From f9a4d8411aa0eca2c600fdba56d529ca853499d6 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 8 Apr 2023 15:21:29 -0400 Subject: [PATCH 001/220] Update FUNDING.yml --- .github/FUNDING.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 30ef0a3..52a0f5f 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1,7 +1,7 @@ # These are supported funding model platforms github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] -PayPal: http://paypal.me/ahmedfgad # Replace with a single Patreon username +PayPal: ahmedfgad # Replace with a single Patreon username open_collective: pygad ko_fi: # Replace with a single Ko-fi username tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel From b141853cd26a21e834a4205b73dec4eaefd651cc Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 8 Apr 2023 15:21:52 -0400 Subject: [PATCH 002/220] Update FUNDING.yml --- .github/FUNDING.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 52a0f5f..6aeb29e 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1,7 +1,7 @@ # These are supported funding model platforms github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] -PayPal: ahmedfgad # Replace with a single Patreon username +paypal: http://paypal.me/ahmedfgad # Replace with a single Patreon username open_collective: pygad ko_fi: # Replace with a single Ko-fi username tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel From d5dbe0e66ae74780c5630489bf46a2fd99c92867 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 8 Apr 2023 15:24:06 -0400 Subject: [PATCH 003/220] Update FUNDING.yml --- .github/FUNDING.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 6aeb29e..d44bede 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1,7 +1,7 @@ # These are supported funding model platforms github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] -paypal: http://paypal.me/ahmedfgad # Replace with a single Patreon username +# paypal: http://paypal.me/ahmedfgad # Replace with a single Patreon username open_collective: pygad ko_fi: # Replace with a single Ko-fi username tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel From ed1fa62bc7ca41aff15ff22581ee971468e3d32e Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 8 Apr 2023 15:36:55 -0400 Subject: [PATCH 004/220] Update FUNDING.yml --- .github/FUNDING.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index d44bede..5892ed6 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -9,4 +9,4 @@ community_bridge: # Replace with a single Community Bridge project-name e.g., cl liberapay: # Replace with a single Liberapay username issuehunt: # Replace with a single IssueHunt username otechie: # Replace with a single Otechie username -custom: https://donate.stripe.com/eVa5kO866elKgM0144 # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] +custom: ['https://donate.stripe.com/eVa5kO866elKgM0144', 'http://paypal.me/ahmedfgad'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] From a62ca2b92ebef81ce557bf78ec2615b72c36d544 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 8 Apr 2023 16:47:05 -0400 Subject: [PATCH 005/220] Update release notes --- docs/source/Footer.rst | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/docs/source/Footer.rst b/docs/source/Footer.rst index 5efa117..6887cbe 100644 --- a/docs/source/Footer.rst +++ b/docs/source/Footer.rst @@ -1196,7 +1196,7 @@ Release Date 23 February 2023 PyGAD 3.0.0 ----------- -Release Date ... 2023 +Release Date 8 April 2023 1. The structure of the library is changed and some methods defined in the ``pygad.py`` module are moved to the ``pygad.utils``, @@ -1204,20 +1204,23 @@ Release Date ... 2023 2. The ``pygad.utils.parent_selection`` module has a class named ``ParentSelection`` where all the parent selection operators exist. + The ``pygad.GA`` class extends this class. 3. The ``pygad.utils.crossover`` module has a class named ``Crossover`` - where all the crossover operators exist. + where all the crossover operators exist. The ``pygad.GA`` class + extends this class. 4. The ``pygad.utils.mutation`` module has a class named ``Mutation`` - where all the mutation operators exist. + where all the mutation operators exist. The ``pygad.GA`` class + extends this class. 5. The ``pygad.helper.unique`` module has a class named ``Unique`` some helper methods exist to solve duplicate genes and make sure every - gene is unique. + gene is unique. The ``pygad.GA`` class extends this class. -6. | The ``pygad.visualize.plot`` module has a class named ``Plot`` - where all the methods that create plots exist. - | The ``pygad.GA`` class extends all of these classes. +6. The ``pygad.visualize.plot`` module has a class named ``Plot`` where + all the methods that create plots exist. The ``pygad.GA`` class + extends this class. .. code:: python From 8ef24c13e0f6d7f98834c6c6485db4fffa7a5ac4 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 8 Apr 2023 16:50:32 -0400 Subject: [PATCH 006/220] Update release 3.0.0 notes --- docs/source/Footer.rst | 70 ++++++++++++++++++------------------------ 1 file changed, 30 insertions(+), 40 deletions(-) diff --git a/docs/source/Footer.rst b/docs/source/Footer.rst index 6887cbe..6de145e 100644 --- a/docs/source/Footer.rst +++ b/docs/source/Footer.rst @@ -1198,84 +1198,74 @@ PyGAD 3.0.0 Release Date 8 April 2023 -1. The structure of the library is changed and some methods defined in - the ``pygad.py`` module are moved to the ``pygad.utils``, - ``pygad.helper``, and ``pygad.visualize`` submodules. +1. The structure of the library is changed and some methods defined in + the ``pygad.py`` module are moved to the ``pygad.utils``, + ``pygad.helper``, and ``pygad.visualize`` submodules. -2. The ``pygad.utils.parent_selection`` module has a class named - ``ParentSelection`` where all the parent selection operators exist. - The ``pygad.GA`` class extends this class. +2. The ``pygad.utils.parent_selection`` module has a class named + ``ParentSelection`` where all the parent selection operators exist. + The ``pygad.GA`` class extends this class. -3. The ``pygad.utils.crossover`` module has a class named ``Crossover`` - where all the crossover operators exist. The ``pygad.GA`` class - extends this class. +3. The ``pygad.utils.crossover`` module has a class named ``Crossover`` + where all the crossover operators exist. The ``pygad.GA`` class + extends this class. -4. The ``pygad.utils.mutation`` module has a class named ``Mutation`` - where all the mutation operators exist. The ``pygad.GA`` class - extends this class. +4. The ``pygad.utils.mutation`` module has a class named ``Mutation`` + where all the mutation operators exist. The ``pygad.GA`` class + extends this class. -5. The ``pygad.helper.unique`` module has a class named ``Unique`` some - helper methods exist to solve duplicate genes and make sure every - gene is unique. The ``pygad.GA`` class extends this class. +5. The ``pygad.helper.unique`` module has a class named ``Unique`` some + helper methods exist to solve duplicate genes and make sure every + gene is unique. The ``pygad.GA`` class extends this class. -6. The ``pygad.visualize.plot`` module has a class named ``Plot`` where - all the methods that create plots exist. The ``pygad.GA`` class - extends this class. +6. The ``pygad.visualize.plot`` module has a class named ``Plot`` where + all the methods that create plots exist. The ``pygad.GA`` class + extends this class. -.. code:: python - - ... - class GA(utils.parent_selection.ParentSelection, - utils.crossover.Crossover, - utils.mutation.Mutation, - helper.unique.Unique, - visualize.plot.Plot): - ... - -1. Support of using the ``logging`` module to log the outputs to both +7. Support of using the ``logging`` module to log the outputs to both the console and text file instead of using the ``print()`` function. This is by assigning the ``logging.Logger`` to the new ``logger`` parameter. Check the `Logging Outputs `__ for more information. -2. A new instance attribute called ``logger`` to save the logger. +8. A new instance attribute called ``logger`` to save the logger. -3. The function/method passed to the ``fitness_func`` parameter accepts +9. The function/method passed to the ``fitness_func`` parameter accepts a new parameter that refers to the instance of the ``pygad.GA`` class. Check this for an example: `Use Functions and Methods to Build Fitness Function and Callbacks `__. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/163 -4. Update the documentation to include an example of using functions +10. Update the documentation to include an example of using functions and methods to calculate the fitness and build callbacks. Check this for more details: `Use Functions and Methods to Build Fitness Function and Callbacks `__. https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92#issuecomment-1443635003 -5. Validate the value passed to the ``initial_population`` parameter. +11. Validate the value passed to the ``initial_population`` parameter. -6. Validate the type and length of the ``pop_fitness`` parameter of the +12. Validate the type and length of the ``pop_fitness`` parameter of the ``best_solution()`` method. -7. Some edits in the documentation. +13. Some edits in the documentation. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/106 -8. Fix an issue when building the initial population as (some) genes +14. Fix an issue when building the initial population as (some) genes have their value taken from the mutation range (defined by the parameters ``random_mutation_min_val`` and ``random_mutation_max_val``) instead of using the parameters ``init_range_low`` and ``init_range_high``. -9. The ``summary()`` method returns the summary as a single-line +15. The ``summary()`` method returns the summary as a single-line string. Just log/print the returned string it to see it properly. -10. The ``callback_generation`` parameter is removed. Use the +16. The ``callback_generation`` parameter is removed. Use the ``on_generation`` parameter instead. -11. There was an issue when using the ``parallel_processing`` parameter +17. There was an issue when using the ``parallel_processing`` parameter with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the ``predict()`` method gives incorrect and weird results when more than 1 thread is used. @@ -1285,7 +1275,7 @@ Release Date 8 April 2023 `StackOverflow answer `__. -12. Replace ``numpy.float`` by ``float`` in the 2 parent selection +18. Replace ``numpy.float`` by ``float`` in the 2 parent selection operators roulette wheel and stochastic universal. https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/168 From 1c43819d30d44ce80e87c38a508ed8c730e41cba Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Mon, 10 Apr 2023 14:34:54 -0400 Subject: [PATCH 007/220] Update README.md --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 015c020..ef5a7ae 100644 --- a/README.md +++ b/README.md @@ -29,9 +29,7 @@ Install PyGAD with the following command: pip install pygad ``` -PyGAD is developed in Python 3.7.3 and depends on NumPy for creating and manipulating arrays and Matplotlib for creating figures. The exact NumPy version used in developing PyGAD is 1.16.4. For Matplotlib, the version is 3.1.0. - -To get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io. +To get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io) https://pygad.readthedocs.io. # PyGAD Source Code From 38e27cbb6b21b4fc4c01661271159938eb5fdcaf Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:40:33 -0400 Subject: [PATCH 008/220] Upload all package files The repository only had the pygad.py script and some few other scripts. Other scripts in the library exist in these repositories: 1) https://github.com/ahmedfgad/NumPyANN 2) https://github.com/ahmedfgad/NumPyCNN 3) https://github.com/ahmedfgad/NeuralGenetic 4) https://github.com/ahmedfgad/CNNGenetic 5) https://github.com/ahmedfgad/KerasGA 6) https://github.com/ahmedfgad/TorchGA Now, the pygad directory has all the scripts in the library. --- pygad/__init__.py | 3 + pygad/cnn/__init__.py | 4 + pygad/cnn/cnn.py | 849 ++++++++++++ pygad/gacnn/__init__.py | 4 + pygad/gacnn/gacnn.py | 97 ++ pygad/gann/__init__.py | 4 + pygad/gann/gann.py | 269 ++++ pygad/helper/__init__.py | 3 + pygad/helper/unique.py | 638 +++++++++ pygad/kerasga/__init__.py | 3 + pygad/kerasga/kerasga.py | 89 ++ pygad/nn/__init__.py | 4 + pygad/nn/nn.py | 399 ++++++ pygad/pygad.py | 2219 +++++++++++++++++++++++++++++++ pygad/torchga/__init__.py | 3 + pygad/torchga/torchga.py | 90 ++ pygad/utils/__init__.py | 5 + pygad/utils/crossover.py | 248 ++++ pygad/utils/mutation.py | 822 ++++++++++++ pygad/utils/parent_selection.py | 191 +++ pygad/visualize/__init__.py | 3 + pygad/visualize/plot.py | 348 +++++ 22 files changed, 6295 insertions(+) create mode 100644 pygad/__init__.py create mode 100644 pygad/cnn/__init__.py create mode 100644 pygad/cnn/cnn.py create mode 100644 pygad/gacnn/__init__.py create mode 100644 pygad/gacnn/gacnn.py create mode 100644 pygad/gann/__init__.py create mode 100644 pygad/gann/gann.py create mode 100644 pygad/helper/__init__.py create mode 100644 pygad/helper/unique.py create mode 100644 pygad/kerasga/__init__.py create mode 100644 pygad/kerasga/kerasga.py create mode 100644 pygad/nn/__init__.py create mode 100644 pygad/nn/nn.py create mode 100644 pygad/pygad.py create mode 100644 pygad/torchga/__init__.py create mode 100644 pygad/torchga/torchga.py create mode 100644 pygad/utils/__init__.py create mode 100644 pygad/utils/crossover.py create mode 100644 pygad/utils/mutation.py create mode 100644 pygad/utils/parent_selection.py create mode 100644 pygad/visualize/__init__.py create mode 100644 pygad/visualize/plot.py diff --git a/pygad/__init__.py b/pygad/__init__.py new file mode 100644 index 0000000..71f207b --- /dev/null +++ b/pygad/__init__.py @@ -0,0 +1,3 @@ +from .pygad import * # Relative import. + +__version__ = "3.0.0" diff --git a/pygad/cnn/__init__.py b/pygad/cnn/__init__.py new file mode 100644 index 0000000..57e6d6f --- /dev/null +++ b/pygad/cnn/__init__.py @@ -0,0 +1,4 @@ +from .cnn import * + +__version__ = "1.0.0" + diff --git a/pygad/cnn/cnn.py b/pygad/cnn/cnn.py new file mode 100644 index 0000000..0e78a52 --- /dev/null +++ b/pygad/cnn/cnn.py @@ -0,0 +1,849 @@ +import numpy +import functools + +""" +Convolutional neural network implementation using NumPy +A tutorial that helps to get started (Building Convolutional Neural Network using NumPy from Scratch) available in these links: + https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad + https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a + https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html +It is also translated into Chinese: http://m.aliyun.com/yunqi/articles/585741 +""" + +# Supported activation functions by the cnn.py module. +supported_activation_functions = ("sigmoid", "relu", "softmax") + +def sigmoid(sop): + + """ + Applies the sigmoid function. + + sop: The input to which the sigmoid function is applied. + + Returns the result of the sigmoid function. + """ + + if type(sop) in [list, tuple]: + sop = numpy.array(sop) + + return 1.0 / (1 + numpy.exp(-1 * sop)) + +def relu(sop): + + """ + Applies the rectified linear unit (ReLU) function. + + sop: The input to which the relu function is applied. + + Returns the result of the ReLU function. + """ + + if not (type(sop) in [list, tuple, numpy.ndarray]): + if sop < 0: + return 0 + else: + return sop + elif type(sop) in [list, tuple]: + sop = numpy.array(sop) + + result = sop + result[sop < 0] = 0 + + return result + +def softmax(layer_outputs): + + """ + Applies the sotmax function. + + sop: The input to which the softmax function is applied. + + Returns the result of the softmax function. + """ + return layer_outputs / (numpy.sum(layer_outputs) + 0.000001) + +def layers_weights(model, initial=True): + + """ + Creates a list holding the weights of all layers in the CNN. + + model: A reference to the instance from the cnn.Model class. + initial: When True, the function returns the initial weights of the layers. When False, the trained weights of the layers are returned. The initial weights are only needed before network training starts. The trained weights are needed to predict the network outputs. + + Returns a list (network_weights) holding the weights of the layers in the CNN. + """ + + network_weights = [] + + layer = model.last_layer + while "previous_layer" in layer.__init__.__code__.co_varnames: + if type(layer) in [Conv2D, Dense]: + # If the 'initial' parameter is True, append the initial weights. Otherwise, append the trained weights. + if initial == True: + network_weights.append(layer.initial_weights) + elif initial == False: + network_weights.append(layer.trained_weights) + else: + raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + + # Go to the previous layer. + layer = layer.previous_layer + + # If the first layer in the network is not an input layer (i.e. an instance of the Input2D class), raise an error. + if not (type(layer) is Input2D): + raise TypeError("The first layer in the network architecture must be an input layer.") + + # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. + # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). + network_weights.reverse() + return numpy.array(network_weights) + +def layers_weights_as_matrix(model, vector_weights): + + """ + Converts the network weights from vectors to matrices. + + model: A reference to the instance from the cnn.Model class. + vector_weights: The network weights as vectors where the weights of each layer form a single vector. + + Returns a list (network_weights) holding the weights of the CNN layers as matrices. + """ + + network_weights = [] + + start = 0 + layer = model.last_layer + vector_weights = vector_weights[::-1] + while "previous_layer" in layer.__init__.__code__.co_varnames: + if type(layer) in [Conv2D, Dense]: + layer_weights_shape = layer.initial_weights.shape + layer_weights_size = layer.initial_weights.size + + weights_vector=vector_weights[start:start + layer_weights_size] + # matrix = pygad.nn.DenseLayer.to_array(vector=weights_vector, shape=layer_weights_shape) + matrix = numpy.reshape(weights_vector, newshape=(layer_weights_shape)) + network_weights.append(matrix) + + start = start + layer_weights_size + + # Go to the previous layer. + layer = layer.previous_layer + + # If the first layer in the network is not an input layer (i.e. an instance of the Input2D class), raise an error. + if not (type(layer) is Input2D): + raise TypeError("The first layer in the network architecture must be an input layer.") + + # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. + # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). + network_weights.reverse() + return numpy.array(network_weights) + +def layers_weights_as_vector(model, initial=True): + + """ + Creates a list holding the weights of each layer (Conv and Dense) in the CNN as a vector. + + model: A reference to the instance from the cnn.Model class. + initial: When True, the function returns the initial weights of the CNN. When False, the trained weights of the CNN layers are returned. The initial weights are only needed before network training starts. The trained weights are needed to predict the network outputs. + + Returns a list (network_weights) holding the weights of the CNN layers as a vector. + """ + + network_weights = [] + + layer = model.last_layer + while "previous_layer" in layer.__init__.__code__.co_varnames: + if type(layer) in [Conv2D, Dense]: + # If the 'initial' parameter is True, append the initial weights. Otherwise, append the trained weights. + if initial == True: + vector = numpy.reshape(layer.initial_weights, newshape=(layer.initial_weights.size)) + # vector = pygad.nn.DenseLayer.to_vector(matrix=layer.initial_weights) + network_weights.extend(vector) + elif initial == False: + vector = numpy.reshape(layer.trained_weights, newshape=(layer.trained_weights.size)) + # vector = pygad.nn.DenseLayer.to_vector(array=layer.trained_weights) + network_weights.extend(vector) + else: + raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + + # Go to the previous layer. + layer = layer.previous_layer + + # If the first layer in the network is not an input layer (i.e. an instance of the Input2D class), raise an error. + if not (type(layer) is Input2D): + raise TypeError("The first layer in the network architecture must be an input layer.") + + # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. + # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). + network_weights.reverse() + return numpy.array(network_weights) + +def update_layers_trained_weights(model, final_weights): + + """ + After the network weights are trained, the 'trained_weights' attribute of each layer is updated by the weights calculated after passing all the epochs (such weights are passed in the 'final_weights' parameter). + By just passing a reference to the last layer in the network (i.e. output layer) in addition to the final weights, this function updates the 'trained_weights' attribute of all layers. + + model: A reference to the instance from the cnn.Model class. + final_weights: An array of layers weights as matrices after passing through all the epochs. + """ + + layer = model.last_layer + layer_idx = len(final_weights) - 1 + while "previous_layer" in layer.__init__.__code__.co_varnames: + if type(layer) in [Conv2D, Dense]: + layer.trained_weights = final_weights[layer_idx] + + layer_idx = layer_idx - 1 + + # Go to the previous layer. + layer = layer.previous_layer + +class Input2D: + + """ + Implementing the input layer of a CNN. + The CNN architecture must start with an input layer. + """ + + def __init__(self, input_shape): + + """ + input_shape: Shape of the input sample to the CNN. + """ + + # If the input sample has less than 2 dimensions, then an exception is raised. + if len(input_shape) < 2: + raise ValueError("The Input2D class creates an input layer for data inputs with at least 2 dimensions but ({num_dim}) dimensions found.".format(num_dim=len(input_shape))) + # If the input sample has exactly 2 dimensions, the third dimension is set to 1. + elif len(input_shape) == 2: + input_shape = (input_shape[0], input_shape[1], 1) + + for dim_idx, dim in enumerate(input_shape): + if dim <= 0: + raise ValueError("The dimension size of the inputs cannot be <= 0. Please pass a valid value to the 'input_size' parameter.") + + self.input_shape = input_shape # Shape of the input sample. + self.layer_output_size = input_shape # Shape of the output from the current layer. For an input layer, it is the same as the shape of the input sample. + +class Conv2D: + + """ + Implementing the convolution layer. + """ + + def __init__(self, num_filters, kernel_size, previous_layer, activation_function=None): + + """ + num_filters: Number of filters in the convolution layer. + kernel_size: Kernel size of the filter. + previous_layer: A reference to the previous layer. + activation_function=None: The name of the activation function to be used in the conv layer. If None, then no activation function is applied besides the convolution operation. The activation function can be applied by a separate layer. + """ + + if num_filters <= 0: + raise ValueError("Number of filters cannot be <= 0. Please pass a valid value to the 'num_filters' parameter.") + # Number of filters in the conv layer. + self.num_filters = num_filters + + if kernel_size <= 0: + raise ValueError("The kernel size cannot be <= 0. Please pass a valid value to the 'kernel_size' parameter.") + # Kernel size of each filter. + self.kernel_size = kernel_size + + # Validating the activation function + if (activation_function is None): + self.activation = None + elif (activation_function == "relu"): + self.activation = relu + elif (activation_function == "sigmoid"): + self.activation = sigmoid + elif (activation_function == "softmax"): + raise ValueError("The softmax activation function cannot be used in a conv layer.") + else: + raise ValueError("The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.".format(activation_function=activation_function, supported_activation_functions=supported_activation_functions)) + + # The activation function used in the current layer. + self.activation_function = activation_function + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + # A reference to the bank of filters. + self.filter_bank_size = (self.num_filters, + self.kernel_size, + self.kernel_size, + self.previous_layer.layer_output_size[-1]) + + # Initializing the filters of the conv layer. + self.initial_weights = numpy.random.uniform(low=-0.1, + high=0.1, + size=self.filter_bank_size) + + # The trained filters of the conv layer. Only assigned a value after the network is trained (i.e. the train_network() function completes). + # Just initialized to be equal to the initial filters + self.trained_weights = self.initial_weights.copy() + + # Size of the input to the layer. + self.layer_input_size = self.previous_layer.layer_output_size + + # Size of the output from the layer. + # Later, it must conider strides and paddings + self.layer_output_size = (self.previous_layer.layer_output_size[0] - self.kernel_size + 1, + self.previous_layer.layer_output_size[1] - self.kernel_size + 1, + num_filters) + + # The layer_output attribute holds the latest output from the layer. + self.layer_output = None + + def conv_(self, input2D, conv_filter): + + """ + Convolves the input (input2D) by a single filter (conv_filter). + + input2D: The input to be convolved by a single filter. + conv_filter: The filter convolving the input. + + Returns the result of convolution. + """ + + result = numpy.zeros(shape=(input2D.shape[0], input2D.shape[1], conv_filter.shape[0])) + # Looping through the image to apply the convolution operation. + for r in numpy.uint16(numpy.arange(self.filter_bank_size[1]/2.0, + input2D.shape[0]-self.filter_bank_size[1]/2.0+1)): + for c in numpy.uint16(numpy.arange(self.filter_bank_size[1]/2.0, + input2D.shape[1]-self.filter_bank_size[1]/2.0+1)): + """ + Getting the current region to get multiplied with the filter. + How to loop through the image and get the region based on + the image and filer sizes is the most tricky part of convolution. + """ + if len(input2D.shape) == 2: + curr_region = input2D[r-numpy.uint16(numpy.floor(self.filter_bank_size[1]/2.0)):r+numpy.uint16(numpy.ceil(self.filter_bank_size[1]/2.0)), + c-numpy.uint16(numpy.floor(self.filter_bank_size[1]/2.0)):c+numpy.uint16(numpy.ceil(self.filter_bank_size[1]/2.0))] + else: + curr_region = input2D[r-numpy.uint16(numpy.floor(self.filter_bank_size[1]/2.0)):r+numpy.uint16(numpy.ceil(self.filter_bank_size[1]/2.0)), + c-numpy.uint16(numpy.floor(self.filter_bank_size[1]/2.0)):c+numpy.uint16(numpy.ceil(self.filter_bank_size[1]/2.0)), :] + # Element-wise multipliplication between the current region and the filter. + + for filter_idx in range(conv_filter.shape[0]): + curr_result = curr_region * conv_filter[filter_idx] + conv_sum = numpy.sum(curr_result) # Summing the result of multiplication. + + if self.activation is None: + result[r, c, filter_idx] = conv_sum # Saving the SOP in the convolution layer feature map. + else: + result[r, c, filter_idx] = self.activation(conv_sum) # Saving the activation function result in the convolution layer feature map. + + # Clipping the outliers of the result matrix. + final_result = result[numpy.uint16(self.filter_bank_size[1]/2.0):result.shape[0]-numpy.uint16(self.filter_bank_size[1]/2.0), + numpy.uint16(self.filter_bank_size[1]/2.0):result.shape[1]-numpy.uint16(self.filter_bank_size[1]/2.0), :] + return final_result + + def conv(self, input2D): + + """ + Convolves the input (input2D) by a filter bank. + + input2D: The input to be convolved by the filter bank. + + The conv() method saves the result of convolving the input by the filter bank in the layer_output attribute. + """ + + if len(input2D.shape) != len(self.initial_weights.shape) - 1: # Check if there is a match in the number of dimensions between the image and the filters. + raise ValueError("Number of dimensions in the conv filter and the input do not match.") + if len(input2D.shape) > 2 or len(self.initial_weights.shape) > 3: # Check if number of image channels matches the filter depth. + if input2D.shape[-1] != self.initial_weights.shape[-1]: + raise ValueError("Number of channels in both the input and the filter must match.") + if self.initial_weights.shape[1] != self.initial_weights.shape[2]: # Check if filter dimensions are equal. + raise ValueError('A filter must be a square matrix. I.e. number of rows and columns must match.') + if self.initial_weights.shape[1]%2==0: # Check if filter diemnsions are odd. + raise ValueError('A filter must have an odd size. I.e. number of rows and columns must be odd.') + + self.layer_output = self.conv_(input2D, self.trained_weights) + +class AveragePooling2D: + + """ + Implementing the average pooling layer. + """ + + def __init__(self, pool_size, previous_layer, stride=2): + + """ + pool_size: Pool size. + previous_layer: Reference to the previous layer in the CNN architecture. + stride=2: Stride + """ + + if not (type(pool_size) is int): + raise ValueError("The expected type of the pool_size is int but {pool_size_type} found.".format(pool_size_type=type(pool_size))) + + if pool_size <= 0: + raise ValueError("The passed value to the pool_size parameter cannot be <= 0.") + self.pool_size = pool_size + + if stride <= 0: + raise ValueError("The passed value to the stride parameter cannot be <= 0.") + self.stride = stride + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + # Size of the input to the layer. + self.layer_input_size = self.previous_layer.layer_output_size + + # Size of the output from the layer. + self.layer_output_size = (numpy.uint16((self.previous_layer.layer_output_size[0] - self.pool_size + 1)/stride + 1), + numpy.uint16((self.previous_layer.layer_output_size[1] - self.pool_size + 1)/stride + 1), + self.previous_layer.layer_output_size[-1]) + + # The layer_output attribute holds the latest output from the layer. + self.layer_output = None + + def average_pooling(self, input2D): + + """ + Applies the average pooling operation. + + input2D: The input to which the average pooling operation is applied. + + The average_pooling() method saves its result in the layer_output attribute. + """ + + # Preparing the output of the pooling operation. + pool_out = numpy.zeros((numpy.uint16((input2D.shape[0]-self.pool_size+1)/self.stride+1), + numpy.uint16((input2D.shape[1]-self.pool_size+1)/self.stride+1), + input2D.shape[-1])) + for map_num in range(input2D.shape[-1]): + r2 = 0 + for r in numpy.arange(0,input2D.shape[0]-self.pool_size+1, self.stride): + c2 = 0 + for c in numpy.arange(0, input2D.shape[1]-self.pool_size+1, self.stride): + pool_out[r2, c2, map_num] = numpy.mean([input2D[r:r+self.pool_size, c:c+self.pool_size, map_num]]) + c2 = c2 + 1 + r2 = r2 +1 + + self.layer_output = pool_out + +class MaxPooling2D: + + """ + Similar to the AveragePooling2D class except that it implements max pooling. + """ + + def __init__(self, pool_size, previous_layer, stride=2): + + """ + pool_size: Pool size. + previous_layer: Reference to the previous layer in the CNN architecture. + stride=2: Stride + """ + + if not (type(pool_size) is int): + raise ValueError("The expected type of the pool_size is int but {pool_size_type} found.".format(pool_size_type=type(pool_size))) + + if pool_size <= 0: + raise ValueError("The passed value to the pool_size parameter cannot be <= 0.") + self.pool_size = pool_size + + if stride <= 0: + raise ValueError("The passed value to the stride parameter cannot be <= 0.") + self.stride = stride + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + # Size of the input to the layer. + self.layer_input_size = self.previous_layer.layer_output_size + + # Size of the output from the layer. + self.layer_output_size = (numpy.uint16((self.previous_layer.layer_output_size[0] - self.pool_size + 1)/stride + 1), + numpy.uint16((self.previous_layer.layer_output_size[1] - self.pool_size + 1)/stride + 1), + self.previous_layer.layer_output_size[-1]) + + # The layer_output attribute holds the latest output from the layer. + self.layer_output = None + + def max_pooling(self, input2D): + + """ + Applies the max pooling operation. + + input2D: The input to which the max pooling operation is applied. + + The max_pooling() method saves its result in the layer_output attribute. + """ + + # Preparing the output of the pooling operation. + pool_out = numpy.zeros((numpy.uint16((input2D.shape[0]-self.pool_size+1)/self.stride+1), + numpy.uint16((input2D.shape[1]-self.pool_size+1)/self.stride+1), + input2D.shape[-1])) + for map_num in range(input2D.shape[-1]): + r2 = 0 + for r in numpy.arange(0,input2D.shape[0]-self.pool_size+1, self.stride): + c2 = 0 + for c in numpy.arange(0, input2D.shape[1]-self.pool_size+1, self.stride): + pool_out[r2, c2, map_num] = numpy.max([input2D[r:r+self.pool_size, c:c+self.pool_size, map_num]]) + c2 = c2 + 1 + r2 = r2 +1 + + self.layer_output = pool_out + +class ReLU: + + """ + Implementing the ReLU layer. + """ + + def __init__(self, previous_layer): + + """ + previous_layer: Reference to the previous layer. + """ + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + # Size of the input to the layer. + self.layer_input_size = self.previous_layer.layer_output_size + + # Size of the output from the layer. + self.layer_output_size = self.previous_layer.layer_output_size + + # The layer_output attribute holds the latest output from the layer. + self.layer_output = None + + def relu_layer(self, layer_input): + + """ + Applies the ReLU function over all elements in input to the ReLU layer. + + layer_input: The input to which the ReLU function is applied. + + The relu_layer() method saves its result in the layer_output attribute. + """ + + self.layer_output_size = layer_input.size + self.layer_output = relu(layer_input) + +class Sigmoid: + + """ + Implementing the sigmoid layer. + """ + + def __init__(self, previous_layer): + + """ + previous_layer: Reference to the previous layer. + """ + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + # Size of the input to the layer. + self.layer_input_size = self.previous_layer.layer_output_size + + # Size of the output from the layer. + self.layer_output_size = self.previous_layer.layer_output_size + + # The layer_output attribute holds the latest output from the layer. + self.layer_output = None + + def sigmoid_layer(self, layer_input): + + """ + Applies the sigmoid function over all elements in input to the sigmoid layer. + + layer_input: The input to which the sigmoid function is applied. + + The sigmoid_layer() method saves its result in the layer_output attribute. + """ + + self.layer_output_size = layer_input.size + self.layer_output = sigmoid(layer_input) + +class Flatten: + + """ + Implementing the flatten layer. + """ + + def __init__(self, previous_layer): + + """ + previous_layer: Reference to the previous layer. + """ + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + # Size of the input to the layer. + self.layer_input_size = self.previous_layer.layer_output_size + + # Size of the output from the layer. + self.layer_output_size = functools.reduce(lambda x, y: x*y, self.previous_layer.layer_output_size) + + # The layer_output attribute holds the latest output from the layer. + self.layer_output = None + + def flatten(self, input2D): + + """ + Reshapes the input into a 1D vector. + + input2D: The input to the Flatten layer that will be converted into a 1D vector. + + The flatten() method saves its result in the layer_output attribute. + """ + + self.layer_output_size = input2D.size + self.layer_output = numpy.ravel(input2D) + +class Dense: + + """ + Implementing the input dense (fully connected) layer of a CNN. + """ + + def __init__(self, num_neurons, previous_layer, activation_function="relu"): + + """ + num_neurons: Number of neurons in the dense layer. + previous_layer: Reference to the previous layer. + activation_function: Name of the activation function to be used in the current layer. + """ + + if num_neurons <= 0: + raise ValueError("Number of neurons cannot be <= 0. Please pass a valid value to the 'num_neurons' parameter.") + + # Number of neurons in the dense layer. + self.num_neurons = num_neurons + + # Validating the activation function + if (activation_function == "relu"): + self.activation = relu + elif (activation_function == "sigmoid"): + self.activation = sigmoid + elif (activation_function == "softmax"): + self.activation = softmax + else: + raise ValueError("The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.".format(activation_function=activation_function, supported_activation_functions=supported_activation_functions)) + + self.activation_function = activation_function + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + if type(self.previous_layer.layer_output_size) in [list, tuple, numpy.ndarray] and len(self.previous_layer.layer_output_size) > 1: + raise ValueError("The input to the dense layer must be of type int but {sh} found.".format(sh=type(self.previous_layer.layer_output_size))) + # Initializing the weights of the layer. + self.initial_weights = numpy.random.uniform(low=-0.1, + high=0.1, + size=(self.previous_layer.layer_output_size, self.num_neurons)) + + # The trained weights of the layer. Only assigned a value after the network is trained (i.e. the train_network() function completes). + # Just initialized to be equal to the initial weights + self.trained_weights = self.initial_weights.copy() + + # Size of the input to the layer. + self.layer_input_size = self.previous_layer.layer_output_size + + # Size of the output from the layer. + self.layer_output_size = num_neurons + + # The layer_output attribute holds the latest output from the layer. + self.layer_output = None + + def dense_layer(self, layer_input): + + """ + Calculates the output of the dense layer. + + layer_input: The input to the dense layer + + The dense_layer() method saves its result in the layer_output attribute. + """ + + if self.trained_weights is None: + raise TypeError("The weights of the dense layer cannot be of Type 'None'.") + + sop = numpy.matmul(layer_input, self.trained_weights) + + self.layer_output = self.activation(sop) + +class Model: + + """ + Creating a CNN model. + """ + + def __init__(self, last_layer, epochs=10, learning_rate=0.01): + + """ + last_layer: A reference to the last layer in the CNN architecture. + epochs=10: Number of epochs. + learning_rate=0.01: Learning rate. + """ + + self.last_layer = last_layer + self.epochs = epochs + self.learning_rate = learning_rate + + # The network_layers attribute is a list holding references to all CNN layers. + self.network_layers = self.get_layers() + + def get_layers(self): + + """ + Prepares a list of all layers in the CNN model. + Returns the list. + """ + + network_layers = [] + + # The last layer in the network archietcture. + layer = self.last_layer + + while "previous_layer" in layer.__init__.__code__.co_varnames: + network_layers.insert(0, layer) + layer = layer.previous_layer + + return network_layers + + def train(self, train_inputs, train_outputs): + + """ + Trains the CNN model. + It is important to note that no learning algorithm is used for training the CNN. Just the learning rate is used for making some changes which is better than leaving the weights unchanged. + + train_inputs: Training data inputs. + train_outputs: Training data outputs. + """ + + if (train_inputs.ndim != 4): + raise ValueError("The training data input has {num_dims} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample.".format(num_dims=train_inputs.ndim)) + + if (train_inputs.shape[0] != len(train_outputs)): + raise ValueError("Mismatch between the number of input samples and number of labels: {num_samples_inputs} != {num_samples_outputs}.".format(num_samples_inputs=train_inputs.shape[0], num_samples_outputs=len(train_outputs))) + + network_predictions = [] + network_error = 0 + + for epoch in range(self.epochs): + print("Epoch {epoch}".format(epoch=epoch)) + for sample_idx in range(train_inputs.shape[0]): + # print("Sample {sample_idx}".format(sample_idx=sample_idx)) + self.feed_sample(train_inputs[sample_idx, :]) + + try: + predicted_label = numpy.where(numpy.max(self.last_layer.layer_output) == self.last_layer.layer_output)[0][0] + except IndexError: + print(self.last_layer.layer_output) + raise IndexError("Index out of range") + network_predictions.append(predicted_label) + + network_error = network_error + abs(predicted_label - train_outputs[sample_idx]) + + self.update_weights(network_error) + + def feed_sample(self, sample): + + """ + Feeds a sample in the CNN layers. + + sample: The samples to be fed to the CNN layers. + + Returns results of the last layer in the CNN. + """ + + last_layer_outputs = sample + for layer in self.network_layers: + if type(layer) is Conv2D: +# import time +# time1 = time.time() + layer.conv(input2D=last_layer_outputs) +# time2 = time.time() +# print(time2 - time1) + elif type(layer) is Dense: + layer.dense_layer(layer_input=last_layer_outputs) + elif type(layer) is MaxPooling2D: + layer.max_pooling(input2D=last_layer_outputs) + elif type(layer) is AveragePooling2D: + layer.average_pooling(input2D=last_layer_outputs) + elif type(layer) is ReLU: + layer.relu_layer(layer_input=last_layer_outputs) + elif type(layer) is Sigmoid: + layer.sigmoid_layer(layer_input=last_layer_outputs) + elif type(layer) is Flatten: + layer.flatten(input2D=last_layer_outputs) + elif type(layer) is Input2D: + pass + else: + print("Other") + raise TypeError("The layer of type {layer_type} is not supported yet.".format(layer_type=type(layer))) + + last_layer_outputs = layer.layer_output + return self.network_layers[-1].layer_output + + def update_weights(self, network_error): + + """ + Updates the weights of the CNN. + It is important to note that no learning algorithm is used for training the CNN. Just the learning rate is used for making some changes which is better than leaving the weights unchanged. + + This method loops through the layers and updates their weights. + + network_error: The network error in the last epoch. + """ + + for layer in self.network_layers: + if "trained_weights" in vars(layer).keys(): + layer.trained_weights = layer.trained_weights - network_error * self.learning_rate * layer.trained_weights + + def predict(self, data_inputs): + + """ + Uses the trained CNN for making predictions. + + data_inputs: The inputs to predict their label. + + Returns a list holding the samples predictions. + """ + + if (data_inputs.ndim != 4): + raise ValueError("The data input has {num_dims} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample.".format(num_dims=data_inputs.ndim)) + + predictions = [] + for sample in data_inputs: + probs = self.feed_sample(sample=sample) + predicted_label = numpy.where(numpy.max(probs) == probs)[0][0] + predictions.append(predicted_label) + return predictions + + def summary(self): + + """ + Prints a summary of the CNN architecture. + """ + + print("\n----------Network Architecture----------") + for layer in self.network_layers: + print(type(layer)) + print("----------------------------------------\n") diff --git a/pygad/gacnn/__init__.py b/pygad/gacnn/__init__.py new file mode 100644 index 0000000..95cb104 --- /dev/null +++ b/pygad/gacnn/__init__.py @@ -0,0 +1,4 @@ +from .gacnn import * + +__version__ = "1.0.0" + diff --git a/pygad/gacnn/gacnn.py b/pygad/gacnn/gacnn.py new file mode 100644 index 0000000..1623c7d --- /dev/null +++ b/pygad/gacnn/gacnn.py @@ -0,0 +1,97 @@ +from ..cnn import cnn +import copy + +def population_as_vectors(population_networks): + + """ + Accepts the population as networks and returns a list holding all weights of the CNN layers of each solution (i.e. network) in the population as a vector. + If the population has 6 solutions (i.e. networks), this function accepts references to such networks and returns a list with 6 vectors, one for each network (i.e. solution). Each vector holds the weights for all layers for a single CNN. + + population_networks: A list holding references to the CNN models used in the population. + + Returns a list holding the weights vectors for all solutions (i.e. networks). + """ + + population_vectors = [] + for solution in population_networks: + # Converting the weights of single layer from the current CNN (i.e. solution) to a vector. + solution_weights_vector = cnn.layers_weights_as_vector(solution) + # Appending the weights vector of the current layer of a CNN (i.e. solution) to the weights of the previous layers of the same CNN (i.e. solution). + population_vectors.append(solution_weights_vector) + + return population_vectors + +def population_as_matrices(population_networks, population_vectors): + + """ + Accepts the population as both networks and weights vectors and returns the weights of all layers of each solution (i.e. CNN) in the population as a matrix. + If the population has 6 solutions (i.e. networks), this function returns a list with 6 matrices, one for each network holding its weights for all layers. + + population_networks: A list holding references to the output (last) layers of the neural networks used in the population. + population_vectors: A list holding the weights of all networks as vectors. Such vectors are to be converted into matrices. + + Returns a list holding the weights matrices for all solutions (i.e. networks). + """ + + population_matrices = [] + for solution, solution_weights_vector in zip(population_networks, population_vectors): + # Converting the weights of single layer from the current CNN (i.e. solution) from a vector to a matrix. + solution_weights_matrix = cnn.layers_weights_as_matrix(solution, solution_weights_vector) + # Appending the weights matrix of the current layer of a CNN (i.e. solution) to the weights of the previous layers of the same network (i.e. solution). + population_matrices.append(solution_weights_matrix) + + return population_matrices + +class GACNN: + + def create_population(self): + + """ + Creates the initial population of the genetic algorithm as a list of CNNs (i.e. solutions). Each element in the list holds a reference to the instance of the cnn.Model class. + + The method returns the list holding the references to the CNN models. + """ + + population_networks = [] + for solution in range(self.num_solutions): + + network = copy.deepcopy(self.model) + + # Appending the CNN model to the list of population networks. + population_networks.append(network) + + return population_networks + + def __init__(self, model, num_solutions): + + """ + Creates an instance of the GACNN class for training a CNN using the genetic algorithm. + The constructor of the GACNN class creates an initial population of multiple CNNs using the create_population() method. + The population returned holds references to instances of the cnn.Model class. + + model: An instance of the pygad.cnn.Model class representing the architecture of all solutions in the population. + num_solutions: Number of CNNs (i.e. solutions) in the population. Based on the value passed to this parameter, a number of identical CNNs are created where their parameters are optimized using the genetic algorithm. + """ + + self.model = model + + self.num_solutions = num_solutions + + # A list holding references to all the solutions (i.e. CNNs) used in the population. + self.population_networks = self.create_population() + + def update_population_trained_weights(self, population_trained_weights): + + """ + The `update_population_trained_weights()` method updates the `trained_weights` attribute of each CNN according to the weights passed in the `population_trained_weights` parameter. + + population_trained_weights: A list holding the trained weights of all networks as matrices. Such matrices are to be assigned to the 'trained_weights' attribute of all layers of all CNNs. + """ + + idx = 0 + # Fetches all layers weights matrices for a single solution (i.e. CNN) + for solution in self.population_networks: + # Calling the cnn.update_layers_trained_weights() function for updating the 'trained_weights' attribute for all layers in the current solution (i.e. CNN). + cnn.update_layers_trained_weights(model=solution, + final_weights=population_trained_weights[idx]) + idx = idx + 1 diff --git a/pygad/gann/__init__.py b/pygad/gann/__init__.py new file mode 100644 index 0000000..ff458d4 --- /dev/null +++ b/pygad/gann/__init__.py @@ -0,0 +1,4 @@ +from .gann import * + +__version__ = "1.0.1" + diff --git a/pygad/gann/gann.py b/pygad/gann/gann.py new file mode 100644 index 0000000..69eea98 --- /dev/null +++ b/pygad/gann/gann.py @@ -0,0 +1,269 @@ +from ..nn import nn + +def validate_network_parameters(num_neurons_input, + num_neurons_output, + num_neurons_hidden_layers, + output_activation, + hidden_activations, + num_solutions=None): + """ + Validating the parameters passed to initial_population_networks() in addition to creating a list of the name(s) of the activation function(s) for the hidden layer(s). + In case that the value passed to the 'hidden_activations' parameter is a string not a list, then a list is created by replicating the passed name a number of times equal to the number of hidden layers (i.e. the length of the 'num_neurons_hidden_layers' parameter). + If an invalid parameter found, an exception is raised and the execution stops. + + The function accepts the same parameters passed to the constructor of the GANN class. + + num_neurons_input: Number of neurons in the input layer. + num_neurons_output: Number of neurons in the output layer. + num_neurons_hidden_layers: A list holding the number of neurons in the hidden layer(s). + output_activation: The name of the activation function of the output layer. + hidden_activations: The name(s) of the activation function(s) of the hidden layer(s). + num_solutions: Number of solutions (i.e. networks) in the population which defaults to None. The reason why this function sets a default value to the `num_solutions` parameter is differentiating whether a population of networks or a single network is to be created. If `None`, then a single network will be created. If not `None`, then a population of networks is to be created. + + Returns a list holding the name(s) of the activation function(s) for the hidden layer(s). + """ + + # Validating the number of solutions within the population. + if not (num_solutions is None): + if num_solutions < 2: + raise ValueError("num_solutions: The number of solutions within the population must be at least 2. The current value is {num_solutions}.".format(num_solutions=num_solutions)) + + # Validating the number of neurons in the input layer. + if num_neurons_input is int and num_neurons_input <= 0: + raise ValueError("num_neurons_input: The number of neurons in the input layer must be > 0.") + + # Validating the number of neurons in the output layer. + if num_neurons_output is int and num_neurons_output <= 0: + raise ValueError("num_neurons_output: The number of neurons in the output layer must be > 0.") + + # Validating the type of the 'num_neurons_hidden_layers' parameter which is expected to be list or tuple. + if not (type(num_neurons_hidden_layers) in [list, tuple]): + raise TypeError("num_neurons_hidden_layers: A list or a tuple is expected but {hidden_layers_neurons_type} found.".format(hidden_layers_neurons_type=type(num_neurons_hidden_layers))) + + # Frequently used error messages. + unexpected_output_activation_value = "Output activation function: The activation function of the output layer is passed as a string not {activation_type}." + unexpected_activation_value = "Activation function: The supported values for the activation function are {supported_activations} but an unexpected value is found:\n{activations}" + unexpected_activation_type = "Activation Function: A list, tuple, or a string is expected but {activations_type} found." + length_mismatch = "Hidden activation functions: When passing the activation function(s) as a list or a tuple, its length must match the length of the 'num_neurons_hidden_layers' parameter but a mismatch is found:\n{mismatched_lengths}" + + # A list of the names of the supported activation functions. + supported_activations = ["sigmoid", "relu", "softmax", "None"] + + # Validating the output layer activation function. + if not (type(output_activation) is str): + raise ValueError(unexpected_output_activation_value.format(activation_type=type(output_activation))) + if not (output_activation in supported_activations): #activation_type + raise ValueError(unexpected_activation_value.format(activations=output_activation, supported_activations=supported_activations)) + + # Number of hidden layers. + num_hidden_layers = len(num_neurons_hidden_layers) + if num_hidden_layers > 1: # In case there are more than 1 hidden layer. + if type(hidden_activations) in [list, tuple]: + num_activations = len(hidden_activations) + if num_activations != num_hidden_layers: + raise ValueError(length_mismatch.format(mismatched_lengths="{num_activations} != {num_layers}".format(num_layers=num_hidden_layers, num_activations=num_activations))) + elif type(hidden_activations) is str: + if hidden_activations in supported_activations: + hidden_activations = [hidden_activations]*num_hidden_layers + else: + raise ValueError(unexpected_activation_value.format(supported_activations=supported_activations, activations=hidden_activations)) + else: + raise TypeError(unexpected_activation_type.format(activations_type=type(hidden_activations))) + elif num_hidden_layers == 1: # In case there is only 1 hidden layer. + if (type(hidden_activations) in [list, tuple]): + if len(hidden_activations) != 1: + raise ValueError(length_mismatch.format(mismatched_lengths="{num_activations} != {num_layers}".format(num_layers=num_hidden_layers, num_activations=len(hidden_activations)))) + elif type(hidden_activations) is str: + if not (hidden_activations in supported_activations): + raise ValueError(unexpected_activation_value.format(supported_activations=supported_activations, activations=hidden_activations)) + else: + hidden_activations = [hidden_activations] + else: + raise TypeError(unexpected_activation_type.format(activations_type=type(hidden_activations))) + else: # In case there are no hidden layers (num_hidden_layers == 0) + print("WARNING: There are no hidden layers however a value is assigned to the parameter 'hidden_activations'. It will be reset to [].".format(hidden_activations=hidden_activations)) + hidden_activations = [] + + # If the value passed to the 'hidden_activations' parameter is actually a list, then its elements are checked to make sure the listed name(s) of the activation function(s) are supported. + for act in hidden_activations: + if not (act in supported_activations): + raise ValueError(unexpected_activation_value.format(supported_activations=supported_activations, activations=act)) + + return hidden_activations + +def create_network(num_neurons_input, + num_neurons_output, + num_neurons_hidden_layers=[], + output_activation="softmax", + hidden_activations="relu", + parameters_validated=False): + """ + Creates a neural network as a linked list between the input, hidden, and output layers where the layer at index N (which is the last/output layer) references the layer at index N-1 (which is a hidden layer) using its previous_layer attribute. The input layer does not reference any layer because it is the last layer in the linked list. + + In addition to the parameters_validated parameter, this function accepts the same parameters passed to the constructor of the gann.GANN class except for the num_solutions parameter because only a single network is created out of the create_network() function. + + num_neurons_input: Number of neurons in the input layer. + num_neurons_output: Number of neurons in the output layer. + num_neurons_hidden_layers=[]: A list holding the number of neurons in the hidden layer(s). If empty [], then no hidden layers are used. For each int value it holds, then a hidden layer is created with number of hidden neurons specified by the corresponding int value. For example, num_neurons_hidden_layers=[10] creates a single hidden layer with 10 neurons. num_neurons_hidden_layers=[10, 5] creates 2 hidden layers with 10 neurons for the first and 5 neurons for the second hidden layer. + output_activation="softmax": The name of the activation function of the output layer which defaults to "softmax". + hidden_activations="relu": The name(s) of the activation function(s) of the hidden layer(s). It defaults to "relu". If passed as a string, this means the specified activation function will be used across all the hidden layers. If passed as a list, then it must has the same length as the length of the num_neurons_hidden_layers list. An exception is raised if there lengths are different. When hidden_activations is a list, a one-to-one mapping between the num_neurons_hidden_layers and hidden_activations lists occurs. + parameters_validated=False: If False, then the parameters are not validated and a call to the validate_network_parameters() function is made. + + Returns the reference to the last layer in the network architecture which is the output layer. Based on such reference, all network layer can be fetched. + """ + + # When parameters_validated is False, then the parameters are not yet validated and a call to validate_network_parameters() is required. + if parameters_validated == False: + # Validating the passed parameters before creating the network. + hidden_activations = validate_network_parameters(num_neurons_input=num_neurons_input, + num_neurons_output=num_neurons_output, + num_neurons_hidden_layers=num_neurons_hidden_layers, + output_activation=output_activation, + hidden_activations=hidden_activations) + + # Creating the input layer as an instance of the nn.InputLayer class. + input_layer = nn.InputLayer(num_neurons_input) + + if len(num_neurons_hidden_layers) > 0: + # If there are hidden layers, then the first hidden layer is connected to the input layer. + hidden_layer = nn.DenseLayer(num_neurons=num_neurons_hidden_layers.pop(0), + previous_layer=input_layer, + activation_function=hidden_activations.pop(0)) + # For the other hidden layers, each hidden layer is connected to its preceding hidden layer. + for hidden_layer_idx in range(len(num_neurons_hidden_layers)): + hidden_layer = nn.DenseLayer(num_neurons=num_neurons_hidden_layers.pop(0), + previous_layer=hidden_layer, + activation_function=hidden_activations.pop(0)) + + # The last hidden layer is connected to the output layer. + # The output layer is created as an instance of the nn.DenseLayer class. + output_layer = nn.DenseLayer(num_neurons=num_neurons_output, + previous_layer=hidden_layer, + activation_function=output_activation) + + # If there are no hidden layers, then the output layer is connected directly to the input layer. + elif len(num_neurons_hidden_layers) == 0: + # The output layer is created as an instance of the nn.DenseLayer class. + output_layer = nn.DenseLayer(num_neurons=num_neurons_output, + previous_layer=input_layer, + activation_function=output_activation) + + # Returning the reference to the last layer in the network architecture which is the output layer. Based on such reference, all network layer can be fetched. + return output_layer + +def population_as_vectors(population_networks): + """ + Accepts the population as networks and returns a list holding all weights of the layers of each solution (i.e. network) in the population as a vector. + If the population has 6 solutions (i.e. networks), this function accepts references to such networks and returns a list with 6 vectors, one for each network (i.e. solution). Each vector holds the weights for all layers for a single network. + + population_networks: A list holding references to the output (last) layers of the neural networks used in the population. + + Returns a list holding the weights vectors for all solutions (i.e. networks). + """ + population_vectors = [] + for solution in population_networks: + # Converting the weights of single layer from the current network (i.e. solution) to a vector. + solution_weights_vector = nn.layers_weights_as_vector(solution) + # Appending the weights vector of the current layer of a network (i.e. solution) to the weights of the previous layers of the same network (i.e. solution). + population_vectors.append(solution_weights_vector) + + return population_vectors + +def population_as_matrices(population_networks, population_vectors): + """ + Accepts the population as both networks and weights vectors and returns the weights of all layers of each solution (i.e. network) in the population as a matrix. + If the population has 6 solutions (i.e. networks), this function returns a list with 6 matrices, one for each network holding its weights for all layers. + + population_networks: A list holding references to the output (last) layers of the neural networks used in the population. + population_vectors: A list holding the weights of all networks as vectors. Such vectors are to be converted into matrices. + + Returns a list holding the weights matrices for all solutions (i.e. networks). + """ + population_matrices = [] + for solution, solution_weights_vector in zip(population_networks, population_vectors): + # Converting the weights of single layer from the current network (i.e. solution) from a vector to a matrix. + solution_weights_matrix = nn.layers_weights_as_matrix(solution, solution_weights_vector) + # Appending the weights matrix of the current layer of a network (i.e. solution) to the weights of the previous layers of the same network (i.e. solution). + population_matrices.append(solution_weights_matrix) + + return population_matrices + +class GANN: + def create_population(self): + """ + Creates the initial population of the genetic algorithm as a list of neural networks (i.e. solutions). Each element in the list holds a reference to the last (i.e. output) layer for the network. The method does not accept any parameter and it accesses all the required details from the `GANN` instance. + + The method returns the list holding the references to the networks. + """ + + population_networks = [] + for solution in range(self.num_solutions): + # Creating a network (i.e. solution) in the population. A network or a solution can be used interchangeably. + # .copy() is so important to avoid modification in the original vale passed to the 'num_neurons_hidden_layers' and 'hidden_activations' parameters. + network = create_network(num_neurons_input=self.num_neurons_input, + num_neurons_output=self.num_neurons_output, + num_neurons_hidden_layers=self.num_neurons_hidden_layers.copy(), + output_activation=self.output_activation, + hidden_activations=self.hidden_activations.copy(), + parameters_validated=True) + + # Appending the created network to the list of population networks. + population_networks.append(network) + + return population_networks + + def __init__(self, + num_solutions, + num_neurons_input, + num_neurons_output, + num_neurons_hidden_layers=[], + output_activation="softmax", + hidden_activations="relu"): + """ + Creates an instance of the GANN class for training a neural network using the genetic algorithm. + The constructor of the GANN class creates an initial population of multiple neural networks using the create_population() method. + The population returned holds references to the last (i.e. output) layers of all created networks. + Besides creating the initial population, the passed parameters are vaidated using the validate_network_parameters() method. + + num_solutions: Number of neural networks (i.e. solutions) in the population. Based on the value passed to this parameter, a number of identical neural networks are created where their parameters are optimized using the genetic algorithm. + num_neurons_input: Number of neurons in the input layer. + num_neurons_output: Number of neurons in the output layer. + num_neurons_hidden_layers=[]: A list holding the number of neurons in the hidden layer(s). If empty [], then no hidden layers are used. For each int value it holds, then a hidden layer is created with number of hidden neurons specified by the corresponding int value. For example, num_neurons_hidden_layers=[10] creates a single hidden layer with 10 neurons. num_neurons_hidden_layers=[10, 5] creates 2 hidden layers with 10 neurons for the first and 5 neurons for the second hidden layer. + output_activation="softmax": The name of the activation function of the output layer which defaults to "softmax". + hidden_activations="relu": The name(s) of the activation function(s) of the hidden layer(s). It defaults to "relu". If passed as a string, this means the specified activation function will be used across all the hidden layers. If passed as a list, then it must has the same length as the length of the num_neurons_hidden_layers list. An exception is raised if there lengths are different. When hidden_activations is a list, a one-to-one mapping between the num_neurons_hidden_layers and hidden_activations lists occurs. + """ + + self.parameters_validated = False # If True, then the parameters passed to the GANN class constructor are valid. + + # Validating the passed parameters before building the initial population. + hidden_activations = validate_network_parameters(num_solutions=num_solutions, + num_neurons_input=num_neurons_input, + num_neurons_output=num_neurons_output, + num_neurons_hidden_layers=num_neurons_hidden_layers, + output_activation=output_activation, + hidden_activations=hidden_activations) + + self.num_solutions = num_solutions + self.num_neurons_input = num_neurons_input + self.num_neurons_output = num_neurons_output + self.num_neurons_hidden_layers = num_neurons_hidden_layers + self.output_activation = output_activation + self.hidden_activations = hidden_activations + self.parameters_validated = True + + # After the parameters are validated, the initial population is created. + self.population_networks = self.create_population() # A list holding references to all the solutions (i.e. neural networks) used in the population. + + def update_population_trained_weights(self, population_trained_weights): + """ + The `update_population_trained_weights()` method updates the `trained_weights` attribute of each network (check the [documentation of the `pygad.nn.DenseLayer` class](https://github.com/ahmedfgad/NumPyANN#nndenselayer-class) for more information) according to the weights passed in the `population_trained_weights` parameter. + + population_trained_weights: A list holding the trained weights of all networks as matrices. Such matrices are to be assigned to the 'trained_weights' attribute of all layers of all networks. + """ + idx = 0 + # Fetches all layers weights matrices for a single solution (i.e. network) + for solution in self.population_networks: + # Calling the nn.update_layers_trained_weights() function for updating the 'trained_weights' attribute for all layers in the current solution (i.e. network). + nn.update_layers_trained_weights(last_layer=solution, + final_weights=population_trained_weights[idx]) + idx = idx + 1 diff --git a/pygad/helper/__init__.py b/pygad/helper/__init__.py new file mode 100644 index 0000000..89accfe --- /dev/null +++ b/pygad/helper/__init__.py @@ -0,0 +1,3 @@ +from pygad.helper import unique + +__version__ = "1.0.0" \ No newline at end of file diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py new file mode 100644 index 0000000..4d5e6ac --- /dev/null +++ b/pygad/helper/unique.py @@ -0,0 +1,638 @@ +""" +The pygad.helper.unique module has helper methods to solve duplicate genes and make sure every gene is unique. +""" + +import numpy +import warnings +import random +import pygad + +class Unique: + def solve_duplicate_genes_randomly(self, + solution, + min_val, + max_val, + mutation_by_replacement, + gene_type, + num_trials=10): + + """ + Solves the duplicates in a solution by randomly selecting new values for the duplicating genes. + + solution: A solution with duplicate values. + min_val: Minimum value of the range to sample a number randomly. + max_val: Maximum value of the range to sample a number randomly. + mutation_by_replacement: Identical to the self.mutation_by_replacement attribute. + gene_type: Exactly the same as the self.gene_type attribute. + num_trials: Maximum number of trials to change the gene value to solve the duplicates. + + Returns: + new_solution: Solution after trying to solve its duplicates. If no duplicates solved, then it is identical to the passed solution parameter. + not_unique_indices: Indices of the genes with duplicate values. + num_unsolved_duplicates: Number of unsolved duplicates. + """ + + new_solution = solution.copy() + + _, unique_gene_indices = numpy.unique(solution, return_index=True) + not_unique_indices = set(range(len(solution))) - set(unique_gene_indices) + + num_unsolved_duplicates = 0 + if len(not_unique_indices) > 0: + for duplicate_index in not_unique_indices: + for trial_index in range(num_trials): + if self.gene_type_single == True: + if gene_type[0] in pygad.GA.supported_int_types: + temp_val = self.unique_int_gene_from_range(solution=new_solution, + gene_index=duplicate_index, + min_val=min_val, + max_val=max_val, + mutation_by_replacement=mutation_by_replacement, + gene_type=gene_type) + else: + temp_val = numpy.random.uniform(low=min_val, + high=max_val, + size=1) + if mutation_by_replacement: + pass + else: + temp_val = new_solution[duplicate_index] + temp_val + else: + if gene_type[duplicate_index] in pygad.GA.supported_int_types: + temp_val = self.unique_int_gene_from_range(solution=new_solution, + gene_index=duplicate_index, + min_val=min_val, + max_val=max_val, + mutation_by_replacement=mutation_by_replacement, + gene_type=gene_type) + else: + temp_val = numpy.random.uniform(low=min_val, + high=max_val, + size=1) + if mutation_by_replacement: + pass + else: + temp_val = new_solution[duplicate_index] + temp_val + + if self.gene_type_single == True: + if not gene_type[1] is None: + temp_val = numpy.round(gene_type[0](temp_val), + gene_type[1]) + else: + temp_val = gene_type[0](temp_val) + else: + if not gene_type[duplicate_index][1] is None: + temp_val = numpy.round(gene_type[duplicate_index][0](temp_val), + gene_type[duplicate_index][1]) + else: + temp_val = gene_type[duplicate_index][0](temp_val) + + if temp_val in new_solution and trial_index == (num_trials - 1): + num_unsolved_duplicates = num_unsolved_duplicates + 1 + if not self.suppress_warnings: warnings.warn("Failed to find a unique value for gene with index {gene_idx} whose value is {gene_value}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.".format(gene_idx=duplicate_index, gene_value=solution[duplicate_index])) + elif temp_val in new_solution: + continue + else: + new_solution[duplicate_index] = temp_val + break + + # Update the list of duplicate indices after each iteration. + _, unique_gene_indices = numpy.unique(new_solution, return_index=True) + not_unique_indices = set(range(len(solution))) - set(unique_gene_indices) + # self.logger.info("not_unique_indices INSIDE", not_unique_indices) + + return new_solution, not_unique_indices, num_unsolved_duplicates + + def solve_duplicate_genes_by_space(self, + solution, + gene_type, + num_trials=10, + build_initial_pop=False): + + """ + Solves the duplicates in a solution by selecting values for the duplicating genes from the gene space. + + solution: A solution with duplicate values. + gene_type: Exactly the same as the self.gene_type attribute. + num_trials: Maximum number of trials to change the gene value to solve the duplicates. + + Returns: + new_solution: Solution after trying to solve its duplicates. If no duplicates solved, then it is identical to the passed solution parameter. + not_unique_indices: Indices of the genes with duplicate values. + num_unsolved_duplicates: Number of unsolved duplicates. + """ + + new_solution = solution.copy() + + _, unique_gene_indices = numpy.unique(solution, return_index=True) + not_unique_indices = set(range(len(solution))) - set(unique_gene_indices) + # self.logger.info("not_unique_indices OUTSIDE", not_unique_indices) + + # First try to solve the duplicates. + # For a solution like [3 2 0 0], the indices of the 2 duplicating genes are 2 and 3. + # The next call to the find_unique_value() method tries to change the value of the gene with index 3 to solve the duplicate. + if len(not_unique_indices) > 0: + new_solution, not_unique_indices, num_unsolved_duplicates = self.unique_genes_by_space(new_solution=new_solution, + gene_type=gene_type, + not_unique_indices=not_unique_indices, + num_trials=10, + build_initial_pop=build_initial_pop) + else: + return new_solution, not_unique_indices, len(not_unique_indices) + + # Do another try if there exist duplicate genes. + # If there are no possible values for the gene 3 with index 3 to solve the duplicate, try to change the value of the other gene with index 2. + if len(not_unique_indices) > 0: + not_unique_indices = set(numpy.where(new_solution == new_solution[list(not_unique_indices)[0]])[0]) - set([list(not_unique_indices)[0]]) + new_solution, not_unique_indices, num_unsolved_duplicates = self.unique_genes_by_space(new_solution=new_solution, + gene_type=gene_type, + not_unique_indices=not_unique_indices, + num_trials=10, + build_initial_pop=build_initial_pop) + else: + # If there exist duplicate genes, then changing either of the 2 duplicating genes (with indices 2 and 3) will not solve the problem. + # This problem can be solved by randomly changing one of the non-duplicating genes that may make a room for a unique value in one the 2 duplicating genes. + # For example, if gene_space=[[3, 0, 1], [4, 1, 2], [0, 2], [3, 2, 0]] and the solution is [3 2 0 0], then the values of the last 2 genes duplicate. + # There are no possible changes in the last 2 genes to solve the problem. But it could be solved by changing the second gene from 2 to 4. + # As a result, any of the last 2 genes can take the value 2 and solve the duplicates. + return new_solution, not_unique_indices, len(not_unique_indices) + + return new_solution, not_unique_indices, num_unsolved_duplicates + + def unique_int_gene_from_range(self, + solution, + gene_index, + min_val, + max_val, + mutation_by_replacement, + gene_type, + step=None): + + """ + Finds a unique integer value for the gene. + + solution: A solution with duplicate values. + gene_index: Index of the gene to find a unique value. + min_val: Minimum value of the range to sample a number randomly. + max_val: Maximum value of the range to sample a number randomly. + mutation_by_replacement: Identical to the self.mutation_by_replacement attribute. + gene_type: Exactly the same as the self.gene_type attribute. + + Returns: + selected_value: The new value of the gene. It may be identical to the original gene value in case there are no possible unique values for the gene. + """ + + if self.gene_type_single == True: + if step is None: + all_gene_values = numpy.arange(min_val, max_val, dtype=gene_type[0]) + else: + # For non-integer steps, the numpy.arange() function returns zeros id the dtype parameter is set to an integer data type. So, this returns zeros if step is non-integer and dtype is set to an int data type: numpy.arange(min_val, max_val, step, dtype=gene_type[0]) + # To solve this issue, the data type casting will not be handled inside numpy.arange(). The range is generated by numpy.arange() and then the data type is converted using the numpy.asarray() function. + all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), dtype=gene_type[0]) + else: + if step is None: + all_gene_values = numpy.arange(min_val, max_val, dtype=gene_type[gene_index][0]) + else: + all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), dtype=gene_type[gene_index][0]) + + if mutation_by_replacement: + pass + else: + all_gene_values = all_gene_values + solution[gene_index] + + if self.gene_type_single == True: + if not gene_type[1] is None: + all_gene_values = numpy.round(gene_type[0](all_gene_values), + gene_type[1]) + else: + if type(all_gene_values) is numpy.ndarray: + all_gene_values = numpy.asarray(all_gene_values, dtype=gene_type[0]) + else: + all_gene_values = gene_type[0](all_gene_values) + else: + if not gene_type[gene_index][1] is None: + all_gene_values = numpy.round(gene_type[gene_index][0](all_gene_values), + gene_type[gene_index][1]) + else: + all_gene_values = gene_type[gene_index][0](all_gene_values) + + values_to_select_from = list(set(all_gene_values) - set(solution)) + + if len(values_to_select_from) == 0: + if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but there is no enough values to prevent duplicates.") + selected_value = solution[gene_index] + else: + selected_value = random.choice(values_to_select_from) + + #if self.gene_type_single == True: + # selected_value = gene_type[0](selected_value) + #else: + # selected_value = gene_type[gene_index][0](selected_value) + + return selected_value + + def unique_genes_by_space(self, + new_solution, + gene_type, + not_unique_indices, + num_trials=10, + build_initial_pop=False): + + """ + Loops through all the duplicating genes to find unique values that from their gene spaces to solve the duplicates. + For each duplicating gene, a call to the unique_gene_by_space() function is made. + + new_solution: A solution with duplicate values. + gene_type: Exactly the same as the self.gene_type attribute. + not_unique_indices: Indices with duplicating values. + num_trials: Maximum number of trials to change the gene value to solve the duplicates. + + Returns: + new_solution: Solution after trying to solve all of its duplicates. If no duplicates solved, then it is identical to the passed solution parameter. + not_unique_indices: Indices of the genes with duplicate values. + num_unsolved_duplicates: Number of unsolved duplicates. + """ + + num_unsolved_duplicates = 0 + for duplicate_index in not_unique_indices: + for trial_index in range(num_trials): + temp_val = self.unique_gene_by_space(solution=new_solution, + gene_idx=duplicate_index, + gene_type=gene_type, + build_initial_pop=build_initial_pop) + + if temp_val in new_solution and trial_index == (num_trials - 1): + # self.logger.info("temp_val, duplicate_index", temp_val, duplicate_index, new_solution) + num_unsolved_duplicates = num_unsolved_duplicates + 1 + if not self.suppress_warnings: warnings.warn("Failed to find a unique value for gene with index {gene_idx} whose value is {gene_value}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.".format(gene_idx=duplicate_index, gene_value=new_solution[duplicate_index])) + elif temp_val in new_solution: + continue + else: + new_solution[duplicate_index] = temp_val + # self.logger.info("SOLVED", duplicate_index) + break + + # Update the list of duplicate indices after each iteration. + _, unique_gene_indices = numpy.unique(new_solution, return_index=True) + not_unique_indices = set(range(len(new_solution))) - set(unique_gene_indices) + # self.logger.info("not_unique_indices INSIDE", not_unique_indices) + + return new_solution, not_unique_indices, num_unsolved_duplicates + + def unique_gene_by_space(self, + solution, + gene_idx, + gene_type, + build_initial_pop=False): + + """ + Returns a unique gene value for a single gene based on its value space to solve the duplicates. + + solution: A solution with duplicate values. + gene_idx: The index of the gene that duplicates its value with another gene. + gene_type: Exactly the same as the self.gene_type attribute. + + Returns: + A unique value, if exists, for the gene. + """ + + if self.gene_space_nested: + # Returning the current gene space from the 'gene_space' attribute. + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: + curr_gene_space = self.gene_space[gene_idx].copy() + else: + curr_gene_space = self.gene_space[gene_idx] + + # If the gene space has only a single value, use it as the new gene value. + if type(curr_gene_space) in pygad.GA.supported_int_float_types: + value_from_space = curr_gene_space + # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + elif curr_gene_space is None: + if self.gene_type_single == True: + if gene_type[0] in pygad.GA.supported_int_types: + if build_initial_pop == True: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + # min_val=self.random_mutation_min_val, + # max_val=self.random_mutation_max_val, + min_val=self.init_range_low, + max_val=self.init_range_high, + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if build_initial_pop == True: + value_from_space = numpy.random.uniform(# low=self.random_mutation_min_val, + # high=self.random_mutation_max_val, + low=self.init_range_low, + high=self.init_range_high, + size=1) + else: + value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + if self.mutation_by_replacement: + pass + else: + value_from_space = solution[gene_idx] + value_from_space + else: + if gene_type[gene_idx] in pygad.GA.supported_int_types: + if build_initial_pop == True: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + # min_val=self.random_mutation_min_val, + # max_val=self.random_mutation_max_val, + min_val=self.init_range_low, + max_val=self.init_range_high, + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if build_initial_pop == True: + value_from_space = numpy.random.uniform(# low=self.random_mutation_min_val, + # high=self.random_mutation_max_val, + low=self.init_range_low, + high=self.init_range_high, + size=1) + else: + value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + if self.mutation_by_replacement: + pass + else: + value_from_space = solution[gene_idx] + value_from_space + + elif type(curr_gene_space) is dict: + if self.gene_type_single == True: + if gene_type[0] in pygad.GA.supported_int_types: + if build_initial_pop == True: + if 'step' in curr_gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=curr_gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if 'step' in curr_gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=curr_gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if 'step' in curr_gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], + stop=curr_gene_space['high'], + step=curr_gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=curr_gene_space['low'], + high=curr_gene_space['high'], + size=1) + if self.mutation_by_replacement: + pass + else: + value_from_space = solution[gene_idx] + value_from_space + else: + if gene_type[gene_idx] in pygad.GA.supported_int_types: + if build_initial_pop == True: + if 'step' in curr_gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=curr_gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if 'step' in curr_gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=curr_gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if 'step' in curr_gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], + stop=curr_gene_space['high'], + step=curr_gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=curr_gene_space['low'], + high=curr_gene_space['high'], + size=1) + if self.mutation_by_replacement: + pass + else: + value_from_space = solution[gene_idx] + value_from_space + + else: + # Selecting a value randomly based on the current gene's space in the 'gene_space' attribute. + # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. + if len(curr_gene_space) == 1: + value_from_space = curr_gene_space[0] + if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but the space of the gene with index {gene_idx} has only a single value. Thus, duplicates are possible.".format(gene_idx=gene_idx)) + # If the gene space has more than 1 value, then select a new one that is different from the current value. + else: + values_to_select_from = list(set(curr_gene_space) - set(solution)) + + if len(values_to_select_from) == 0: + if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but the gene space does not have enough values to prevent duplicates.") + value_from_space = solution[gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + else: + # Selecting a value randomly from the global gene space in the 'gene_space' attribute. + if type(self.gene_space) is dict: + if self.gene_type_single == True: + if gene_type[0] in pygad.GA.supported_int_types: + if build_initial_pop == True: + if 'step' in self.gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=self.gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if 'step' in self.gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=self.gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + # When the gene_space is assigned a dict object, then it specifies the lower and upper limits of all genes in the space. + if 'step' in self.gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=1) + if self.mutation_by_replacement: + pass + else: + value_from_space = solution[gene_idx] + value_from_space + else: + if gene_type[gene_idx] in pygad.GA.supported_int_types: + if build_initial_pop == True: + if 'step' in self.gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=self.gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + if 'step' in self.gene_space.keys(): + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=self.gene_space['step'], + mutation_by_replacement=True, + gene_type=gene_type) + else: + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=None, + mutation_by_replacement=True, + gene_type=gene_type) + else: + # When the gene_space is assigned a dict object, then it specifies the lower and upper limits of all genes in the space. + if 'step' in self.gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=1) + if self.mutation_by_replacement: + pass + else: + value_from_space = solution[gene_idx] + value_from_space + + else: + # If the space type is not of type dict, then a value is randomly selected from the gene_space attribute. + # Remove all the genes in the current solution from the gene_space. + # This only leaves the unique values that could be selected for the gene. + values_to_select_from = list(set(self.gene_space) - set(solution)) + + if len(values_to_select_from) == 0: + if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but the gene space does not have enough values to prevent duplicates.") + value_from_space = solution[gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + + if value_from_space is None: + if build_initial_pop == True: + value_from_space = numpy.random.uniform(# low=self.random_mutation_min_val, + # high=self.random_mutation_max_val, + low=self.init_range_low, + high=self.init_range_high, + size=1) + else: + value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + + if self.gene_type_single == True: + if not gene_type[1] is None: + value_from_space = numpy.round(gene_type[0](value_from_space), + gene_type[1]) + else: + value_from_space = gene_type[0](value_from_space) + else: + if not gene_type[gene_idx][1] is None: + value_from_space = numpy.round(gene_type[gene_idx][0](value_from_space), + gene_type[gene_idx][1]) + else: + value_from_space = gene_type[gene_idx][0](value_from_space) + + return value_from_space diff --git a/pygad/kerasga/__init__.py b/pygad/kerasga/__init__.py new file mode 100644 index 0000000..7e6b139 --- /dev/null +++ b/pygad/kerasga/__init__.py @@ -0,0 +1,3 @@ +from .kerasga import * + +__version__ = "1.2.0" diff --git a/pygad/kerasga/kerasga.py b/pygad/kerasga/kerasga.py new file mode 100644 index 0000000..0e1b618 --- /dev/null +++ b/pygad/kerasga/kerasga.py @@ -0,0 +1,89 @@ +import copy +import numpy +import tensorflow.keras + +def model_weights_as_vector(model): + weights_vector = [] + + for layer in model.layers: # model.get_weights(): + if layer.trainable: + layer_weights = layer.get_weights() + for l_weights in layer_weights: + vector = numpy.reshape(l_weights, newshape=(l_weights.size)) + weights_vector.extend(vector) + + return numpy.array(weights_vector) + +def model_weights_as_matrix(model, weights_vector): + weights_matrix = [] + + start = 0 + for layer_idx, layer in enumerate(model.layers): # model.get_weights(): + # for w_matrix in model.get_weights(): + layer_weights = layer.get_weights() + if layer.trainable: + for l_weights in layer_weights: + layer_weights_shape = l_weights.shape + layer_weights_size = l_weights.size + + layer_weights_vector = weights_vector[start:start + layer_weights_size] + layer_weights_matrix = numpy.reshape(layer_weights_vector, newshape=(layer_weights_shape)) + weights_matrix.append(layer_weights_matrix) + + start = start + layer_weights_size + else: + for l_weights in layer_weights: + weights_matrix.append(l_weights) + + return weights_matrix + +def predict(model, solution, data): + # Fetch the parameters of the best solution. + solution_weights = model_weights_as_matrix(model=model, + weights_vector=solution) + _model = tensorflow.keras.models.clone_model(model) + _model.set_weights(solution_weights) + predictions = _model.predict(data) + + return predictions + +class KerasGA: + + def __init__(self, model, num_solutions): + + """ + Creates an instance of the KerasGA class to build a population of model parameters. + + model: A Keras model class. + num_solutions: Number of solutions in the population. Each solution has different model parameters. + """ + + self.model = model + + self.num_solutions = num_solutions + + # A list holding references to all the solutions (i.e. networks) used in the population. + self.population_weights = self.create_population() + + def create_population(self): + + """ + Creates the initial population of the genetic algorithm as a list of networks' weights (i.e. solutions). Each element in the list holds a different weights of the Keras model. + + The method returns a list holding the weights of all solutions. + """ + + model_weights_vector = model_weights_as_vector(model=self.model) + + net_population_weights = [] + net_population_weights.append(model_weights_vector) + + for idx in range(self.num_solutions-1): + + net_weights = copy.deepcopy(model_weights_vector) + net_weights = numpy.array(net_weights) + numpy.random.uniform(low=-1.0, high=1.0, size=model_weights_vector.size) + + # Appending the weights to the population. + net_population_weights.append(net_weights) + + return net_population_weights diff --git a/pygad/nn/__init__.py b/pygad/nn/__init__.py new file mode 100644 index 0000000..224d984 --- /dev/null +++ b/pygad/nn/__init__.py @@ -0,0 +1,4 @@ +from .nn import * + +__version__ = "1.2.1" + diff --git a/pygad/nn/nn.py b/pygad/nn/nn.py new file mode 100644 index 0000000..04933d7 --- /dev/null +++ b/pygad/nn/nn.py @@ -0,0 +1,399 @@ +import numpy +import functools + +""" +This project creates a neural network where the architecture has input and dense layers only. More layers will be added in the future. +The project only implements the forward pass of a neural network and no training algorithm is used. +For training a neural network using the genetic algorithm, check this project (https://github.com/ahmedfgad/NeuralGenetic) in which the genetic algorithm is used for training the network. +Feel free to leave an issue in this project (https://github.com/ahmedfgad/NumPyANN) in case something is not working properly or to ask for questions. I am also available for e-mails at ahmed.f.gad@gmail.com +""" + +def layers_weights(last_layer, initial=True): + """ + Creates a list holding the weights of all layers in the neural network. + + last_layer: A reference to the last (output) layer in the network architecture. + initial: When True, the function returns the initial weights of the layers. When False, the trained weights of the layers are returned. The initial weights are only needed before network training starts. The trained weights are needed to predict the network outputs. + + Returns a list (network_weights) holding the weights of the layers. + """ + network_weights = [] + + layer = last_layer + while "previous_layer" in layer.__init__.__code__.co_varnames: + # If the 'initial' parameter is True, append the initial weights. Otherwise, append the trained weights. + if initial == True: + network_weights.append(layer.initial_weights) + elif initial == False: + network_weights.append(layer.trained_weights) + else: + raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + + # Go to the previous layer. + layer = layer.previous_layer + + # If the first layer in the network is not an input layer (i.e. an instance of the InputLayer class), raise an error. + if not (type(layer) is InputLayer): + raise TypeError("The first layer in the network architecture must be an input layer.") + + # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. + # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). + network_weights.reverse() + return network_weights + +def layers_weights_as_vector(last_layer, initial=True): + """ + Creates a list holding the weights of each layer in the network as a vector. + + last_layer: A reference to the last (output) layer in the network architecture. + initial: When True, the function returns the initial weights of the layers. When False, the trained weights of the layers are returned. The initial weights are only needed before network training starts. The trained weights are needed to predict the network outputs. + + Returns a list (network_weights) holding the weights of the layers as a vector. + """ + network_weights = [] + + layer = last_layer + while "previous_layer" in layer.__init__.__code__.co_varnames: + # If the 'initial' parameter is True, append the initial weights. Otherwise, append the trained weights. + if initial == True: + vector = numpy.reshape(layer.initial_weights, newshape=(layer.initial_weights.size)) +# vector = DenseLayer.to_vector(matrix=layer.initial_weights) + network_weights.extend(vector) + elif initial == False: + vector = numpy.reshape(layer.trained_weights, newshape=(layer.trained_weights.size)) +# vector = DenseLayer.to_vector(array=layer.trained_weights) + network_weights.extend(vector) + else: + raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + + # Go to the previous layer. + layer = layer.previous_layer + + # If the first layer in the network is not an input layer (i.e. an instance of the InputLayer class), raise an error. + if not (type(layer) is InputLayer): + raise TypeError("The first layer in the network architecture must be an input layer.") + + # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. + # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). + network_weights.reverse() + return numpy.array(network_weights) + +def layers_weights_as_matrix(last_layer, vector_weights): + """ + Converts the network weights from vectors to matrices. + + last_layer: A reference to the last (output) layer in the network architecture. + vector_weights: The network weights as vectors where the weights of each layer form a single vector. + + Returns a list (network_weights) holding the weights of the layers as matrices. + """ + network_weights = [] + + start = 0 + layer = last_layer + vector_weights = vector_weights[::-1] + while "previous_layer" in layer.__init__.__code__.co_varnames: + layer_weights_shape = layer.initial_weights.shape + layer_weights_size = layer.initial_weights.size + + weights_vector=vector_weights[start:start + layer_weights_size] +# matrix = DenseLayer.to_array(vector=weights_vector, shape=layer_weights_shape) + matrix = numpy.reshape(weights_vector, newshape=(layer_weights_shape)) + network_weights.append(matrix) + + start = start + layer_weights_size + + # Go to the previous layer. + layer = layer.previous_layer + + # If the first layer in the network is not an input layer (i.e. an instance of the InputLayer class), raise an error. + if not (type(layer) is InputLayer): + raise TypeError("The first layer in the network architecture must be an input layer.") + + # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. + # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). + network_weights.reverse() + return network_weights + +def layers_activations(last_layer): + """ + Creates a list holding the activation functions of all layers in the network. + + last_layer: A reference to the last (output) layer in the network architecture. + + Returns a list (activations) holding the activation functions of the layers. + """ + activations = [] + + layer = last_layer + while "previous_layer" in layer.__init__.__code__.co_varnames: + activations.append(layer.activation_function) + + # Go to the previous layer. + layer = layer.previous_layer + + if not (type(layer) is InputLayer): + raise TypeError("The first layer in the network architecture must be an input layer.") + + # Currently, the activations of layers are in the reverse order. In other words, the activation function of the first layer are at the last index of the 'activations' list while the activation function of the last layer are at the first index. + # Reversing the 'activations' list to order the layers' weights according to their location in the network architecture (i.e. the activation function of the first layer appears at index 0 of the list). + activations.reverse() + return activations + +def sigmoid(sop): + + """ + Applies the sigmoid function. + + sop: The input to which the sigmoid function is applied. + + Returns the result of the sigmoid function. + """ + + if type(sop) in [list, tuple]: + sop = numpy.array(sop) + + return 1.0 / (1 + numpy.exp(-1 * sop)) + +def relu(sop): + + """ + Applies the rectified linear unit (ReLU) function. + + sop: The input to which the relu function is applied. + + Returns the result of the ReLU function. + """ + + if not (type(sop) in [list, tuple, numpy.ndarray]): + if sop < 0: + return 0 + else: + return sop + elif type(sop) in [list, tuple]: + sop = numpy.array(sop) + + result = sop + result[sop < 0] = 0 + + return result + +def softmax(layer_outputs): + + """ + Applies the sotmax function. + + sop: The input to which the softmax function is applied. + + Returns the result of the softmax function. + """ + return layer_outputs / (numpy.sum(layer_outputs) + 0.000001) + +def train(num_epochs, + last_layer, + data_inputs, + data_outputs, + problem_type="classification", + learning_rate=0.01): + """ + Trains the neural network. + + num_epochs: Number of epochs. + last_layer: Reference to the last (output) layer in the network architecture. + data_inputs: Data features. + data_outputs: Data outputs. + problem_type: Can be either classification or regression to define the problem type. + learning_rate: Learning rate which defaults to 0.01. + """ + + if not (problem_type in ["classification", "regression"]): + raise ValueError("The value of the problem_type parameter can be either classification or regression but {problem_type_val} found.".format(problem_type_val=problem_type)) + + # To fetch the initial weights of the layer, the 'initial' argument is set to True. + weights = layers_weights(last_layer, initial=True) + activations = layers_activations(last_layer) + + network_error = 0 + for epoch in range(num_epochs): + print("Epoch ", epoch) + for sample_idx in range(data_inputs.shape[0]): + r1 = data_inputs[sample_idx, :] + for idx in range(len(weights) - 1): + curr_weights = weights[idx] + r1 = numpy.matmul(r1, curr_weights) + if activations[idx] == "relu": + r1 = relu(r1) + elif activations[idx] == "sigmoid": + r1 = sigmoid(r1) + elif activations[idx] == "softmax": + r1 = softmax(r1) + elif activations[idx] == None: + pass + + curr_weights = weights[-1] + r1 = numpy.matmul(r1, curr_weights) + + if problem_type == "classification": + prediction = numpy.where(r1 == numpy.max(r1))[0][0] + else: + prediction = r1 + + network_error = network_error + numpy.mean(numpy.abs((prediction - data_outputs[sample_idx]))) + + # Updating the network weights once after completing an epoch (i.e. passing through all the samples). + weights = update_weights(weights=weights, + network_error=network_error, + learning_rate=learning_rate) + + # Initially, the 'trained_weights' attribute of the layers are set to None. After the is trained, the 'trained_weights' attribute is updated by the trained weights using the update_layers_trained_weights() function. + update_layers_trained_weights(last_layer, weights) + +def update_weights(weights, network_error, learning_rate): + """ + Updates the network weights using the learning rate only. + The purpose of this project is to only apply the forward pass of training a neural network. Thus, there is no optimization algorithm is used like the gradient descent. + For optimizing the neural network, check this project (https://github.com/ahmedfgad/NeuralGenetic) in which the genetic algorithm is used for training the network. + + weights: The current weights of the network. + network_error: The network error. + learning_rate: The learning rate. + + It returns the new weights. + """ + # weights = numpy.array(weights) + for layer_idx in range(len(weights)): + weights[layer_idx] = network_error * learning_rate * weights[layer_idx] + + return weights + +def update_layers_trained_weights(last_layer, final_weights): + """ + After the network weights are trained, the 'trained_weights' attribute of each layer is updated by the weights calculated after passing all the epochs (such weights are passed in the 'final_weights' parameter). + By just passing a reference to the last layer in the network (i.e. output layer) in addition to the final weights, this function updates the 'trained_weights' attribute of all layers. + + last_layer: A reference to the last (output) layer in the network architecture. + final_weights: An array of weights of all layers in the network after passing through all the epochs. + """ + layer = last_layer + layer_idx = len(final_weights) - 1 + while "previous_layer" in layer.__init__.__code__.co_varnames: + layer.trained_weights = final_weights[layer_idx] + + layer_idx = layer_idx - 1 + # Go to the previous layer. + layer = layer.previous_layer + +def predict(last_layer, data_inputs, problem_type="classification"): + """ + Uses the trained weights for predicting the samples' outputs. + + last_layer: A reference to the last (output) layer in the network architecture. + data_inputs: Data features. + problem_type: Can be either classification or regression to define the problem type. + + Returns the predictions of all samples. + """ + if not (problem_type in ["classification", "regression"]): + raise ValueError("The value of the problem_type parameter can be either classification or regression but {problem_type_val} found.".format(problem_type_val=problem_type)) + + # To fetch the trained weights of the layer, the 'initial' argument is set to False. + weights = layers_weights(last_layer, initial=False) + activations = layers_activations(last_layer) + + if len(weights) != len(activations): + raise TypeError("The length of layers {num_layers} is not equal to the number of activations functions {num_activations} and they must be equal.".format(num_layers=len(weights), num_activations=len(activations))) + + predictions = [] + for sample_idx in range(data_inputs.shape[0]): + r1 = data_inputs[sample_idx, :] + for curr_weights, activation in zip(weights, activations): + r1 = numpy.matmul(r1, curr_weights) + if activation == "relu": + r1 = relu(r1) + elif activation == "sigmoid": + r1 = sigmoid(r1) + elif activation == "softmax": + r1 = softmax(r1) + elif activation == None: + pass + + if problem_type == "classification": + prediction = numpy.where(r1 == numpy.max(r1))[0][0] + else: + prediction = r1 + + predictions.append(prediction) + + return predictions + +def to_vector(array): + """ + Converts a passed NumPy array (of any dimensionality) to its `array` parameter into a 1D vector and returns the vector. + + array: The NumPy array to be converted into a 1D vector. + + Returns the array after being reshaped into a NumPy 1D vector. + + Example: weights_vector = nn.DenseLayer.to_vector(array=array) + """ + if not (type(array) is numpy.ndarray): + raise TypeError("An input of type numpy.ndarray is expected but an input of type {in_type} found.".format(in_type=type(array))) + return numpy.reshape(array, newshape=(array.size)) + +def to_array(vector, shape): + """ + Converts a passed vector to its `vector` parameter into a NumPy array and returns the array. + + vector: The 1D vector to be converted into an array. + shape: The target shape of the array. + + Returns the NumPy 1D vector after being reshaped into an array. + + Example: weights_matrix = nn.DenseLayer.to_array(vector=vector, shape=shape) + """ + if not (type(vector) is numpy.ndarray): + raise TypeError("An input of type numpy.ndarray is expected but an input of type {in_type} found.".format(in_type=type(vector))) + if vector.ndim > 1: + raise ValueError("A 1D NumPy array is expected but an array of {ndim} dimensions found.".format(ndim=vector.ndim)) + if vector.size != functools.reduce(lambda x,y:x*y, shape, 1): # (operator.mul == lambda x,y:x*y + raise ValueError("Mismatch between the vector length and the array shape. A vector of length {vector_length} cannot be converted into a array of shape ({array_shape}).".format(vector_length=vector.size, array_shape=shape)) + return numpy.reshape(vector, newshape=shape) + +class InputLayer: + """ + Implementing the input layer of a neural network. + """ + def __init__(self, num_inputs): + if num_inputs <= 0: + raise ValueError("Number of input neurons cannot be <= 0. Please pass a valid value to the 'num_inputs' parameter.") + # The number of neurons in the input layer. + self.num_neurons = num_inputs + +class DenseLayer: + """ + Implementing the input dense (fully connected) layer of a neural network. + """ + def __init__(self, num_neurons, previous_layer, activation_function="sigmoid"): + if num_neurons <= 0: + raise ValueError("Number of neurons cannot be <= 0. Please pass a valid value to the 'num_neurons' parameter.") + # Number of neurons in the dense layer. + self.num_neurons = num_neurons + + supported_activation_functions = ("sigmoid", "relu", "softmax", "None") + if not (activation_function in supported_activation_functions): + raise ValueError("The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.".format(activation_function=activation_function, supported_activation_functions=supported_activation_functions)) + self.activation_function = activation_function + + if previous_layer is None: + raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + # A reference to the layer that preceeds the current layer in the network architecture. + self.previous_layer = previous_layer + + # Initializing the weights of the layer. + self.initial_weights = numpy.random.uniform(low=-0.1, + high=0.1, + size=(previous_layer.num_neurons, num_neurons)) + + # The trained weights of the layer. Only assigned a value after the network is trained (i.e. the train() function completes). + # Just initialized to be equal to the initial weights + self.trained_weights = self.initial_weights.copy() \ No newline at end of file diff --git a/pygad/pygad.py b/pygad/pygad.py new file mode 100644 index 0000000..9916a15 --- /dev/null +++ b/pygad/pygad.py @@ -0,0 +1,2219 @@ +import numpy +import random +import cloudpickle +import time +import warnings +import concurrent.futures +import inspect +import logging +from pygad import utils +from pygad import helper +from pygad import visualize + +class GA(utils.parent_selection.ParentSelection, + utils.crossover.Crossover, + utils.mutation.Mutation, + helper.unique.Unique, + visualize.plot.Plot): + + supported_int_types = [int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64] + supported_float_types = [float, numpy.float16, numpy.float32, numpy.float64] + supported_int_float_types = supported_int_types + supported_float_types + + def __init__(self, + num_generations, + num_parents_mating, + fitness_func, + fitness_batch_size=None, + initial_population=None, + sol_per_pop=None, + num_genes=None, + init_range_low=-4, + init_range_high=4, + gene_type=float, + parent_selection_type="sss", + keep_parents=-1, + keep_elitism=1, + K_tournament=3, + crossover_type="single_point", + crossover_probability=None, + mutation_type="random", + mutation_probability=None, + mutation_by_replacement=False, + mutation_percent_genes='default', + mutation_num_genes=None, + random_mutation_min_val=-1.0, + random_mutation_max_val=1.0, + gene_space=None, + allow_duplicate_genes=True, + on_start=None, + on_fitness=None, + on_parents=None, + on_crossover=None, + on_mutation=None, + on_generation=None, + on_stop=None, + delay_after_gen=0.0, + save_best_solutions=False, + save_solutions=False, + suppress_warnings=False, + stop_criteria=None, + parallel_processing=None, + random_seed=None, + logger=None): + + """ + The constructor of the GA class accepts all parameters required to create an instance of the GA class. It validates such parameters. + + num_generations: Number of generations. + num_parents_mating: Number of solutions to be selected as parents in the mating pool. + + fitness_func: Accepts a function/method and returns the fitness value of the solution. In PyGAD 2.20.0, a third parameter is passed referring to the 'pygad.GA' instance. If method, then it must accept 4 parameters where the fourth one refers to the method's object. + fitness_batch_size: Added in PyGAD 2.19.0. Supports calculating the fitness in batches. If the value is 1 or None, then the fitness function is called for each invidiaul solution. If given another value X where X is neither 1 nor None (e.g. X=3), then the fitness function is called once for each X (3) solutions. + + initial_population: A user-defined initial population. It is useful when the user wants to start the generations with a custom initial population. It defaults to None which means no initial population is specified by the user. In this case, PyGAD creates an initial population using the 'sol_per_pop' and 'num_genes' parameters. An exception is raised if the 'initial_population' is None while any of the 2 parameters ('sol_per_pop' or 'num_genes') is also None. + sol_per_pop: Number of solutions in the population. + num_genes: Number of parameters in the function. + + init_range_low: The lower value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20 and higher. + init_range_high: The upper value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20. + # It is OK to set the value of any of the 2 parameters ('init_range_low' and 'init_range_high') to be equal, higher or lower than the other parameter (i.e. init_range_low is not needed to be lower than init_range_high). + + gene_type: The type of the gene. It is assigned to any of these types (int, float, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32, numpy.float64) and forces all the genes to be of that type. + + parent_selection_type: Type of parent selection. + keep_parents: If 0, this means no parent in the current population will be used in the next population. If -1, this means all parents in the current population will be used in the next population. If set to a value > 0, then the specified value refers to the number of parents in the current population to be used in the next population. Some parent selection operators such as rank selection, favor population diversity and therefore keeping the parents in the next generation can be beneficial. However, some other parent selection operators, such as roulette wheel selection (RWS), have higher selection pressure and keeping more than one parent in the next generation can seriously harm population diversity. This parameter have an effect only when the keep_elitism parameter is 0. Thanks to Prof. Fernando Jiménez Barrionuevo (http://webs.um.es/fernan) for editing this sentence. + K_tournament: When the value of 'parent_selection_type' is 'tournament', the 'K_tournament' parameter specifies the number of solutions from which a parent is selected randomly. + + keep_elitism: Added in PyGAD 2.18.0. It can take the value 0 or a positive integer that satisfies (0 <= keep_elitism <= sol_per_pop). It defaults to 1 which means only the best solution in the current generation is kept in the next generation. If assigned 0, this means it has no effect. If assigned a positive integer K, then the best K solutions are kept in the next generation. It cannot be assigned a value greater than the value assigned to the sol_per_pop parameter. If this parameter has a value different than 0, then the keep_parents parameter will have no effect. + + crossover_type: Type of the crossover opreator. If crossover_type=None, then the crossover step is bypassed which means no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. + crossover_probability: The probability of selecting a solution for the crossover operation. If the solution probability is <= crossover_probability, the solution is selected. The value must be between 0 and 1 inclusive. + + mutation_type: Type of the mutation opreator. If mutation_type=None, then the mutation step is bypassed which means no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. + mutation_probability: The probability of selecting a gene for the mutation operation. If the gene probability is <= mutation_probability, the gene is selected. It accepts either a single value for fixed mutation or a list/tuple/numpy.ndarray of 2 values for adaptive mutation. The values must be between 0 and 1 inclusive. If specified, then no need for the 2 parameters mutation_percent_genes and mutation_num_genes. + + mutation_by_replacement: An optional bool parameter. It works only when the selected type of mutation is random (mutation_type="random"). In this case, setting mutation_by_replacement=True means replace the gene by the randomly generated value. If False, then it has no effect and random mutation works by adding the random value to the gene. + + mutation_percent_genes: Percentage of genes to mutate which defaults to the string 'default' which means 10%. This parameter has no action if any of the 2 parameters mutation_probability or mutation_num_genes exist. + mutation_num_genes: Number of genes to mutate which defaults to None. If the parameter mutation_num_genes exists, then no need for the parameter mutation_percent_genes. This parameter has no action if the mutation_probability parameter exists. + random_mutation_min_val: The minimum value of the range from which a random value is selected to be added to the selected gene(s) to mutate. It defaults to -1.0. + random_mutation_max_val: The maximum value of the range from which a random value is selected to be added to the selected gene(s) to mutate. It defaults to 1.0. + + gene_space: It accepts a list of all possible values of the gene. This list is used in the mutation step. Should be used only if the gene space is a set of discrete values. No need for the 2 parameters (random_mutation_min_val and random_mutation_max_val) if the parameter gene_space exists. Added in PyGAD 2.5.0. In PyGAD 2.11.0, the gene_space can be assigned a dict. + + on_start: Accepts a function/method to be called only once before the genetic algorithm starts its evolution. If function, then it must accept a single parameter representing the instance of the genetic algorithm. If method, then it must accept 2 parameters where the second one refers to the method's object. Added in PyGAD 2.6.0. + on_fitness: Accepts a function/method to be called after calculating the fitness values of all solutions in the population. If function, then it must accept 2 parameters: 1) a list of all solutions' fitness values 2) the instance of the genetic algorithm. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. + on_parents: Accepts a function/method to be called after selecting the parents that mates. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one represents the selected parents. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. + on_crossover: Accepts a function/method to be called each time the crossover operation is applied. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one represents the offspring generated using crossover. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. + on_mutation: Accepts a function/method to be called each time the mutation operation is applied. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one represents the offspring after applying the mutation. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. + on_generation: Accepts a function/method to be called after each generation. If function, then it must accept a single parameter representing the instance of the genetic algorithm. If the function returned "stop", then the run() method stops without completing the other generations. If method, then it must accept 2 parameters where the second one refers to the method's object. Added in PyGAD 2.6.0. + on_stop: Accepts a function/method to be called only once exactly before the genetic algorithm stops or when it completes all the generations. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one is a list of fitness values of the last population's solutions. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. + + delay_after_gen: Added in PyGAD 2.4.0. It accepts a non-negative number specifying the number of seconds to wait after a generation completes and before going to the next generation. It defaults to 0.0 which means no delay after the generation. + + save_best_solutions: Added in PyGAD 2.9.0 and its type is bool. If True, then the best solution in each generation is saved into the 'best_solutions' attribute. Use this parameter with caution as it may cause memory overflow when either the number of generations or the number of genes is large. + save_solutions: Added in PyGAD 2.15.0 and its type is bool. If True, then all solutions in each generation are saved into the 'solutions' attribute. Use this parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large. + + suppress_warnings: Added in PyGAD 2.10.0 and its type is bool. If True, then no warning messages will be displayed. It defaults to False. + + allow_duplicate_genes: Added in PyGAD 2.13.0. If True, then a solution/chromosome may have duplicate gene values. If False, then each gene will have a unique value in its solution. + + stop_criteria: Added in PyGAD 2.15.0. It is assigned to some criteria to stop the evolution if at least one criterion holds. + + parallel_processing: Added in PyGAD 2.17.0. Defaults to `None` which means no parallel processing is used. If a positive integer is assigned, it specifies the number of threads to be used. If a list or a tuple of exactly 2 elements is assigned, then: 1) The first element can be either "process" or "thread" to specify whether processes or threads are used, respectively. 2) The second element can be: 1) A positive integer to select the maximum number of processes or threads to be used. 2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'. 3) None to use the default value as calculated by the concurrent.futures module. + + random_seed: Added in PyGAD 2.18.0. It defines the random seed to be used by the random function generators (we use random functions in the NumPy and random modules). This helps to reproduce the same results by setting the same random seed. + + logger: Added in PyGAD 2.20.0. It accepts a logger object of the 'logging.Logger' class to log the messages. If no logger is passed, then a default logger is created to log/print the messages to the console exactly like using the 'print()' function. + """ + + # If no logger is passed, then create a logger that logs only the messages to the console. + if logger is None: + # Create a logger named with the module name. + logger = logging.getLogger(__name__) + # Set the logger log level to 'DEBUG' to log all kinds of messages. + logger.setLevel(logging.DEBUG) + + # Clear any attached handlers to the logger from the previous runs. + # If the handlers are not cleared, then the new handler will be appended to the list of handlers. + # This makes the single log message be repeated according to the length of the list of handlers. + logger.handlers.clear() + + # Create the handlers. + stream_handler = logging.StreamHandler() + # Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger. + stream_handler.setLevel(logging.DEBUG) + + # Create the formatter that just includes the log message. + formatter = logging.Formatter('%(message)s') + + # Add the formatter to the handler. + stream_handler.setFormatter(formatter) + + # Add the handler to the logger. + logger.addHandler(stream_handler) + else: + # Validate that the passed logger is of type 'logging.Logger'. + if isinstance(logger, logging.Logger): + pass + else: + raise TypeError("The expected type of the 'logger' parameter is 'logging.Logger' but {logger_type} found.".format(logger_type=type(logger))) + + # Create the 'self.logger' attribute to hold the logger. + # Instead of using 'print()', use 'self.logger.info()' + self.logger = logger + + self.random_seed = random_seed + if random_seed is None: + pass + else: + numpy.random.seed(self.random_seed) + random.seed(self.random_seed) + + # If suppress_warnings is bool and its valud is False, then print warning messages. + if type(suppress_warnings) is bool: + self.suppress_warnings = suppress_warnings + else: + self.valid_parameters = False + self.logger.error("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) + raise TypeError("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) + + # Validating mutation_by_replacement + if not (type(mutation_by_replacement) is bool): + self.valid_parameters = False + self.logger.error("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) + raise TypeError("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) + + self.mutation_by_replacement = mutation_by_replacement + + # Validate gene_space + self.gene_space_nested = False + if type(gene_space) is type(None): + pass + elif type(gene_space) in [list, tuple, range, numpy.ndarray]: + if len(gene_space) == 0: + self.valid_parameters = False + self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") + raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") + else: + for index, el in enumerate(gene_space): + if type(el) in [list, tuple, range, numpy.ndarray]: + if len(el) == 0: + self.valid_parameters = False + self.logger.error("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) + raise ValueError("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) + else: + for val in el: + if not (type(val) in [type(None)] + GA.supported_int_float_types): + self.logger.error("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) + raise TypeError("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) + self.gene_space_nested = True + elif type(el) == type(None): + pass + # self.gene_space_nested = True + elif type(el) is dict: + if len(el.items()) == 2: + if ('low' in el.keys()) and ('high' in el.keys()): + pass + else: + self.valid_parameters = False + self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) + raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) + elif len(el.items()) == 3: + if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): + pass + else: + self.valid_parameters = False + self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) + raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) + else: + self.valid_parameters = False + self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) + raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) + self.gene_space_nested = True + elif not (type(el) in GA.supported_int_float_types): + self.valid_parameters = False + self.logger.error("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) + raise TypeError("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) + + elif type(gene_space) is dict: + if len(gene_space.items()) == 2: + if ('low' in gene_space.keys()) and ('high' in gene_space.keys()): + pass + else: + self.valid_parameters = False + self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) + raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) + elif len(gene_space.items()) == 3: + if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): + pass + else: + self.valid_parameters = False + self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) + raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) + else: + self.valid_parameters = False + self.logger.error("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) + raise ValueError("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) + + else: + self.valid_parameters = False + self.logger.error("The expected type of 'gene_space' is list, tuple, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) + raise TypeError("The expected type of 'gene_space' is list, tuple, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) + + self.gene_space = gene_space + + # Validate init_range_low and init_range_high + if type(init_range_low) in GA.supported_int_float_types: + if type(init_range_high) in GA.supported_int_float_types: + self.init_range_low = init_range_low + self.init_range_high = init_range_high + else: + self.valid_parameters = False + self.logger.error("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) + raise ValueError("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) + else: + self.valid_parameters = False + self.logger.error("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) + raise ValueError("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) + + # Validate random_mutation_min_val and random_mutation_max_val + if type(random_mutation_min_val) in GA.supported_int_float_types: + if type(random_mutation_max_val) in GA.supported_int_float_types: + if random_mutation_min_val == random_mutation_max_val: + if not self.suppress_warnings: warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") + else: + self.valid_parameters = False + self.logger.error("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) + raise TypeError("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) + else: + self.valid_parameters = False + self.logger.error("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) + raise TypeError("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) + self.random_mutation_min_val = random_mutation_min_val + self.random_mutation_max_val = random_mutation_max_val + + # Validate gene_type + if gene_type in GA.supported_int_float_types: + self.gene_type = [gene_type, None] + self.gene_type_single = True + # A single data type of float with precision. + elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): + self.gene_type = gene_type + self.gene_type_single = True + elif type(gene_type) in [list, tuple, numpy.ndarray]: + if num_genes is None: + if initial_population is None: + self.valid_parameters = False + self.logger.error("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") + raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") + elif not len(gene_type) == len(initial_population[0]): + self.valid_parameters = False + self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) + raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) + elif not len(gene_type) == num_genes: + self.valid_parameters = False + self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) + raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) + for gene_type_idx, gene_type_val in enumerate(gene_type): + if gene_type_val in GA.supported_float_types: + # If the gene type is float and no precision is passed, set it to None. + gene_type[gene_type_idx] = [gene_type_val, None] + elif gene_type_val in GA.supported_int_types: + gene_type[gene_type_idx] = [gene_type_val, None] + elif type(gene_type_val) in [list, tuple, numpy.ndarray]: + # A float type is expected in a list/tuple/numpy.ndarray of length 2. + if len(gene_type_val) == 2: + if gene_type_val[0] in GA.supported_float_types: + if type(gene_type_val[1]) in GA.supported_int_types: + pass + else: + self.valid_parameters = False + self.logger.error("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) + raise TypeError("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) + else: + self.valid_parameters = False + self.logger.error("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) + raise TypeError("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) + else: + self.valid_parameters = False + self.logger.error("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) + raise ValueError("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) + else: + self.valid_parameters = False + self.logger.error("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) + raise ValueError("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) + self.gene_type = gene_type + self.gene_type_single = False + else: + self.valid_parameters = False + self.logger.error("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) + raise ValueError("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) + + # Build the initial population + if initial_population is None: + if (sol_per_pop is None) or (num_genes is None): + self.valid_parameters = False + self.logger.error("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") + raise TypeError("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") + elif (type(sol_per_pop) is int) and (type(num_genes) is int): + # Validating the number of solutions in the population (sol_per_pop) + if sol_per_pop <= 0: + self.valid_parameters = False + self.logger.error("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) + raise ValueError("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) + # Validating the number of gene. + if (num_genes <= 0): + self.valid_parameters = False + self.logger.error("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) + raise ValueError("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) + # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. + # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. + self.num_genes = num_genes # Number of genes in the solution. + + # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. + if self.gene_space_nested: + if len(gene_space) != self.num_genes: + self.valid_parameters = False + self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) + raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) + + self.sol_per_pop = sol_per_pop # Number of solutions in the population. + self.initialize_population(self.init_range_low, + self.init_range_high, + allow_duplicate_genes, + True, + self.gene_type) + else: + self.valid_parameters = False + self.logger.error("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) + raise TypeError("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) + elif not type(initial_population) in [list, tuple, numpy.ndarray]: + self.valid_parameters = False + self.logger.error("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) + raise TypeError("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) + elif numpy.array(initial_population).ndim != 2: + self.valid_parameters = False + self.logger.error("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) + raise ValueError("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) + else: + # Validate the type of each value in the 'initial_population' parameter. + for row_idx in range(len(initial_population)): + for col_idx in range(len(initial_population[0])): + if type(initial_population[row_idx][col_idx]) in GA.supported_int_float_types: + pass + else: + self.valid_parameters = False + self.logger.error("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) + raise TypeError("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) + + # Forcing the initial_population array to have the data type assigned to the gene_type parameter. + if self.gene_type_single == True: + if self.gene_type[1] == None: + self.initial_population = numpy.array(initial_population, dtype=self.gene_type[0]) + else: + self.initial_population = numpy.round(numpy.array(initial_population, dtype=self.gene_type[0]), self.gene_type[1]) + else: + initial_population = numpy.array(initial_population) + self.initial_population = numpy.zeros(shape=(initial_population.shape[0], initial_population.shape[1]), dtype=object) + for gene_idx in range(initial_population.shape[1]): + if self.gene_type[gene_idx][1] is None: + self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], + dtype=self.gene_type[gene_idx][0]) + else: + self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], + dtype=self.gene_type[gene_idx][0]), + self.gene_type[gene_idx][1]) + + self.population = self.initial_population.copy() # A NumPy array holding the initial population. + self.num_genes = self.initial_population.shape[1] # Number of genes in the solution. + self.sol_per_pop = self.initial_population.shape[0] # Number of solutions in the population. + self.pop_size = (self.sol_per_pop,self.num_genes) # The population size. + + # Round initial_population and population + self.initial_population = self.round_genes(self.initial_population) + self.population = self.round_genes(self.population) + + # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. + if self.gene_space_nested: + if len(gene_space) != self.num_genes: + self.valid_parameters = False + self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) + raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) + + # Validating the number of parents to be selected for mating (num_parents_mating) + if num_parents_mating <= 0: + self.valid_parameters = False + self.logger.error("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) + raise ValueError("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) + + # Validating the number of parents to be selected for mating: num_parents_mating + if (num_parents_mating > self.sol_per_pop): + self.valid_parameters = False + self.logger.error("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) + raise ValueError("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) + + self.num_parents_mating = num_parents_mating + + # crossover: Refers to the method that applies the crossover operator based on the selected type of crossover in the crossover_type property. + # Validating the crossover type: crossover_type + if (crossover_type is None): + self.crossover = None + elif inspect.ismethod(crossover_type): + # Check if the crossover_type is a method that accepts 4 paramaters. + if (crossover_type.__code__.co_argcount == 4): + # The crossover method assigned to the crossover_type parameter is validated. + self.crossover = crossover_type + else: + self.valid_parameters = False + self.logger.error("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) + raise ValueError("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) + elif callable(crossover_type): + # Check if the crossover_type is a function that accepts 2 paramaters. + if (crossover_type.__code__.co_argcount == 3): + # The crossover function assigned to the crossover_type parameter is validated. + self.crossover = crossover_type + else: + self.valid_parameters = False + self.logger.error("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) + raise ValueError("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) + elif not (type(crossover_type) is str): + self.valid_parameters = False + self.logger.error("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) + raise TypeError("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) + else: # type crossover_type is str + crossover_type = crossover_type.lower() + if (crossover_type == "single_point"): + self.crossover = self.single_point_crossover + elif (crossover_type == "two_points"): + self.crossover = self.two_points_crossover + elif (crossover_type == "uniform"): + self.crossover = self.uniform_crossover + elif (crossover_type == "scattered"): + self.crossover = self.scattered_crossover + else: + self.valid_parameters = False + self.logger.error("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) + raise TypeError("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) + + self.crossover_type = crossover_type + + # Calculate the value of crossover_probability + if crossover_probability is None: + self.crossover_probability = None + elif type(crossover_probability) in GA.supported_int_float_types: + if crossover_probability >= 0 and crossover_probability <= 1: + self.crossover_probability = crossover_probability + else: + self.valid_parameters = False + self.logger.error("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) + raise ValueError("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) + else: + self.valid_parameters = False + self.logger.error("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) + raise TypeError("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) + + # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. + # Validating the mutation type: mutation_type + # "adaptive" mutation is supported starting from PyGAD 2.10.0 + if mutation_type is None: + self.mutation = None + elif inspect.ismethod(mutation_type): + # Check if the mutation_type is a method that accepts 3 paramater. + if (mutation_type.__code__.co_argcount == 3): + # The mutation method assigned to the mutation_type parameter is validated. + self.mutation = mutation_type + else: + self.valid_parameters = False + self.logger.error("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) + raise ValueError("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) + elif callable(mutation_type): + # Check if the mutation_type is a function that accepts 2 paramater. + if (mutation_type.__code__.co_argcount == 2): + # The mutation function assigned to the mutation_type parameter is validated. + self.mutation = mutation_type + else: + self.valid_parameters = False + self.logger.error("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) + raise ValueError("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) + elif not (type(mutation_type) is str): + self.valid_parameters = False + self.logger.error("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) + raise TypeError("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) + else: # type mutation_type is str + mutation_type = mutation_type.lower() + if (mutation_type == "random"): + self.mutation = self.random_mutation + elif (mutation_type == "swap"): + self.mutation = self.swap_mutation + elif (mutation_type == "scramble"): + self.mutation = self.scramble_mutation + elif (mutation_type == "inversion"): + self.mutation = self.inversion_mutation + elif (mutation_type == "adaptive"): + self.mutation = self.adaptive_mutation + else: + self.valid_parameters = False + self.logger.error("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) + raise TypeError("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) + + self.mutation_type = mutation_type + + # Calculate the value of mutation_probability + if not (self.mutation_type is None): + if mutation_probability is None: + self.mutation_probability = None + elif (mutation_type != "adaptive"): + # Mutation probability is fixed not adaptive. + if type(mutation_probability) in GA.supported_int_float_types: + if mutation_probability >= 0 and mutation_probability <= 1: + self.mutation_probability = mutation_probability + else: + self.valid_parameters = False + self.logger.error("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) + raise ValueError("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) + else: + self.valid_parameters = False + self.logger.error("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) + raise TypeError("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) + else: + # Mutation probability is adaptive not fixed. + if type(mutation_probability) in [list, tuple, numpy.ndarray]: + if len(mutation_probability) == 2: + for el in mutation_probability: + if type(el) in GA.supported_int_float_types: + if el >= 0 and el <= 1: + pass + else: + self.valid_parameters = False + self.logger.error("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) + raise ValueError("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) + else: + self.valid_parameters = False + self.logger.error("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) + raise TypeError("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) + if mutation_probability[0] < mutation_probability[1]: + if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_probability' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_probability[0], second_el=mutation_probability[1])) + self.mutation_probability = mutation_probability + else: + self.valid_parameters = False + self.logger.error("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) + raise ValueError("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) + else: + self.valid_parameters = False + self.logger.error("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) + raise TypeError("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) + else: + pass + + # Calculate the value of mutation_num_genes + if not (self.mutation_type is None): + if mutation_num_genes is None: + # The mutation_num_genes parameter does not exist. Checking whether adaptive mutation is used. + if (mutation_type != "adaptive"): + # The percent of genes to mutate is fixed not adaptive. + if mutation_percent_genes == 'default'.lower(): + mutation_percent_genes = 10 + # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. + mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) + # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. + if mutation_num_genes == 0: + if self.mutation_probability is None: + if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) + mutation_num_genes = 1 + + elif type(mutation_percent_genes) in GA.supported_int_float_types: + if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): + self.valid_parameters = False + self.logger.error("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) + raise ValueError("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) + else: + # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. + if mutation_percent_genes == 'default'.lower(): + mutation_percent_genes = 10 + + # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. + mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) + # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. + if mutation_num_genes == 0: + if self.mutation_probability is None: + if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) + mutation_num_genes = 1 + else: + self.valid_parameters = False + self.logger.error("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) + raise TypeError("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) + else: + # The percent of genes to mutate is adaptive not fixed. + if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: + if len(mutation_percent_genes) == 2: + mutation_num_genes = numpy.zeros_like(mutation_percent_genes, dtype=numpy.uint32) + for idx, el in enumerate(mutation_percent_genes): + if type(el) in GA.supported_int_float_types: + if (el <= 0 or el > 100): + self.valid_parameters = False + self.logger.error("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) + raise ValueError("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) + else: + self.valid_parameters = False + self.logger.error("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) + raise TypeError("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) + # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. + # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. + mutation_num_genes[idx] = numpy.uint32((mutation_percent_genes[idx]*self.num_genes)/100) + # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. + if mutation_num_genes[idx] == 0: + if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate ({mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes[idx], mutation_num=mutation_num_genes[idx])) + mutation_num_genes[idx] = 1 + if mutation_percent_genes[0] < mutation_percent_genes[1]: + if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_percent_genes' parameter is ({first_el}) which is smaller than the second element ({second_el}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.".format(first_el=mutation_percent_genes[0], second_el=mutation_percent_genes[1])) + # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. + else: + self.valid_parameters = False + self.logger.error("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) + raise ValueError("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) + else: + if self.mutation_probability is None: + self.valid_parameters = False + self.logger.error("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) + raise TypeError("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) + # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. + elif (mutation_type != "adaptive"): + # Number of genes to mutate is fixed not adaptive. + if type(mutation_num_genes) in GA.supported_int_types: + if (mutation_num_genes <= 0): + self.valid_parameters = False + self.logger.error("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) + raise ValueError("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) + elif (mutation_num_genes > self.num_genes): + self.valid_parameters = False + self.logger.error("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) + raise ValueError("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) + else: + self.valid_parameters = False + self.logger.error("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) + raise TypeError("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) + else: + # Number of genes to mutate is adaptive not fixed. + if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: + if len(mutation_num_genes) == 2: + for el in mutation_num_genes: + if type(el) in GA.supported_int_types: + if (el <= 0): + self.valid_parameters = False + self.logger.error("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) + raise ValueError("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) + elif (el > self.num_genes): + self.valid_parameters = False + self.logger.error("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) + raise ValueError("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) + else: + self.valid_parameters = False + self.logger.error("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) + raise TypeError("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) + # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. + if mutation_num_genes[0] < mutation_num_genes[1]: + if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_num_genes' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_num_genes[0], second_el=mutation_num_genes[1])) + # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. + else: + self.valid_parameters = False + self.logger.error("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) + raise ValueError("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) + else: + self.valid_parameters = False + self.logger.error("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) + raise TypeError("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) + else: + pass + + # Validating mutation_by_replacement and mutation_type + if self.mutation_type != "random" and self.mutation_by_replacement: + if not self.suppress_warnings: warnings.warn("The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mut_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.".format(mut_type=mutation_type)) + + # Check if crossover and mutation are both disabled. + if (self.mutation_type is None) and (self.crossover_type is None): + if not self.suppress_warnings: warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") + + # select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute. + # Validating the selected type of parent selection: parent_selection_type + if inspect.ismethod(parent_selection_type): + # Check if the parent_selection_type is a method that accepts 4 paramaters. + if (parent_selection_type.__code__.co_argcount == 4): + # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. + # The parent selection method assigned to the parent_selection_type parameter is validated. + self.select_parents = parent_selection_type + else: + self.valid_parameters = False + self.logger.error("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) + raise ValueError("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) + elif callable(parent_selection_type): + # Check if the parent_selection_type is a function that accepts 3 paramaters. + if (parent_selection_type.__code__.co_argcount == 3): + # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. + # The parent selection function assigned to the parent_selection_type parameter is validated. + self.select_parents = parent_selection_type + else: + self.valid_parameters = False + self.logger.error("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) + raise ValueError("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) + elif not (type(parent_selection_type) is str): + self.valid_parameters = False + self.logger.error("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) + raise TypeError("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) + else: + parent_selection_type = parent_selection_type.lower() + if (parent_selection_type == "sss"): + self.select_parents = self.steady_state_selection + elif (parent_selection_type == "rws"): + self.select_parents = self.roulette_wheel_selection + elif (parent_selection_type == "sus"): + self.select_parents = self.stochastic_universal_selection + elif (parent_selection_type == "random"): + self.select_parents = self.random_selection + elif (parent_selection_type == "tournament"): + self.select_parents = self.tournament_selection + elif (parent_selection_type == "rank"): + self.select_parents = self.rank_selection + else: + self.valid_parameters = False + self.logger.error("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) + raise TypeError("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) + + # For tournament selection, validate the K value. + if(parent_selection_type == "tournament"): + if (K_tournament > self.sol_per_pop): + K_tournament = self.sol_per_pop + if not self.suppress_warnings: warnings.warn("K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n".format(K_tournament=K_tournament, sol_per_pop=self.sol_per_pop)) + elif (K_tournament <= 0): + self.valid_parameters = False + self.logger.error("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) + raise ValueError("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) + + self.K_tournament = K_tournament + + # Validating the number of parents to keep in the next population: keep_parents + if not (type(keep_parents) in GA.supported_int_types): + self.valid_parameters = False + self.logger.error("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) + raise TypeError("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) + elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): + self.valid_parameters = False + self.logger.error("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) + raise ValueError("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) + + self.keep_parents = keep_parents + + if parent_selection_type == "sss" and self.keep_parents == 0: + if not self.suppress_warnings: warnings.warn("The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") + + # Validating the number of elitism to keep in the next population: keep_elitism + if not (type(keep_elitism) in GA.supported_int_types): + self.valid_parameters = False + self.logger.error("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) + raise TypeError("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) + elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): + self.valid_parameters = False + self.logger.error("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) + raise ValueError("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) + + self.keep_elitism = keep_elitism + + # Validate keep_parents. + if self.keep_elitism == 0: + if (self.keep_parents == -1): # Keep all parents in the next population. + self.num_offspring = self.sol_per_pop - self.num_parents_mating + elif (self.keep_parents == 0): # Keep no parents in the next population. + self.num_offspring = self.sol_per_pop + elif (self.keep_parents > 0): # Keep the specified number of parents in the next population. + self.num_offspring = self.sol_per_pop - self.keep_parents + else: + self.num_offspring = self.sol_per_pop - self.keep_elitism + + # Check if the fitness_func is a method. + # In PyGAD 2.19.0, a method can be passed to the fitness function. If function is passed, then it accepts 2 parameters. If method, then it accepts 3 parameters. + # In PyGAD 2.20.0, a new parameter is passed referring to the instance of the `pygad.GA` class. So, the function accepts 3 parameters and the method accepts 4 parameters. + if inspect.ismethod(fitness_func): + # If the fitness is calculated through a method, not a function, then there is a fourth 'self` paramaters. + if (fitness_func.__code__.co_argcount == 4): + self.fitness_func = fitness_func + else: + self.valid_parameters = False + self.logger.error("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) + raise ValueError("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) + elif callable(fitness_func): + # Check if the fitness function accepts 2 paramaters. + if (fitness_func.__code__.co_argcount == 3): + self.fitness_func = fitness_func + else: + self.valid_parameters = False + self.logger.error("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) + raise ValueError("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) + raise TypeError("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) + + if fitness_batch_size is None: + pass + elif not (type(fitness_batch_size) in GA.supported_int_types): + self.valid_parameters = False + self.logger.error("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) + raise TypeError("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) + elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: + self.valid_parameters = False + self.logger.error("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) + raise ValueError("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) + + self.fitness_batch_size = fitness_batch_size + + # Check if the on_start exists. + if not (on_start is None): + if inspect.ismethod(on_start): + # Check if the on_start method accepts 2 paramaters. + if (on_start.__code__.co_argcount == 2): + self.on_start = on_start + else: + self.valid_parameters = False + self.logger.error("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) + raise ValueError("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) + # Check if the on_start is a function. + elif callable(on_start): + # Check if the on_start function accepts only a single paramater. + if (on_start.__code__.co_argcount == 1): + self.on_start = on_start + else: + self.valid_parameters = False + self.logger.error("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) + raise ValueError("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) + raise TypeError("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) + else: + self.on_start = None + + # Check if the on_fitness exists. + if not (on_fitness is None): + # Check if the on_fitness is a method. + if inspect.ismethod(on_fitness): + # Check if the on_fitness method accepts 3 paramaters. + if (on_fitness.__code__.co_argcount == 3): + self.on_fitness = on_fitness + else: + self.valid_parameters = False + self.logger.error("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) + raise ValueError("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) + # Check if the on_fitness is a function. + elif callable(on_fitness): + # Check if the on_fitness function accepts 2 paramaters. + if (on_fitness.__code__.co_argcount == 2): + self.on_fitness = on_fitness + else: + self.valid_parameters = False + self.logger.error("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) + raise ValueError("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) + raise TypeError("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) + else: + self.on_fitness = None + + # Check if the on_parents exists. + if not (on_parents is None): + # Check if the on_parents is a method. + if inspect.ismethod(on_parents): + # Check if the on_parents method accepts 3 paramaters. + if (on_parents.__code__.co_argcount == 3): + self.on_parents = on_parents + else: + self.valid_parameters = False + self.logger.error("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) + raise ValueError("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) + # Check if the on_parents is a function. + elif callable(on_parents): + # Check if the on_parents function accepts 2 paramaters. + if (on_parents.__code__.co_argcount == 2): + self.on_parents = on_parents + else: + self.valid_parameters = False + self.logger.error("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) + raise ValueError("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) + raise TypeError("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) + else: + self.on_parents = None + + # Check if the on_crossover exists. + if not (on_crossover is None): + # Check if the on_crossover is a method. + if inspect.ismethod(on_crossover): + # Check if the on_crossover method accepts 3 paramaters. + if (on_crossover.__code__.co_argcount == 3): + self.on_crossover = on_crossover + else: + self.valid_parameters = False + self.logger.error("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) + raise ValueError("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) + # Check if the on_crossover is a function. + elif callable(on_crossover): + # Check if the on_crossover function accepts 2 paramaters. + if (on_crossover.__code__.co_argcount == 2): + self.on_crossover = on_crossover + else: + self.valid_parameters = False + self.logger.error("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) + raise ValueError("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) + raise TypeError("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) + else: + self.on_crossover = None + + # Check if the on_mutation exists. + if not (on_mutation is None): + # Check if the on_mutation is a method. + if inspect.ismethod(on_mutation): + # Check if the on_mutation method accepts 3 paramaters. + if (on_mutation.__code__.co_argcount == 3): + self.on_mutation = on_mutation + else: + self.valid_parameters = False + self.logger.error("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) + raise ValueError("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) + # Check if the on_mutation is a function. + elif callable(on_mutation): + # Check if the on_mutation function accepts 2 paramaters. + if (on_mutation.__code__.co_argcount == 2): + self.on_mutation = on_mutation + else: + self.valid_parameters = False + self.logger.error("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) + raise ValueError("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) + raise TypeError("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) + else: + self.on_mutation = None + + # Check if the on_generation exists. + if not (on_generation is None): + # Check if the on_generation is a method. + if inspect.ismethod(on_generation): + # Check if the on_generation method accepts 2 paramaters. + if (on_generation.__code__.co_argcount == 2): + self.on_generation = on_generation + else: + self.valid_parameters = False + self.logger.error("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) + raise ValueError("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) + # Check if the on_generation is a function. + elif callable(on_generation): + # Check if the on_generation function accepts only a single paramater. + if (on_generation.__code__.co_argcount == 1): + self.on_generation = on_generation + else: + self.valid_parameters = False + self.logger.error("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) + raise ValueError("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) + raise TypeError("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) + else: + self.on_generation = None + + # Check if the on_stop exists. + if not (on_stop is None): + # Check if the on_stop is a method. + if inspect.ismethod(on_stop): + # Check if the on_stop method accepts 3 paramaters. + if (on_stop.__code__.co_argcount == 3): + self.on_stop = on_stop + else: + self.valid_parameters = False + self.logger.error("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) + raise ValueError("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) + # Check if the on_stop is a function. + elif callable(on_stop): + # Check if the on_stop function accepts 2 paramaters. + if (on_stop.__code__.co_argcount == 2): + self.on_stop = on_stop + else: + self.valid_parameters = False + self.logger.error("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) + raise ValueError("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) + else: + self.valid_parameters = False + self.logger.error("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) + raise TypeError("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) + else: + self.on_stop = None + + # Validate delay_after_gen + if type(delay_after_gen) in GA.supported_int_float_types: + if delay_after_gen >= 0.0: + self.delay_after_gen = delay_after_gen + else: + self.valid_parameters = False + self.logger.error("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) + raise ValueError("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) + else: + self.valid_parameters = False + self.logger.error("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) + raise TypeError("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) + + # Validate save_best_solutions + if type(save_best_solutions) is bool: + if save_best_solutions == True: + if not self.suppress_warnings: warnings.warn("Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") + else: + self.valid_parameters = False + self.logger.error("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) + raise TypeError("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) + + # Validate save_solutions + if type(save_solutions) is bool: + if save_solutions == True: + if not self.suppress_warnings: warnings.warn("Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") + else: + self.valid_parameters = False + self.logger.error("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) + raise TypeError("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) + + # Validate allow_duplicate_genes + if not (type(allow_duplicate_genes) is bool): + self.valid_parameters = False + self.logger.error("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) + raise TypeError("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) + + self.allow_duplicate_genes = allow_duplicate_genes + + self.stop_criteria = [] + self.supported_stop_words = ["reach", "saturate"] + if stop_criteria is None: + # None: Stop after passing through all generations. + self.stop_criteria = None + elif type(stop_criteria) is str: + # reach_{target_fitness}: Stop if the target fitness value is reached. + # saturate_{num_generations}: Stop if the fitness value does not change (saturates) for the given number of generations. + criterion = stop_criteria.split("_") + if len(criterion) == 2: + stop_word = criterion[0] + number = criterion[1] + + if stop_word in self.supported_stop_words: + pass + else: + self.valid_parameters = False + self.logger.error("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + raise ValueError("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + + if number.replace(".", "").isnumeric(): + number = float(number) + else: + self.valid_parameters = False + self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) + raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) + + self.stop_criteria.append([stop_word, number]) + + else: + self.valid_parameters = False + self.logger.error("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) + raise ValueError("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) + + elif type(stop_criteria) in [list, tuple, numpy.ndarray]: + # Remove duplicate criterira by converting the list to a set then back to a list. + stop_criteria = list(set(stop_criteria)) + for idx, val in enumerate(stop_criteria): + if type(val) is str: + criterion = val.split("_") + if len(criterion) == 2: + stop_word = criterion[0] + number = criterion[1] + + if stop_word in self.supported_stop_words: + pass + else: + self.valid_parameters = False + self.logger.error("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + raise ValueError("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + + if number.replace(".", "").isnumeric(): + number = float(number) + else: + self.valid_parameters = False + self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) + raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) + + self.stop_criteria.append([stop_word, number]) + + else: + self.valid_parameters = False + self.logger.error("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) + raise ValueError("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) + else: + self.valid_parameters = False + self.logger.error("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) + raise TypeError("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) + else: + self.valid_parameters = False + self.logger.error("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) + raise TypeError("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) + + if parallel_processing is None: + self.parallel_processing = None + elif type(parallel_processing) in GA.supported_int_types: + if parallel_processing > 0: + self.parallel_processing = ["thread", parallel_processing] + else: + self.valid_parameters = False + self.logger.error("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) + raise ValueError("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) + elif type(parallel_processing) in [list, tuple]: + if len(parallel_processing) == 2: + if type(parallel_processing[0]) is str: + if parallel_processing[0] in ["process", "thread"]: + if (type(parallel_processing[1]) in GA.supported_int_types and parallel_processing[1] > 0) or (parallel_processing[1] == 0) or (parallel_processing[1] is None): + if parallel_processing[1] == 0: + # If the number of processes/threads is 0, this means no parallel processing is used. It is equivelant to setting parallel_processing=None. + self.parallel_processing = None + else: + # Whether the second value is None or a positive integer. + self.parallel_processing = parallel_processing + else: + self.valid_parameters = False + self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) + raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) + else: + self.valid_parameters = False + self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) + raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) + else: + self.valid_parameters = False + self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) + raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) + else: + self.valid_parameters = False + self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) + raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) + else: + self.valid_parameters = False + self.logger.error("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) + raise ValueError("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) + + # Set the `run_completed` property to False. It is set to `True` only after the `run()` method is complete. + self.run_completed = False + + # The number of completed generations. + self.generations_completed = 0 + + # At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid. + self.valid_parameters = True # Set to True when all the parameters passed in the GA class constructor are valid. + + # Parameters of the genetic algorithm. + self.num_generations = abs(num_generations) + self.parent_selection_type = parent_selection_type + + # Parameters of the mutation operation. + self.mutation_percent_genes = mutation_percent_genes + self.mutation_num_genes = mutation_num_genes + + # Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object. + self.best_solutions_fitness = [] # A list holding the fitness value of the best solution for each generation. + + self.best_solution_generation = -1 # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. + + self.save_best_solutions = save_best_solutions + self.best_solutions = [] # Holds the best solution in each generation. + + self.save_solutions = save_solutions + self.solutions = [] # Holds the solutions in each generation. + self.solutions_fitness = [] # Holds the fitness of the solutions in each generation. + + self.last_generation_fitness = None # A list holding the fitness values of all solutions in the last generation. + self.last_generation_parents = None # A list holding the parents of the last generation. + self.last_generation_offspring_crossover = None # A list holding the offspring after applying crossover in the last generation. + self.last_generation_offspring_mutation = None # A list holding the offspring after applying mutation in the last generation. + self.previous_generation_fitness = None # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. + self.last_generation_elitism = None # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. + self.last_generation_elitism_indices = None # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. + + def round_genes(self, solutions): + for gene_idx in range(self.num_genes): + if self.gene_type_single: + if not self.gene_type[1] is None: + solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], self.gene_type[1]) + else: + if not self.gene_type[gene_idx][1] is None: + solutions[:, gene_idx] = numpy.round(numpy.asarray(solutions[:, gene_idx], + dtype=self.gene_type[gene_idx][0]), + self.gene_type[gene_idx][1]) + return solutions + + def initialize_population(self, + low, + high, + allow_duplicate_genes, + mutation_by_replacement, + gene_type): + + """ + Creates an initial population randomly as a NumPy array. The array is saved in the instance attribute named 'population'. + + low: The lower value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20 and higher. + high: The upper value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20. + + This method assigns the values of the following 3 instance attributes: + 1. pop_size: Size of the population. + 2. population: Initially, holds the initial population and later updated after each generation. + 3. init_population: Keeping the initial population. + """ + + # Population size = (number of chromosomes, number of genes per chromosome) + self.pop_size = (self.sol_per_pop,self.num_genes) # The population will have sol_per_pop chromosome where each chromosome has num_genes genes. + + if self.gene_space is None: + # Creating the initial population randomly. + if self.gene_type_single == True: + self.population = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=self.pop_size), + dtype=self.gene_type[0]) # A NumPy array holding the initial population. + else: + # Create an empty population of dtype=object to support storing mixed data types within the same array. + self.population = numpy.zeros(shape=self.pop_size, dtype=object) + # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. + for gene_idx in range(self.num_genes): + # A vector of all values of this single gene across all solutions in the population. + gene_values = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=self.pop_size[0]), + dtype=self.gene_type[gene_idx][0]) + # Adding the current gene values to the population. + self.population[:, gene_idx] = gene_values + + if allow_duplicate_genes == False: + for solution_idx in range(self.population.shape[0]): + # self.logger.info("Before", self.population[solution_idx]) + self.population[solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=self.population[solution_idx], + min_val=low, + max_val=high, + mutation_by_replacement=True, + gene_type=gene_type, + num_trials=10) + # self.logger.info("After", self.population[solution_idx]) + + elif self.gene_space_nested: + if self.gene_type_single == True: + self.population = numpy.zeros(shape=self.pop_size, dtype=self.gene_type[0]) + for sol_idx in range(self.sol_per_pop): + for gene_idx in range(self.num_genes): + if type(self.gene_space[gene_idx]) in [list, tuple, range]: + # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. + if type(self.gene_space[gene_idx]) is range: + temp = self.gene_space[gene_idx] + else: + temp = self.gene_space[gene_idx].copy() + for idx, val in enumerate(self.gene_space[gene_idx]): + if val is None: + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[0])[0] + self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[0](self.population[sol_idx, gene_idx]) + self.gene_space[gene_idx] = temp + elif type(self.gene_space[gene_idx]) is dict: + if 'step' in self.gene_space[gene_idx].keys(): + self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], + stop=self.gene_space[gene_idx]['high'], + step=self.gene_space[gene_idx]['step']), + size=1), + dtype=self.gene_type[0])[0] + else: + self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], + high=self.gene_space[gene_idx]['high'], + size=1), + dtype=self.gene_type[0])[0] + elif type(self.gene_space[gene_idx]) == type(None): + + # The following commented code replace the None value with a single number that will not change again. + # This means the gene value will be the same across all solutions. + # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, + # high=high, + # size=1), dtype=self.gene_type[0])[0] + # self.population[sol_idx, gene_idx] = self.gene_space[gene_idx].copy() + + # The above problem is solved by keeping the None value in the gene_space parameter. This forces PyGAD to generate this value for each solution. + self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[0])[0] + elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: + self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + else: + self.population = numpy.zeros(shape=self.pop_size, dtype=object) + for sol_idx in range(self.sol_per_pop): + for gene_idx in range(self.num_genes): + if type(self.gene_space[gene_idx]) in [list, tuple, range]: + # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. + temp = self.gene_space[gene_idx].copy() + for idx, val in enumerate(self.gene_space[gene_idx]): + if val is None: + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[gene_idx][0])[0] + self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0](self.population[sol_idx, gene_idx]) + self.gene_space[gene_idx] = temp.copy() + elif type(self.gene_space[gene_idx]) is dict: + if 'step' in self.gene_space[gene_idx].keys(): + self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], + stop=self.gene_space[gene_idx]['high'], + step=self.gene_space[gene_idx]['step']), + size=1), + dtype=self.gene_type[gene_idx][0])[0] + else: + self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], + high=self.gene_space[gene_idx]['high'], + size=1), + dtype=self.gene_type[gene_idx][0])[0] + elif type(self.gene_space[gene_idx]) == type(None): + # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, + # high=high, + # size=1), + # dtype=self.gene_type[gene_idx][0])[0] + + # self.population[sol_idx, gene_idx] = self.gene_space[gene_idx].copy() + + temp = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[gene_idx][0])[0] + self.population[sol_idx, gene_idx] = temp + elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: + self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + else: + if self.gene_type_single == True: + # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. + for idx, curr_gene_space in enumerate(self.gene_space): + if curr_gene_space is None: + self.gene_space[idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[0])[0] + + # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. + if type(self.gene_space) is dict: + if 'step' in self.gene_space.keys(): + self.population = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), + size=self.pop_size), + dtype=self.gene_type[0]) + else: + self.population = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=self.pop_size), + dtype=self.gene_type[0]) # A NumPy array holding the initial population. + else: + self.population = numpy.asarray(numpy.random.choice(self.gene_space, + size=self.pop_size), + dtype=self.gene_type[0]) # A NumPy array holding the initial population. + else: + # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. + for gene_idx, curr_gene_space in enumerate(self.gene_space): + if curr_gene_space is None: + self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[gene_idx][0])[0] + + # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. + if type(self.gene_space) is dict: + # Create an empty population of dtype=object to support storing mixed data types within the same array. + self.population = numpy.zeros(shape=self.pop_size, dtype=object) + # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. + for gene_idx in range(self.num_genes): + # A vector of all values of this single gene across all solutions in the population. + if 'step' in self.gene_space[gene_idx].keys(): + gene_values = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], + stop=self.gene_space[gene_idx]['high'], + step=self.gene_space[gene_idx]['step']), + size=self.pop_size[0]), + dtype=self.gene_type[gene_idx][0]) + else: + gene_values = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=self.pop_size[0]), + dtype=self.gene_type[gene_idx][0]) + # Adding the current gene values to the population. + self.population[:, gene_idx] = gene_values + + else: + # Create an empty population of dtype=object to support storing mixed data types within the same array. + self.population = numpy.zeros(shape=self.pop_size, dtype=object) + # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. + for gene_idx in range(self.num_genes): + # A vector of all values of this single gene across all solutions in the population. + gene_values = numpy.asarray(numpy.random.choice(self.gene_space, + size=self.pop_size[0]), + dtype=self.gene_type[gene_idx][0]) + # Adding the current gene values to the population. + self.population[:, gene_idx] = gene_values + + if not (self.gene_space is None): + if allow_duplicate_genes == False: + for sol_idx in range(self.population.shape[0]): + self.population[sol_idx], _, _ = self.solve_duplicate_genes_by_space(solution=self.population[sol_idx], + gene_type=self.gene_type, + num_trials=10, + build_initial_pop=True) + + # Keeping the initial population in the initial_population attribute. + self.initial_population = self.population.copy() + + def cal_pop_fitness(self): + + """ + Calculating the fitness values of batches of solutions in the current population. + It returns: + -fitness: An array of the calculated fitness values. + """ + + if self.valid_parameters == False: + self.logger.error("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") + raise Exception("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") + + # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' + # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. + if self.last_generation_parents is not None: + last_generation_parents_as_list = [list(gen_parent) for gen_parent in self.last_generation_parents] + + # 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism' + # It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. + if self.last_generation_elitism is not None: + last_generation_elitism_as_list = [list(gen_elitism) for gen_elitism in self.last_generation_elitism] + + pop_fitness = ["undefined"] * len(self.population) + if self.parallel_processing is None: + # Calculating the fitness value of each solution in the current population. + for sol_idx, sol in enumerate(self.population): + # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. + # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. + # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. + # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. + # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1))): + # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1))): + if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): + solution_idx = self.solutions.index(list(sol)) + fitness = self.solutions_fitness[solution_idx] + elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): + # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. + # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. + elitism_idx = last_generation_elitism_as_list.index(list(sol)) + # Use the returned elitism index to return its index in the last population. + elitism_idx = self.last_generation_elitism_indices[elitism_idx] + # Use the elitism's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[elitism_idx] + # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. + # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. + # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): + elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): + # Index of the parent in the 'self.last_generation_parents' array. + # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. + # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] + parent_idx = last_generation_parents_as_list.index(list(sol)) + # Use the returned parent index to return its index in the last population. + parent_idx = self.last_generation_parents_indices[parent_idx] + # Use the parent's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[parent_idx] + else: + # Check if batch processing is used. If not, then calculate this missing fitness value. + if self.fitness_batch_size in [1, None]: + fitness = self.fitness_func(self, sol, sol_idx) + if type(fitness) in GA.supported_int_float_types: + pass + else: + self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + else: + # Reaching this point means that batch processing is in effect to calculate the fitness values. + # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. + continue + + # This is only executed if the fitness value was already calculated. + pop_fitness[sol_idx] = fitness + + if self.fitness_batch_size not in [1, None]: + # Reaching this block means that batch fitness calculation is used. + + # Indices of the solutions to calculate their fitness. + solutions_indices = numpy.where(numpy.array(pop_fitness) == "undefined")[0] + # Number of batches. + num_batches = int(numpy.ceil(len(solutions_indices) / self.fitness_batch_size)) + # For each batch, get its indices and call the fitness function. + for batch_idx in range(num_batches): + batch_first_index = batch_idx * self.fitness_batch_size + batch_last_index = (batch_idx + 1) * self.fitness_batch_size + batch_indices = solutions_indices[batch_first_index:batch_last_index] + batch_solutions = self.population[batch_indices, :] + + batch_fitness = self.fitness_func(self, batch_solutions, batch_indices) + if type(batch_fitness) not in [list, tuple, numpy.ndarray]: + self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) + raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) + elif len(numpy.array(batch_fitness)) != len(batch_indices): + self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) + raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) + + for index, fitness in zip(batch_indices, batch_fitness): + if type(fitness) in GA.supported_int_float_types: + pop_fitness[index] = fitness + else: + self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + else: + # Calculating the fitness value of each solution in the current population. + for sol_idx, sol in enumerate(self.population): + # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. + # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. + # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. + # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. + if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): + solution_idx = self.solutions.index(list(sol)) + fitness = self.solutions_fitness[solution_idx] + pop_fitness[sol_idx] = fitness + elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): + # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. + # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. + elitism_idx = last_generation_elitism_as_list.index(list(sol)) + # Use the returned elitism index to return its index in the last population. + elitism_idx = self.last_generation_elitism_indices[elitism_idx] + # Use the elitism's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[elitism_idx] + + pop_fitness[sol_idx] = fitness + # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. + # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. + # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): + elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): + # Index of the parent in the 'self.last_generation_parents' array. + # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. + # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] + parent_idx = last_generation_parents_as_list.index(list(sol)) + # Use the returned parent index to return its index in the last population. + parent_idx = self.last_generation_parents_indices[parent_idx] + # Use the parent's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[parent_idx] + + pop_fitness[sol_idx] = fitness + + # Decide which class to use based on whether the user selected "process" or "thread" + if self.parallel_processing[0] == "process": + ExecutorClass = concurrent.futures.ProcessPoolExecutor + else: + ExecutorClass = concurrent.futures.ThreadPoolExecutor + + # We can use a with statement to ensure threads are cleaned up promptly (https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example) + with ExecutorClass(max_workers=self.parallel_processing[1]) as executor: + solutions_to_submit_indices = [] + solutions_to_submit = [] + for sol_idx, sol in enumerate(self.population): + # The "undefined" value means that the fitness of this solution must be calculated. + if pop_fitness[sol_idx] == "undefined": + solutions_to_submit.append(sol.copy()) + solutions_to_submit_indices.append(sol_idx) + + # Check if batch processing is used. If not, then calculate the fitness value for individual solutions. + if self.fitness_batch_size in [1, None]: + for index, fitness in zip(solutions_to_submit_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), solutions_to_submit, solutions_to_submit_indices)): + if type(fitness) in GA.supported_int_float_types: + pop_fitness[index] = fitness + else: + self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + else: + # Reaching this block means that batch processing is used. The fitness values are calculated in batches. + + # Number of batches. + num_batches = int(numpy.ceil(len(solutions_to_submit_indices) / self.fitness_batch_size)) + # Each element of the `batches_solutions` list represents the solutions in one batch. + batches_solutions = [] + # Each element of the `batches_indices` list represents the solutions' indices in one batch. + batches_indices = [] + # For each batch, get its indices and call the fitness function. + for batch_idx in range(num_batches): + batch_first_index = batch_idx * self.fitness_batch_size + batch_last_index = (batch_idx + 1) * self.fitness_batch_size + batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] + batch_solutions = self.population[batch_indices, :] + + batches_solutions.append(batch_solutions) + batches_indices.append(batch_indices) + + for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): + if type(batch_fitness) not in [list, tuple, numpy.ndarray]: + self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) + raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) + elif len(numpy.array(batch_fitness)) != len(batch_indices): + self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) + raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) + + for index, fitness in zip(batch_indices, batch_fitness): + if type(fitness) in GA.supported_int_float_types: + pop_fitness[index] = fitness + else: + self.logger.error("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + raise ValueError("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + + pop_fitness = numpy.array(pop_fitness) + + return pop_fitness + + def run(self): + + """ + Runs the genetic algorithm. This is the main method in which the genetic algorithm is evolved through a number of generations. + """ + + if self.valid_parameters == False: + self.logger.error("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") + raise Exception("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") + + # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. + # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. + + # self.best_solutions: Holds the best solution in each generation. + if type(self.best_solutions) is numpy.ndarray: + self.best_solutions = list(self.best_solutions) + # self.best_solutions_fitness: A list holding the fitness value of the best solution for each generation. + if type(self.best_solutions_fitness) is numpy.ndarray: + self.best_solutions_fitness = list(self.best_solutions_fitness) + # self.solutions: Holds the solutions in each generation. + if type(self.solutions) is numpy.ndarray: + self.solutions = list(self.solutions) + # self.solutions_fitness: Holds the fitness of the solutions in each generation. + if type(self.solutions_fitness) is numpy.ndarray: + self.solutions_fitness = list(self.solutions_fitness) + + if not (self.on_start is None): + self.on_start(self) + + stop_run = False + + # To continue from where we stopped, the first generation index should start from the value of the 'self.generations_completed' parameter. + if self.generations_completed != 0 and type(self.generations_completed) in GA.supported_int_types: + # If the 'self.generations_completed' parameter is not '0', then this means we continue execution. + generation_first_idx = self.generations_completed + generation_last_idx = self.num_generations + self.generations_completed + else: + # If the 'self.generations_completed' parameter is '0', then stat from scratch. + generation_first_idx = 0 + generation_last_idx = self.num_generations + + # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. + self.last_generation_fitness = self.cal_pop_fitness() + + best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) + + # Appending the best solution in the initial population to the best_solutions list. + if self.save_best_solutions: + self.best_solutions.append(best_solution) + + for generation in range(generation_first_idx, generation_last_idx): + if not (self.on_fitness is None): + self.on_fitness(self, self.last_generation_fitness) + + # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. + self.best_solutions_fitness.append(best_solution_fitness) + + # Appending the solutions in the current generation to the solutions list. + if self.save_solutions: + # self.solutions.extend(self.population.copy()) + population_as_list = self.population.copy() + population_as_list = [list(item) for item in population_as_list] + self.solutions.extend(population_as_list) + + self.solutions_fitness.extend(self.last_generation_fitness) + + # Selecting the best parents in the population for mating. + if callable(self.parent_selection_type): + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self, + self.last_generation_fitness, + self.num_parents_mating, self) + if not type(self.last_generation_parents) is numpy.ndarray: + self.logger.error("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) + raise TypeError("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) + if not type(self.last_generation_parents_indices) is numpy.ndarray: + self.logger.error("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) + raise TypeError("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) + else: + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + num_parents=self.num_parents_mating) + + # Validate the output of the parent selection step: self.select_parents() + if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): + if self.last_generation_parents.shape[0] != self.num_parents_mating: + self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) + raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) + elif self.last_generation_parents.shape[1] != self.num_genes: + self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) + raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) + + if self.last_generation_parents_indices.ndim != 1: + self.logger.error("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) + raise ValueError("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) + elif len(self.last_generation_parents_indices) != self.num_parents_mating: + self.logger.error("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) + raise ValueError("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) + + if not (self.on_parents is None): + self.on_parents(self, self.last_generation_parents) + + # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. + if self.crossover_type is None: + if self.keep_elitism == 0: + num_parents_to_keep = self.num_parents_mating if self.keep_parents == -1 else self.keep_parents + if self.num_offspring <= num_parents_to_keep: + self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] + else: + self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) + else: + # The steady_state_selection() function is called to select the best solutions (i.e. elitism). The keep_elitism parameter defines the number of these solutions. + # The steady_state_selection() function is still called here even if its output may not be used given that the condition of the next if statement is True. The reason is that it will be used later. + self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_elitism) + if self.num_offspring <= self.keep_elitism: + self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] + else: + self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) + else: + # Generating offspring using crossover. + if callable(self.crossover_type): + self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, + (self.num_offspring, self.num_genes), + self) + if not type(self.last_generation_offspring_crossover) is numpy.ndarray: + self.logger.error("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) + raise TypeError("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) + else: + self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, + offspring_size=(self.num_offspring, self.num_genes)) + if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): + if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: + self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) + raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) + elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: + self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) + raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) + + # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. + if not (self.on_crossover is None): + self.on_crossover(self, self.last_generation_offspring_crossover) + + # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. + if self.mutation_type is None: + self.last_generation_offspring_mutation = self.last_generation_offspring_crossover + else: + # Adding some variations to the offspring using mutation. + if callable(self.mutation_type): + self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, + self) + if not type(self.last_generation_offspring_mutation) is numpy.ndarray: + self.logger.error("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) + raise TypeError("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) + else: + self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover) + + if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): + if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: + self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) + raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) + elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: + self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) + raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) + + # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. + if not (self.on_mutation is None): + self.on_mutation(self, self.last_generation_offspring_mutation) + + # Update the population attribute according to the offspring generated. + if self.keep_elitism == 0: + # If the keep_elitism parameter is 0, then the keep_parents parameter will be used to decide if the parents are kept in the next generation. + if (self.keep_parents == 0): + self.population = self.last_generation_offspring_mutation + elif (self.keep_parents == -1): + # Creating the new population based on the parents and offspring. + self.population[0:self.last_generation_parents.shape[0], :] = self.last_generation_parents + self.population[self.last_generation_parents.shape[0]:, :] = self.last_generation_offspring_mutation + elif (self.keep_parents > 0): + parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_parents) + self.population[0:parents_to_keep.shape[0], :] = parents_to_keep + self.population[parents_to_keep.shape[0]:, :] = self.last_generation_offspring_mutation + else: + self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_elitism) + self.population[0:self.last_generation_elitism.shape[0], :] = self.last_generation_elitism + self.population[self.last_generation_elitism.shape[0]:, :] = self.last_generation_offspring_mutation + + self.generations_completed = generation + 1 # The generations_completed attribute holds the number of the last completed generation. + + self.previous_generation_fitness = self.last_generation_fitness.copy() + # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. + self.last_generation_fitness = self.cal_pop_fitness() + + best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) + + # Appending the best solution in the current generation to the best_solutions list. + if self.save_best_solutions: + self.best_solutions.append(best_solution) + + # If the on_generation attribute is not None, then cal the callback function after the generation. + if not (self.on_generation is None): + r = self.on_generation(self) + if type(r) is str and r.lower() == "stop": + # Before aborting the loop, save the fitness value of the best solution. + # _, best_solution_fitness, _ = self.best_solution() + self.best_solutions_fitness.append(best_solution_fitness) + break + + if not self.stop_criteria is None: + for criterion in self.stop_criteria: + if criterion[0] == "reach": + if max(self.last_generation_fitness) >= criterion[1]: + stop_run = True + break + elif criterion[0] == "saturate": + criterion[1] = int(criterion[1]) + if (self.generations_completed >= criterion[1]): + if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0: + stop_run = True + break + + if stop_run: + break + + time.sleep(self.delay_after_gen) + + # Save the fitness of the last generation. + if self.save_solutions: + # self.solutions.extend(self.population.copy()) + population_as_list = self.population.copy() + population_as_list = [list(item) for item in population_as_list] + self.solutions.extend(population_as_list) + + self.solutions_fitness.extend(self.last_generation_fitness) + + # Save the fitness value of the best solution. + _, best_solution_fitness, _ = self.best_solution(pop_fitness=self.last_generation_fitness) + self.best_solutions_fitness.append(best_solution_fitness) + + self.best_solution_generation = numpy.where(numpy.array(self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] + # After the run() method completes, the run_completed flag is changed from False to True. + self.run_completed = True # Set to True only after the run() method completes gracefully. + + if not (self.on_stop is None): + self.on_stop(self, self.last_generation_fitness) + + # Converting the 'best_solutions' list into a NumPy array. + self.best_solutions = numpy.array(self.best_solutions) + + # Converting the 'solutions' list into a NumPy array. + # self.solutions = numpy.array(self.solutions) + + def best_solution(self, pop_fitness=None): + + """ + Returns information about the best solution found by the genetic algorithm. + Accepts the following parameters: + pop_fitness: An optional parameter holding the fitness values of the solutions in the latest population. If passed, then it save time calculating the fitness. If None, then the 'cal_pop_fitness()' method is called to calculate the fitness of the latest population. + The following are returned: + -best_solution: Best solution in the current population. + -best_solution_fitness: Fitness value of the best solution. + -best_match_idx: Index of the best solution in the current population. + """ + + if pop_fitness is None: + # If the 'pop_fitness' parameter is not passed, then we have to call the 'cal_pop_fitness()' method to calculate the fitness of all solutions in the lastest population. + pop_fitness = self.cal_pop_fitness() + # Verify the type of the 'pop_fitness' parameter. + elif type(pop_fitness) in [tuple, list, numpy.ndarray]: + # Verify that the length of the passed population fitness matches the length of the 'self.population' attribute. + if len(pop_fitness) == len(self.population): + # This successfully verifies the 'pop_fitness' parameter. + pass + else: + self.logger.error("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) + raise ValueError("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) + else: + self.logger.error("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) + raise ValueError("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) + + # Return the index of the best solution that has the best fitness value. + best_match_idx = numpy.where(pop_fitness == numpy.max(pop_fitness))[0][0] + + best_solution = self.population[best_match_idx, :].copy() + best_solution_fitness = pop_fitness[best_match_idx] + + return best_solution, best_solution_fitness, best_match_idx + + def save(self, filename): + + """ + Saves the genetic algorithm instance: + -filename: Name of the file to save the instance. No extension is needed. + """ + + cloudpickle_serialized_object = cloudpickle.dumps(self) + with open(filename + ".pkl", 'wb') as file: + file.write(cloudpickle_serialized_object) + cloudpickle.dump(self, file) + + def summary(self, + line_length=70, + fill_character=" ", + line_character="-", + line_character2="=", + columns_equal_len=False, + print_step_parameters=True, + print_parameters_summary=True): + """ + The summary() method prints a summary of the PyGAD lifecycle in a Keras style. + The parameters are: + line_length: An integer representing the length of the single line in characters. + fill_character: A character to fill the lines. + line_character: A character for creating a line separator. + line_character2: A secondary character to create a line separator. + columns_equal_len: The table rows are split into equal-sized columns or split subjective to the width needed. + print_step_parameters: Whether to print extra parameters about each step inside the step. If print_step_parameters=False and print_parameters_summary=True, then the parameters of each step are printed at the end of the table. + print_parameters_summary: Whether to print parameters summary at the end of the table. If print_step_parameters=False, then the parameters of each step are printed at the end of the table too. + """ + + summary_output = "" + + def fill_message(msg, line_length=line_length, fill_character=fill_character): + num_spaces = int((line_length - len(msg))/2) + num_spaces = int(num_spaces / len(fill_character)) + msg = "{spaces}{msg}{spaces}".format(msg=msg, spaces=fill_character * num_spaces) + return msg + + def line_separator(line_length=line_length, line_character=line_character): + num_characters = int(line_length / len(line_character)) + return line_character * num_characters + + def create_row(columns, line_length=line_length, fill_character=fill_character, split_percentages=None): + filled_columns = [] + if split_percentages == None: + split_percentages = [int(100/len(columns))] * 3 + columns_lengths = [int((split_percentages[idx] * line_length) / 100) for idx in range(len(split_percentages))] + for column_idx, column in enumerate(columns): + current_column_length = len(column) + extra_characters = columns_lengths[column_idx] - current_column_length + filled_column = column + fill_character * extra_characters + filled_column = column + fill_character * extra_characters + filled_columns.append(filled_column) + + return "".join(filled_columns) + + def print_parent_selection_params(): + nonlocal summary_output + m = "Number of Parents: {num_parents_mating}".format(num_parents_mating=self.num_parents_mating) + self.logger.info(m) + summary_output = summary_output + m + "\n" + if self.parent_selection_type == "tournament": + m = "K Tournament: {K_tournament}".format(K_tournament=self.K_tournament) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + def print_fitness_params(): + nonlocal summary_output + if not self.fitness_batch_size is None: + m = "Fitness batch size: {fitness_batch_size}".format(fitness_batch_size=self.fitness_batch_size) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + def print_crossover_params(): + nonlocal summary_output + if not self.crossover_probability is None: + m = "Crossover probability: {crossover_probability}".format(crossover_probability=self.crossover_probability) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + def print_mutation_params(): + nonlocal summary_output + if not self.mutation_probability is None: + m = "Mutation Probability: {mutation_probability}".format(mutation_probability=self.mutation_probability) + self.logger.info(m) + summary_output = summary_output + m + "\n" + if self.mutation_percent_genes == "default": + m = "Mutation Percentage: {mutation_percent_genes}".format(mutation_percent_genes=self.mutation_percent_genes) + self.logger.info(m) + summary_output = summary_output + m + "\n" + # Number of mutation genes is already showed above. + m = "Mutation Genes: {mutation_num_genes}".format(mutation_num_genes=self.mutation_num_genes) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Random Mutation Range: ({random_mutation_min_val}, {random_mutation_max_val})".format(random_mutation_min_val=self.random_mutation_min_val, random_mutation_max_val=self.random_mutation_max_val) + self.logger.info(m) + summary_output = summary_output + m + "\n" + if not self.gene_space is None: + m = "Gene Space: {gene_space}".format(gene_space=self.gene_space) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Mutation by Replacement: {mutation_by_replacement}".format(mutation_by_replacement=self.mutation_by_replacement) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Allow Duplicated Genes: {allow_duplicate_genes}".format(allow_duplicate_genes=self.allow_duplicate_genes) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + def print_on_generation_params(): + nonlocal summary_output + if not self.stop_criteria is None: + m = "Stop Criteria: {stop_criteria}".format(stop_criteria=self.stop_criteria) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + def print_params_summary(): + nonlocal summary_output + m = "Population Size: ({sol_per_pop}, {num_genes})".format(sol_per_pop=self.sol_per_pop, num_genes=self.num_genes) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Number of Generations: {num_generations}".format(num_generations=self.num_generations) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Initial Population Range: ({init_range_low}, {init_range_high})".format(init_range_low=self.init_range_low, init_range_high=self.init_range_high) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + if not print_step_parameters: + print_fitness_params() + + if not print_step_parameters: + print_parent_selection_params() + + if self.keep_elitism != 0: + m = "Keep Elitism: {keep_elitism}".format(keep_elitism=self.keep_elitism) + self.logger.info(m) + summary_output = summary_output + m + "\n" + else: + m = "Keep Parents: {keep_parents}".format(keep_parents=self.keep_parents) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Gene DType: {gene_type}".format(gene_type=self.gene_type) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + if not print_step_parameters: + print_crossover_params() + + if not print_step_parameters: + print_mutation_params() + + if self.delay_after_gen != 0: + m = "Post-Generation Delay: {delay_after_gen}".format(delay_after_gen=self.delay_after_gen) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + if not print_step_parameters: + print_on_generation_params() + + if not self.parallel_processing is None: + m = "Parallel Processing: {parallel_processing}".format(parallel_processing=self.parallel_processing) + self.logger.info(m) + summary_output = summary_output + m + "\n" + if not self.random_seed is None: + m = "Random Seed: {random_seed}".format(random_seed=self.random_seed) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Save Best Solutions: {save_best_solutions}".format(save_best_solutions=self.save_best_solutions) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = "Save Solutions: {save_solutions}".format(save_solutions=self.save_solutions) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + m = line_separator(line_character=line_character) + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = fill_message("PyGAD Lifecycle") + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = line_separator(line_character=line_character2) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + lifecycle_steps = ["on_start()", "Fitness Function", "On Fitness", "Parent Selection", "On Parents", "Crossover", "On Crossover", "Mutation", "On Mutation", "On Generation", "On Stop"] + lifecycle_functions = [self.on_start, self.fitness_func, self.on_fitness, self.select_parents, self.on_parents, self.crossover, self.on_crossover, self.mutation, self.on_mutation, self.on_generation, self.on_stop] + lifecycle_functions = [getattr(lifecycle_func, '__name__', "None") for lifecycle_func in lifecycle_functions] + lifecycle_functions = [lifecycle_func + "()" if lifecycle_func != "None" else "None" for lifecycle_func in lifecycle_functions] + lifecycle_output = ["None", "(1)", "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "None", "None"] + lifecycle_step_parameters = [None, print_fitness_params, None, print_parent_selection_params, None, print_crossover_params, None, print_mutation_params, None, print_on_generation_params, None] + + if not columns_equal_len: + max_lengthes = [max(list(map(len, lifecycle_steps))), max(list(map(len, lifecycle_functions))), max(list(map(len, lifecycle_output)))] + split_percentages = [int((column_len / sum(max_lengthes)) * 100) for column_len in max_lengthes] + else: + split_percentages = None + + header_columns = ["Step", "Handler", "Output Shape"] + header_row = create_row(header_columns, split_percentages=split_percentages) + m = header_row + self.logger.info(m) + summary_output = summary_output + m + "\n" + m = line_separator(line_character=line_character2) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + for lifecycle_idx in range(len(lifecycle_steps)): + lifecycle_column = [lifecycle_steps[lifecycle_idx], lifecycle_functions[lifecycle_idx], lifecycle_output[lifecycle_idx]] + if lifecycle_column[1] == "None": + continue + lifecycle_row = create_row(lifecycle_column, split_percentages=split_percentages) + m = lifecycle_row + self.logger.info(m) + summary_output = summary_output + m + "\n" + if print_step_parameters: + if not lifecycle_step_parameters[lifecycle_idx] is None: + lifecycle_step_parameters[lifecycle_idx]() + m = line_separator(line_character=line_character) + self.logger.info(m) + summary_output = summary_output + m + "\n" + + m = line_separator(line_character=line_character2) + self.logger.info(m) + summary_output = summary_output + m + "\n" + if print_parameters_summary: + print_params_summary() + m = line_separator(line_character=line_character2) + self.logger.info(m) + summary_output = summary_output + m + "\n" + return summary_output + +def load(filename): + + """ + Reads a saved instance of the genetic algorithm: + -filename: Name of the file to read the instance. No extension is needed. + Returns the genetic algorithm instance. + """ + + try: + with open(filename + ".pkl", 'rb') as file: + ga_in = cloudpickle.load(file) + except FileNotFoundError: + raise FileNotFoundError("Error reading the file {filename}. Please check your inputs.".format(filename=filename)) + except: + # raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).") + raise BaseException("Error loading the file.") + return ga_in diff --git a/pygad/torchga/__init__.py b/pygad/torchga/__init__.py new file mode 100644 index 0000000..7e51570 --- /dev/null +++ b/pygad/torchga/__init__.py @@ -0,0 +1,3 @@ +from .torchga import * + +__version__ = "1.3.0" diff --git a/pygad/torchga/torchga.py b/pygad/torchga/torchga.py new file mode 100644 index 0000000..cff6d2e --- /dev/null +++ b/pygad/torchga/torchga.py @@ -0,0 +1,90 @@ +import copy +import numpy +import torch + +def model_weights_as_vector(model): + weights_vector = [] + + for curr_weights in model.state_dict().values(): + # Calling detach() to remove the computational graph from the layer. + # cpu() is called for making shore the data is moved from GPU to cpu + # numpy() is called for converting the tensor into a NumPy array. + curr_weights = curr_weights.cpu().detach().numpy() + vector = numpy.reshape(curr_weights, newshape=(curr_weights.size)) + weights_vector.extend(vector) + + return numpy.array(weights_vector) + +def model_weights_as_dict(model, weights_vector): + weights_dict = model.state_dict() + + start = 0 + for key in weights_dict: + # Calling detach() to remove the computational graph from the layer. + # cpu() is called for making shore the data is moved from GPU to cpu + # numpy() is called for converting the tensor into a NumPy array. + w_matrix = weights_dict[key].cpu().detach().numpy() + layer_weights_shape = w_matrix.shape + layer_weights_size = w_matrix.size + + layer_weights_vector = weights_vector[start:start + layer_weights_size] + layer_weights_matrix = numpy.reshape(layer_weights_vector, newshape=(layer_weights_shape)) + weights_dict[key] = torch.from_numpy(layer_weights_matrix) + + start = start + layer_weights_size + + return weights_dict + +def predict(model, solution, data): + # Fetch the parameters of the best solution. + model_weights_dict = model_weights_as_dict(model=model, + weights_vector=solution) + + # Use the current solution as the model parameters. + _model = copy.deepcopy(model) + _model.load_state_dict(model_weights_dict) + + predictions = _model(data) + + return predictions + +class TorchGA: + + def __init__(self, model, num_solutions): + + """ + Creates an instance of the TorchGA class to build a population of model parameters. + + model: A PyTorch model class. + num_solutions: Number of solutions in the population. Each solution has different model parameters. + """ + + self.model = model + + self.num_solutions = num_solutions + + # A list holding references to all the solutions (i.e. networks) used in the population. + self.population_weights = self.create_population() + + def create_population(self): + + """ + Creates the initial population of the genetic algorithm as a list of networks' weights (i.e. solutions). Each element in the list holds a different weights of the PyTorch model. + + The method returns a list holding the weights of all solutions. + """ + + model_weights_vector = model_weights_as_vector(model=self.model) + + net_population_weights = [] + net_population_weights.append(model_weights_vector) + + for idx in range(self.num_solutions-1): + + net_weights = copy.deepcopy(model_weights_vector) + net_weights = numpy.array(net_weights) + numpy.random.uniform(low=-1.0, high=1.0, size=model_weights_vector.size) + + # Appending the weights to the population. + net_population_weights.append(net_weights) + + return net_population_weights diff --git a/pygad/utils/__init__.py b/pygad/utils/__init__.py new file mode 100644 index 0000000..dde5633 --- /dev/null +++ b/pygad/utils/__init__.py @@ -0,0 +1,5 @@ +from pygad.utils import parent_selection +from pygad.utils import crossover +from pygad.utils import mutation + +__version__ = "1.0.0" \ No newline at end of file diff --git a/pygad/utils/crossover.py b/pygad/utils/crossover.py new file mode 100644 index 0000000..8eff279 --- /dev/null +++ b/pygad/utils/crossover.py @@ -0,0 +1,248 @@ +""" +The pygad.utils.crossover module has all the built-in crossover operators. +""" + +import numpy +import random + +class Crossover: + def single_point_crossover(self, parents, offspring_size): + + """ + Applies the single-point crossover. It selects a point randomly at which crossover takes place between the pairs of parents. + It accepts 2 parameters: + -parents: The parents to mate for producing the offspring. + -offspring_size: The size of the offspring to produce. + It returns an array the produced offspring. + """ + + if self.gene_type_single == True: + offspring = numpy.empty(offspring_size, dtype=self.gene_type[0]) + else: + offspring = numpy.empty(offspring_size, dtype=object) + + for k in range(offspring_size[0]): + # The point at which crossover takes place between two parents. Usually, it is at the center. + crossover_point = numpy.random.randint(low=0, high=parents.shape[1], size=1)[0] + + if not (self.crossover_probability is None): + probs = numpy.random.random(size=parents.shape[0]) + indices = numpy.where(probs <= self.crossover_probability)[0] + + # If no parent satisfied the probability, no crossover is applied and a parent is selected. + if len(indices) == 0: + offspring[k, :] = parents[k % parents.shape[0], :] + continue + elif len(indices) == 1: + parent1_idx = indices[0] + parent2_idx = parent1_idx + else: + indices = random.sample(list(set(indices)), 2) + parent1_idx = indices[0] + parent2_idx = indices[1] + else: + # Index of the first parent to mate. + parent1_idx = k % parents.shape[0] + # Index of the second parent to mate. + parent2_idx = (k+1) % parents.shape[0] + + # The new offspring has its first half of its genes from the first parent. + offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point] + # The new offspring has its second half of its genes from the second parent. + offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:] + + if self.allow_duplicate_genes == False: + if self.gene_space is None: + offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + else: + offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k], + gene_type=self.gene_type, + num_trials=10) + + return offspring + + def two_points_crossover(self, parents, offspring_size): + + """ + Applies the 2 points crossover. It selects the 2 points randomly at which crossover takes place between the pairs of parents. + It accepts 2 parameters: + -parents: The parents to mate for producing the offspring. + -offspring_size: The size of the offspring to produce. + It returns an array the produced offspring. + """ + + if self.gene_type_single == True: + offspring = numpy.empty(offspring_size, dtype=self.gene_type[0]) + else: + offspring = numpy.empty(offspring_size, dtype=object) + + for k in range(offspring_size[0]): + if (parents.shape[1] == 1): # If the chromosome has only a single gene. In this case, this gene is copied from the second parent. + crossover_point1 = 0 + else: + crossover_point1 = numpy.random.randint(low=0, high=numpy.ceil(parents.shape[1]/2 + 1), size=1)[0] + + crossover_point2 = crossover_point1 + int(parents.shape[1]/2) # The second point must always be greater than the first point. + + if not (self.crossover_probability is None): + probs = numpy.random.random(size=parents.shape[0]) + indices = numpy.where(probs <= self.crossover_probability)[0] + + # If no parent satisfied the probability, no crossover is applied and a parent is selected. + if len(indices) == 0: + offspring[k, :] = parents[k % parents.shape[0], :] + continue + elif len(indices) == 1: + parent1_idx = indices[0] + parent2_idx = parent1_idx + else: + indices = random.sample(list(set(indices)), 2) + parent1_idx = indices[0] + parent2_idx = indices[1] + else: + # Index of the first parent to mate. + parent1_idx = k % parents.shape[0] + # Index of the second parent to mate. + parent2_idx = (k+1) % parents.shape[0] + + # The genes from the beginning of the chromosome up to the first point are copied from the first parent. + offspring[k, 0:crossover_point1] = parents[parent1_idx, 0:crossover_point1] + # The genes from the second point up to the end of the chromosome are copied from the first parent. + offspring[k, crossover_point2:] = parents[parent1_idx, crossover_point2:] + # The genes between the 2 points are copied from the second parent. + offspring[k, crossover_point1:crossover_point2] = parents[parent2_idx, crossover_point1:crossover_point2] + + if self.allow_duplicate_genes == False: + if self.gene_space is None: + offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + else: + offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k], + gene_type=self.gene_type, + num_trials=10) + return offspring + + def uniform_crossover(self, parents, offspring_size): + + """ + Applies the uniform crossover. For each gene, a parent out of the 2 mating parents is selected randomly and the gene is copied from it. + It accepts 2 parameters: + -parents: The parents to mate for producing the offspring. + -offspring_size: The size of the offspring to produce. + It returns an array the produced offspring. + """ + + if self.gene_type_single == True: + offspring = numpy.empty(offspring_size, dtype=self.gene_type[0]) + else: + offspring = numpy.empty(offspring_size, dtype=object) + + for k in range(offspring_size[0]): + if not (self.crossover_probability is None): + probs = numpy.random.random(size=parents.shape[0]) + indices = numpy.where(probs <= self.crossover_probability)[0] + + # If no parent satisfied the probability, no crossover is applied and a parent is selected. + if len(indices) == 0: + offspring[k, :] = parents[k % parents.shape[0], :] + continue + elif len(indices) == 1: + parent1_idx = indices[0] + parent2_idx = parent1_idx + else: + indices = random.sample(list(set(indices)), 2) + parent1_idx = indices[0] + parent2_idx = indices[1] + else: + # Index of the first parent to mate. + parent1_idx = k % parents.shape[0] + # Index of the second parent to mate. + parent2_idx = (k+1) % parents.shape[0] + + genes_source = numpy.random.randint(low=0, high=2, size=offspring_size[1]) + for gene_idx in range(offspring_size[1]): + if (genes_source[gene_idx] == 0): + # The gene will be copied from the first parent if the current gene index is 0. + offspring[k, gene_idx] = parents[parent1_idx, gene_idx] + elif (genes_source[gene_idx] == 1): + # The gene will be copied from the second parent if the current gene index is 1. + offspring[k, gene_idx] = parents[parent2_idx, gene_idx] + + if self.allow_duplicate_genes == False: + if self.gene_space is None: + offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + else: + offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k], + gene_type=self.gene_type, + num_trials=10) + + return offspring + + def scattered_crossover(self, parents, offspring_size): + + """ + Applies the scattered crossover. It randomly selects the gene from one of the 2 parents. + It accepts 2 parameters: + -parents: The parents to mate for producing the offspring. + -offspring_size: The size of the offspring to produce. + It returns an array the produced offspring. + """ + + if self.gene_type_single == True: + offspring = numpy.empty(offspring_size, dtype=self.gene_type[0]) + else: + offspring = numpy.empty(offspring_size, dtype=object) + + for k in range(offspring_size[0]): + if not (self.crossover_probability is None): + probs = numpy.random.random(size=parents.shape[0]) + indices = numpy.where(probs <= self.crossover_probability)[0] + + # If no parent satisfied the probability, no crossover is applied and a parent is selected. + if len(indices) == 0: + offspring[k, :] = parents[k % parents.shape[0], :] + continue + elif len(indices) == 1: + parent1_idx = indices[0] + parent2_idx = parent1_idx + else: + indices = random.sample(list(set(indices)), 2) + parent1_idx = indices[0] + parent2_idx = indices[1] + else: + # Index of the first parent to mate. + parent1_idx = k % parents.shape[0] + # Index of the second parent to mate. + parent2_idx = (k+1) % parents.shape[0] + + # A 0/1 vector where 0 means the gene is taken from the first parent and 1 means the gene is taken from the second parent. + gene_sources = numpy.random.randint(0, 2, size=self.num_genes) + offspring[k, :] = numpy.where(gene_sources == 0, parents[parent1_idx, :], parents[parent2_idx, :]) + + if self.allow_duplicate_genes == False: + if self.gene_space is None: + offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + else: + offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k], + gene_type=self.gene_type, + num_trials=10) + return offspring diff --git a/pygad/utils/mutation.py b/pygad/utils/mutation.py new file mode 100644 index 0000000..a5eb867 --- /dev/null +++ b/pygad/utils/mutation.py @@ -0,0 +1,822 @@ +""" +The pygad.utils.mutation module has all the built-in mutation operators. +""" + +import numpy +import random + +import pygad + +class Mutation: + def random_mutation(self, offspring): + + """ + Applies the random mutation which changes the values of a number of genes randomly. + The random value is selected either using the 'gene_space' parameter or the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + # If the mutation values are selected from the mutation space, the attribute 'gene_space' is not None. Otherwise, it is None. + # When the 'mutation_probability' parameter exists (i.e. not None), then it is used in the mutation. Otherwise, the 'mutation_num_genes' parameter is used. + + if self.mutation_probability is None: + # When the 'mutation_probability' parameter does not exist (i.e. None), then the parameter 'mutation_num_genes' is used in the mutation. + if not (self.gene_space is None): + # When the attribute 'gene_space' exists (i.e. not None), the mutation values are selected randomly from the space of values of each gene. + offspring = self.mutation_by_space(offspring) + else: + offspring = self.mutation_randomly(offspring) + else: + # When the 'mutation_probability' parameter exists (i.e. not None), then it is used in the mutation. + if not (self.gene_space is None): + # When the attribute 'gene_space' does not exist (i.e. None), the mutation values are selected randomly based on the continuous range specified by the 2 attributes 'random_mutation_min_val' and 'random_mutation_max_val'. + offspring = self.mutation_probs_by_space(offspring) + else: + offspring = self.mutation_probs_randomly(offspring) + + return offspring + + def mutation_by_space(self, offspring): + + """ + Applies the random mutation using the mutation values' space. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring using the mutation space. + """ + + # For each offspring, a value from the gene space is selected randomly and assigned to the selected mutated gene. + for offspring_idx in range(offspring.shape[0]): + mutation_indices = numpy.array(random.sample(range(0, self.num_genes), self.mutation_num_genes)) + for gene_idx in mutation_indices: + + if self.gene_space_nested: + # Returning the current gene space from the 'gene_space' attribute. + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: + curr_gene_space = self.gene_space[gene_idx].copy() + else: + curr_gene_space = self.gene_space[gene_idx] + + # If the gene space has only a single value, use it as the new gene value. + if type(curr_gene_space) in pygad.GA.supported_int_float_types: + value_from_space = curr_gene_space + # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + elif curr_gene_space is None: + rand_val = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + if self.mutation_by_replacement: + value_from_space = rand_val + else: + value_from_space = offspring[offspring_idx, gene_idx] + rand_val + elif type(curr_gene_space) is dict: + # The gene's space of type dict specifies the lower and upper limits of a gene. + if 'step' in curr_gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], + stop=curr_gene_space['high'], + step=curr_gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=curr_gene_space['low'], + high=curr_gene_space['high'], + size=1) + else: + # Selecting a value randomly based on the current gene's space in the 'gene_space' attribute. + # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. + if len(curr_gene_space) == 1: + value_from_space = curr_gene_space[0] + # If the gene space has more than 1 value, then select a new one that is different from the current value. + else: + values_to_select_from = list(set(curr_gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + else: + # Selecting a value randomly from the global gene space in the 'gene_space' attribute. + if type(self.gene_space) is dict: + # When the gene_space is assigned a dict object, then it specifies the lower and upper limits of all genes in the space. + if 'step' in self.gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=1) + else: + # If the space type is not of type dict, then a value is randomly selected from the gene_space attribute. + values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + # value_from_space = random.choice(self.gene_space) + + if value_from_space is None: + value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + + # Assinging the selected value from the space to the gene. + if self.gene_type_single == True: + if not self.gene_type[1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[0](value_from_space), + self.gene_type[1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[0](value_from_space) + else: + if not self.gene_type[gene_idx][1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[gene_idx][0](value_from_space), + self.gene_type[gene_idx][1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[gene_idx][0](value_from_space) + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[offspring_idx], + gene_type=self.gene_type, + num_trials=10) + return offspring + + def mutation_probs_by_space(self, offspring): + + """ + Applies the random mutation using the mutation values' space and the mutation probability. For each gene, if its probability is <= that mutation probability, then it will be mutated based on the mutation space. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring using the mutation space. + """ + + # For each offspring, a value from the gene space is selected randomly and assigned to the selected mutated gene. + for offspring_idx in range(offspring.shape[0]): + probs = numpy.random.random(size=offspring.shape[1]) + for gene_idx in range(offspring.shape[1]): + if probs[gene_idx] <= self.mutation_probability: + if self.gene_space_nested: + # Returning the current gene space from the 'gene_space' attribute. + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: + curr_gene_space = self.gene_space[gene_idx].copy() + else: + curr_gene_space = self.gene_space[gene_idx] + + # If the gene space has only a single value, use it as the new gene value. + if type(curr_gene_space) in pygad.GA.supported_int_float_types: + value_from_space = curr_gene_space + # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + elif curr_gene_space is None: + rand_val = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + if self.mutation_by_replacement: + value_from_space = rand_val + else: + value_from_space = offspring[offspring_idx, gene_idx] + rand_val + elif type(curr_gene_space) is dict: + # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. + if 'step' in curr_gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], + stop=curr_gene_space['high'], + step=curr_gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=curr_gene_space['low'], + high=curr_gene_space['high'], + size=1) + else: + # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. + # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. + if len(curr_gene_space) == 1: + value_from_space = curr_gene_space[0] + # If the gene space has more than 1 value, then select a new one that is different from the current value. + else: + values_to_select_from = list(set(curr_gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + else: + # Selecting a value randomly from the global gene space in the 'gene_space' attribute. + if type(self.gene_space) is dict: + if 'step' in self.gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=1) + else: + values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + + # Assigning the selected value from the space to the gene. + if self.gene_type_single == True: + if not self.gene_type[1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[0](value_from_space), + self.gene_type[1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[0](value_from_space) + else: + if not self.gene_type[gene_idx][1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[gene_idx][0](value_from_space), + self.gene_type[gene_idx][1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[gene_idx][0](value_from_space) + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[offspring_idx], + gene_type=self.gene_type, + num_trials=10) + return offspring + + def mutation_randomly(self, offspring): + + """ + Applies the random mutation the mutation probability. For each gene, if its probability is <= that mutation probability, then it will be mutated randomly. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + # Random mutation changes one or more genes in each offspring randomly. + for offspring_idx in range(offspring.shape[0]): + mutation_indices = numpy.array(random.sample(range(0, self.num_genes), self.mutation_num_genes)) + for gene_idx in mutation_indices: + # Generating a random value. + random_value = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. + if self.mutation_by_replacement: + if self.gene_type_single == True: + random_value = self.gene_type[0](random_value) + else: + random_value = self.gene_type[gene_idx][0](random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + # If the mutation_by_replacement attribute is False, then the random value is added to the gene value. + else: + if self.gene_type_single == True: + random_value = self.gene_type[0](offspring[offspring_idx, gene_idx] + random_value) + else: + random_value = self.gene_type[gene_idx][0](offspring[offspring_idx, gene_idx] + random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + + # Round the gene + if self.gene_type_single == True: + if not self.gene_type[1] is None: + random_value = numpy.round(random_value, self.gene_type[1]) + else: + if not self.gene_type[gene_idx][1] is None: + random_value = numpy.round(random_value, self.gene_type[gene_idx][1]) + + offspring[offspring_idx, gene_idx] = random_value + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + + return offspring + + def mutation_probs_randomly(self, offspring): + + """ + Applies the random mutation using the mutation probability. For each gene, if its probability is <= that mutation probability, then it will be mutated randomly. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + # Random mutation changes one or more gene in each offspring randomly. + for offspring_idx in range(offspring.shape[0]): + probs = numpy.random.random(size=offspring.shape[1]) + for gene_idx in range(offspring.shape[1]): + if probs[gene_idx] <= self.mutation_probability: + # Generating a random value. + random_value = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. + if self.mutation_by_replacement: + if self.gene_type_single == True: + random_value = self.gene_type[0](random_value) + else: + random_value = self.gene_type[gene_idx][0](random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + # If the mutation_by_replacement attribute is False, then the random value is added to the gene value. + else: + if self.gene_type_single == True: + random_value = self.gene_type[0](offspring[offspring_idx, gene_idx] + random_value) + else: + random_value = self.gene_type[gene_idx][0](offspring[offspring_idx, gene_idx] + random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + + # Round the gene + if self.gene_type_single == True: + if not self.gene_type[1] is None: + random_value = numpy.round(random_value, self.gene_type[1]) + else: + if not self.gene_type[gene_idx][1] is None: + random_value = numpy.round(random_value, self.gene_type[gene_idx][1]) + + offspring[offspring_idx, gene_idx] = random_value + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + return offspring + + def swap_mutation(self, offspring): + + """ + Applies the swap mutation which interchanges the values of 2 randomly selected genes. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + for idx in range(offspring.shape[0]): + mutation_gene1 = numpy.random.randint(low=0, high=offspring.shape[1]/2, size=1)[0] + mutation_gene2 = mutation_gene1 + int(offspring.shape[1]/2) + + temp = offspring[idx, mutation_gene1] + offspring[idx, mutation_gene1] = offspring[idx, mutation_gene2] + offspring[idx, mutation_gene2] = temp + return offspring + + def inversion_mutation(self, offspring): + + """ + Applies the inversion mutation which selects a subset of genes and inverts them (in order). + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + for idx in range(offspring.shape[0]): + mutation_gene1 = numpy.random.randint(low=0, high=numpy.ceil(offspring.shape[1]/2 + 1), size=1)[0] + mutation_gene2 = mutation_gene1 + int(offspring.shape[1]/2) + + genes_to_scramble = numpy.flip(offspring[idx, mutation_gene1:mutation_gene2]) + offspring[idx, mutation_gene1:mutation_gene2] = genes_to_scramble + return offspring + + def scramble_mutation(self, offspring): + + """ + Applies the scramble mutation which selects a subset of genes and shuffles their order randomly. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + for idx in range(offspring.shape[0]): + mutation_gene1 = numpy.random.randint(low=0, high=numpy.ceil(offspring.shape[1]/2 + 1), size=1)[0] + mutation_gene2 = mutation_gene1 + int(offspring.shape[1]/2) + genes_range = numpy.arange(start=mutation_gene1, stop=mutation_gene2) + numpy.random.shuffle(genes_range) + + genes_to_scramble = numpy.flip(offspring[idx, genes_range]) + offspring[idx, genes_range] = genes_to_scramble + return offspring + + def adaptive_mutation_population_fitness(self, offspring): + + """ + A helper method to calculate the average fitness of the solutions before applying the adaptive mutation. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns the average fitness to be used in adaptive mutation. + """ + + fitness = self.last_generation_fitness.copy() + temp_population = numpy.zeros_like(self.population) + + if (self.keep_elitism == 0): + if (self.keep_parents == 0): + parents_to_keep = [] + elif (self.keep_parents == -1): + parents_to_keep = self.last_generation_parents.copy() + temp_population[0:len(parents_to_keep), :] = parents_to_keep + elif (self.keep_parents > 0): + parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, num_parents=self.keep_parents) + temp_population[0:len(parents_to_keep), :] = parents_to_keep + else: + parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, num_parents=self.keep_elitism) + temp_population[0:len(parents_to_keep), :] = parents_to_keep + + temp_population[len(parents_to_keep):, :] = offspring + + fitness[:self.last_generation_parents.shape[0]] = self.last_generation_fitness[self.last_generation_parents_indices] + + for idx in range(len(parents_to_keep), fitness.shape[0]): + fitness[idx] = self.fitness_func(self, temp_population[idx], None) + average_fitness = numpy.mean(fitness) + + return average_fitness, fitness[len(parents_to_keep):] + + def adaptive_mutation(self, offspring): + + """ + Applies the adaptive mutation which changes the values of a number of genes randomly. In adaptive mutation, the number of genes to mutate differs based on the fitness value of the solution. + The random value is selected either using the 'gene_space' parameter or the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + # If the attribute 'gene_space' exists (i.e. not None), then the mutation values are selected from the 'gene_space' parameter according to the space of values of each gene. Otherwise, it is selected randomly based on the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + # When the 'mutation_probability' parameter exists (i.e. not None), then it is used in the mutation. Otherwise, the 'mutation_num_genes' parameter is used. + + if self.mutation_probability is None: + # When the 'mutation_probability' parameter does not exist (i.e. None), then the parameter 'mutation_num_genes' is used in the mutation. + if not (self.gene_space is None): + # When the attribute 'gene_space' exists (i.e. not None), the mutation values are selected randomly from the space of values of each gene. + offspring = self.adaptive_mutation_by_space(offspring) + else: + # When the attribute 'gene_space' does not exist (i.e. None), the mutation values are selected randomly based on the continuous range specified by the 2 attributes 'random_mutation_min_val' and 'random_mutation_max_val'. + offspring = self.adaptive_mutation_randomly(offspring) + else: + # When the 'mutation_probability' parameter exists (i.e. not None), then it is used in the mutation. + if not (self.gene_space is None): + # When the attribute 'gene_space' exists (i.e. not None), the mutation values are selected randomly from the space of values of each gene. + offspring = self.adaptive_mutation_probs_by_space(offspring) + else: + # When the attribute 'gene_space' does not exist (i.e. None), the mutation values are selected randomly based on the continuous range specified by the 2 attributes 'random_mutation_min_val' and 'random_mutation_max_val'. + offspring = self.adaptive_mutation_probs_randomly(offspring) + + return offspring + + def adaptive_mutation_by_space(self, offspring): + + """ + Applies the adaptive mutation based on the 2 parameters 'mutation_num_genes' and 'gene_space'. + A number of genes equal are selected randomly for mutation. This number depends on the fitness of the solution. + The random values are selected from the 'gene_space' parameter. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + # For each offspring, a value from the gene space is selected randomly and assigned to the selected gene for mutation. + + average_fitness, offspring_fitness = self.adaptive_mutation_population_fitness(offspring) + + # Adaptive mutation changes one or more genes in each offspring randomly. + # The number of genes to mutate depends on the solution's fitness value. + for offspring_idx in range(offspring.shape[0]): + if offspring_fitness[offspring_idx] < average_fitness: + adaptive_mutation_num_genes = self.mutation_num_genes[0] + else: + adaptive_mutation_num_genes = self.mutation_num_genes[1] + mutation_indices = numpy.array(random.sample(range(0, self.num_genes), adaptive_mutation_num_genes)) + for gene_idx in mutation_indices: + + if self.gene_space_nested: + # Returning the current gene space from the 'gene_space' attribute. + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: + curr_gene_space = self.gene_space[gene_idx].copy() + else: + curr_gene_space = self.gene_space[gene_idx] + + # If the gene space has only a single value, use it as the new gene value. + if type(curr_gene_space) in pygad.GA.supported_int_float_types: + value_from_space = curr_gene_space + # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + elif curr_gene_space is None: + rand_val = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + if self.mutation_by_replacement: + value_from_space = rand_val + else: + value_from_space = offspring[offspring_idx, gene_idx] + rand_val + elif type(curr_gene_space) is dict: + # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. + if 'step' in curr_gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], + stop=curr_gene_space['high'], + step=curr_gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=curr_gene_space['low'], + high=curr_gene_space['high'], + size=1) + else: + # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. + # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. + if len(curr_gene_space) == 1: + value_from_space = curr_gene_space[0] + # If the gene space has more than 1 value, then select a new one that is different from the current value. + else: + values_to_select_from = list(set(curr_gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + else: + # Selecting a value randomly from the global gene space in the 'gene_space' attribute. + if type(self.gene_space) is dict: + if 'step' in self.gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=1) + else: + values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + + + if value_from_space is None: + value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + + # Assinging the selected value from the space to the gene. + if self.gene_type_single == True: + if not self.gene_type[1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[0](value_from_space), + self.gene_type[1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[0](value_from_space) + else: + if not self.gene_type[gene_idx][1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[gene_idx][0](value_from_space), + self.gene_type[gene_idx][1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[gene_idx][0](value_from_space) + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[offspring_idx], + gene_type=self.gene_type, + num_trials=10) + return offspring + + def adaptive_mutation_randomly(self, offspring): + + """ + Applies the adaptive mutation based on the 'mutation_num_genes' parameter. + A number of genes equal are selected randomly for mutation. This number depends on the fitness of the solution. + The random values are selected based on the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + average_fitness, offspring_fitness = self.adaptive_mutation_population_fitness(offspring) + + # Adaptive random mutation changes one or more genes in each offspring randomly. + # The number of genes to mutate depends on the solution's fitness value. + for offspring_idx in range(offspring.shape[0]): + if offspring_fitness[offspring_idx] < average_fitness: + adaptive_mutation_num_genes = self.mutation_num_genes[0] + else: + adaptive_mutation_num_genes = self.mutation_num_genes[1] + mutation_indices = numpy.array(random.sample(range(0, self.num_genes), adaptive_mutation_num_genes)) + for gene_idx in mutation_indices: + # Generating a random value. + random_value = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. + if self.mutation_by_replacement: + if self.gene_type_single == True: + random_value = self.gene_type[0](random_value) + else: + random_value = self.gene_type[gene_idx][0](random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + # If the mutation_by_replacement attribute is False, then the random value is added to the gene value. + else: + if self.gene_type_single == True: + random_value = self.gene_type[0](offspring[offspring_idx, gene_idx] + random_value) + else: + random_value = self.gene_type[gene_idx][0](offspring[offspring_idx, gene_idx] + random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + + if self.gene_type_single == True: + if not self.gene_type[1] is None: + random_value = numpy.round(random_value, self.gene_type[1]) + else: + if not self.gene_type[gene_idx][1] is None: + random_value = numpy.round(random_value, self.gene_type[gene_idx][1]) + + offspring[offspring_idx, gene_idx] = random_value + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + return offspring + + def adaptive_mutation_probs_by_space(self, offspring): + + """ + Applies the adaptive mutation based on the 2 parameters 'mutation_probability' and 'gene_space'. + Based on whether the solution fitness is above or below a threshold, the mutation is applied diffrently by mutating high or low number of genes. + The random values are selected based on space of values for each gene. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + # For each offspring, a value from the gene space is selected randomly and assigned to the selected gene for mutation. + + average_fitness, offspring_fitness = self.adaptive_mutation_population_fitness(offspring) + + # Adaptive random mutation changes one or more genes in each offspring randomly. + # The probability of mutating a gene depends on the solution's fitness value. + for offspring_idx in range(offspring.shape[0]): + if offspring_fitness[offspring_idx] < average_fitness: + adaptive_mutation_probability = self.mutation_probability[0] + else: + adaptive_mutation_probability = self.mutation_probability[1] + + probs = numpy.random.random(size=offspring.shape[1]) + for gene_idx in range(offspring.shape[1]): + if probs[gene_idx] <= adaptive_mutation_probability: + if self.gene_space_nested: + # Returning the current gene space from the 'gene_space' attribute. + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: + curr_gene_space = self.gene_space[gene_idx].copy() + else: + curr_gene_space = self.gene_space[gene_idx] + + # If the gene space has only a single value, use it as the new gene value. + if type(curr_gene_space) in pygad.GA.supported_int_float_types: + value_from_space = curr_gene_space + # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + elif curr_gene_space is None: + rand_val = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + if self.mutation_by_replacement: + value_from_space = rand_val + else: + value_from_space = offspring[offspring_idx, gene_idx] + rand_val + elif type(curr_gene_space) is dict: + # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. + if 'step' in curr_gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], + stop=curr_gene_space['high'], + step=curr_gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=curr_gene_space['low'], + high=curr_gene_space['high'], + size=1) + else: + # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. + # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. + if len(curr_gene_space) == 1: + value_from_space = curr_gene_space[0] + # If the gene space has more than 1 value, then select a new one that is different from the current value. + else: + values_to_select_from = list(set(curr_gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + else: + # Selecting a value randomly from the global gene space in the 'gene_space' attribute. + if type(self.gene_space) is dict: + if 'step' in self.gene_space.keys(): + value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), + size=1) + else: + value_from_space = numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=1) + else: + values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) + + if len(values_to_select_from) == 0: + value_from_space = offspring[offspring_idx, gene_idx] + else: + value_from_space = random.choice(values_to_select_from) + + if value_from_space is None: + value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + + # Assinging the selected value from the space to the gene. + if self.gene_type_single == True: + if not self.gene_type[1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[0](value_from_space), + self.gene_type[1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[0](value_from_space) + else: + if not self.gene_type[gene_idx][1] is None: + offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[gene_idx][0](value_from_space), + self.gene_type[gene_idx][1]) + else: + offspring[offspring_idx, gene_idx] = self.gene_type[gene_idx][0](value_from_space) + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[offspring_idx], + gene_type=self.gene_type, + num_trials=10) + return offspring + + def adaptive_mutation_probs_randomly(self, offspring): + + """ + Applies the adaptive mutation based on the 'mutation_probability' parameter. + Based on whether the solution fitness is above or below a threshold, the mutation is applied diffrently by mutating high or low number of genes. + The random values are selected based on the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. + It accepts a single parameter: + -offspring: The offspring to mutate. + It returns an array of the mutated offspring. + """ + + average_fitness, offspring_fitness = self.adaptive_mutation_population_fitness(offspring) + + # Adaptive random mutation changes one or more genes in each offspring randomly. + # The probability of mutating a gene depends on the solution's fitness value. + for offspring_idx in range(offspring.shape[0]): + if offspring_fitness[offspring_idx] < average_fitness: + adaptive_mutation_probability = self.mutation_probability[0] + else: + adaptive_mutation_probability = self.mutation_probability[1] + + probs = numpy.random.random(size=offspring.shape[1]) + for gene_idx in range(offspring.shape[1]): + if probs[gene_idx] <= adaptive_mutation_probability: + # Generating a random value. + random_value = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. + if self.mutation_by_replacement: + if self.gene_type_single == True: + random_value = self.gene_type[0](random_value) + else: + random_value = self.gene_type[gene_idx][0](random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + # If the mutation_by_replacement attribute is False, then the random value is added to the gene value. + else: + if self.gene_type_single == True: + random_value = self.gene_type[0](offspring[offspring_idx, gene_idx] + random_value) + else: + random_value = self.gene_type[gene_idx][0](offspring[offspring_idx, gene_idx] + random_value) + if type(random_value) is numpy.ndarray: + random_value = random_value[0] + + if self.gene_type_single == True: + if not self.gene_type[1] is None: + random_value = numpy.round(random_value, self.gene_type[1]) + else: + if not self.gene_type[gene_idx][1] is None: + random_value = numpy.round(random_value, self.gene_type[gene_idx][1]) + + offspring[offspring_idx, gene_idx] = random_value + + if self.allow_duplicate_genes == False: + offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + return offspring diff --git a/pygad/utils/parent_selection.py b/pygad/utils/parent_selection.py new file mode 100644 index 0000000..7f32181 --- /dev/null +++ b/pygad/utils/parent_selection.py @@ -0,0 +1,191 @@ +""" +The pygad.utils.parent_selection module has all the built-in parent selection operators. +""" + +import numpy + +class ParentSelection: + def steady_state_selection(self, fitness, num_parents): + + """ + Selects the parents using the steady-state selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + fitness_sorted.reverse() + # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + for parent_num in range(num_parents): + parents[parent_num, :] = self.population[fitness_sorted[parent_num], :].copy() + + return parents, numpy.array(fitness_sorted[:num_parents]) + + def rank_selection(self, fitness, num_parents): + + """ + Selects the parents using the rank selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + fitness_sorted.reverse() + # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + for parent_num in range(num_parents): + parents[parent_num, :] = self.population[fitness_sorted[parent_num], :].copy() + + return parents, numpy.array(fitness_sorted[:num_parents]) + + def random_selection(self, fitness, num_parents): + + """ + Selects the parents randomly. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + rand_indices = numpy.random.randint(low=0.0, high=fitness.shape[0], size=num_parents) + + for parent_num in range(num_parents): + parents[parent_num, :] = self.population[rand_indices[parent_num], :].copy() + + return parents, rand_indices + + def tournament_selection(self, fitness, num_parents): + + """ + Selects the parents using the tournament selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + parents_indices = [] + + for parent_num in range(num_parents): + rand_indices = numpy.random.randint(low=0.0, high=len(fitness), size=self.K_tournament) + K_fitnesses = fitness[rand_indices] + selected_parent_idx = numpy.where(K_fitnesses == numpy.max(K_fitnesses))[0][0] + parents_indices.append(rand_indices[selected_parent_idx]) + parents[parent_num, :] = self.population[rand_indices[selected_parent_idx], :].copy() + + return parents, numpy.array(parents_indices) + + def roulette_wheel_selection(self, fitness, num_parents): + + """ + Selects the parents using the roulette wheel selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + fitness_sum = numpy.sum(fitness) + if fitness_sum == 0: + self.logger.error("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + raise ZeroDivisionError("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + probs = fitness / fitness_sum + probs_start = numpy.zeros(probs.shape, dtype=float) # An array holding the start values of the ranges of probabilities. + probs_end = numpy.zeros(probs.shape, dtype=float) # An array holding the end values of the ranges of probabilities. + + curr = 0.0 + + # Calculating the probabilities of the solutions to form a roulette wheel. + for _ in range(probs.shape[0]): + min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0] + probs_start[min_probs_idx] = curr + curr = curr + probs[min_probs_idx] + probs_end[min_probs_idx] = curr + probs[min_probs_idx] = 99999999999 + + # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + parents_indices = [] + + for parent_num in range(num_parents): + rand_prob = numpy.random.rand() + for idx in range(probs.shape[0]): + if (rand_prob >= probs_start[idx] and rand_prob < probs_end[idx]): + parents[parent_num, :] = self.population[idx, :].copy() + parents_indices.append(idx) + break + return parents, numpy.array(parents_indices) + + def stochastic_universal_selection(self, fitness, num_parents): + + """ + Selects the parents using the stochastic universal selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + fitness_sum = numpy.sum(fitness) + if fitness_sum == 0: + self.logger.error("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + raise ZeroDivisionError("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + probs = fitness / fitness_sum + probs_start = numpy.zeros(probs.shape, dtype=float) # An array holding the start values of the ranges of probabilities. + probs_end = numpy.zeros(probs.shape, dtype=float) # An array holding the end values of the ranges of probabilities. + + curr = 0.0 + + # Calculating the probabilities of the solutions to form a roulette wheel. + for _ in range(probs.shape[0]): + min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0] + probs_start[min_probs_idx] = curr + curr = curr + probs[min_probs_idx] + probs_end[min_probs_idx] = curr + probs[min_probs_idx] = 99999999999 + + pointers_distance = 1.0 / self.num_parents_mating # Distance between different pointers. + first_pointer = numpy.random.uniform(low=0.0, high=pointers_distance, size=1) # Location of the first pointer. + + # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + parents_indices = [] + + for parent_num in range(num_parents): + rand_pointer = first_pointer + parent_num*pointers_distance + for idx in range(probs.shape[0]): + if (rand_pointer >= probs_start[idx] and rand_pointer < probs_end[idx]): + parents[parent_num, :] = self.population[idx, :].copy() + parents_indices.append(idx) + break + return parents, numpy.array(parents_indices) diff --git a/pygad/visualize/__init__.py b/pygad/visualize/__init__.py new file mode 100644 index 0000000..6b79646 --- /dev/null +++ b/pygad/visualize/__init__.py @@ -0,0 +1,3 @@ +from pygad.visualize import plot + +__version__ = "1.0.0" \ No newline at end of file diff --git a/pygad/visualize/plot.py b/pygad/visualize/plot.py new file mode 100644 index 0000000..a79f848 --- /dev/null +++ b/pygad/visualize/plot.py @@ -0,0 +1,348 @@ +""" +The pygad.visualize.plot module has methods to create plots. +""" + +import numpy +import warnings +import matplotlib.pyplot + +class Plot: + def plot_result(self, + title="PyGAD - Generation vs. Fitness", + xlabel="Generation", + ylabel="Fitness", + linewidth=3, + font_size=14, + plot_type="plot", + color="#3870FF", + save_dir=None): + + if not self.suppress_warnings: + warnings.warn("Please use the plot_fitness() method instead of plot_result(). The plot_result() method will be removed in the future.") + + return self.plot_fitness(title=title, + xlabel=xlabel, + ylabel=ylabel, + linewidth=linewidth, + font_size=font_size, + plot_type=plot_type, + color=color, + save_dir=save_dir) + + def plot_fitness(self, + title="PyGAD - Generation vs. Fitness", + xlabel="Generation", + ylabel="Fitness", + linewidth=3, + font_size=14, + plot_type="plot", + color="#3870FF", + save_dir=None): + + """ + Creates, shows, and returns a figure that summarizes how the fitness value evolved by generation. Can only be called after completing at least 1 generation. If no generation is completed, an exception is raised. + + Accepts the following: + title: Figure title. + xlabel: Label on the X-axis. + ylabel: Label on the Y-axis. + linewidth: Line width of the plot. Defaults to 3. + font_size: Font size for the labels and title. Defaults to 14. + plot_type: Type of the plot which can be either "plot" (default), "scatter", or "bar". + color: Color of the plot which defaults to "#3870FF". + save_dir: Directory to save the figure. + + Returns the figure. + """ + + if self.generations_completed < 1: + self.logger.error("The plot_fitness() (i.e. plot_result()) method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + raise RuntimeError("The plot_fitness() (i.e. plot_result()) method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + + fig = matplotlib.pyplot.figure() + if plot_type == "plot": + matplotlib.pyplot.plot(self.best_solutions_fitness, linewidth=linewidth, color=color) + elif plot_type == "scatter": + matplotlib.pyplot.scatter(range(len(self.best_solutions_fitness)), self.best_solutions_fitness, linewidth=linewidth, color=color) + elif plot_type == "bar": + matplotlib.pyplot.bar(range(len(self.best_solutions_fitness)), self.best_solutions_fitness, linewidth=linewidth, color=color) + matplotlib.pyplot.title(title, fontsize=font_size) + matplotlib.pyplot.xlabel(xlabel, fontsize=font_size) + matplotlib.pyplot.ylabel(ylabel, fontsize=font_size) + + if not save_dir is None: + matplotlib.pyplot.savefig(fname=save_dir, + bbox_inches='tight') + matplotlib.pyplot.show() + + return fig + + def plot_new_solution_rate(self, + title="PyGAD - Generation vs. New Solution Rate", + xlabel="Generation", + ylabel="New Solution Rate", + linewidth=3, + font_size=14, + plot_type="plot", + color="#3870FF", + save_dir=None): + + """ + Creates, shows, and returns a figure that summarizes the rate of exploring new solutions. This method works only when save_solutions=True in the constructor of the pygad.GA class. + + Accepts the following: + title: Figure title. + xlabel: Label on the X-axis. + ylabel: Label on the Y-axis. + linewidth: Line width of the plot. Defaults to 3. + font_size: Font size for the labels and title. Defaults to 14. + plot_type: Type of the plot which can be either "plot" (default), "scatter", or "bar". + color: Color of the plot which defaults to "#3870FF". + save_dir: Directory to save the figure. + + Returns the figure. + """ + + if self.generations_completed < 1: + self.logger.error("The plot_new_solution_rate() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + raise RuntimeError("The plot_new_solution_rate() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + + if self.save_solutions == False: + self.logger.error("The plot_new_solution_rate() method works only when save_solutions=True in the constructor of the pygad.GA class.") + raise RuntimeError("The plot_new_solution_rate() method works only when save_solutions=True in the constructor of the pygad.GA class.") + + unique_solutions = set() + num_unique_solutions_per_generation = [] + for generation_idx in range(self.generations_completed): + + len_before = len(unique_solutions) + + start = generation_idx * self.sol_per_pop + end = start + self.sol_per_pop + + for sol in self.solutions[start:end]: + unique_solutions.add(tuple(sol)) + + len_after = len(unique_solutions) + + generation_num_unique_solutions = len_after - len_before + num_unique_solutions_per_generation.append(generation_num_unique_solutions) + + fig = matplotlib.pyplot.figure() + if plot_type == "plot": + matplotlib.pyplot.plot(num_unique_solutions_per_generation, linewidth=linewidth, color=color) + elif plot_type == "scatter": + matplotlib.pyplot.scatter(range(self.generations_completed), num_unique_solutions_per_generation, linewidth=linewidth, color=color) + elif plot_type == "bar": + matplotlib.pyplot.bar(range(self.generations_completed), num_unique_solutions_per_generation, linewidth=linewidth, color=color) + matplotlib.pyplot.title(title, fontsize=font_size) + matplotlib.pyplot.xlabel(xlabel, fontsize=font_size) + matplotlib.pyplot.ylabel(ylabel, fontsize=font_size) + + if not save_dir is None: + matplotlib.pyplot.savefig(fname=save_dir, + bbox_inches='tight') + matplotlib.pyplot.show() + + return fig + + def plot_genes(self, + title="PyGAD - Gene", + xlabel="Gene", + ylabel="Value", + linewidth=3, + font_size=14, + plot_type="plot", + graph_type="plot", + fill_color="#3870FF", + color="black", + solutions="all", + save_dir=None): + + """ + Creates, shows, and returns a figure with number of subplots equal to the number of genes. Each subplot shows the gene value for each generation. + This method works only when save_solutions=True in the constructor of the pygad.GA class. + It also works only after completing at least 1 generation. If no generation is completed, an exception is raised. + + Accepts the following: + title: Figure title. + xlabel: Label on the X-axis. + ylabel: Label on the Y-axis. + linewidth: Line width of the plot. Defaults to 3. + font_size: Font size for the labels and title. Defaults to 14. + plot_type: Type of the plot which can be either "plot" (default), "scatter", or "bar". + graph_type: Type of the graph which can be either "plot" (default), "boxplot", or "histogram". + fill_color: Fill color of the graph which defaults to "#3870FF". This has no effect if graph_type="plot". + color: Color of the plot which defaults to "black". + solutions: Defaults to "all" which means use all solutions. If "best" then only the best solutions are used. + save_dir: Directory to save the figure. + + Returns the figure. + """ + + if self.generations_completed < 1: + self.logger.error("The plot_genes() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + raise RuntimeError("The plot_genes() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + + if type(solutions) is str: + if solutions == 'all': + if self.save_solutions: + solutions_to_plot = numpy.array(self.solutions) + else: + self.logger.error("The plot_genes() method with solutions='all' can only be called if 'save_solutions=True' in the pygad.GA class constructor.") + raise RuntimeError("The plot_genes() method with solutions='all' can only be called if 'save_solutions=True' in the pygad.GA class constructor.") + elif solutions == 'best': + if self.save_best_solutions: + solutions_to_plot = self.best_solutions + else: + self.logger.error("The plot_genes() method with solutions='best' can only be called if 'save_best_solutions=True' in the pygad.GA class constructor.") + raise RuntimeError("The plot_genes() method with solutions='best' can only be called if 'save_best_solutions=True' in the pygad.GA class constructor.") + else: + self.logger.error("The solutions parameter can be either 'all' or 'best' but {solutions} found.".format(solutions=solutions)) + raise RuntimeError("The solutions parameter can be either 'all' or 'best' but {solutions} found.".format(solutions=solutions)) + else: + self.logger.error("The solutions parameter must be a string but {solutions_type} found.".format(solutions_type=type(solutions))) + raise RuntimeError("The solutions parameter must be a string but {solutions_type} found.".format(solutions_type=type(solutions))) + + if graph_type == "plot": + # num_rows will be always be >= 1 + # num_cols can only be 0 if num_genes=1 + num_rows = int(numpy.ceil(self.num_genes/5.0)) + num_cols = int(numpy.ceil(self.num_genes/num_rows)) + + if num_cols == 0: + figsize = (10, 8) + # There is only a single gene + fig, ax = matplotlib.pyplot.subplots(num_rows, figsize=figsize) + if plot_type == "plot": + ax.plot(solutions_to_plot[:, 0], linewidth=linewidth, color=fill_color) + elif plot_type == "scatter": + ax.scatter(range(self.generations_completed + 1), solutions_to_plot[:, 0], linewidth=linewidth, color=fill_color) + elif plot_type == "bar": + ax.bar(range(self.generations_completed + 1), solutions_to_plot[:, 0], linewidth=linewidth, color=fill_color) + ax.set_xlabel(0, fontsize=font_size) + else: + fig, axs = matplotlib.pyplot.subplots(num_rows, num_cols) + + if num_cols == 1 and num_rows == 1: + fig.set_figwidth(5 * num_cols) + fig.set_figheight(4) + axs.plot(solutions_to_plot[:, 0], linewidth=linewidth, color=fill_color) + axs.set_xlabel("Gene " + str(0), fontsize=font_size) + elif num_cols == 1 or num_rows == 1: + fig.set_figwidth(5 * num_cols) + fig.set_figheight(4) + for gene_idx in range(len(axs)): + if plot_type == "plot": + axs[gene_idx].plot(solutions_to_plot[:, gene_idx], linewidth=linewidth, color=fill_color) + elif plot_type == "scatter": + axs[gene_idx].scatter(range(solutions_to_plot.shape[0]), solutions_to_plot[:, gene_idx], linewidth=linewidth, color=fill_color) + elif plot_type == "bar": + axs[gene_idx].bar(range(solutions_to_plot.shape[0]), solutions_to_plot[:, gene_idx], linewidth=linewidth, color=fill_color) + axs[gene_idx].set_xlabel("Gene " + str(gene_idx), fontsize=font_size) + else: + gene_idx = 0 + fig.set_figwidth(25) + fig.set_figheight(4*num_rows) + for row_idx in range(num_rows): + for col_idx in range(num_cols): + if gene_idx >= self.num_genes: + # axs[row_idx, col_idx].remove() + break + if plot_type == "plot": + axs[row_idx, col_idx].plot(solutions_to_plot[:, gene_idx], linewidth=linewidth, color=fill_color) + elif plot_type == "scatter": + axs[row_idx, col_idx].scatter(range(solutions_to_plot.shape[0]), solutions_to_plot[:, gene_idx], linewidth=linewidth, color=fill_color) + elif plot_type == "bar": + axs[row_idx, col_idx].bar(range(solutions_to_plot.shape[0]), solutions_to_plot[:, gene_idx], linewidth=linewidth, color=fill_color) + axs[row_idx, col_idx].set_xlabel("Gene " + str(gene_idx), fontsize=font_size) + gene_idx += 1 + + fig.suptitle(title, fontsize=font_size, y=1.001) + matplotlib.pyplot.tight_layout() + + elif graph_type == "boxplot": + fig = matplotlib.pyplot.figure(1, figsize=(0.7*self.num_genes, 6)) + + # Create an axes instance + ax = fig.add_subplot(111) + boxeplots = ax.boxplot(solutions_to_plot, + labels=range(self.num_genes), + patch_artist=True) + # adding horizontal grid lines + ax.yaxis.grid(True) + + for box in boxeplots['boxes']: + # change outline color + box.set(color='black', linewidth=linewidth) + # change fill color https://color.adobe.com/create/color-wheel + box.set_facecolor(fill_color) + + for whisker in boxeplots['whiskers']: + whisker.set(color=color, linewidth=linewidth) + for median in boxeplots['medians']: + median.set(color=color, linewidth=linewidth) + for cap in boxeplots['caps']: + cap.set(color=color, linewidth=linewidth) + + matplotlib.pyplot.title(title, fontsize=font_size) + matplotlib.pyplot.xlabel(xlabel, fontsize=font_size) + matplotlib.pyplot.ylabel(ylabel, fontsize=font_size) + matplotlib.pyplot.tight_layout() + + elif graph_type == "histogram": + # num_rows will always be >= 1 + # num_cols can only be 0 if num_genes=1 + num_rows = int(numpy.ceil(self.num_genes/5.0)) + num_cols = int(numpy.ceil(self.num_genes/num_rows)) + + if num_cols == 0: + figsize = (10, 8) + # There is only a single gene + fig, ax = matplotlib.pyplot.subplots(num_rows, + figsize=figsize) + ax.hist(solutions_to_plot[:, 0], color=fill_color) + ax.set_xlabel(0, fontsize=font_size) + else: + fig, axs = matplotlib.pyplot.subplots(num_rows, num_cols) + + if num_cols == 1 and num_rows == 1: + fig.set_figwidth(4 * num_cols) + fig.set_figheight(3) + axs.hist(solutions_to_plot[:, 0], + color=fill_color, + rwidth=0.95) + axs.set_xlabel("Gene " + str(0), fontsize=font_size) + elif num_cols == 1 or num_rows == 1: + fig.set_figwidth(4 * num_cols) + fig.set_figheight(3) + for gene_idx in range(len(axs)): + axs[gene_idx].hist(solutions_to_plot[:, gene_idx], + color=fill_color, + rwidth=0.95) + axs[gene_idx].set_xlabel("Gene " + str(gene_idx), fontsize=font_size) + else: + gene_idx = 0 + fig.set_figwidth(20) + fig.set_figheight(3*num_rows) + for row_idx in range(num_rows): + for col_idx in range(num_cols): + if gene_idx >= self.num_genes: + # axs[row_idx, col_idx].remove() + break + axs[row_idx, col_idx].hist(solutions_to_plot[:, gene_idx], + color=fill_color, + rwidth=0.95) + axs[row_idx, col_idx].set_xlabel("Gene " + str(gene_idx), fontsize=font_size) + gene_idx += 1 + + fig.suptitle(title, fontsize=font_size, y=1.001) + matplotlib.pyplot.tight_layout() + + if not save_dir is None: + matplotlib.pyplot.savefig(fname=save_dir, + bbox_inches='tight') + + matplotlib.pyplot.show() + + return fig From 05ace78a3d0cd2577a70fd85ed1a5a9d12e3aa8f Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:40:53 -0400 Subject: [PATCH 009/220] Delete pygad.py --- pygad/pygad.py | 2219 ------------------------------------------------ 1 file changed, 2219 deletions(-) delete mode 100644 pygad/pygad.py diff --git a/pygad/pygad.py b/pygad/pygad.py deleted file mode 100644 index 9916a15..0000000 --- a/pygad/pygad.py +++ /dev/null @@ -1,2219 +0,0 @@ -import numpy -import random -import cloudpickle -import time -import warnings -import concurrent.futures -import inspect -import logging -from pygad import utils -from pygad import helper -from pygad import visualize - -class GA(utils.parent_selection.ParentSelection, - utils.crossover.Crossover, - utils.mutation.Mutation, - helper.unique.Unique, - visualize.plot.Plot): - - supported_int_types = [int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64] - supported_float_types = [float, numpy.float16, numpy.float32, numpy.float64] - supported_int_float_types = supported_int_types + supported_float_types - - def __init__(self, - num_generations, - num_parents_mating, - fitness_func, - fitness_batch_size=None, - initial_population=None, - sol_per_pop=None, - num_genes=None, - init_range_low=-4, - init_range_high=4, - gene_type=float, - parent_selection_type="sss", - keep_parents=-1, - keep_elitism=1, - K_tournament=3, - crossover_type="single_point", - crossover_probability=None, - mutation_type="random", - mutation_probability=None, - mutation_by_replacement=False, - mutation_percent_genes='default', - mutation_num_genes=None, - random_mutation_min_val=-1.0, - random_mutation_max_val=1.0, - gene_space=None, - allow_duplicate_genes=True, - on_start=None, - on_fitness=None, - on_parents=None, - on_crossover=None, - on_mutation=None, - on_generation=None, - on_stop=None, - delay_after_gen=0.0, - save_best_solutions=False, - save_solutions=False, - suppress_warnings=False, - stop_criteria=None, - parallel_processing=None, - random_seed=None, - logger=None): - - """ - The constructor of the GA class accepts all parameters required to create an instance of the GA class. It validates such parameters. - - num_generations: Number of generations. - num_parents_mating: Number of solutions to be selected as parents in the mating pool. - - fitness_func: Accepts a function/method and returns the fitness value of the solution. In PyGAD 2.20.0, a third parameter is passed referring to the 'pygad.GA' instance. If method, then it must accept 4 parameters where the fourth one refers to the method's object. - fitness_batch_size: Added in PyGAD 2.19.0. Supports calculating the fitness in batches. If the value is 1 or None, then the fitness function is called for each invidiaul solution. If given another value X where X is neither 1 nor None (e.g. X=3), then the fitness function is called once for each X (3) solutions. - - initial_population: A user-defined initial population. It is useful when the user wants to start the generations with a custom initial population. It defaults to None which means no initial population is specified by the user. In this case, PyGAD creates an initial population using the 'sol_per_pop' and 'num_genes' parameters. An exception is raised if the 'initial_population' is None while any of the 2 parameters ('sol_per_pop' or 'num_genes') is also None. - sol_per_pop: Number of solutions in the population. - num_genes: Number of parameters in the function. - - init_range_low: The lower value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20 and higher. - init_range_high: The upper value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20. - # It is OK to set the value of any of the 2 parameters ('init_range_low' and 'init_range_high') to be equal, higher or lower than the other parameter (i.e. init_range_low is not needed to be lower than init_range_high). - - gene_type: The type of the gene. It is assigned to any of these types (int, float, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32, numpy.float64) and forces all the genes to be of that type. - - parent_selection_type: Type of parent selection. - keep_parents: If 0, this means no parent in the current population will be used in the next population. If -1, this means all parents in the current population will be used in the next population. If set to a value > 0, then the specified value refers to the number of parents in the current population to be used in the next population. Some parent selection operators such as rank selection, favor population diversity and therefore keeping the parents in the next generation can be beneficial. However, some other parent selection operators, such as roulette wheel selection (RWS), have higher selection pressure and keeping more than one parent in the next generation can seriously harm population diversity. This parameter have an effect only when the keep_elitism parameter is 0. Thanks to Prof. Fernando Jiménez Barrionuevo (http://webs.um.es/fernan) for editing this sentence. - K_tournament: When the value of 'parent_selection_type' is 'tournament', the 'K_tournament' parameter specifies the number of solutions from which a parent is selected randomly. - - keep_elitism: Added in PyGAD 2.18.0. It can take the value 0 or a positive integer that satisfies (0 <= keep_elitism <= sol_per_pop). It defaults to 1 which means only the best solution in the current generation is kept in the next generation. If assigned 0, this means it has no effect. If assigned a positive integer K, then the best K solutions are kept in the next generation. It cannot be assigned a value greater than the value assigned to the sol_per_pop parameter. If this parameter has a value different than 0, then the keep_parents parameter will have no effect. - - crossover_type: Type of the crossover opreator. If crossover_type=None, then the crossover step is bypassed which means no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. - crossover_probability: The probability of selecting a solution for the crossover operation. If the solution probability is <= crossover_probability, the solution is selected. The value must be between 0 and 1 inclusive. - - mutation_type: Type of the mutation opreator. If mutation_type=None, then the mutation step is bypassed which means no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. - mutation_probability: The probability of selecting a gene for the mutation operation. If the gene probability is <= mutation_probability, the gene is selected. It accepts either a single value for fixed mutation or a list/tuple/numpy.ndarray of 2 values for adaptive mutation. The values must be between 0 and 1 inclusive. If specified, then no need for the 2 parameters mutation_percent_genes and mutation_num_genes. - - mutation_by_replacement: An optional bool parameter. It works only when the selected type of mutation is random (mutation_type="random"). In this case, setting mutation_by_replacement=True means replace the gene by the randomly generated value. If False, then it has no effect and random mutation works by adding the random value to the gene. - - mutation_percent_genes: Percentage of genes to mutate which defaults to the string 'default' which means 10%. This parameter has no action if any of the 2 parameters mutation_probability or mutation_num_genes exist. - mutation_num_genes: Number of genes to mutate which defaults to None. If the parameter mutation_num_genes exists, then no need for the parameter mutation_percent_genes. This parameter has no action if the mutation_probability parameter exists. - random_mutation_min_val: The minimum value of the range from which a random value is selected to be added to the selected gene(s) to mutate. It defaults to -1.0. - random_mutation_max_val: The maximum value of the range from which a random value is selected to be added to the selected gene(s) to mutate. It defaults to 1.0. - - gene_space: It accepts a list of all possible values of the gene. This list is used in the mutation step. Should be used only if the gene space is a set of discrete values. No need for the 2 parameters (random_mutation_min_val and random_mutation_max_val) if the parameter gene_space exists. Added in PyGAD 2.5.0. In PyGAD 2.11.0, the gene_space can be assigned a dict. - - on_start: Accepts a function/method to be called only once before the genetic algorithm starts its evolution. If function, then it must accept a single parameter representing the instance of the genetic algorithm. If method, then it must accept 2 parameters where the second one refers to the method's object. Added in PyGAD 2.6.0. - on_fitness: Accepts a function/method to be called after calculating the fitness values of all solutions in the population. If function, then it must accept 2 parameters: 1) a list of all solutions' fitness values 2) the instance of the genetic algorithm. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. - on_parents: Accepts a function/method to be called after selecting the parents that mates. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one represents the selected parents. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. - on_crossover: Accepts a function/method to be called each time the crossover operation is applied. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one represents the offspring generated using crossover. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. - on_mutation: Accepts a function/method to be called each time the mutation operation is applied. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one represents the offspring after applying the mutation. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. - on_generation: Accepts a function/method to be called after each generation. If function, then it must accept a single parameter representing the instance of the genetic algorithm. If the function returned "stop", then the run() method stops without completing the other generations. If method, then it must accept 2 parameters where the second one refers to the method's object. Added in PyGAD 2.6.0. - on_stop: Accepts a function/method to be called only once exactly before the genetic algorithm stops or when it completes all the generations. If function, then it must accept 2 parameters: the first one represents the instance of the genetic algorithm and the second one is a list of fitness values of the last population's solutions. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0. - - delay_after_gen: Added in PyGAD 2.4.0. It accepts a non-negative number specifying the number of seconds to wait after a generation completes and before going to the next generation. It defaults to 0.0 which means no delay after the generation. - - save_best_solutions: Added in PyGAD 2.9.0 and its type is bool. If True, then the best solution in each generation is saved into the 'best_solutions' attribute. Use this parameter with caution as it may cause memory overflow when either the number of generations or the number of genes is large. - save_solutions: Added in PyGAD 2.15.0 and its type is bool. If True, then all solutions in each generation are saved into the 'solutions' attribute. Use this parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large. - - suppress_warnings: Added in PyGAD 2.10.0 and its type is bool. If True, then no warning messages will be displayed. It defaults to False. - - allow_duplicate_genes: Added in PyGAD 2.13.0. If True, then a solution/chromosome may have duplicate gene values. If False, then each gene will have a unique value in its solution. - - stop_criteria: Added in PyGAD 2.15.0. It is assigned to some criteria to stop the evolution if at least one criterion holds. - - parallel_processing: Added in PyGAD 2.17.0. Defaults to `None` which means no parallel processing is used. If a positive integer is assigned, it specifies the number of threads to be used. If a list or a tuple of exactly 2 elements is assigned, then: 1) The first element can be either "process" or "thread" to specify whether processes or threads are used, respectively. 2) The second element can be: 1) A positive integer to select the maximum number of processes or threads to be used. 2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'. 3) None to use the default value as calculated by the concurrent.futures module. - - random_seed: Added in PyGAD 2.18.0. It defines the random seed to be used by the random function generators (we use random functions in the NumPy and random modules). This helps to reproduce the same results by setting the same random seed. - - logger: Added in PyGAD 2.20.0. It accepts a logger object of the 'logging.Logger' class to log the messages. If no logger is passed, then a default logger is created to log/print the messages to the console exactly like using the 'print()' function. - """ - - # If no logger is passed, then create a logger that logs only the messages to the console. - if logger is None: - # Create a logger named with the module name. - logger = logging.getLogger(__name__) - # Set the logger log level to 'DEBUG' to log all kinds of messages. - logger.setLevel(logging.DEBUG) - - # Clear any attached handlers to the logger from the previous runs. - # If the handlers are not cleared, then the new handler will be appended to the list of handlers. - # This makes the single log message be repeated according to the length of the list of handlers. - logger.handlers.clear() - - # Create the handlers. - stream_handler = logging.StreamHandler() - # Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger. - stream_handler.setLevel(logging.DEBUG) - - # Create the formatter that just includes the log message. - formatter = logging.Formatter('%(message)s') - - # Add the formatter to the handler. - stream_handler.setFormatter(formatter) - - # Add the handler to the logger. - logger.addHandler(stream_handler) - else: - # Validate that the passed logger is of type 'logging.Logger'. - if isinstance(logger, logging.Logger): - pass - else: - raise TypeError("The expected type of the 'logger' parameter is 'logging.Logger' but {logger_type} found.".format(logger_type=type(logger))) - - # Create the 'self.logger' attribute to hold the logger. - # Instead of using 'print()', use 'self.logger.info()' - self.logger = logger - - self.random_seed = random_seed - if random_seed is None: - pass - else: - numpy.random.seed(self.random_seed) - random.seed(self.random_seed) - - # If suppress_warnings is bool and its valud is False, then print warning messages. - if type(suppress_warnings) is bool: - self.suppress_warnings = suppress_warnings - else: - self.valid_parameters = False - self.logger.error("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) - raise TypeError("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) - - # Validating mutation_by_replacement - if not (type(mutation_by_replacement) is bool): - self.valid_parameters = False - self.logger.error("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) - raise TypeError("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) - - self.mutation_by_replacement = mutation_by_replacement - - # Validate gene_space - self.gene_space_nested = False - if type(gene_space) is type(None): - pass - elif type(gene_space) in [list, tuple, range, numpy.ndarray]: - if len(gene_space) == 0: - self.valid_parameters = False - self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") - raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") - else: - for index, el in enumerate(gene_space): - if type(el) in [list, tuple, range, numpy.ndarray]: - if len(el) == 0: - self.valid_parameters = False - self.logger.error("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) - raise ValueError("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) - else: - for val in el: - if not (type(val) in [type(None)] + GA.supported_int_float_types): - self.logger.error("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) - raise TypeError("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) - self.gene_space_nested = True - elif type(el) == type(None): - pass - # self.gene_space_nested = True - elif type(el) is dict: - if len(el.items()) == 2: - if ('low' in el.keys()) and ('high' in el.keys()): - pass - else: - self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - elif len(el.items()) == 3: - if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): - pass - else: - self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - else: - self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) - self.gene_space_nested = True - elif not (type(el) in GA.supported_int_float_types): - self.valid_parameters = False - self.logger.error("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) - raise TypeError("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) - - elif type(gene_space) is dict: - if len(gene_space.items()) == 2: - if ('low' in gene_space.keys()) and ('high' in gene_space.keys()): - pass - else: - self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - elif len(gene_space.items()) == 3: - if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): - pass - else: - self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - else: - self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) - raise ValueError("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) - - else: - self.valid_parameters = False - self.logger.error("The expected type of 'gene_space' is list, tuple, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) - raise TypeError("The expected type of 'gene_space' is list, tuple, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) - - self.gene_space = gene_space - - # Validate init_range_low and init_range_high - if type(init_range_low) in GA.supported_int_float_types: - if type(init_range_high) in GA.supported_int_float_types: - self.init_range_low = init_range_low - self.init_range_high = init_range_high - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) - raise ValueError("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) - raise ValueError("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) - - # Validate random_mutation_min_val and random_mutation_max_val - if type(random_mutation_min_val) in GA.supported_int_float_types: - if type(random_mutation_max_val) in GA.supported_int_float_types: - if random_mutation_min_val == random_mutation_max_val: - if not self.suppress_warnings: warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") - else: - self.valid_parameters = False - self.logger.error("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) - raise TypeError("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) - else: - self.valid_parameters = False - self.logger.error("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) - raise TypeError("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) - self.random_mutation_min_val = random_mutation_min_val - self.random_mutation_max_val = random_mutation_max_val - - # Validate gene_type - if gene_type in GA.supported_int_float_types: - self.gene_type = [gene_type, None] - self.gene_type_single = True - # A single data type of float with precision. - elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): - self.gene_type = gene_type - self.gene_type_single = True - elif type(gene_type) in [list, tuple, numpy.ndarray]: - if num_genes is None: - if initial_population is None: - self.valid_parameters = False - self.logger.error("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - elif not len(gene_type) == len(initial_population[0]): - self.valid_parameters = False - self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) - raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) - elif not len(gene_type) == num_genes: - self.valid_parameters = False - self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) - raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) - for gene_type_idx, gene_type_val in enumerate(gene_type): - if gene_type_val in GA.supported_float_types: - # If the gene type is float and no precision is passed, set it to None. - gene_type[gene_type_idx] = [gene_type_val, None] - elif gene_type_val in GA.supported_int_types: - gene_type[gene_type_idx] = [gene_type_val, None] - elif type(gene_type_val) in [list, tuple, numpy.ndarray]: - # A float type is expected in a list/tuple/numpy.ndarray of length 2. - if len(gene_type_val) == 2: - if gene_type_val[0] in GA.supported_float_types: - if type(gene_type_val[1]) in GA.supported_int_types: - pass - else: - self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) - raise TypeError("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) - else: - self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) - raise TypeError("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) - else: - self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) - raise ValueError("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) - else: - self.valid_parameters = False - self.logger.error("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) - raise ValueError("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) - self.gene_type = gene_type - self.gene_type_single = False - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) - raise ValueError("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) - - # Build the initial population - if initial_population is None: - if (sol_per_pop is None) or (num_genes is None): - self.valid_parameters = False - self.logger.error("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") - raise TypeError("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") - elif (type(sol_per_pop) is int) and (type(num_genes) is int): - # Validating the number of solutions in the population (sol_per_pop) - if sol_per_pop <= 0: - self.valid_parameters = False - self.logger.error("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) - raise ValueError("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) - # Validating the number of gene. - if (num_genes <= 0): - self.valid_parameters = False - self.logger.error("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) - raise ValueError("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) - # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. - # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. - self.num_genes = num_genes # Number of genes in the solution. - - # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. - if self.gene_space_nested: - if len(gene_space) != self.num_genes: - self.valid_parameters = False - self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) - raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) - - self.sol_per_pop = sol_per_pop # Number of solutions in the population. - self.initialize_population(self.init_range_low, - self.init_range_high, - allow_duplicate_genes, - True, - self.gene_type) - else: - self.valid_parameters = False - self.logger.error("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) - raise TypeError("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) - elif not type(initial_population) in [list, tuple, numpy.ndarray]: - self.valid_parameters = False - self.logger.error("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) - raise TypeError("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) - elif numpy.array(initial_population).ndim != 2: - self.valid_parameters = False - self.logger.error("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) - raise ValueError("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) - else: - # Validate the type of each value in the 'initial_population' parameter. - for row_idx in range(len(initial_population)): - for col_idx in range(len(initial_population[0])): - if type(initial_population[row_idx][col_idx]) in GA.supported_int_float_types: - pass - else: - self.valid_parameters = False - self.logger.error("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) - raise TypeError("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) - - # Forcing the initial_population array to have the data type assigned to the gene_type parameter. - if self.gene_type_single == True: - if self.gene_type[1] == None: - self.initial_population = numpy.array(initial_population, dtype=self.gene_type[0]) - else: - self.initial_population = numpy.round(numpy.array(initial_population, dtype=self.gene_type[0]), self.gene_type[1]) - else: - initial_population = numpy.array(initial_population) - self.initial_population = numpy.zeros(shape=(initial_population.shape[0], initial_population.shape[1]), dtype=object) - for gene_idx in range(initial_population.shape[1]): - if self.gene_type[gene_idx][1] is None: - self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], - dtype=self.gene_type[gene_idx][0]) - else: - self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], - dtype=self.gene_type[gene_idx][0]), - self.gene_type[gene_idx][1]) - - self.population = self.initial_population.copy() # A NumPy array holding the initial population. - self.num_genes = self.initial_population.shape[1] # Number of genes in the solution. - self.sol_per_pop = self.initial_population.shape[0] # Number of solutions in the population. - self.pop_size = (self.sol_per_pop,self.num_genes) # The population size. - - # Round initial_population and population - self.initial_population = self.round_genes(self.initial_population) - self.population = self.round_genes(self.population) - - # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. - if self.gene_space_nested: - if len(gene_space) != self.num_genes: - self.valid_parameters = False - self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) - raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) - - # Validating the number of parents to be selected for mating (num_parents_mating) - if num_parents_mating <= 0: - self.valid_parameters = False - self.logger.error("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) - raise ValueError("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) - - # Validating the number of parents to be selected for mating: num_parents_mating - if (num_parents_mating > self.sol_per_pop): - self.valid_parameters = False - self.logger.error("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) - raise ValueError("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) - - self.num_parents_mating = num_parents_mating - - # crossover: Refers to the method that applies the crossover operator based on the selected type of crossover in the crossover_type property. - # Validating the crossover type: crossover_type - if (crossover_type is None): - self.crossover = None - elif inspect.ismethod(crossover_type): - # Check if the crossover_type is a method that accepts 4 paramaters. - if (crossover_type.__code__.co_argcount == 4): - # The crossover method assigned to the crossover_type parameter is validated. - self.crossover = crossover_type - else: - self.valid_parameters = False - self.logger.error("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - raise ValueError("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - elif callable(crossover_type): - # Check if the crossover_type is a function that accepts 2 paramaters. - if (crossover_type.__code__.co_argcount == 3): - # The crossover function assigned to the crossover_type parameter is validated. - self.crossover = crossover_type - else: - self.valid_parameters = False - self.logger.error("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - raise ValueError("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - elif not (type(crossover_type) is str): - self.valid_parameters = False - self.logger.error("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) - raise TypeError("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) - else: # type crossover_type is str - crossover_type = crossover_type.lower() - if (crossover_type == "single_point"): - self.crossover = self.single_point_crossover - elif (crossover_type == "two_points"): - self.crossover = self.two_points_crossover - elif (crossover_type == "uniform"): - self.crossover = self.uniform_crossover - elif (crossover_type == "scattered"): - self.crossover = self.scattered_crossover - else: - self.valid_parameters = False - self.logger.error("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) - raise TypeError("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) - - self.crossover_type = crossover_type - - # Calculate the value of crossover_probability - if crossover_probability is None: - self.crossover_probability = None - elif type(crossover_probability) in GA.supported_int_float_types: - if crossover_probability >= 0 and crossover_probability <= 1: - self.crossover_probability = crossover_probability - else: - self.valid_parameters = False - self.logger.error("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) - raise ValueError("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) - raise TypeError("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) - - # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. - # Validating the mutation type: mutation_type - # "adaptive" mutation is supported starting from PyGAD 2.10.0 - if mutation_type is None: - self.mutation = None - elif inspect.ismethod(mutation_type): - # Check if the mutation_type is a method that accepts 3 paramater. - if (mutation_type.__code__.co_argcount == 3): - # The mutation method assigned to the mutation_type parameter is validated. - self.mutation = mutation_type - else: - self.valid_parameters = False - self.logger.error("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - raise ValueError("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - elif callable(mutation_type): - # Check if the mutation_type is a function that accepts 2 paramater. - if (mutation_type.__code__.co_argcount == 2): - # The mutation function assigned to the mutation_type parameter is validated. - self.mutation = mutation_type - else: - self.valid_parameters = False - self.logger.error("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - raise ValueError("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - elif not (type(mutation_type) is str): - self.valid_parameters = False - self.logger.error("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) - raise TypeError("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) - else: # type mutation_type is str - mutation_type = mutation_type.lower() - if (mutation_type == "random"): - self.mutation = self.random_mutation - elif (mutation_type == "swap"): - self.mutation = self.swap_mutation - elif (mutation_type == "scramble"): - self.mutation = self.scramble_mutation - elif (mutation_type == "inversion"): - self.mutation = self.inversion_mutation - elif (mutation_type == "adaptive"): - self.mutation = self.adaptive_mutation - else: - self.valid_parameters = False - self.logger.error("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) - raise TypeError("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) - - self.mutation_type = mutation_type - - # Calculate the value of mutation_probability - if not (self.mutation_type is None): - if mutation_probability is None: - self.mutation_probability = None - elif (mutation_type != "adaptive"): - # Mutation probability is fixed not adaptive. - if type(mutation_probability) in GA.supported_int_float_types: - if mutation_probability >= 0 and mutation_probability <= 1: - self.mutation_probability = mutation_probability - else: - self.valid_parameters = False - self.logger.error("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) - raise ValueError("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - raise TypeError("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - else: - # Mutation probability is adaptive not fixed. - if type(mutation_probability) in [list, tuple, numpy.ndarray]: - if len(mutation_probability) == 2: - for el in mutation_probability: - if type(el) in GA.supported_int_float_types: - if el >= 0 and el <= 1: - pass - else: - self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) - raise ValueError("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) - if mutation_probability[0] < mutation_probability[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_probability' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_probability[0], second_el=mutation_probability[1])) - self.mutation_probability = mutation_probability - else: - self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - raise TypeError("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - else: - pass - - # Calculate the value of mutation_num_genes - if not (self.mutation_type is None): - if mutation_num_genes is None: - # The mutation_num_genes parameter does not exist. Checking whether adaptive mutation is used. - if (mutation_type != "adaptive"): - # The percent of genes to mutate is fixed not adaptive. - if mutation_percent_genes == 'default'.lower(): - mutation_percent_genes = 10 - # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) - # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. - if mutation_num_genes == 0: - if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) - mutation_num_genes = 1 - - elif type(mutation_percent_genes) in GA.supported_int_float_types: - if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): - self.valid_parameters = False - self.logger.error("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - raise ValueError("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - else: - # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. - if mutation_percent_genes == 'default'.lower(): - mutation_percent_genes = 10 - - # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) - # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. - if mutation_num_genes == 0: - if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) - mutation_num_genes = 1 - else: - self.valid_parameters = False - self.logger.error("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) - raise TypeError("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) - else: - # The percent of genes to mutate is adaptive not fixed. - if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: - if len(mutation_percent_genes) == 2: - mutation_num_genes = numpy.zeros_like(mutation_percent_genes, dtype=numpy.uint32) - for idx, el in enumerate(mutation_percent_genes): - if type(el) in GA.supported_int_float_types: - if (el <= 0 or el > 100): - self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - raise ValueError("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) - # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. - # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes[idx] = numpy.uint32((mutation_percent_genes[idx]*self.num_genes)/100) - # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. - if mutation_num_genes[idx] == 0: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate ({mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes[idx], mutation_num=mutation_num_genes[idx])) - mutation_num_genes[idx] = 1 - if mutation_percent_genes[0] < mutation_percent_genes[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_percent_genes' parameter is ({first_el}) which is smaller than the second element ({second_el}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.".format(first_el=mutation_percent_genes[0], second_el=mutation_percent_genes[1])) - # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. - else: - self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) - else: - if self.mutation_probability is None: - self.valid_parameters = False - self.logger.error("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) - raise TypeError("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) - # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. - elif (mutation_type != "adaptive"): - # Number of genes to mutate is fixed not adaptive. - if type(mutation_num_genes) in GA.supported_int_types: - if (mutation_num_genes <= 0): - self.valid_parameters = False - self.logger.error("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) - raise ValueError("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) - elif (mutation_num_genes > self.num_genes): - self.valid_parameters = False - self.logger.error("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) - raise ValueError("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) - else: - self.valid_parameters = False - self.logger.error("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - raise TypeError("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - else: - # Number of genes to mutate is adaptive not fixed. - if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: - if len(mutation_num_genes) == 2: - for el in mutation_num_genes: - if type(el) in GA.supported_int_types: - if (el <= 0): - self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) - raise ValueError("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) - elif (el > self.num_genes): - self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) - raise ValueError("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) - # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. - if mutation_num_genes[0] < mutation_num_genes[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_num_genes' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_num_genes[0], second_el=mutation_num_genes[1])) - # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. - else: - self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - raise TypeError("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - else: - pass - - # Validating mutation_by_replacement and mutation_type - if self.mutation_type != "random" and self.mutation_by_replacement: - if not self.suppress_warnings: warnings.warn("The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mut_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.".format(mut_type=mutation_type)) - - # Check if crossover and mutation are both disabled. - if (self.mutation_type is None) and (self.crossover_type is None): - if not self.suppress_warnings: warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") - - # select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute. - # Validating the selected type of parent selection: parent_selection_type - if inspect.ismethod(parent_selection_type): - # Check if the parent_selection_type is a method that accepts 4 paramaters. - if (parent_selection_type.__code__.co_argcount == 4): - # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. - # The parent selection method assigned to the parent_selection_type parameter is validated. - self.select_parents = parent_selection_type - else: - self.valid_parameters = False - self.logger.error("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - raise ValueError("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - elif callable(parent_selection_type): - # Check if the parent_selection_type is a function that accepts 3 paramaters. - if (parent_selection_type.__code__.co_argcount == 3): - # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. - # The parent selection function assigned to the parent_selection_type parameter is validated. - self.select_parents = parent_selection_type - else: - self.valid_parameters = False - self.logger.error("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - raise ValueError("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - elif not (type(parent_selection_type) is str): - self.valid_parameters = False - self.logger.error("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) - raise TypeError("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) - else: - parent_selection_type = parent_selection_type.lower() - if (parent_selection_type == "sss"): - self.select_parents = self.steady_state_selection - elif (parent_selection_type == "rws"): - self.select_parents = self.roulette_wheel_selection - elif (parent_selection_type == "sus"): - self.select_parents = self.stochastic_universal_selection - elif (parent_selection_type == "random"): - self.select_parents = self.random_selection - elif (parent_selection_type == "tournament"): - self.select_parents = self.tournament_selection - elif (parent_selection_type == "rank"): - self.select_parents = self.rank_selection - else: - self.valid_parameters = False - self.logger.error("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) - raise TypeError("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) - - # For tournament selection, validate the K value. - if(parent_selection_type == "tournament"): - if (K_tournament > self.sol_per_pop): - K_tournament = self.sol_per_pop - if not self.suppress_warnings: warnings.warn("K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n".format(K_tournament=K_tournament, sol_per_pop=self.sol_per_pop)) - elif (K_tournament <= 0): - self.valid_parameters = False - self.logger.error("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) - raise ValueError("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) - - self.K_tournament = K_tournament - - # Validating the number of parents to keep in the next population: keep_parents - if not (type(keep_parents) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) - raise TypeError("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) - elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): - self.valid_parameters = False - self.logger.error("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) - raise ValueError("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) - - self.keep_parents = keep_parents - - if parent_selection_type == "sss" and self.keep_parents == 0: - if not self.suppress_warnings: warnings.warn("The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") - - # Validating the number of elitism to keep in the next population: keep_elitism - if not (type(keep_elitism) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) - raise TypeError("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) - elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): - self.valid_parameters = False - self.logger.error("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) - raise ValueError("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) - - self.keep_elitism = keep_elitism - - # Validate keep_parents. - if self.keep_elitism == 0: - if (self.keep_parents == -1): # Keep all parents in the next population. - self.num_offspring = self.sol_per_pop - self.num_parents_mating - elif (self.keep_parents == 0): # Keep no parents in the next population. - self.num_offspring = self.sol_per_pop - elif (self.keep_parents > 0): # Keep the specified number of parents in the next population. - self.num_offspring = self.sol_per_pop - self.keep_parents - else: - self.num_offspring = self.sol_per_pop - self.keep_elitism - - # Check if the fitness_func is a method. - # In PyGAD 2.19.0, a method can be passed to the fitness function. If function is passed, then it accepts 2 parameters. If method, then it accepts 3 parameters. - # In PyGAD 2.20.0, a new parameter is passed referring to the instance of the `pygad.GA` class. So, the function accepts 3 parameters and the method accepts 4 parameters. - if inspect.ismethod(fitness_func): - # If the fitness is calculated through a method, not a function, then there is a fourth 'self` paramaters. - if (fitness_func.__code__.co_argcount == 4): - self.fitness_func = fitness_func - else: - self.valid_parameters = False - self.logger.error("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - raise ValueError("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - elif callable(fitness_func): - # Check if the fitness function accepts 2 paramaters. - if (fitness_func.__code__.co_argcount == 3): - self.fitness_func = fitness_func - else: - self.valid_parameters = False - self.logger.error("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - raise ValueError("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) - raise TypeError("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) - - if fitness_batch_size is None: - pass - elif not (type(fitness_batch_size) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) - raise TypeError("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) - elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: - self.valid_parameters = False - self.logger.error("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) - raise ValueError("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) - - self.fitness_batch_size = fitness_batch_size - - # Check if the on_start exists. - if not (on_start is None): - if inspect.ismethod(on_start): - # Check if the on_start method accepts 2 paramaters. - if (on_start.__code__.co_argcount == 2): - self.on_start = on_start - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - raise ValueError("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - # Check if the on_start is a function. - elif callable(on_start): - # Check if the on_start function accepts only a single paramater. - if (on_start.__code__.co_argcount == 1): - self.on_start = on_start - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - raise ValueError("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) - raise TypeError("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) - else: - self.on_start = None - - # Check if the on_fitness exists. - if not (on_fitness is None): - # Check if the on_fitness is a method. - if inspect.ismethod(on_fitness): - # Check if the on_fitness method accepts 3 paramaters. - if (on_fitness.__code__.co_argcount == 3): - self.on_fitness = on_fitness - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - raise ValueError("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - # Check if the on_fitness is a function. - elif callable(on_fitness): - # Check if the on_fitness function accepts 2 paramaters. - if (on_fitness.__code__.co_argcount == 2): - self.on_fitness = on_fitness - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - raise ValueError("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) - raise TypeError("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) - else: - self.on_fitness = None - - # Check if the on_parents exists. - if not (on_parents is None): - # Check if the on_parents is a method. - if inspect.ismethod(on_parents): - # Check if the on_parents method accepts 3 paramaters. - if (on_parents.__code__.co_argcount == 3): - self.on_parents = on_parents - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - raise ValueError("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - # Check if the on_parents is a function. - elif callable(on_parents): - # Check if the on_parents function accepts 2 paramaters. - if (on_parents.__code__.co_argcount == 2): - self.on_parents = on_parents - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - raise ValueError("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) - raise TypeError("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) - else: - self.on_parents = None - - # Check if the on_crossover exists. - if not (on_crossover is None): - # Check if the on_crossover is a method. - if inspect.ismethod(on_crossover): - # Check if the on_crossover method accepts 3 paramaters. - if (on_crossover.__code__.co_argcount == 3): - self.on_crossover = on_crossover - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - raise ValueError("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - # Check if the on_crossover is a function. - elif callable(on_crossover): - # Check if the on_crossover function accepts 2 paramaters. - if (on_crossover.__code__.co_argcount == 2): - self.on_crossover = on_crossover - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - raise ValueError("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) - raise TypeError("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) - else: - self.on_crossover = None - - # Check if the on_mutation exists. - if not (on_mutation is None): - # Check if the on_mutation is a method. - if inspect.ismethod(on_mutation): - # Check if the on_mutation method accepts 3 paramaters. - if (on_mutation.__code__.co_argcount == 3): - self.on_mutation = on_mutation - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - raise ValueError("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - # Check if the on_mutation is a function. - elif callable(on_mutation): - # Check if the on_mutation function accepts 2 paramaters. - if (on_mutation.__code__.co_argcount == 2): - self.on_mutation = on_mutation - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - raise ValueError("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) - raise TypeError("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) - else: - self.on_mutation = None - - # Check if the on_generation exists. - if not (on_generation is None): - # Check if the on_generation is a method. - if inspect.ismethod(on_generation): - # Check if the on_generation method accepts 2 paramaters. - if (on_generation.__code__.co_argcount == 2): - self.on_generation = on_generation - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - raise ValueError("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - # Check if the on_generation is a function. - elif callable(on_generation): - # Check if the on_generation function accepts only a single paramater. - if (on_generation.__code__.co_argcount == 1): - self.on_generation = on_generation - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - raise ValueError("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) - raise TypeError("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) - else: - self.on_generation = None - - # Check if the on_stop exists. - if not (on_stop is None): - # Check if the on_stop is a method. - if inspect.ismethod(on_stop): - # Check if the on_stop method accepts 3 paramaters. - if (on_stop.__code__.co_argcount == 3): - self.on_stop = on_stop - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - raise ValueError("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - # Check if the on_stop is a function. - elif callable(on_stop): - # Check if the on_stop function accepts 2 paramaters. - if (on_stop.__code__.co_argcount == 2): - self.on_stop = on_stop - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - raise ValueError("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) - raise TypeError("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) - else: - self.on_stop = None - - # Validate delay_after_gen - if type(delay_after_gen) in GA.supported_int_float_types: - if delay_after_gen >= 0.0: - self.delay_after_gen = delay_after_gen - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) - raise ValueError("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) - raise TypeError("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) - - # Validate save_best_solutions - if type(save_best_solutions) is bool: - if save_best_solutions == True: - if not self.suppress_warnings: warnings.warn("Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) - raise TypeError("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) - - # Validate save_solutions - if type(save_solutions) is bool: - if save_solutions == True: - if not self.suppress_warnings: warnings.warn("Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) - raise TypeError("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) - - # Validate allow_duplicate_genes - if not (type(allow_duplicate_genes) is bool): - self.valid_parameters = False - self.logger.error("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) - raise TypeError("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) - - self.allow_duplicate_genes = allow_duplicate_genes - - self.stop_criteria = [] - self.supported_stop_words = ["reach", "saturate"] - if stop_criteria is None: - # None: Stop after passing through all generations. - self.stop_criteria = None - elif type(stop_criteria) is str: - # reach_{target_fitness}: Stop if the target fitness value is reached. - # saturate_{num_generations}: Stop if the fitness value does not change (saturates) for the given number of generations. - criterion = stop_criteria.split("_") - if len(criterion) == 2: - stop_word = criterion[0] - number = criterion[1] - - if stop_word in self.supported_stop_words: - pass - else: - self.valid_parameters = False - self.logger.error("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - raise ValueError("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - - if number.replace(".", "").isnumeric(): - number = float(number) - else: - self.valid_parameters = False - self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - - self.stop_criteria.append([stop_word, number]) - - else: - self.valid_parameters = False - self.logger.error("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) - raise ValueError("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) - - elif type(stop_criteria) in [list, tuple, numpy.ndarray]: - # Remove duplicate criterira by converting the list to a set then back to a list. - stop_criteria = list(set(stop_criteria)) - for idx, val in enumerate(stop_criteria): - if type(val) is str: - criterion = val.split("_") - if len(criterion) == 2: - stop_word = criterion[0] - number = criterion[1] - - if stop_word in self.supported_stop_words: - pass - else: - self.valid_parameters = False - self.logger.error("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - raise ValueError("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - - if number.replace(".", "").isnumeric(): - number = float(number) - else: - self.valid_parameters = False - self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - - self.stop_criteria.append([stop_word, number]) - - else: - self.valid_parameters = False - self.logger.error("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) - raise ValueError("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) - else: - self.valid_parameters = False - self.logger.error("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) - raise TypeError("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) - else: - self.valid_parameters = False - self.logger.error("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) - raise TypeError("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) - - if parallel_processing is None: - self.parallel_processing = None - elif type(parallel_processing) in GA.supported_int_types: - if parallel_processing > 0: - self.parallel_processing = ["thread", parallel_processing] - else: - self.valid_parameters = False - self.logger.error("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) - raise ValueError("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) - elif type(parallel_processing) in [list, tuple]: - if len(parallel_processing) == 2: - if type(parallel_processing[0]) is str: - if parallel_processing[0] in ["process", "thread"]: - if (type(parallel_processing[1]) in GA.supported_int_types and parallel_processing[1] > 0) or (parallel_processing[1] == 0) or (parallel_processing[1] is None): - if parallel_processing[1] == 0: - # If the number of processes/threads is 0, this means no parallel processing is used. It is equivelant to setting parallel_processing=None. - self.parallel_processing = None - else: - # Whether the second value is None or a positive integer. - self.parallel_processing = parallel_processing - else: - self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) - raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) - else: - self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) - raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) - else: - self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) - raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) - else: - self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) - raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) - else: - self.valid_parameters = False - self.logger.error("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) - raise ValueError("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) - - # Set the `run_completed` property to False. It is set to `True` only after the `run()` method is complete. - self.run_completed = False - - # The number of completed generations. - self.generations_completed = 0 - - # At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid. - self.valid_parameters = True # Set to True when all the parameters passed in the GA class constructor are valid. - - # Parameters of the genetic algorithm. - self.num_generations = abs(num_generations) - self.parent_selection_type = parent_selection_type - - # Parameters of the mutation operation. - self.mutation_percent_genes = mutation_percent_genes - self.mutation_num_genes = mutation_num_genes - - # Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object. - self.best_solutions_fitness = [] # A list holding the fitness value of the best solution for each generation. - - self.best_solution_generation = -1 # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. - - self.save_best_solutions = save_best_solutions - self.best_solutions = [] # Holds the best solution in each generation. - - self.save_solutions = save_solutions - self.solutions = [] # Holds the solutions in each generation. - self.solutions_fitness = [] # Holds the fitness of the solutions in each generation. - - self.last_generation_fitness = None # A list holding the fitness values of all solutions in the last generation. - self.last_generation_parents = None # A list holding the parents of the last generation. - self.last_generation_offspring_crossover = None # A list holding the offspring after applying crossover in the last generation. - self.last_generation_offspring_mutation = None # A list holding the offspring after applying mutation in the last generation. - self.previous_generation_fitness = None # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. - self.last_generation_elitism = None # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. - self.last_generation_elitism_indices = None # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. - - def round_genes(self, solutions): - for gene_idx in range(self.num_genes): - if self.gene_type_single: - if not self.gene_type[1] is None: - solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], self.gene_type[1]) - else: - if not self.gene_type[gene_idx][1] is None: - solutions[:, gene_idx] = numpy.round(numpy.asarray(solutions[:, gene_idx], - dtype=self.gene_type[gene_idx][0]), - self.gene_type[gene_idx][1]) - return solutions - - def initialize_population(self, - low, - high, - allow_duplicate_genes, - mutation_by_replacement, - gene_type): - - """ - Creates an initial population randomly as a NumPy array. The array is saved in the instance attribute named 'population'. - - low: The lower value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20 and higher. - high: The upper value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20. - - This method assigns the values of the following 3 instance attributes: - 1. pop_size: Size of the population. - 2. population: Initially, holds the initial population and later updated after each generation. - 3. init_population: Keeping the initial population. - """ - - # Population size = (number of chromosomes, number of genes per chromosome) - self.pop_size = (self.sol_per_pop,self.num_genes) # The population will have sol_per_pop chromosome where each chromosome has num_genes genes. - - if self.gene_space is None: - # Creating the initial population randomly. - if self.gene_type_single == True: - self.population = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. - else: - # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) - # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. - for gene_idx in range(self.num_genes): - # A vector of all values of this single gene across all solutions in the population. - gene_values = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=self.pop_size[0]), - dtype=self.gene_type[gene_idx][0]) - # Adding the current gene values to the population. - self.population[:, gene_idx] = gene_values - - if allow_duplicate_genes == False: - for solution_idx in range(self.population.shape[0]): - # self.logger.info("Before", self.population[solution_idx]) - self.population[solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=self.population[solution_idx], - min_val=low, - max_val=high, - mutation_by_replacement=True, - gene_type=gene_type, - num_trials=10) - # self.logger.info("After", self.population[solution_idx]) - - elif self.gene_space_nested: - if self.gene_type_single == True: - self.population = numpy.zeros(shape=self.pop_size, dtype=self.gene_type[0]) - for sol_idx in range(self.sol_per_pop): - for gene_idx in range(self.num_genes): - if type(self.gene_space[gene_idx]) in [list, tuple, range]: - # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. - if type(self.gene_space[gene_idx]) is range: - temp = self.gene_space[gene_idx] - else: - temp = self.gene_space[gene_idx].copy() - for idx, val in enumerate(self.gene_space[gene_idx]): - if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[0])[0] - self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) - self.population[sol_idx, gene_idx] = self.gene_type[0](self.population[sol_idx, gene_idx]) - self.gene_space[gene_idx] = temp - elif type(self.gene_space[gene_idx]) is dict: - if 'step' in self.gene_space[gene_idx].keys(): - self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], - step=self.gene_space[gene_idx]['step']), - size=1), - dtype=self.gene_type[0])[0] - else: - self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[gene_idx]['high'], - size=1), - dtype=self.gene_type[0])[0] - elif type(self.gene_space[gene_idx]) == type(None): - - # The following commented code replace the None value with a single number that will not change again. - # This means the gene value will be the same across all solutions. - # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - # high=high, - # size=1), dtype=self.gene_type[0])[0] - # self.population[sol_idx, gene_idx] = self.gene_space[gene_idx].copy() - - # The above problem is solved by keeping the None value in the gene_space parameter. This forces PyGAD to generate this value for each solution. - self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[0])[0] - elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] - else: - self.population = numpy.zeros(shape=self.pop_size, dtype=object) - for sol_idx in range(self.sol_per_pop): - for gene_idx in range(self.num_genes): - if type(self.gene_space[gene_idx]) in [list, tuple, range]: - # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. - temp = self.gene_space[gene_idx].copy() - for idx, val in enumerate(self.gene_space[gene_idx]): - if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) - self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0](self.population[sol_idx, gene_idx]) - self.gene_space[gene_idx] = temp.copy() - elif type(self.gene_space[gene_idx]) is dict: - if 'step' in self.gene_space[gene_idx].keys(): - self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], - step=self.gene_space[gene_idx]['step']), - size=1), - dtype=self.gene_type[gene_idx][0])[0] - else: - self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[gene_idx]['high'], - size=1), - dtype=self.gene_type[gene_idx][0])[0] - elif type(self.gene_space[gene_idx]) == type(None): - # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - # high=high, - # size=1), - # dtype=self.gene_type[gene_idx][0])[0] - - # self.population[sol_idx, gene_idx] = self.gene_space[gene_idx].copy() - - temp = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = temp - elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] - else: - if self.gene_type_single == True: - # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. - for idx, curr_gene_space in enumerate(self.gene_space): - if curr_gene_space is None: - self.gene_space[idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[0])[0] - - # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. - if type(self.gene_space) is dict: - if 'step' in self.gene_space.keys(): - self.population = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space['low'], - stop=self.gene_space['high'], - step=self.gene_space['step']), - size=self.pop_size), - dtype=self.gene_type[0]) - else: - self.population = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], - high=self.gene_space['high'], - size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. - else: - self.population = numpy.asarray(numpy.random.choice(self.gene_space, - size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. - else: - # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. - for gene_idx, curr_gene_space in enumerate(self.gene_space): - if curr_gene_space is None: - self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[gene_idx][0])[0] - - # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. - if type(self.gene_space) is dict: - # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) - # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. - for gene_idx in range(self.num_genes): - # A vector of all values of this single gene across all solutions in the population. - if 'step' in self.gene_space[gene_idx].keys(): - gene_values = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], - step=self.gene_space[gene_idx]['step']), - size=self.pop_size[0]), - dtype=self.gene_type[gene_idx][0]) - else: - gene_values = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], - high=self.gene_space['high'], - size=self.pop_size[0]), - dtype=self.gene_type[gene_idx][0]) - # Adding the current gene values to the population. - self.population[:, gene_idx] = gene_values - - else: - # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) - # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. - for gene_idx in range(self.num_genes): - # A vector of all values of this single gene across all solutions in the population. - gene_values = numpy.asarray(numpy.random.choice(self.gene_space, - size=self.pop_size[0]), - dtype=self.gene_type[gene_idx][0]) - # Adding the current gene values to the population. - self.population[:, gene_idx] = gene_values - - if not (self.gene_space is None): - if allow_duplicate_genes == False: - for sol_idx in range(self.population.shape[0]): - self.population[sol_idx], _, _ = self.solve_duplicate_genes_by_space(solution=self.population[sol_idx], - gene_type=self.gene_type, - num_trials=10, - build_initial_pop=True) - - # Keeping the initial population in the initial_population attribute. - self.initial_population = self.population.copy() - - def cal_pop_fitness(self): - - """ - Calculating the fitness values of batches of solutions in the current population. - It returns: - -fitness: An array of the calculated fitness values. - """ - - if self.valid_parameters == False: - self.logger.error("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") - raise Exception("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") - - # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' - # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. - if self.last_generation_parents is not None: - last_generation_parents_as_list = [list(gen_parent) for gen_parent in self.last_generation_parents] - - # 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism' - # It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. - if self.last_generation_elitism is not None: - last_generation_elitism_as_list = [list(gen_elitism) for gen_elitism in self.last_generation_elitism] - - pop_fitness = ["undefined"] * len(self.population) - if self.parallel_processing is None: - # Calculating the fitness value of each solution in the current population. - for sol_idx, sol in enumerate(self.population): - # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. - # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. - # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. - # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. - # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1))): - # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1))): - if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): - solution_idx = self.solutions.index(list(sol)) - fitness = self.solutions_fitness[solution_idx] - elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): - # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. - # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index(list(sol)) - # Use the returned elitism index to return its index in the last population. - elitism_idx = self.last_generation_elitism_indices[elitism_idx] - # Use the elitism's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[elitism_idx] - # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. - # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. - # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): - elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. - # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. - # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index(list(sol)) - # Use the returned parent index to return its index in the last population. - parent_idx = self.last_generation_parents_indices[parent_idx] - # Use the parent's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[parent_idx] - else: - # Check if batch processing is used. If not, then calculate this missing fitness value. - if self.fitness_batch_size in [1, None]: - fitness = self.fitness_func(self, sol, sol_idx) - if type(fitness) in GA.supported_int_float_types: - pass - else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - else: - # Reaching this point means that batch processing is in effect to calculate the fitness values. - # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. - continue - - # This is only executed if the fitness value was already calculated. - pop_fitness[sol_idx] = fitness - - if self.fitness_batch_size not in [1, None]: - # Reaching this block means that batch fitness calculation is used. - - # Indices of the solutions to calculate their fitness. - solutions_indices = numpy.where(numpy.array(pop_fitness) == "undefined")[0] - # Number of batches. - num_batches = int(numpy.ceil(len(solutions_indices) / self.fitness_batch_size)) - # For each batch, get its indices and call the fitness function. - for batch_idx in range(num_batches): - batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * self.fitness_batch_size - batch_indices = solutions_indices[batch_first_index:batch_last_index] - batch_solutions = self.population[batch_indices, :] - - batch_fitness = self.fitness_func(self, batch_solutions, batch_indices) - if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - - for index, fitness in zip(batch_indices, batch_fitness): - if type(fitness) in GA.supported_int_float_types: - pop_fitness[index] = fitness - else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - else: - # Calculating the fitness value of each solution in the current population. - for sol_idx, sol in enumerate(self.population): - # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. - # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. - # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. - # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. - if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): - solution_idx = self.solutions.index(list(sol)) - fitness = self.solutions_fitness[solution_idx] - pop_fitness[sol_idx] = fitness - elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): - # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. - # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index(list(sol)) - # Use the returned elitism index to return its index in the last population. - elitism_idx = self.last_generation_elitism_indices[elitism_idx] - # Use the elitism's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[elitism_idx] - - pop_fitness[sol_idx] = fitness - # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. - # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. - # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): - elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. - # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. - # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index(list(sol)) - # Use the returned parent index to return its index in the last population. - parent_idx = self.last_generation_parents_indices[parent_idx] - # Use the parent's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[parent_idx] - - pop_fitness[sol_idx] = fitness - - # Decide which class to use based on whether the user selected "process" or "thread" - if self.parallel_processing[0] == "process": - ExecutorClass = concurrent.futures.ProcessPoolExecutor - else: - ExecutorClass = concurrent.futures.ThreadPoolExecutor - - # We can use a with statement to ensure threads are cleaned up promptly (https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example) - with ExecutorClass(max_workers=self.parallel_processing[1]) as executor: - solutions_to_submit_indices = [] - solutions_to_submit = [] - for sol_idx, sol in enumerate(self.population): - # The "undefined" value means that the fitness of this solution must be calculated. - if pop_fitness[sol_idx] == "undefined": - solutions_to_submit.append(sol.copy()) - solutions_to_submit_indices.append(sol_idx) - - # Check if batch processing is used. If not, then calculate the fitness value for individual solutions. - if self.fitness_batch_size in [1, None]: - for index, fitness in zip(solutions_to_submit_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), solutions_to_submit, solutions_to_submit_indices)): - if type(fitness) in GA.supported_int_float_types: - pop_fitness[index] = fitness - else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - else: - # Reaching this block means that batch processing is used. The fitness values are calculated in batches. - - # Number of batches. - num_batches = int(numpy.ceil(len(solutions_to_submit_indices) / self.fitness_batch_size)) - # Each element of the `batches_solutions` list represents the solutions in one batch. - batches_solutions = [] - # Each element of the `batches_indices` list represents the solutions' indices in one batch. - batches_indices = [] - # For each batch, get its indices and call the fitness function. - for batch_idx in range(num_batches): - batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * self.fitness_batch_size - batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] - batch_solutions = self.population[batch_indices, :] - - batches_solutions.append(batch_solutions) - batches_indices.append(batch_indices) - - for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): - if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - - for index, fitness in zip(batch_indices, batch_fitness): - if type(fitness) in GA.supported_int_float_types: - pop_fitness[index] = fitness - else: - self.logger.error("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - - pop_fitness = numpy.array(pop_fitness) - - return pop_fitness - - def run(self): - - """ - Runs the genetic algorithm. This is the main method in which the genetic algorithm is evolved through a number of generations. - """ - - if self.valid_parameters == False: - self.logger.error("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - raise Exception("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - - # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. - # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. - - # self.best_solutions: Holds the best solution in each generation. - if type(self.best_solutions) is numpy.ndarray: - self.best_solutions = list(self.best_solutions) - # self.best_solutions_fitness: A list holding the fitness value of the best solution for each generation. - if type(self.best_solutions_fitness) is numpy.ndarray: - self.best_solutions_fitness = list(self.best_solutions_fitness) - # self.solutions: Holds the solutions in each generation. - if type(self.solutions) is numpy.ndarray: - self.solutions = list(self.solutions) - # self.solutions_fitness: Holds the fitness of the solutions in each generation. - if type(self.solutions_fitness) is numpy.ndarray: - self.solutions_fitness = list(self.solutions_fitness) - - if not (self.on_start is None): - self.on_start(self) - - stop_run = False - - # To continue from where we stopped, the first generation index should start from the value of the 'self.generations_completed' parameter. - if self.generations_completed != 0 and type(self.generations_completed) in GA.supported_int_types: - # If the 'self.generations_completed' parameter is not '0', then this means we continue execution. - generation_first_idx = self.generations_completed - generation_last_idx = self.num_generations + self.generations_completed - else: - # If the 'self.generations_completed' parameter is '0', then stat from scratch. - generation_first_idx = 0 - generation_last_idx = self.num_generations - - # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. - self.last_generation_fitness = self.cal_pop_fitness() - - best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) - - # Appending the best solution in the initial population to the best_solutions list. - if self.save_best_solutions: - self.best_solutions.append(best_solution) - - for generation in range(generation_first_idx, generation_last_idx): - if not (self.on_fitness is None): - self.on_fitness(self, self.last_generation_fitness) - - # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. - self.best_solutions_fitness.append(best_solution_fitness) - - # Appending the solutions in the current generation to the solutions list. - if self.save_solutions: - # self.solutions.extend(self.population.copy()) - population_as_list = self.population.copy() - population_as_list = [list(item) for item in population_as_list] - self.solutions.extend(population_as_list) - - self.solutions_fitness.extend(self.last_generation_fitness) - - # Selecting the best parents in the population for mating. - if callable(self.parent_selection_type): - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self, - self.last_generation_fitness, - self.num_parents_mating, self) - if not type(self.last_generation_parents) is numpy.ndarray: - self.logger.error("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) - raise TypeError("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) - if not type(self.last_generation_parents_indices) is numpy.ndarray: - self.logger.error("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) - raise TypeError("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) - else: - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, - num_parents=self.num_parents_mating) - - # Validate the output of the parent selection step: self.select_parents() - if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): - if self.last_generation_parents.shape[0] != self.num_parents_mating: - self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) - raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) - elif self.last_generation_parents.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) - raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) - - if self.last_generation_parents_indices.ndim != 1: - self.logger.error("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) - raise ValueError("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) - elif len(self.last_generation_parents_indices) != self.num_parents_mating: - self.logger.error("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) - raise ValueError("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) - - if not (self.on_parents is None): - self.on_parents(self, self.last_generation_parents) - - # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. - if self.crossover_type is None: - if self.keep_elitism == 0: - num_parents_to_keep = self.num_parents_mating if self.keep_parents == -1 else self.keep_parents - if self.num_offspring <= num_parents_to_keep: - self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] - else: - self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) - else: - # The steady_state_selection() function is called to select the best solutions (i.e. elitism). The keep_elitism parameter defines the number of these solutions. - # The steady_state_selection() function is still called here even if its output may not be used given that the condition of the next if statement is True. The reason is that it will be used later. - self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_elitism) - if self.num_offspring <= self.keep_elitism: - self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] - else: - self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) - else: - # Generating offspring using crossover. - if callable(self.crossover_type): - self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, - (self.num_offspring, self.num_genes), - self) - if not type(self.last_generation_offspring_crossover) is numpy.ndarray: - self.logger.error("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) - raise TypeError("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) - else: - self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, - offspring_size=(self.num_offspring, self.num_genes)) - if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): - if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: - self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) - raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) - elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) - raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) - - # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. - if not (self.on_crossover is None): - self.on_crossover(self, self.last_generation_offspring_crossover) - - # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. - if self.mutation_type is None: - self.last_generation_offspring_mutation = self.last_generation_offspring_crossover - else: - # Adding some variations to the offspring using mutation. - if callable(self.mutation_type): - self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, - self) - if not type(self.last_generation_offspring_mutation) is numpy.ndarray: - self.logger.error("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) - raise TypeError("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) - else: - self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover) - - if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): - if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: - self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) - raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) - elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) - raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) - - # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. - if not (self.on_mutation is None): - self.on_mutation(self, self.last_generation_offspring_mutation) - - # Update the population attribute according to the offspring generated. - if self.keep_elitism == 0: - # If the keep_elitism parameter is 0, then the keep_parents parameter will be used to decide if the parents are kept in the next generation. - if (self.keep_parents == 0): - self.population = self.last_generation_offspring_mutation - elif (self.keep_parents == -1): - # Creating the new population based on the parents and offspring. - self.population[0:self.last_generation_parents.shape[0], :] = self.last_generation_parents - self.population[self.last_generation_parents.shape[0]:, :] = self.last_generation_offspring_mutation - elif (self.keep_parents > 0): - parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_parents) - self.population[0:parents_to_keep.shape[0], :] = parents_to_keep - self.population[parents_to_keep.shape[0]:, :] = self.last_generation_offspring_mutation - else: - self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_elitism) - self.population[0:self.last_generation_elitism.shape[0], :] = self.last_generation_elitism - self.population[self.last_generation_elitism.shape[0]:, :] = self.last_generation_offspring_mutation - - self.generations_completed = generation + 1 # The generations_completed attribute holds the number of the last completed generation. - - self.previous_generation_fitness = self.last_generation_fitness.copy() - # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. - self.last_generation_fitness = self.cal_pop_fitness() - - best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) - - # Appending the best solution in the current generation to the best_solutions list. - if self.save_best_solutions: - self.best_solutions.append(best_solution) - - # If the on_generation attribute is not None, then cal the callback function after the generation. - if not (self.on_generation is None): - r = self.on_generation(self) - if type(r) is str and r.lower() == "stop": - # Before aborting the loop, save the fitness value of the best solution. - # _, best_solution_fitness, _ = self.best_solution() - self.best_solutions_fitness.append(best_solution_fitness) - break - - if not self.stop_criteria is None: - for criterion in self.stop_criteria: - if criterion[0] == "reach": - if max(self.last_generation_fitness) >= criterion[1]: - stop_run = True - break - elif criterion[0] == "saturate": - criterion[1] = int(criterion[1]) - if (self.generations_completed >= criterion[1]): - if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0: - stop_run = True - break - - if stop_run: - break - - time.sleep(self.delay_after_gen) - - # Save the fitness of the last generation. - if self.save_solutions: - # self.solutions.extend(self.population.copy()) - population_as_list = self.population.copy() - population_as_list = [list(item) for item in population_as_list] - self.solutions.extend(population_as_list) - - self.solutions_fitness.extend(self.last_generation_fitness) - - # Save the fitness value of the best solution. - _, best_solution_fitness, _ = self.best_solution(pop_fitness=self.last_generation_fitness) - self.best_solutions_fitness.append(best_solution_fitness) - - self.best_solution_generation = numpy.where(numpy.array(self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] - # After the run() method completes, the run_completed flag is changed from False to True. - self.run_completed = True # Set to True only after the run() method completes gracefully. - - if not (self.on_stop is None): - self.on_stop(self, self.last_generation_fitness) - - # Converting the 'best_solutions' list into a NumPy array. - self.best_solutions = numpy.array(self.best_solutions) - - # Converting the 'solutions' list into a NumPy array. - # self.solutions = numpy.array(self.solutions) - - def best_solution(self, pop_fitness=None): - - """ - Returns information about the best solution found by the genetic algorithm. - Accepts the following parameters: - pop_fitness: An optional parameter holding the fitness values of the solutions in the latest population. If passed, then it save time calculating the fitness. If None, then the 'cal_pop_fitness()' method is called to calculate the fitness of the latest population. - The following are returned: - -best_solution: Best solution in the current population. - -best_solution_fitness: Fitness value of the best solution. - -best_match_idx: Index of the best solution in the current population. - """ - - if pop_fitness is None: - # If the 'pop_fitness' parameter is not passed, then we have to call the 'cal_pop_fitness()' method to calculate the fitness of all solutions in the lastest population. - pop_fitness = self.cal_pop_fitness() - # Verify the type of the 'pop_fitness' parameter. - elif type(pop_fitness) in [tuple, list, numpy.ndarray]: - # Verify that the length of the passed population fitness matches the length of the 'self.population' attribute. - if len(pop_fitness) == len(self.population): - # This successfully verifies the 'pop_fitness' parameter. - pass - else: - self.logger.error("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) - raise ValueError("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) - else: - self.logger.error("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) - raise ValueError("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) - - # Return the index of the best solution that has the best fitness value. - best_match_idx = numpy.where(pop_fitness == numpy.max(pop_fitness))[0][0] - - best_solution = self.population[best_match_idx, :].copy() - best_solution_fitness = pop_fitness[best_match_idx] - - return best_solution, best_solution_fitness, best_match_idx - - def save(self, filename): - - """ - Saves the genetic algorithm instance: - -filename: Name of the file to save the instance. No extension is needed. - """ - - cloudpickle_serialized_object = cloudpickle.dumps(self) - with open(filename + ".pkl", 'wb') as file: - file.write(cloudpickle_serialized_object) - cloudpickle.dump(self, file) - - def summary(self, - line_length=70, - fill_character=" ", - line_character="-", - line_character2="=", - columns_equal_len=False, - print_step_parameters=True, - print_parameters_summary=True): - """ - The summary() method prints a summary of the PyGAD lifecycle in a Keras style. - The parameters are: - line_length: An integer representing the length of the single line in characters. - fill_character: A character to fill the lines. - line_character: A character for creating a line separator. - line_character2: A secondary character to create a line separator. - columns_equal_len: The table rows are split into equal-sized columns or split subjective to the width needed. - print_step_parameters: Whether to print extra parameters about each step inside the step. If print_step_parameters=False and print_parameters_summary=True, then the parameters of each step are printed at the end of the table. - print_parameters_summary: Whether to print parameters summary at the end of the table. If print_step_parameters=False, then the parameters of each step are printed at the end of the table too. - """ - - summary_output = "" - - def fill_message(msg, line_length=line_length, fill_character=fill_character): - num_spaces = int((line_length - len(msg))/2) - num_spaces = int(num_spaces / len(fill_character)) - msg = "{spaces}{msg}{spaces}".format(msg=msg, spaces=fill_character * num_spaces) - return msg - - def line_separator(line_length=line_length, line_character=line_character): - num_characters = int(line_length / len(line_character)) - return line_character * num_characters - - def create_row(columns, line_length=line_length, fill_character=fill_character, split_percentages=None): - filled_columns = [] - if split_percentages == None: - split_percentages = [int(100/len(columns))] * 3 - columns_lengths = [int((split_percentages[idx] * line_length) / 100) for idx in range(len(split_percentages))] - for column_idx, column in enumerate(columns): - current_column_length = len(column) - extra_characters = columns_lengths[column_idx] - current_column_length - filled_column = column + fill_character * extra_characters - filled_column = column + fill_character * extra_characters - filled_columns.append(filled_column) - - return "".join(filled_columns) - - def print_parent_selection_params(): - nonlocal summary_output - m = "Number of Parents: {num_parents_mating}".format(num_parents_mating=self.num_parents_mating) - self.logger.info(m) - summary_output = summary_output + m + "\n" - if self.parent_selection_type == "tournament": - m = "K Tournament: {K_tournament}".format(K_tournament=self.K_tournament) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - def print_fitness_params(): - nonlocal summary_output - if not self.fitness_batch_size is None: - m = "Fitness batch size: {fitness_batch_size}".format(fitness_batch_size=self.fitness_batch_size) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - def print_crossover_params(): - nonlocal summary_output - if not self.crossover_probability is None: - m = "Crossover probability: {crossover_probability}".format(crossover_probability=self.crossover_probability) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - def print_mutation_params(): - nonlocal summary_output - if not self.mutation_probability is None: - m = "Mutation Probability: {mutation_probability}".format(mutation_probability=self.mutation_probability) - self.logger.info(m) - summary_output = summary_output + m + "\n" - if self.mutation_percent_genes == "default": - m = "Mutation Percentage: {mutation_percent_genes}".format(mutation_percent_genes=self.mutation_percent_genes) - self.logger.info(m) - summary_output = summary_output + m + "\n" - # Number of mutation genes is already showed above. - m = "Mutation Genes: {mutation_num_genes}".format(mutation_num_genes=self.mutation_num_genes) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Random Mutation Range: ({random_mutation_min_val}, {random_mutation_max_val})".format(random_mutation_min_val=self.random_mutation_min_val, random_mutation_max_val=self.random_mutation_max_val) - self.logger.info(m) - summary_output = summary_output + m + "\n" - if not self.gene_space is None: - m = "Gene Space: {gene_space}".format(gene_space=self.gene_space) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Mutation by Replacement: {mutation_by_replacement}".format(mutation_by_replacement=self.mutation_by_replacement) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Allow Duplicated Genes: {allow_duplicate_genes}".format(allow_duplicate_genes=self.allow_duplicate_genes) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - def print_on_generation_params(): - nonlocal summary_output - if not self.stop_criteria is None: - m = "Stop Criteria: {stop_criteria}".format(stop_criteria=self.stop_criteria) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - def print_params_summary(): - nonlocal summary_output - m = "Population Size: ({sol_per_pop}, {num_genes})".format(sol_per_pop=self.sol_per_pop, num_genes=self.num_genes) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Number of Generations: {num_generations}".format(num_generations=self.num_generations) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Initial Population Range: ({init_range_low}, {init_range_high})".format(init_range_low=self.init_range_low, init_range_high=self.init_range_high) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - if not print_step_parameters: - print_fitness_params() - - if not print_step_parameters: - print_parent_selection_params() - - if self.keep_elitism != 0: - m = "Keep Elitism: {keep_elitism}".format(keep_elitism=self.keep_elitism) - self.logger.info(m) - summary_output = summary_output + m + "\n" - else: - m = "Keep Parents: {keep_parents}".format(keep_parents=self.keep_parents) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Gene DType: {gene_type}".format(gene_type=self.gene_type) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - if not print_step_parameters: - print_crossover_params() - - if not print_step_parameters: - print_mutation_params() - - if self.delay_after_gen != 0: - m = "Post-Generation Delay: {delay_after_gen}".format(delay_after_gen=self.delay_after_gen) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - if not print_step_parameters: - print_on_generation_params() - - if not self.parallel_processing is None: - m = "Parallel Processing: {parallel_processing}".format(parallel_processing=self.parallel_processing) - self.logger.info(m) - summary_output = summary_output + m + "\n" - if not self.random_seed is None: - m = "Random Seed: {random_seed}".format(random_seed=self.random_seed) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Save Best Solutions: {save_best_solutions}".format(save_best_solutions=self.save_best_solutions) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = "Save Solutions: {save_solutions}".format(save_solutions=self.save_solutions) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - m = line_separator(line_character=line_character) - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = fill_message("PyGAD Lifecycle") - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = line_separator(line_character=line_character2) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - lifecycle_steps = ["on_start()", "Fitness Function", "On Fitness", "Parent Selection", "On Parents", "Crossover", "On Crossover", "Mutation", "On Mutation", "On Generation", "On Stop"] - lifecycle_functions = [self.on_start, self.fitness_func, self.on_fitness, self.select_parents, self.on_parents, self.crossover, self.on_crossover, self.mutation, self.on_mutation, self.on_generation, self.on_stop] - lifecycle_functions = [getattr(lifecycle_func, '__name__', "None") for lifecycle_func in lifecycle_functions] - lifecycle_functions = [lifecycle_func + "()" if lifecycle_func != "None" else "None" for lifecycle_func in lifecycle_functions] - lifecycle_output = ["None", "(1)", "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "None", "None"] - lifecycle_step_parameters = [None, print_fitness_params, None, print_parent_selection_params, None, print_crossover_params, None, print_mutation_params, None, print_on_generation_params, None] - - if not columns_equal_len: - max_lengthes = [max(list(map(len, lifecycle_steps))), max(list(map(len, lifecycle_functions))), max(list(map(len, lifecycle_output)))] - split_percentages = [int((column_len / sum(max_lengthes)) * 100) for column_len in max_lengthes] - else: - split_percentages = None - - header_columns = ["Step", "Handler", "Output Shape"] - header_row = create_row(header_columns, split_percentages=split_percentages) - m = header_row - self.logger.info(m) - summary_output = summary_output + m + "\n" - m = line_separator(line_character=line_character2) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - for lifecycle_idx in range(len(lifecycle_steps)): - lifecycle_column = [lifecycle_steps[lifecycle_idx], lifecycle_functions[lifecycle_idx], lifecycle_output[lifecycle_idx]] - if lifecycle_column[1] == "None": - continue - lifecycle_row = create_row(lifecycle_column, split_percentages=split_percentages) - m = lifecycle_row - self.logger.info(m) - summary_output = summary_output + m + "\n" - if print_step_parameters: - if not lifecycle_step_parameters[lifecycle_idx] is None: - lifecycle_step_parameters[lifecycle_idx]() - m = line_separator(line_character=line_character) - self.logger.info(m) - summary_output = summary_output + m + "\n" - - m = line_separator(line_character=line_character2) - self.logger.info(m) - summary_output = summary_output + m + "\n" - if print_parameters_summary: - print_params_summary() - m = line_separator(line_character=line_character2) - self.logger.info(m) - summary_output = summary_output + m + "\n" - return summary_output - -def load(filename): - - """ - Reads a saved instance of the genetic algorithm: - -filename: Name of the file to read the instance. No extension is needed. - Returns the genetic algorithm instance. - """ - - try: - with open(filename + ".pkl", 'rb') as file: - ga_in = cloudpickle.load(file) - except FileNotFoundError: - raise FileNotFoundError("Error reading the file {filename}. Please check your inputs.".format(filename=filename)) - except: - # raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).") - raise BaseException("Error loading the file.") - return ga_in From bf87baad1109117a469bf20f441fa78e9be7b459 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:40:55 -0400 Subject: [PATCH 010/220] Delete __init__.py --- pygad/__init__.py | 3 --- 1 file changed, 3 deletions(-) delete mode 100644 pygad/__init__.py diff --git a/pygad/__init__.py b/pygad/__init__.py deleted file mode 100644 index 71f207b..0000000 --- a/pygad/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .pygad import * # Relative import. - -__version__ = "3.0.0" From 8ee348f03a409d6643929c95c3d7ed961b23fad0 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:41:12 -0400 Subject: [PATCH 011/220] Rename __init__.py to pygad/__init__.py --- __init__.py => pygad/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename __init__.py => pygad/__init__.py (100%) diff --git a/__init__.py b/pygad/__init__.py similarity index 100% rename from __init__.py rename to pygad/__init__.py From 78daf35ec68b0392c8e2e742dab7ab75ede6869b Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:41:24 -0400 Subject: [PATCH 012/220] Rename pygad.py to pygad/pygad.py --- pygad.py => pygad/pygad.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename pygad.py => pygad/pygad.py (100%) diff --git a/pygad.py b/pygad/pygad.py similarity index 100% rename from pygad.py rename to pygad/pygad.py From 7aedc6de0211756f3acd0598fb6052dd6256d699 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:42:21 -0400 Subject: [PATCH 013/220] Rename example.py to examples/example.py --- example.py => examples/example.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename example.py => examples/example.py (100%) diff --git a/example.py b/examples/example.py similarity index 100% rename from example.py rename to examples/example.py From 34d3879cebbf0f3d98ff04da00c9b079cbc474d5 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:42:31 -0400 Subject: [PATCH 014/220] Rename lifecycle.py to examples/lifecycle.py --- lifecycle.py => examples/lifecycle.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename lifecycle.py => examples/lifecycle.py (100%) diff --git a/lifecycle.py b/examples/lifecycle.py similarity index 100% rename from lifecycle.py rename to examples/lifecycle.py From 8f2c6d40b10f0169acc66b34c47166ed5c0eafca Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:42:33 -0400 Subject: [PATCH 015/220] Rename example_logger.py to examples/example_logger.py --- example_logger.py => examples/example_logger.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename example_logger.py => examples/example_logger.py (100%) diff --git a/example_logger.py b/examples/example_logger.py similarity index 100% rename from example_logger.py rename to examples/example_logger.py From e356f3d0afaf8cb99196c5826bf271a0f7f3fc39 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:42:36 -0400 Subject: [PATCH 016/220] Rename example_custom_operators.py to examples/example_custom_operators.py --- .../example_custom_operators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename example_custom_operators.py => examples/example_custom_operators.py (96%) diff --git a/example_custom_operators.py b/examples/example_custom_operators.py similarity index 96% rename from example_custom_operators.py rename to examples/example_custom_operators.py index 0acd5a5..88d3e6a 100644 --- a/example_custom_operators.py +++ b/examples/example_custom_operators.py @@ -71,4 +71,4 @@ def mutation_func(offspring, ga_instance): mutation_type=mutation_func) ga_instance.run() -ga_instance.plot_fitness() \ No newline at end of file +ga_instance.plot_fitness() From ffc3bf90fb53f5b84abc2ebb1a26ce7d0e588e5b Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:42:38 -0400 Subject: [PATCH 017/220] Rename example_clustering_3.py to examples/example_clustering_3.py --- example_clustering_3.py => examples/example_clustering_3.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename example_clustering_3.py => examples/example_clustering_3.py (100%) diff --git a/example_clustering_3.py b/examples/example_clustering_3.py similarity index 100% rename from example_clustering_3.py rename to examples/example_clustering_3.py From 28c64cd9956a4680a7ce440d491fe2404b88f39c Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:42:40 -0400 Subject: [PATCH 018/220] Rename example_clustering_2.py to examples/example_clustering_2.py --- example_clustering_2.py => examples/example_clustering_2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename example_clustering_2.py => examples/example_clustering_2.py (97%) diff --git a/example_clustering_2.py b/examples/example_clustering_2.py similarity index 97% rename from example_clustering_2.py rename to examples/example_clustering_2.py index 43c99df..fa14bb7 100644 --- a/example_clustering_2.py +++ b/examples/example_clustering_2.py @@ -119,4 +119,4 @@ def fitness_func(ga_instance, solution, solution_idx): matplotlib.pyplot.scatter(cluster_x, cluster_y) matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) matplotlib.pyplot.title("Clustering using PyGAD") -matplotlib.pyplot.show() \ No newline at end of file +matplotlib.pyplot.show() From c63706a83340c4203d3da6a45710abdec30ba555 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 00:45:21 -0400 Subject: [PATCH 019/220] Create a workflow YAML file. --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) create mode 100644 .github/workflows/main.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1 @@ + From 7d09cded61276303a03b87ca5051434e228d5d15 Mon Sep 17 00:00:00 2001 From: Joe Lewis Date: Sun, 16 Apr 2023 20:51:24 +0100 Subject: [PATCH 020/220] Add travelling salesman .ipynb example --- example_travelling_salesman.ipynb | 26535 ++++++++++++++++++++++++++++ 1 file changed, 26535 insertions(+) create mode 100644 example_travelling_salesman.ipynb diff --git a/example_travelling_salesman.ipynb b/example_travelling_salesman.ipynb new file mode 100644 index 0000000..179b94d --- /dev/null +++ b/example_travelling_salesman.ipynb @@ -0,0 +1,26535 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "95ba26f7" + }, + "source": [ + "# The Travelling Coffee Drinker - Genetic Algorithm\n", + "\n", + "Solving a travelling salesman problem for United Kingdom Starbucks Cafés" + ], + "id": "95ba26f7" + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Load and transform data\n", + "\n", + "The data comes from Kaggle, which is accessed using the API wrapper.\n", + "\n", + "The transformation needed is just to filter only GB Starbucks restaurants with a valid lon/lat pair." + ], + "metadata": { + "id": "sEtmfvLsvMC2" + }, + "id": "sEtmfvLsvMC2" + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_HBlGrIyYa7G", + "outputId": "cf89fc50-e566-4fe2-f54c-178b9d6e1acd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: pygad==2.17 in /usr/local/lib/python3.7/dist-packages (2.17.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pygad==2.17) (1.21.6)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pygad==2.17) (3.2.2)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (1.4.4)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (3.0.9)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (0.11.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->pygad==2.17) (4.1.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->pygad==2.17) (1.15.0)\n" + ] + } + ], + "source": [ + "!pip install pygad==2.17" + ], + "id": "_HBlGrIyYa7G" + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "id": "buoFAtjz2duB" + }, + "outputs": [], + "source": [ + "!pip install -q kaggle" + ], + "id": "buoFAtjz2duB" + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 375, + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": 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Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m 39\u001b[0m \"\"\"\n\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m 116\u001b[0m result = _output.eval_js(\n\u001b[1;32m 117\u001b[0m 'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m--> 118\u001b[0;31m input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m if (reply.get('type') == 'colab_reply' and\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from google.colab import files\n", + "files.upload() # upload a Kaggle JSON file to make request for data " + ], + "id": "WYcYq9YV2gi9" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TBtskqtn29H3" + }, + "outputs": [], + "source": [ + "!mkdir kaggle " + ], + "id": "TBtskqtn29H3" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oK1Z1Yr12tEv" + }, + "outputs": [], + "source": [ + "!cp kaggle.json ~/.kaggle/\n", + "!chmod 600 ~/.kaggle/kaggle.json" + ], + "id": "oK1Z1Yr12tEv" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8U2rpJ1j191n" + }, + "outputs": [], + "source": [ + "!kaggle datasets download kukuroo3/starbucks-locations-worldwide-2021-version -p /content/sample_data/ --unzip" + ], + "id": "8U2rpJ1j191n" + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "id": "sTkY5cLb3age", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a064aedc-8e4d-4712-d223-e35db3d1d7ae" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'storeNumber', 'countryCode', 'ownershipTypeCode',\n", + " 'schedule', 'slug', 'latitude', 'longitude', 'streetAddressLine1',\n", + " 'streetAddressLine2', 'streetAddressLine3', 'city',\n", + " 'countrySubdivisionCode', 'postalCode', 'currentTimeOffset',\n", + " 'windowsTimeZoneId', 'olsonTimeZoneId'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 165 + } + ], + "source": [ + "import pandas as pd \n", + "\n", + "# read in data and check column names \n", + "data = pd.read_csv('/content/sample_data/startbucks.csv')\n", + "data.columns" + ], + "id": "sTkY5cLb3age" + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "id": "SImMYnfe3n_w", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4ee033d9-9f66-489a-c470-b45a1a3b0ac0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0" + ] + }, + "metadata": {}, + "execution_count": 166 + } + ], + "source": [ + "df = data[data['countryCode']=='GB']\n", + "df.reset_index(inplace=True)\n", + "\n", + "# check for invalid lon/lat pairs\n", + "len(df.dropna(subset=['latitude', 'longitude'])) - len(df)" + ], + "id": "SImMYnfe3n_w" + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Exploratory analysis\n", + "\n", + "Find the distribution of cafés across the United Kingdom. \n", + "\n", + "How are restaurants distributed across towns?\n", + "What does a geospatial representation of the data look like?" + ], + "metadata": { + "id": "ovWqqNFIvydy" + }, + "id": "ovWqqNFIvydy" + }, + { + "cell_type": "markdown", + "source": [ + "### 2.1 Distribution of cafés by town" + ], + "metadata": { + "id": "SokLPN9j2fn1" + }, + "id": "SokLPN9j2fn1" + }, + { + "cell_type": "code", + "source": [ + "import plotly.express as px\n", + "vis = df.groupby('city').storeNumber.count().reset_index()\n", + "px.bar(vis, x='city', y='storeNumber', template='seaborn')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "HGOBgqnX2lIo", + "outputId": "0f19d0f2-0063-458a-cd02-987e30b3d9cf" + }, + "id": "HGOBgqnX2lIo", + "execution_count": 167, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "### 2.2 Map of cafés in the UK" + ], + "metadata": { + "id": "z9a3NPSM2lb4" + }, + "id": "z9a3NPSM2lb4" + }, + { + "cell_type": "code", + "source": [ + "import folium" + ], + "metadata": { + "id": "dAq1AQyfwN_l" + }, + "id": "dAq1AQyfwN_l", + "execution_count": 168, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")" + ], + "metadata": { + "id": "V9b0_8g_xM5K" + }, + "id": "V9b0_8g_xM5K", + "execution_count": 169, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for _, r in df.iterrows():\n", + " folium.Marker(\n", + " [r['latitude'], r['longitude']], popup=f'{r[\"storeNumber\"]}'\n", + " ).add_to(map)" + ], + "metadata": { + "id": "ZiprKw6ExhQR" + }, + "id": "ZiprKw6ExhQR", + "execution_count": 170, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "iVnEBooHc0ze", + "outputId": "b6ee729b-739f-4db5-975f-12a6a2c82246" + }, + "id": "iVnEBooHc0ze", + "execution_count": 171, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "metadata": {}, + "execution_count": 171 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 3. Testing the distance methodology\n", + "\n", + "To assess how good each solution is there needs to be a measure of fitness. For the purpose of this example the distance 'as the crow flies' is used without taking into account actual road distances however this could be explored in future." + ], + "metadata": { + "id": "1sg-1A4Ih1L4" + }, + "id": "1sg-1A4Ih1L4" + }, + { + "cell_type": "code", + "source": [ + "from geopy.distance import geodesic" + ], + "metadata": { + "id": "IbG9XRkLh0Aj" + }, + "id": "IbG9XRkLh0Aj", + "execution_count": 173, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The tested origin is the first Starbucks in the data and the destination is the second Starbucks in the dataset." + ], + "metadata": { + "id": "zgmh6b1l3liN" + }, + "id": "zgmh6b1l3liN" + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "id": "TyoeO2I-4bGq" + }, + "outputs": [], + "source": [ + "origin = (df['latitude'][0], df['longitude'][0])\n", + "dest = (df['latitude'][100], df['longitude'][100])" + ], + "id": "TyoeO2I-4bGq" + }, + { + "cell_type": "markdown", + "source": [ + "The distance between the two points as the crow flies in kilometres is given below." + ], + "metadata": { + "id": "VUr7ZAzw3srI" + }, + "id": "VUr7ZAzw3srI" + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "id": "zXo1I-5Q4Lwn", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4672edf2-9142-4276-ab6a-2b35b65b8b3d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "81.63683980420957" + ] + }, + "metadata": {}, + "execution_count": 175 + } + ], + "source": [ + "geodesic(origin, dest).kilometers" + ], + "id": "zXo1I-5Q4Lwn" + }, + { + "cell_type": "markdown", + "source": [ + "## 4. Preparing data structures\n", + "\n", + "The data structures needed for testing solutions are the \"genes\" or store options to select from named *genes*\n", + "\n", + "A lookup to access these genes known as *stores* \n", + "\n", + "A *check_range* which is used to check that every option is given in a solution (a key criteria in the TSP).\n" + ], + "metadata": { + "id": "GeRIsd5G378L" + }, + "id": "GeRIsd5G378L" + }, + { + "cell_type": "code", + "source": [ + "test = df.head(10)\n", + "genes = {store_num:[lat, lon] for store_num, lat, lon in zip(test['storeNumber'], test['latitude'], test['longitude'])}\n", + "stores = list(genes.keys())\n", + "check_range = [i for i in range(0, 10)]" + ], + "metadata": { + "id": "rkKStcn4iIrN" + }, + "id": "rkKStcn4iIrN", + "execution_count": 176, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 5. Defining functions \n", + "\n", + "The algorithm requires a set of functions to be pre-defined as the out of the box genetic algorithm does not support a TSP.\n", + "\n", + " 1. build_population: builds a population of chromosomes to test with proper restrictions applied\n", + " 2. fitness_func: Used to test a solution to see how well it performs, in this case the fitness_func will be assessed based on the distance as the crow flies between each successive point\n", + " 3. pmx_crossover: performs the crossover of a parent and child with proper Partially Matched Crossover (PMX) logic\n", + " 4. crossover_func: applies the crossover\n", + " 5. on_crossover: applies the mutation after crossover\n", + " 6. on_generation: used to print the progress and results at each generation" + ], + "metadata": { + "id": "5mKC2lKO4tRY" + }, + "id": "5mKC2lKO4tRY" + }, + { + "cell_type": "code", + "source": [ + "import random\n", + "import numpy as np\n", + "from geopy.distance import geodesic" + ], + "metadata": { + "id": "15-IiuRNljOC" + }, + "id": "15-IiuRNljOC", + "execution_count": 177, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Assess the quality or fitness of a solution so that only the fittest are selected for the next generation and to breed." + ], + "metadata": { + "id": "BhCEN9165in3" + }, + "id": "BhCEN9165in3" + }, + { + "cell_type": "code", + "source": [ + "def build_population(size, chromosome_size):\n", + " population = []\n", + " for i in range(size):\n", + " home_city = 0\n", + " added = {home_city:'Added'}\n", + " chromosome = [home_city]\n", + "\n", + " while len(chromosome) < chromosome_size:\n", + " proposed_gene = random.randint(0, chromosome_size-1)\n", + " if added.get(proposed_gene) is None:\n", + " chromosome.append(proposed_gene)\n", + " added.update({proposed_gene:'Added'})\n", + " else:\n", + " pass\n", + "\n", + " chromosome.append(home_city)\n", + "\n", + " population.append(chromosome)\n", + "\n", + " return np.array(population)" + ], + "metadata": { + "id": "BLz2sU2n78Ui" + }, + "id": "BLz2sU2n78Ui", + "execution_count": 178, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "population = build_population(100, 10)\n", + "population.shape" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vs8CrTNV9iqe", + "outputId": "4178c652-2593-44e9-ca0d-1829f11c6d5e" + }, + "id": "vs8CrTNV9iqe", + "execution_count": 179, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(100, 11)" + ] + }, + "metadata": {}, + "execution_count": 179 + } + ] + }, + { + "cell_type": "code", + "source": [ + "def fitness_func(solution, solution_idx):\n", + " # loop through the length of the chromosome finding the distance between each\n", + " # gene added \n", + "\n", + " # to increment\n", + " total_dist = 0\n", + "\n", + " for gene in range(0, len(solution)):\n", + "\n", + " # get the lon lat of the two points\n", + " a = genes.get(stores[solution[gene]])\n", + " \n", + " try:\n", + " b = genes.get(stores[solution[gene + 1]])\n", + "\n", + " # find the distance (crow flies)\n", + " dist = geodesic(a, b).kilometers\n", + "\n", + " except IndexError:\n", + " dist = 0\n", + "\n", + " total_dist += dist\n", + "\n", + " # to optimise this value in the positive direction the inverse of dist is used\n", + " fitness = 1 / total_dist\n", + "\n", + " return fitness " + ], + "metadata": { + "id": "5u-5msoj-84i" + }, + "id": "5u-5msoj-84i", + "execution_count": 180, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def pmx_crossover(parent1, parent2, sequence_start, sequence_end):\n", + " # initialise a child\n", + " child = np.zeros(parent1.shape[0])\n", + "\n", + " # get the genes for parent one that are passed on to child one\n", + " parent1_to_child1_genes = parent1[sequence_start:sequence_end]\n", + "\n", + " # get the position of genes for each respective combination\n", + " parent1_to_child1 = np.isin(parent1,parent1_to_child1_genes).nonzero()[0]\n", + "\n", + " for gene in parent1_to_child1:\n", + " child[gene] = parent1[gene]\n", + "\n", + " # gene of parent 2 not in the child\n", + " genes_not_in_child = parent2[np.isin(parent2, parent1_to_child1_genes, invert=True).nonzero()[0]]\n", + " \n", + " # if the gene is not already\n", + " if genes_not_in_child.shape[0] >= 1:\n", + " for gene in genes_not_in_child:\n", + " if gene >= 1:\n", + " lookup = gene\n", + " not_in_sequence = True\n", + "\n", + " while not_in_sequence:\n", + " position_in_parent2 = np.where(parent2==lookup)[0][0]\n", + "\n", + " if position_in_parent2 in range(sequence_start, sequence_end):\n", + " lookup = parent1[position_in_parent2]\n", + "\n", + " else:\n", + " child[position_in_parent2] = gene\n", + " not_in_sequence = False\n", + "\n", + " return child" + ], + "metadata": { + "id": "OpbfyShQes_q" + }, + "id": "OpbfyShQes_q", + "execution_count": 181, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def crossover_func(parents, offspring_size, ga_instance):\n", + " offspring = []\n", + " idx = 0\n", + " while len(offspring) != offspring_size[0]:\n", + "\n", + " # locate the parents\n", + " parent1 = parents[idx % parents.shape[0], :].copy()\n", + " parent2 = parents[(idx + 1) % parents.shape[0], :].copy()\n", + "\n", + " # find gene sequence in parent 1 \n", + " sequence_start = random.randint(1, parent1.shape[0]-4)\n", + " sequence_end = random.randint(sequence_start, parent1.shape[0]-1)\n", + "\n", + " # perform crossover\n", + " child1 = pmx_crossover(parent1, parent2, sequence_start, sequence_end)\n", + " child2 = pmx_crossover(parent2, parent1, sequence_start, sequence_end) \n", + "\n", + " offspring.append(child1)\n", + " offspring.append(child2)\n", + "\n", + "\n", + " idx += 1\n", + "\n", + " return np.array(offspring)" + ], + "metadata": { + "id": "shgFWqH2NinO" + }, + "id": "shgFWqH2NinO", + "execution_count": 182, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The mutation function chosen is inversion as it does not invalidate the solution." + ], + "metadata": { + "id": "bCjaaeofA7Bg" + }, + "id": "bCjaaeofA7Bg" + }, + { + "cell_type": "code", + "source": [ + "def mutation_func(offspring, ga_instance):\n", + "\n", + " for chromosome_idx in range(offspring.shape[0]):\n", + " # define a sequence of genes to reverse\n", + " sequence_start = random.randint(1, offspring[chromosome_idx].shape[0] - 2)\n", + " sequence_end = random.randint(sequence_start, offspring[chromosome_idx].shape[0] - 1)\n", + " \n", + " genes = offspring[chromosome_idx, sequence_start:sequence_end]\n", + "\n", + " # start at the start of the sequence assigning the reverse sequence back to the chromosome\n", + " index = 0\n", + " if len(genes) > 0:\n", + " for gene in range(sequence_start, sequence_end):\n", + "\n", + " offspring[chromosome_idx, gene] = genes[index]\n", + "\n", + " index += 1\n", + "\n", + " return offspring" + ], + "metadata": { + "id": "0kMm7J1WAsvH" + }, + "id": "0kMm7J1WAsvH", + "execution_count": 183, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Used in the genetic algorithm flow to apply the custom mutation after crossover" + ], + "metadata": { + "id": "HTeKwuPl5vVt" + }, + "id": "HTeKwuPl5vVt" + }, + { + "cell_type": "code", + "source": [ + "def on_crossover(ga_instance, offspring_crossover):\n", + " # apply mutation to ensure uniqueness \n", + " offspring_mutation = mutation_func(offspring_crossover, ga_instance)\n", + "\n", + " # save the new offspring set as the parents of the next generation\n", + " ga_instance.last_generation_offspring_mutation = offspring_mutation" + ], + "metadata": { + "id": "ucwYe4rgwLQC" + }, + "id": "ucwYe4rgwLQC", + "execution_count": 184, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Added for debugging and assessing progress by generation at runtime" + ], + "metadata": { + "id": "Xf7rgtuO532X" + }, + "id": "Xf7rgtuO532X" + }, + { + "cell_type": "code", + "source": [ + "def on_generation(ga):\n", + " print(\"Generation\", ga.generations_completed)\n", + " print(ga.population)" + ], + "metadata": { + "id": "eqRwVsSBMf_B" + }, + "id": "eqRwVsSBMf_B", + "execution_count": 185, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 6. Executing the algorithm\n", + "\n", + "The genetic algorithm is set up as instance and at initialisation several parameters are given. \n", + "\n", + "The algorithm then runs to find the best solution for a set number of generations." + ], + "metadata": { + "id": "C9AtXoqx58x0" + }, + "id": "C9AtXoqx58x0" + }, + { + "cell_type": "code", + "source": [ + "import pygad" + ], + "metadata": { + "id": "BrJVLZlBW07R" + }, + "id": "BrJVLZlBW07R", + "execution_count": 186, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 6.1 Example Initialising the algorithm\n", + "\n", + "The algorithm is initialised below.\n", + "\n", + "Notable parameters include:\n", + " - The use of gene space to limit the possible genes chosen to just be those in the TSP range\n", + " - Mutations being turned off temporarily\n", + " - Implementation of custom on_ functions \n", + " - Allow duplication of genes parameter set to false to ensure any newly introduced chromosomes/chromosomes created as population is initialised have no duplicate genes" + ], + "metadata": { + "id": "6ni9VkQv6TJR" + }, + "id": "6ni9VkQv6TJR" + }, + { + "cell_type": "code", + "source": [ + "ga_instance = pygad.GA(num_generations=100,\n", + " num_parents_mating=40,\n", + " fitness_func=fitness_func,\n", + " sol_per_pop=200,\n", + " initial_population=population,\n", + " gene_space=range(0, 10),\n", + " gene_type=int,\n", + " mutation_type=mutation_func,\n", + " on_generation=on_generation,\n", + " crossover_type=crossover_func, \n", + " keep_parents=6,\n", + " mutation_probability=0.4)" + ], + "metadata": { + "id": "FfFdncf-G3Mr" + }, + "id": "FfFdncf-G3Mr", + "execution_count": 187, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 6.2 Running the algorithm \n", + "\n", + "The genetic algorithm is run with a simple function call" + ], + "metadata": { + "id": "sfqme_5461A4" + }, + "id": "sfqme_5461A4" + }, + { + "cell_type": "code", + "source": [ + "ga_instance.run()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g-3CjxWYZh3H", + "outputId": "dc1fbda5-6b22-4245-9b99-e22b0e41684f" + }, + "id": "g-3CjxWYZh3H", + "execution_count": 188, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 1\n", + "[[0 3 2 ... 4 5 0]\n", + " [0 3 6 ... 1 2 0]\n", + " [0 8 3 ... 6 1 0]\n", + " ...\n", + " [0 9 5 ... 7 4 0]\n", + " [0 2 7 ... 8 6 0]\n", + " [0 3 5 ... 6 8 0]]\n", + "Generation 2\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 9 ... 8 3 0]\n", + " [0 3 2 ... 4 5 0]\n", + " ...\n", + " [0 3 6 ... 1 2 0]\n", + " [0 3 6 ... 1 2 0]\n", + " [0 3 1 ... 6 2 0]]\n", + "Generation 3\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 9 ... 8 3 0]\n", + " [0 3 2 ... 4 5 0]\n", + " [0 9 2 ... 8 3 0]]\n", + "Generation 4\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 5\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 6\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 7\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 8\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 9\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 10\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 11\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 12\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 13\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 14\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 15\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 16\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 17\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 18\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 19\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 20\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 21\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 22\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 23\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 24\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 25\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 26\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 27\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 28\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 29\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 30\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 31\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 32\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 33\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 34\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 35\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 36\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 37\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 38\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 39\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 40\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 41\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 42\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 43\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 44\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 45\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 46\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 47\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 48\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 49\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 50\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 51\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 52\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 53\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 54\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 55\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 56\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 57\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 58\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 59\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 60\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 61\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 62\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 63\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 64\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 65\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 66\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 67\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 68\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 69\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 70\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 71\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 72\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 73\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 74\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 75\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 76\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 77\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 78\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 79\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 80\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 81\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 82\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 83\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 84\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 85\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 86\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 87\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 88\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 89\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 90\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 91\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 92\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 93\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 94\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 95\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 96\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 97\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 98\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 99\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 100\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 7. Assessing results \n", + "\n", + "The result solution can be checked and analysed using the ga_instance itself" + ], + "metadata": { + "id": "woJTWlOrYLwl" + }, + "id": "woJTWlOrYLwl" + }, + { + "cell_type": "code", + "source": [ + "solution, solution_fitness, solution_idx = ga_instance.best_solution()" + ], + "metadata": { + "id": "BZxvMCZ-aFtJ" + }, + "id": "BZxvMCZ-aFtJ", + "execution_count": 189, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "solution, solution_fitness, solution_idx = ga_instance.best_solution()\n", + "print(f'Generation of best solution: {ga_instance.best_solution_generation}')\n", + "print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n", + "print(\"Index of the best solution : {solution_idx}\".format(solution_idx=solution_idx))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IOTGnna-QXsB", + "outputId": "30d494d6-e88e-4061-a453-9a44a9409176" + }, + "id": "IOTGnna-QXsB", + "execution_count": 190, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation of best solution: 1\n", + "Fitness value of the best solution = 0.010681933534441102\n", + "Index of the best solution : 0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "if ga_instance.best_solution_generation != -1:\n", + " print(\"Best fitness value reached after {best_solution_generation} generations.\".format(best_solution_generation=ga_instance.best_solution_generation))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WsjTUVloQhez", + "outputId": "fdd41ccb-9ead-49d7-d180-ccc1dd2d13f9" + }, + "id": "WsjTUVloQhez", + "execution_count": 191, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best fitness value reached after 1 generations.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 7.1 Verifying a solution\n", + "\n", + "For a solution to be valid it needs to have:\n", + " - A maximum gene value that matches the total number of stores \n", + " - A minimum gene value of 0 \n", + " - Each gene must be unique" + ], + "metadata": { + "id": "FiLlGlILYWhw" + }, + "id": "FiLlGlILYWhw" + }, + { + "cell_type": "code", + "source": [ + "def verify_solution(solution, max_gene):\n", + " if min(solution) != 0:\n", + " print('Failed values below 0')\n", + "\n", + " if max(solution) != max_gene:\n", + " print('Failed values less than or above max possible value')\n", + "\n", + " if len(set(solution)) - len(solution) != -1:\n", + " print(len(set(solution)) - len(solution))\n", + " print('Failed solution does not contain unique values')" + ], + "metadata": { + "id": "vEhveNG4VQBF" + }, + "id": "vEhveNG4VQBF", + "execution_count": 192, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "verify_solution(solution, 9)" + ], + "metadata": { + "id": "IQg1sULKYRZe" + }, + "id": "IQg1sULKYRZe", + "execution_count": 193, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "solution" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VqjdGMGnYqz-", + "outputId": "152c6b82-4787-4776-b9e9-bc3ceb1ccec1" + }, + "id": "VqjdGMGnYqz-", + "execution_count": 194, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 7, 9, 8, 6, 0])" + ] + }, + "metadata": {}, + "execution_count": 194 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 7.2 Interpreting the result \n", + "\n", + "The result sequence can be used to access latitude and longitude for each store in the solution." + ], + "metadata": { + "id": "5ixLDfvtY3cI" + }, + "id": "5ixLDfvtY3cI" + }, + { + "cell_type": "code", + "source": [ + "points = [genes.get(stores[id]) + [stores[id]] for id in solution]\n", + "points[:5]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tv-AjzcoQwGc", + "outputId": "a5187ac8-ea84-4dc9-992c-60cfa4dcab8a" + }, + "id": "tv-AjzcoQwGc", + "execution_count": 195, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[[51.483556, -1.557143, '9155-152277'],\n", + " [51.482387, -1.555109, '22194-218828'],\n", + " [51.481264, -1.556526, '18362-190424'],\n", + " [51.481177, -1.557422, '9136-152279'],\n", + " [51.562617, -1.798111, '47832-260044']]" + ] + }, + "metadata": {}, + "execution_count": 195 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import folium \n", + "\n", + "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")\n", + "\n", + "for point in range(0, len(points)):\n", + " folium.Marker(\n", + " [points[point][0], points[point][1]], popup=f'{points[point][2]}'\n", + " ).add_to(map)\n", + "\n", + " try:\n", + " folium.PolyLine([(points[point][0], points[point][1]), \n", + " (points[point+1][0], points[point+1][1])],\n", + " color='red',\n", + " weight=5,\n", + " opacity=0.8).add_to(map)\n", + "\n", + " except IndexError:\n", + " pass\n", + " " + ], + "metadata": { + "id": "Tq0hTc5cstWX" + }, + "id": "Tq0hTc5cstWX", + "execution_count": 196, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "ebkOuHleuGU1", + "outputId": "801c457d-49d9-452d-9c4b-872aef5816d4" + }, + "id": "ebkOuHleuGU1", + "execution_count": 197, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "metadata": {}, + "execution_count": 197 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The map shows the shortest path that has been found. So that the travelling coffee drinker can maximise the time on coffee and minimise the time on travelling.\n", + "\n", + "Now the algorithm can be scaled up for the whole of the UK, or tailored to just one town. An example of the solution scaled to the UK is given below." + ], + "metadata": { + "id": "5Sz8ykeEYDS3" + }, + "id": "5Sz8ykeEYDS3" + }, + { + "cell_type": "markdown", + "source": [ + "## 8. Scaling up the solution\n", + "\n", + "This is where the fun begins!" + ], + "metadata": { + "id": "gscT4SjxZ9h4" + }, + "id": "gscT4SjxZ9h4" + }, + { + "cell_type": "code", + "source": [ + "df = df[df['city'] == 'London']\n", + "genes = {store_num:[lat, lon] for store_num, lat, lon in zip(df['storeNumber'], df['latitude'], df['longitude'])}\n", + "stores = list(genes.keys())\n", + "len(stores)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yYY3-gP1aFkH", + "outputId": "907e2a7c-3292-4409-e83e-4db372f09a45" + }, + "id": "yYY3-gP1aFkH", + "execution_count": 157, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "165" + ] + }, + "metadata": {}, + "execution_count": 157 + } + ] + }, + { + "cell_type": "code", + "source": [ + "population = build_population(200, 165)\n", + "len(population[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oykOz7l4KhJE", + "outputId": "fe7fdabc-a909-40c4-e69a-daba8169b07c" + }, + "id": "oykOz7l4KhJE", + "execution_count": 155, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "166" + ] + }, + "metadata": {}, + "execution_count": 155 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 8.1 Building the final algorithm\n", + "\n", + "The code to build the algorithm has to be re-run with the above data structures altered." + ], + "metadata": { + "id": "ioy8EGjEbnBJ" + }, + "id": "ioy8EGjEbnBJ" + }, + { + "cell_type": "code", + "source": [ + "def fitness_func(solution, solution_idx):\n", + " # loop through the length of the chromosome finding the distance between each\n", + " # gene added \n", + "\n", + " # to increment\n", + " total_dist = 0\n", + "\n", + " for gene in range(0, len(solution)):\n", + "\n", + " # get the lon lat of the two points\n", + " a = genes.get(stores[solution[gene]])\n", + " \n", + " try:\n", + " b = genes.get(stores[solution[gene + 1]])\n", + "\n", + " # find the distance (crow flies)\n", + " dist = geodesic(a, b).kilometers\n", + "\n", + " except IndexError:\n", + " dist = 0\n", + "\n", + " total_dist += dist\n", + "\n", + " # to optimise this value in the positive direction the inverse of dist is used\n", + " fitness = 1 / total_dist\n", + "\n", + " return fitness " + ], + "metadata": { + "id": "uBfcikkma5hP" + }, + "id": "uBfcikkma5hP", + "execution_count": 108, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def pmx_crossover(parent1, parent2, sequence_start, sequence_end):\n", + " # initialise a child\n", + " child = np.zeros(parent1.shape[0])\n", + "\n", + " # get the genes for parent one that are passed on to child one\n", + " parent1_to_child1_genes = parent1[sequence_start:sequence_end]\n", + "\n", + " # get the position of genes for each respective combination\n", + " parent1_to_child1 = np.isin(parent1,parent1_to_child1_genes).nonzero()[0]\n", + "\n", + " for gene in parent1_to_child1:\n", + " child[gene] = parent1[gene]\n", + "\n", + " # gene of parent 2 not in the child\n", + " genes_not_in_child = parent2[np.isin(parent2, parent1_to_child1_genes, invert=True).nonzero()[0]]\n", + " \n", + " if genes_not_in_child.shape[0] >= 1:\n", + " for gene in genes_not_in_child:\n", + " if gene >= 1:\n", + " lookup = gene\n", + " not_in_sequence = True\n", + "\n", + " while not_in_sequence:\n", + " position_in_parent2 = np.where(parent2==lookup)[0][0]\n", + "\n", + " if position_in_parent2 in range(sequence_start, sequence_end):\n", + " lookup = parent1[position_in_parent2]\n", + "\n", + " else:\n", + " child[position_in_parent2] = gene\n", + " not_in_sequence = False\n", + "\n", + " return child" + ], + "metadata": { + "id": "FuOkiStta7Pz" + }, + "id": "FuOkiStta7Pz", + "execution_count": 109, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def crossover_func(parents, offspring_size, ga_instance):\n", + " offspring = []\n", + " idx = 0\n", + " while len(offspring) != offspring_size[0]:\n", + "\n", + " # locate the parents\n", + " parent1 = parents[idx % parents.shape[0], :].copy()\n", + " parent2 = parents[(idx + 1) % parents.shape[0], :].copy()\n", + "\n", + " # find gene sequence in parent 1 \n", + " sequence_start = random.randint(1, parent1.shape[0]-4)\n", + " sequence_end = random.randint(sequence_start, parent1.shape[0]-1)\n", + "\n", + " # perform crossover\n", + " child1 = pmx_crossover(parent1, parent2, sequence_start, sequence_end)\n", + " child2 = pmx_crossover(parent2, parent1, sequence_start, sequence_end)\n", + " \n", + "\n", + " offspring.append(child1)\n", + " offspring.append(child2)\n", + "\n", + " idx += 1\n", + "\n", + " return np.array(offspring)" + ], + "metadata": { + "id": "O10ZgScUa_bj" + }, + "id": "O10ZgScUa_bj", + "execution_count": 130, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def mutation_func(offspring, ga_instance):\n", + "\n", + " for chromosome_idx in range(offspring.shape[0]):\n", + " # define a sequence of genes to reverse\n", + " sequence_start = random.randint(1, offspring[chromosome_idx].shape[0] - 2)\n", + " sequence_end = random.randint(sequence_start, offspring[chromosome_idx].shape[0] - 1)\n", + " \n", + " genes = offspring[chromosome_idx, sequence_start:sequence_end]\n", + "\n", + " # start at the start of the sequence assigning the reverse sequence back to the chromosome\n", + " index = 0\n", + " if len(genes) > 0:\n", + " for gene in range(sequence_start, sequence_end):\n", + "\n", + " offspring[chromosome_idx, gene] = genes[index]\n", + "\n", + " index += 1\n", + "\n", + " return offspring" + ], + "metadata": { + "id": "mLLY7Ub4K_Y5" + }, + "id": "mLLY7Ub4K_Y5", + "execution_count": 144, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def on_crossover(ga_instance, offspring_crossover):\n", + " # apply mutation to ensure uniqueness \n", + " offspring_mutation = mutation_func(offspring_crossover, ga_instance)\n", + "\n", + " # save the new offspring set as the parents of the next generation\n", + " ga_instance.last_generation_offspring_mutation = offspring_mutation" + ], + "metadata": { + "id": "QLtP6in4LFSw" + }, + "id": "QLtP6in4LFSw", + "execution_count": 126, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def on_generation(ga):\n", + " print(\"Generation\", ga.generations_completed)\n", + " print(ga.population)" + ], + "metadata": { + "id": "SnR2LaDJLGRj" + }, + "id": "SnR2LaDJLGRj", + "execution_count": 127, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "ga_instance = pygad.GA(num_generations=100,\n", + " num_parents_mating=40,\n", + " fitness_func=fitness_func,\n", + " sol_per_pop=200,\n", + " initial_population=population,\n", + " gene_space=range(0, 165),\n", + " gene_type=int,\n", + " mutation_type=mutation_func,\n", + " on_generation=on_generation,\n", + " crossover_type=crossover_func, \n", + " keep_parents=6,\n", + " mutation_probability=0.4)" + ], + "metadata": { + "id": "j2J5jlh9bDxR" + }, + "id": "j2J5jlh9bDxR", + "execution_count": 145, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "ga_instance.run()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lhW7JkMAbS6E", + "outputId": "c6130b2a-1e6b-4b7d-c2d6-f891b178fef9" + }, + "id": "lhW7JkMAbS6E", + "execution_count": 146, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 1\n", + "[[ 0 1 111 ... 127 108 0]\n", + " [ 0 62 141 ... 26 161 0]\n", + " [ 0 137 155 ... 158 3 0]\n", + " ...\n", + " [ 0 142 162 ... 2 159 0]\n", + " [ 0 161 159 ... 112 66 0]\n", + " [ 0 152 108 ... 72 58 0]]\n", + "Generation 2\n", + "[[ 0 1 111 ... 127 108 0]\n", + " [ 0 1 111 ... 127 108 0]\n", + " [ 0 62 141 ... 26 161 0]\n", + " ...\n", + " [ 0 137 155 ... 135 76 0]\n", + " [ 0 137 155 ... 158 3 0]\n", + " [ 0 96 40 ... 135 5 0]]\n", + "Generation 3\n", + "[[ 0 1 145 ... 26 94 0]\n", + " [ 0 1 111 ... 127 108 0]\n", + " [ 0 1 111 ... 127 108 0]\n", + " ...\n", + " [ 0 89 155 ... 158 32 0]\n", + " [ 0 1 110 ... 127 94 0]\n", + " [ 0 96 40 ... 64 90 0]]\n", + "Generation 4\n", + "[[ 0 1 56 ... 26 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 110 ... 127 94 0]\n", + " ...\n", + " [ 0 1 111 ... 127 108 0]\n", + " [ 0 1 145 ... 26 22 0]\n", + " [ 0 1 77 ... 127 142 0]]\n", + "Generation 5\n", + "[[ 0 1 111 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 56 ... 127 81 0]\n", + " ...\n", + " [ 0 1 60 ... 127 118 0]\n", + " [ 0 1 154 ... 127 7 0]\n", + " [ 0 1 164 ... 127 94 0]]\n", + "Generation 6\n", + "[[ 0 1 56 ... 26 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 93 ... 127 108 0]\n", + " [ 0 1 30 ... 127 108 0]\n", + " [ 0 1 111 ... 127 108 0]]\n", + "Generation 7\n", + "[[ 0 1 56 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 56 ... 26 81 0]\n", + " [ 0 1 56 ... 127 81 0]]\n", + "Generation 8\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 26 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 56 ... 127 94 0]]\n", + "Generation 9\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 33 ... 26 94 0]\n", + " [ 0 1 110 ... 127 108 0]]\n", + "Generation 10\n", + "[[ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 26 108 0]]\n", + "Generation 11\n", + "[[ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 81 0]]\n", + "Generation 12\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 81 0]]\n", + "Generation 13\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 81 0]]\n", + "Generation 14\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 15\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 16\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]]\n", + "Generation 17\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 18\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 19\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 20\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 21\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 22\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 23\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 24\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 25\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 26\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 27\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 28\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 29\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 30\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 31\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 32\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 33\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 34\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 35\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 36\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 37\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 38\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 39\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 40\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 41\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 42\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 43\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 44\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 45\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 46\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 47\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 48\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 49\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 50\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 51\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 52\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 53\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 54\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 55\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 56\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 57\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 58\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 59\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 60\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 61\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 62\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 63\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 64\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 65\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 66\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 67\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 68\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 69\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 70\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 71\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 72\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 73\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 74\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 75\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 76\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 77\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 78\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 79\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 80\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 81\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 82\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 83\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 84\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 85\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 86\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 87\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 88\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 89\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 90\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 91\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 92\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 93\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 94\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 95\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 96\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 97\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 98\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 99\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 100\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 8.2 Evaluating the final algorithm \n", + "\n", + "The overall solution can now be assessed." + ], + "metadata": { + "id": "KJY5PcFabWdL" + }, + "id": "KJY5PcFabWdL" + }, + { + "cell_type": "code", + "source": [ + "solution, solution_fitness, solution_idx = ga_instance.best_solution()\n", + "print(f'Generation of best solution: {ga_instance.best_solution_generation}')\n", + "print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n", + "print(\"Index of the best solution : {solution_idx}\".format(solution_idx=solution_idx))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yxGGtFfE3j_1", + "outputId": "d5b68eaa-167f-47c8-aadd-10ecb3eb080c" + }, + "id": "yxGGtFfE3j_1", + "execution_count": 147, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation of best solution: 25\n", + "Fitness value of the best solution = 0.0010087414431375688\n", + "Index of the best solution : 0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "verify_solution(solution, len(stores))\n", + "solution" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NKvGE63abjmU", + "outputId": "75249196-afeb-4ee6-9b96-9674ea8493c9" + }, + "id": "NKvGE63abjmU", + "execution_count": 148, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Failed values less than or above max possible value\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 0, 1, 164, 19, 77, 23, 10, 9, 154, 158, 157, 26, 22,\n", + " 92, 42, 137, 75, 143, 149, 12, 100, 85, 86, 124, 128, 135,\n", + " 147, 54, 24, 3, 58, 123, 153, 51, 29, 69, 20, 110, 59,\n", + " 95, 113, 115, 121, 91, 36, 64, 65, 32, 53, 35, 105, 52,\n", + " 21, 34, 133, 109, 47, 71, 98, 106, 131, 89, 108, 56, 152,\n", + " 150, 7, 38, 43, 94, 8, 132, 155, 4, 16, 84, 90, 27,\n", + " 2, 144, 151, 39, 45, 159, 125, 79, 156, 40, 6, 74, 139,\n", + " 141, 145, 76, 104, 50, 37, 129, 130, 72, 142, 97, 25, 93,\n", + " 134, 126, 138, 140, 148, 120, 96, 28, 160, 116, 18, 112, 31,\n", + " 41, 55, 63, 73, 122, 162, 161, 163, 66, 107, 17, 87, 103,\n", + " 80, 81, 88, 83, 82, 14, 33, 11, 46, 61, 60, 136, 146,\n", + " 15, 70, 44, 48, 67, 78, 111, 13, 62, 30, 118, 114, 99,\n", + " 102, 5, 68, 49, 57, 101, 117, 127, 119, 0])" + ] + }, + "metadata": {}, + "execution_count": 148 + } + ] + }, + { + "cell_type": "code", + "source": [ + "points = [genes.get(stores[id]) + [stores[id]] for id in solution]\n", + "points[:5]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G8bOm7PGcPx_", + "outputId": "f78d25f6-3679-489b-c533-2971ac389941" + }, + "id": "G8bOm7PGcPx_", + "execution_count": 150, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[[51.877854, -0.376379, '12851-253386'],\n", + " [51.877854, -0.376289, '7187-253385'],\n", + " [51.655847, -0.277039, '47771-259784'],\n", + " [51.51402, -0.13925, '12021-10341'],\n", + " [51.54541, -0.16269, '12158-22023']]" + ] + }, + "metadata": {}, + "execution_count": 150 + } + ] + }, + { + "cell_type": "code", + "source": [ + "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")\n", + "\n", + "for point in range(0, len(points)):\n", + " folium.Marker(\n", + " [points[point][0], points[point][1]], popup=f'{points[point][2]}'\n", + " ).add_to(map)\n", + "\n", + " try:\n", + " folium.PolyLine([(points[point][0], points[point][1]), \n", + " (points[point+1][0], points[point+1][1])],\n", + " color='red',\n", + " weight=5,\n", + " opacity=0.8).add_to(map)\n", + "\n", + " except IndexError:\n", + " pass" + ], + "metadata": { + "id": "_KtJJkkvcY-E" + }, + "id": "_KtJJkkvcY-E", + "execution_count": 151, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 531 + }, + "id": "PMH_yECHcaH8", + "outputId": "708aca8b-c6c9-4008-cc54-e14bb37b180f" + }, + "id": "PMH_yECHcaH8", + "execution_count": 152, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "metadata": {}, + "execution_count": 152 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 10. Total result \n", + "\n", + "The total resulting distance around London after optimising the solution is:" + ], + "metadata": { + "id": "4ek_Es2DcbXU" + }, + "id": "4ek_Es2DcbXU" + }, + { + "cell_type": "code", + "source": [ + "def distance(solution):\n", + " # loop through the length of the chromosome finding the distance between each\n", + " # gene added \n", + "\n", + " # to increment\n", + " total_dist = 0\n", + "\n", + " for gene in range(0, len(solution)):\n", + "\n", + " # get the lon lat of the two points\n", + " a = genes.get(stores[solution[gene]])\n", + " \n", + " try:\n", + " b = genes.get(stores[solution[gene + 1]])\n", + "\n", + " # find the distance (crow flies)\n", + " dist = geodesic(a, b).kilometers\n", + "\n", + " except IndexError:\n", + " dist = 0\n", + "\n", + " \n", + " total_dist += dist\n", + "\n", + " # to optimise this value in the positive direction the inverse of dist is used\n", + "\n", + " return total_dist " + ], + "metadata": { + "id": "SiCFxx7WcwxV" + }, + "id": "SiCFxx7WcwxV", + "execution_count": 153, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "distance(solution)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nds6G5tmnJ8K", + "outputId": "fe4ccfc6-a5c0-4e53-c0cd-acfe62bd6052" + }, + "id": "nds6G5tmnJ8K", + "execution_count": 154, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "991.3343075204886" + ] + }, + "metadata": {}, + "execution_count": 154 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Which is not too bad for 975 cups of joe. 🥤" + ], + "metadata": { + "id": "bukLealWcxOz" + }, + "id": "bukLealWcxOz" + } + ], + "metadata": { + "colab": { + "provenance": [], + "collapsed_sections": [ + "sEtmfvLsvMC2", + "1sg-1A4Ih1L4" + ], + "toc_visible": true + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "accelerator": "GPU" + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 88013f7910420805a6ec5f6550d88c0d78265c4f Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 16:00:03 -0400 Subject: [PATCH 021/220] Rename example_travelling_salesman.ipynb to examples/example_travelling_salesman.ipynb --- .../example_travelling_salesman.ipynb | 53070 ++++++++-------- 1 file changed, 26535 insertions(+), 26535 deletions(-) rename example_travelling_salesman.ipynb => examples/example_travelling_salesman.ipynb (98%) diff --git a/example_travelling_salesman.ipynb b/examples/example_travelling_salesman.ipynb similarity index 98% rename from example_travelling_salesman.ipynb rename to examples/example_travelling_salesman.ipynb index 179b94d..f59d57e 100644 --- a/example_travelling_salesman.ipynb +++ b/examples/example_travelling_salesman.ipynb @@ -1,26535 +1,26535 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "95ba26f7" - }, - "source": [ - "# The Travelling Coffee Drinker - Genetic Algorithm\n", - "\n", - "Solving a travelling salesman problem for United Kingdom Starbucks Cafés" - ], - "id": "95ba26f7" - }, - { - "cell_type": "markdown", - "source": [ - "## 1. Load and transform data\n", - "\n", - "The data comes from Kaggle, which is accessed using the API wrapper.\n", - "\n", - "The transformation needed is just to filter only GB Starbucks restaurants with a valid lon/lat pair." - ], - "metadata": { - "id": "sEtmfvLsvMC2" - }, - "id": "sEtmfvLsvMC2" - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_HBlGrIyYa7G", - "outputId": "cf89fc50-e566-4fe2-f54c-178b9d6e1acd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Requirement already satisfied: pygad==2.17 in /usr/local/lib/python3.7/dist-packages (2.17.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pygad==2.17) (1.21.6)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pygad==2.17) (3.2.2)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (2.8.2)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (1.4.4)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (3.0.9)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (0.11.0)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->pygad==2.17) (4.1.1)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->pygad==2.17) (1.15.0)\n" - ] - } - ], - "source": [ - "!pip install pygad==2.17" - ], - "id": "_HBlGrIyYa7G" - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "id": "buoFAtjz2duB" - }, - "outputs": [], - "source": [ - "!pip install -q kaggle" - ], - "id": "buoFAtjz2duB" - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 375, - "resources": { - "http://localhost:8080/nbextensions/google.colab/files.js": { - "data": 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Please rerun this cell to enable.\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "error", - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m 39\u001b[0m \"\"\"\n\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m 116\u001b[0m result = _output.eval_js(\n\u001b[1;32m 117\u001b[0m 'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m--> 118\u001b[0;31m input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m if (reply.get('type') == 'colab_reply' and\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "from google.colab import files\n", - "files.upload() # upload a Kaggle JSON file to make request for data " - ], - "id": "WYcYq9YV2gi9" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "TBtskqtn29H3" - }, - "outputs": [], - "source": [ - "!mkdir kaggle " - ], - "id": "TBtskqtn29H3" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oK1Z1Yr12tEv" - }, - "outputs": [], - "source": [ - "!cp kaggle.json ~/.kaggle/\n", - "!chmod 600 ~/.kaggle/kaggle.json" - ], - "id": "oK1Z1Yr12tEv" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8U2rpJ1j191n" - }, - "outputs": [], - "source": [ - "!kaggle datasets download kukuroo3/starbucks-locations-worldwide-2021-version -p /content/sample_data/ --unzip" - ], - "id": "8U2rpJ1j191n" - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "id": "sTkY5cLb3age", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a064aedc-8e4d-4712-d223-e35db3d1d7ae" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Index(['Unnamed: 0', 'storeNumber', 'countryCode', 'ownershipTypeCode',\n", - " 'schedule', 'slug', 'latitude', 'longitude', 'streetAddressLine1',\n", - " 'streetAddressLine2', 'streetAddressLine3', 'city',\n", - " 'countrySubdivisionCode', 'postalCode', 'currentTimeOffset',\n", - " 'windowsTimeZoneId', 'olsonTimeZoneId'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "execution_count": 165 - } - ], - "source": [ - "import pandas as pd \n", - "\n", - "# read in data and check column names \n", - "data = pd.read_csv('/content/sample_data/startbucks.csv')\n", - "data.columns" - ], - "id": "sTkY5cLb3age" - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": { - "id": "SImMYnfe3n_w", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4ee033d9-9f66-489a-c470-b45a1a3b0ac0" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0" - ] - }, - "metadata": {}, - "execution_count": 166 - } - ], - "source": [ - "df = data[data['countryCode']=='GB']\n", - "df.reset_index(inplace=True)\n", - "\n", - "# check for invalid lon/lat pairs\n", - "len(df.dropna(subset=['latitude', 'longitude'])) - len(df)" - ], - "id": "SImMYnfe3n_w" - }, - { - "cell_type": "markdown", - "source": [ - "## 2. Exploratory analysis\n", - "\n", - "Find the distribution of cafés across the United Kingdom. \n", - "\n", - "How are restaurants distributed across towns?\n", - "What does a geospatial representation of the data look like?" - ], - "metadata": { - "id": "ovWqqNFIvydy" - }, - "id": "ovWqqNFIvydy" - }, - { - "cell_type": "markdown", - "source": [ - "### 2.1 Distribution of cafés by town" - ], - "metadata": { - "id": "SokLPN9j2fn1" - }, - "id": "SokLPN9j2fn1" - }, - { - "cell_type": "code", - "source": [ - "import plotly.express as px\n", - "vis = df.groupby('city').storeNumber.count().reset_index()\n", - "px.bar(vis, x='city', y='storeNumber', template='seaborn')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "HGOBgqnX2lIo", - "outputId": "0f19d0f2-0063-458a-cd02-987e30b3d9cf" - }, - "id": "HGOBgqnX2lIo", - "execution_count": 167, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
\n", - "
\n", - "\n", - "" - ] - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "### 2.2 Map of cafés in the UK" - ], - "metadata": { - "id": "z9a3NPSM2lb4" - }, - "id": "z9a3NPSM2lb4" - }, - { - "cell_type": "code", - "source": [ - "import folium" - ], - "metadata": { - "id": "dAq1AQyfwN_l" - }, - "id": "dAq1AQyfwN_l", - "execution_count": 168, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")" - ], - "metadata": { - "id": "V9b0_8g_xM5K" - }, - "id": "V9b0_8g_xM5K", - "execution_count": 169, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "for _, r in df.iterrows():\n", - " folium.Marker(\n", - " [r['latitude'], r['longitude']], popup=f'{r[\"storeNumber\"]}'\n", - " ).add_to(map)" - ], - "metadata": { - "id": "ZiprKw6ExhQR" - }, - "id": "ZiprKw6ExhQR", - "execution_count": 170, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "iVnEBooHc0ze", - "outputId": "b6ee729b-739f-4db5-975f-12a6a2c82246" - }, - "id": "iVnEBooHc0ze", - "execution_count": 171, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ] - }, - "metadata": {}, - "execution_count": 171 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 3. Testing the distance methodology\n", - "\n", - "To assess how good each solution is there needs to be a measure of fitness. For the purpose of this example the distance 'as the crow flies' is used without taking into account actual road distances however this could be explored in future." - ], - "metadata": { - "id": "1sg-1A4Ih1L4" - }, - "id": "1sg-1A4Ih1L4" - }, - { - "cell_type": "code", - "source": [ - "from geopy.distance import geodesic" - ], - "metadata": { - "id": "IbG9XRkLh0Aj" - }, - "id": "IbG9XRkLh0Aj", - "execution_count": 173, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "The tested origin is the first Starbucks in the data and the destination is the second Starbucks in the dataset." - ], - "metadata": { - "id": "zgmh6b1l3liN" - }, - "id": "zgmh6b1l3liN" - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "id": "TyoeO2I-4bGq" - }, - "outputs": [], - "source": [ - "origin = (df['latitude'][0], df['longitude'][0])\n", - "dest = (df['latitude'][100], df['longitude'][100])" - ], - "id": "TyoeO2I-4bGq" - }, - { - "cell_type": "markdown", - "source": [ - "The distance between the two points as the crow flies in kilometres is given below." - ], - "metadata": { - "id": "VUr7ZAzw3srI" - }, - "id": "VUr7ZAzw3srI" - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": { - "id": "zXo1I-5Q4Lwn", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4672edf2-9142-4276-ab6a-2b35b65b8b3d" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "81.63683980420957" - ] - }, - "metadata": {}, - "execution_count": 175 - } - ], - "source": [ - "geodesic(origin, dest).kilometers" - ], - "id": "zXo1I-5Q4Lwn" - }, - { - "cell_type": "markdown", - "source": [ - "## 4. Preparing data structures\n", - "\n", - "The data structures needed for testing solutions are the \"genes\" or store options to select from named *genes*\n", - "\n", - "A lookup to access these genes known as *stores* \n", - "\n", - "A *check_range* which is used to check that every option is given in a solution (a key criteria in the TSP).\n" - ], - "metadata": { - "id": "GeRIsd5G378L" - }, - "id": "GeRIsd5G378L" - }, - { - "cell_type": "code", - "source": [ - "test = df.head(10)\n", - "genes = {store_num:[lat, lon] for store_num, lat, lon in zip(test['storeNumber'], test['latitude'], test['longitude'])}\n", - "stores = list(genes.keys())\n", - "check_range = [i for i in range(0, 10)]" - ], - "metadata": { - "id": "rkKStcn4iIrN" - }, - "id": "rkKStcn4iIrN", - "execution_count": 176, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## 5. Defining functions \n", - "\n", - "The algorithm requires a set of functions to be pre-defined as the out of the box genetic algorithm does not support a TSP.\n", - "\n", - " 1. build_population: builds a population of chromosomes to test with proper restrictions applied\n", - " 2. fitness_func: Used to test a solution to see how well it performs, in this case the fitness_func will be assessed based on the distance as the crow flies between each successive point\n", - " 3. pmx_crossover: performs the crossover of a parent and child with proper Partially Matched Crossover (PMX) logic\n", - " 4. crossover_func: applies the crossover\n", - " 5. on_crossover: applies the mutation after crossover\n", - " 6. on_generation: used to print the progress and results at each generation" - ], - "metadata": { - "id": "5mKC2lKO4tRY" - }, - "id": "5mKC2lKO4tRY" - }, - { - "cell_type": "code", - "source": [ - "import random\n", - "import numpy as np\n", - "from geopy.distance import geodesic" - ], - "metadata": { - "id": "15-IiuRNljOC" - }, - "id": "15-IiuRNljOC", - "execution_count": 177, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Assess the quality or fitness of a solution so that only the fittest are selected for the next generation and to breed." - ], - "metadata": { - "id": "BhCEN9165in3" - }, - "id": "BhCEN9165in3" - }, - { - "cell_type": "code", - "source": [ - "def build_population(size, chromosome_size):\n", - " population = []\n", - " for i in range(size):\n", - " home_city = 0\n", - " added = {home_city:'Added'}\n", - " chromosome = [home_city]\n", - "\n", - " while len(chromosome) < chromosome_size:\n", - " proposed_gene = random.randint(0, chromosome_size-1)\n", - " if added.get(proposed_gene) is None:\n", - " chromosome.append(proposed_gene)\n", - " added.update({proposed_gene:'Added'})\n", - " else:\n", - " pass\n", - "\n", - " chromosome.append(home_city)\n", - "\n", - " population.append(chromosome)\n", - "\n", - " return np.array(population)" - ], - "metadata": { - "id": "BLz2sU2n78Ui" - }, - "id": "BLz2sU2n78Ui", - "execution_count": 178, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "population = build_population(100, 10)\n", - "population.shape" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vs8CrTNV9iqe", - "outputId": "4178c652-2593-44e9-ca0d-1829f11c6d5e" - }, - "id": "vs8CrTNV9iqe", - "execution_count": 179, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(100, 11)" - ] - }, - "metadata": {}, - "execution_count": 179 - } - ] - }, - { - "cell_type": "code", - "source": [ - "def fitness_func(solution, solution_idx):\n", - " # loop through the length of the chromosome finding the distance between each\n", - " # gene added \n", - "\n", - " # to increment\n", - " total_dist = 0\n", - "\n", - " for gene in range(0, len(solution)):\n", - "\n", - " # get the lon lat of the two points\n", - " a = genes.get(stores[solution[gene]])\n", - " \n", - " try:\n", - " b = genes.get(stores[solution[gene + 1]])\n", - "\n", - " # find the distance (crow flies)\n", - " dist = geodesic(a, b).kilometers\n", - "\n", - " except IndexError:\n", - " dist = 0\n", - "\n", - " total_dist += dist\n", - "\n", - " # to optimise this value in the positive direction the inverse of dist is used\n", - " fitness = 1 / total_dist\n", - "\n", - " return fitness " - ], - "metadata": { - "id": "5u-5msoj-84i" - }, - "id": "5u-5msoj-84i", - "execution_count": 180, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def pmx_crossover(parent1, parent2, sequence_start, sequence_end):\n", - " # initialise a child\n", - " child = np.zeros(parent1.shape[0])\n", - "\n", - " # get the genes for parent one that are passed on to child one\n", - " parent1_to_child1_genes = parent1[sequence_start:sequence_end]\n", - "\n", - " # get the position of genes for each respective combination\n", - " parent1_to_child1 = np.isin(parent1,parent1_to_child1_genes).nonzero()[0]\n", - "\n", - " for gene in parent1_to_child1:\n", - " child[gene] = parent1[gene]\n", - "\n", - " # gene of parent 2 not in the child\n", - " genes_not_in_child = parent2[np.isin(parent2, parent1_to_child1_genes, invert=True).nonzero()[0]]\n", - " \n", - " # if the gene is not already\n", - " if genes_not_in_child.shape[0] >= 1:\n", - " for gene in genes_not_in_child:\n", - " if gene >= 1:\n", - " lookup = gene\n", - " not_in_sequence = True\n", - "\n", - " while not_in_sequence:\n", - " position_in_parent2 = np.where(parent2==lookup)[0][0]\n", - "\n", - " if position_in_parent2 in range(sequence_start, sequence_end):\n", - " lookup = parent1[position_in_parent2]\n", - "\n", - " else:\n", - " child[position_in_parent2] = gene\n", - " not_in_sequence = False\n", - "\n", - " return child" - ], - "metadata": { - "id": "OpbfyShQes_q" - }, - "id": "OpbfyShQes_q", - "execution_count": 181, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def crossover_func(parents, offspring_size, ga_instance):\n", - " offspring = []\n", - " idx = 0\n", - " while len(offspring) != offspring_size[0]:\n", - "\n", - " # locate the parents\n", - " parent1 = parents[idx % parents.shape[0], :].copy()\n", - " parent2 = parents[(idx + 1) % parents.shape[0], :].copy()\n", - "\n", - " # find gene sequence in parent 1 \n", - " sequence_start = random.randint(1, parent1.shape[0]-4)\n", - " sequence_end = random.randint(sequence_start, parent1.shape[0]-1)\n", - "\n", - " # perform crossover\n", - " child1 = pmx_crossover(parent1, parent2, sequence_start, sequence_end)\n", - " child2 = pmx_crossover(parent2, parent1, sequence_start, sequence_end) \n", - "\n", - " offspring.append(child1)\n", - " offspring.append(child2)\n", - "\n", - "\n", - " idx += 1\n", - "\n", - " return np.array(offspring)" - ], - "metadata": { - "id": "shgFWqH2NinO" - }, - "id": "shgFWqH2NinO", - "execution_count": 182, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "The mutation function chosen is inversion as it does not invalidate the solution." - ], - "metadata": { - "id": "bCjaaeofA7Bg" - }, - "id": "bCjaaeofA7Bg" - }, - { - "cell_type": "code", - "source": [ - "def mutation_func(offspring, ga_instance):\n", - "\n", - " for chromosome_idx in range(offspring.shape[0]):\n", - " # define a sequence of genes to reverse\n", - " sequence_start = random.randint(1, offspring[chromosome_idx].shape[0] - 2)\n", - " sequence_end = random.randint(sequence_start, offspring[chromosome_idx].shape[0] - 1)\n", - " \n", - " genes = offspring[chromosome_idx, sequence_start:sequence_end]\n", - "\n", - " # start at the start of the sequence assigning the reverse sequence back to the chromosome\n", - " index = 0\n", - " if len(genes) > 0:\n", - " for gene in range(sequence_start, sequence_end):\n", - "\n", - " offspring[chromosome_idx, gene] = genes[index]\n", - "\n", - " index += 1\n", - "\n", - " return offspring" - ], - "metadata": { - "id": "0kMm7J1WAsvH" - }, - "id": "0kMm7J1WAsvH", - "execution_count": 183, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Used in the genetic algorithm flow to apply the custom mutation after crossover" - ], - "metadata": { - "id": "HTeKwuPl5vVt" - }, - "id": "HTeKwuPl5vVt" - }, - { - "cell_type": "code", - "source": [ - "def on_crossover(ga_instance, offspring_crossover):\n", - " # apply mutation to ensure uniqueness \n", - " offspring_mutation = mutation_func(offspring_crossover, ga_instance)\n", - "\n", - " # save the new offspring set as the parents of the next generation\n", - " ga_instance.last_generation_offspring_mutation = offspring_mutation" - ], - "metadata": { - "id": "ucwYe4rgwLQC" - }, - "id": "ucwYe4rgwLQC", - "execution_count": 184, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Added for debugging and assessing progress by generation at runtime" - ], - "metadata": { - "id": "Xf7rgtuO532X" - }, - "id": "Xf7rgtuO532X" - }, - { - "cell_type": "code", - "source": [ - "def on_generation(ga):\n", - " print(\"Generation\", ga.generations_completed)\n", - " print(ga.population)" - ], - "metadata": { - "id": "eqRwVsSBMf_B" - }, - "id": "eqRwVsSBMf_B", - "execution_count": 185, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## 6. Executing the algorithm\n", - "\n", - "The genetic algorithm is set up as instance and at initialisation several parameters are given. \n", - "\n", - "The algorithm then runs to find the best solution for a set number of generations." - ], - "metadata": { - "id": "C9AtXoqx58x0" - }, - "id": "C9AtXoqx58x0" - }, - { - "cell_type": "code", - "source": [ - "import pygad" - ], - "metadata": { - "id": "BrJVLZlBW07R" - }, - "id": "BrJVLZlBW07R", - "execution_count": 186, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### 6.1 Example Initialising the algorithm\n", - "\n", - "The algorithm is initialised below.\n", - "\n", - "Notable parameters include:\n", - " - The use of gene space to limit the possible genes chosen to just be those in the TSP range\n", - " - Mutations being turned off temporarily\n", - " - Implementation of custom on_ functions \n", - " - Allow duplication of genes parameter set to false to ensure any newly introduced chromosomes/chromosomes created as population is initialised have no duplicate genes" - ], - "metadata": { - "id": "6ni9VkQv6TJR" - }, - "id": "6ni9VkQv6TJR" - }, - { - "cell_type": "code", - "source": [ - "ga_instance = pygad.GA(num_generations=100,\n", - " num_parents_mating=40,\n", - " fitness_func=fitness_func,\n", - " sol_per_pop=200,\n", - " initial_population=population,\n", - " gene_space=range(0, 10),\n", - " gene_type=int,\n", - " mutation_type=mutation_func,\n", - " on_generation=on_generation,\n", - " crossover_type=crossover_func, \n", - " keep_parents=6,\n", - " mutation_probability=0.4)" - ], - "metadata": { - "id": "FfFdncf-G3Mr" - }, - "id": "FfFdncf-G3Mr", - "execution_count": 187, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### 6.2 Running the algorithm \n", - "\n", - "The genetic algorithm is run with a simple function call" - ], - "metadata": { - "id": "sfqme_5461A4" - }, - "id": "sfqme_5461A4" - }, - { - "cell_type": "code", - "source": [ - "ga_instance.run()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "g-3CjxWYZh3H", - "outputId": "dc1fbda5-6b22-4245-9b99-e22b0e41684f" - }, - "id": "g-3CjxWYZh3H", - "execution_count": 188, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generation 1\n", - "[[0 3 2 ... 4 5 0]\n", - " [0 3 6 ... 1 2 0]\n", - " [0 8 3 ... 6 1 0]\n", - " ...\n", - " [0 9 5 ... 7 4 0]\n", - " [0 2 7 ... 8 6 0]\n", - " [0 3 5 ... 6 8 0]]\n", - "Generation 2\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 9 ... 8 3 0]\n", - " [0 3 2 ... 4 5 0]\n", - " ...\n", - " [0 3 6 ... 1 2 0]\n", - " [0 3 6 ... 1 2 0]\n", - " [0 3 1 ... 6 2 0]]\n", - "Generation 3\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 9 ... 8 3 0]\n", - " [0 3 2 ... 4 5 0]\n", - " [0 9 2 ... 8 3 0]]\n", - "Generation 4\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 5\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 6\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 7\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 8\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 9\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 10\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 11\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 12\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 13\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 14\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 15\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 16\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 17\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 18\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 19\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 20\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 21\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 22\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 23\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 24\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 25\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 26\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 27\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 28\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 29\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 30\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 31\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 32\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 33\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 34\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 35\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 36\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 37\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 38\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 39\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 40\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 41\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 42\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 43\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 44\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 45\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 46\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 47\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 48\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 49\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 50\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 51\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 52\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 53\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 54\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 55\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 56\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 57\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 58\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 59\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 60\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 61\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 62\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 63\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 64\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 65\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 66\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 67\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 68\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 69\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 70\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 71\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 72\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 73\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 74\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 75\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 76\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 77\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 78\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 79\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 80\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 81\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 82\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 83\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 84\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 85\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 86\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 87\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 88\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 89\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 90\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 91\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 92\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 93\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 94\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 95\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 96\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 97\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 98\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 99\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n", - "Generation 100\n", - "[[0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " ...\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]\n", - " [0 1 2 ... 8 6 0]]\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 7. Assessing results \n", - "\n", - "The result solution can be checked and analysed using the ga_instance itself" - ], - "metadata": { - "id": "woJTWlOrYLwl" - }, - "id": "woJTWlOrYLwl" - }, - { - "cell_type": "code", - "source": [ - "solution, solution_fitness, solution_idx = ga_instance.best_solution()" - ], - "metadata": { - "id": "BZxvMCZ-aFtJ" - }, - "id": "BZxvMCZ-aFtJ", - "execution_count": 189, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "solution, solution_fitness, solution_idx = ga_instance.best_solution()\n", - "print(f'Generation of best solution: {ga_instance.best_solution_generation}')\n", - "print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n", - "print(\"Index of the best solution : {solution_idx}\".format(solution_idx=solution_idx))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IOTGnna-QXsB", - "outputId": "30d494d6-e88e-4061-a453-9a44a9409176" - }, - "id": "IOTGnna-QXsB", - "execution_count": 190, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generation of best solution: 1\n", - "Fitness value of the best solution = 0.010681933534441102\n", - "Index of the best solution : 0\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "if ga_instance.best_solution_generation != -1:\n", - " print(\"Best fitness value reached after {best_solution_generation} generations.\".format(best_solution_generation=ga_instance.best_solution_generation))\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WsjTUVloQhez", - "outputId": "fdd41ccb-9ead-49d7-d180-ccc1dd2d13f9" - }, - "id": "WsjTUVloQhez", - "execution_count": 191, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Best fitness value reached after 1 generations.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### 7.1 Verifying a solution\n", - "\n", - "For a solution to be valid it needs to have:\n", - " - A maximum gene value that matches the total number of stores \n", - " - A minimum gene value of 0 \n", - " - Each gene must be unique" - ], - "metadata": { - "id": "FiLlGlILYWhw" - }, - "id": "FiLlGlILYWhw" - }, - { - "cell_type": "code", - "source": [ - "def verify_solution(solution, max_gene):\n", - " if min(solution) != 0:\n", - " print('Failed values below 0')\n", - "\n", - " if max(solution) != max_gene:\n", - " print('Failed values less than or above max possible value')\n", - "\n", - " if len(set(solution)) - len(solution) != -1:\n", - " print(len(set(solution)) - len(solution))\n", - " print('Failed solution does not contain unique values')" - ], - "metadata": { - "id": "vEhveNG4VQBF" - }, - "id": "vEhveNG4VQBF", - "execution_count": 192, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "verify_solution(solution, 9)" - ], - "metadata": { - "id": "IQg1sULKYRZe" - }, - "id": "IQg1sULKYRZe", - "execution_count": 193, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "solution" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VqjdGMGnYqz-", - "outputId": "152c6b82-4787-4776-b9e9-bc3ceb1ccec1" - }, - "id": "VqjdGMGnYqz-", - "execution_count": 194, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 7, 9, 8, 6, 0])" - ] - }, - "metadata": {}, - "execution_count": 194 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### 7.2 Interpreting the result \n", - "\n", - "The result sequence can be used to access latitude and longitude for each store in the solution." - ], - "metadata": { - "id": "5ixLDfvtY3cI" - }, - "id": "5ixLDfvtY3cI" - }, - { - "cell_type": "code", - "source": [ - "points = [genes.get(stores[id]) + [stores[id]] for id in solution]\n", - "points[:5]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tv-AjzcoQwGc", - "outputId": "a5187ac8-ea84-4dc9-992c-60cfa4dcab8a" - }, - "id": "tv-AjzcoQwGc", - "execution_count": 195, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[[51.483556, -1.557143, '9155-152277'],\n", - " [51.482387, -1.555109, '22194-218828'],\n", - " [51.481264, -1.556526, '18362-190424'],\n", - " [51.481177, -1.557422, '9136-152279'],\n", - " [51.562617, -1.798111, '47832-260044']]" - ] - }, - "metadata": {}, - "execution_count": 195 - } - ] - }, - { - "cell_type": "code", - "source": [ - "import folium \n", - "\n", - "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")\n", - "\n", - "for point in range(0, len(points)):\n", - " folium.Marker(\n", - " [points[point][0], points[point][1]], popup=f'{points[point][2]}'\n", - " ).add_to(map)\n", - "\n", - " try:\n", - " folium.PolyLine([(points[point][0], points[point][1]), \n", - " (points[point+1][0], points[point+1][1])],\n", - " color='red',\n", - " weight=5,\n", - " opacity=0.8).add_to(map)\n", - "\n", - " except IndexError:\n", - " pass\n", - " " - ], - "metadata": { - "id": "Tq0hTc5cstWX" - }, - "id": "Tq0hTc5cstWX", - "execution_count": 196, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "ebkOuHleuGU1", - "outputId": "801c457d-49d9-452d-9c4b-872aef5816d4" - }, - "id": "ebkOuHleuGU1", - "execution_count": 197, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ] - }, - "metadata": {}, - "execution_count": 197 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "The map shows the shortest path that has been found. So that the travelling coffee drinker can maximise the time on coffee and minimise the time on travelling.\n", - "\n", - "Now the algorithm can be scaled up for the whole of the UK, or tailored to just one town. An example of the solution scaled to the UK is given below." - ], - "metadata": { - "id": "5Sz8ykeEYDS3" - }, - "id": "5Sz8ykeEYDS3" - }, - { - "cell_type": "markdown", - "source": [ - "## 8. Scaling up the solution\n", - "\n", - "This is where the fun begins!" - ], - "metadata": { - "id": "gscT4SjxZ9h4" - }, - "id": "gscT4SjxZ9h4" - }, - { - "cell_type": "code", - "source": [ - "df = df[df['city'] == 'London']\n", - "genes = {store_num:[lat, lon] for store_num, lat, lon in zip(df['storeNumber'], df['latitude'], df['longitude'])}\n", - "stores = list(genes.keys())\n", - "len(stores)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yYY3-gP1aFkH", - "outputId": "907e2a7c-3292-4409-e83e-4db372f09a45" - }, - "id": "yYY3-gP1aFkH", - "execution_count": 157, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "165" - ] - }, - "metadata": {}, - "execution_count": 157 - } - ] - }, - { - "cell_type": "code", - "source": [ - "population = build_population(200, 165)\n", - "len(population[0])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oykOz7l4KhJE", - "outputId": "fe7fdabc-a909-40c4-e69a-daba8169b07c" - }, - "id": "oykOz7l4KhJE", - "execution_count": 155, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "166" - ] - }, - "metadata": {}, - "execution_count": 155 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### 8.1 Building the final algorithm\n", - "\n", - "The code to build the algorithm has to be re-run with the above data structures altered." - ], - "metadata": { - "id": "ioy8EGjEbnBJ" - }, - "id": "ioy8EGjEbnBJ" - }, - { - "cell_type": "code", - "source": [ - "def fitness_func(solution, solution_idx):\n", - " # loop through the length of the chromosome finding the distance between each\n", - " # gene added \n", - "\n", - " # to increment\n", - " total_dist = 0\n", - "\n", - " for gene in range(0, len(solution)):\n", - "\n", - " # get the lon lat of the two points\n", - " a = genes.get(stores[solution[gene]])\n", - " \n", - " try:\n", - " b = genes.get(stores[solution[gene + 1]])\n", - "\n", - " # find the distance (crow flies)\n", - " dist = geodesic(a, b).kilometers\n", - "\n", - " except IndexError:\n", - " dist = 0\n", - "\n", - " total_dist += dist\n", - "\n", - " # to optimise this value in the positive direction the inverse of dist is used\n", - " fitness = 1 / total_dist\n", - "\n", - " return fitness " - ], - "metadata": { - "id": "uBfcikkma5hP" - }, - "id": "uBfcikkma5hP", - "execution_count": 108, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def pmx_crossover(parent1, parent2, sequence_start, sequence_end):\n", - " # initialise a child\n", - " child = np.zeros(parent1.shape[0])\n", - "\n", - " # get the genes for parent one that are passed on to child one\n", - " parent1_to_child1_genes = parent1[sequence_start:sequence_end]\n", - "\n", - " # get the position of genes for each respective combination\n", - " parent1_to_child1 = np.isin(parent1,parent1_to_child1_genes).nonzero()[0]\n", - "\n", - " for gene in parent1_to_child1:\n", - " child[gene] = parent1[gene]\n", - "\n", - " # gene of parent 2 not in the child\n", - " genes_not_in_child = parent2[np.isin(parent2, parent1_to_child1_genes, invert=True).nonzero()[0]]\n", - " \n", - " if genes_not_in_child.shape[0] >= 1:\n", - " for gene in genes_not_in_child:\n", - " if gene >= 1:\n", - " lookup = gene\n", - " not_in_sequence = True\n", - "\n", - " while not_in_sequence:\n", - " position_in_parent2 = np.where(parent2==lookup)[0][0]\n", - "\n", - " if position_in_parent2 in range(sequence_start, sequence_end):\n", - " lookup = parent1[position_in_parent2]\n", - "\n", - " else:\n", - " child[position_in_parent2] = gene\n", - " not_in_sequence = False\n", - "\n", - " return child" - ], - "metadata": { - "id": "FuOkiStta7Pz" - }, - "id": "FuOkiStta7Pz", - "execution_count": 109, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def crossover_func(parents, offspring_size, ga_instance):\n", - " offspring = []\n", - " idx = 0\n", - " while len(offspring) != offspring_size[0]:\n", - "\n", - " # locate the parents\n", - " parent1 = parents[idx % parents.shape[0], :].copy()\n", - " parent2 = parents[(idx + 1) % parents.shape[0], :].copy()\n", - "\n", - " # find gene sequence in parent 1 \n", - " sequence_start = random.randint(1, parent1.shape[0]-4)\n", - " sequence_end = random.randint(sequence_start, parent1.shape[0]-1)\n", - "\n", - " # perform crossover\n", - " child1 = pmx_crossover(parent1, parent2, sequence_start, sequence_end)\n", - " child2 = pmx_crossover(parent2, parent1, sequence_start, sequence_end)\n", - " \n", - "\n", - " offspring.append(child1)\n", - " offspring.append(child2)\n", - "\n", - " idx += 1\n", - "\n", - " return np.array(offspring)" - ], - "metadata": { - "id": "O10ZgScUa_bj" - }, - "id": "O10ZgScUa_bj", - "execution_count": 130, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def mutation_func(offspring, ga_instance):\n", - "\n", - " for chromosome_idx in range(offspring.shape[0]):\n", - " # define a sequence of genes to reverse\n", - " sequence_start = random.randint(1, offspring[chromosome_idx].shape[0] - 2)\n", - " sequence_end = random.randint(sequence_start, offspring[chromosome_idx].shape[0] - 1)\n", - " \n", - " genes = offspring[chromosome_idx, sequence_start:sequence_end]\n", - "\n", - " # start at the start of the sequence assigning the reverse sequence back to the chromosome\n", - " index = 0\n", - " if len(genes) > 0:\n", - " for gene in range(sequence_start, sequence_end):\n", - "\n", - " offspring[chromosome_idx, gene] = genes[index]\n", - "\n", - " index += 1\n", - "\n", - " return offspring" - ], - "metadata": { - "id": "mLLY7Ub4K_Y5" - }, - "id": "mLLY7Ub4K_Y5", - "execution_count": 144, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def on_crossover(ga_instance, offspring_crossover):\n", - " # apply mutation to ensure uniqueness \n", - " offspring_mutation = mutation_func(offspring_crossover, ga_instance)\n", - "\n", - " # save the new offspring set as the parents of the next generation\n", - " ga_instance.last_generation_offspring_mutation = offspring_mutation" - ], - "metadata": { - "id": "QLtP6in4LFSw" - }, - "id": "QLtP6in4LFSw", - "execution_count": 126, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def on_generation(ga):\n", - " print(\"Generation\", ga.generations_completed)\n", - " print(ga.population)" - ], - "metadata": { - "id": "SnR2LaDJLGRj" - }, - "id": "SnR2LaDJLGRj", - "execution_count": 127, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "ga_instance = pygad.GA(num_generations=100,\n", - " num_parents_mating=40,\n", - " fitness_func=fitness_func,\n", - " sol_per_pop=200,\n", - " initial_population=population,\n", - " gene_space=range(0, 165),\n", - " gene_type=int,\n", - " mutation_type=mutation_func,\n", - " on_generation=on_generation,\n", - " crossover_type=crossover_func, \n", - " keep_parents=6,\n", - " mutation_probability=0.4)" - ], - "metadata": { - "id": "j2J5jlh9bDxR" - }, - "id": "j2J5jlh9bDxR", - "execution_count": 145, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "ga_instance.run()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lhW7JkMAbS6E", - "outputId": "c6130b2a-1e6b-4b7d-c2d6-f891b178fef9" - }, - "id": "lhW7JkMAbS6E", - "execution_count": 146, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generation 1\n", - "[[ 0 1 111 ... 127 108 0]\n", - " [ 0 62 141 ... 26 161 0]\n", - " [ 0 137 155 ... 158 3 0]\n", - " ...\n", - " [ 0 142 162 ... 2 159 0]\n", - " [ 0 161 159 ... 112 66 0]\n", - " [ 0 152 108 ... 72 58 0]]\n", - "Generation 2\n", - "[[ 0 1 111 ... 127 108 0]\n", - " [ 0 1 111 ... 127 108 0]\n", - " [ 0 62 141 ... 26 161 0]\n", - " ...\n", - " [ 0 137 155 ... 135 76 0]\n", - " [ 0 137 155 ... 158 3 0]\n", - " [ 0 96 40 ... 135 5 0]]\n", - "Generation 3\n", - "[[ 0 1 145 ... 26 94 0]\n", - " [ 0 1 111 ... 127 108 0]\n", - " [ 0 1 111 ... 127 108 0]\n", - " ...\n", - " [ 0 89 155 ... 158 32 0]\n", - " [ 0 1 110 ... 127 94 0]\n", - " [ 0 96 40 ... 64 90 0]]\n", - "Generation 4\n", - "[[ 0 1 56 ... 26 81 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 110 ... 127 94 0]\n", - " ...\n", - " [ 0 1 111 ... 127 108 0]\n", - " [ 0 1 145 ... 26 22 0]\n", - " [ 0 1 77 ... 127 142 0]]\n", - "Generation 5\n", - "[[ 0 1 111 ... 127 108 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 56 ... 127 81 0]\n", - " ...\n", - " [ 0 1 60 ... 127 118 0]\n", - " [ 0 1 154 ... 127 7 0]\n", - " [ 0 1 164 ... 127 94 0]]\n", - "Generation 6\n", - "[[ 0 1 56 ... 26 81 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " ...\n", - " [ 0 1 93 ... 127 108 0]\n", - " [ 0 1 30 ... 127 108 0]\n", - " [ 0 1 111 ... 127 108 0]]\n", - "Generation 7\n", - "[[ 0 1 56 ... 127 81 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 94 0]\n", - " ...\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 56 ... 26 81 0]\n", - " [ 0 1 56 ... 127 81 0]]\n", - "Generation 8\n", - "[[ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 26 108 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " ...\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 56 ... 127 94 0]]\n", - "Generation 9\n", - "[[ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " ...\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 33 ... 26 94 0]\n", - " [ 0 1 110 ... 127 108 0]]\n", - "Generation 10\n", - "[[ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 26 108 0]]\n", - "Generation 11\n", - "[[ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 81 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " ...\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 81 0]]\n", - "Generation 12\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " ...\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 81 0]\n", - " [ 0 1 164 ... 127 81 0]]\n", - "Generation 13\n", - "[[ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 81 0]\n", - " ...\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 81 0]]\n", - "Generation 14\n", - "[[ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 81 0]\n", - " ...\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 108 0]]\n", - "Generation 15\n", - "[[ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 81 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " ...\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 94 0]\n", - " [ 0 1 164 ... 127 108 0]]\n", - "Generation 16\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 81 0]\n", - " ...\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 94 0]]\n", - "Generation 17\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 81 0]\n", - " [ 0 1 164 ... 127 108 0]\n", - " [ 0 1 164 ... 127 108 0]]\n", - "Generation 18\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 108 0]]\n", - "Generation 19\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 20\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 21\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 22\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 23\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 24\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 25\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 26\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 27\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 28\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 29\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 30\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 31\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 32\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 33\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 34\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 35\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 36\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 37\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 38\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 39\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 40\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 41\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 42\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 43\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 44\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 45\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 46\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 47\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 48\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 49\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 50\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 51\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 52\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 53\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 54\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 55\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 56\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 57\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 58\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 59\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 60\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 61\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 62\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 63\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 64\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 65\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 66\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 67\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 68\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 69\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 70\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 71\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 72\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 73\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 74\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 75\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 76\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 77\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 78\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 79\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 80\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 81\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 82\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 83\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 84\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 85\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 86\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 87\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 88\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 89\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 90\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 91\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 92\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 93\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 94\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 95\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 96\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 97\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 98\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 99\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n", - "Generation 100\n", - "[[ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " ...\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]\n", - " [ 0 1 164 ... 127 119 0]]\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 8.2 Evaluating the final algorithm \n", - "\n", - "The overall solution can now be assessed." - ], - "metadata": { - "id": "KJY5PcFabWdL" - }, - "id": "KJY5PcFabWdL" - }, - { - "cell_type": "code", - "source": [ - "solution, solution_fitness, solution_idx = ga_instance.best_solution()\n", - "print(f'Generation of best solution: {ga_instance.best_solution_generation}')\n", - "print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n", - "print(\"Index of the best solution : {solution_idx}\".format(solution_idx=solution_idx))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yxGGtFfE3j_1", - "outputId": "d5b68eaa-167f-47c8-aadd-10ecb3eb080c" - }, - "id": "yxGGtFfE3j_1", - "execution_count": 147, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generation of best solution: 25\n", - "Fitness value of the best solution = 0.0010087414431375688\n", - "Index of the best solution : 0\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "verify_solution(solution, len(stores))\n", - "solution" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NKvGE63abjmU", - "outputId": "75249196-afeb-4ee6-9b96-9674ea8493c9" - }, - "id": "NKvGE63abjmU", - "execution_count": 148, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Failed values less than or above max possible value\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([ 0, 1, 164, 19, 77, 23, 10, 9, 154, 158, 157, 26, 22,\n", - " 92, 42, 137, 75, 143, 149, 12, 100, 85, 86, 124, 128, 135,\n", - " 147, 54, 24, 3, 58, 123, 153, 51, 29, 69, 20, 110, 59,\n", - " 95, 113, 115, 121, 91, 36, 64, 65, 32, 53, 35, 105, 52,\n", - " 21, 34, 133, 109, 47, 71, 98, 106, 131, 89, 108, 56, 152,\n", - " 150, 7, 38, 43, 94, 8, 132, 155, 4, 16, 84, 90, 27,\n", - " 2, 144, 151, 39, 45, 159, 125, 79, 156, 40, 6, 74, 139,\n", - " 141, 145, 76, 104, 50, 37, 129, 130, 72, 142, 97, 25, 93,\n", - " 134, 126, 138, 140, 148, 120, 96, 28, 160, 116, 18, 112, 31,\n", - " 41, 55, 63, 73, 122, 162, 161, 163, 66, 107, 17, 87, 103,\n", - " 80, 81, 88, 83, 82, 14, 33, 11, 46, 61, 60, 136, 146,\n", - " 15, 70, 44, 48, 67, 78, 111, 13, 62, 30, 118, 114, 99,\n", - " 102, 5, 68, 49, 57, 101, 117, 127, 119, 0])" - ] - }, - "metadata": {}, - "execution_count": 148 - } - ] - }, - { - "cell_type": "code", - "source": [ - "points = [genes.get(stores[id]) + [stores[id]] for id in solution]\n", - "points[:5]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "G8bOm7PGcPx_", - "outputId": "f78d25f6-3679-489b-c533-2971ac389941" - }, - "id": "G8bOm7PGcPx_", - "execution_count": 150, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[[51.877854, -0.376379, '12851-253386'],\n", - " [51.877854, -0.376289, '7187-253385'],\n", - " [51.655847, -0.277039, '47771-259784'],\n", - " [51.51402, -0.13925, '12021-10341'],\n", - " [51.54541, -0.16269, '12158-22023']]" - ] - }, - "metadata": {}, - "execution_count": 150 - } - ] - }, - { - "cell_type": "code", - "source": [ - "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")\n", - "\n", - "for point in range(0, len(points)):\n", - " folium.Marker(\n", - " [points[point][0], points[point][1]], popup=f'{points[point][2]}'\n", - " ).add_to(map)\n", - "\n", - " try:\n", - " folium.PolyLine([(points[point][0], points[point][1]), \n", - " (points[point+1][0], points[point+1][1])],\n", - " color='red',\n", - " weight=5,\n", - " opacity=0.8).add_to(map)\n", - "\n", - " except IndexError:\n", - " pass" - ], - "metadata": { - "id": "_KtJJkkvcY-E" - }, - "id": "_KtJJkkvcY-E", - "execution_count": 151, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 531 - }, - "id": "PMH_yECHcaH8", - "outputId": "708aca8b-c6c9-4008-cc54-e14bb37b180f" - }, - "id": "PMH_yECHcaH8", - "execution_count": 152, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ] - }, - "metadata": {}, - "execution_count": 152 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 10. Total result \n", - "\n", - "The total resulting distance around London after optimising the solution is:" - ], - "metadata": { - "id": "4ek_Es2DcbXU" - }, - "id": "4ek_Es2DcbXU" - }, - { - "cell_type": "code", - "source": [ - "def distance(solution):\n", - " # loop through the length of the chromosome finding the distance between each\n", - " # gene added \n", - "\n", - " # to increment\n", - " total_dist = 0\n", - "\n", - " for gene in range(0, len(solution)):\n", - "\n", - " # get the lon lat of the two points\n", - " a = genes.get(stores[solution[gene]])\n", - " \n", - " try:\n", - " b = genes.get(stores[solution[gene + 1]])\n", - "\n", - " # find the distance (crow flies)\n", - " dist = geodesic(a, b).kilometers\n", - "\n", - " except IndexError:\n", - " dist = 0\n", - "\n", - " \n", - " total_dist += dist\n", - "\n", - " # to optimise this value in the positive direction the inverse of dist is used\n", - "\n", - " return total_dist " - ], - "metadata": { - "id": "SiCFxx7WcwxV" - }, - "id": "SiCFxx7WcwxV", - "execution_count": 153, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "distance(solution)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nds6G5tmnJ8K", - "outputId": "fe4ccfc6-a5c0-4e53-c0cd-acfe62bd6052" - }, - "id": "nds6G5tmnJ8K", - "execution_count": 154, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "991.3343075204886" - ] - }, - "metadata": {}, - "execution_count": 154 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Which is not too bad for 975 cups of joe. 🥤" - ], - "metadata": { - "id": "bukLealWcxOz" - }, - "id": "bukLealWcxOz" - } - ], - "metadata": { - "colab": { - "provenance": [], - "collapsed_sections": [ - "sEtmfvLsvMC2", - "1sg-1A4Ih1L4" - ], - "toc_visible": true - }, - "gpuClass": "standard", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "accelerator": "GPU" - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "95ba26f7" + }, + "source": [ + "# The Travelling Coffee Drinker - Genetic Algorithm\n", + "\n", + "Solving a travelling salesman problem for United Kingdom Starbucks Cafés" + ], + "id": "95ba26f7" + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Load and transform data\n", + "\n", + "The data comes from Kaggle, which is accessed using the API wrapper.\n", + "\n", + "The transformation needed is just to filter only GB Starbucks restaurants with a valid lon/lat pair." + ], + "metadata": { + "id": "sEtmfvLsvMC2" + }, + "id": "sEtmfvLsvMC2" + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_HBlGrIyYa7G", + "outputId": "cf89fc50-e566-4fe2-f54c-178b9d6e1acd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: pygad==2.17 in /usr/local/lib/python3.7/dist-packages (2.17.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pygad==2.17) (1.21.6)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pygad==2.17) (3.2.2)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (1.4.4)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (3.0.9)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pygad==2.17) (0.11.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->pygad==2.17) (4.1.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->pygad==2.17) (1.15.0)\n" + ] + } + ], + "source": [ + "!pip install pygad==2.17" + ], + "id": "_HBlGrIyYa7G" + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "id": "buoFAtjz2duB" + }, + "outputs": [], + "source": [ + "!pip install -q kaggle" + ], + "id": "buoFAtjz2duB" + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 375, + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": 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Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m 39\u001b[0m \"\"\"\n\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m 116\u001b[0m result = _output.eval_js(\n\u001b[1;32m 117\u001b[0m 'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m--> 118\u001b[0;31m input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m if (reply.get('type') == 'colab_reply' and\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from google.colab import files\n", + "files.upload() # upload a Kaggle JSON file to make request for data " + ], + "id": "WYcYq9YV2gi9" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TBtskqtn29H3" + }, + "outputs": [], + "source": [ + "!mkdir kaggle " + ], + "id": "TBtskqtn29H3" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oK1Z1Yr12tEv" + }, + "outputs": [], + "source": [ + "!cp kaggle.json ~/.kaggle/\n", + "!chmod 600 ~/.kaggle/kaggle.json" + ], + "id": "oK1Z1Yr12tEv" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8U2rpJ1j191n" + }, + "outputs": [], + "source": [ + "!kaggle datasets download kukuroo3/starbucks-locations-worldwide-2021-version -p /content/sample_data/ --unzip" + ], + "id": "8U2rpJ1j191n" + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "id": "sTkY5cLb3age", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a064aedc-8e4d-4712-d223-e35db3d1d7ae" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'storeNumber', 'countryCode', 'ownershipTypeCode',\n", + " 'schedule', 'slug', 'latitude', 'longitude', 'streetAddressLine1',\n", + " 'streetAddressLine2', 'streetAddressLine3', 'city',\n", + " 'countrySubdivisionCode', 'postalCode', 'currentTimeOffset',\n", + " 'windowsTimeZoneId', 'olsonTimeZoneId'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 165 + } + ], + "source": [ + "import pandas as pd \n", + "\n", + "# read in data and check column names \n", + "data = pd.read_csv('/content/sample_data/startbucks.csv')\n", + "data.columns" + ], + "id": "sTkY5cLb3age" + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "id": "SImMYnfe3n_w", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4ee033d9-9f66-489a-c470-b45a1a3b0ac0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0" + ] + }, + "metadata": {}, + "execution_count": 166 + } + ], + "source": [ + "df = data[data['countryCode']=='GB']\n", + "df.reset_index(inplace=True)\n", + "\n", + "# check for invalid lon/lat pairs\n", + "len(df.dropna(subset=['latitude', 'longitude'])) - len(df)" + ], + "id": "SImMYnfe3n_w" + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Exploratory analysis\n", + "\n", + "Find the distribution of cafés across the United Kingdom. \n", + "\n", + "How are restaurants distributed across towns?\n", + "What does a geospatial representation of the data look like?" + ], + "metadata": { + "id": "ovWqqNFIvydy" + }, + "id": "ovWqqNFIvydy" + }, + { + "cell_type": "markdown", + "source": [ + "### 2.1 Distribution of cafés by town" + ], + "metadata": { + "id": "SokLPN9j2fn1" + }, + "id": "SokLPN9j2fn1" + }, + { + "cell_type": "code", + "source": [ + "import plotly.express as px\n", + "vis = df.groupby('city').storeNumber.count().reset_index()\n", + "px.bar(vis, x='city', y='storeNumber', template='seaborn')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "HGOBgqnX2lIo", + "outputId": "0f19d0f2-0063-458a-cd02-987e30b3d9cf" + }, + "id": "HGOBgqnX2lIo", + "execution_count": 167, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "### 2.2 Map of cafés in the UK" + ], + "metadata": { + "id": "z9a3NPSM2lb4" + }, + "id": "z9a3NPSM2lb4" + }, + { + "cell_type": "code", + "source": [ + "import folium" + ], + "metadata": { + "id": "dAq1AQyfwN_l" + }, + "id": "dAq1AQyfwN_l", + "execution_count": 168, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")" + ], + "metadata": { + "id": "V9b0_8g_xM5K" + }, + "id": "V9b0_8g_xM5K", + "execution_count": 169, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for _, r in df.iterrows():\n", + " folium.Marker(\n", + " [r['latitude'], r['longitude']], popup=f'{r[\"storeNumber\"]}'\n", + " ).add_to(map)" + ], + "metadata": { + "id": "ZiprKw6ExhQR" + }, + "id": "ZiprKw6ExhQR", + "execution_count": 170, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "iVnEBooHc0ze", + "outputId": "b6ee729b-739f-4db5-975f-12a6a2c82246" + }, + "id": "iVnEBooHc0ze", + "execution_count": 171, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "metadata": {}, + "execution_count": 171 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 3. Testing the distance methodology\n", + "\n", + "To assess how good each solution is there needs to be a measure of fitness. For the purpose of this example the distance 'as the crow flies' is used without taking into account actual road distances however this could be explored in future." + ], + "metadata": { + "id": "1sg-1A4Ih1L4" + }, + "id": "1sg-1A4Ih1L4" + }, + { + "cell_type": "code", + "source": [ + "from geopy.distance import geodesic" + ], + "metadata": { + "id": "IbG9XRkLh0Aj" + }, + "id": "IbG9XRkLh0Aj", + "execution_count": 173, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The tested origin is the first Starbucks in the data and the destination is the second Starbucks in the dataset." + ], + "metadata": { + "id": "zgmh6b1l3liN" + }, + "id": "zgmh6b1l3liN" + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "id": "TyoeO2I-4bGq" + }, + "outputs": [], + "source": [ + "origin = (df['latitude'][0], df['longitude'][0])\n", + "dest = (df['latitude'][100], df['longitude'][100])" + ], + "id": "TyoeO2I-4bGq" + }, + { + "cell_type": "markdown", + "source": [ + "The distance between the two points as the crow flies in kilometres is given below." + ], + "metadata": { + "id": "VUr7ZAzw3srI" + }, + "id": "VUr7ZAzw3srI" + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "id": "zXo1I-5Q4Lwn", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4672edf2-9142-4276-ab6a-2b35b65b8b3d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "81.63683980420957" + ] + }, + "metadata": {}, + "execution_count": 175 + } + ], + "source": [ + "geodesic(origin, dest).kilometers" + ], + "id": "zXo1I-5Q4Lwn" + }, + { + "cell_type": "markdown", + "source": [ + "## 4. Preparing data structures\n", + "\n", + "The data structures needed for testing solutions are the \"genes\" or store options to select from named *genes*\n", + "\n", + "A lookup to access these genes known as *stores* \n", + "\n", + "A *check_range* which is used to check that every option is given in a solution (a key criteria in the TSP).\n" + ], + "metadata": { + "id": "GeRIsd5G378L" + }, + "id": "GeRIsd5G378L" + }, + { + "cell_type": "code", + "source": [ + "test = df.head(10)\n", + "genes = {store_num:[lat, lon] for store_num, lat, lon in zip(test['storeNumber'], test['latitude'], test['longitude'])}\n", + "stores = list(genes.keys())\n", + "check_range = [i for i in range(0, 10)]" + ], + "metadata": { + "id": "rkKStcn4iIrN" + }, + "id": "rkKStcn4iIrN", + "execution_count": 176, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 5. Defining functions \n", + "\n", + "The algorithm requires a set of functions to be pre-defined as the out of the box genetic algorithm does not support a TSP.\n", + "\n", + " 1. build_population: builds a population of chromosomes to test with proper restrictions applied\n", + " 2. fitness_func: Used to test a solution to see how well it performs, in this case the fitness_func will be assessed based on the distance as the crow flies between each successive point\n", + " 3. pmx_crossover: performs the crossover of a parent and child with proper Partially Matched Crossover (PMX) logic\n", + " 4. crossover_func: applies the crossover\n", + " 5. on_crossover: applies the mutation after crossover\n", + " 6. on_generation: used to print the progress and results at each generation" + ], + "metadata": { + "id": "5mKC2lKO4tRY" + }, + "id": "5mKC2lKO4tRY" + }, + { + "cell_type": "code", + "source": [ + "import random\n", + "import numpy as np\n", + "from geopy.distance import geodesic" + ], + "metadata": { + "id": "15-IiuRNljOC" + }, + "id": "15-IiuRNljOC", + "execution_count": 177, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Assess the quality or fitness of a solution so that only the fittest are selected for the next generation and to breed." + ], + "metadata": { + "id": "BhCEN9165in3" + }, + "id": "BhCEN9165in3" + }, + { + "cell_type": "code", + "source": [ + "def build_population(size, chromosome_size):\n", + " population = []\n", + " for i in range(size):\n", + " home_city = 0\n", + " added = {home_city:'Added'}\n", + " chromosome = [home_city]\n", + "\n", + " while len(chromosome) < chromosome_size:\n", + " proposed_gene = random.randint(0, chromosome_size-1)\n", + " if added.get(proposed_gene) is None:\n", + " chromosome.append(proposed_gene)\n", + " added.update({proposed_gene:'Added'})\n", + " else:\n", + " pass\n", + "\n", + " chromosome.append(home_city)\n", + "\n", + " population.append(chromosome)\n", + "\n", + " return np.array(population)" + ], + "metadata": { + "id": "BLz2sU2n78Ui" + }, + "id": "BLz2sU2n78Ui", + "execution_count": 178, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "population = build_population(100, 10)\n", + "population.shape" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vs8CrTNV9iqe", + "outputId": "4178c652-2593-44e9-ca0d-1829f11c6d5e" + }, + "id": "vs8CrTNV9iqe", + "execution_count": 179, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(100, 11)" + ] + }, + "metadata": {}, + "execution_count": 179 + } + ] + }, + { + "cell_type": "code", + "source": [ + "def fitness_func(solution, solution_idx):\n", + " # loop through the length of the chromosome finding the distance between each\n", + " # gene added \n", + "\n", + " # to increment\n", + " total_dist = 0\n", + "\n", + " for gene in range(0, len(solution)):\n", + "\n", + " # get the lon lat of the two points\n", + " a = genes.get(stores[solution[gene]])\n", + " \n", + " try:\n", + " b = genes.get(stores[solution[gene + 1]])\n", + "\n", + " # find the distance (crow flies)\n", + " dist = geodesic(a, b).kilometers\n", + "\n", + " except IndexError:\n", + " dist = 0\n", + "\n", + " total_dist += dist\n", + "\n", + " # to optimise this value in the positive direction the inverse of dist is used\n", + " fitness = 1 / total_dist\n", + "\n", + " return fitness " + ], + "metadata": { + "id": "5u-5msoj-84i" + }, + "id": "5u-5msoj-84i", + "execution_count": 180, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def pmx_crossover(parent1, parent2, sequence_start, sequence_end):\n", + " # initialise a child\n", + " child = np.zeros(parent1.shape[0])\n", + "\n", + " # get the genes for parent one that are passed on to child one\n", + " parent1_to_child1_genes = parent1[sequence_start:sequence_end]\n", + "\n", + " # get the position of genes for each respective combination\n", + " parent1_to_child1 = np.isin(parent1,parent1_to_child1_genes).nonzero()[0]\n", + "\n", + " for gene in parent1_to_child1:\n", + " child[gene] = parent1[gene]\n", + "\n", + " # gene of parent 2 not in the child\n", + " genes_not_in_child = parent2[np.isin(parent2, parent1_to_child1_genes, invert=True).nonzero()[0]]\n", + " \n", + " # if the gene is not already\n", + " if genes_not_in_child.shape[0] >= 1:\n", + " for gene in genes_not_in_child:\n", + " if gene >= 1:\n", + " lookup = gene\n", + " not_in_sequence = True\n", + "\n", + " while not_in_sequence:\n", + " position_in_parent2 = np.where(parent2==lookup)[0][0]\n", + "\n", + " if position_in_parent2 in range(sequence_start, sequence_end):\n", + " lookup = parent1[position_in_parent2]\n", + "\n", + " else:\n", + " child[position_in_parent2] = gene\n", + " not_in_sequence = False\n", + "\n", + " return child" + ], + "metadata": { + "id": "OpbfyShQes_q" + }, + "id": "OpbfyShQes_q", + "execution_count": 181, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def crossover_func(parents, offspring_size, ga_instance):\n", + " offspring = []\n", + " idx = 0\n", + " while len(offspring) != offspring_size[0]:\n", + "\n", + " # locate the parents\n", + " parent1 = parents[idx % parents.shape[0], :].copy()\n", + " parent2 = parents[(idx + 1) % parents.shape[0], :].copy()\n", + "\n", + " # find gene sequence in parent 1 \n", + " sequence_start = random.randint(1, parent1.shape[0]-4)\n", + " sequence_end = random.randint(sequence_start, parent1.shape[0]-1)\n", + "\n", + " # perform crossover\n", + " child1 = pmx_crossover(parent1, parent2, sequence_start, sequence_end)\n", + " child2 = pmx_crossover(parent2, parent1, sequence_start, sequence_end) \n", + "\n", + " offspring.append(child1)\n", + " offspring.append(child2)\n", + "\n", + "\n", + " idx += 1\n", + "\n", + " return np.array(offspring)" + ], + "metadata": { + "id": "shgFWqH2NinO" + }, + "id": "shgFWqH2NinO", + "execution_count": 182, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The mutation function chosen is inversion as it does not invalidate the solution." + ], + "metadata": { + "id": "bCjaaeofA7Bg" + }, + "id": "bCjaaeofA7Bg" + }, + { + "cell_type": "code", + "source": [ + "def mutation_func(offspring, ga_instance):\n", + "\n", + " for chromosome_idx in range(offspring.shape[0]):\n", + " # define a sequence of genes to reverse\n", + " sequence_start = random.randint(1, offspring[chromosome_idx].shape[0] - 2)\n", + " sequence_end = random.randint(sequence_start, offspring[chromosome_idx].shape[0] - 1)\n", + " \n", + " genes = offspring[chromosome_idx, sequence_start:sequence_end]\n", + "\n", + " # start at the start of the sequence assigning the reverse sequence back to the chromosome\n", + " index = 0\n", + " if len(genes) > 0:\n", + " for gene in range(sequence_start, sequence_end):\n", + "\n", + " offspring[chromosome_idx, gene] = genes[index]\n", + "\n", + " index += 1\n", + "\n", + " return offspring" + ], + "metadata": { + "id": "0kMm7J1WAsvH" + }, + "id": "0kMm7J1WAsvH", + "execution_count": 183, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Used in the genetic algorithm flow to apply the custom mutation after crossover" + ], + "metadata": { + "id": "HTeKwuPl5vVt" + }, + "id": "HTeKwuPl5vVt" + }, + { + "cell_type": "code", + "source": [ + "def on_crossover(ga_instance, offspring_crossover):\n", + " # apply mutation to ensure uniqueness \n", + " offspring_mutation = mutation_func(offspring_crossover, ga_instance)\n", + "\n", + " # save the new offspring set as the parents of the next generation\n", + " ga_instance.last_generation_offspring_mutation = offspring_mutation" + ], + "metadata": { + "id": "ucwYe4rgwLQC" + }, + "id": "ucwYe4rgwLQC", + "execution_count": 184, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Added for debugging and assessing progress by generation at runtime" + ], + "metadata": { + "id": "Xf7rgtuO532X" + }, + "id": "Xf7rgtuO532X" + }, + { + "cell_type": "code", + "source": [ + "def on_generation(ga):\n", + " print(\"Generation\", ga.generations_completed)\n", + " print(ga.population)" + ], + "metadata": { + "id": "eqRwVsSBMf_B" + }, + "id": "eqRwVsSBMf_B", + "execution_count": 185, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 6. Executing the algorithm\n", + "\n", + "The genetic algorithm is set up as instance and at initialisation several parameters are given. \n", + "\n", + "The algorithm then runs to find the best solution for a set number of generations." + ], + "metadata": { + "id": "C9AtXoqx58x0" + }, + "id": "C9AtXoqx58x0" + }, + { + "cell_type": "code", + "source": [ + "import pygad" + ], + "metadata": { + "id": "BrJVLZlBW07R" + }, + "id": "BrJVLZlBW07R", + "execution_count": 186, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 6.1 Example Initialising the algorithm\n", + "\n", + "The algorithm is initialised below.\n", + "\n", + "Notable parameters include:\n", + " - The use of gene space to limit the possible genes chosen to just be those in the TSP range\n", + " - Mutations being turned off temporarily\n", + " - Implementation of custom on_ functions \n", + " - Allow duplication of genes parameter set to false to ensure any newly introduced chromosomes/chromosomes created as population is initialised have no duplicate genes" + ], + "metadata": { + "id": "6ni9VkQv6TJR" + }, + "id": "6ni9VkQv6TJR" + }, + { + "cell_type": "code", + "source": [ + "ga_instance = pygad.GA(num_generations=100,\n", + " num_parents_mating=40,\n", + " fitness_func=fitness_func,\n", + " sol_per_pop=200,\n", + " initial_population=population,\n", + " gene_space=range(0, 10),\n", + " gene_type=int,\n", + " mutation_type=mutation_func,\n", + " on_generation=on_generation,\n", + " crossover_type=crossover_func, \n", + " keep_parents=6,\n", + " mutation_probability=0.4)" + ], + "metadata": { + "id": "FfFdncf-G3Mr" + }, + "id": "FfFdncf-G3Mr", + "execution_count": 187, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 6.2 Running the algorithm \n", + "\n", + "The genetic algorithm is run with a simple function call" + ], + "metadata": { + "id": "sfqme_5461A4" + }, + "id": "sfqme_5461A4" + }, + { + "cell_type": "code", + "source": [ + "ga_instance.run()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g-3CjxWYZh3H", + "outputId": "dc1fbda5-6b22-4245-9b99-e22b0e41684f" + }, + "id": "g-3CjxWYZh3H", + "execution_count": 188, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 1\n", + "[[0 3 2 ... 4 5 0]\n", + " [0 3 6 ... 1 2 0]\n", + " [0 8 3 ... 6 1 0]\n", + " ...\n", + " [0 9 5 ... 7 4 0]\n", + " [0 2 7 ... 8 6 0]\n", + " [0 3 5 ... 6 8 0]]\n", + "Generation 2\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 9 ... 8 3 0]\n", + " [0 3 2 ... 4 5 0]\n", + " ...\n", + " [0 3 6 ... 1 2 0]\n", + " [0 3 6 ... 1 2 0]\n", + " [0 3 1 ... 6 2 0]]\n", + "Generation 3\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 9 ... 8 3 0]\n", + " [0 3 2 ... 4 5 0]\n", + " [0 9 2 ... 8 3 0]]\n", + "Generation 4\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 5\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 6\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 7\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 8\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 9\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 10\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 11\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 12\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 13\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 14\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 15\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 16\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 17\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 18\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 19\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 20\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 21\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 22\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 23\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 24\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 25\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 26\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 27\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 28\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 29\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 30\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 31\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 32\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 33\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 34\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 35\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 36\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 37\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 38\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 39\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 40\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 41\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 42\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 43\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 44\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 45\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 46\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 47\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 48\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 49\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 50\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 51\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 52\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 53\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 54\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 55\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 56\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 57\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 58\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 59\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 60\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 61\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 62\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 63\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 64\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 65\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 66\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 67\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 68\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 69\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 70\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 71\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 72\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 73\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 74\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 75\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 76\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 77\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 78\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 79\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 80\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 81\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 82\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 83\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 84\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 85\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 86\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 87\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 88\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 89\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 90\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 91\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 92\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 93\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 94\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 95\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 96\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 97\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 98\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 99\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n", + "Generation 100\n", + "[[0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " ...\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]\n", + " [0 1 2 ... 8 6 0]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 7. Assessing results \n", + "\n", + "The result solution can be checked and analysed using the ga_instance itself" + ], + "metadata": { + "id": "woJTWlOrYLwl" + }, + "id": "woJTWlOrYLwl" + }, + { + "cell_type": "code", + "source": [ + "solution, solution_fitness, solution_idx = ga_instance.best_solution()" + ], + "metadata": { + "id": "BZxvMCZ-aFtJ" + }, + "id": "BZxvMCZ-aFtJ", + "execution_count": 189, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "solution, solution_fitness, solution_idx = ga_instance.best_solution()\n", + "print(f'Generation of best solution: {ga_instance.best_solution_generation}')\n", + "print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n", + "print(\"Index of the best solution : {solution_idx}\".format(solution_idx=solution_idx))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IOTGnna-QXsB", + "outputId": "30d494d6-e88e-4061-a453-9a44a9409176" + }, + "id": "IOTGnna-QXsB", + "execution_count": 190, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation of best solution: 1\n", + "Fitness value of the best solution = 0.010681933534441102\n", + "Index of the best solution : 0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "if ga_instance.best_solution_generation != -1:\n", + " print(\"Best fitness value reached after {best_solution_generation} generations.\".format(best_solution_generation=ga_instance.best_solution_generation))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WsjTUVloQhez", + "outputId": "fdd41ccb-9ead-49d7-d180-ccc1dd2d13f9" + }, + "id": "WsjTUVloQhez", + "execution_count": 191, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best fitness value reached after 1 generations.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 7.1 Verifying a solution\n", + "\n", + "For a solution to be valid it needs to have:\n", + " - A maximum gene value that matches the total number of stores \n", + " - A minimum gene value of 0 \n", + " - Each gene must be unique" + ], + "metadata": { + "id": "FiLlGlILYWhw" + }, + "id": "FiLlGlILYWhw" + }, + { + "cell_type": "code", + "source": [ + "def verify_solution(solution, max_gene):\n", + " if min(solution) != 0:\n", + " print('Failed values below 0')\n", + "\n", + " if max(solution) != max_gene:\n", + " print('Failed values less than or above max possible value')\n", + "\n", + " if len(set(solution)) - len(solution) != -1:\n", + " print(len(set(solution)) - len(solution))\n", + " print('Failed solution does not contain unique values')" + ], + "metadata": { + "id": "vEhveNG4VQBF" + }, + "id": "vEhveNG4VQBF", + "execution_count": 192, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "verify_solution(solution, 9)" + ], + "metadata": { + "id": "IQg1sULKYRZe" + }, + "id": "IQg1sULKYRZe", + "execution_count": 193, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "solution" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VqjdGMGnYqz-", + "outputId": "152c6b82-4787-4776-b9e9-bc3ceb1ccec1" + }, + "id": "VqjdGMGnYqz-", + "execution_count": 194, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 7, 9, 8, 6, 0])" + ] + }, + "metadata": {}, + "execution_count": 194 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 7.2 Interpreting the result \n", + "\n", + "The result sequence can be used to access latitude and longitude for each store in the solution." + ], + "metadata": { + "id": "5ixLDfvtY3cI" + }, + "id": "5ixLDfvtY3cI" + }, + { + "cell_type": "code", + "source": [ + "points = [genes.get(stores[id]) + [stores[id]] for id in solution]\n", + "points[:5]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tv-AjzcoQwGc", + "outputId": "a5187ac8-ea84-4dc9-992c-60cfa4dcab8a" + }, + "id": "tv-AjzcoQwGc", + "execution_count": 195, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[[51.483556, -1.557143, '9155-152277'],\n", + " [51.482387, -1.555109, '22194-218828'],\n", + " [51.481264, -1.556526, '18362-190424'],\n", + " [51.481177, -1.557422, '9136-152279'],\n", + " [51.562617, -1.798111, '47832-260044']]" + ] + }, + "metadata": {}, + "execution_count": 195 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import folium \n", + "\n", + "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")\n", + "\n", + "for point in range(0, len(points)):\n", + " folium.Marker(\n", + " [points[point][0], points[point][1]], popup=f'{points[point][2]}'\n", + " ).add_to(map)\n", + "\n", + " try:\n", + " folium.PolyLine([(points[point][0], points[point][1]), \n", + " (points[point+1][0], points[point+1][1])],\n", + " color='red',\n", + " weight=5,\n", + " opacity=0.8).add_to(map)\n", + "\n", + " except IndexError:\n", + " pass\n", + " " + ], + "metadata": { + "id": "Tq0hTc5cstWX" + }, + "id": "Tq0hTc5cstWX", + "execution_count": 196, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "ebkOuHleuGU1", + "outputId": "801c457d-49d9-452d-9c4b-872aef5816d4" + }, + "id": "ebkOuHleuGU1", + "execution_count": 197, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "metadata": {}, + "execution_count": 197 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The map shows the shortest path that has been found. So that the travelling coffee drinker can maximise the time on coffee and minimise the time on travelling.\n", + "\n", + "Now the algorithm can be scaled up for the whole of the UK, or tailored to just one town. An example of the solution scaled to the UK is given below." + ], + "metadata": { + "id": "5Sz8ykeEYDS3" + }, + "id": "5Sz8ykeEYDS3" + }, + { + "cell_type": "markdown", + "source": [ + "## 8. Scaling up the solution\n", + "\n", + "This is where the fun begins!" + ], + "metadata": { + "id": "gscT4SjxZ9h4" + }, + "id": "gscT4SjxZ9h4" + }, + { + "cell_type": "code", + "source": [ + "df = df[df['city'] == 'London']\n", + "genes = {store_num:[lat, lon] for store_num, lat, lon in zip(df['storeNumber'], df['latitude'], df['longitude'])}\n", + "stores = list(genes.keys())\n", + "len(stores)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yYY3-gP1aFkH", + "outputId": "907e2a7c-3292-4409-e83e-4db372f09a45" + }, + "id": "yYY3-gP1aFkH", + "execution_count": 157, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "165" + ] + }, + "metadata": {}, + "execution_count": 157 + } + ] + }, + { + "cell_type": "code", + "source": [ + "population = build_population(200, 165)\n", + "len(population[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oykOz7l4KhJE", + "outputId": "fe7fdabc-a909-40c4-e69a-daba8169b07c" + }, + "id": "oykOz7l4KhJE", + "execution_count": 155, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "166" + ] + }, + "metadata": {}, + "execution_count": 155 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 8.1 Building the final algorithm\n", + "\n", + "The code to build the algorithm has to be re-run with the above data structures altered." + ], + "metadata": { + "id": "ioy8EGjEbnBJ" + }, + "id": "ioy8EGjEbnBJ" + }, + { + "cell_type": "code", + "source": [ + "def fitness_func(solution, solution_idx):\n", + " # loop through the length of the chromosome finding the distance between each\n", + " # gene added \n", + "\n", + " # to increment\n", + " total_dist = 0\n", + "\n", + " for gene in range(0, len(solution)):\n", + "\n", + " # get the lon lat of the two points\n", + " a = genes.get(stores[solution[gene]])\n", + " \n", + " try:\n", + " b = genes.get(stores[solution[gene + 1]])\n", + "\n", + " # find the distance (crow flies)\n", + " dist = geodesic(a, b).kilometers\n", + "\n", + " except IndexError:\n", + " dist = 0\n", + "\n", + " total_dist += dist\n", + "\n", + " # to optimise this value in the positive direction the inverse of dist is used\n", + " fitness = 1 / total_dist\n", + "\n", + " return fitness " + ], + "metadata": { + "id": "uBfcikkma5hP" + }, + "id": "uBfcikkma5hP", + "execution_count": 108, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def pmx_crossover(parent1, parent2, sequence_start, sequence_end):\n", + " # initialise a child\n", + " child = np.zeros(parent1.shape[0])\n", + "\n", + " # get the genes for parent one that are passed on to child one\n", + " parent1_to_child1_genes = parent1[sequence_start:sequence_end]\n", + "\n", + " # get the position of genes for each respective combination\n", + " parent1_to_child1 = np.isin(parent1,parent1_to_child1_genes).nonzero()[0]\n", + "\n", + " for gene in parent1_to_child1:\n", + " child[gene] = parent1[gene]\n", + "\n", + " # gene of parent 2 not in the child\n", + " genes_not_in_child = parent2[np.isin(parent2, parent1_to_child1_genes, invert=True).nonzero()[0]]\n", + " \n", + " if genes_not_in_child.shape[0] >= 1:\n", + " for gene in genes_not_in_child:\n", + " if gene >= 1:\n", + " lookup = gene\n", + " not_in_sequence = True\n", + "\n", + " while not_in_sequence:\n", + " position_in_parent2 = np.where(parent2==lookup)[0][0]\n", + "\n", + " if position_in_parent2 in range(sequence_start, sequence_end):\n", + " lookup = parent1[position_in_parent2]\n", + "\n", + " else:\n", + " child[position_in_parent2] = gene\n", + " not_in_sequence = False\n", + "\n", + " return child" + ], + "metadata": { + "id": "FuOkiStta7Pz" + }, + "id": "FuOkiStta7Pz", + "execution_count": 109, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def crossover_func(parents, offspring_size, ga_instance):\n", + " offspring = []\n", + " idx = 0\n", + " while len(offspring) != offspring_size[0]:\n", + "\n", + " # locate the parents\n", + " parent1 = parents[idx % parents.shape[0], :].copy()\n", + " parent2 = parents[(idx + 1) % parents.shape[0], :].copy()\n", + "\n", + " # find gene sequence in parent 1 \n", + " sequence_start = random.randint(1, parent1.shape[0]-4)\n", + " sequence_end = random.randint(sequence_start, parent1.shape[0]-1)\n", + "\n", + " # perform crossover\n", + " child1 = pmx_crossover(parent1, parent2, sequence_start, sequence_end)\n", + " child2 = pmx_crossover(parent2, parent1, sequence_start, sequence_end)\n", + " \n", + "\n", + " offspring.append(child1)\n", + " offspring.append(child2)\n", + "\n", + " idx += 1\n", + "\n", + " return np.array(offspring)" + ], + "metadata": { + "id": "O10ZgScUa_bj" + }, + "id": "O10ZgScUa_bj", + "execution_count": 130, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def mutation_func(offspring, ga_instance):\n", + "\n", + " for chromosome_idx in range(offspring.shape[0]):\n", + " # define a sequence of genes to reverse\n", + " sequence_start = random.randint(1, offspring[chromosome_idx].shape[0] - 2)\n", + " sequence_end = random.randint(sequence_start, offspring[chromosome_idx].shape[0] - 1)\n", + " \n", + " genes = offspring[chromosome_idx, sequence_start:sequence_end]\n", + "\n", + " # start at the start of the sequence assigning the reverse sequence back to the chromosome\n", + " index = 0\n", + " if len(genes) > 0:\n", + " for gene in range(sequence_start, sequence_end):\n", + "\n", + " offspring[chromosome_idx, gene] = genes[index]\n", + "\n", + " index += 1\n", + "\n", + " return offspring" + ], + "metadata": { + "id": "mLLY7Ub4K_Y5" + }, + "id": "mLLY7Ub4K_Y5", + "execution_count": 144, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def on_crossover(ga_instance, offspring_crossover):\n", + " # apply mutation to ensure uniqueness \n", + " offspring_mutation = mutation_func(offspring_crossover, ga_instance)\n", + "\n", + " # save the new offspring set as the parents of the next generation\n", + " ga_instance.last_generation_offspring_mutation = offspring_mutation" + ], + "metadata": { + "id": "QLtP6in4LFSw" + }, + "id": "QLtP6in4LFSw", + "execution_count": 126, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def on_generation(ga):\n", + " print(\"Generation\", ga.generations_completed)\n", + " print(ga.population)" + ], + "metadata": { + "id": "SnR2LaDJLGRj" + }, + "id": "SnR2LaDJLGRj", + "execution_count": 127, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "ga_instance = pygad.GA(num_generations=100,\n", + " num_parents_mating=40,\n", + " fitness_func=fitness_func,\n", + " sol_per_pop=200,\n", + " initial_population=population,\n", + " gene_space=range(0, 165),\n", + " gene_type=int,\n", + " mutation_type=mutation_func,\n", + " on_generation=on_generation,\n", + " crossover_type=crossover_func, \n", + " keep_parents=6,\n", + " mutation_probability=0.4)" + ], + "metadata": { + "id": "j2J5jlh9bDxR" + }, + "id": "j2J5jlh9bDxR", + "execution_count": 145, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "ga_instance.run()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lhW7JkMAbS6E", + "outputId": "c6130b2a-1e6b-4b7d-c2d6-f891b178fef9" + }, + "id": "lhW7JkMAbS6E", + "execution_count": 146, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 1\n", + "[[ 0 1 111 ... 127 108 0]\n", + " [ 0 62 141 ... 26 161 0]\n", + " [ 0 137 155 ... 158 3 0]\n", + " ...\n", + " [ 0 142 162 ... 2 159 0]\n", + " [ 0 161 159 ... 112 66 0]\n", + " [ 0 152 108 ... 72 58 0]]\n", + "Generation 2\n", + "[[ 0 1 111 ... 127 108 0]\n", + " [ 0 1 111 ... 127 108 0]\n", + " [ 0 62 141 ... 26 161 0]\n", + " ...\n", + " [ 0 137 155 ... 135 76 0]\n", + " [ 0 137 155 ... 158 3 0]\n", + " [ 0 96 40 ... 135 5 0]]\n", + "Generation 3\n", + "[[ 0 1 145 ... 26 94 0]\n", + " [ 0 1 111 ... 127 108 0]\n", + " [ 0 1 111 ... 127 108 0]\n", + " ...\n", + " [ 0 89 155 ... 158 32 0]\n", + " [ 0 1 110 ... 127 94 0]\n", + " [ 0 96 40 ... 64 90 0]]\n", + "Generation 4\n", + "[[ 0 1 56 ... 26 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 110 ... 127 94 0]\n", + " ...\n", + " [ 0 1 111 ... 127 108 0]\n", + " [ 0 1 145 ... 26 22 0]\n", + " [ 0 1 77 ... 127 142 0]]\n", + "Generation 5\n", + "[[ 0 1 111 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 56 ... 127 81 0]\n", + " ...\n", + " [ 0 1 60 ... 127 118 0]\n", + " [ 0 1 154 ... 127 7 0]\n", + " [ 0 1 164 ... 127 94 0]]\n", + "Generation 6\n", + "[[ 0 1 56 ... 26 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 93 ... 127 108 0]\n", + " [ 0 1 30 ... 127 108 0]\n", + " [ 0 1 111 ... 127 108 0]]\n", + "Generation 7\n", + "[[ 0 1 56 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 56 ... 26 81 0]\n", + " [ 0 1 56 ... 127 81 0]]\n", + "Generation 8\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 26 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 56 ... 127 94 0]]\n", + "Generation 9\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 33 ... 26 94 0]\n", + " [ 0 1 110 ... 127 108 0]]\n", + "Generation 10\n", + "[[ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 26 108 0]]\n", + "Generation 11\n", + "[[ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 81 0]]\n", + "Generation 12\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 81 0]]\n", + "Generation 13\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 81 0]]\n", + "Generation 14\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " ...\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 15\n", + "[[ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 16\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 81 0]\n", + " ...\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 94 0]]\n", + "Generation 17\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 81 0]\n", + " [ 0 1 164 ... 127 108 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 18\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 108 0]]\n", + "Generation 19\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 20\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 21\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 22\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 23\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 24\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 25\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 26\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 27\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 28\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 29\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 30\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 31\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 32\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 33\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 34\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 35\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 36\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 37\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 38\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 39\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 40\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 41\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 42\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 43\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 44\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 45\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 46\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 47\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 48\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 49\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 50\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 51\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 52\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 53\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 54\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 55\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 56\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 57\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 58\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 59\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 60\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 61\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 62\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 63\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 64\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 65\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 66\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 67\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 68\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 69\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 70\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 71\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 72\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 73\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 74\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 75\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 76\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 77\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 78\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 79\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 80\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 81\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 82\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 83\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 84\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 85\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 86\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 87\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 88\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 89\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 90\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 91\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 92\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 93\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 94\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 95\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 96\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 97\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 98\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 99\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n", + "Generation 100\n", + "[[ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " ...\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]\n", + " [ 0 1 164 ... 127 119 0]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 8.2 Evaluating the final algorithm \n", + "\n", + "The overall solution can now be assessed." + ], + "metadata": { + "id": "KJY5PcFabWdL" + }, + "id": "KJY5PcFabWdL" + }, + { + "cell_type": "code", + "source": [ + "solution, solution_fitness, solution_idx = ga_instance.best_solution()\n", + "print(f'Generation of best solution: {ga_instance.best_solution_generation}')\n", + "print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n", + "print(\"Index of the best solution : {solution_idx}\".format(solution_idx=solution_idx))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yxGGtFfE3j_1", + "outputId": "d5b68eaa-167f-47c8-aadd-10ecb3eb080c" + }, + "id": "yxGGtFfE3j_1", + "execution_count": 147, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation of best solution: 25\n", + "Fitness value of the best solution = 0.0010087414431375688\n", + "Index of the best solution : 0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "verify_solution(solution, len(stores))\n", + "solution" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NKvGE63abjmU", + "outputId": "75249196-afeb-4ee6-9b96-9674ea8493c9" + }, + "id": "NKvGE63abjmU", + "execution_count": 148, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Failed values less than or above max possible value\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 0, 1, 164, 19, 77, 23, 10, 9, 154, 158, 157, 26, 22,\n", + " 92, 42, 137, 75, 143, 149, 12, 100, 85, 86, 124, 128, 135,\n", + " 147, 54, 24, 3, 58, 123, 153, 51, 29, 69, 20, 110, 59,\n", + " 95, 113, 115, 121, 91, 36, 64, 65, 32, 53, 35, 105, 52,\n", + " 21, 34, 133, 109, 47, 71, 98, 106, 131, 89, 108, 56, 152,\n", + " 150, 7, 38, 43, 94, 8, 132, 155, 4, 16, 84, 90, 27,\n", + " 2, 144, 151, 39, 45, 159, 125, 79, 156, 40, 6, 74, 139,\n", + " 141, 145, 76, 104, 50, 37, 129, 130, 72, 142, 97, 25, 93,\n", + " 134, 126, 138, 140, 148, 120, 96, 28, 160, 116, 18, 112, 31,\n", + " 41, 55, 63, 73, 122, 162, 161, 163, 66, 107, 17, 87, 103,\n", + " 80, 81, 88, 83, 82, 14, 33, 11, 46, 61, 60, 136, 146,\n", + " 15, 70, 44, 48, 67, 78, 111, 13, 62, 30, 118, 114, 99,\n", + " 102, 5, 68, 49, 57, 101, 117, 127, 119, 0])" + ] + }, + "metadata": {}, + "execution_count": 148 + } + ] + }, + { + "cell_type": "code", + "source": [ + "points = [genes.get(stores[id]) + [stores[id]] for id in solution]\n", + "points[:5]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G8bOm7PGcPx_", + "outputId": "f78d25f6-3679-489b-c533-2971ac389941" + }, + "id": "G8bOm7PGcPx_", + "execution_count": 150, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[[51.877854, -0.376379, '12851-253386'],\n", + " [51.877854, -0.376289, '7187-253385'],\n", + " [51.655847, -0.277039, '47771-259784'],\n", + " [51.51402, -0.13925, '12021-10341'],\n", + " [51.54541, -0.16269, '12158-22023']]" + ] + }, + "metadata": {}, + "execution_count": 150 + } + ] + }, + { + "cell_type": "code", + "source": [ + "map = folium.Map(location=[51.509685, -0.118092], zoom_start=6, tiles=\"stamentoner\")\n", + "\n", + "for point in range(0, len(points)):\n", + " folium.Marker(\n", + " [points[point][0], points[point][1]], popup=f'{points[point][2]}'\n", + " ).add_to(map)\n", + "\n", + " try:\n", + " folium.PolyLine([(points[point][0], points[point][1]), \n", + " (points[point+1][0], points[point+1][1])],\n", + " color='red',\n", + " weight=5,\n", + " opacity=0.8).add_to(map)\n", + "\n", + " except IndexError:\n", + " pass" + ], + "metadata": { + "id": "_KtJJkkvcY-E" + }, + "id": "_KtJJkkvcY-E", + "execution_count": 151, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "map" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 531 + }, + "id": "PMH_yECHcaH8", + "outputId": "708aca8b-c6c9-4008-cc54-e14bb37b180f" + }, + "id": "PMH_yECHcaH8", + "execution_count": 152, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "metadata": {}, + "execution_count": 152 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 10. Total result \n", + "\n", + "The total resulting distance around London after optimising the solution is:" + ], + "metadata": { + "id": "4ek_Es2DcbXU" + }, + "id": "4ek_Es2DcbXU" + }, + { + "cell_type": "code", + "source": [ + "def distance(solution):\n", + " # loop through the length of the chromosome finding the distance between each\n", + " # gene added \n", + "\n", + " # to increment\n", + " total_dist = 0\n", + "\n", + " for gene in range(0, len(solution)):\n", + "\n", + " # get the lon lat of the two points\n", + " a = genes.get(stores[solution[gene]])\n", + " \n", + " try:\n", + " b = genes.get(stores[solution[gene + 1]])\n", + "\n", + " # find the distance (crow flies)\n", + " dist = geodesic(a, b).kilometers\n", + "\n", + " except IndexError:\n", + " dist = 0\n", + "\n", + " \n", + " total_dist += dist\n", + "\n", + " # to optimise this value in the positive direction the inverse of dist is used\n", + "\n", + " return total_dist " + ], + "metadata": { + "id": "SiCFxx7WcwxV" + }, + "id": "SiCFxx7WcwxV", + "execution_count": 153, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "distance(solution)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nds6G5tmnJ8K", + "outputId": "fe4ccfc6-a5c0-4e53-c0cd-acfe62bd6052" + }, + "id": "nds6G5tmnJ8K", + "execution_count": 154, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "991.3343075204886" + ] + }, + "metadata": {}, + "execution_count": 154 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Which is not too bad for 975 cups of joe. 🥤" + ], + "metadata": { + "id": "bukLealWcxOz" + }, + "id": "bukLealWcxOz" + } + ], + "metadata": { + "colab": { + "provenance": [], + "collapsed_sections": [ + "sEtmfvLsvMC2", + "1sg-1A4Ih1L4" + ], + "toc_visible": true + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "accelerator": "GPU" + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ab66e04c9d44344fb4bbec6b58c0ef0d9ac32fab Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 16:25:07 -0400 Subject: [PATCH 022/220] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index f5bab50..7ce2f32 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ author="Ahmed Fawzy Gad", install_requires=["numpy", "matplotlib", "cloudpickle",], author_email="ahmed.f.gad@gmail.com", - description="PyGAD: A Python 3 Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch).", + description="PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch).", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/ahmedfgad/GeneticAlgorithmPython", From b42bcd0d5edf45507a8e4bab628d7f9f6124be7d Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 18:30:53 -0400 Subject: [PATCH 023/220] Metadata pyproject.toml File Create a metadata TOML pyproject.toml file for the project. --- pyproject.toml | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..4849c4a --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,35 @@ +[project] +name = "pygad" +version = "3.0.0" +description = "PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch)." +readme = {file = "README.md", content-type = "text/markdown"} +requires-python = ">=3" +license = {file = "LICENSE"} +authors = [ + {name = "Ahmed Gad", email = "ahmed.f.gad@gmail.com"}, +] +maintainers = [ +{name = "Ahmed Gad", email = "ahmed.f.gad@gmail.com"} +] +classifiers = [ + "Programming Language :: Python" +] +keywords = ["genetic algorithm", "optimization", "natural evolution", "pygad", "machine learning", "deep learning", "neural networks", "tensorflow", "keras", "pytorch"] +dependencies = [ + "numpy", + "matplotlib", + "cloudpickle", +] + +[project.urls] +homepage = "https://github.com/ahmedfgad/GeneticAlgorithmPython" +documentation = "https://pygad.readthedocs.io" +github_repository = "https://github.com/ahmedfgad/GeneticAlgorithmPython" +pypi_project = "https://pypi.org/project/pygad" +conda_forge_project = "https://anaconda.org/conda-forge/pygad" +donation_stripe = "https://donate.stripe.com/eVa5kO866elKgM0144" +donation_open_collective = "https://opencollective.com/pygad" +donation_paypal = "http://paypal.me/ahmedfgad" + +[project.optional-dependencies] +deep_learning = ["keras", "torch"] From 97f3c1652218cb0aeae962e0e38d1ed81962d1da Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 18:57:59 -0400 Subject: [PATCH 024/220] Add more classifiers --- pyproject.toml | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4849c4a..fc1ddb4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,9 +12,23 @@ maintainers = [ {name = "Ahmed Gad", email = "ahmed.f.gad@gmail.com"} ] classifiers = [ - "Programming Language :: Python" + "License :: OSI Approved :: BSD License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Natural Language :: English", + "Operating System :: OS Independent", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Bio-Informatics", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Software Development", + "Topic :: Utilities", + "Intended Audience :: Information Technology", + "Intended Audience :: Science/Research", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Other Audience" ] -keywords = ["genetic algorithm", "optimization", "natural evolution", "pygad", "machine learning", "deep learning", "neural networks", "tensorflow", "keras", "pytorch"] +keywords = ["genetic algorithm", "GA", "optimization", "evolutionary algorithm", "natural evolution", "pygad", "machine learning", "deep learning", "neural networks", "tensorflow", "keras", "pytorch"] dependencies = [ "numpy", "matplotlib", From 2b79ddb535f351828146750df3b8d0d73731c310 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 19:05:34 -0400 Subject: [PATCH 025/220] Edit links --- pyproject.toml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index fc1ddb4..31a3fbf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,14 +36,14 @@ dependencies = [ ] [project.urls] -homepage = "https://github.com/ahmedfgad/GeneticAlgorithmPython" -documentation = "https://pygad.readthedocs.io" -github_repository = "https://github.com/ahmedfgad/GeneticAlgorithmPython" -pypi_project = "https://pypi.org/project/pygad" -conda_forge_project = "https://anaconda.org/conda-forge/pygad" -donation_stripe = "https://donate.stripe.com/eVa5kO866elKgM0144" -donation_open_collective = "https://opencollective.com/pygad" -donation_paypal = "http://paypal.me/ahmedfgad" +"Homepage" = "https://github.com/ahmedfgad/GeneticAlgorithmPython" +"Documentation" = "https://pygad.readthedocs.io" +"GitHub Repository" = "https://github.com/ahmedfgad/GeneticAlgorithmPython" +"PyPI Project" = "https://pypi.org/project/pygad" +"Conda Forge Project" = "https://anaconda.org/conda-forge/pygad" +"Donation Stripe" = "https://donate.stripe.com/eVa5kO866elKgM0144" +"Donation Open Collective" = "https://opencollective.com/pygad" +"Donation Paypal" = "http://paypal.me/ahmedfgad" [project.optional-dependencies] deep_learning = ["keras", "torch"] From ab972476109cd1d52333907b2ac4a324e583ac65 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sun, 16 Apr 2023 19:07:22 -0400 Subject: [PATCH 026/220] Add [build-system] table --- pyproject.toml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 31a3fbf..2e20768 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,7 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + [project] name = "pygad" version = "3.0.0" From 330e7bdcbdb9e85e4b48c2c7c54511428f1b1f72 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Thu, 20 Apr 2023 09:35:05 -0400 Subject: [PATCH 027/220] PyGAD 3.0.1 Fix an issue with passing user-defined function/method for parent selection. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/179 --- __init__.py | 2 +- pygad.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/__init__.py b/__init__.py index 71f207b..316b67c 100644 --- a/__init__.py +++ b/__init__.py @@ -1,3 +1,3 @@ from .pygad import * # Relative import. -__version__ = "3.0.0" +__version__ = "3.0.1" diff --git a/pygad.py b/pygad.py index 9916a15..b63e613 100644 --- a/pygad.py +++ b/pygad.py @@ -1747,9 +1747,9 @@ def run(self): # Selecting the best parents in the population for mating. if callable(self.parent_selection_type): - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self, - self.last_generation_fitness, - self.num_parents_mating, self) + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + self.num_parents_mating, + self) if not type(self.last_generation_parents) is numpy.ndarray: self.logger.error("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) raise TypeError("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) From 7dac6f05a8f45b83ef8ca8412b333e49610c1380 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Thu, 20 Apr 2023 09:37:48 -0400 Subject: [PATCH 028/220] PyGAD 3.0.1 Documentation --- docs/source/Footer.rst | 11 +++++++++++ docs/source/conf.py | 2 +- 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/docs/source/Footer.rst b/docs/source/Footer.rst index 6de145e..54814ba 100644 --- a/docs/source/Footer.rst +++ b/docs/source/Footer.rst @@ -1279,6 +1279,17 @@ Release Date 8 April 2023 operators roulette wheel and stochastic universal. https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/168 +.. _pygad-301: + +PyGAD 3.0.1 +----------- + +Release Date 20 April 2023 + +1. Fix an issue with passing user-defined function/method for parent + selection. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/179 + PyGAD Projects at GitHub ======================== diff --git a/docs/source/conf.py b/docs/source/conf.py index 0799f5e..eca9313 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,7 @@ author = 'Ahmed Fawzy Gad' # The full version, including alpha/beta/rc tags -release = '3.0.0' +release = '3.0.1' master_doc = 'index' From a1ab6aaeaff1c1001964e1950a29064072bb32c2 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Thu, 20 Apr 2023 09:48:26 -0400 Subject: [PATCH 029/220] Add tests --- pyproject.toml | 9 ++ tests/example_clustering_2.py | 122 +++++++++++++++++++++++++++ tests/example_clustering_3.py | 134 ++++++++++++++++++++++++++++++ tests/example_custom_operators.py | 74 +++++++++++++++++ tests/example_logger.py | 45 ++++++++++ tests/lifecycle.py | 48 +++++++++++ tests/test_example.py | 38 +++++++++ 7 files changed, 470 insertions(+) create mode 100644 tests/example_clustering_2.py create mode 100644 tests/example_clustering_3.py create mode 100644 tests/example_custom_operators.py create mode 100644 tests/example_logger.py create mode 100644 tests/lifecycle.py create mode 100644 tests/test_example.py diff --git a/pyproject.toml b/pyproject.toml index 2e20768..cc30824 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,3 +51,12 @@ dependencies = [ [project.optional-dependencies] deep_learning = ["keras", "torch"] + +# PyTest Configuration. Later, PyTest will support the [tool.pytest] table. +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "-ra -q" +testpaths = [ + "tests", + "integration", +] diff --git a/tests/example_clustering_2.py b/tests/example_clustering_2.py new file mode 100644 index 0000000..877e318 --- /dev/null +++ b/tests/example_clustering_2.py @@ -0,0 +1,122 @@ +import numpy +import matplotlib.pyplot +import pygad + +cluster1_num_samples = 10 +cluster1_x1_start = 0 +cluster1_x1_end = 5 +cluster1_x2_start = 2 +cluster1_x2_end = 6 +cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start +cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start + +cluster2_num_samples = 10 +cluster2_x1_start = 10 +cluster2_x1_end = 15 +cluster2_x2_start = 8 +cluster2_x2_end = 12 +cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start +cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start + +c1 = numpy.array([cluster1_x1, cluster1_x2]).T +c2 = numpy.array([cluster2_x1, cluster2_x2]).T + +data = numpy.concatenate((c1, c2), axis=0) + +matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) +matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) +matplotlib.pyplot.title("Optimal Clustering") +matplotlib.pyplot.show() + +def euclidean_distance(X, Y): + """ + Calculate the euclidean distance between X and Y. It accepts: + :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. + :Y should be of size f. In other words, it is a single sample. + + Returns a vector of N elements with the distances between the N samples and the Y. + """ + + return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) + +def cluster_data(solution, solution_idx): + """ + Clusters the data based on the current solution. + """ + + global num_cluster, data + feature_vector_length = data.shape[1] + cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. + all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. + clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. + clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. + + for clust_idx in range(num_clusters): + # Return the current cluster center. + cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) + # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. + cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) + all_clusters_dists.append(numpy.array(cluster_center_dists)) + + cluster_centers = numpy.array(cluster_centers) + all_clusters_dists = numpy.array(all_clusters_dists) + + # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. + # In other words, the array holds the sample's cluster index. + cluster_indices = numpy.argmin(all_clusters_dists, axis=0) + for clust_idx in range(num_clusters): + clusters.append(numpy.where(cluster_indices == clust_idx)[0]) + # Calculate the sum of distances for the cluster. + if len(clusters[clust_idx]) == 0: + # In case the cluster is empty (i.e. has zero samples). + clusters_sum_dist.append(0) + else: + # When the cluster is not empty (i.e. has at least 1 sample). + clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) + # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) + + clusters_sum_dist = numpy.array(clusters_sum_dist) + + return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist + +def fitness_func(ga_instance, solution, solution_idx): + _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) + + # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. + fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) + + return fitness + +num_clusters = 2 +num_genes = num_clusters * data.shape[1] + +ga_instance = pygad.GA(num_generations=100, + sol_per_pop=10, + num_parents_mating=5, + init_range_low=-6, + init_range_high=20, + keep_parents=2, + num_genes=num_genes, + fitness_func=fitness_func, + suppress_warnings=True) + +ga_instance.run() + +best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() +print("Best solution is {bs}".format(bs=best_solution)) +print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) +print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) + +cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) + +for cluster_idx in range(num_clusters): + cluster_x = data[clusters[cluster_idx], 0] + cluster_y = data[clusters[cluster_idx], 1] + matplotlib.pyplot.scatter(cluster_x, cluster_y) + matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) +matplotlib.pyplot.title("Clustering using PyGAD") +matplotlib.pyplot.show() diff --git a/tests/example_clustering_3.py b/tests/example_clustering_3.py new file mode 100644 index 0000000..608d54b --- /dev/null +++ b/tests/example_clustering_3.py @@ -0,0 +1,134 @@ +import numpy +import matplotlib.pyplot +import pygad + +cluster1_num_samples = 20 +cluster1_x1_start = 0 +cluster1_x1_end = 5 +cluster1_x2_start = 2 +cluster1_x2_end = 6 +cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start +cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start + +cluster2_num_samples = 20 +cluster2_x1_start = 4 +cluster2_x1_end = 12 +cluster2_x2_start = 14 +cluster2_x2_end = 18 +cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start +cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start + +cluster3_num_samples = 20 +cluster3_x1_start = 12 +cluster3_x1_end = 18 +cluster3_x2_start = 8 +cluster3_x2_end = 11 +cluster3_x1 = numpy.random.random(size=(cluster3_num_samples)) +cluster3_x1 = cluster3_x1 * (cluster3_x1_end - cluster3_x1_start) + cluster3_x1_start +cluster3_x2 = numpy.random.random(size=(cluster3_num_samples)) +cluster3_x2 = cluster3_x2 * (cluster3_x2_end - cluster3_x2_start) + cluster3_x2_start + +c1 = numpy.array([cluster1_x1, cluster1_x2]).T +c2 = numpy.array([cluster2_x1, cluster2_x2]).T +c3 = numpy.array([cluster3_x1, cluster3_x2]).T + +data = numpy.concatenate((c1, c2, c3), axis=0) + +matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) +matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) +matplotlib.pyplot.scatter(cluster3_x1, cluster3_x2) +matplotlib.pyplot.title("Optimal Clustering") +matplotlib.pyplot.show() + +def euclidean_distance(X, Y): + """ + Calculate the euclidean distance between X and Y. It accepts: + :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. + :Y should be of size f. In other words, it is a single sample. + + Returns a vector of N elements with the distances between the N samples and the Y. + """ + + return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) + +def cluster_data(solution, solution_idx): + """ + Clusters the data based on the current solution. + """ + + global num_clusters, feature_vector_length, data + cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. + all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. + clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. + clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. + + for clust_idx in range(num_clusters): + # Return the current cluster center. + cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) + # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. + cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) + all_clusters_dists.append(numpy.array(cluster_center_dists)) + + cluster_centers = numpy.array(cluster_centers) + all_clusters_dists = numpy.array(all_clusters_dists) + + # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. + # In other words, the array holds the sample's cluster index. + cluster_indices = numpy.argmin(all_clusters_dists, axis=0) + for clust_idx in range(num_clusters): + clusters.append(numpy.where(cluster_indices == clust_idx)[0]) + # Calculate the sum of distances for the cluster. + if len(clusters[clust_idx]) == 0: + # In case the cluster is empty (i.e. has zero samples). + clusters_sum_dist.append(0) + else: + # When the cluster is not empty (i.e. has at least 1 sample). + clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) + # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) + + clusters_sum_dist = numpy.array(clusters_sum_dist) + + return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist + +def fitness_func(ga_instance, solution, solution_idx): + _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) + + # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. + fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) + + return fitness + +num_clusters = 3 +feature_vector_length = data.shape[1] +num_genes = num_clusters * feature_vector_length + +ga_instance = pygad.GA(num_generations=100, + sol_per_pop=10, + init_range_low=0, + init_range_high=20, + num_parents_mating=5, + keep_parents=2, + num_genes=num_genes, + fitness_func=fitness_func, + suppress_warnings=True) + +ga_instance.run() + +best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() +print("Best solution is {bs}".format(bs=best_solution)) +print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) +print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) + +cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) + +for cluster_idx in range(num_clusters): + cluster_x = data[clusters[cluster_idx], 0] + cluster_y = data[clusters[cluster_idx], 1] + matplotlib.pyplot.scatter(cluster_x, cluster_y) + matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) +matplotlib.pyplot.title("Clustering using PyGAD") +matplotlib.pyplot.show() diff --git a/tests/example_custom_operators.py b/tests/example_custom_operators.py new file mode 100644 index 0000000..7261a54 --- /dev/null +++ b/tests/example_custom_operators.py @@ -0,0 +1,74 @@ +import pygad +import numpy + +""" +This script gives an example of using custom user-defined functions for the 3 operators: + 1) Parent selection. + 2) Crossover. + 3) Mutation. +For more information, check the User-Defined Crossover, Mutation, and Parent Selection Operators section in the documentation: + https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#user-defined-crossover-mutation-and-parent-selection-operators +""" + +equation_inputs = [4,-2,3.5] +desired_output = 44 + +def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + + return fitness + +def parent_selection_func(fitness, num_parents, ga_instance): + # Selects the best {num_parents} parents. Works as steady-state selection. + + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + fitness_sorted.reverse() + + parents = numpy.empty((num_parents, ga_instance.population.shape[1])) + + for parent_num in range(num_parents): + parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() + + return parents, numpy.array(fitness_sorted[:num_parents]) + +def crossover_func(parents, offspring_size, ga_instance): + # This is single-point crossover. + offspring = [] + idx = 0 + while len(offspring) != offspring_size[0]: + parent1 = parents[idx % parents.shape[0], :].copy() + parent2 = parents[(idx + 1) % parents.shape[0], :].copy() + + random_split_point = numpy.random.choice(range(offspring_size[0])) + + parent1[random_split_point:] = parent2[random_split_point:] + + offspring.append(parent1) + + idx += 1 + + return numpy.array(offspring) + +def mutation_func(offspring, ga_instance): + # This is random mutation that mutates a single gene. + for chromosome_idx in range(offspring.shape[0]): + # Make some random changes in 1 or more genes. + random_gene_idx = numpy.random.choice(range(offspring.shape[1])) + + offspring[chromosome_idx, random_gene_idx] += numpy.random.random() + + return offspring + +ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + parent_selection_type=parent_selection_func, + crossover_type=crossover_func, + mutation_type=mutation_func) + +ga_instance.run() +ga_instance.plot_fitness() diff --git a/tests/example_logger.py b/tests/example_logger.py new file mode 100644 index 0000000..d38a179 --- /dev/null +++ b/tests/example_logger.py @@ -0,0 +1,45 @@ +import logging +import pygad +import numpy + +level = logging.DEBUG +name = 'logfile.txt' + +logger = logging.getLogger(name) +logger.setLevel(level) + +file_handler = logging.FileHandler(name,'a+','utf-8') +file_handler.setLevel(logging.DEBUG) +file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') +file_handler.setFormatter(file_format) +logger.addHandler(file_handler) + +console_handler = logging.StreamHandler() +console_handler.setLevel(logging.INFO) +console_format = logging.Formatter('%(message)s') +console_handler.setFormatter(console_format) +logger.addHandler(console_handler) + +equation_inputs = [4, -2, 8] +desired_output = 2671.1234 + +def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + +def on_generation(ga_instance): + ga_instance.logger.info("Generation = {generation}".format(generation=ga_instance.generations_completed)) + ga_instance.logger.info("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1])) + +ga_instance = pygad.GA(num_generations=10, + sol_per_pop=40, + num_parents_mating=2, + keep_parents=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + on_generation=on_generation, + logger=logger) +ga_instance.run() + +logger.handlers.clear() diff --git a/tests/lifecycle.py b/tests/lifecycle.py new file mode 100644 index 0000000..8eeae5b --- /dev/null +++ b/tests/lifecycle.py @@ -0,0 +1,48 @@ +import pygad +import numpy + +function_inputs = [4,-2,3.5,5,-11,-4.7] +desired_output = 44 + +def fitness_func(ga_instanse, solution, solution_idx): + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + +fitness_function = fitness_func + +def on_start(ga_instance): + print("on_start()") + +def on_fitness(ga_instance, population_fitness): + print("on_fitness()") + +def on_parents(ga_instance, selected_parents): + print("on_parents()") + +def on_crossover(ga_instance, offspring_crossover): + print("on_crossover()") + +def on_mutation(ga_instance, offspring_mutation): + print("on_mutation()") + +def on_generation(ga_instance): + print("on_generation()") + +def on_stop(ga_instance, last_population_fitness): + print("on_stop") + +ga_instance = pygad.GA(num_generations=3, + num_parents_mating=5, + fitness_func=fitness_function, + sol_per_pop=10, + num_genes=len(function_inputs), + on_start=on_start, + on_fitness=on_fitness, + on_parents=on_parents, + on_crossover=on_crossover, + on_mutation=on_mutation, + on_generation=on_generation, + on_stop=on_stop) + +ga_instance.run() diff --git a/tests/test_example.py b/tests/test_example.py new file mode 100644 index 0000000..67c82f2 --- /dev/null +++ b/tests/test_example.py @@ -0,0 +1,38 @@ +import pygad +import numpy as np + +def fitness_func(ga_instance, x, x_idx): + rng_noise = np.random.default_rng(678910) + dummy_fit = rng_noise.random()*100 + x = np.sort(x) + return dummy_fit + + +gene_space = np.arange(1,41,1) + +ga_instance = pygad.GA(num_generations = 20, + num_parents_mating = 40, + sol_per_pop = 50, + num_genes = 6, + init_range_low = gene_space[0], + init_range_high = gene_space[-1], + gene_space = gene_space, + gene_type = int, + keep_elitism = 2, + mutation_probability = 0.025, + fitness_func = fitness_func, + save_solutions = True, + allow_duplicate_genes = True, + suppress_warnings=True, + random_seed=12345) +ga_instance.run() + +trial = ga_instance.solutions +trial = np.sort(trial) + +unique_genes = [] +for i_genes in range(trial.shape[0]): + unique_genes.append(np.unique(trial[i_genes,:])) + +for unique_gene in unique_genes: + assert len(unique_gene) == len(trial[0]) From c96411865f117a2ed9bf12389dd95e9377bf4888 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 25 Apr 2023 16:27:28 -0400 Subject: [PATCH 030/220] PyTest metadata --- pyproject.toml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index cc30824..f7afdf5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,9 +54,4 @@ deep_learning = ["keras", "torch"] # PyTest Configuration. Later, PyTest will support the [tool.pytest] table. [tool.pytest.ini_options] -minversion = "6.0" -addopts = "-ra -q" -testpaths = [ - "tests", - "integration", -] +testpaths = ["tests"] \ No newline at end of file From 44cf9fb997c270f2749f1bcaf931d3801186af31 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 25 Apr 2023 16:31:34 -0400 Subject: [PATCH 031/220] Running tests using GitHub Actions --- .github/workflows/main.yml | 27 ++++ tests/example_clustering_2.py | 122 ------------------ tests/example_clustering_3.py | 134 -------------------- tests/example_custom_operators.py | 74 ----------- tests/example_logger.py | 45 ------- tests/lifecycle.py | 48 ------- tests/test_example.py | 38 ------ tests/test_number_fitness_function_calls.py | 85 +++++++++++++ 8 files changed, 112 insertions(+), 461 deletions(-) delete mode 100644 tests/example_clustering_2.py delete mode 100644 tests/example_clustering_3.py delete mode 100644 tests/example_custom_operators.py delete mode 100644 tests/example_logger.py delete mode 100644 tests/lifecycle.py delete mode 100644 tests/test_example.py create mode 100644 tests/test_number_fitness_function_calls.py diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 8b13789..099980d 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1 +1,28 @@ +name: Testing PyGAD using PyTest +on: + push: + branches: + - main + - master + +jobs: + job_id_1: + runs-on: ubuntu-latest + name: PyTest Workflow Job + + steps: + - name: Checkout Pre-Built Action + uses: actions/checkout@v3 + + - name: Setup Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.10' + + - name: Install PyTest + run: pip install pytest + + - name: Call PyTest + run: | + pytest diff --git a/tests/example_clustering_2.py b/tests/example_clustering_2.py deleted file mode 100644 index 877e318..0000000 --- a/tests/example_clustering_2.py +++ /dev/null @@ -1,122 +0,0 @@ -import numpy -import matplotlib.pyplot -import pygad - -cluster1_num_samples = 10 -cluster1_x1_start = 0 -cluster1_x1_end = 5 -cluster1_x2_start = 2 -cluster1_x2_end = 6 -cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start -cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start - -cluster2_num_samples = 10 -cluster2_x1_start = 10 -cluster2_x1_end = 15 -cluster2_x2_start = 8 -cluster2_x2_end = 12 -cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start -cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start - -c1 = numpy.array([cluster1_x1, cluster1_x2]).T -c2 = numpy.array([cluster2_x1, cluster2_x2]).T - -data = numpy.concatenate((c1, c2), axis=0) - -matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) -matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) -matplotlib.pyplot.title("Optimal Clustering") -matplotlib.pyplot.show() - -def euclidean_distance(X, Y): - """ - Calculate the euclidean distance between X and Y. It accepts: - :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. - :Y should be of size f. In other words, it is a single sample. - - Returns a vector of N elements with the distances between the N samples and the Y. - """ - - return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) - -def cluster_data(solution, solution_idx): - """ - Clusters the data based on the current solution. - """ - - global num_cluster, data - feature_vector_length = data.shape[1] - cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. - all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. - clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. - clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. - - for clust_idx in range(num_clusters): - # Return the current cluster center. - cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) - # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. - cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) - all_clusters_dists.append(numpy.array(cluster_center_dists)) - - cluster_centers = numpy.array(cluster_centers) - all_clusters_dists = numpy.array(all_clusters_dists) - - # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. - # In other words, the array holds the sample's cluster index. - cluster_indices = numpy.argmin(all_clusters_dists, axis=0) - for clust_idx in range(num_clusters): - clusters.append(numpy.where(cluster_indices == clust_idx)[0]) - # Calculate the sum of distances for the cluster. - if len(clusters[clust_idx]) == 0: - # In case the cluster is empty (i.e. has zero samples). - clusters_sum_dist.append(0) - else: - # When the cluster is not empty (i.e. has at least 1 sample). - clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) - # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) - - clusters_sum_dist = numpy.array(clusters_sum_dist) - - return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist - -def fitness_func(ga_instance, solution, solution_idx): - _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) - - # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. - fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) - - return fitness - -num_clusters = 2 -num_genes = num_clusters * data.shape[1] - -ga_instance = pygad.GA(num_generations=100, - sol_per_pop=10, - num_parents_mating=5, - init_range_low=-6, - init_range_high=20, - keep_parents=2, - num_genes=num_genes, - fitness_func=fitness_func, - suppress_warnings=True) - -ga_instance.run() - -best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() -print("Best solution is {bs}".format(bs=best_solution)) -print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) -print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) - -cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) - -for cluster_idx in range(num_clusters): - cluster_x = data[clusters[cluster_idx], 0] - cluster_y = data[clusters[cluster_idx], 1] - matplotlib.pyplot.scatter(cluster_x, cluster_y) - matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) -matplotlib.pyplot.title("Clustering using PyGAD") -matplotlib.pyplot.show() diff --git a/tests/example_clustering_3.py b/tests/example_clustering_3.py deleted file mode 100644 index 608d54b..0000000 --- a/tests/example_clustering_3.py +++ /dev/null @@ -1,134 +0,0 @@ -import numpy -import matplotlib.pyplot -import pygad - -cluster1_num_samples = 20 -cluster1_x1_start = 0 -cluster1_x1_end = 5 -cluster1_x2_start = 2 -cluster1_x2_end = 6 -cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start -cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start - -cluster2_num_samples = 20 -cluster2_x1_start = 4 -cluster2_x1_end = 12 -cluster2_x2_start = 14 -cluster2_x2_end = 18 -cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start -cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start - -cluster3_num_samples = 20 -cluster3_x1_start = 12 -cluster3_x1_end = 18 -cluster3_x2_start = 8 -cluster3_x2_end = 11 -cluster3_x1 = numpy.random.random(size=(cluster3_num_samples)) -cluster3_x1 = cluster3_x1 * (cluster3_x1_end - cluster3_x1_start) + cluster3_x1_start -cluster3_x2 = numpy.random.random(size=(cluster3_num_samples)) -cluster3_x2 = cluster3_x2 * (cluster3_x2_end - cluster3_x2_start) + cluster3_x2_start - -c1 = numpy.array([cluster1_x1, cluster1_x2]).T -c2 = numpy.array([cluster2_x1, cluster2_x2]).T -c3 = numpy.array([cluster3_x1, cluster3_x2]).T - -data = numpy.concatenate((c1, c2, c3), axis=0) - -matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) -matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) -matplotlib.pyplot.scatter(cluster3_x1, cluster3_x2) -matplotlib.pyplot.title("Optimal Clustering") -matplotlib.pyplot.show() - -def euclidean_distance(X, Y): - """ - Calculate the euclidean distance between X and Y. It accepts: - :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. - :Y should be of size f. In other words, it is a single sample. - - Returns a vector of N elements with the distances between the N samples and the Y. - """ - - return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) - -def cluster_data(solution, solution_idx): - """ - Clusters the data based on the current solution. - """ - - global num_clusters, feature_vector_length, data - cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. - all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. - clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. - clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. - - for clust_idx in range(num_clusters): - # Return the current cluster center. - cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) - # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. - cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) - all_clusters_dists.append(numpy.array(cluster_center_dists)) - - cluster_centers = numpy.array(cluster_centers) - all_clusters_dists = numpy.array(all_clusters_dists) - - # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. - # In other words, the array holds the sample's cluster index. - cluster_indices = numpy.argmin(all_clusters_dists, axis=0) - for clust_idx in range(num_clusters): - clusters.append(numpy.where(cluster_indices == clust_idx)[0]) - # Calculate the sum of distances for the cluster. - if len(clusters[clust_idx]) == 0: - # In case the cluster is empty (i.e. has zero samples). - clusters_sum_dist.append(0) - else: - # When the cluster is not empty (i.e. has at least 1 sample). - clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) - # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) - - clusters_sum_dist = numpy.array(clusters_sum_dist) - - return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist - -def fitness_func(ga_instance, solution, solution_idx): - _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) - - # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. - fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) - - return fitness - -num_clusters = 3 -feature_vector_length = data.shape[1] -num_genes = num_clusters * feature_vector_length - -ga_instance = pygad.GA(num_generations=100, - sol_per_pop=10, - init_range_low=0, - init_range_high=20, - num_parents_mating=5, - keep_parents=2, - num_genes=num_genes, - fitness_func=fitness_func, - suppress_warnings=True) - -ga_instance.run() - -best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() -print("Best solution is {bs}".format(bs=best_solution)) -print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) -print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) - -cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) - -for cluster_idx in range(num_clusters): - cluster_x = data[clusters[cluster_idx], 0] - cluster_y = data[clusters[cluster_idx], 1] - matplotlib.pyplot.scatter(cluster_x, cluster_y) - matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) -matplotlib.pyplot.title("Clustering using PyGAD") -matplotlib.pyplot.show() diff --git a/tests/example_custom_operators.py b/tests/example_custom_operators.py deleted file mode 100644 index 7261a54..0000000 --- a/tests/example_custom_operators.py +++ /dev/null @@ -1,74 +0,0 @@ -import pygad -import numpy - -""" -This script gives an example of using custom user-defined functions for the 3 operators: - 1) Parent selection. - 2) Crossover. - 3) Mutation. -For more information, check the User-Defined Crossover, Mutation, and Parent Selection Operators section in the documentation: - https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#user-defined-crossover-mutation-and-parent-selection-operators -""" - -equation_inputs = [4,-2,3.5] -desired_output = 44 - -def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - - return fitness - -def parent_selection_func(fitness, num_parents, ga_instance): - # Selects the best {num_parents} parents. Works as steady-state selection. - - fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) - fitness_sorted.reverse() - - parents = numpy.empty((num_parents, ga_instance.population.shape[1])) - - for parent_num in range(num_parents): - parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() - - return parents, numpy.array(fitness_sorted[:num_parents]) - -def crossover_func(parents, offspring_size, ga_instance): - # This is single-point crossover. - offspring = [] - idx = 0 - while len(offspring) != offspring_size[0]: - parent1 = parents[idx % parents.shape[0], :].copy() - parent2 = parents[(idx + 1) % parents.shape[0], :].copy() - - random_split_point = numpy.random.choice(range(offspring_size[0])) - - parent1[random_split_point:] = parent2[random_split_point:] - - offspring.append(parent1) - - idx += 1 - - return numpy.array(offspring) - -def mutation_func(offspring, ga_instance): - # This is random mutation that mutates a single gene. - for chromosome_idx in range(offspring.shape[0]): - # Make some random changes in 1 or more genes. - random_gene_idx = numpy.random.choice(range(offspring.shape[1])) - - offspring[chromosome_idx, random_gene_idx] += numpy.random.random() - - return offspring - -ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - parent_selection_type=parent_selection_func, - crossover_type=crossover_func, - mutation_type=mutation_func) - -ga_instance.run() -ga_instance.plot_fitness() diff --git a/tests/example_logger.py b/tests/example_logger.py deleted file mode 100644 index d38a179..0000000 --- a/tests/example_logger.py +++ /dev/null @@ -1,45 +0,0 @@ -import logging -import pygad -import numpy - -level = logging.DEBUG -name = 'logfile.txt' - -logger = logging.getLogger(name) -logger.setLevel(level) - -file_handler = logging.FileHandler(name,'a+','utf-8') -file_handler.setLevel(logging.DEBUG) -file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') -file_handler.setFormatter(file_format) -logger.addHandler(file_handler) - -console_handler = logging.StreamHandler() -console_handler.setLevel(logging.INFO) -console_format = logging.Formatter('%(message)s') -console_handler.setFormatter(console_format) -logger.addHandler(console_handler) - -equation_inputs = [4, -2, 8] -desired_output = 2671.1234 - -def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - -def on_generation(ga_instance): - ga_instance.logger.info("Generation = {generation}".format(generation=ga_instance.generations_completed)) - ga_instance.logger.info("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1])) - -ga_instance = pygad.GA(num_generations=10, - sol_per_pop=40, - num_parents_mating=2, - keep_parents=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - on_generation=on_generation, - logger=logger) -ga_instance.run() - -logger.handlers.clear() diff --git a/tests/lifecycle.py b/tests/lifecycle.py deleted file mode 100644 index 8eeae5b..0000000 --- a/tests/lifecycle.py +++ /dev/null @@ -1,48 +0,0 @@ -import pygad -import numpy - -function_inputs = [4,-2,3.5,5,-11,-4.7] -desired_output = 44 - -def fitness_func(ga_instanse, solution, solution_idx): - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - -fitness_function = fitness_func - -def on_start(ga_instance): - print("on_start()") - -def on_fitness(ga_instance, population_fitness): - print("on_fitness()") - -def on_parents(ga_instance, selected_parents): - print("on_parents()") - -def on_crossover(ga_instance, offspring_crossover): - print("on_crossover()") - -def on_mutation(ga_instance, offspring_mutation): - print("on_mutation()") - -def on_generation(ga_instance): - print("on_generation()") - -def on_stop(ga_instance, last_population_fitness): - print("on_stop") - -ga_instance = pygad.GA(num_generations=3, - num_parents_mating=5, - fitness_func=fitness_function, - sol_per_pop=10, - num_genes=len(function_inputs), - on_start=on_start, - on_fitness=on_fitness, - on_parents=on_parents, - on_crossover=on_crossover, - on_mutation=on_mutation, - on_generation=on_generation, - on_stop=on_stop) - -ga_instance.run() diff --git a/tests/test_example.py b/tests/test_example.py deleted file mode 100644 index 67c82f2..0000000 --- a/tests/test_example.py +++ /dev/null @@ -1,38 +0,0 @@ -import pygad -import numpy as np - -def fitness_func(ga_instance, x, x_idx): - rng_noise = np.random.default_rng(678910) - dummy_fit = rng_noise.random()*100 - x = np.sort(x) - return dummy_fit - - -gene_space = np.arange(1,41,1) - -ga_instance = pygad.GA(num_generations = 20, - num_parents_mating = 40, - sol_per_pop = 50, - num_genes = 6, - init_range_low = gene_space[0], - init_range_high = gene_space[-1], - gene_space = gene_space, - gene_type = int, - keep_elitism = 2, - mutation_probability = 0.025, - fitness_func = fitness_func, - save_solutions = True, - allow_duplicate_genes = True, - suppress_warnings=True, - random_seed=12345) -ga_instance.run() - -trial = ga_instance.solutions -trial = np.sort(trial) - -unique_genes = [] -for i_genes in range(trial.shape[0]): - unique_genes.append(np.unique(trial[i_genes,:])) - -for unique_gene in unique_genes: - assert len(unique_gene) == len(trial[0]) diff --git a/tests/test_number_fitness_function_calls.py b/tests/test_number_fitness_function_calls.py new file mode 100644 index 0000000..8863632 --- /dev/null +++ b/tests/test_number_fitness_function_calls.py @@ -0,0 +1,85 @@ +import pygad + +actual_num_fitness_calls_default_keep = 0 +actual_num_fitness_calls_no_keep = 0 +actual_num_fitness_calls_keep_elitism = 0 +actual_num_fitness_calls_keep_parents = 0 + +num_generations = 100 +sol_per_pop = 10 +num_parents_mating = 5 + +def number_calls_fitness_function(keep_elitism, + keep_parents): + + actual_num_fitness_calls = 0 + def fitness_func(ga, solution, idx): + nonlocal actual_num_fitness_calls + actual_num_fitness_calls = actual_num_fitness_calls + 1 + return 1 + + ga_optimizer = pygad.GA(num_generations=num_generations, + sol_per_pop=sol_per_pop, + num_genes=6, + num_parents_mating=num_parents_mating, + fitness_func=fitness_func, + keep_elitism=keep_elitism, + keep_parents=keep_parents, + suppress_warnings=True) + + ga_optimizer.run() + if keep_elitism == 0: + if keep_parents == 0: + # 10 (for initial population) + 100*10 (for other generations) = 1010 + expected_num_fitness_calls = sol_per_pop + num_generations * sol_per_pop + elif keep_parents == -1: + # 10 (for initial population) + 100*num_parents_mating (for other generations) + expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - num_parents_mating) + else: + # 10 (for initial population) + 100*keep_parents (for other generations) + expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - keep_parents) + else: + # 10 (for initial population) + 100*keep_elitism (for other generations) + expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - keep_elitism) + + print("Expected number of fitness function calls is {expected_num_fitness_calls}.".format(expected_num_fitness_calls=expected_num_fitness_calls)) + print("Actual number of fitness function calls is {actual_num_fitness_calls}.".format(actual_num_fitness_calls=actual_num_fitness_calls)) + return actual_num_fitness_calls, expected_num_fitness_calls + +def test_number_calls_fitness_function_default_keep(): + actual, expected = number_calls_fitness_function(keep_elitism=1, + keep_parents=-1) + assert actual == expected + +def test_number_calls_fitness_function_no_keep(): + actual, expected = number_calls_fitness_function(keep_elitism=0, + keep_parents=0) + assert actual == expected + +def test_number_calls_fitness_function_keep_elitism(): + actual, expected = number_calls_fitness_function(keep_elitism=3, + keep_parents=0) + assert actual == expected + +def test_number_calls_fitness_function_keep_parents(): + actual, expected = number_calls_fitness_function(keep_elitism=0, + keep_parents=4) + assert actual == expected + +def test_number_calls_fitness_function_both_keep(): + actual, expected = number_calls_fitness_function(keep_elitism=3, + keep_parents=4) + assert actual == expected + +if __name__ == "__main__": + print() + test_number_calls_fitness_function_default_keep() + print() + test_number_calls_fitness_function_no_keep() + print() + test_number_calls_fitness_function_keep_elitism() + print() + test_number_calls_fitness_function_keep_parents() + print() + test_number_calls_fitness_function_both_keep() + print() From 36b7bc3126123dd15f79180fa50cde38da0a7fa7 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 25 Apr 2023 16:32:52 -0400 Subject: [PATCH 032/220] Running tests on github-actions branch --- .github/workflows/main.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 099980d..6a4aae4 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -3,8 +3,8 @@ name: Testing PyGAD using PyTest on: push: branches: - - main - - master + - github-actions + # - master jobs: job_id_1: From b3d55d27c751dd986e39d536efc25126445b70dd Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 25 Apr 2023 16:45:35 -0400 Subject: [PATCH 033/220] Build and install PyGAD in Actions Workflow --- .github/workflows/main.yml | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6a4aae4..7e45fa9 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -19,10 +19,19 @@ jobs: uses: actions/setup-python@v4 with: python-version: '3.10' - + + - name: Build PyGAD from the Repository + run: | + python3 -m pip install --upgrade build + python3 -m build + + - name: Install PyGAD after Building the .whl File + run: | + find ./dist/*.whl | xargs pip install + - name: Install PyTest run: pip install pytest - - name: Call PyTest + - name: Run the Tests by Calling PyTest run: | pytest From 6e19c042127adb4098d022660679175f6c7d8c94 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Thu, 27 Apr 2023 19:09:34 -0400 Subject: [PATCH 034/220] Update the version --- pyproject.toml | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f7afdf5..413e391 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "pygad" -version = "3.0.0" +version = "3.0.1" description = "PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch)." readme = {file = "README.md", content-type = "text/markdown"} requires-python = ">=3" diff --git a/setup.py b/setup.py index 7ce2f32..4978a72 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name="pygad", - version="3.0.0", + version="3.0.1", author="Ahmed Fawzy Gad", install_requires=["numpy", "matplotlib", "cloudpickle",], author_email="ahmed.f.gad@gmail.com", From 262463ee85259cc167cd3935ea331004bf3fcbe1 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Thu, 27 Apr 2023 19:57:27 -0400 Subject: [PATCH 035/220] Test number of callbacks calls --- tests/test_lifecycle.py | 182 ++++++++++++++++++++ tests/test_number_fitness_function_calls.py | 52 +++++- 2 files changed, 226 insertions(+), 8 deletions(-) create mode 100644 tests/test_lifecycle.py diff --git a/tests/test_lifecycle.py b/tests/test_lifecycle.py new file mode 100644 index 0000000..65b38c5 --- /dev/null +++ b/tests/test_lifecycle.py @@ -0,0 +1,182 @@ +import pygad + +num_generations = 100 + +def number_lifecycle_callback_functions_calls(stop_criteria=None, + on_generation_stop=None): + actual_num_callbacks_calls = 0 + + def fitness_func(ga_instanse, solution, solution_idx): + return 1 + + def on_start(ga_instance): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_fitness(ga_instance, population_fitness): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_parents(ga_instance, selected_parents): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_crossover(ga_instance, offspring_crossover): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_mutation(ga_instance, offspring_mutation): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_generation(ga_instance): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + if on_generation_stop: + if ga_instance.generations_completed == on_generation_stop: + return "stop" + + def on_stop(ga_instance, last_population_fitness): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=5, + fitness_func=fitness_func, + sol_per_pop=10, + num_genes=5, + on_start=on_start, + on_fitness=on_fitness, + on_parents=on_parents, + on_crossover=on_crossover, + on_mutation=on_mutation, + on_generation=on_generation, + on_stop=on_stop, + stop_criteria=stop_criteria, + suppress_warnings=True) + + ga_instance.run() + + # The total number is: + # 1 [for on_start()] + + # num_generations [for on_fitness()] + + # num_generations [for on_parents()] + + # num_generations [for on_crossover()] + + # num_generations [for on_mutation()] + + # num_generations [for on_generation()] + + # 1 [for on_stop()] + # = 1 + num_generations * 5 + 1 + + # Use 'generations_completed' instead of 'num_generations' because the evolution may stops in the on_generation() callback. + expected_num_callbacks_calls = 1 + ga_instance.generations_completed * 5 + 1 + + print("Expected number of callbacks calls is {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls)) + print("Actual number of callbacks calls is {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls)) + return actual_num_callbacks_calls, expected_num_callbacks_calls + +def number_lifecycle_callback_methods_calls(stop_criteria=None, + on_generation_stop=None): + actual_num_callbacks_calls = 0 + + class Callbacks: + def fitness_func(self, ga_instanse, solution, solution_idx): + return 1 + + def on_start(self, ga_instance): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_fitness(self, ga_instance, population_fitness): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_parents(self, ga_instance, selected_parents): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_crossover(self, ga_instance, offspring_crossover): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_mutation(self, ga_instance, offspring_mutation): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + def on_generation(self, ga_instance): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + if on_generation_stop: + if ga_instance.generations_completed == on_generation_stop: + return "stop" + + def on_stop(self, ga_instance, last_population_fitness): + nonlocal actual_num_callbacks_calls + actual_num_callbacks_calls = actual_num_callbacks_calls + 1 + + Callbacks_obj = Callbacks() + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=5, + fitness_func=Callbacks_obj.fitness_func, + sol_per_pop=10, + num_genes=5, + on_start=Callbacks_obj.on_start, + on_fitness=Callbacks_obj.on_fitness, + on_parents=Callbacks_obj.on_parents, + on_crossover=Callbacks_obj.on_crossover, + on_mutation=Callbacks_obj.on_mutation, + on_generation=Callbacks_obj.on_generation, + on_stop=Callbacks_obj.on_stop, + stop_criteria=stop_criteria, + suppress_warnings=True) + + ga_instance.run() + + # The total number is: + # 1 [for on_start()] + + # num_generations [for on_fitness()] + + # num_generations [for on_parents()] + + # num_generations [for on_crossover()] + + # num_generations [for on_mutation()] + + # num_generations [for on_generation()] + + # 1 [for on_stop()] + # = 1 + num_generations * 5 + 1 + + # Use 'generations_completed' instead of 'num_generations' because the evolution may stops in the on_generation() callback. + expected_num_callbacks_calls = 1 + ga_instance.generations_completed * 5 + 1 + + print("Expected number of callbacks calls is {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls)) + print("Actual number of callbacks calls is {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls)) + return actual_num_callbacks_calls, expected_num_callbacks_calls + +def test_number_lifecycle_callback_functions_calls(): + actual, expected = number_lifecycle_callback_functions_calls() + + assert actual == expected + +def test_number_lifecycle_callback_functions_calls_stop_criteria(): + actual, expected = number_lifecycle_callback_functions_calls(on_generation_stop=30) + + assert actual == expected + +def test_number_lifecycle_callback_methods_calls(): + actual, expected = number_lifecycle_callback_methods_calls() + + assert actual == expected + +def test_number_lifecycle_callback_methods_calls_stop_criteria(): + actual, expected = number_lifecycle_callback_methods_calls(on_generation_stop=30) + + assert actual == expected + +if __name__ == "__main__": + print() + test_number_lifecycle_callback_functions_calls() + print() + test_number_lifecycle_callback_functions_calls_stop_criteria() + print() + test_number_lifecycle_callback_methods_calls() + print() + test_number_lifecycle_callback_methods_calls_stop_criteria() + print() diff --git a/tests/test_number_fitness_function_calls.py b/tests/test_number_fitness_function_calls.py index 8863632..c628606 100644 --- a/tests/test_number_fitness_function_calls.py +++ b/tests/test_number_fitness_function_calls.py @@ -9,8 +9,10 @@ sol_per_pop = 10 num_parents_mating = 5 -def number_calls_fitness_function(keep_elitism, - keep_parents): +def number_calls_fitness_function(keep_elitism=1, + keep_parents=-1, + mutation_type="random", + mutation_percent_genes="default"): actual_num_fitness_calls = 0 def fitness_func(ga, solution, idx): @@ -23,6 +25,8 @@ def fitness_func(ga, solution, idx): num_genes=6, num_parents_mating=num_parents_mating, fitness_func=fitness_func, + mutation_type=mutation_type, + mutation_percent_genes=mutation_percent_genes, keep_elitism=keep_elitism, keep_parents=keep_parents, suppress_warnings=True) @@ -32,43 +36,69 @@ def fitness_func(ga, solution, idx): if keep_parents == 0: # 10 (for initial population) + 100*10 (for other generations) = 1010 expected_num_fitness_calls = sol_per_pop + num_generations * sol_per_pop + if mutation_type == "adaptive": + expected_num_fitness_calls += num_generations * sol_per_pop elif keep_parents == -1: # 10 (for initial population) + 100*num_parents_mating (for other generations) expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - num_parents_mating) + if mutation_type == "adaptive": + expected_num_fitness_calls += num_generations * (sol_per_pop - num_parents_mating) else: # 10 (for initial population) + 100*keep_parents (for other generations) expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - keep_parents) + if mutation_type == "adaptive": + expected_num_fitness_calls += num_generations * (sol_per_pop - keep_parents) else: # 10 (for initial population) + 100*keep_elitism (for other generations) expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - keep_elitism) + if mutation_type == "adaptive": + expected_num_fitness_calls += num_generations * (sol_per_pop - keep_elitism) print("Expected number of fitness function calls is {expected_num_fitness_calls}.".format(expected_num_fitness_calls=expected_num_fitness_calls)) print("Actual number of fitness function calls is {actual_num_fitness_calls}.".format(actual_num_fitness_calls=actual_num_fitness_calls)) return actual_num_fitness_calls, expected_num_fitness_calls def test_number_calls_fitness_function_default_keep(): - actual, expected = number_calls_fitness_function(keep_elitism=1, - keep_parents=-1) + actual, expected = number_calls_fitness_function() assert actual == expected def test_number_calls_fitness_function_no_keep(): actual, expected = number_calls_fitness_function(keep_elitism=0, - keep_parents=0) + keep_parents=0) assert actual == expected def test_number_calls_fitness_function_keep_elitism(): actual, expected = number_calls_fitness_function(keep_elitism=3, - keep_parents=0) + keep_parents=0) assert actual == expected def test_number_calls_fitness_function_keep_parents(): actual, expected = number_calls_fitness_function(keep_elitism=0, - keep_parents=4) + keep_parents=4) assert actual == expected def test_number_calls_fitness_function_both_keep(): actual, expected = number_calls_fitness_function(keep_elitism=3, - keep_parents=4) + keep_parents=4) + assert actual == expected + +def test_number_calls_fitness_function_no_keep_adaptive_mutation(): + actual, expected = number_calls_fitness_function(keep_elitism=0, + keep_parents=0, + mutation_type="adaptive", + mutation_percent_genes=[10, 5]) + assert actual == expected + +def test_number_calls_fitness_function_default_adaptive_mutation(): + actual, expected = number_calls_fitness_function(mutation_type="adaptive", + mutation_percent_genes=[10, 5]) + assert actual == expected + +def test_number_calls_fitness_function_both_keep_adaptive_mutation(): + actual, expected = number_calls_fitness_function(keep_elitism=3, + keep_parents=4, + mutation_type="adaptive", + mutation_percent_genes=[10, 5]) assert actual == expected if __name__ == "__main__": @@ -83,3 +113,9 @@ def test_number_calls_fitness_function_both_keep(): print() test_number_calls_fitness_function_both_keep() print() + test_number_calls_fitness_function_no_keep_adaptive_mutation() + print() + test_number_calls_fitness_function_default_adaptive_mutation() + print() + test_number_calls_fitness_function_both_keep_adaptive_mutation() + print() From d7747697badcb8dc3dd578de241a42247683caaa Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Thu, 27 Apr 2023 19:58:11 -0400 Subject: [PATCH 036/220] Change test file name --- tests/{test_lifecycle.py => test_lifecycle_callbacks_calls.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/{test_lifecycle.py => test_lifecycle_callbacks_calls.py} (100%) diff --git a/tests/test_lifecycle.py b/tests/test_lifecycle_callbacks_calls.py similarity index 100% rename from tests/test_lifecycle.py rename to tests/test_lifecycle_callbacks_calls.py From 66ec89550edafb1f8f76aa46e09c694814db6df2 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Mon, 1 May 2023 19:11:03 -0400 Subject: [PATCH 037/220] Solve duplicates deeply --- examples/example_fitness_wrapper.py | 41 +++ pygad/helper/unique.py | 547 +++++++++++++++++----------- pygad/pygad.py | 67 ++-- pygad/utils/mutation.py | 1 + tests/test_allow_duplicate_genes.py | 150 ++++++++ 5 files changed, 574 insertions(+), 232 deletions(-) create mode 100644 examples/example_fitness_wrapper.py create mode 100644 tests/test_allow_duplicate_genes.py diff --git a/examples/example_fitness_wrapper.py b/examples/example_fitness_wrapper.py new file mode 100644 index 0000000..6a9acf1 --- /dev/null +++ b/examples/example_fitness_wrapper.py @@ -0,0 +1,41 @@ +import pygad +import numpy + +""" +All the callback functions/methods in PyGAD have limits in the number of arguments passed. +For example, the fitness function accepts only 3 arguments: + 1. The pygad.GA instance. + 2. The solution(s). + 3. The index (indices) of the passed solution(s). +If it is necessary to pass extra arguments to the fitness function, for example, then follow these steps: + 1. Create a wrapper function that accepts only the number of arguments meeded by PyGAD. + 2. Define the extra arguments in the body of the wrapper function. + 3. Create an inner fitness function inside the wrapper function with whatever extra arguments needed. + 4. Call the inner fitness function from the wrapper function while passing the extra arguments. + +This is an example that passes a list ([10, 20, 30]) to the inner fitness function. The list has 3 numbers. +A number is randomly selected from the list and added to the calculated fitness. +""" + +function_inputs = [4,-2,3.5,5,-11,-4.7] +desired_output = 44 + +def fitness_func_wrapper(ga_instanse, solution, solution_idx): + def fitness_func(ga_instanse, solution, solution_idx, *args): + output = numpy.sum(solution*function_inputs) + output += numpy.random.choice(args) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + args = [10, 20, 30] + fitness = fitness_func(ga_instanse, solution, solution_idx, *args) + return fitness + +ga_instance = pygad.GA(num_generations=3, + num_parents_mating=5, + fitness_func=fitness_func_wrapper, + sol_per_pop=10, + num_genes=len(function_inputs), + suppress_warnings=True) + +ga_instance.run() +ga_instance.plot_fitness() diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py index 4d5e6ac..bd2da60 100644 --- a/pygad/helper/unique.py +++ b/pygad/helper/unique.py @@ -1,13 +1,16 @@ """ The pygad.helper.unique module has helper methods to solve duplicate genes and make sure every gene is unique. """ - + import numpy import warnings import random import pygad class Unique: + # DEEP-DUPLICATE-REMOVAL-NEEDED + NUM_DUPLICATE1 = 0 + NUM_DUPLICATE2 = 0 def solve_duplicate_genes_randomly(self, solution, min_val, @@ -73,7 +76,9 @@ def solve_duplicate_genes_randomly(self, pass else: temp_val = new_solution[duplicate_index] + temp_val - + + # Similar to the round_genes() method in the pygad module, + # Create a round_gene() method to round a single gene. if self.gene_type_single == True: if not gene_type[1] is None: temp_val = numpy.round(gene_type[0](temp_val), @@ -121,13 +126,12 @@ def solve_duplicate_genes_by_space(self, not_unique_indices: Indices of the genes with duplicate values. num_unsolved_duplicates: Number of unsolved duplicates. """ - new_solution = solution.copy() - + _, unique_gene_indices = numpy.unique(solution, return_index=True) not_unique_indices = set(range(len(solution))) - set(unique_gene_indices) # self.logger.info("not_unique_indices OUTSIDE", not_unique_indices) - + # First try to solve the duplicates. # For a solution like [3 2 0 0], the indices of the 2 duplicating genes are 2 and 3. # The next call to the find_unique_value() method tries to change the value of the gene with index 3 to solve the duplicate. @@ -150,13 +154,18 @@ def solve_duplicate_genes_by_space(self, num_trials=10, build_initial_pop=build_initial_pop) else: + # DEEP-DUPLICATE-REMOVAL-NEEDED # If there exist duplicate genes, then changing either of the 2 duplicating genes (with indices 2 and 3) will not solve the problem. # This problem can be solved by randomly changing one of the non-duplicating genes that may make a room for a unique value in one the 2 duplicating genes. # For example, if gene_space=[[3, 0, 1], [4, 1, 2], [0, 2], [3, 2, 0]] and the solution is [3 2 0 0], then the values of the last 2 genes duplicate. # There are no possible changes in the last 2 genes to solve the problem. But it could be solved by changing the second gene from 2 to 4. # As a result, any of the last 2 genes can take the value 2 and solve the duplicates. + # print("DEEP-DUPLICATE-REMOVAL-NEEDED1") + # print("DEEP-DUPLICATE-REMOVAL-NEEDED1\n", new_solution, not_unique_indices, len(not_unique_indices)) + # DEEP-DUPLICATE-REMOVAL-NEEDED + Unique.NUM_DUPLICATE1 += 1 return new_solution, not_unique_indices, len(not_unique_indices) - + return new_solution, not_unique_indices, num_unsolved_duplicates def unique_int_gene_from_range(self, @@ -167,7 +176,7 @@ def unique_int_gene_from_range(self, mutation_by_replacement, gene_type, step=None): - + """ Finds a unique integer value for the gene. @@ -188,7 +197,8 @@ def unique_int_gene_from_range(self, else: # For non-integer steps, the numpy.arange() function returns zeros id the dtype parameter is set to an integer data type. So, this returns zeros if step is non-integer and dtype is set to an int data type: numpy.arange(min_val, max_val, step, dtype=gene_type[0]) # To solve this issue, the data type casting will not be handled inside numpy.arange(). The range is generated by numpy.arange() and then the data type is converted using the numpy.asarray() function. - all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), dtype=gene_type[0]) + all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), + dtype=gene_type[0]) else: if step is None: all_gene_values = numpy.arange(min_val, max_val, dtype=gene_type[gene_index][0]) @@ -199,7 +209,9 @@ def unique_int_gene_from_range(self, pass else: all_gene_values = all_gene_values + solution[gene_index] - + + # Similar to the round_genes() method in the pygad module, + # Create a round_gene() method to round a single gene. if self.gene_type_single == True: if not gene_type[1] is None: all_gene_values = numpy.round(gene_type[0](all_gene_values), @@ -230,7 +242,7 @@ def unique_int_gene_from_range(self, # selected_value = gene_type[gene_index][0](selected_value) return selected_value - + def unique_genes_by_space(self, new_solution, gene_type, @@ -252,7 +264,7 @@ def unique_genes_by_space(self, not_unique_indices: Indices of the genes with duplicate values. num_unsolved_duplicates: Number of unsolved duplicates. """ - + num_unsolved_duplicates = 0 for duplicate_index in not_unique_indices: for trial_index in range(num_trials): @@ -278,7 +290,7 @@ def unique_genes_by_space(self, # self.logger.info("not_unique_indices INSIDE", not_unique_indices) return new_solution, not_unique_indices, num_unsolved_duplicates - + def unique_gene_by_space(self, solution, gene_idx, @@ -295,14 +307,15 @@ def unique_gene_by_space(self, Returns: A unique value, if exists, for the gene. """ - + if self.gene_space_nested: - # Returning the current gene space from the 'gene_space' attribute. if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: + # Return the current gene space from the 'gene_space' attribute. curr_gene_space = self.gene_space[gene_idx].copy() else: - curr_gene_space = self.gene_space[gene_idx] - + # Return the entire gene space from the 'gene_space' attribute. + curr_gene_space = list(self.gene_space[gene_idx]).copy() + # If the gene space has only a single value, use it as the new gene value. if type(curr_gene_space) in pygad.GA.supported_int_float_types: value_from_space = curr_gene_space @@ -311,65 +324,61 @@ def unique_gene_by_space(self, if self.gene_type_single == True: if gene_type[0] in pygad.GA.supported_int_types: if build_initial_pop == True: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - # min_val=self.random_mutation_min_val, - # max_val=self.random_mutation_max_val, - min_val=self.init_range_low, - max_val=self.init_range_high, - mutation_by_replacement=True, - gene_type=gene_type) + # If we are building the initial population, then use the range of the initial population. + min_val = self.init_range_low + max_val = self.init_range_high else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, - mutation_by_replacement=True, - gene_type=gene_type) + # If we are NOT building the initial population, then use the range of the random mutation. + min_val = self.random_mutation_min_val + max_val = self.random_mutation_max_val + + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=min_val, + max_val=max_val, + mutation_by_replacement=True, + gene_type=gene_type) else: if build_initial_pop == True: - value_from_space = numpy.random.uniform(# low=self.random_mutation_min_val, - # high=self.random_mutation_max_val, - low=self.init_range_low, - high=self.init_range_high, - size=1) + low = self.init_range_low + high = self.init_range_high else: - value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, - size=1) + low = self.random_mutation_min_val + high = self.random_mutation_max_val + + value_from_space = numpy.random.uniform(low=low, + high=high, + size=1) if self.mutation_by_replacement: pass else: value_from_space = solution[gene_idx] + value_from_space else: - if gene_type[gene_idx] in pygad.GA.supported_int_types: + if gene_type[gene_idx][0] in pygad.GA.supported_int_types: if build_initial_pop == True: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - # min_val=self.random_mutation_min_val, - # max_val=self.random_mutation_max_val, - min_val=self.init_range_low, - max_val=self.init_range_high, - mutation_by_replacement=True, - gene_type=gene_type) + min_val = self.init_range_low + max_val = self.init_range_high else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, - mutation_by_replacement=True, - gene_type=gene_type) + min_val = self.random_mutation_min_val + max_val = self.random_mutation_max_val + + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=min_val, + max_val=max_val, + mutation_by_replacement=True, + gene_type=gene_type) else: if build_initial_pop == True: - value_from_space = numpy.random.uniform(# low=self.random_mutation_min_val, - # high=self.random_mutation_max_val, - low=self.init_range_low, - high=self.init_range_high, - size=1) + low = self.init_range_low + high = self.init_range_high else: - value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, - size=1) + low = self.random_mutation_min_val + high = self.random_mutation_max_val + + value_from_space = numpy.random.uniform(low=low, + high=high, + size=1) if self.mutation_by_replacement: pass else: @@ -378,40 +387,18 @@ def unique_gene_by_space(self, elif type(curr_gene_space) is dict: if self.gene_type_single == True: if gene_type[0] in pygad.GA.supported_int_types: - if build_initial_pop == True: - if 'step' in curr_gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=curr_gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + if 'step' in curr_gene_space.keys(): + step = curr_gene_space['step'] else: - if 'step' in curr_gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=curr_gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + step = None + + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=step, + mutation_by_replacement=True, + gene_type=gene_type) else: if 'step' in curr_gene_space.keys(): value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], @@ -427,41 +414,20 @@ def unique_gene_by_space(self, else: value_from_space = solution[gene_idx] + value_from_space else: - if gene_type[gene_idx] in pygad.GA.supported_int_types: - if build_initial_pop == True: - if 'step' in curr_gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=curr_gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + # Use index 0 to return the type from the list (e.g. [int, None] or [float, 2]). + if gene_type[gene_idx][0] in pygad.GA.supported_int_types: + if 'step' in curr_gene_space.keys(): + step = curr_gene_space['step'] else: - if 'step' in curr_gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=curr_gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=curr_gene_space['low'], - max_val=curr_gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + step = None + + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=curr_gene_space['low'], + max_val=curr_gene_space['high'], + step=step, + mutation_by_replacement=True, + gene_type=gene_type) else: if 'step' in curr_gene_space.keys(): value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], @@ -476,7 +442,7 @@ def unique_gene_by_space(self, pass else: value_from_space = solution[gene_idx] + value_from_space - + else: # Selecting a value randomly based on the current gene's space in the 'gene_space' attribute. # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. @@ -488,8 +454,20 @@ def unique_gene_by_space(self, values_to_select_from = list(set(curr_gene_space) - set(solution)) if len(values_to_select_from) == 0: + # DEEP-DUPLICATE-REMOVAL-NEEDED + # Reaching this block means there is no value in the gene space of this gene to solve the duplicates. + # To solve the duplicate between the 2 genes, the solution is to change the value of a third gene that makes a room to solve the duplicate. + if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but the gene space does not have enough values to prevent duplicates.") + solution2 = self.solve_duplicates_deeply(solution) + if solution2 is None: + # print("DEEP-DUPLICATE-REMOVAL-NEEDED2") + # print("DEEP-DUPLICATE-REMOVAL-NEEDED2", solution, gene_idx, solution[gene_idx]) + Unique.NUM_DUPLICATE2 += 1 + else: + solution = solution2 value_from_space = solution[gene_idx] + else: value_from_space = random.choice(values_to_select_from) else: @@ -497,40 +475,18 @@ def unique_gene_by_space(self, if type(self.gene_space) is dict: if self.gene_type_single == True: if gene_type[0] in pygad.GA.supported_int_types: - if build_initial_pop == True: - if 'step' in self.gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=self.gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + if 'step' in self.gene_space.keys(): + step = self.gene_space['step'] else: - if 'step' in self.gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=self.gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + step = None + + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=step, + mutation_by_replacement=True, + gene_type=gene_type) else: # When the gene_space is assigned a dict object, then it specifies the lower and upper limits of all genes in the space. if 'step' in self.gene_space.keys(): @@ -547,41 +503,19 @@ def unique_gene_by_space(self, else: value_from_space = solution[gene_idx] + value_from_space else: - if gene_type[gene_idx] in pygad.GA.supported_int_types: - if build_initial_pop == True: - if 'step' in self.gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=self.gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + if gene_type[gene_idx][0] in pygad.GA.supported_int_types: + if 'step' in self.gene_space.keys(): + step = self.gene_space['step'] else: - if 'step' in self.gene_space.keys(): - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=self.gene_space['step'], - mutation_by_replacement=True, - gene_type=gene_type) - else: - value_from_space = self.unique_int_gene_from_range(solution=solution, - gene_index=gene_idx, - min_val=self.gene_space['low'], - max_val=self.gene_space['high'], - step=None, - mutation_by_replacement=True, - gene_type=gene_type) + step = None + + value_from_space = self.unique_int_gene_from_range(solution=solution, + gene_index=gene_idx, + min_val=self.gene_space['low'], + max_val=self.gene_space['high'], + step=step, + mutation_by_replacement=True, + gene_type=gene_type) else: # When the gene_space is assigned a dict object, then it specifies the lower and upper limits of all genes in the space. if 'step' in self.gene_space.keys(): @@ -609,19 +543,21 @@ def unique_gene_by_space(self, value_from_space = solution[gene_idx] else: value_from_space = random.choice(values_to_select_from) - + if value_from_space is None: if build_initial_pop == True: - value_from_space = numpy.random.uniform(# low=self.random_mutation_min_val, - # high=self.random_mutation_max_val, - low=self.init_range_low, - high=self.init_range_high, - size=1) + low = self.init_range_low + high = self.init_range_high else: - value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, - size=1) - + low = self.random_mutation_min_val + high = self.random_mutation_max_val + + value_from_space = numpy.random.uniform(low=low, + high=high, + size=1) + + # Similar to the round_genes() method in the pygad module, + # Create a round_gene() method to round a single gene. if self.gene_type_single == True: if not gene_type[1] is None: value_from_space = numpy.round(gene_type[0](value_from_space), @@ -634,5 +570,202 @@ def unique_gene_by_space(self, gene_type[gene_idx][1]) else: value_from_space = gene_type[gene_idx][0](value_from_space) - + return value_from_space + + def find_two_duplicates(self, + solution, + gene_space_unpacked): + """ + Returns the first occurrence of duplicate genes. + It returns: + The index of a gene with a duplicate value. + The value of the gene. + """ + for gene in set(solution): + gene_indices = numpy.where(numpy.array(solution) == gene)[0] + if len(gene_indices) == 1: + continue + # print("Gene value", gene, "Gene indices", gene_indices) + for gene_idx in gene_indices: + # print(" Current Gene Index", gene_idx) + number_alternate_values = len(set(gene_space_unpacked[gene_idx])) + if number_alternate_values > 1: + return gene_idx, gene + # This means there is no way to solve the duplicates between the genes. + # Because the space of the duplicates genes only has a single value and there is no alternatives. + # print("Cannot solve duplicates between the genes with value {gene} at indices {gene_indices}.".format(gene_indices=gene_indices, gene=gene)) + return None, gene + + def unpack_gene_space(self, num_values_from_range=100): + """ + Unpack the gene_space for the purpose of selecting a value that solves the duplicates. + This is by replacing each range by a list of values. + For the infinite range of float values, a number of values equal to num_values_from_range is selected using the numpy.linspace() function. + It returns the unpacked gene space. + """ + + # Copy the gene_space to keep it isolated form the changes. + gene_space_unpacked = self.gene_space.copy() + for space_idx, space in enumerate(gene_space_unpacked): + if type(space) in pygad.GA.supported_int_float_types: + gene_space_unpacked[space_idx] = [space] + elif type(space) is None: + # Randomly generate the value using the mutation range. + gene_space_unpacked[space_idx] = numpy.arange(start=self.random_mutation_min_val, + stop=self.random_mutation_max_val) + elif type(space) is range: + # Convert the range to a list. + gene_space_unpacked[space_idx] = list(range) + elif type(space) is dict: + # Create a list of values using the dict range. + # Use numpy.linspace() + if self.gene_type_single == True: # self.gene_type_single + if self.gene_type[0] in pygad.GA.supported_int_types: + if 'step' in space.keys(): + step = space['step'] + else: + step = 1 + + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], + stop=space['high'], + step=step) + else: + if 'step' in space.keys(): + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], + stop=space['high'], + step=space['step']) + else: + gene_space_unpacked[space_idx] = numpy.linspace(start=space['low'], + stop=space['high'], + num=num_values_from_range, + endpoint=False) + else: + if self.gene_type[space_idx][0] in pygad.GA.supported_int_types: + if 'step' in space.keys(): + step = space['step'] + else: + step = 1 + + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], + stop=space['high'], + step=step) + else: + if 'step' in space.keys(): + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], + stop=space['high'], + step=space['step']) + else: + gene_space_unpacked[space_idx] = numpy.linspace(start=space['low'], + stop=space['high'], + num=num_values_from_range, + endpoint=False) + + elif type(space) in [list, tuple, numpy.ndarray]: + # list/tuple/numpy.ndarray + # Convert all to list + gene_space_unpacked[space_idx] = list(space) + + # Check if there is an item with the value None. If so, replace it with a random value using the mutation range. + none_indices = numpy.where(numpy.array(gene_space_unpacked[space_idx]) == None)[0] + if len(none_indices) > 0: + for idx in none_indices: + random_value = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + gene_space_unpacked[space_idx][idx] = random_value + + if self.gene_type_single == True: # self.gene_type_single + # Change the data type. + gene_space_unpacked[space_idx] = numpy.array(gene_space_unpacked[space_idx], + dtype=self.gene_type[0]) + if not self.gene_type[1] is None: + # Round the values for float (non-int) data types. + gene_space_unpacked[space_idx] = numpy.round(gene_space_unpacked[space_idx], + self.gene_type[1]) + else: + # Change the data type. + gene_space_unpacked[space_idx] = numpy.array(gene_space_unpacked[space_idx], + self.gene_type[space_idx][0]) + if not self.gene_type[space_idx][1] is None: + # Round the values for float (non-int) data types. + gene_space_unpacked[space_idx] = numpy.round(gene_space_unpacked[space_idx], + self.gene_type[space_idx][1]) + + return gene_space_unpacked + + def solve_duplicates_deeply(self, + solution): + """ + Sometimes it is impossible to solve the duplicate genes by simply selecting another value for either genes. + This function solve the duplicates between 2 genes by searching for a third gene that can make assist in the solution. + It returns: + The solution after solving the duplicates or the None if duplicates cannot be solved. + """ + + gene_space_unpacked = self.unpack_gene_space() + self.gene_space_unpacked = gene_space_unpacked.copy() + + duplicate_index, duplicate_value = self.find_two_duplicates(solution, + gene_space_unpacked) + # print() + # print("Duplicate_index, Duplicate_value", duplicate_index, duplicate_value) + + if duplicate_index is None: + # Impossible to solve the duplicates for the genes with value duplicate_value. + return None + + # gene_duplicate_value = solution[duplicate_index] + + + # Without copy(), the gene will be removed from the gene_space. + # Convert the space to list because tuples do not have copy() + gene_other_values = list(gene_space_unpacked[duplicate_index]).copy() + # This removes all the occurrences of this value using the __ne__ magic function. + # gene_other_values = list(filter((duplicate_value).__ne__, gene_other_values)) + gene_other_values = [v for v in gene_other_values if v != duplicate_value] + # The remove() function only removes the first occurrence of the value. + # gene_other_values.remove(duplicate_value) + # if len(gene_other_values) == 0: return None + + # print("Gene_other_values", gene_other_values) + # Two conditions to solve the duplicates of the value D: + # 1. From gene_other_values, select a value V such that it is available in the gene space of another gene X. + # 2. Find an alternate value for the gene X that will not cause any duplicates. + # 2.1 If the gene X does not have alternatives, then go back to step 1 to find another gene. + # 2.2 Set the gene X to the value D. + # 2.3 Set the target gene to the value V. + # search_gene_space = gene_space_unpacked.copy() + # Set the space of the duplicate gene to empty list []. Do not remove it to not alter the indices of the gene spaces. + # search_gene_space[duplicate_index] = [] + gene_space_unpacked[duplicate_index] = [] + # print("search_gene_space", search_gene_space) + + for other_value in gene_other_values: + for space_idx, space in enumerate(gene_space_unpacked): + # print("other_value in space", other_value, space) + if other_value in space: + if other_value in solution and list(solution).index(other_value) != space_idx: + continue + else: + # print(" Current Space", space, space_idx) + # Find an alternate value for the third gene. + # Copy the space so that the original space is not changed after removing the value. + space_other_values = space.copy() + # This removes all the occurrences of this value. It is not enough to use the remove() function because it only removes the first occurrence. + space_other_values = [v for v in space_other_values if v != other_value] + # print("Space_other_values", space_other_values, other_value) + for val in space_other_values: + # print("val", val) + if val in solution: + # If the value exists in another gene of the solution, then we cannot use this value as it will cause another duplicate. + # End the current iteration and go check another value. + continue + else: + solution[space_idx] = val + solution[duplicate_index] = other_value + # print("solution", solution) + return solution + # print("Cannot solve the duplicate genes with value {duplicate_value}.".format(duplicate_value=duplicate_value)) + return None + diff --git a/pygad/pygad.py b/pygad/pygad.py index b63e613..ef12c94 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -191,7 +191,12 @@ def __init__(self, self.gene_space_nested = False if type(gene_space) is type(None): pass - elif type(gene_space) in [list, tuple, range, numpy.ndarray]: + elif type(gene_space) is range: + if len(gene_space) == 0: + self.valid_parameters = False + self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") + raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") + elif type(gene_space) in [list, numpy.ndarray]: if len(gene_space) == 0: self.valid_parameters = False self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") @@ -259,8 +264,8 @@ def __init__(self, else: self.valid_parameters = False - self.logger.error("The expected type of 'gene_space' is list, tuple, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) - raise TypeError("The expected type of 'gene_space' is list, tuple, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) + self.logger.error("The expected type of 'gene_space' is list, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) + raise TypeError("The expected type of 'gene_space' is list, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) self.gene_space = gene_space @@ -302,6 +307,11 @@ def __init__(self, elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): self.gene_type = gene_type self.gene_type_single = True + # A single data type of int with precision. + elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): + self.gene_type_single = False + self.logger.error("Integers cannot have precision. Please use the integer data type directly instead of {gene_type_val}.".format(gene_type_val=gene_type)) + raise ValueError("Integers cannot have precision. Please use the integer data type directly instead of {gene_type_val}.".format(gene_type_val=gene_type)) elif type(gene_type) in [list, tuple, numpy.ndarray]: if num_genes is None: if initial_population is None: @@ -334,8 +344,8 @@ def __init__(self, raise TypeError("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) else: self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) - raise TypeError("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) + self.logger.error("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) + raise TypeError("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) else: self.valid_parameters = False self.logger.error("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) @@ -1315,21 +1325,42 @@ def initialize_population(self, self.population = numpy.zeros(shape=self.pop_size, dtype=self.gene_type[0]) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): - if type(self.gene_space[gene_idx]) in [list, tuple, range]: + if type(self.gene_space[gene_idx]) == type(None): + + # The following commented code replace the None value with a single number that will not change again. + # This means the gene value will be the same across all solutions. + # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, + # high=high, + # size=1), dtype=self.gene_type[0])[0] + # self.population[sol_idx, gene_idx] = list(self.gene_space[gene_idx]).copy() + + # The above problem is solved by keeping the None value in the gene_space parameter. This forces PyGAD to generate this value for each solution. + self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[0])[0] + elif type(self.gene_space[gene_idx]) in [list, tuple, range]: # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. if type(self.gene_space[gene_idx]) is range: temp = self.gene_space[gene_idx] else: - temp = self.gene_space[gene_idx].copy() + temp = list(self.gene_space[gene_idx]).copy() + for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, high=high, size=1), dtype=self.gene_type[0])[0] - self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) + # Find the difference between the current gene space and the current values in the solution. + unique_gene_values = list(set(self.gene_space[gene_idx]).difference(set(self.population[sol_idx, :gene_idx]))) + if len(unique_gene_values) > 0: + self.population[sol_idx, gene_idx] = random.choice(unique_gene_values) + else: + # If there is no unique values, then we have to select a duplicate value. + self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) self.population[sol_idx, gene_idx] = self.gene_type[0](self.population[sol_idx, gene_idx]) - self.gene_space[gene_idx] = temp + self.gene_space[gene_idx] = list(temp).copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], @@ -1342,20 +1373,6 @@ def initialize_population(self, high=self.gene_space[gene_idx]['high'], size=1), dtype=self.gene_type[0])[0] - elif type(self.gene_space[gene_idx]) == type(None): - - # The following commented code replace the None value with a single number that will not change again. - # This means the gene value will be the same across all solutions. - # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - # high=high, - # size=1), dtype=self.gene_type[0])[0] - # self.population[sol_idx, gene_idx] = self.gene_space[gene_idx].copy() - - # The above problem is solved by keeping the None value in the gene_space parameter. This forces PyGAD to generate this value for each solution. - self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] else: @@ -1364,7 +1381,7 @@ def initialize_population(self, for gene_idx in range(self.num_genes): if type(self.gene_space[gene_idx]) in [list, tuple, range]: # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. - temp = self.gene_space[gene_idx].copy() + temp = list(self.gene_space[gene_idx]).copy() for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, @@ -1392,7 +1409,7 @@ def initialize_population(self, # size=1), # dtype=self.gene_type[gene_idx][0])[0] - # self.population[sol_idx, gene_idx] = self.gene_space[gene_idx].copy() + # self.population[sol_idx, gene_idx] = list(self.gene_space[gene_idx]).copy() temp = numpy.asarray(numpy.random.uniform(low=low, high=high, diff --git a/pygad/utils/mutation.py b/pygad/utils/mutation.py index a5eb867..9ce8722 100644 --- a/pygad/utils/mutation.py +++ b/pygad/utils/mutation.py @@ -8,6 +8,7 @@ import pygad class Mutation: + def random_mutation(self, offspring): """ diff --git a/tests/test_allow_duplicate_genes.py b/tests/test_allow_duplicate_genes.py new file mode 100644 index 0000000..cc3db96 --- /dev/null +++ b/tests/test_allow_duplicate_genes.py @@ -0,0 +1,150 @@ +import pygad +import random +import numpy + +num_generations = 100 + +def number_duplicate_genes(gene_space=None, + gene_type=float, + num_genes=10, + mutation_by_replacement=False, + random_mutation_min_val=-1, + random_mutation_max_val=1, + init_range_low=-4, + init_range_high=4): + + def fitness_func(ga, solution, idx): + return random.random() + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=5, + fitness_func=fitness_func, + sol_per_pop=10, + num_genes=num_genes, + gene_space=gene_space, + gene_type=gene_type, + init_range_low=init_range_low, + init_range_high=init_range_high, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val, + allow_duplicate_genes=False, + mutation_by_replacement=mutation_by_replacement, + save_solutions=True, + suppress_warnings=True) + + ga_instance.run() + + num_duplicates = 0 + for solution in ga_instance.solutions: + num = len(solution) - len(set(solution)) + if num != 0: + print(solution) + num_duplicates += num + + print("Number of duplicates is {num_duplicates}.".format(num_duplicates=num_duplicates)) + return num_duplicates + +def test_number_duplicates_default(): + num_duplicates = number_duplicate_genes() + + assert num_duplicates == 0 + +def test_number_duplicates_float_gene_type(): + num_genes = 10 + num_duplicates = number_duplicate_genes(gene_type=float, + num_genes=num_genes, + init_range_low=0, + init_range_high=1, + random_mutation_min_val=0, + random_mutation_max_val=1) + + assert num_duplicates == 0 + +def test_number_duplicates_int_gene_type(): + num_genes = 10 + init_range_low = 0 + init_range_high = init_range_low + num_genes + random_mutation_min_val = 0 + random_mutation_max_val = random_mutation_min_val + num_genes + num_duplicates = number_duplicate_genes(gene_type=int, + mutation_by_replacement=False, + num_genes=num_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val) + + assert num_duplicates == 0 + +def test_number_duplicates_int_gene_type_replacement(): + num_genes = 10 + init_range_low = 0 + init_range_high = init_range_low + num_genes + random_mutation_min_val = 0 + random_mutation_max_val = random_mutation_min_val + num_genes + num_duplicates = number_duplicate_genes(gene_type=int, + mutation_by_replacement=True, + num_genes=num_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val) + + assert num_duplicates == 0 + +def test_number_duplicates_single_gene_space(): + num_duplicates = number_duplicate_genes(gene_space=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + num_genes=10) + + assert num_duplicates == 0 + +def test_number_duplicates_single_range_gene_space(): + num_genes = 10 + num_duplicates = number_duplicate_genes(gene_space=range(num_genes), + num_genes=num_genes) + + assert num_duplicates == 0 + +def test_number_duplicates_single_numpy_range_gene_space(): + num_genes = 10 + num_duplicates = number_duplicate_genes(gene_space=numpy.arange(num_genes), + num_genes=num_genes) + + assert num_duplicates == 0 + +def test_number_duplicates_nested_gene_space(): + num_duplicates = number_duplicate_genes(gene_space=[[0, 1], + [1, 2], + [2, 3], + [3, 4], + [4, 5], + [5, 6], + [6, 7], + [7, 8], + [8, 9], + [9, 10]], + gene_type=int, + random_mutation_min_val=11, + random_mutation_max_val=20, + mutation_by_replacement=True, + num_genes=10) + + assert num_duplicates == 0 + +if __name__ == "__main__": + # print() + # test_number_duplicates_default() + # print() + # test_number_duplicates_float_gene_type() + # print() + # test_number_duplicates_int_gene_type() + # print() + # test_number_duplicates_single_gene_space() + # print() + # test_number_duplicates_single_range_gene_space() + # print() + # test_number_duplicates_single_numpy_range_gene_space() + print() + test_number_duplicates_nested_gene_space() + print() + From 160a8365d0ba6dcda32774409fd55c4430ed0c53 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Mon, 1 May 2023 19:31:05 -0400 Subject: [PATCH 038/220] Refactor unique.py --- pygad/helper/unique.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py index bd2da60..b3a1e61 100644 --- a/pygad/helper/unique.py +++ b/pygad/helper/unique.py @@ -8,9 +8,7 @@ import pygad class Unique: - # DEEP-DUPLICATE-REMOVAL-NEEDED - NUM_DUPLICATE1 = 0 - NUM_DUPLICATE2 = 0 + def solve_duplicate_genes_randomly(self, solution, min_val, @@ -155,15 +153,13 @@ def solve_duplicate_genes_by_space(self, build_initial_pop=build_initial_pop) else: # DEEP-DUPLICATE-REMOVAL-NEEDED + # Search by this phrase to find where deep duplicates removal should be applied. + # If there exist duplicate genes, then changing either of the 2 duplicating genes (with indices 2 and 3) will not solve the problem. # This problem can be solved by randomly changing one of the non-duplicating genes that may make a room for a unique value in one the 2 duplicating genes. # For example, if gene_space=[[3, 0, 1], [4, 1, 2], [0, 2], [3, 2, 0]] and the solution is [3 2 0 0], then the values of the last 2 genes duplicate. # There are no possible changes in the last 2 genes to solve the problem. But it could be solved by changing the second gene from 2 to 4. # As a result, any of the last 2 genes can take the value 2 and solve the duplicates. - # print("DEEP-DUPLICATE-REMOVAL-NEEDED1") - # print("DEEP-DUPLICATE-REMOVAL-NEEDED1\n", new_solution, not_unique_indices, len(not_unique_indices)) - # DEEP-DUPLICATE-REMOVAL-NEEDED - Unique.NUM_DUPLICATE1 += 1 return new_solution, not_unique_indices, len(not_unique_indices) return new_solution, not_unique_indices, num_unsolved_duplicates @@ -288,7 +284,7 @@ def unique_genes_by_space(self, _, unique_gene_indices = numpy.unique(new_solution, return_index=True) not_unique_indices = set(range(len(new_solution))) - set(unique_gene_indices) # self.logger.info("not_unique_indices INSIDE", not_unique_indices) - + return new_solution, not_unique_indices, num_unsolved_duplicates def unique_gene_by_space(self, @@ -455,15 +451,18 @@ def unique_gene_by_space(self, if len(values_to_select_from) == 0: # DEEP-DUPLICATE-REMOVAL-NEEDED + # Search by this phrase to find where deep duplicates removal should be applied. + # Reaching this block means there is no value in the gene space of this gene to solve the duplicates. # To solve the duplicate between the 2 genes, the solution is to change the value of a third gene that makes a room to solve the duplicate. if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but the gene space does not have enough values to prevent duplicates.") + solution2 = self.solve_duplicates_deeply(solution) if solution2 is None: - # print("DEEP-DUPLICATE-REMOVAL-NEEDED2") - # print("DEEP-DUPLICATE-REMOVAL-NEEDED2", solution, gene_idx, solution[gene_idx]) - Unique.NUM_DUPLICATE2 += 1 + # Cannot solve duplicates. At the moment, we are changing the value of a third gene to solve the duplicates between 2 genes. + # Maybe a 4th, 5th, 6th, or even more genes need to be changed to solve the duplicates. + pass else: solution = solution2 value_from_space = solution[gene_idx] From 19be8b0f38b00a8d85191a9c248c92464dc1a1a4 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 2 May 2023 11:06:52 -0400 Subject: [PATCH 039/220] Refactoring the code --- pygad/helper/unique.py | 83 +++++++++++++++++------------------------- 1 file changed, 33 insertions(+), 50 deletions(-) diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py index b3a1e61..74ecbeb 100644 --- a/pygad/helper/unique.py +++ b/pygad/helper/unique.py @@ -59,7 +59,7 @@ def solve_duplicate_genes_randomly(self, else: temp_val = new_solution[duplicate_index] + temp_val else: - if gene_type[duplicate_index] in pygad.GA.supported_int_types: + if gene_type[duplicate_index][0] in pygad.GA.supported_int_types: temp_val = self.unique_int_gene_from_range(solution=new_solution, gene_index=duplicate_index, min_val=min_val, @@ -186,43 +186,43 @@ def unique_int_gene_from_range(self, Returns: selected_value: The new value of the gene. It may be identical to the original gene value in case there are no possible unique values for the gene. """ - + if self.gene_type_single == True: if step is None: - all_gene_values = numpy.arange(min_val, max_val, dtype=gene_type[0]) + # all_gene_values = numpy.arange(min_val, + # max_val, + # dtype=gene_type[0]) + all_gene_values = numpy.asarray(numpy.arange(min_val, max_val), + dtype=gene_type[0]) else: - # For non-integer steps, the numpy.arange() function returns zeros id the dtype parameter is set to an integer data type. So, this returns zeros if step is non-integer and dtype is set to an int data type: numpy.arange(min_val, max_val, step, dtype=gene_type[0]) + # For non-integer steps, the numpy.arange() function returns zeros if the dtype parameter is set to an integer data type. So, this returns zeros if step is non-integer and dtype is set to an int data type: numpy.arange(min_val, max_val, step, dtype=gene_type[0]) # To solve this issue, the data type casting will not be handled inside numpy.arange(). The range is generated by numpy.arange() and then the data type is converted using the numpy.asarray() function. all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), dtype=gene_type[0]) else: if step is None: - all_gene_values = numpy.arange(min_val, max_val, dtype=gene_type[gene_index][0]) + # all_gene_values = numpy.arange(min_val, + # max_val, + # dtype=gene_type[gene_index][0]) + all_gene_values = numpy.asarray(numpy.arange(min_val, max_val), + dtype=gene_type[gene_index][0]) else: - all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), dtype=gene_type[gene_index][0]) + all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), + dtype=gene_type[gene_index][0]) if mutation_by_replacement: pass else: all_gene_values = all_gene_values + solution[gene_index] - # Similar to the round_genes() method in the pygad module, - # Create a round_gene() method to round a single gene. if self.gene_type_single == True: - if not gene_type[1] is None: - all_gene_values = numpy.round(gene_type[0](all_gene_values), - gene_type[1]) - else: - if type(all_gene_values) is numpy.ndarray: - all_gene_values = numpy.asarray(all_gene_values, dtype=gene_type[0]) - else: - all_gene_values = gene_type[0](all_gene_values) + # Note that we already know that the data type is integer. + all_gene_values = numpy.asarray(all_gene_values, + dtype=gene_type[0]) else: - if not gene_type[gene_index][1] is None: - all_gene_values = numpy.round(gene_type[gene_index][0](all_gene_values), - gene_type[gene_index][1]) - else: - all_gene_values = gene_type[gene_index][0](all_gene_values) + # Note that we already know that the data type is integer. + all_gene_values = numpy.asarray(all_gene_values, + gene_type[gene_index][0]) values_to_select_from = list(set(all_gene_values) - set(solution)) @@ -232,11 +232,6 @@ def unique_int_gene_from_range(self, else: selected_value = random.choice(values_to_select_from) - #if self.gene_type_single == True: - # selected_value = gene_type[0](selected_value) - #else: - # selected_value = gene_type[gene_index][0](selected_value) - return selected_value def unique_genes_by_space(self, @@ -310,7 +305,8 @@ def unique_gene_by_space(self, curr_gene_space = self.gene_space[gene_idx].copy() else: # Return the entire gene space from the 'gene_space' attribute. - curr_gene_space = list(self.gene_space[gene_idx]).copy() + # curr_gene_space = list(self.gene_space[gene_idx]).copy() + curr_gene_space = self.gene_space[gene_idx] # If the gene space has only a single value, use it as the new gene value. if type(curr_gene_space) in pygad.GA.supported_int_float_types: @@ -585,15 +581,12 @@ def find_two_duplicates(self, gene_indices = numpy.where(numpy.array(solution) == gene)[0] if len(gene_indices) == 1: continue - # print("Gene value", gene, "Gene indices", gene_indices) for gene_idx in gene_indices: - # print(" Current Gene Index", gene_idx) number_alternate_values = len(set(gene_space_unpacked[gene_idx])) if number_alternate_values > 1: return gene_idx, gene # This means there is no way to solve the duplicates between the genes. # Because the space of the duplicates genes only has a single value and there is no alternatives. - # print("Cannot solve duplicates between the genes with value {gene} at indices {gene_indices}.".format(gene_indices=gene_indices, gene=gene)) return None, gene def unpack_gene_space(self, num_values_from_range=100): @@ -609,13 +602,13 @@ def unpack_gene_space(self, num_values_from_range=100): for space_idx, space in enumerate(gene_space_unpacked): if type(space) in pygad.GA.supported_int_float_types: gene_space_unpacked[space_idx] = [space] - elif type(space) is None: + elif space is None: # Randomly generate the value using the mutation range. gene_space_unpacked[space_idx] = numpy.arange(start=self.random_mutation_min_val, stop=self.random_mutation_max_val) elif type(space) is range: # Convert the range to a list. - gene_space_unpacked[space_idx] = list(range) + gene_space_unpacked[space_idx] = list(space) elif type(space) is dict: # Create a list of values using the dict range. # Use numpy.linspace() @@ -703,59 +696,50 @@ def solve_duplicates_deeply(self, """ gene_space_unpacked = self.unpack_gene_space() + # Create a copy into the attribute because it will be changed later. self.gene_space_unpacked = gene_space_unpacked.copy() duplicate_index, duplicate_value = self.find_two_duplicates(solution, gene_space_unpacked) - # print() - # print("Duplicate_index, Duplicate_value", duplicate_index, duplicate_value) if duplicate_index is None: # Impossible to solve the duplicates for the genes with value duplicate_value. return None - # gene_duplicate_value = solution[duplicate_index] - # Without copy(), the gene will be removed from the gene_space. # Convert the space to list because tuples do not have copy() gene_other_values = list(gene_space_unpacked[duplicate_index]).copy() - # This removes all the occurrences of this value using the __ne__ magic function. - # gene_other_values = list(filter((duplicate_value).__ne__, gene_other_values)) + + # This removes all the occurrences of this value. gene_other_values = [v for v in gene_other_values if v != duplicate_value] + # The remove() function only removes the first occurrence of the value. + # Do not use it. # gene_other_values.remove(duplicate_value) - # if len(gene_other_values) == 0: return None - # print("Gene_other_values", gene_other_values) # Two conditions to solve the duplicates of the value D: # 1. From gene_other_values, select a value V such that it is available in the gene space of another gene X. # 2. Find an alternate value for the gene X that will not cause any duplicates. # 2.1 If the gene X does not have alternatives, then go back to step 1 to find another gene. # 2.2 Set the gene X to the value D. # 2.3 Set the target gene to the value V. - # search_gene_space = gene_space_unpacked.copy() # Set the space of the duplicate gene to empty list []. Do not remove it to not alter the indices of the gene spaces. - # search_gene_space[duplicate_index] = [] gene_space_unpacked[duplicate_index] = [] - # print("search_gene_space", search_gene_space) for other_value in gene_other_values: for space_idx, space in enumerate(gene_space_unpacked): - # print("other_value in space", other_value, space) if other_value in space: if other_value in solution and list(solution).index(other_value) != space_idx: continue else: - # print(" Current Space", space, space_idx) # Find an alternate value for the third gene. # Copy the space so that the original space is not changed after removing the value. space_other_values = space.copy() # This removes all the occurrences of this value. It is not enough to use the remove() function because it only removes the first occurrence. space_other_values = [v for v in space_other_values if v != other_value] - # print("Space_other_values", space_other_values, other_value) + for val in space_other_values: - # print("val", val) if val in solution: # If the value exists in another gene of the solution, then we cannot use this value as it will cause another duplicate. # End the current iteration and go check another value. @@ -763,8 +747,7 @@ def solve_duplicates_deeply(self, else: solution[space_idx] = val solution[duplicate_index] = other_value - # print("solution", solution) return solution - # print("Cannot solve the duplicate genes with value {duplicate_value}.".format(duplicate_value=duplicate_value)) - return None + # Reaching here means we cannot solve the duplicate genes. + return None From 1c446922c1434fd3c8adc3107ed45d615fafe5f3 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 2 May 2023 18:54:21 -0400 Subject: [PATCH 040/220] More tests --- pygad/helper/unique.py | 314 ++++++++++++++----------- pygad/pygad.py | 147 ++++++++---- pygad/utils/crossover.py | 43 ++-- tests/test_allow_duplicate_genes.py | 227 ++++++++++++++++-- tests/test_gene_space.py | 345 ++++++++++++++++++++++++++++ 5 files changed, 862 insertions(+), 214 deletions(-) create mode 100644 tests/test_gene_space.py diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py index 74ecbeb..3224445 100644 --- a/pygad/helper/unique.py +++ b/pygad/helper/unique.py @@ -19,14 +19,14 @@ def solve_duplicate_genes_randomly(self, """ Solves the duplicates in a solution by randomly selecting new values for the duplicating genes. - + solution: A solution with duplicate values. min_val: Minimum value of the range to sample a number randomly. max_val: Maximum value of the range to sample a number randomly. mutation_by_replacement: Identical to the self.mutation_by_replacement attribute. gene_type: Exactly the same as the self.gene_type attribute. num_trials: Maximum number of trials to change the gene value to solve the duplicates. - + Returns: new_solution: Solution after trying to solve its duplicates. If no duplicates solved, then it is identical to the passed solution parameter. not_unique_indices: Indices of the genes with duplicate values. @@ -197,17 +197,22 @@ def unique_int_gene_from_range(self, else: # For non-integer steps, the numpy.arange() function returns zeros if the dtype parameter is set to an integer data type. So, this returns zeros if step is non-integer and dtype is set to an int data type: numpy.arange(min_val, max_val, step, dtype=gene_type[0]) # To solve this issue, the data type casting will not be handled inside numpy.arange(). The range is generated by numpy.arange() and then the data type is converted using the numpy.asarray() function. - all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), + all_gene_values = numpy.asarray(numpy.arange(min_val, + max_val, + step), dtype=gene_type[0]) else: if step is None: # all_gene_values = numpy.arange(min_val, # max_val, # dtype=gene_type[gene_index][0]) - all_gene_values = numpy.asarray(numpy.arange(min_val, max_val), + all_gene_values = numpy.asarray(numpy.arange(min_val, + max_val), dtype=gene_type[gene_index][0]) else: - all_gene_values = numpy.asarray(numpy.arange(min_val, max_val, step), + all_gene_values = numpy.asarray(numpy.arange(min_val, + max_val, + step), dtype=gene_type[gene_index][0]) if mutation_by_replacement: @@ -215,6 +220,7 @@ def unique_int_gene_from_range(self, else: all_gene_values = all_gene_values + solution[gene_index] + # TODO: The gene data type is converted twine. One above and one here. if self.gene_type_single == True: # Note that we already know that the data type is integer. all_gene_values = numpy.asarray(all_gene_values, @@ -227,6 +233,7 @@ def unique_int_gene_from_range(self, values_to_select_from = list(set(all_gene_values) - set(solution)) if len(values_to_select_from) == 0: + # If there is no values, then keep the current gene value. if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but there is no enough values to prevent duplicates.") selected_value = solution[gene_index] else: @@ -240,47 +247,47 @@ def unique_genes_by_space(self, not_unique_indices, num_trials=10, build_initial_pop=False): - - """ - Loops through all the duplicating genes to find unique values that from their gene spaces to solve the duplicates. - For each duplicating gene, a call to the unique_gene_by_space() function is made. - - new_solution: A solution with duplicate values. - gene_type: Exactly the same as the self.gene_type attribute. - not_unique_indices: Indices with duplicating values. - num_trials: Maximum number of trials to change the gene value to solve the duplicates. - - Returns: - new_solution: Solution after trying to solve all of its duplicates. If no duplicates solved, then it is identical to the passed solution parameter. - not_unique_indices: Indices of the genes with duplicate values. - num_unsolved_duplicates: Number of unsolved duplicates. - """ - num_unsolved_duplicates = 0 - for duplicate_index in not_unique_indices: - for trial_index in range(num_trials): - temp_val = self.unique_gene_by_space(solution=new_solution, - gene_idx=duplicate_index, - gene_type=gene_type, - build_initial_pop=build_initial_pop) - - if temp_val in new_solution and trial_index == (num_trials - 1): - # self.logger.info("temp_val, duplicate_index", temp_val, duplicate_index, new_solution) - num_unsolved_duplicates = num_unsolved_duplicates + 1 - if not self.suppress_warnings: warnings.warn("Failed to find a unique value for gene with index {gene_idx} whose value is {gene_value}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.".format(gene_idx=duplicate_index, gene_value=new_solution[duplicate_index])) - elif temp_val in new_solution: - continue - else: - new_solution[duplicate_index] = temp_val - # self.logger.info("SOLVED", duplicate_index) - break + """ + Loops through all the duplicating genes to find unique values that from their gene spaces to solve the duplicates. + For each duplicating gene, a call to the unique_gene_by_space() function is made. + + new_solution: A solution with duplicate values. + gene_type: Exactly the same as the self.gene_type attribute. + not_unique_indices: Indices with duplicating values. + num_trials: Maximum number of trials to change the gene value to solve the duplicates. + + Returns: + new_solution: Solution after trying to solve all of its duplicates. If no duplicates solved, then it is identical to the passed solution parameter. + not_unique_indices: Indices of the genes with duplicate values. + num_unsolved_duplicates: Number of unsolved duplicates. + """ + + num_unsolved_duplicates = 0 + for duplicate_index in not_unique_indices: + for trial_index in range(num_trials): + temp_val = self.unique_gene_by_space(solution=new_solution, + gene_idx=duplicate_index, + gene_type=gene_type, + build_initial_pop=build_initial_pop) + + if temp_val in new_solution and trial_index == (num_trials - 1): + # self.logger.info("temp_val, duplicate_index", temp_val, duplicate_index, new_solution) + num_unsolved_duplicates = num_unsolved_duplicates + 1 + if not self.suppress_warnings: warnings.warn("Failed to find a unique value for gene with index {gene_idx} whose value is {gene_value}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.".format(gene_idx=duplicate_index, gene_value=new_solution[duplicate_index])) + elif temp_val in new_solution: + continue + else: + new_solution[duplicate_index] = temp_val + # self.logger.info("SOLVED", duplicate_index) + break - # Update the list of duplicate indices after each iteration. - _, unique_gene_indices = numpy.unique(new_solution, return_index=True) - not_unique_indices = set(range(len(new_solution))) - set(unique_gene_indices) - # self.logger.info("not_unique_indices INSIDE", not_unique_indices) + # Update the list of duplicate indices after each iteration. + _, unique_gene_indices = numpy.unique(new_solution, return_index=True) + not_unique_indices = set(range(len(new_solution))) - set(unique_gene_indices) + # self.logger.info("not_unique_indices INSIDE", not_unique_indices) - return new_solution, not_unique_indices, num_unsolved_duplicates + return new_solution, not_unique_indices, num_unsolved_duplicates def unique_gene_by_space(self, solution, @@ -300,9 +307,9 @@ def unique_gene_by_space(self, """ if self.gene_space_nested: - if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list, tuple]: # Return the current gene space from the 'gene_space' attribute. - curr_gene_space = self.gene_space[gene_idx].copy() + curr_gene_space = list(self.gene_space[gene_idx]).copy() else: # Return the entire gene space from the 'gene_space' attribute. # curr_gene_space = list(self.gene_space[gene_idx]).copy() @@ -341,6 +348,7 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=low, high=high, size=1) + # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. if self.mutation_by_replacement: pass else: @@ -371,6 +379,7 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=low, high=high, size=1) + # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. if self.mutation_by_replacement: pass else: @@ -401,6 +410,7 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], size=1) + # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. if self.mutation_by_replacement: pass else: @@ -430,6 +440,7 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], size=1) + # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. if self.mutation_by_replacement: pass else: @@ -493,6 +504,7 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], size=1) + # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. if self.mutation_by_replacement: pass else: @@ -522,6 +534,7 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], size=1) + # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. if self.mutation_by_replacement: pass else: @@ -589,100 +602,145 @@ def find_two_duplicates(self, # Because the space of the duplicates genes only has a single value and there is no alternatives. return None, gene - def unpack_gene_space(self, num_values_from_range=100): + def unpack_gene_space(self, + num_values_from_inf_range=100): """ Unpack the gene_space for the purpose of selecting a value that solves the duplicates. This is by replacing each range by a list of values. - For the infinite range of float values, a number of values equal to num_values_from_range is selected using the numpy.linspace() function. + It accepts: + num_values_from_inf_range: For infinite range of float values, a fixed number of values equal to num_values_from_inf_range is selected using the numpy.linspace() function. It returns the unpacked gene space. """ # Copy the gene_space to keep it isolated form the changes. - gene_space_unpacked = self.gene_space.copy() - for space_idx, space in enumerate(gene_space_unpacked): - if type(space) in pygad.GA.supported_int_float_types: - gene_space_unpacked[space_idx] = [space] - elif space is None: - # Randomly generate the value using the mutation range. - gene_space_unpacked[space_idx] = numpy.arange(start=self.random_mutation_min_val, - stop=self.random_mutation_max_val) - elif type(space) is range: - # Convert the range to a list. - gene_space_unpacked[space_idx] = list(space) - elif type(space) is dict: - # Create a list of values using the dict range. - # Use numpy.linspace() - if self.gene_type_single == True: # self.gene_type_single - if self.gene_type[0] in pygad.GA.supported_int_types: - if 'step' in space.keys(): - step = space['step'] - else: - step = 1 + if self.gene_space is None: + return None - gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], - stop=space['high'], - step=step) - else: - if 'step' in space.keys(): + if self.gene_space_nested == False: + if type(self.gene_space) is range: + gene_space_unpacked = list(self.gene_space) + elif type(self.gene_space) in [numpy.ndarray, list]: + gene_space_unpacked = self.gene_space.copy() + elif type(self.gene_space) is dict: + if 'step' in self.gene_space.keys(): + gene_space_unpacked = numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']) + else: + gene_space_unpacked = numpy.linspace(start=self.gene_space['low'], + stop=self.gene_space['high'], + num=num_values_from_inf_range, + endpoint=False) + + if self.gene_type_single == True: + # Change the data type. + gene_space_unpacked = numpy.array(gene_space_unpacked, + dtype=self.gene_type[0]) + if not self.gene_type[1] is None: + # Round the values for float (non-int) data types. + gene_space_unpacked = numpy.round(gene_space_unpacked, + self.gene_type[1]) + else: + temp_gene_space_unpacked = gene_space_unpacked.copy() + gene_space_unpacked = [] + # Get the number of genes from the length of gene_type. + # The num_genes attribute is not set yet when this method (unpack_gene_space) is called for the first time. + for gene_idx in range(len(self.gene_type)): + # Change the data type. + gene_space_item_unpacked = numpy.array(temp_gene_space_unpacked, + self.gene_type[gene_idx][0]) + if not self.gene_type[gene_idx][1] is None: + # Round the values for float (non-int) data types. + gene_space_item_unpacked = numpy.round(temp_gene_space_unpacked, + self.gene_type[gene_idx][1]) + gene_space_unpacked.append(gene_space_item_unpacked) + + elif self.gene_space_nested == True: + gene_space_unpacked = self.gene_space.copy() + for space_idx, space in enumerate(gene_space_unpacked): + if type(space) in pygad.GA.supported_int_float_types: + gene_space_unpacked[space_idx] = [space] + elif space is None: + # Randomly generate the value using the mutation range. + gene_space_unpacked[space_idx] = numpy.arange(start=self.random_mutation_min_val, + stop=self.random_mutation_max_val) + elif type(space) is range: + # Convert the range to a list. + gene_space_unpacked[space_idx] = list(space) + elif type(space) is dict: + # Create a list of values using the dict range. + # Use numpy.linspace() + if self.gene_type_single == True: # self.gene_type_single + if self.gene_type[0] in pygad.GA.supported_int_types: + if 'step' in space.keys(): + step = space['step'] + else: + step = 1 + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], stop=space['high'], - step=space['step']) - else: - gene_space_unpacked[space_idx] = numpy.linspace(start=space['low'], - stop=space['high'], - num=num_values_from_range, - endpoint=False) - else: - if self.gene_type[space_idx][0] in pygad.GA.supported_int_types: - if 'step' in space.keys(): - step = space['step'] + step=step) else: - step = 1 - - gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], - stop=space['high'], - step=step) + if 'step' in space.keys(): + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], + stop=space['high'], + step=space['step']) + else: + gene_space_unpacked[space_idx] = numpy.linspace(start=space['low'], + stop=space['high'], + num=num_values_from_inf_range, + endpoint=False) else: - if 'step' in space.keys(): + if self.gene_type[space_idx][0] in pygad.GA.supported_int_types: + if 'step' in space.keys(): + step = space['step'] + else: + step = 1 + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], stop=space['high'], - step=space['step']) + step=step) else: - gene_space_unpacked[space_idx] = numpy.linspace(start=space['low'], - stop=space['high'], - num=num_values_from_range, - endpoint=False) - - elif type(space) in [list, tuple, numpy.ndarray]: - # list/tuple/numpy.ndarray - # Convert all to list - gene_space_unpacked[space_idx] = list(space) - - # Check if there is an item with the value None. If so, replace it with a random value using the mutation range. - none_indices = numpy.where(numpy.array(gene_space_unpacked[space_idx]) == None)[0] - if len(none_indices) > 0: - for idx in none_indices: - random_value = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, - size=1) - gene_space_unpacked[space_idx][idx] = random_value - - if self.gene_type_single == True: # self.gene_type_single - # Change the data type. - gene_space_unpacked[space_idx] = numpy.array(gene_space_unpacked[space_idx], - dtype=self.gene_type[0]) - if not self.gene_type[1] is None: - # Round the values for float (non-int) data types. - gene_space_unpacked[space_idx] = numpy.round(gene_space_unpacked[space_idx], - self.gene_type[1]) - else: - # Change the data type. - gene_space_unpacked[space_idx] = numpy.array(gene_space_unpacked[space_idx], - self.gene_type[space_idx][0]) - if not self.gene_type[space_idx][1] is None: - # Round the values for float (non-int) data types. - gene_space_unpacked[space_idx] = numpy.round(gene_space_unpacked[space_idx], - self.gene_type[space_idx][1]) + if 'step' in space.keys(): + gene_space_unpacked[space_idx] = numpy.arange(start=space['low'], + stop=space['high'], + step=space['step']) + else: + gene_space_unpacked[space_idx] = numpy.linspace(start=space['low'], + stop=space['high'], + num=num_values_from_inf_range, + endpoint=False) + + elif type(space) in [numpy.ndarray, list, tuple]: + # list/tuple/numpy.ndarray + # Convert all to list + gene_space_unpacked[space_idx] = list(space) + + # Check if there is an item with the value None. If so, replace it with a random value using the mutation range. + none_indices = numpy.where(numpy.array(gene_space_unpacked[space_idx]) == None)[0] + if len(none_indices) > 0: + for idx in none_indices: + random_value = numpy.random.uniform(low=self.random_mutation_min_val, + high=self.random_mutation_max_val, + size=1) + gene_space_unpacked[space_idx][idx] = random_value + + if self.gene_type_single == True: # self.gene_type_single + # Change the data type. + gene_space_unpacked[space_idx] = numpy.array(gene_space_unpacked[space_idx], + dtype=self.gene_type[0]) + if not self.gene_type[1] is None: + # Round the values for float (non-int) data types. + gene_space_unpacked[space_idx] = numpy.round(gene_space_unpacked[space_idx], + self.gene_type[1]) + else: + # Change the data type. + gene_space_unpacked[space_idx] = numpy.array(gene_space_unpacked[space_idx], + self.gene_type[space_idx][0]) + if not self.gene_type[space_idx][1] is None: + # Round the values for float (non-int) data types. + gene_space_unpacked[space_idx] = numpy.round(gene_space_unpacked[space_idx], + self.gene_type[space_idx][1]) return gene_space_unpacked @@ -695,9 +753,9 @@ def solve_duplicates_deeply(self, The solution after solving the duplicates or the None if duplicates cannot be solved. """ - gene_space_unpacked = self.unpack_gene_space() - # Create a copy into the attribute because it will be changed later. - self.gene_space_unpacked = gene_space_unpacked.copy() + # gene_space_unpacked = self.unpack_gene_space() + # Create a copy of the gene_space_unpacked attribute because it will be changed later. + gene_space_unpacked = self.gene_space_unpacked.copy() duplicate_index, duplicate_value = self.find_two_duplicates(solution, gene_space_unpacked) @@ -726,7 +784,7 @@ def solve_duplicates_deeply(self, # 2.3 Set the target gene to the value V. # Set the space of the duplicate gene to empty list []. Do not remove it to not alter the indices of the gene spaces. gene_space_unpacked[duplicate_index] = [] - + for other_value in gene_other_values: for space_idx, space in enumerate(gene_space_unpacked): if other_value in space: diff --git a/pygad/pygad.py b/pygad/pygad.py index ef12c94..b7382cc 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -187,6 +187,14 @@ def __init__(self, self.mutation_by_replacement = mutation_by_replacement + # Validate allow_duplicate_genes + if not (type(allow_duplicate_genes) is bool): + self.valid_parameters = False + self.logger.error("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) + raise TypeError("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) + + self.allow_duplicate_genes = allow_duplicate_genes + # Validate gene_space self.gene_space_nested = False if type(gene_space) is type(None): @@ -203,7 +211,7 @@ def __init__(self, raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") else: for index, el in enumerate(gene_space): - if type(el) in [list, tuple, range, numpy.ndarray]: + if type(el) in [numpy.ndarray, list, tuple, range]: if len(el) == 0: self.valid_parameters = False self.logger.error("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) @@ -361,6 +369,9 @@ def __init__(self, self.logger.error("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) raise ValueError("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) + # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. + self.gene_space_unpacked = self.unpack_gene_space() + # Build the initial population if initial_population is None: if (sol_per_pop is None) or (num_genes is None): @@ -421,21 +432,43 @@ def __init__(self, # Forcing the initial_population array to have the data type assigned to the gene_type parameter. if self.gene_type_single == True: if self.gene_type[1] == None: - self.initial_population = numpy.array(initial_population, dtype=self.gene_type[0]) + self.initial_population = numpy.array(initial_population, + dtype=self.gene_type[0]) else: - self.initial_population = numpy.round(numpy.array(initial_population, dtype=self.gene_type[0]), self.gene_type[1]) + # This block is reached only for non-integer data types (i.e. float). + self.initial_population = numpy.round(numpy.array(initial_population, + dtype=self.gene_type[0]), + self.gene_type[1]) else: initial_population = numpy.array(initial_population) - self.initial_population = numpy.zeros(shape=(initial_population.shape[0], initial_population.shape[1]), dtype=object) + self.initial_population = numpy.zeros(shape=(initial_population.shape[0], + initial_population.shape[1]), + dtype=object) for gene_idx in range(initial_population.shape[1]): if self.gene_type[gene_idx][1] is None: self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], dtype=self.gene_type[gene_idx][0]) else: + # This block is reached only for non-integer data types (i.e. float). self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], dtype=self.gene_type[gene_idx][0]), self.gene_type[gene_idx][1]) + # Check if duplicates are allowed. If not, then solve any exisiting duplicates in the passed initial population. + if self.allow_duplicate_genes == False: + for initial_solution_idx, initial_solution in enumerate(self.initial_population): + if self.gene_space is None: + self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=initial_solution, + min_val=self.init_range_low, + max_val=self.init_range_high, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + else: + self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=initial_solution, + gene_type=self.gene_type, + num_trials=10) + self.population = self.initial_population.copy() # A NumPy array holding the initial population. self.num_genes = self.initial_population.shape[1] # Number of genes in the solution. self.sol_per_pop = self.initial_population.shape[0] # Number of solutions in the population. @@ -1096,14 +1129,6 @@ def __init__(self, self.logger.error("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) raise TypeError("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) - # Validate allow_duplicate_genes - if not (type(allow_duplicate_genes) is bool): - self.valid_parameters = False - self.logger.error("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) - raise TypeError("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) - - self.allow_duplicate_genes = allow_duplicate_genes - self.stop_criteria = [] self.supported_stop_words = ["reach", "saturate"] if stop_criteria is None: @@ -1259,7 +1284,8 @@ def round_genes(self, solutions): for gene_idx in range(self.num_genes): if self.gene_type_single: if not self.gene_type[1] is None: - solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], self.gene_type[1]) + solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], + self.gene_type[1]) else: if not self.gene_type[gene_idx][1] is None: solutions[:, gene_idx] = numpy.round(numpy.asarray(solutions[:, gene_idx], @@ -1322,10 +1348,14 @@ def initialize_population(self, elif self.gene_space_nested: if self.gene_type_single == True: - self.population = numpy.zeros(shape=self.pop_size, dtype=self.gene_type[0]) + # Reaching this block means: + # 1) gene_space is nested (gene_space_nested is True). + # 2) gene_type is not nested (gene_type_single is True). + self.population = numpy.zeros(shape=self.pop_size, + dtype=self.gene_type[0]) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): - if type(self.gene_space[gene_idx]) == type(None): + if self.gene_space[gene_idx] is None: # The following commented code replace the None value with a single number that will not change again. # This means the gene value will be the same across all solutions. @@ -1339,12 +1369,16 @@ def initialize_population(self, high=high, size=1), dtype=self.gene_type[0])[0] - elif type(self.gene_space[gene_idx]) in [list, tuple, range]: + elif type(self.gene_space[gene_idx]) in [numpy.ndarray, list, tuple, range]: # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. if type(self.gene_space[gene_idx]) is range: - temp = self.gene_space[gene_idx] + temp_gene_space = self.gene_space[gene_idx] else: - temp = list(self.gene_space[gene_idx]).copy() + # Convert to list because tuple and range do not have copy(). + # We copy the gene_space to a temp variable to keep its original value. + # In the next for loop, the gene_space is changed. + # Later, the gene_space is restored to its original value using the temp variable. + temp_gene_space = list(self.gene_space[gene_idx]).copy() for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: @@ -1359,8 +1393,10 @@ def initialize_population(self, else: # If there is no unique values, then we have to select a duplicate value. self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[0](self.population[sol_idx, gene_idx]) - self.gene_space[gene_idx] = list(temp).copy() + # Restore the gene_space from the temp_gene_space variable. + self.gene_space[gene_idx] = list(temp_gene_space).copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], @@ -1375,22 +1411,36 @@ def initialize_population(self, dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + else: + # There is no more options. + pass else: - self.population = numpy.zeros(shape=self.pop_size, dtype=object) + # Reaching this block means: + # 1) gene_space is nested (gene_space_nested is True). + # 2) gene_type is nested (gene_type_single is False). + self.population = numpy.zeros(shape=self.pop_size, + dtype=object) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): - if type(self.gene_space[gene_idx]) in [list, tuple, range]: + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list, tuple, range]: + # Convert to list because tuple and range do not have copy(). + # We copy the gene_space to a temp variable to keep its original value. + # In the next for loop, the gene_space is changed. + # Later, the gene_space is restored to its original value using the temp variable. + temp_gene_space = list(self.gene_space[gene_idx]).copy() + # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. - temp = list(self.gene_space[gene_idx]).copy() for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, high=high, size=1), dtype=self.gene_type[gene_idx][0])[0] + self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0](self.population[sol_idx, gene_idx]) - self.gene_space[gene_idx] = temp.copy() + # Restore the gene_space from the temp_gene_space variable. + self.gene_space[gene_idx] = temp_gene_space.copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], @@ -1404,22 +1454,24 @@ def initialize_population(self, size=1), dtype=self.gene_type[gene_idx][0])[0] elif type(self.gene_space[gene_idx]) == type(None): - # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - # high=high, - # size=1), - # dtype=self.gene_type[gene_idx][0])[0] + temp_gene_value = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), + dtype=self.gene_type[gene_idx][0])[0] - # self.population[sol_idx, gene_idx] = list(self.gene_space[gene_idx]).copy() - - temp = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = temp + self.population[sol_idx, gene_idx] = temp_gene_value.copy() elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + else: + # There is no more options. + pass else: + # Handle the non-nested gene_space. It can be assigned a numeric value, list, numpy.ndarray, or a dict. if self.gene_type_single == True: + # Reaching this block means: + # 1) gene_space is not nested (gene_space_nested is False). + # 2) gene_type is not nested (gene_type_single is True). + # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. for idx, curr_gene_space in enumerate(self.gene_space): if curr_gene_space is None: @@ -1446,25 +1498,23 @@ def initialize_population(self, size=self.pop_size), dtype=self.gene_type[0]) # A NumPy array holding the initial population. else: - # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. - for gene_idx, curr_gene_space in enumerate(self.gene_space): - if curr_gene_space is None: - self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[gene_idx][0])[0] - + # Reaching this block means: + # 1) gene_space is not nested (gene_space_nested is False). + # 2) gene_type is nested (gene_type_single is False). + # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. if type(self.gene_space) is dict: # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) + self.population = numpy.zeros(shape=self.pop_size, + dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): + # Generate the values of the current gene across all solutions. # A vector of all values of this single gene across all solutions in the population. - if 'step' in self.gene_space[gene_idx].keys(): - gene_values = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], - step=self.gene_space[gene_idx]['step']), + if 'step' in self.gene_space.keys(): + gene_values = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space['low'], + stop=self.gene_space['high'], + step=self.gene_space['step']), size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) else: @@ -1476,6 +1526,9 @@ def initialize_population(self, self.population[:, gene_idx] = gene_values else: + # Reaching this block means that the gene_space is not None or dict. + # It can be either range, numpy.ndarray, or list. + # Create an empty population of dtype=object to support storing mixed data types within the same array. self.population = numpy.zeros(shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. diff --git a/pygad/utils/crossover.py b/pygad/utils/crossover.py index 8eff279..6cc9a27 100644 --- a/pygad/utils/crossover.py +++ b/pygad/utils/crossover.py @@ -64,6 +64,7 @@ def single_point_crossover(self, parents, offspring_size): gene_type=self.gene_type, num_trials=10) + return offspring def two_points_crossover(self, parents, offspring_size): @@ -120,15 +121,15 @@ def two_points_crossover(self, parents, offspring_size): if self.allow_duplicate_genes == False: if self.gene_space is None: offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k], - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, - mutation_by_replacement=self.mutation_by_replacement, - gene_type=self.gene_type, - num_trials=10) + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) else: offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k], - gene_type=self.gene_type, - num_trials=10) + gene_type=self.gene_type, + num_trials=10) return offspring def uniform_crossover(self, parents, offspring_size): @@ -180,15 +181,15 @@ def uniform_crossover(self, parents, offspring_size): if self.allow_duplicate_genes == False: if self.gene_space is None: offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k], - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, - mutation_by_replacement=self.mutation_by_replacement, - gene_type=self.gene_type, - num_trials=10) + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) else: offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k], - gene_type=self.gene_type, - num_trials=10) + gene_type=self.gene_type, + num_trials=10) return offspring @@ -236,13 +237,13 @@ def scattered_crossover(self, parents, offspring_size): if self.allow_duplicate_genes == False: if self.gene_space is None: offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k], - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, - mutation_by_replacement=self.mutation_by_replacement, - gene_type=self.gene_type, - num_trials=10) + min_val=self.random_mutation_min_val, + max_val=self.random_mutation_max_val, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) else: offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k], - gene_type=self.gene_type, - num_trials=10) + gene_type=self.gene_type, + num_trials=10) return offspring diff --git a/tests/test_allow_duplicate_genes.py b/tests/test_allow_duplicate_genes.py index cc3db96..af7b652 100644 --- a/tests/test_allow_duplicate_genes.py +++ b/tests/test_allow_duplicate_genes.py @@ -2,7 +2,18 @@ import random import numpy -num_generations = 100 +num_generations = 1 + +initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]] def number_duplicate_genes(gene_space=None, gene_type=float, @@ -11,7 +22,9 @@ def number_duplicate_genes(gene_space=None, random_mutation_min_val=-1, random_mutation_max_val=1, init_range_low=-4, - init_range_high=4): + init_range_high=4, + random_seed=123, + initial_population=None): def fitness_func(ga, solution, idx): return random.random() @@ -23,12 +36,14 @@ def fitness_func(ga, solution, idx): num_genes=num_genes, gene_space=gene_space, gene_type=gene_type, + initial_population=initial_population, init_range_low=init_range_low, init_range_high=init_range_high, random_mutation_min_val=random_mutation_min_val, random_mutation_max_val=random_mutation_max_val, allow_duplicate_genes=False, mutation_by_replacement=mutation_by_replacement, + random_seed=random_seed, save_solutions=True, suppress_warnings=True) @@ -49,6 +64,11 @@ def test_number_duplicates_default(): assert num_duplicates == 0 +def test_number_duplicates_default_initial_population(): + num_duplicates = number_duplicate_genes(initial_population=initial_population) + + assert num_duplicates == 0 + def test_number_duplicates_float_gene_type(): num_genes = 10 num_duplicates = number_duplicate_genes(gene_type=float, @@ -60,6 +80,18 @@ def test_number_duplicates_float_gene_type(): assert num_duplicates == 0 +def test_number_duplicates_float_gene_type_initial_population(): + num_genes = 10 + num_duplicates = number_duplicate_genes(gene_type=float, + num_genes=num_genes, + init_range_low=0, + init_range_high=1, + initial_population=initial_population, + random_mutation_min_val=0, + random_mutation_max_val=1) + + assert num_duplicates == 0 + def test_number_duplicates_int_gene_type(): num_genes = 10 init_range_low = 0 @@ -76,6 +108,23 @@ def test_number_duplicates_int_gene_type(): assert num_duplicates == 0 +def test_number_duplicates_int_gene_type_initial_population(): + num_genes = 10 + init_range_low = 0 + init_range_high = init_range_low + num_genes + random_mutation_min_val = 0 + random_mutation_max_val = random_mutation_min_val + num_genes + num_duplicates = number_duplicate_genes(gene_type=int, + mutation_by_replacement=False, + num_genes=num_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + initial_population=initial_population, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val) + + assert num_duplicates == 0 + def test_number_duplicates_int_gene_type_replacement(): num_genes = 10 init_range_low = 0 @@ -92,12 +141,36 @@ def test_number_duplicates_int_gene_type_replacement(): assert num_duplicates == 0 +def test_number_duplicates_int_gene_type_replacement_initial_population(): + num_genes = 10 + init_range_low = 0 + init_range_high = init_range_low + num_genes + random_mutation_min_val = 0 + random_mutation_max_val = random_mutation_min_val + num_genes + num_duplicates = number_duplicate_genes(gene_type=int, + mutation_by_replacement=True, + num_genes=num_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + initial_population=initial_population, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val) + + assert num_duplicates == 0 + def test_number_duplicates_single_gene_space(): num_duplicates = number_duplicate_genes(gene_space=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], num_genes=10) assert num_duplicates == 0 +def test_number_duplicates_single_gene_space_initial_population(): + num_duplicates = number_duplicate_genes(gene_space=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + num_genes=10, + initial_population=initial_population) + + assert num_duplicates == 0 + def test_number_duplicates_single_range_gene_space(): num_genes = 10 num_duplicates = number_duplicate_genes(gene_space=range(num_genes), @@ -105,6 +178,14 @@ def test_number_duplicates_single_range_gene_space(): assert num_duplicates == 0 +def test_number_duplicates_single_range_gene_space_initial_population(): + num_genes = 10 + num_duplicates = number_duplicate_genes(gene_space=range(num_genes), + num_genes=num_genes, + initial_population=initial_population) + + assert num_duplicates == 0 + def test_number_duplicates_single_numpy_range_gene_space(): num_genes = 10 num_duplicates = number_duplicate_genes(gene_space=numpy.arange(num_genes), @@ -112,6 +193,14 @@ def test_number_duplicates_single_numpy_range_gene_space(): assert num_duplicates == 0 +def test_number_duplicates_single_numpy_range_gene_space_initial_population(): + num_genes = 10 + num_duplicates = number_duplicate_genes(gene_space=numpy.arange(num_genes), + num_genes=num_genes, + initial_population=initial_population) + + assert num_duplicates == 0 + def test_number_duplicates_nested_gene_space(): num_duplicates = number_duplicate_genes(gene_space=[[0, 1], [1, 2], @@ -124,27 +213,129 @@ def test_number_duplicates_nested_gene_space(): [8, 9], [9, 10]], gene_type=int, - random_mutation_min_val=11, - random_mutation_max_val=20, - mutation_by_replacement=True, num_genes=10) assert num_duplicates == 0 +def test_number_duplicates_nested_gene_space_initial_population(): + num_duplicates = number_duplicate_genes(gene_space=[[0, 1], + [1, 2], + [2, 3], + [3, 4], + [4, 5], + [5, 6], + [6, 7], + [7, 8], + [8, 9], + [9, 10]], + gene_type=int, + num_genes=10, + initial_population=initial_population) + + assert num_duplicates == 0 + + +# def test_number_duplicates_nested_gene_space_nested_gene_type(): + """ + This example causes duplicate genes that can only be solved by changing the values of a chain of genes. + Let's explain it using this solution: [0, 2, 3, 4, 5, 6, 6, 7, 8, 9] + It has 2 genes with the value 6 at indices 5 and 6. + According to the gene space, none of these genes can has a different value that solves the duplicates. + -If the value of the gene at index 5 is changed from 6 to 5, then it causes another duplicate with the gene at index 4. + -If the value of the gene at index 6 is changed from 6 to 7, then it causes another duplicate with the gene at index 7. + The solution is to change a chain of genes that make a room to solve the duplicates between the 2 genes. + 1) Change the second gene from 2 to 1. + 2) Change the third gene from 3 to 2. + 3) Change the fourth gene from 4 to 3. + 4) Change the fifth gene from 5 to 4. + 5) Change the sixth gene from 6 to 5. This solves the duplicates. + But this is NOT SUPPORTED yet. + We support changing only a single gene that makes a room to solve the duplicates. + + Let's explain it using this solution: [1, 2, 2, 4, 5, 6, 6, 7, 8, 9] + It has 2 genes with the value 2 at indices 1 and 2. + This is how the duplicates are solved: + 1) Change the first gene from 1 to 0. + 2) Change the second gene from 2 to 1. This solves the duplicates. + The result is [0, 1, 2, 4, 5, 6, 6, 7, 8, 9] + """ + # num_duplicates = number_duplicate_genes(gene_space=[[0, 1], + # [1, 2], + # [2, 3], + # [3, 4], + # [4, 5], + # [5, 6], + # [6, 7], + # [7, 8], + # [8, 9], + # [9, 10]], + # gene_type=[int, int, int, int, int, int, int, int, int, int], + # num_genes=10) + + # assert num_duplicates == 0 + +def test_number_duplicates_nested_gene_space_nested_gene_type_initial_population(): + num_duplicates = number_duplicate_genes(gene_space=[[0, 1], + [1, 2], + [2, 3], + [3, 4], + [4, 5], + [5, 6], + [6, 7], + [7, 8], + [8, 9], + [9, 10]], + gene_type=[int, int, int, int, int, int, int, int, int, int], + num_genes=10, + initial_population=initial_population) + + assert num_duplicates == 0 + if __name__ == "__main__": - # print() - # test_number_duplicates_default() - # print() - # test_number_duplicates_float_gene_type() - # print() - # test_number_duplicates_int_gene_type() - # print() - # test_number_duplicates_single_gene_space() - # print() - # test_number_duplicates_single_range_gene_space() - # print() - # test_number_duplicates_single_numpy_range_gene_space() print() + test_number_duplicates_default() + print() + test_number_duplicates_default_initial_population() + print() + + test_number_duplicates_float_gene_type() + print() + test_number_duplicates_float_gene_type_initial_population() + print() + + test_number_duplicates_int_gene_type() + print() + test_number_duplicates_int_gene_type_initial_population() + print() + + test_number_duplicates_int_gene_type_replacement() + print() + test_number_duplicates_int_gene_type_replacement_initial_population() + print() + + test_number_duplicates_single_gene_space() + print() + test_number_duplicates_single_gene_space_initial_population() + print() + + test_number_duplicates_single_range_gene_space() + print() + test_number_duplicates_single_range_gene_space_initial_population() + print() + + test_number_duplicates_single_numpy_range_gene_space() + print() + test_number_duplicates_single_numpy_range_gene_space_initial_population() + print() + test_number_duplicates_nested_gene_space() print() - + test_number_duplicates_nested_gene_space_initial_population() + print() + + # This example causes duplicates that can only be solved by changing a chain of genes. + # test_number_duplicates_nested_gene_space_nested_gene_type() + # print() + test_number_duplicates_nested_gene_space_nested_gene_type_initial_population() + print() + diff --git a/tests/test_gene_space.py b/tests/test_gene_space.py new file mode 100644 index 0000000..bdda870 --- /dev/null +++ b/tests/test_gene_space.py @@ -0,0 +1,345 @@ +import pygad +import random +import numpy + +num_generations = 100 + +initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]] + +# Test single gene space with nested gene type. + +def number_respect_gene_space(gene_space=None, + gene_type=float, + num_genes=10, + mutation_by_replacement=False, + random_mutation_min_val=-1, + random_mutation_max_val=1, + init_range_low=-4, + init_range_high=4, + initial_population=None): + + def fitness_func(ga, solution, idx): + return random.random() + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=5, + fitness_func=fitness_func, + sol_per_pop=10, + num_genes=num_genes, + gene_space=gene_space, + gene_type=gene_type, + initial_population=initial_population, + init_range_low=init_range_low, + init_range_high=init_range_high, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val, + allow_duplicate_genes=True, + mutation_by_replacement=mutation_by_replacement, + save_solutions=True, + suppress_warnings=True) + + ga_instance.run() + ga_instance.solutions = numpy.array(ga_instance.solutions, + dtype=object) + + # gene_space_unpacked = ga_instance.unpack_gene_space(num_values_from_inf_range=100) + num_outside = 0 + if ga_instance.gene_space_nested == True: + for gene_idx in range(ga_instance.num_genes): + all_gene_values = ga_instance.solutions[:, gene_idx] + if type(ga_instance.gene_space[gene_idx]) in [list, tuple, range, numpy.ndarray]: + current_gene_space = list(ga_instance.gene_space[gene_idx]) + for val in all_gene_values: + if val in current_gene_space: + # print(val, current_gene_space) + pass + else: + # print(gene_idx, val, current_gene_space) + num_outside += 1 + elif type(ga_instance.gene_space[gene_idx]) is dict: + if not "step" in ga_instance.gene_space[gene_idx].keys(): + for val in all_gene_values: + if val >= ga_instance.gene_space[gene_idx]["low"] and val < ga_instance.gene_space[gene_idx]["high"]: + pass + else: + num_outside += 1 + else: + gene_space_values = numpy.arange(ga_instance.gene_space[gene_idx]["low"], + ga_instance.gene_space[gene_idx]["high"], + ga_instance.gene_space[gene_idx]["step"]) + for val in all_gene_values: + if val in gene_space_values: + pass + else: + num_outside += 1 + elif type(ga_instance.gene_space[gene_idx]) in ga_instance.supported_int_float_types: + for val in all_gene_values: + if val == ga_instance.gene_space[gene_idx]: + pass + else: + num_outside += 1 + else: + for gene_idx in range(ga_instance.num_genes): + all_gene_values = ga_instance.solutions[:, gene_idx] + # print("all_gene_values", gene_idx, all_gene_values) + if type(ga_instance.gene_space) in [list, tuple, range, numpy.ndarray]: + current_gene_space = list(ga_instance.gene_space) + for val in all_gene_values: + if val in current_gene_space: + pass + else: + num_outside += 1 + elif type(ga_instance.gene_space) is dict: + if not "step" in ga_instance.gene_space.keys(): + for val in all_gene_values: + if val >= ga_instance.gene_space["low"] and val < ga_instance.gene_space["high"]: + pass + else: + num_outside += 1 + else: + gene_space_values = numpy.arange(ga_instance.gene_space["low"], + ga_instance.gene_space["high"], + ga_instance.gene_space["step"]) + for val in all_gene_values: + if val in gene_space_values: + pass + else: + num_outside += 1 + + print("Number of outside range is {num_outside}.".format(num_outside=num_outside)) + return num_outside, ga_instance + +def test_gene_space_range(): + num_outside, _ = number_respect_gene_space(gene_space=range(10)) + + assert num_outside == 0 + +def test_gene_space_numpy_arange(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10)) + + assert num_outside == 0 + +def test_gene_space_list(): + num_outside, _ = number_respect_gene_space(gene_space=list(range(10))) + + assert num_outside == 0 + +def test_gene_space_numpy(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10)))) + + assert num_outside == 0 + +def test_gene_space_dict_without_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10}) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_gene_space_dict_with_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2}) + + assert num_outside == 0 + +def test_gene_space_list_single_value(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[5]) + + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_range(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[range(0, 10), + range(10, 20), + range(20, 30), + range(30, 40), + range(40, 50), + range(50, 60), + range(60, 70), + range(70, 80), + range(80, 90), + range(90, 100)]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_dict_without_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10}, + {"low": 10, "high": 20}, + {"low": 20, "high": 30}, + {"low": 30, "high": 40}, + {"low": 40, "high": 50}, + {"low": 50, "high": 60}, + {"low": 60, "high": 70}, + {"low": 70, "high": 80}, + {"low": 80, "high": 90}, + {"low": 90, "high": 100}]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_dict_with_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10, "step": 1}, + {"low": 10, "high": 20, "step": 1.5}, + {"low": 20, "high": 30, "step": 2}, + {"low": 30, "high": 40, "step": 2.5}, + {"low": 40, "high": 50, "step": 3}, + {"low": 50, "high": 60, "step": 3.5}, + {"low": 60, "high": 70, "step": 4}, + {"low": 70, "high": 80, "step": 4.5}, + {"low": 80, "high": 90, "step": 5}, + {"low": 90, "high": 100, "step": 5.5}]) + # print(ga_instance.population) + + assert num_outside == 0 + + +def test_nested_gene_space_numpy_arange(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[numpy.arange(0, 10), + numpy.arange(10, 20), + numpy.arange(20, 30), + numpy.arange(30, 40), + numpy.arange(40, 50), + numpy.arange(50, 60), + numpy.arange(60, 70), + numpy.arange(70, 80), + numpy.arange(80, 90), + numpy.arange(90, 100)]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_list(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [-10, 10, 20, 30, 40, 50, 60, 70, 80, 90], + [-11, 11, 22, 33, 44, 55, 66, 77, 88, 99], + [-100, 100, 200, 300, 400, 500, 600, 700, 800, 900], + [-4.1, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], + [-5.1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9], + [-10.5, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9], + [-15, 15, 25, 35, 45, 55, 65, 75, 85, 95], + [30, 31, 32, 33, 34, 35, 36, 37, 38, 39], + [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_list2(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1], + [1, 2], + [2, 3], + [3, 4], + [4, 5], + [5, 6], + [6, 7], + [7, 8], + [8, 9], + [9, 10]]) + + assert num_outside == 0 + +def test_nested_gene_space_mix(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]], + gene_type=int) + + assert num_outside == 0 + +def test_nested_gene_space_mix_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_mix_initial_population(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + numpy.arange(0, 10), + range(0, 10), + {"low": 0, "high": 10}, + {"low": 00, "high": 10, "step": 1}, + range(0, 10), + numpy.arange(0, 10), + numpy.arange(0, 10), + {"low": 0, "high": 10}, + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]], + initial_population=initial_population) + # print(ga_instance.population) + + assert num_outside == 0 + +if __name__ == "__main__": + print() + test_gene_space_range() + print() + + test_gene_space_numpy_arange() + print() + + test_gene_space_list() + print() + + test_gene_space_list_single_value() + print() + + test_gene_space_numpy() + print() + + test_gene_space_dict_without_step() + print() + + test_gene_space_dict_with_step() + print() + + test_nested_gene_space_range() + print() + + test_nested_gene_space_dict_without_step() + print() + + test_nested_gene_space_dict_with_step() + print() + + test_nested_gene_space_numpy_arange() + print() + + test_nested_gene_space_list() + print() + + test_nested_gene_space_list2() + print() + + test_nested_gene_space_mix() + print() + + test_nested_gene_space_mix_nested_gene_type() + print() + + test_nested_gene_space_mix_initial_population() + print() \ No newline at end of file From 6b941522548559f389b84e6487ea749eff49a9d7 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 2 May 2023 22:40:35 -0400 Subject: [PATCH 041/220] Fixes and more tests --- pygad/helper/unique.py | 70 ++-- pygad/utils/mutation.py | 41 +- pygad/utils/parent_selection.py | 4 +- tests/test_gene_space.py | 47 ++- .../test_gene_space_allow_duplicate_genes.py | 391 ++++++++++++++++++ 5 files changed, 497 insertions(+), 56 deletions(-) create mode 100644 tests/test_gene_space_allow_duplicate_genes.py diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py index 3224445..ad4bd8a 100644 --- a/pygad/helper/unique.py +++ b/pygad/helper/unique.py @@ -53,7 +53,7 @@ def solve_duplicate_genes_randomly(self, else: temp_val = numpy.random.uniform(low=min_val, high=max_val, - size=1) + size=1)[0] if mutation_by_replacement: pass else: @@ -69,7 +69,7 @@ def solve_duplicate_genes_randomly(self, else: temp_val = numpy.random.uniform(low=min_val, high=max_val, - size=1) + size=1)[0] if mutation_by_replacement: pass else: @@ -229,7 +229,7 @@ def unique_int_gene_from_range(self, # Note that we already know that the data type is integer. all_gene_values = numpy.asarray(all_gene_values, gene_type[gene_index][0]) - + values_to_select_from = list(set(all_gene_values) - set(solution)) if len(values_to_select_from) == 0: @@ -347,12 +347,12 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=low, high=high, - size=1) + size=1)[0] # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. - if self.mutation_by_replacement: - pass - else: - value_from_space = solution[gene_idx] + value_from_space + # if self.mutation_by_replacement: + # pass + # else: + # value_from_space = solution[gene_idx] + value_from_space else: if gene_type[gene_idx][0] in pygad.GA.supported_int_types: if build_initial_pop == True: @@ -378,12 +378,12 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=low, high=high, - size=1) + size=1)[0] # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. - if self.mutation_by_replacement: - pass - else: - value_from_space = solution[gene_idx] + value_from_space + # if self.mutation_by_replacement: + # pass + # else: + # value_from_space = solution[gene_idx] + value_from_space elif type(curr_gene_space) is dict: if self.gene_type_single == True: @@ -409,12 +409,12 @@ def unique_gene_by_space(self, else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], - size=1) + size=1)[0] # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. - if self.mutation_by_replacement: - pass - else: - value_from_space = solution[gene_idx] + value_from_space + # if self.mutation_by_replacement: + # pass + # else: + # value_from_space = solution[gene_idx] + value_from_space else: # Use index 0 to return the type from the list (e.g. [int, None] or [float, 2]). if gene_type[gene_idx][0] in pygad.GA.supported_int_types: @@ -439,12 +439,12 @@ def unique_gene_by_space(self, else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], - size=1) + size=1)[0] # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. - if self.mutation_by_replacement: - pass - else: - value_from_space = solution[gene_idx] + value_from_space + # if self.mutation_by_replacement: + # pass + # else: + # value_from_space = solution[gene_idx] + value_from_space else: # Selecting a value randomly based on the current gene's space in the 'gene_space' attribute. @@ -503,12 +503,12 @@ def unique_gene_by_space(self, else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], - size=1) + size=1)[0] # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. - if self.mutation_by_replacement: - pass - else: - value_from_space = solution[gene_idx] + value_from_space + # if self.mutation_by_replacement: + # pass + # else: + # value_from_space = solution[gene_idx] + value_from_space else: if gene_type[gene_idx][0] in pygad.GA.supported_int_types: if 'step' in self.gene_space.keys(): @@ -533,12 +533,12 @@ def unique_gene_by_space(self, else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], - size=1) + size=1)[0] # TODO: Remove check for mutation_by_replacement when solving duplicates. Just replace the gene by the selected value from space. - if self.mutation_by_replacement: - pass - else: - value_from_space = solution[gene_idx] + value_from_space + # if self.mutation_by_replacement: + # pass + # else: + # value_from_space = solution[gene_idx] + value_from_space else: # If the space type is not of type dict, then a value is randomly selected from the gene_space attribute. @@ -562,7 +562,7 @@ def unique_gene_by_space(self, value_from_space = numpy.random.uniform(low=low, high=high, - size=1) + size=1)[0] # Similar to the round_genes() method in the pygad module, # Create a round_gene() method to round a single gene. @@ -722,7 +722,7 @@ def unpack_gene_space(self, for idx in none_indices: random_value = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] gene_space_unpacked[space_idx][idx] = random_value if self.gene_type_single == True: # self.gene_type_single diff --git a/pygad/utils/mutation.py b/pygad/utils/mutation.py index 9ce8722..e6b67ff 100644 --- a/pygad/utils/mutation.py +++ b/pygad/utils/mutation.py @@ -67,7 +67,7 @@ def mutation_by_space(self, offspring): elif curr_gene_space is None: rand_val = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val else: @@ -82,7 +82,7 @@ def mutation_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], - size=1) + size=1)[0] else: # Selecting a value randomly based on the current gene's space in the 'gene_space' attribute. # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. @@ -108,7 +108,7 @@ def mutation_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], - size=1) + size=1)[0] else: # If the space type is not of type dict, then a value is randomly selected from the gene_space attribute. values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) @@ -120,9 +120,11 @@ def mutation_by_space(self, offspring): # value_from_space = random.choice(self.gene_space) if value_from_space is None: + # TODO: Return index 0. + # TODO: Check if this if statement is necessary. value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] # Assinging the selected value from the space to the gene. if self.gene_type_single == True: @@ -135,6 +137,7 @@ def mutation_by_space(self, offspring): if not self.gene_type[gene_idx][1] is None: offspring[offspring_idx, gene_idx] = numpy.round(self.gene_type[gene_idx][0](value_from_space), self.gene_type[gene_idx][1]) + else: offspring[offspring_idx, gene_idx] = self.gene_type[gene_idx][0](value_from_space) @@ -172,7 +175,7 @@ def mutation_probs_by_space(self, offspring): elif curr_gene_space is None: rand_val = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val else: @@ -187,7 +190,7 @@ def mutation_probs_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], - size=1) + size=1)[0] else: # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. @@ -212,7 +215,7 @@ def mutation_probs_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], - size=1) + size=1)[0] else: values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) @@ -257,7 +260,7 @@ def mutation_randomly(self, offspring): # Generating a random value. random_value = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: if self.gene_type_single == True: @@ -312,7 +315,7 @@ def mutation_probs_randomly(self, offspring): # Generating a random value. random_value = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: if self.gene_type_single == True: @@ -509,7 +512,7 @@ def adaptive_mutation_by_space(self, offspring): elif curr_gene_space is None: rand_val = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val else: @@ -524,7 +527,7 @@ def adaptive_mutation_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], - size=1) + size=1)[0] else: # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. @@ -549,7 +552,7 @@ def adaptive_mutation_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], - size=1) + size=1)[0] else: values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) @@ -562,7 +565,7 @@ def adaptive_mutation_by_space(self, offspring): if value_from_space is None: value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] # Assinging the selected value from the space to the gene. if self.gene_type_single == True: @@ -609,7 +612,7 @@ def adaptive_mutation_randomly(self, offspring): # Generating a random value. random_value = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: if self.gene_type_single == True: @@ -685,7 +688,7 @@ def adaptive_mutation_probs_by_space(self, offspring): elif curr_gene_space is None: rand_val = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val else: @@ -700,7 +703,7 @@ def adaptive_mutation_probs_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], - size=1) + size=1)[0] else: # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. @@ -725,7 +728,7 @@ def adaptive_mutation_probs_by_space(self, offspring): else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], - size=1) + size=1)[0] else: values_to_select_from = list(set(self.gene_space) - set([offspring[offspring_idx, gene_idx]])) @@ -737,7 +740,7 @@ def adaptive_mutation_probs_by_space(self, offspring): if value_from_space is None: value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] # Assinging the selected value from the space to the gene. if self.gene_type_single == True: @@ -786,7 +789,7 @@ def adaptive_mutation_probs_randomly(self, offspring): # Generating a random value. random_value = numpy.random.uniform(low=self.random_mutation_min_val, high=self.random_mutation_max_val, - size=1) + size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: if self.gene_type_single == True: diff --git a/pygad/utils/parent_selection.py b/pygad/utils/parent_selection.py index 7f32181..976e130 100644 --- a/pygad/utils/parent_selection.py +++ b/pygad/utils/parent_selection.py @@ -171,7 +171,9 @@ def stochastic_universal_selection(self, fitness, num_parents): probs[min_probs_idx] = 99999999999 pointers_distance = 1.0 / self.num_parents_mating # Distance between different pointers. - first_pointer = numpy.random.uniform(low=0.0, high=pointers_distance, size=1) # Location of the first pointer. + first_pointer = numpy.random.uniform(low=0.0, + high=pointers_distance, + size=1)[0] # Location of the first pointer. # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. if self.gene_type_single == True: diff --git a/tests/test_gene_space.py b/tests/test_gene_space.py index bdda870..09bc221 100644 --- a/tests/test_gene_space.py +++ b/tests/test_gene_space.py @@ -1,3 +1,8 @@ +""" +This script is identical to the test_gene_space_allow_duplicate_genes.py script except for: + Setting allow_duplicate_genes=False instead of True. +""" + import pygad import random import numpy @@ -45,7 +50,8 @@ def fitness_func(ga, solution, idx): allow_duplicate_genes=True, mutation_by_replacement=mutation_by_replacement, save_solutions=True, - suppress_warnings=True) + suppress_warnings=True, + random_seed=1) ga_instance.run() ga_instance.solutions = numpy.array(ga_instance.solutions, @@ -186,6 +192,22 @@ def test_nested_gene_space_dict_without_step(): assert num_outside == 0 +def test_nested_gene_space_dict_without_step_float_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10}, + {"low": 10, "high": 20}, + {"low": 20, "high": 30}, + {"low": 30, "high": 40}, + {"low": 40, "high": 50}, + {"low": 50, "high": 60}, + {"low": 60, "high": 70}, + {"low": 70, "high": 80}, + {"low": 80, "high": 90}, + {"low": 90, "high": 100}], + gene_type=[float, 3]) + # print(ga_instance.population) + + assert num_outside == 0 + def test_nested_gene_space_dict_with_step(): num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10, "step": 1}, {"low": 10, "high": 20, "step": 1.5}, @@ -294,6 +316,23 @@ def test_nested_gene_space_mix_initial_population(): assert num_outside == 0 +def test_nested_gene_space_mix_initial_population_single_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + numpy.arange(0, 10), + range(0, 10), + {"low": 0, "high": 10}, + {"low": 0, "high": 10}, + range(0, 10), + numpy.arange(0, 10), + numpy.arange(0, 10), + {"low": 0, "high": 10}, + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], + gene_type=[float, 2], + initial_population=initial_population) + # print(ga_instance.population) + + assert num_outside == 0 + if __name__ == "__main__": print() test_gene_space_range() @@ -323,6 +362,9 @@ def test_nested_gene_space_mix_initial_population(): test_nested_gene_space_dict_without_step() print() + test_nested_gene_space_dict_without_step_float_gene_type() + print() + test_nested_gene_space_dict_with_step() print() @@ -342,4 +384,7 @@ def test_nested_gene_space_mix_initial_population(): print() test_nested_gene_space_mix_initial_population() + print() + + test_nested_gene_space_mix_initial_population_single_gene_type() print() \ No newline at end of file diff --git a/tests/test_gene_space_allow_duplicate_genes.py b/tests/test_gene_space_allow_duplicate_genes.py new file mode 100644 index 0000000..804129a --- /dev/null +++ b/tests/test_gene_space_allow_duplicate_genes.py @@ -0,0 +1,391 @@ +""" +This script is identical to the test_gene_space.py script except for: + Setting allow_duplicate_genes=False instead of True. +""" + +import pygad +import random +import numpy + +num_generations = 100 + +initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]] + +# Test single gene space with nested gene type. + +def number_respect_gene_space(gene_space=None, + gene_type=float, + num_genes=10, + mutation_by_replacement=False, + random_mutation_min_val=-1, + random_mutation_max_val=1, + init_range_low=-4, + init_range_high=4, + initial_population=None): + + def fitness_func(ga, solution, idx): + return random.random() + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=5, + fitness_func=fitness_func, + sol_per_pop=10, + num_genes=num_genes, + gene_space=gene_space, + gene_type=gene_type, + initial_population=initial_population, + init_range_low=init_range_low, + init_range_high=init_range_high, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val, + allow_duplicate_genes=False, + mutation_by_replacement=mutation_by_replacement, + save_solutions=True, + suppress_warnings=True, + random_seed=2) + + ga_instance.run() + ga_instance.solutions = numpy.array(ga_instance.solutions, + dtype=object) + + # gene_space_unpacked = ga_instance.unpack_gene_space(num_values_from_inf_range=100) + num_outside = 0 + if ga_instance.gene_space_nested == True: + for gene_idx in range(ga_instance.num_genes): + all_gene_values = ga_instance.solutions[:, gene_idx] + if type(ga_instance.gene_space[gene_idx]) in [list, tuple, range, numpy.ndarray]: + current_gene_space = list(ga_instance.gene_space[gene_idx]) + for val in all_gene_values: + if val in current_gene_space: + # print(val, current_gene_space) + pass + else: + # print(gene_idx, val, current_gene_space) + num_outside += 1 + elif type(ga_instance.gene_space[gene_idx]) is dict: + if not "step" in ga_instance.gene_space[gene_idx].keys(): + for val in all_gene_values: + if val >= ga_instance.gene_space[gene_idx]["low"] and val < ga_instance.gene_space[gene_idx]["high"]: + pass + else: + print(gene_idx, val, current_gene_space, all_gene_values) + num_outside += 1 + else: + gene_space_values = numpy.arange(ga_instance.gene_space[gene_idx]["low"], + ga_instance.gene_space[gene_idx]["high"], + ga_instance.gene_space[gene_idx]["step"]) + for val in all_gene_values: + if val in gene_space_values: + pass + else: + num_outside += 1 + elif type(ga_instance.gene_space[gene_idx]) in ga_instance.supported_int_float_types: + for val in all_gene_values: + if val == ga_instance.gene_space[gene_idx]: + pass + else: + num_outside += 1 + else: + for gene_idx in range(ga_instance.num_genes): + all_gene_values = ga_instance.solutions[:, gene_idx] + # print("all_gene_values", gene_idx, all_gene_values) + if type(ga_instance.gene_space) in [list, tuple, range, numpy.ndarray]: + current_gene_space = list(ga_instance.gene_space) + for val in all_gene_values: + if val in current_gene_space: + pass + else: + num_outside += 1 + elif type(ga_instance.gene_space) is dict: + if not "step" in ga_instance.gene_space.keys(): + for val in all_gene_values: + if val >= ga_instance.gene_space["low"] and val < ga_instance.gene_space["high"]: + pass + else: + num_outside += 1 + else: + gene_space_values = numpy.arange(ga_instance.gene_space["low"], + ga_instance.gene_space["high"], + ga_instance.gene_space["step"]) + for val in all_gene_values: + if val in gene_space_values: + pass + else: + num_outside += 1 + + print("Number of outside range is {num_outside}.".format(num_outside=num_outside)) + return num_outside, ga_instance + +def test_gene_space_range(): + num_outside, _ = number_respect_gene_space(gene_space=range(10)) + + assert num_outside == 0 + +def test_gene_space_numpy_arange(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10)) + + assert num_outside == 0 + +def test_gene_space_list(): + num_outside, _ = number_respect_gene_space(gene_space=list(range(10))) + + assert num_outside == 0 + +def test_gene_space_numpy(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10)))) + + assert num_outside == 0 + +def test_gene_space_dict_without_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10}) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_gene_space_dict_with_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2}) + + assert num_outside == 0 + +def test_gene_space_list_single_value(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[5]) + + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_range(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[range(0, 10), + range(10, 20), + range(20, 30), + range(30, 40), + range(40, 50), + range(50, 60), + range(60, 70), + range(70, 80), + range(80, 90), + range(90, 100)]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_dict_without_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10}, + {"low": 10, "high": 20}, + {"low": 20, "high": 30}, + {"low": 30, "high": 40}, + {"low": 40, "high": 50}, + {"low": 50, "high": 60}, + {"low": 60, "high": 70}, + {"low": 70, "high": 80}, + {"low": 80, "high": 90}, + {"low": 90, "high": 100}]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_dict_without_step_float_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10}, + {"low": 10, "high": 20}, + {"low": 20, "high": 30}, + {"low": 30, "high": 40}, + {"low": 40, "high": 50}, + {"low": 50, "high": 60}, + {"low": 60, "high": 70}, + {"low": 70, "high": 80}, + {"low": 80, "high": 90}, + {"low": 90, "high": 100}], + gene_type=[float, 3]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_dict_with_step(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10, "step": 1}, + {"low": 10, "high": 20, "step": 1.5}, + {"low": 20, "high": 30, "step": 2}, + {"low": 30, "high": 40, "step": 2.5}, + {"low": 40, "high": 50, "step": 3}, + {"low": 50, "high": 60, "step": 3.5}, + {"low": 60, "high": 70, "step": 4}, + {"low": 70, "high": 80, "step": 4.5}, + {"low": 80, "high": 90, "step": 5}, + {"low": 90, "high": 100, "step": 5.5}]) + # print(ga_instance.population) + + assert num_outside == 0 + + +def test_nested_gene_space_numpy_arange(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[numpy.arange(0, 10), + numpy.arange(10, 20), + numpy.arange(20, 30), + numpy.arange(30, 40), + numpy.arange(40, 50), + numpy.arange(50, 60), + numpy.arange(60, 70), + numpy.arange(70, 80), + numpy.arange(80, 90), + numpy.arange(90, 100)]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_list(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [-10, 10, 20, 30, 40, 50, 60, 70, 80, 90], + [-11, 11, 22, 33, 44, 55, 66, 77, 88, 99], + [-100, 100, 200, 300, 400, 500, 600, 700, 800, 900], + [-4.1, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], + [-5.1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9], + [-10.5, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9], + [-15, 15, 25, 35, 45, 55, 65, 75, 85, 95], + [30, 31, 32, 33, 34, 35, 36, 37, 38, 39], + [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_list2(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1], + [1, 2], + [2, 3], + [3, 4], + [4, 5], + [5, 6], + [6, 7], + [7, 8], + [8, 9], + [9, 10]]) + + assert num_outside == 0 + +def test_nested_gene_space_mix(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]], + gene_type=int) + + assert num_outside == 0 + +def test_nested_gene_space_mix_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_mix_initial_population(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + numpy.arange(0, 10), + range(0, 10), + {"low": 0, "high": 10}, + {"low": 00, "high": 10, "step": 1}, + range(0, 10), + numpy.arange(0, 10), + numpy.arange(0, 10), + {"low": 0, "high": 10}, + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]], + initial_population=initial_population) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_mix_initial_population_single_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + numpy.arange(0, 10), + range(0, 10), + {"low": 0, "high": 10}, + {"low": 0, "high": 10}, + range(0, 10), + numpy.arange(0, 10), + numpy.arange(0, 10), + {"low": 0, "high": 10}, + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], + gene_type=[float, 2], + initial_population=initial_population) + # print(ga_instance.population) + + assert num_outside == 0 + +if __name__ == "__main__": + print() + test_gene_space_range() + print() + + test_gene_space_numpy_arange() + print() + + test_gene_space_list() + print() + + test_gene_space_list_single_value() + print() + + test_gene_space_numpy() + print() + + test_gene_space_dict_without_step() + print() + + test_gene_space_dict_with_step() + print() + + test_nested_gene_space_range() + print() + + test_nested_gene_space_dict_without_step() + print() + + test_nested_gene_space_dict_without_step_float_gene_type() + print() + + test_nested_gene_space_dict_with_step() + print() + + test_nested_gene_space_numpy_arange() + print() + + test_nested_gene_space_list() + print() + + test_nested_gene_space_list2() + print() + + test_nested_gene_space_mix() + print() + + test_nested_gene_space_mix_nested_gene_type() + print() + + test_nested_gene_space_mix_initial_population() + print() + + test_nested_gene_space_mix_initial_population_single_gene_type() + print() \ No newline at end of file From 2f139d3e2c226ce4ccb5cdea37f695c7b549527f Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Wed, 3 May 2023 00:38:54 -0400 Subject: [PATCH 042/220] Fixes and more tests --- pygad/utils/mutation.py | 25 ++- tests/test_crossover_mutation.py | 188 ++++++++++++++++++ tests/test_gene_space.py | 63 +++++- .../test_gene_space_allow_duplicate_genes.py | 100 ++++++++-- 4 files changed, 345 insertions(+), 31 deletions(-) create mode 100644 tests/test_crossover_mutation.py diff --git a/pygad/utils/mutation.py b/pygad/utils/mutation.py index e6b67ff..326ba6b 100644 --- a/pygad/utils/mutation.py +++ b/pygad/utils/mutation.py @@ -75,10 +75,13 @@ def mutation_by_space(self, offspring): elif type(curr_gene_space) is dict: # The gene's space of type dict specifies the lower and upper limits of a gene. if 'step' in curr_gene_space.keys(): + # The numpy.random.choice() and numpy.random.uniform() functions return a NumPy array as the output even if the array has a single value. + # We have to return the output at index 0 to force a numeric value to be returned not an object of type numpy.ndarray. + # If numpy.ndarray is returned, then it will cause an issue later while using the set() function. value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], stop=curr_gene_space['high'], step=curr_gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], @@ -104,7 +107,7 @@ def mutation_by_space(self, offspring): value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], stop=self.gene_space['high'], step=self.gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], @@ -186,7 +189,7 @@ def mutation_probs_by_space(self, offspring): value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], stop=curr_gene_space['high'], step=curr_gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], @@ -211,7 +214,7 @@ def mutation_probs_by_space(self, offspring): value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], stop=self.gene_space['high'], step=self.gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], @@ -520,10 +523,13 @@ def adaptive_mutation_by_space(self, offspring): elif type(curr_gene_space) is dict: # Selecting a value randomly from the current gene's space in the 'gene_space' attribute. if 'step' in curr_gene_space.keys(): + # The numpy.random.choice() and numpy.random.uniform() functions return a NumPy array as the output even if the array has a single value. + # We have to return the output at index 0 to force a numeric value to be returned not an object of type numpy.ndarray. + # If numpy.ndarray is returned, then it will cause an issue later while using the set() function. value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], stop=curr_gene_space['high'], step=curr_gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], @@ -548,7 +554,7 @@ def adaptive_mutation_by_space(self, offspring): value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], stop=self.gene_space['high'], step=self.gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], @@ -699,7 +705,7 @@ def adaptive_mutation_probs_by_space(self, offspring): value_from_space = numpy.random.choice(numpy.arange(start=curr_gene_space['low'], stop=curr_gene_space['high'], step=curr_gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=curr_gene_space['low'], high=curr_gene_space['high'], @@ -721,10 +727,13 @@ def adaptive_mutation_probs_by_space(self, offspring): # Selecting a value randomly from the global gene space in the 'gene_space' attribute. if type(self.gene_space) is dict: if 'step' in self.gene_space.keys(): + # The numpy.random.choice() and numpy.random.uniform() functions return a NumPy array as the output even if the array has a single value. + # We have to return the output at index 0 to force a numeric value to be returned not an object of type numpy.ndarray. + # If numpy.ndarray is returned, then it will cause an issue later while using the set() function. value_from_space = numpy.random.choice(numpy.arange(start=self.gene_space['low'], stop=self.gene_space['high'], step=self.gene_space['step']), - size=1) + size=1)[0] else: value_from_space = numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], diff --git a/tests/test_crossover_mutation.py b/tests/test_crossover_mutation.py new file mode 100644 index 0000000..f65a795 --- /dev/null +++ b/tests/test_crossover_mutation.py @@ -0,0 +1,188 @@ +import pygad +import random +import numpy + +num_generations = 1 + +initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]] + +def output_crossover_mutation(gene_space=None, + gene_type=float, + num_genes=10, + mutation_by_replacement=False, + random_mutation_min_val=-1, + random_mutation_max_val=1, + init_range_low=-4, + init_range_high=4, + initial_population=None, + crossover_probability=None, + mutation_probability=None, + crossover_type=None, + mutation_type=None): + + def fitness_func(ga, solution, idx): + return random.random() + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=5, + fitness_func=fitness_func, + sol_per_pop=10, + num_genes=num_genes, + gene_space=gene_space, + gene_type=gene_type, + initial_population=initial_population, + init_range_low=init_range_low, + init_range_high=init_range_high, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val, + allow_duplicate_genes=True, + mutation_by_replacement=mutation_by_replacement, + save_solutions=True, + crossover_probability=crossover_probability, + mutation_probability=mutation_probability, + crossover_type=crossover_type, + mutation_type=mutation_type, + suppress_warnings=True, + random_seed=1) + + ga_instance.run() + + comparison_result = [] + for solution_idx, solution in enumerate(ga_instance.population): + if list(solution) in ga_instance.initial_population.tolist(): + comparison_result.append(True) + else: + comparison_result.append(False) + + comparison_result = numpy.array(comparison_result) + result = numpy.all(comparison_result == True) + + print("Comparison result is {result}".format(result=result)) + return result, ga_instance + +def test_no_crossover_no_mutation(): + result, ga_instance = output_crossover_mutation() + + assert result == True + +def test_no_crossover_no_mutation_gene_space(): + result, ga_instance = output_crossover_mutation(gene_space=range(10)) + + assert result == True + +def test_no_crossover_no_mutation_int_gene_type(): + result, ga_instance = output_crossover_mutation(gene_type=int) + + assert result == True + + +def test_no_crossover_no_mutation_gene_space_gene_type(): + result, ga_instance = output_crossover_mutation(gene_space={"low": 0, "high": 10}, + gene_type=[float, 2]) + + assert result == True + + +def test_no_crossover_no_mutation_nested_gene_space(): + result, ga_instance = output_crossover_mutation(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]]) + assert result == True + +def test_no_crossover_no_mutation_nested_gene_type(): + result, ga_instance = output_crossover_mutation(gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert result == True + +def test_no_crossover_no_mutation_nested_gene_space_nested_gene_type(): + result, ga_instance = output_crossover_mutation(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert result == True + +def test_no_crossover_no_mutation_initial_population(): + global initial_population + result, ga_instance = output_crossover_mutation(initial_population=initial_population) + + assert result == True + +def test_no_crossover_no_mutation_initial_population_nested_gene_type(): + global initial_population + result, ga_instance = output_crossover_mutation(initial_population=initial_population, + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert result == True + +def test_crossover_no_mutation_zero_crossover_probability(): + global initial_population + result, ga_instance = output_crossover_mutation(crossover_type="single_point", + crossover_probability=0.0) + + assert result == True + +def test_zero_crossover_probability_zero_mutation_probability(): + global initial_population + result, ga_instance = output_crossover_mutation(crossover_type="single_point", + crossover_probability=0.0, + mutation_type="random", + mutation_probability=0.0) + + assert result == True + +if __name__ == "__main__": + print() + test_no_crossover_no_mutation() + print() + + test_no_crossover_no_mutation_int_gene_type() + print() + + test_no_crossover_no_mutation_gene_space() + print() + + test_no_crossover_no_mutation_gene_space_gene_type() + print() + + test_no_crossover_no_mutation_nested_gene_space() + print() + + test_no_crossover_no_mutation_nested_gene_type() + print() + + test_no_crossover_no_mutation_initial_population() + print() + + test_no_crossover_no_mutation_initial_population_nested_gene_type() + print() + + test_crossover_no_mutation_zero_crossover_probability() + print() + + test_zero_crossover_probability_zero_mutation_probability() + print() + diff --git a/tests/test_gene_space.py b/tests/test_gene_space.py index 09bc221..063acf7 100644 --- a/tests/test_gene_space.py +++ b/tests/test_gene_space.py @@ -1,6 +1,6 @@ """ This script is identical to the test_gene_space_allow_duplicate_genes.py script except for: - Setting allow_duplicate_genes=False instead of True. + Setting allow_duplicate_genes=True instead of False. """ import pygad @@ -51,7 +51,7 @@ def fitness_func(ga, solution, idx): mutation_by_replacement=mutation_by_replacement, save_solutions=True, suppress_warnings=True, - random_seed=1) + random_seed=2) ga_instance.run() ga_instance.solutions = numpy.array(ga_instance.solutions, @@ -162,6 +162,51 @@ def test_gene_space_list_single_value(): assert num_outside == 0 +def test_gene_space_range_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=range(10), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_numpy_arange_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_list_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=list(range(10)), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_numpy_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10))), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_dict_without_step_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10}, + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_gene_space_dict_with_step_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2}, + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_list_single_value_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[5], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + # print(ga_instance.population) + + assert num_outside == 0 + def test_nested_gene_space_range(): num_outside, ga_instance = number_respect_gene_space(gene_space=[range(0, 10), range(10, 20), @@ -337,24 +382,38 @@ def test_nested_gene_space_mix_initial_population_single_gene_type(): print() test_gene_space_range() print() + test_gene_space_range_nested_gene_type() + print() test_gene_space_numpy_arange() print() + test_gene_space_numpy_arange_nested_gene_type() + print() test_gene_space_list() print() + test_gene_space_list_nested_gene_type() + print() test_gene_space_list_single_value() print() + test_gene_space_list_single_value_nested_gene_type() + print() test_gene_space_numpy() print() + test_gene_space_numpy_nested_gene_type() + print() test_gene_space_dict_without_step() print() + test_gene_space_dict_without_step_nested_gene_type() + print() test_gene_space_dict_with_step() print() + test_gene_space_dict_with_step_nested_gene_type() + print() test_nested_gene_space_range() print() diff --git a/tests/test_gene_space_allow_duplicate_genes.py b/tests/test_gene_space_allow_duplicate_genes.py index 804129a..2a65f1a 100644 --- a/tests/test_gene_space_allow_duplicate_genes.py +++ b/tests/test_gene_space_allow_duplicate_genes.py @@ -77,7 +77,6 @@ def fitness_func(ga, solution, idx): if val >= ga_instance.gene_space[gene_idx]["low"] and val < ga_instance.gene_space[gene_idx]["high"]: pass else: - print(gene_idx, val, current_gene_space, all_gene_values) num_outside += 1 else: gene_space_values = numpy.arange(ga_instance.gene_space[gene_idx]["low"], @@ -163,6 +162,51 @@ def test_gene_space_list_single_value(): assert num_outside == 0 +def test_gene_space_range_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=range(10), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_numpy_arange_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_list_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=list(range(10)), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_numpy_nested_gene_type(): + num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10))), + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_dict_without_step_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10}, + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + # print(ga_instance.population) + + assert num_outside == 0 + +def test_gene_space_dict_with_step_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2}, + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + assert num_outside == 0 + +def test_gene_space_list_single_value_nested_gene_type(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[5], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + # print(ga_instance.population) + + assert num_outside == 0 + def test_nested_gene_space_range(): num_outside, ga_instance = number_respect_gene_space(gene_space=[range(0, 10), range(10, 20), @@ -335,26 +379,40 @@ def test_nested_gene_space_mix_initial_population_single_gene_type(): assert num_outside == 0 if __name__ == "__main__": - print() - test_gene_space_range() - print() - - test_gene_space_numpy_arange() - print() - - test_gene_space_list() - print() - - test_gene_space_list_single_value() - print() - - test_gene_space_numpy() - print() - - test_gene_space_dict_without_step() - print() - - test_gene_space_dict_with_step() + # print() + # test_gene_space_range() + # print() + # test_gene_space_range_nested_gene_type() + # print() + + # test_gene_space_numpy_arange() + # print() + # test_gene_space_numpy_arange_nested_gene_type() + # print() + + # test_gene_space_list() + # print() + # test_gene_space_list_nested_gene_type() + # print() + + # test_gene_space_list_single_value() + # print() + # test_gene_space_list_single_value_nested_gene_type() + # print() + + # test_gene_space_numpy() + # print() + # test_gene_space_numpy_nested_gene_type() + # print() + + # test_gene_space_dict_without_step() + # print() + # test_gene_space_dict_without_step_nested_gene_type() + # print() + + # test_gene_space_dict_with_step() + # print() + test_gene_space_dict_with_step_nested_gene_type() print() test_nested_gene_space_range() From d8b637209401f1e3d56cec38bf74bc30ab21fe88 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Wed, 3 May 2023 12:26:29 -0400 Subject: [PATCH 043/220] Test lifecycle without crossover & mutation --- tests/test_lifecycle_callbacks_calls.py | 76 +++++++++++++++++++++++-- 1 file changed, 70 insertions(+), 6 deletions(-) diff --git a/tests/test_lifecycle_callbacks_calls.py b/tests/test_lifecycle_callbacks_calls.py index 65b38c5..a6cce83 100644 --- a/tests/test_lifecycle_callbacks_calls.py +++ b/tests/test_lifecycle_callbacks_calls.py @@ -3,7 +3,9 @@ num_generations = 100 def number_lifecycle_callback_functions_calls(stop_criteria=None, - on_generation_stop=None): + on_generation_stop=None, + crossover_type="single_point", + mutation_type="random"): actual_num_callbacks_calls = 0 def fitness_func(ga_instanse, solution, solution_idx): @@ -46,6 +48,8 @@ def on_stop(ga_instance, last_population_fitness): fitness_func=fitness_func, sol_per_pop=10, num_genes=5, + crossover_type=crossover_type, + mutation_type=mutation_type, on_start=on_start, on_fitness=on_fitness, on_parents=on_parents, @@ -71,12 +75,14 @@ def on_stop(ga_instance, last_population_fitness): # Use 'generations_completed' instead of 'num_generations' because the evolution may stops in the on_generation() callback. expected_num_callbacks_calls = 1 + ga_instance.generations_completed * 5 + 1 - print("Expected number of callbacks calls is {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls)) - print("Actual number of callbacks calls is {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls)) + print("Expected {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls)) + print("Actual {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls)) return actual_num_callbacks_calls, expected_num_callbacks_calls def number_lifecycle_callback_methods_calls(stop_criteria=None, - on_generation_stop=None): + on_generation_stop=None, + crossover_type="single_point", + mutation_type="random"): actual_num_callbacks_calls = 0 class Callbacks: @@ -121,6 +127,8 @@ def on_stop(self, ga_instance, last_population_fitness): fitness_func=Callbacks_obj.fitness_func, sol_per_pop=10, num_genes=5, + crossover_type=crossover_type, + mutation_type=mutation_type, on_start=Callbacks_obj.on_start, on_fitness=Callbacks_obj.on_fitness, on_parents=Callbacks_obj.on_parents, @@ -146,8 +154,8 @@ def on_stop(self, ga_instance, last_population_fitness): # Use 'generations_completed' instead of 'num_generations' because the evolution may stops in the on_generation() callback. expected_num_callbacks_calls = 1 + ga_instance.generations_completed * 5 + 1 - print("Expected number of callbacks calls is {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls)) - print("Actual number of callbacks calls is {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls)) + print("Expected {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls)) + print("Actual {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls)) return actual_num_callbacks_calls, expected_num_callbacks_calls def test_number_lifecycle_callback_functions_calls(): @@ -170,13 +178,69 @@ def test_number_lifecycle_callback_methods_calls_stop_criteria(): assert actual == expected +def test_number_lifecycle_callback_functions_calls_no_crossover(): + actual, expected = number_lifecycle_callback_functions_calls(crossover_type=None) + + assert actual == expected + +def test_number_lifecycle_callback_functions_calls_no_mutation(): + actual, expected = number_lifecycle_callback_functions_calls(mutation_type=None) + + assert actual == expected + +def test_number_lifecycle_callback_functions_calls_no_crossover_no_mutation(): + actual, expected = number_lifecycle_callback_functions_calls(crossover_type=None, + mutation_type=None) + + assert actual == expected + +def test_number_lifecycle_callback_methods_calls_no_crossover(): + actual, expected = number_lifecycle_callback_methods_calls(crossover_type=None) + + assert actual == expected + +def test_number_lifecycle_callback_methods_calls_no_mutation(): + actual, expected = number_lifecycle_callback_methods_calls(mutation_type=None) + + assert actual == expected + +def test_number_lifecycle_callback_methods_calls_no_crossover_no_mutation(): + actual, expected = number_lifecycle_callback_methods_calls(crossover_type=None, + mutation_type=None) + + assert actual == expected + if __name__ == "__main__": print() test_number_lifecycle_callback_functions_calls() print() + test_number_lifecycle_callback_functions_calls_stop_criteria() print() + test_number_lifecycle_callback_methods_calls() print() + test_number_lifecycle_callback_methods_calls_stop_criteria() print() + + test_number_lifecycle_callback_functions_calls_no_crossover() + print() + + test_number_lifecycle_callback_functions_calls_no_crossover() + print() + + test_number_lifecycle_callback_functions_calls_no_mutation() + print() + + test_number_lifecycle_callback_functions_calls_no_crossover_no_mutation() + print() + + test_number_lifecycle_callback_methods_calls_no_crossover() + print() + + test_number_lifecycle_callback_methods_calls_no_mutation() + print() + + test_number_lifecycle_callback_methods_calls_no_crossover_no_mutation() + print() From d2d9b7ac2441b0914640835ad5e28e4c0b0d73c5 Mon Sep 17 00:00:00 2001 From: King Lok Chung Date: Thu, 4 May 2023 13:36:45 +0100 Subject: [PATCH 044/220] Changing all the string to fstring --- pygad/pygad.py | 628 ++++++++++++++++++++++++------------------------- 1 file changed, 314 insertions(+), 314 deletions(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index b7382cc..ea6d249 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -158,7 +158,7 @@ def __init__(self, if isinstance(logger, logging.Logger): pass else: - raise TypeError("The expected type of the 'logger' parameter is 'logging.Logger' but {logger_type} found.".format(logger_type=type(logger))) + raise TypeError(f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") # Create the 'self.logger' attribute to hold the logger. # Instead of using 'print()', use 'self.logger.info()' @@ -176,22 +176,22 @@ def __init__(self, self.suppress_warnings = suppress_warnings else: self.valid_parameters = False - self.logger.error("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) - raise TypeError("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) + self.logger.error(f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") + raise TypeError(f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") # Validating mutation_by_replacement if not (type(mutation_by_replacement) is bool): self.valid_parameters = False - self.logger.error("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) - raise TypeError("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) + self.logger.error(f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") + raise TypeError(f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") self.mutation_by_replacement = mutation_by_replacement # Validate allow_duplicate_genes if not (type(allow_duplicate_genes) is bool): self.valid_parameters = False - self.logger.error("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) - raise TypeError("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) + self.logger.error(f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") + raise TypeError(f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") self.allow_duplicate_genes = allow_duplicate_genes @@ -214,13 +214,13 @@ def __init__(self, if type(el) in [numpy.ndarray, list, tuple, range]: if len(el) == 0: self.valid_parameters = False - self.logger.error("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) - raise ValueError("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) + self.logger.error(f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") + raise ValueError(f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") else: for val in el: if not (type(val) in [type(None)] + GA.supported_int_float_types): - self.logger.error("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) - raise TypeError("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) + self.logger.error(f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") + raise TypeError(f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") self.gene_space_nested = True elif type(el) == type(None): pass @@ -231,24 +231,24 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) + self.logger.error(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") elif len(el.items()) == 3: if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): pass else: self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) + self.logger.error(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") else: self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) + self.logger.error(f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") + raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") self.gene_space_nested = True elif not (type(el) in GA.supported_int_float_types): self.valid_parameters = False - self.logger.error("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) - raise TypeError("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) + self.logger.error(f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") + raise TypeError(f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") elif type(gene_space) is dict: if len(gene_space.items()) == 2: @@ -256,24 +256,24 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) + self.logger.error(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") elif len(gene_space.items()) == 3: if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): pass else: self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) + self.logger.error(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") else: self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) - raise ValueError("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) + self.logger.error(f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") + raise ValueError(f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") else: self.valid_parameters = False - self.logger.error("The expected type of 'gene_space' is list, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) - raise TypeError("The expected type of 'gene_space' is list, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) + self.logger.error(f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") + raise TypeError(f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") self.gene_space = gene_space @@ -284,12 +284,12 @@ def __init__(self, self.init_range_high = init_range_high else: self.valid_parameters = False - self.logger.error("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) - raise ValueError("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) + self.logger.error(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") + raise ValueError(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") else: self.valid_parameters = False - self.logger.error("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) - raise ValueError("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) + self.logger.error(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") + raise ValueError(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") # Validate random_mutation_min_val and random_mutation_max_val if type(random_mutation_min_val) in GA.supported_int_float_types: @@ -298,12 +298,12 @@ def __init__(self, if not self.suppress_warnings: warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") else: self.valid_parameters = False - self.logger.error("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) - raise TypeError("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) + self.logger.error(f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") + raise TypeError(f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") else: self.valid_parameters = False - self.logger.error("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) - raise TypeError("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) + self.logger.error(f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") + raise TypeError(f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") self.random_mutation_min_val = random_mutation_min_val self.random_mutation_max_val = random_mutation_max_val @@ -318,8 +318,8 @@ def __init__(self, # A single data type of int with precision. elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): self.gene_type_single = False - self.logger.error("Integers cannot have precision. Please use the integer data type directly instead of {gene_type_val}.".format(gene_type_val=gene_type)) - raise ValueError("Integers cannot have precision. Please use the integer data type directly instead of {gene_type_val}.".format(gene_type_val=gene_type)) + self.logger.error(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") + raise ValueError(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") elif type(gene_type) in [list, tuple, numpy.ndarray]: if num_genes is None: if initial_population is None: @@ -328,12 +328,12 @@ def __init__(self, raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") elif not len(gene_type) == len(initial_population[0]): self.valid_parameters = False - self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) - raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) + self.logger.error(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") + raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") elif not len(gene_type) == num_genes: self.valid_parameters = False - self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) - raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) + self.logger.error(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") + raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") for gene_type_idx, gene_type_val in enumerate(gene_type): if gene_type_val in GA.supported_float_types: # If the gene type is float and no precision is passed, set it to None. @@ -348,26 +348,26 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) - raise TypeError("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) + self.logger.error(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") + raise TypeError(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") else: self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) - raise TypeError("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) + self.logger.error(f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") + raise TypeError(f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") else: self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) - raise ValueError("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) + self.logger.error(f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") + raise ValueError(f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") else: self.valid_parameters = False - self.logger.error("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) - raise ValueError("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) + self.logger.error(f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") + raise ValueError(f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") self.gene_type = gene_type self.gene_type_single = False else: self.valid_parameters = False - self.logger.error("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) - raise ValueError("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) + self.logger.error(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") + raise ValueError(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. self.gene_space_unpacked = self.unpack_gene_space() @@ -382,13 +382,13 @@ def __init__(self, # Validating the number of solutions in the population (sol_per_pop) if sol_per_pop <= 0: self.valid_parameters = False - self.logger.error("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) - raise ValueError("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) + self.logger.error(f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + raise ValueError(f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") # Validating the number of gene. if (num_genes <= 0): self.valid_parameters = False - self.logger.error("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) - raise ValueError("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) + self.logger.error(f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") + raise ValueError(f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. self.num_genes = num_genes # Number of genes in the solution. @@ -397,8 +397,8 @@ def __init__(self, if self.gene_space_nested: if len(gene_space) != self.num_genes: self.valid_parameters = False - self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) - raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) + self.logger.error(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") self.sol_per_pop = sol_per_pop # Number of solutions in the population. self.initialize_population(self.init_range_low, @@ -408,16 +408,16 @@ def __init__(self, self.gene_type) else: self.valid_parameters = False - self.logger.error("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) - raise TypeError("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) + self.logger.error(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") + raise TypeError(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") elif not type(initial_population) in [list, tuple, numpy.ndarray]: self.valid_parameters = False - self.logger.error("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) - raise TypeError("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) + self.logger.error(f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") + raise TypeError(f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") elif numpy.array(initial_population).ndim != 2: self.valid_parameters = False - self.logger.error("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) - raise ValueError("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) + self.logger.error(f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") + raise ValueError(f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") else: # Validate the type of each value in the 'initial_population' parameter. for row_idx in range(len(initial_population)): @@ -426,8 +426,8 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) - raise TypeError("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) + self.logger.error(f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") + raise TypeError(f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") # Forcing the initial_population array to have the data type assigned to the gene_type parameter. if self.gene_type_single == True: @@ -482,20 +482,20 @@ def __init__(self, if self.gene_space_nested: if len(gene_space) != self.num_genes: self.valid_parameters = False - self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) - raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) + self.logger.error(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") # Validating the number of parents to be selected for mating (num_parents_mating) if num_parents_mating <= 0: self.valid_parameters = False - self.logger.error("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) - raise ValueError("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) + self.logger.error(f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + raise ValueError(f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") # Validating the number of parents to be selected for mating: num_parents_mating if (num_parents_mating > self.sol_per_pop): self.valid_parameters = False - self.logger.error("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) - raise ValueError("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) + self.logger.error(f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") + raise ValueError(f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") self.num_parents_mating = num_parents_mating @@ -510,8 +510,8 @@ def __init__(self, self.crossover = crossover_type else: self.valid_parameters = False - self.logger.error("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - raise ValueError("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) + self.logger.error(f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + raise ValueError(f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") elif callable(crossover_type): # Check if the crossover_type is a function that accepts 2 paramaters. if (crossover_type.__code__.co_argcount == 3): @@ -519,12 +519,12 @@ def __init__(self, self.crossover = crossover_type else: self.valid_parameters = False - self.logger.error("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - raise ValueError("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) + self.logger.error(f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + raise ValueError(f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") elif not (type(crossover_type) is str): self.valid_parameters = False - self.logger.error("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) - raise TypeError("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) + self.logger.error(f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") + raise TypeError(f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") else: # type crossover_type is str crossover_type = crossover_type.lower() if (crossover_type == "single_point"): @@ -537,8 +537,8 @@ def __init__(self, self.crossover = self.scattered_crossover else: self.valid_parameters = False - self.logger.error("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) - raise TypeError("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) + self.logger.error(f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") + raise TypeError(f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") self.crossover_type = crossover_type @@ -550,12 +550,12 @@ def __init__(self, self.crossover_probability = crossover_probability else: self.valid_parameters = False - self.logger.error("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) - raise ValueError("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) + self.logger.error(f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") + raise ValueError(f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") else: self.valid_parameters = False - self.logger.error("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) - raise TypeError("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) + self.logger.error(f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") + raise TypeError(f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. # Validating the mutation type: mutation_type @@ -569,8 +569,8 @@ def __init__(self, self.mutation = mutation_type else: self.valid_parameters = False - self.logger.error("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - raise ValueError("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) + self.logger.error(f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + raise ValueError(f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") elif callable(mutation_type): # Check if the mutation_type is a function that accepts 2 paramater. if (mutation_type.__code__.co_argcount == 2): @@ -578,12 +578,12 @@ def __init__(self, self.mutation = mutation_type else: self.valid_parameters = False - self.logger.error("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - raise ValueError("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) + self.logger.error(f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + raise ValueError(f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") elif not (type(mutation_type) is str): self.valid_parameters = False - self.logger.error("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) - raise TypeError("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) + self.logger.error(f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") + raise TypeError(f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") else: # type mutation_type is str mutation_type = mutation_type.lower() if (mutation_type == "random"): @@ -598,8 +598,8 @@ def __init__(self, self.mutation = self.adaptive_mutation else: self.valid_parameters = False - self.logger.error("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) - raise TypeError("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) + self.logger.error(f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") + raise TypeError(f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") self.mutation_type = mutation_type @@ -614,12 +614,12 @@ def __init__(self, self.mutation_probability = mutation_probability else: self.valid_parameters = False - self.logger.error("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) - raise ValueError("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) + self.logger.error(f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") + raise ValueError(f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") else: self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - raise TypeError("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) + self.logger.error(f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") else: # Mutation probability is adaptive not fixed. if type(mutation_probability) in [list, tuple, numpy.ndarray]: @@ -630,23 +630,23 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) - raise ValueError("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) + self.logger.error(f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") + raise ValueError(f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") else: self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) + self.logger.error(f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") + raise TypeError(f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") if mutation_probability[0] < mutation_probability[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_probability' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_probability[0], second_el=mutation_probability[1])) + if not self.suppress_warnings: warnings.warn(f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") self.mutation_probability = mutation_probability else: self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) + self.logger.error(f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") + raise ValueError(f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") else: self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - raise TypeError("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) + self.logger.error(f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") else: pass @@ -663,14 +663,14 @@ def __init__(self, # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes == 0: if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) + if not self.suppress_warnings: warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes = 1 elif type(mutation_percent_genes) in GA.supported_int_float_types: if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): self.valid_parameters = False - self.logger.error("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - raise ValueError("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) + self.logger.error(f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + raise ValueError(f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") else: # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. if mutation_percent_genes == 'default'.lower(): @@ -681,12 +681,12 @@ def __init__(self, # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes == 0: if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) + if not self.suppress_warnings: warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes = 1 else: self.valid_parameters = False - self.logger.error("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) - raise TypeError("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) + self.logger.error(f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") + raise TypeError(f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") else: # The percent of genes to mutate is adaptive not fixed. if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: @@ -696,47 +696,47 @@ def __init__(self, if type(el) in GA.supported_int_float_types: if (el <= 0 or el > 100): self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - raise ValueError("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) + self.logger.error(f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + raise ValueError(f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") else: self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) + self.logger.error(f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + raise TypeError(f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. mutation_num_genes[idx] = numpy.uint32((mutation_percent_genes[idx]*self.num_genes)/100) # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes[idx] == 0: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate ({mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes[idx], mutation_num=mutation_num_genes[idx])) + if not self.suppress_warnings: warnings.warn(f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes[idx] = 1 if mutation_percent_genes[0] < mutation_percent_genes[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_percent_genes' parameter is ({first_el}) which is smaller than the second element ({second_el}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.".format(first_el=mutation_percent_genes[0], second_el=mutation_percent_genes[1])) + if not self.suppress_warnings: warnings.warn(f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.") # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. else: self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) + self.logger.error(f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") + raise ValueError(f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") else: if self.mutation_probability is None: self.valid_parameters = False - self.logger.error("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) - raise TypeError("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) + self.logger.error(f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") + raise TypeError(f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. elif (mutation_type != "adaptive"): # Number of genes to mutate is fixed not adaptive. if type(mutation_num_genes) in GA.supported_int_types: if (mutation_num_genes <= 0): self.valid_parameters = False - self.logger.error("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) - raise ValueError("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) + self.logger.error(f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") + raise ValueError(f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") elif (mutation_num_genes > self.num_genes): self.valid_parameters = False - self.logger.error("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) - raise ValueError("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) + self.logger.error(f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") + raise ValueError(f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") else: self.valid_parameters = False - self.logger.error("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - raise TypeError("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) + self.logger.error(f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") + raise TypeError(f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") else: # Number of genes to mutate is adaptive not fixed. if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: @@ -745,34 +745,34 @@ def __init__(self, if type(el) in GA.supported_int_types: if (el <= 0): self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) - raise ValueError("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) + self.logger.error(f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") + raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") elif (el > self.num_genes): self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) - raise ValueError("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) + self.logger.error(f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") + raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") else: self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) + self.logger.error(f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + raise TypeError(f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. if mutation_num_genes[0] < mutation_num_genes[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_num_genes' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_num_genes[0], second_el=mutation_num_genes[1])) + if not self.suppress_warnings: warnings.warn(f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. else: self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) + self.logger.error(f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") + raise ValueError(f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") else: self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - raise TypeError("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) + self.logger.error(f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") + raise TypeError(f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") else: pass # Validating mutation_by_replacement and mutation_type if self.mutation_type != "random" and self.mutation_by_replacement: - if not self.suppress_warnings: warnings.warn("The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mut_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.".format(mut_type=mutation_type)) + if not self.suppress_warnings: warnings.warn(f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") # Check if crossover and mutation are both disabled. if (self.mutation_type is None) and (self.crossover_type is None): @@ -788,8 +788,8 @@ def __init__(self, self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - raise ValueError("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) + self.logger.error(f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + raise ValueError(f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") elif callable(parent_selection_type): # Check if the parent_selection_type is a function that accepts 3 paramaters. if (parent_selection_type.__code__.co_argcount == 3): @@ -798,12 +798,12 @@ def __init__(self, self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - raise ValueError("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) + self.logger.error(f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + raise ValueError(f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") elif not (type(parent_selection_type) is str): self.valid_parameters = False - self.logger.error("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) - raise TypeError("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) + self.logger.error(f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") + raise TypeError(f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") else: parent_selection_type = parent_selection_type.lower() if (parent_selection_type == "sss"): @@ -820,30 +820,30 @@ def __init__(self, self.select_parents = self.rank_selection else: self.valid_parameters = False - self.logger.error("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) - raise TypeError("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) + self.logger.error(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n") + raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n") # For tournament selection, validate the K value. if(parent_selection_type == "tournament"): if (K_tournament > self.sol_per_pop): K_tournament = self.sol_per_pop - if not self.suppress_warnings: warnings.warn("K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n".format(K_tournament=K_tournament, sol_per_pop=self.sol_per_pop)) + if not self.suppress_warnings: warnings.warn(f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") elif (K_tournament <= 0): self.valid_parameters = False - self.logger.error("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) - raise ValueError("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) + self.logger.error(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") + raise ValueError(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") self.K_tournament = K_tournament # Validating the number of parents to keep in the next population: keep_parents if not (type(keep_parents) in GA.supported_int_types): self.valid_parameters = False - self.logger.error("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) - raise TypeError("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) + self.logger.error(f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") + raise TypeError(f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): self.valid_parameters = False - self.logger.error("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) - raise ValueError("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) + self.logger.error(f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") + raise ValueError(f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") self.keep_parents = keep_parents @@ -853,12 +853,12 @@ def __init__(self, # Validating the number of elitism to keep in the next population: keep_elitism if not (type(keep_elitism) in GA.supported_int_types): self.valid_parameters = False - self.logger.error("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) - raise TypeError("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) + self.logger.error(f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") + raise TypeError(f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): self.valid_parameters = False - self.logger.error("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) - raise ValueError("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) + self.logger.error(f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") + raise ValueError(f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") self.keep_elitism = keep_elitism @@ -882,31 +882,31 @@ def __init__(self, self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - raise ValueError("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) + self.logger.error(f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + raise ValueError(f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") elif callable(fitness_func): # Check if the fitness function accepts 2 paramaters. if (fitness_func.__code__.co_argcount == 3): self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - raise ValueError("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) + self.logger.error(f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + raise ValueError(f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) - raise TypeError("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) + self.logger.error(f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") + raise TypeError(f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") if fitness_batch_size is None: pass elif not (type(fitness_batch_size) in GA.supported_int_types): self.valid_parameters = False - self.logger.error("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) - raise TypeError("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) + self.logger.error(f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") + raise TypeError(f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: self.valid_parameters = False - self.logger.error("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) - raise ValueError("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) + self.logger.error(f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") + raise ValueError(f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") self.fitness_batch_size = fitness_batch_size @@ -918,8 +918,8 @@ def __init__(self, self.on_start = on_start else: self.valid_parameters = False - self.logger.error("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - raise ValueError("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) + self.logger.error(f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + raise ValueError(f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") # Check if the on_start is a function. elif callable(on_start): # Check if the on_start function accepts only a single paramater. @@ -927,12 +927,12 @@ def __init__(self, self.on_start = on_start else: self.valid_parameters = False - self.logger.error("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - raise ValueError("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) + self.logger.error(f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + raise ValueError(f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) - raise TypeError("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) + self.logger.error(f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") + raise TypeError(f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") else: self.on_start = None @@ -945,8 +945,8 @@ def __init__(self, self.on_fitness = on_fitness else: self.valid_parameters = False - self.logger.error("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - raise ValueError("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) + self.logger.error(f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + raise ValueError(f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") # Check if the on_fitness is a function. elif callable(on_fitness): # Check if the on_fitness function accepts 2 paramaters. @@ -954,12 +954,12 @@ def __init__(self, self.on_fitness = on_fitness else: self.valid_parameters = False - self.logger.error("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - raise ValueError("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) + self.logger.error(f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + raise ValueError(f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) - raise TypeError("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) + self.logger.error(f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") + raise TypeError(f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") else: self.on_fitness = None @@ -972,8 +972,8 @@ def __init__(self, self.on_parents = on_parents else: self.valid_parameters = False - self.logger.error("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - raise ValueError("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) + self.logger.error(f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + raise ValueError(f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") # Check if the on_parents is a function. elif callable(on_parents): # Check if the on_parents function accepts 2 paramaters. @@ -981,12 +981,12 @@ def __init__(self, self.on_parents = on_parents else: self.valid_parameters = False - self.logger.error("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - raise ValueError("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) + self.logger.error(f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + raise ValueError(f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) - raise TypeError("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) + self.logger.error(f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") + raise TypeError(f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") else: self.on_parents = None @@ -999,8 +999,8 @@ def __init__(self, self.on_crossover = on_crossover else: self.valid_parameters = False - self.logger.error("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - raise ValueError("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) + self.logger.error(f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + raise ValueError(f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") # Check if the on_crossover is a function. elif callable(on_crossover): # Check if the on_crossover function accepts 2 paramaters. @@ -1008,12 +1008,12 @@ def __init__(self, self.on_crossover = on_crossover else: self.valid_parameters = False - self.logger.error("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - raise ValueError("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) + self.logger.error(f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + raise ValueError(f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) - raise TypeError("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) + self.logger.error(f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") + raise TypeError(f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") else: self.on_crossover = None @@ -1026,8 +1026,8 @@ def __init__(self, self.on_mutation = on_mutation else: self.valid_parameters = False - self.logger.error("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - raise ValueError("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) + self.logger.error(f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + raise ValueError(f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") # Check if the on_mutation is a function. elif callable(on_mutation): # Check if the on_mutation function accepts 2 paramaters. @@ -1035,12 +1035,12 @@ def __init__(self, self.on_mutation = on_mutation else: self.valid_parameters = False - self.logger.error("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - raise ValueError("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) + self.logger.error(f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + raise ValueError(f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) - raise TypeError("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) + self.logger.error(f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") + raise TypeError(f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") else: self.on_mutation = None @@ -1053,8 +1053,8 @@ def __init__(self, self.on_generation = on_generation else: self.valid_parameters = False - self.logger.error("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - raise ValueError("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) + self.logger.error(f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + raise ValueError(f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") # Check if the on_generation is a function. elif callable(on_generation): # Check if the on_generation function accepts only a single paramater. @@ -1062,12 +1062,12 @@ def __init__(self, self.on_generation = on_generation else: self.valid_parameters = False - self.logger.error("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - raise ValueError("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) + self.logger.error(f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + raise ValueError(f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) - raise TypeError("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) + self.logger.error(f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") + raise TypeError(f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") else: self.on_generation = None @@ -1080,8 +1080,8 @@ def __init__(self, self.on_stop = on_stop else: self.valid_parameters = False - self.logger.error("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - raise ValueError("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) + self.logger.error(f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + raise ValueError(f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") # Check if the on_stop is a function. elif callable(on_stop): # Check if the on_stop function accepts 2 paramaters. @@ -1089,12 +1089,12 @@ def __init__(self, self.on_stop = on_stop else: self.valid_parameters = False - self.logger.error("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - raise ValueError("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) + self.logger.error(f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + raise ValueError(f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) - raise TypeError("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) + self.logger.error(f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") + raise TypeError(f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") else: self.on_stop = None @@ -1104,12 +1104,12 @@ def __init__(self, self.delay_after_gen = delay_after_gen else: self.valid_parameters = False - self.logger.error("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) - raise ValueError("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) + self.logger.error(f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") + raise ValueError(f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") else: self.valid_parameters = False - self.logger.error("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) - raise TypeError("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) + self.logger.error(f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") + raise TypeError(f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") # Validate save_best_solutions if type(save_best_solutions) is bool: @@ -1117,8 +1117,8 @@ def __init__(self, if not self.suppress_warnings: warnings.warn("Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") else: self.valid_parameters = False - self.logger.error("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) - raise TypeError("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) + self.logger.error(f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") + raise TypeError(f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") # Validate save_solutions if type(save_solutions) is bool: @@ -1126,8 +1126,8 @@ def __init__(self, if not self.suppress_warnings: warnings.warn("Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") else: self.valid_parameters = False - self.logger.error("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) - raise TypeError("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) + self.logger.error(f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") + raise TypeError(f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") self.stop_criteria = [] self.supported_stop_words = ["reach", "saturate"] @@ -1146,22 +1146,22 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - raise ValueError("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + self.logger.error(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") + raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") if number.replace(".", "").isnumeric(): number = float(number) else: self.valid_parameters = False - self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) + self.logger.error(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") self.stop_criteria.append([stop_word, number]) else: self.valid_parameters = False - self.logger.error("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) - raise ValueError("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) + self.logger.error(f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") + raise ValueError(f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") elif type(stop_criteria) in [list, tuple, numpy.ndarray]: # Remove duplicate criterira by converting the list to a set then back to a list. @@ -1177,30 +1177,30 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - raise ValueError("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + self.logger.error(f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") + raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") if number.replace(".", "").isnumeric(): number = float(number) else: self.valid_parameters = False - self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) + self.logger.error(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") self.stop_criteria.append([stop_word, number]) else: self.valid_parameters = False - self.logger.error("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) - raise ValueError("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) + self.logger.error(f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") + raise ValueError(f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") else: self.valid_parameters = False - self.logger.error("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) - raise TypeError("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) + self.logger.error(f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") + raise TypeError(f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") else: self.valid_parameters = False - self.logger.error("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) - raise TypeError("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) + self.logger.error(f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") + raise TypeError(f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") if parallel_processing is None: self.parallel_processing = None @@ -1209,8 +1209,8 @@ def __init__(self, self.parallel_processing = ["thread", parallel_processing] else: self.valid_parameters = False - self.logger.error("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) - raise ValueError("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) + self.logger.error(f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") + raise ValueError(f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") elif type(parallel_processing) in [list, tuple]: if len(parallel_processing) == 2: if type(parallel_processing[0]) is str: @@ -1224,24 +1224,24 @@ def __init__(self, self.parallel_processing = parallel_processing else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) - raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) + self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") + raise TypeError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) - raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) + self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") + raise ValueError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) - raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) + self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") + raise TypeError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) - raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) + self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") + raise ValueError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") else: self.valid_parameters = False - self.logger.error("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) - raise ValueError("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) + self.logger.error(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") + raise ValueError(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") # Set the `run_completed` property to False. It is set to `True` only after the `run()` method is complete. self.run_completed = False @@ -1613,8 +1613,8 @@ def cal_pop_fitness(self): if type(fitness) in GA.supported_int_float_types: pass else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + self.logger.error(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: # Reaching this point means that batch processing is in effect to calculate the fitness values. # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. @@ -1639,18 +1639,18 @@ def cal_pop_fitness(self): batch_fitness = self.fitness_func(self, batch_solutions, batch_indices) if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) + self.logger.error(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) + self.logger.error(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") for index, fitness in zip(batch_indices, batch_fitness): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + self.logger.error(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: # Calculating the fitness value of each solution in the current population. for sol_idx, sol in enumerate(self.population): @@ -1709,8 +1709,8 @@ def cal_pop_fitness(self): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + self.logger.error(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: # Reaching this block means that batch processing is used. The fitness values are calculated in batches. @@ -1732,18 +1732,18 @@ def cal_pop_fitness(self): for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) + self.logger.error(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) + self.logger.error(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") for index, fitness in zip(batch_indices, batch_fitness): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + self.logger.error(f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") + raise ValueError(f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") pop_fitness = numpy.array(pop_fitness) @@ -1821,11 +1821,11 @@ def run(self): self.num_parents_mating, self) if not type(self.last_generation_parents) is numpy.ndarray: - self.logger.error("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) - raise TypeError("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) + self.logger.error(f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") + raise TypeError(f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") if not type(self.last_generation_parents_indices) is numpy.ndarray: - self.logger.error("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) - raise TypeError("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) + self.logger.error(f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") + raise TypeError(f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") else: self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, num_parents=self.num_parents_mating) @@ -1833,18 +1833,18 @@ def run(self): # Validate the output of the parent selection step: self.select_parents() if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): if self.last_generation_parents.shape[0] != self.num_parents_mating: - self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) - raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) + self.logger.error(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") + raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") elif self.last_generation_parents.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) - raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) + self.logger.error(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") + raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") if self.last_generation_parents_indices.ndim != 1: - self.logger.error("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) - raise ValueError("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) + self.logger.error(f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") + raise ValueError(f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") elif len(self.last_generation_parents_indices) != self.num_parents_mating: - self.logger.error("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) - raise ValueError("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) + self.logger.error(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") + raise ValueError(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") if not (self.on_parents is None): self.on_parents(self, self.last_generation_parents) @@ -1873,18 +1873,18 @@ def run(self): (self.num_offspring, self.num_genes), self) if not type(self.last_generation_offspring_crossover) is numpy.ndarray: - self.logger.error("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) - raise TypeError("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) + self.logger.error(f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") + raise TypeError(f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") else: self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, offspring_size=(self.num_offspring, self.num_genes)) if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: - self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) - raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) + self.logger.error(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") + raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) - raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) + self.logger.error(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") + raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. if not (self.on_crossover is None): @@ -1899,18 +1899,18 @@ def run(self): self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, self) if not type(self.last_generation_offspring_mutation) is numpy.ndarray: - self.logger.error("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) - raise TypeError("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) + self.logger.error(f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") + raise TypeError(f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") else: self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover) if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: - self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) - raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) + self.logger.error(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") + raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) - raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) + self.logger.error(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") + raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. if not (self.on_mutation is None): @@ -2023,11 +2023,11 @@ def best_solution(self, pop_fitness=None): # This successfully verifies the 'pop_fitness' parameter. pass else: - self.logger.error("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) - raise ValueError("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) + self.logger.error(f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") + raise ValueError(f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") else: - self.logger.error("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) - raise ValueError("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) + self.logger.error(f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") + raise ValueError(f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") # Return the index of the best solution that has the best fitness value. best_match_idx = numpy.where(pop_fitness == numpy.max(pop_fitness))[0][0] @@ -2097,72 +2097,72 @@ def create_row(columns, line_length=line_length, fill_character=fill_character, def print_parent_selection_params(): nonlocal summary_output - m = "Number of Parents: {num_parents_mating}".format(num_parents_mating=self.num_parents_mating) + m = f"Number of Parents: {self.num_parents_mating}" self.logger.info(m) summary_output = summary_output + m + "\n" if self.parent_selection_type == "tournament": - m = "K Tournament: {K_tournament}".format(K_tournament=self.K_tournament) + m = f"K Tournament: {self.K_tournament}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_fitness_params(): nonlocal summary_output if not self.fitness_batch_size is None: - m = "Fitness batch size: {fitness_batch_size}".format(fitness_batch_size=self.fitness_batch_size) + m = f"Fitness batch size: {self.fitness_batch_size}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_crossover_params(): nonlocal summary_output if not self.crossover_probability is None: - m = "Crossover probability: {crossover_probability}".format(crossover_probability=self.crossover_probability) + m = f"Crossover probability: {self.crossover_probability}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_mutation_params(): nonlocal summary_output if not self.mutation_probability is None: - m = "Mutation Probability: {mutation_probability}".format(mutation_probability=self.mutation_probability) + m = f"Mutation Probability: {self.mutation_probability}" self.logger.info(m) summary_output = summary_output + m + "\n" if self.mutation_percent_genes == "default": - m = "Mutation Percentage: {mutation_percent_genes}".format(mutation_percent_genes=self.mutation_percent_genes) + m = f"Mutation Percentage: {self.mutation_percent_genes}" self.logger.info(m) summary_output = summary_output + m + "\n" # Number of mutation genes is already showed above. - m = "Mutation Genes: {mutation_num_genes}".format(mutation_num_genes=self.mutation_num_genes) + m = f"Mutation Genes: {self.mutation_num_genes}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Random Mutation Range: ({random_mutation_min_val}, {random_mutation_max_val})".format(random_mutation_min_val=self.random_mutation_min_val, random_mutation_max_val=self.random_mutation_max_val) + m = f"Random Mutation Range: ({self.random_mutation_min_val}, {self.random_mutation_max_val})" self.logger.info(m) summary_output = summary_output + m + "\n" if not self.gene_space is None: - m = "Gene Space: {gene_space}".format(gene_space=self.gene_space) + m = f"Gene Space: {self.gene_space}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Mutation by Replacement: {mutation_by_replacement}".format(mutation_by_replacement=self.mutation_by_replacement) + m = f"Mutation by Replacement: {self.mutation_by_replacement}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Allow Duplicated Genes: {allow_duplicate_genes}".format(allow_duplicate_genes=self.allow_duplicate_genes) + m = f"Allow Duplicated Genes: {self.allow_duplicate_genes}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_on_generation_params(): nonlocal summary_output if not self.stop_criteria is None: - m = "Stop Criteria: {stop_criteria}".format(stop_criteria=self.stop_criteria) + m = f"Stop Criteria: {self.stop_criteria}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_params_summary(): nonlocal summary_output - m = "Population Size: ({sol_per_pop}, {num_genes})".format(sol_per_pop=self.sol_per_pop, num_genes=self.num_genes) + m = f"Population Size: ({self.sol_per_pop}, {self.num_genes})" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Number of Generations: {num_generations}".format(num_generations=self.num_generations) + m = f"Number of Generations: {self.num_generations}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Initial Population Range: ({init_range_low}, {init_range_high})".format(init_range_low=self.init_range_low, init_range_high=self.init_range_high) + m = f"Initial Population Range: ({self.init_range_low}, {self.init_range_high})" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2173,14 +2173,14 @@ def print_params_summary(): print_parent_selection_params() if self.keep_elitism != 0: - m = "Keep Elitism: {keep_elitism}".format(keep_elitism=self.keep_elitism) + m = f"Keep Elitism: {self.keep_elitism}" self.logger.info(m) summary_output = summary_output + m + "\n" else: - m = "Keep Parents: {keep_parents}".format(keep_parents=self.keep_parents) + m = f"Keep Parents: {self.keep_parents}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Gene DType: {gene_type}".format(gene_type=self.gene_type) + m = f"Gene DType: {self.gene_type}" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2191,7 +2191,7 @@ def print_params_summary(): print_mutation_params() if self.delay_after_gen != 0: - m = "Post-Generation Delay: {delay_after_gen}".format(delay_after_gen=self.delay_after_gen) + m = f"Post-Generation Delay: {self.delay_after_gen}" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2199,17 +2199,17 @@ def print_params_summary(): print_on_generation_params() if not self.parallel_processing is None: - m = "Parallel Processing: {parallel_processing}".format(parallel_processing=self.parallel_processing) + m = f"Parallel Processing: {self.parallel_processing}" self.logger.info(m) summary_output = summary_output + m + "\n" if not self.random_seed is None: - m = "Random Seed: {random_seed}".format(random_seed=self.random_seed) + m = f"Random Seed: {self.random_seed}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Save Best Solutions: {save_best_solutions}".format(save_best_solutions=self.save_best_solutions) + m = f"Save Best Solutions: {self.save_best_solutions}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Save Solutions: {save_solutions}".format(save_solutions=self.save_solutions) + m = f"Save Solutions: {self.save_solutions}" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2227,7 +2227,7 @@ def print_params_summary(): lifecycle_functions = [self.on_start, self.fitness_func, self.on_fitness, self.select_parents, self.on_parents, self.crossover, self.on_crossover, self.mutation, self.on_mutation, self.on_generation, self.on_stop] lifecycle_functions = [getattr(lifecycle_func, '__name__', "None") for lifecycle_func in lifecycle_functions] lifecycle_functions = [lifecycle_func + "()" if lifecycle_func != "None" else "None" for lifecycle_func in lifecycle_functions] - lifecycle_output = ["None", "(1)", "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "None", "None"] + lifecycle_output = ["None", "(1)", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", "None", "None"] lifecycle_step_parameters = [None, print_fitness_params, None, print_parent_selection_params, None, print_crossover_params, None, print_mutation_params, None, print_on_generation_params, None] if not columns_equal_len: @@ -2282,7 +2282,7 @@ def load(filename): with open(filename + ".pkl", 'rb') as file: ga_in = cloudpickle.load(file) except FileNotFoundError: - raise FileNotFoundError("Error reading the file {filename}. Please check your inputs.".format(filename=filename)) + raise FileNotFoundError(f"Error reading the file {filename}. Please check your inputs.") except: # raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).") raise BaseException("Error loading the file.") From 866fc457047ef7759bbc6ee53a9dbf3320ff53b6 Mon Sep 17 00:00:00 2001 From: King Lok Chung Date: Thu, 4 May 2023 13:38:48 +0100 Subject: [PATCH 045/220] update formatting to pep8 --- pygad/pygad.py | 1395 +++++++++++++++++++++++++++++++----------------- 1 file changed, 896 insertions(+), 499 deletions(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index ea6d249..8d95941 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -10,23 +10,25 @@ from pygad import helper from pygad import visualize -class GA(utils.parent_selection.ParentSelection, - utils.crossover.Crossover, - utils.mutation.Mutation, +class GA(utils.parent_selection.ParentSelection, + utils.crossover.Crossover, + utils.mutation.Mutation, helper.unique.Unique, visualize.plot.Plot): - supported_int_types = [int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64] - supported_float_types = [float, numpy.float16, numpy.float32, numpy.float64] + supported_int_types = [int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, + numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64] + supported_float_types = [ + float, numpy.float16, numpy.float32, numpy.float64] supported_int_float_types = supported_int_types + supported_float_types - def __init__(self, - num_generations, - num_parents_mating, + def __init__(self, + num_generations, + num_parents_mating, fitness_func, fitness_batch_size=None, initial_population=None, - sol_per_pop=None, + sol_per_pop=None, num_genes=None, init_range_low=-4, init_range_high=4, @@ -61,7 +63,6 @@ def __init__(self, parallel_processing=None, random_seed=None, logger=None): - """ The constructor of the GA class accepts all parameters required to create an instance of the GA class. It validates such parameters. @@ -158,7 +159,8 @@ def __init__(self, if isinstance(logger, logging.Logger): pass else: - raise TypeError(f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") + raise TypeError( + f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") # Create the 'self.logger' attribute to hold the logger. # Instead of using 'print()', use 'self.logger.info()' @@ -176,22 +178,28 @@ def __init__(self, self.suppress_warnings = suppress_warnings else: self.valid_parameters = False - self.logger.error(f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") - raise TypeError(f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") + self.logger.error( + f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") + raise TypeError( + f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") # Validating mutation_by_replacement if not (type(mutation_by_replacement) is bool): self.valid_parameters = False - self.logger.error(f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") - raise TypeError(f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") + self.logger.error( + f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") + raise TypeError( + f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") self.mutation_by_replacement = mutation_by_replacement # Validate allow_duplicate_genes if not (type(allow_duplicate_genes) is bool): self.valid_parameters = False - self.logger.error(f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") - raise TypeError(f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") + self.logger.error( + f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") + raise TypeError( + f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") self.allow_duplicate_genes = allow_duplicate_genes @@ -202,25 +210,33 @@ def __init__(self, elif type(gene_space) is range: if len(gene_space) == 0: self.valid_parameters = False - self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") - raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") + self.logger.error( + "'gene_space' cannot be empty (i.e. its length must be >= 0).") + raise ValueError( + "'gene_space' cannot be empty (i.e. its length must be >= 0).") elif type(gene_space) in [list, numpy.ndarray]: if len(gene_space) == 0: self.valid_parameters = False - self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") - raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") + self.logger.error( + "'gene_space' cannot be empty (i.e. its length must be >= 0).") + raise ValueError( + "'gene_space' cannot be empty (i.e. its length must be >= 0).") else: for index, el in enumerate(gene_space): if type(el) in [numpy.ndarray, list, tuple, range]: if len(el) == 0: self.valid_parameters = False - self.logger.error(f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") - raise ValueError(f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") + self.logger.error( + f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") + raise ValueError( + f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") else: for val in el: if not (type(val) in [type(None)] + GA.supported_int_float_types): - self.logger.error(f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") - raise TypeError(f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") + self.logger.error( + f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") + raise TypeError( + f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") self.gene_space_nested = True elif type(el) == type(None): pass @@ -231,24 +247,32 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") - raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + self.logger.error( + f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + raise ValueError( + f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") elif len(el.items()) == 3: if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): pass else: self.valid_parameters = False - self.logger.error(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") - raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + self.logger.error( + f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + raise ValueError( + f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") else: self.valid_parameters = False - self.logger.error(f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") - raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") + self.logger.error( + f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") + raise ValueError( + f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") self.gene_space_nested = True elif not (type(el) in GA.supported_int_float_types): self.valid_parameters = False - self.logger.error(f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") - raise TypeError(f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") + self.logger.error( + f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") + raise TypeError( + f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") elif type(gene_space) is dict: if len(gene_space.items()) == 2: @@ -256,24 +280,32 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") - raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + self.logger.error( + f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + raise ValueError( + f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") elif len(gene_space.items()) == 3: - if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): + if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): pass else: self.valid_parameters = False - self.logger.error(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") - raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + self.logger.error( + f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + raise ValueError( + f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") else: self.valid_parameters = False - self.logger.error(f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") - raise ValueError(f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") + self.logger.error( + f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") + raise ValueError( + f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") else: self.valid_parameters = False - self.logger.error(f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") - raise TypeError(f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") + self.logger.error( + f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") + raise TypeError( + f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") self.gene_space = gene_space @@ -284,26 +316,36 @@ def __init__(self, self.init_range_high = init_range_high else: self.valid_parameters = False - self.logger.error(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") - raise ValueError(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") + self.logger.error( + f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") + raise ValueError( + f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") else: self.valid_parameters = False - self.logger.error(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") - raise ValueError(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") + self.logger.error( + f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") + raise ValueError( + f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") # Validate random_mutation_min_val and random_mutation_max_val if type(random_mutation_min_val) in GA.supported_int_float_types: if type(random_mutation_max_val) in GA.supported_int_float_types: if random_mutation_min_val == random_mutation_max_val: - if not self.suppress_warnings: warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") + if not self.suppress_warnings: + warnings.warn( + "The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") else: self.valid_parameters = False - self.logger.error(f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") - raise TypeError(f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") + self.logger.error( + f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") + raise TypeError( + f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") else: self.valid_parameters = False - self.logger.error(f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") - raise TypeError(f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") + self.logger.error( + f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") + raise TypeError( + f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") self.random_mutation_min_val = random_mutation_min_val self.random_mutation_max_val = random_mutation_max_val @@ -318,22 +360,30 @@ def __init__(self, # A single data type of int with precision. elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): self.gene_type_single = False - self.logger.error(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") - raise ValueError(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") + self.logger.error( + f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") + raise ValueError( + f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") elif type(gene_type) in [list, tuple, numpy.ndarray]: if num_genes is None: if initial_population is None: self.valid_parameters = False - self.logger.error("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") + self.logger.error( + "When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") + raise TypeError( + "When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") elif not len(gene_type) == len(initial_population[0]): self.valid_parameters = False - self.logger.error(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") - raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") + self.logger.error( + f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") + raise ValueError( + f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") elif not len(gene_type) == num_genes: self.valid_parameters = False - self.logger.error(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") - raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") + self.logger.error( + f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") + raise ValueError( + f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") for gene_type_idx, gene_type_val in enumerate(gene_type): if gene_type_val in GA.supported_float_types: # If the gene type is float and no precision is passed, set it to None. @@ -348,26 +398,36 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") - raise TypeError(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") + self.logger.error( + f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") + raise TypeError( + f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") else: self.valid_parameters = False - self.logger.error(f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") - raise TypeError(f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") + self.logger.error( + f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") + raise TypeError( + f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") else: self.valid_parameters = False - self.logger.error(f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") - raise ValueError(f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") + self.logger.error( + f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") + raise ValueError( + f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") else: self.valid_parameters = False - self.logger.error(f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") - raise ValueError(f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") + self.logger.error( + f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") + raise ValueError( + f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") self.gene_type = gene_type self.gene_type_single = False else: self.valid_parameters = False - self.logger.error(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") - raise ValueError(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") + self.logger.error( + f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") + raise ValueError( + f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. self.gene_space_unpacked = self.unpack_gene_space() @@ -382,42 +442,55 @@ def __init__(self, # Validating the number of solutions in the population (sol_per_pop) if sol_per_pop <= 0: self.valid_parameters = False - self.logger.error(f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") - raise ValueError(f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + self.logger.error( + f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + raise ValueError( + f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") # Validating the number of gene. if (num_genes <= 0): self.valid_parameters = False - self.logger.error(f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") - raise ValueError(f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") + self.logger.error( + f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") + raise ValueError( + f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. - self.num_genes = num_genes # Number of genes in the solution. + self.num_genes = num_genes # Number of genes in the solution. # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. if self.gene_space_nested: if len(gene_space) != self.num_genes: self.valid_parameters = False - self.logger.error(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") - raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") - - self.sol_per_pop = sol_per_pop # Number of solutions in the population. - self.initialize_population(self.init_range_low, - self.init_range_high, - allow_duplicate_genes, - True, + self.logger.error( + f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + raise ValueError( + f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + + # Number of solutions in the population. + self.sol_per_pop = sol_per_pop + self.initialize_population(self.init_range_low, + self.init_range_high, + allow_duplicate_genes, + True, self.gene_type) else: self.valid_parameters = False - self.logger.error(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") - raise TypeError(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") + self.logger.error( + f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") + raise TypeError( + f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") elif not type(initial_population) in [list, tuple, numpy.ndarray]: self.valid_parameters = False - self.logger.error(f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") - raise TypeError(f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") + self.logger.error( + f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") + raise TypeError( + f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") elif numpy.array(initial_population).ndim != 2: self.valid_parameters = False - self.logger.error(f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") - raise ValueError(f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") + self.logger.error( + f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") + raise ValueError( + f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") else: # Validate the type of each value in the 'initial_population' parameter. for row_idx in range(len(initial_population)): @@ -426,32 +499,34 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error(f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") - raise TypeError(f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") + self.logger.error( + f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") + raise TypeError( + f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") # Forcing the initial_population array to have the data type assigned to the gene_type parameter. if self.gene_type_single == True: if self.gene_type[1] == None: - self.initial_population = numpy.array(initial_population, + self.initial_population = numpy.array(initial_population, dtype=self.gene_type[0]) else: # This block is reached only for non-integer data types (i.e. float). - self.initial_population = numpy.round(numpy.array(initial_population, - dtype=self.gene_type[0]), + self.initial_population = numpy.round(numpy.array(initial_population, + dtype=self.gene_type[0]), self.gene_type[1]) else: initial_population = numpy.array(initial_population) - self.initial_population = numpy.zeros(shape=(initial_population.shape[0], - initial_population.shape[1]), + self.initial_population = numpy.zeros(shape=(initial_population.shape[0], + initial_population.shape[1]), dtype=object) for gene_idx in range(initial_population.shape[1]): if self.gene_type[gene_idx][1] is None: - self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], + self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], dtype=self.gene_type[gene_idx][0]) else: # This block is reached only for non-integer data types (i.e. float). - self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], - dtype=self.gene_type[gene_idx][0]), + self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], + dtype=self.gene_type[gene_idx][0]), self.gene_type[gene_idx][1]) # Check if duplicates are allowed. If not, then solve any exisiting duplicates in the passed initial population. @@ -469,10 +544,14 @@ def __init__(self, gene_type=self.gene_type, num_trials=10) - self.population = self.initial_population.copy() # A NumPy array holding the initial population. - self.num_genes = self.initial_population.shape[1] # Number of genes in the solution. - self.sol_per_pop = self.initial_population.shape[0] # Number of solutions in the population. - self.pop_size = (self.sol_per_pop,self.num_genes) # The population size. + # A NumPy array holding the initial population. + self.population = self.initial_population.copy() + # Number of genes in the solution. + self.num_genes = self.initial_population.shape[1] + # Number of solutions in the population. + self.sol_per_pop = self.initial_population.shape[0] + # The population size. + self.pop_size = (self.sol_per_pop, self.num_genes) # Round initial_population and population self.initial_population = self.round_genes(self.initial_population) @@ -482,20 +561,26 @@ def __init__(self, if self.gene_space_nested: if len(gene_space) != self.num_genes: self.valid_parameters = False - self.logger.error(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") - raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + self.logger.error( + f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + raise ValueError( + f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") # Validating the number of parents to be selected for mating (num_parents_mating) if num_parents_mating <= 0: self.valid_parameters = False - self.logger.error(f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") - raise ValueError(f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + self.logger.error( + f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + raise ValueError( + f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") # Validating the number of parents to be selected for mating: num_parents_mating if (num_parents_mating > self.sol_per_pop): self.valid_parameters = False - self.logger.error(f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") - raise ValueError(f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") + self.logger.error( + f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") + raise ValueError( + f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") self.num_parents_mating = num_parents_mating @@ -510,8 +595,10 @@ def __init__(self, self.crossover = crossover_type else: self.valid_parameters = False - self.logger.error(f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") - raise ValueError(f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + self.logger.error( + f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + raise ValueError( + f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") elif callable(crossover_type): # Check if the crossover_type is a function that accepts 2 paramaters. if (crossover_type.__code__.co_argcount == 3): @@ -519,13 +606,17 @@ def __init__(self, self.crossover = crossover_type else: self.valid_parameters = False - self.logger.error(f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") - raise ValueError(f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + self.logger.error( + f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + raise ValueError( + f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") elif not (type(crossover_type) is str): self.valid_parameters = False - self.logger.error(f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") - raise TypeError(f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") - else: # type crossover_type is str + self.logger.error( + f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") + raise TypeError( + f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") + else: # type crossover_type is str crossover_type = crossover_type.lower() if (crossover_type == "single_point"): self.crossover = self.single_point_crossover @@ -537,8 +628,10 @@ def __init__(self, self.crossover = self.scattered_crossover else: self.valid_parameters = False - self.logger.error(f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") - raise TypeError(f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") + self.logger.error( + f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") + raise TypeError( + f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") self.crossover_type = crossover_type @@ -550,12 +643,16 @@ def __init__(self, self.crossover_probability = crossover_probability else: self.valid_parameters = False - self.logger.error(f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") - raise ValueError(f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") + self.logger.error( + f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") + raise ValueError( + f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") else: self.valid_parameters = False - self.logger.error(f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") - raise TypeError(f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") + self.logger.error( + f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") + raise TypeError( + f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. # Validating the mutation type: mutation_type @@ -569,8 +666,10 @@ def __init__(self, self.mutation = mutation_type else: self.valid_parameters = False - self.logger.error(f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") - raise ValueError(f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + self.logger.error( + f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + raise ValueError( + f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") elif callable(mutation_type): # Check if the mutation_type is a function that accepts 2 paramater. if (mutation_type.__code__.co_argcount == 2): @@ -578,13 +677,17 @@ def __init__(self, self.mutation = mutation_type else: self.valid_parameters = False - self.logger.error(f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") - raise ValueError(f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + self.logger.error( + f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + raise ValueError( + f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") elif not (type(mutation_type) is str): self.valid_parameters = False - self.logger.error(f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") - raise TypeError(f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") - else: # type mutation_type is str + self.logger.error( + f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") + raise TypeError( + f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") + else: # type mutation_type is str mutation_type = mutation_type.lower() if (mutation_type == "random"): self.mutation = self.random_mutation @@ -598,8 +701,10 @@ def __init__(self, self.mutation = self.adaptive_mutation else: self.valid_parameters = False - self.logger.error(f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") - raise TypeError(f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") + self.logger.error( + f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") + raise TypeError( + f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") self.mutation_type = mutation_type @@ -614,12 +719,16 @@ def __init__(self, self.mutation_probability = mutation_probability else: self.valid_parameters = False - self.logger.error(f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") - raise ValueError(f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") + self.logger.error( + f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") + raise ValueError( + f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") else: self.valid_parameters = False - self.logger.error(f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") - raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + self.logger.error( + f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + raise TypeError( + f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") else: # Mutation probability is adaptive not fixed. if type(mutation_probability) in [list, tuple, numpy.ndarray]: @@ -630,23 +739,33 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error(f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") - raise ValueError(f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") + self.logger.error( + f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") + raise ValueError( + f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") else: self.valid_parameters = False - self.logger.error(f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") - raise TypeError(f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") + self.logger.error( + f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") + raise TypeError( + f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") if mutation_probability[0] < mutation_probability[1]: - if not self.suppress_warnings: warnings.warn(f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") + if not self.suppress_warnings: + warnings.warn( + f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") self.mutation_probability = mutation_probability else: self.valid_parameters = False - self.logger.error(f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") - raise ValueError(f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") + self.logger.error( + f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") + raise ValueError( + f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") else: self.valid_parameters = False - self.logger.error(f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") - raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + self.logger.error( + f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + raise TypeError( + f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") else: pass @@ -659,84 +778,114 @@ def __init__(self, if mutation_percent_genes == 'default'.lower(): mutation_percent_genes = 10 # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) + mutation_num_genes = numpy.uint32( + (mutation_percent_genes*self.num_genes)/100) # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes == 0: if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") + if not self.suppress_warnings: + warnings.warn( + f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes = 1 elif type(mutation_percent_genes) in GA.supported_int_float_types: if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): self.valid_parameters = False - self.logger.error(f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") - raise ValueError(f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + self.logger.error( + f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + raise ValueError( + f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") else: # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. if mutation_percent_genes == 'default'.lower(): mutation_percent_genes = 10 # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) + mutation_num_genes = numpy.uint32( + (mutation_percent_genes*self.num_genes)/100) # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes == 0: if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") + if not self.suppress_warnings: + warnings.warn( + f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes = 1 else: self.valid_parameters = False - self.logger.error(f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") - raise TypeError(f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") + self.logger.error( + f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") + raise TypeError( + f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") else: # The percent of genes to mutate is adaptive not fixed. if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: if len(mutation_percent_genes) == 2: - mutation_num_genes = numpy.zeros_like(mutation_percent_genes, dtype=numpy.uint32) + mutation_num_genes = numpy.zeros_like( + mutation_percent_genes, dtype=numpy.uint32) for idx, el in enumerate(mutation_percent_genes): if type(el) in GA.supported_int_float_types: if (el <= 0 or el > 100): self.valid_parameters = False - self.logger.error(f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") - raise ValueError(f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + self.logger.error( + f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + raise ValueError( + f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") else: self.valid_parameters = False - self.logger.error(f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") - raise TypeError(f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + self.logger.error( + f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + raise TypeError( + f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes[idx] = numpy.uint32((mutation_percent_genes[idx]*self.num_genes)/100) + mutation_num_genes[idx] = numpy.uint32( + (mutation_percent_genes[idx]*self.num_genes)/100) # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes[idx] == 0: - if not self.suppress_warnings: warnings.warn(f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") + if not self.suppress_warnings: + warnings.warn( + f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes[idx] = 1 if mutation_percent_genes[0] < mutation_percent_genes[1]: - if not self.suppress_warnings: warnings.warn(f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.") + if not self.suppress_warnings: + warnings.warn( + f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.") # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. else: self.valid_parameters = False - self.logger.error(f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") - raise ValueError(f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") + self.logger.error( + f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") + raise ValueError( + f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") else: if self.mutation_probability is None: self.valid_parameters = False - self.logger.error(f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") - raise TypeError(f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") + self.logger.error( + f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") + raise TypeError( + f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. elif (mutation_type != "adaptive"): # Number of genes to mutate is fixed not adaptive. if type(mutation_num_genes) in GA.supported_int_types: if (mutation_num_genes <= 0): self.valid_parameters = False - self.logger.error(f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") - raise ValueError(f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") + self.logger.error( + f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") + raise ValueError( + f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") elif (mutation_num_genes > self.num_genes): self.valid_parameters = False - self.logger.error(f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") - raise ValueError(f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") + self.logger.error( + f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") + raise ValueError( + f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") else: self.valid_parameters = False - self.logger.error(f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") - raise TypeError(f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") + self.logger.error( + f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") + raise TypeError( + f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") else: # Number of genes to mutate is adaptive not fixed. if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: @@ -745,38 +894,53 @@ def __init__(self, if type(el) in GA.supported_int_types: if (el <= 0): self.valid_parameters = False - self.logger.error(f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") - raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") + self.logger.error( + f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") + raise ValueError( + f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") elif (el > self.num_genes): self.valid_parameters = False - self.logger.error(f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") - raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") + self.logger.error( + f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") + raise ValueError( + f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") else: self.valid_parameters = False - self.logger.error(f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") - raise TypeError(f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + self.logger.error( + f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + raise TypeError( + f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. if mutation_num_genes[0] < mutation_num_genes[1]: - if not self.suppress_warnings: warnings.warn(f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") + if not self.suppress_warnings: + warnings.warn( + f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. else: self.valid_parameters = False - self.logger.error(f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") - raise ValueError(f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") + self.logger.error( + f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") + raise ValueError( + f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") else: self.valid_parameters = False - self.logger.error(f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") - raise TypeError(f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") + self.logger.error( + f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") + raise TypeError( + f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") else: pass - + # Validating mutation_by_replacement and mutation_type if self.mutation_type != "random" and self.mutation_by_replacement: - if not self.suppress_warnings: warnings.warn(f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") + if not self.suppress_warnings: + warnings.warn( + f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") # Check if crossover and mutation are both disabled. if (self.mutation_type is None) and (self.crossover_type is None): - if not self.suppress_warnings: warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") + if not self.suppress_warnings: + warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") # select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute. # Validating the selected type of parent selection: parent_selection_type @@ -788,8 +952,10 @@ def __init__(self, self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error(f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") - raise ValueError(f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + self.logger.error( + f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + raise ValueError( + f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") elif callable(parent_selection_type): # Check if the parent_selection_type is a function that accepts 3 paramaters. if (parent_selection_type.__code__.co_argcount == 3): @@ -798,12 +964,16 @@ def __init__(self, self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error(f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") - raise ValueError(f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + self.logger.error( + f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + raise ValueError( + f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") elif not (type(parent_selection_type) is str): self.valid_parameters = False - self.logger.error(f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") - raise TypeError(f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") + self.logger.error( + f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") + raise TypeError( + f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") else: parent_selection_type = parent_selection_type.lower() if (parent_selection_type == "sss"): @@ -824,51 +994,68 @@ def __init__(self, raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n") # For tournament selection, validate the K value. - if(parent_selection_type == "tournament"): + if (parent_selection_type == "tournament"): if (K_tournament > self.sol_per_pop): K_tournament = self.sol_per_pop - if not self.suppress_warnings: warnings.warn(f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") + if not self.suppress_warnings: + warnings.warn( + f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") elif (K_tournament <= 0): self.valid_parameters = False - self.logger.error(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") - raise ValueError(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") + self.logger.error( + f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") + raise ValueError( + f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") self.K_tournament = K_tournament # Validating the number of parents to keep in the next population: keep_parents if not (type(keep_parents) in GA.supported_int_types): self.valid_parameters = False - self.logger.error(f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") - raise TypeError(f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") + self.logger.error( + f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") + raise TypeError( + f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): self.valid_parameters = False - self.logger.error(f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") - raise ValueError(f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") + self.logger.error( + f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") + raise ValueError( + f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") self.keep_parents = keep_parents if parent_selection_type == "sss" and self.keep_parents == 0: - if not self.suppress_warnings: warnings.warn("The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") + if not self.suppress_warnings: + warnings.warn( + "The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") # Validating the number of elitism to keep in the next population: keep_elitism if not (type(keep_elitism) in GA.supported_int_types): self.valid_parameters = False - self.logger.error(f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") - raise TypeError(f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") + self.logger.error( + f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") + raise TypeError( + f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): self.valid_parameters = False - self.logger.error(f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") - raise ValueError(f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") + self.logger.error( + f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") + raise ValueError( + f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") self.keep_elitism = keep_elitism # Validate keep_parents. if self.keep_elitism == 0: - if (self.keep_parents == -1): # Keep all parents in the next population. + # Keep all parents in the next population. + if (self.keep_parents == -1): self.num_offspring = self.sol_per_pop - self.num_parents_mating - elif (self.keep_parents == 0): # Keep no parents in the next population. + # Keep no parents in the next population. + elif (self.keep_parents == 0): self.num_offspring = self.sol_per_pop - elif (self.keep_parents > 0): # Keep the specified number of parents in the next population. + # Keep the specified number of parents in the next population. + elif (self.keep_parents > 0): self.num_offspring = self.sol_per_pop - self.keep_parents else: self.num_offspring = self.sol_per_pop - self.keep_elitism @@ -882,31 +1069,41 @@ def __init__(self, self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error(f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") - raise ValueError(f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + self.logger.error( + f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + raise ValueError( + f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") elif callable(fitness_func): # Check if the fitness function accepts 2 paramaters. if (fitness_func.__code__.co_argcount == 3): self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error(f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") - raise ValueError(f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + self.logger.error( + f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + raise ValueError( + f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") - raise TypeError(f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") - + self.logger.error( + f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") + raise TypeError( + f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") + if fitness_batch_size is None: pass elif not (type(fitness_batch_size) in GA.supported_int_types): self.valid_parameters = False - self.logger.error(f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") - raise TypeError(f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") + self.logger.error( + f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") + raise TypeError( + f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: self.valid_parameters = False - self.logger.error(f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") - raise ValueError(f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") + self.logger.error( + f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") + raise ValueError( + f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") self.fitness_batch_size = fitness_batch_size @@ -918,8 +1115,10 @@ def __init__(self, self.on_start = on_start else: self.valid_parameters = False - self.logger.error(f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") - raise ValueError(f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") # Check if the on_start is a function. elif callable(on_start): # Check if the on_start function accepts only a single paramater. @@ -927,12 +1126,16 @@ def __init__(self, self.on_start = on_start else: self.valid_parameters = False - self.logger.error(f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") - raise ValueError(f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") - raise TypeError(f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") + self.logger.error( + f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") + raise TypeError( + f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") else: self.on_start = None @@ -945,8 +1148,10 @@ def __init__(self, self.on_fitness = on_fitness else: self.valid_parameters = False - self.logger.error(f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") - raise ValueError(f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") # Check if the on_fitness is a function. elif callable(on_fitness): # Check if the on_fitness function accepts 2 paramaters. @@ -954,12 +1159,16 @@ def __init__(self, self.on_fitness = on_fitness else: self.valid_parameters = False - self.logger.error(f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") - raise ValueError(f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") - raise TypeError(f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") + self.logger.error( + f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") + raise TypeError( + f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") else: self.on_fitness = None @@ -972,8 +1181,10 @@ def __init__(self, self.on_parents = on_parents else: self.valid_parameters = False - self.logger.error(f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") - raise ValueError(f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") # Check if the on_parents is a function. elif callable(on_parents): # Check if the on_parents function accepts 2 paramaters. @@ -981,12 +1192,16 @@ def __init__(self, self.on_parents = on_parents else: self.valid_parameters = False - self.logger.error(f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") - raise ValueError(f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") - raise TypeError(f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") + self.logger.error( + f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") + raise TypeError( + f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") else: self.on_parents = None @@ -999,8 +1214,10 @@ def __init__(self, self.on_crossover = on_crossover else: self.valid_parameters = False - self.logger.error(f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") - raise ValueError(f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") # Check if the on_crossover is a function. elif callable(on_crossover): # Check if the on_crossover function accepts 2 paramaters. @@ -1008,12 +1225,16 @@ def __init__(self, self.on_crossover = on_crossover else: self.valid_parameters = False - self.logger.error(f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") - raise ValueError(f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") - raise TypeError(f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") + self.logger.error( + f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") + raise TypeError( + f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") else: self.on_crossover = None @@ -1026,8 +1247,10 @@ def __init__(self, self.on_mutation = on_mutation else: self.valid_parameters = False - self.logger.error(f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") - raise ValueError(f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") # Check if the on_mutation is a function. elif callable(on_mutation): # Check if the on_mutation function accepts 2 paramaters. @@ -1035,12 +1258,16 @@ def __init__(self, self.on_mutation = on_mutation else: self.valid_parameters = False - self.logger.error(f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") - raise ValueError(f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") - raise TypeError(f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") + self.logger.error( + f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") + raise TypeError( + f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") else: self.on_mutation = None @@ -1053,8 +1280,10 @@ def __init__(self, self.on_generation = on_generation else: self.valid_parameters = False - self.logger.error(f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") - raise ValueError(f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") # Check if the on_generation is a function. elif callable(on_generation): # Check if the on_generation function accepts only a single paramater. @@ -1062,12 +1291,16 @@ def __init__(self, self.on_generation = on_generation else: self.valid_parameters = False - self.logger.error(f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") - raise ValueError(f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") - raise TypeError(f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") + self.logger.error( + f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") + raise TypeError( + f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") else: self.on_generation = None @@ -1080,8 +1313,10 @@ def __init__(self, self.on_stop = on_stop else: self.valid_parameters = False - self.logger.error(f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") - raise ValueError(f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") # Check if the on_stop is a function. elif callable(on_stop): # Check if the on_stop function accepts 2 paramaters. @@ -1089,12 +1324,16 @@ def __init__(self, self.on_stop = on_stop else: self.valid_parameters = False - self.logger.error(f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") - raise ValueError(f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + self.logger.error( + f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + raise ValueError( + f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error(f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") - raise TypeError(f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") + self.logger.error( + f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") + raise TypeError( + f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") else: self.on_stop = None @@ -1104,30 +1343,42 @@ def __init__(self, self.delay_after_gen = delay_after_gen else: self.valid_parameters = False - self.logger.error(f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") - raise ValueError(f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") + self.logger.error( + f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") + raise ValueError( + f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") else: self.valid_parameters = False - self.logger.error(f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") - raise TypeError(f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") + self.logger.error( + f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") + raise TypeError( + f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") # Validate save_best_solutions if type(save_best_solutions) is bool: if save_best_solutions == True: - if not self.suppress_warnings: warnings.warn("Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") + if not self.suppress_warnings: + warnings.warn( + "Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") else: self.valid_parameters = False - self.logger.error(f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") - raise TypeError(f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") + self.logger.error( + f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") + raise TypeError( + f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") # Validate save_solutions if type(save_solutions) is bool: if save_solutions == True: - if not self.suppress_warnings: warnings.warn("Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") + if not self.suppress_warnings: + warnings.warn( + "Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") else: self.valid_parameters = False - self.logger.error(f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") - raise TypeError(f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") + self.logger.error( + f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") + raise TypeError( + f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") self.stop_criteria = [] self.supported_stop_words = ["reach", "saturate"] @@ -1146,22 +1397,28 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") - raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") + self.logger.error( + f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") + raise ValueError( + f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") if number.replace(".", "").isnumeric(): number = float(number) else: self.valid_parameters = False - self.logger.error(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") - raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") - + self.logger.error( + f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + raise ValueError( + f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + self.stop_criteria.append([stop_word, number]) else: self.valid_parameters = False - self.logger.error(f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") - raise ValueError(f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") + self.logger.error( + f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") + raise ValueError( + f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") elif type(stop_criteria) in [list, tuple, numpy.ndarray]: # Remove duplicate criterira by converting the list to a set then back to a list. @@ -1177,30 +1434,40 @@ def __init__(self, pass else: self.valid_parameters = False - self.logger.error(f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") - raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") + self.logger.error( + f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") + raise ValueError( + f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") if number.replace(".", "").isnumeric(): number = float(number) else: self.valid_parameters = False - self.logger.error(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") - raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + self.logger.error( + f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + raise ValueError( + f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") self.stop_criteria.append([stop_word, number]) else: self.valid_parameters = False - self.logger.error(f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") - raise ValueError(f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") + self.logger.error( + f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") + raise ValueError( + f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") else: self.valid_parameters = False - self.logger.error(f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") - raise TypeError(f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") + self.logger.error( + f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") + raise TypeError( + f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") else: self.valid_parameters = False - self.logger.error(f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") - raise TypeError(f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") + self.logger.error( + f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") + raise TypeError( + f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") if parallel_processing is None: self.parallel_processing = None @@ -1209,8 +1476,10 @@ def __init__(self, self.parallel_processing = ["thread", parallel_processing] else: self.valid_parameters = False - self.logger.error(f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") - raise ValueError(f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") + self.logger.error( + f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") + raise ValueError( + f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") elif type(parallel_processing) in [list, tuple]: if len(parallel_processing) == 2: if type(parallel_processing[0]) is str: @@ -1224,20 +1493,28 @@ def __init__(self, self.parallel_processing = parallel_processing else: self.valid_parameters = False - self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") - raise TypeError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") + self.logger.error( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") + raise TypeError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") else: self.valid_parameters = False - self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") - raise ValueError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") + self.logger.error( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") + raise ValueError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") else: self.valid_parameters = False - self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") - raise TypeError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") + self.logger.error( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") + raise TypeError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") else: self.valid_parameters = False - self.logger.error(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") - raise ValueError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") + self.logger.error( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") + raise ValueError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") else: self.valid_parameters = False self.logger.error(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") @@ -1250,7 +1527,8 @@ def __init__(self, self.generations_completed = 0 # At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid. - self.valid_parameters = True # Set to True when all the parameters passed in the GA class constructor are valid. + # Set to True when all the parameters passed in the GA class constructor are valid. + self.valid_parameters = True # Parameters of the genetic algorithm. self.num_generations = abs(num_generations) @@ -1261,45 +1539,54 @@ def __init__(self, self.mutation_num_genes = mutation_num_genes # Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object. - self.best_solutions_fitness = [] # A list holding the fitness value of the best solution for each generation. + # A list holding the fitness value of the best solution for each generation. + self.best_solutions_fitness = [] - self.best_solution_generation = -1 # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. + # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. + self.best_solution_generation = -1 self.save_best_solutions = save_best_solutions - self.best_solutions = [] # Holds the best solution in each generation. + self.best_solutions = [] # Holds the best solution in each generation. self.save_solutions = save_solutions - self.solutions = [] # Holds the solutions in each generation. - self.solutions_fitness = [] # Holds the fitness of the solutions in each generation. - - self.last_generation_fitness = None # A list holding the fitness values of all solutions in the last generation. - self.last_generation_parents = None # A list holding the parents of the last generation. - self.last_generation_offspring_crossover = None # A list holding the offspring after applying crossover in the last generation. - self.last_generation_offspring_mutation = None # A list holding the offspring after applying mutation in the last generation. - self.previous_generation_fitness = None # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. - self.last_generation_elitism = None # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. - self.last_generation_elitism_indices = None # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. + self.solutions = [] # Holds the solutions in each generation. + # Holds the fitness of the solutions in each generation. + self.solutions_fitness = [] + + # A list holding the fitness values of all solutions in the last generation. + self.last_generation_fitness = None + # A list holding the parents of the last generation. + self.last_generation_parents = None + # A list holding the offspring after applying crossover in the last generation. + self.last_generation_offspring_crossover = None + # A list holding the offspring after applying mutation in the last generation. + self.last_generation_offspring_mutation = None + # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. + self.previous_generation_fitness = None + # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. + self.last_generation_elitism = None + # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. + self.last_generation_elitism_indices = None def round_genes(self, solutions): for gene_idx in range(self.num_genes): if self.gene_type_single: if not self.gene_type[1] is None: - solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], + solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], self.gene_type[1]) else: if not self.gene_type[gene_idx][1] is None: - solutions[:, gene_idx] = numpy.round(numpy.asarray(solutions[:, gene_idx], - dtype=self.gene_type[gene_idx][0]), + solutions[:, gene_idx] = numpy.round(numpy.asarray(solutions[:, gene_idx], + dtype=self.gene_type[gene_idx][0]), self.gene_type[gene_idx][1]) return solutions - def initialize_population(self, - low, - high, - allow_duplicate_genes, - mutation_by_replacement, + def initialize_population(self, + low, + high, + allow_duplicate_genes, + mutation_by_replacement, gene_type): - """ Creates an initial population randomly as a NumPy array. The array is saved in the instance attribute named 'population'. @@ -1313,24 +1600,26 @@ def initialize_population(self, """ # Population size = (number of chromosomes, number of genes per chromosome) - self.pop_size = (self.sol_per_pop,self.num_genes) # The population will have sol_per_pop chromosome where each chromosome has num_genes genes. + # The population will have sol_per_pop chromosome where each chromosome has num_genes genes. + self.pop_size = (self.sol_per_pop, self.num_genes) if self.gene_space is None: # Creating the initial population randomly. if self.gene_type_single == True: - self.population = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. + self.population = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=self.pop_size), + dtype=self.gene_type[0]) # A NumPy array holding the initial population. else: # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) + self.population = numpy.zeros( + shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): # A vector of all values of this single gene across all solutions in the population. - gene_values = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=self.pop_size[0]), + gene_values = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) # Adding the current gene values to the population. self.population[:, gene_idx] = gene_values @@ -1339,7 +1628,7 @@ def initialize_population(self, for solution_idx in range(self.population.shape[0]): # self.logger.info("Before", self.population[solution_idx]) self.population[solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=self.population[solution_idx], - min_val=low, + min_val=low, max_val=high, mutation_by_replacement=True, gene_type=gene_type, @@ -1349,25 +1638,25 @@ def initialize_population(self, elif self.gene_space_nested: if self.gene_type_single == True: # Reaching this block means: - # 1) gene_space is nested (gene_space_nested is True). - # 2) gene_type is not nested (gene_type_single is True). - self.population = numpy.zeros(shape=self.pop_size, + # 1) gene_space is nested (gene_space_nested is True). + # 2) gene_type is not nested (gene_type_single is True). + self.population = numpy.zeros(shape=self.pop_size, dtype=self.gene_type[0]) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): if self.gene_space[gene_idx] is None: - # The following commented code replace the None value with a single number that will not change again. + # The following commented code replace the None value with a single number that will not change again. # This means the gene value will be the same across all solutions. # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - # high=high, + # high=high, # size=1), dtype=self.gene_type[0])[0] # self.population[sol_idx, gene_idx] = list(self.gene_space[gene_idx]).copy() # The above problem is solved by keeping the None value in the gene_space parameter. This forces PyGAD to generate this value for each solution. self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + high=high, + size=1), dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in [numpy.ndarray, list, tuple, range]: # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. @@ -1378,47 +1667,56 @@ def initialize_population(self, # We copy the gene_space to a temp variable to keep its original value. # In the next for loop, the gene_space is changed. # Later, the gene_space is restored to its original value using the temp variable. - temp_gene_space = list(self.gene_space[gene_idx]).copy() + temp_gene_space = list( + self.gene_space[gene_idx]).copy() for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), dtype=self.gene_type[0])[0] # Find the difference between the current gene space and the current values in the solution. - unique_gene_values = list(set(self.gene_space[gene_idx]).difference(set(self.population[sol_idx, :gene_idx]))) + unique_gene_values = list(set(self.gene_space[gene_idx]).difference( + set(self.population[sol_idx, :gene_idx]))) if len(unique_gene_values) > 0: - self.population[sol_idx, gene_idx] = random.choice(unique_gene_values) + self.population[sol_idx, gene_idx] = random.choice( + unique_gene_values) else: # If there is no unique values, then we have to select a duplicate value. - self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = random.choice( + self.gene_space[gene_idx]) - self.population[sol_idx, gene_idx] = self.gene_type[0](self.population[sol_idx, gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[0]( + self.population[sol_idx, gene_idx]) # Restore the gene_space from the temp_gene_space variable. - self.gene_space[gene_idx] = list(temp_gene_space).copy() + self.gene_space[gene_idx] = list( + temp_gene_space).copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], + stop=self.gene_space[ + gene_idx]['high'], step=self.gene_space[gene_idx]['step']), size=1), dtype=self.gene_type[0])[0] else: self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[gene_idx]['high'], + high=self.gene_space[ + gene_idx]['high'], size=1), dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + self.population[sol_idx, + gene_idx] = self.gene_space[gene_idx] else: # There is no more options. pass else: # Reaching this block means: - # 1) gene_space is nested (gene_space_nested is True). - # 2) gene_type is nested (gene_type_single is False). - self.population = numpy.zeros(shape=self.pop_size, + # 1) gene_space is nested (gene_space_nested is True). + # 2) gene_type is nested (gene_type_single is False). + self.population = numpy.zeros(shape=self.pop_size, dtype=object) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): @@ -1427,59 +1725,66 @@ def initialize_population(self, # We copy the gene_space to a temp variable to keep its original value. # In the next for loop, the gene_space is changed. # Later, the gene_space is restored to its original value using the temp variable. - temp_gene_space = list(self.gene_space[gene_idx]).copy() + temp_gene_space = list( + self.gene_space[gene_idx]).copy() # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) - self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0](self.population[sol_idx, gene_idx]) + self.population[sol_idx, gene_idx] = random.choice( + self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0]( + self.population[sol_idx, gene_idx]) # Restore the gene_space from the temp_gene_space variable. self.gene_space[gene_idx] = temp_gene_space.copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], + stop=self.gene_space[ + gene_idx]['high'], step=self.gene_space[gene_idx]['step']), size=1), dtype=self.gene_type[gene_idx][0])[0] else: self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[gene_idx]['high'], - size=1), + high=self.gene_space[ + gene_idx]['high'], + size=1), dtype=self.gene_type[gene_idx][0])[0] elif type(self.gene_space[gene_idx]) == type(None): temp_gene_value = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + high=high, + size=1), dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = temp_gene_value.copy() + self.population[sol_idx, + gene_idx] = temp_gene_value.copy() elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + self.population[sol_idx, + gene_idx] = self.gene_space[gene_idx] else: - # There is no more options. + # There is no more options. pass else: # Handle the non-nested gene_space. It can be assigned a numeric value, list, numpy.ndarray, or a dict. if self.gene_type_single == True: # Reaching this block means: - # 1) gene_space is not nested (gene_space_nested is False). - # 2) gene_type is not nested (gene_type_single is True). + # 1) gene_space is not nested (gene_space_nested is False). + # 2) gene_type is not nested (gene_type_single is True). # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. for idx, curr_gene_space in enumerate(self.gene_space): if curr_gene_space is None: - self.gene_space[idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + self.gene_space[idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), dtype=self.gene_type[0])[0] - + # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. if type(self.gene_space) is dict: if 'step' in self.gene_space.keys(): @@ -1492,20 +1797,20 @@ def initialize_population(self, self.population = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. + dtype=self.gene_type[0]) # A NumPy array holding the initial population. else: self.population = numpy.asarray(numpy.random.choice(self.gene_space, size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. + dtype=self.gene_type[0]) # A NumPy array holding the initial population. else: # Reaching this block means: - # 1) gene_space is not nested (gene_space_nested is False). - # 2) gene_type is nested (gene_type_single is False). + # 1) gene_space is not nested (gene_space_nested is False). + # 2) gene_type is nested (gene_type_single is False). # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. if type(self.gene_space) is dict: # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, + self.population = numpy.zeros(shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): @@ -1518,24 +1823,25 @@ def initialize_population(self, size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) else: - gene_values = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], - high=self.gene_space['high'], - size=self.pop_size[0]), + gene_values = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) # Adding the current gene values to the population. self.population[:, gene_idx] = gene_values - + else: # Reaching this block means that the gene_space is not None or dict. # It can be either range, numpy.ndarray, or list. # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) + self.population = numpy.zeros( + shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): # A vector of all values of this single gene across all solutions in the population. - gene_values = numpy.asarray(numpy.random.choice(self.gene_space, - size=self.pop_size[0]), + gene_values = numpy.asarray(numpy.random.choice(self.gene_space, + size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) # Adding the current gene values to the population. self.population[:, gene_idx] = gene_values @@ -1552,7 +1858,6 @@ def initialize_population(self, self.initial_population = self.population.copy() def cal_pop_fitness(self): - """ Calculating the fitness values of batches of solutions in the current population. It returns: @@ -1560,18 +1865,22 @@ def cal_pop_fitness(self): """ if self.valid_parameters == False: - self.logger.error("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") - raise Exception("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") + self.logger.error( + "ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") + raise Exception( + "ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. if self.last_generation_parents is not None: - last_generation_parents_as_list = [list(gen_parent) for gen_parent in self.last_generation_parents] + last_generation_parents_as_list = [ + list(gen_parent) for gen_parent in self.last_generation_parents] # 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism' # It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. if self.last_generation_elitism is not None: - last_generation_elitism_as_list = [list(gen_elitism) for gen_elitism in self.last_generation_elitism] + last_generation_elitism_as_list = [ + list(gen_elitism) for gen_elitism in self.last_generation_elitism] pop_fitness = ["undefined"] * len(self.population) if self.parallel_processing is None: @@ -1589,7 +1898,8 @@ def cal_pop_fitness(self): elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index(list(sol)) + elitism_idx = last_generation_elitism_as_list.index( + list(sol)) # Use the returned elitism index to return its index in the last population. elitism_idx = self.last_generation_elitism_indices[elitism_idx] # Use the elitism's index to return its pre-calculated fitness value. @@ -1598,10 +1908,11 @@ def cal_pop_fitness(self): # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. + # Index of the parent in the 'self.last_generation_parents' array. # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index(list(sol)) + parent_idx = last_generation_parents_as_list.index( + list(sol)) # Use the returned parent index to return its index in the last population. parent_idx = self.last_generation_parents_indices[parent_idx] # Use the parent's index to return its pre-calculated fitness value. @@ -1613,8 +1924,10 @@ def cal_pop_fitness(self): if type(fitness) in GA.supported_int_float_types: pass else: - self.logger.error(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + self.logger.error( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + raise ValueError( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: # Reaching this point means that batch processing is in effect to calculate the fitness values. # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. @@ -1627,30 +1940,40 @@ def cal_pop_fitness(self): # Reaching this block means that batch fitness calculation is used. # Indices of the solutions to calculate their fitness. - solutions_indices = numpy.where(numpy.array(pop_fitness) == "undefined")[0] + solutions_indices = numpy.where( + numpy.array(pop_fitness) == "undefined")[0] # Number of batches. - num_batches = int(numpy.ceil(len(solutions_indices) / self.fitness_batch_size)) + num_batches = int(numpy.ceil( + len(solutions_indices) / self.fitness_batch_size)) # For each batch, get its indices and call the fitness function. for batch_idx in range(num_batches): batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * self.fitness_batch_size + batch_last_index = (batch_idx + 1) * \ + self.fitness_batch_size batch_indices = solutions_indices[batch_first_index:batch_last_index] batch_solutions = self.population[batch_indices, :] - batch_fitness = self.fitness_func(self, batch_solutions, batch_indices) + batch_fitness = self.fitness_func( + self, batch_solutions, batch_indices) if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") - raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + self.logger.error( + f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + raise TypeError( + f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") - raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + self.logger.error( + f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + raise ValueError( + f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") for index, fitness in zip(batch_indices, batch_fitness): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + self.logger.error( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + raise ValueError( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: # Calculating the fitness value of each solution in the current population. for sol_idx, sol in enumerate(self.population): @@ -1665,7 +1988,8 @@ def cal_pop_fitness(self): elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index(list(sol)) + elitism_idx = last_generation_elitism_as_list.index( + list(sol)) # Use the returned elitism index to return its index in the last population. elitism_idx = self.last_generation_elitism_indices[elitism_idx] # Use the elitism's index to return its pre-calculated fitness value. @@ -1676,10 +2000,11 @@ def cal_pop_fitness(self): # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. + # Index of the parent in the 'self.last_generation_parents' array. # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index(list(sol)) + parent_idx = last_generation_parents_as_list.index( + list(sol)) # Use the returned parent index to return its index in the last population. parent_idx = self.last_generation_parents_indices[parent_idx] # Use the parent's index to return its pre-calculated fitness value. @@ -1709,13 +2034,16 @@ def cal_pop_fitness(self): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + self.logger.error( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + raise ValueError( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: # Reaching this block means that batch processing is used. The fitness values are calculated in batches. # Number of batches. - num_batches = int(numpy.ceil(len(solutions_to_submit_indices) / self.fitness_batch_size)) + num_batches = int(numpy.ceil( + len(solutions_to_submit_indices) / self.fitness_batch_size)) # Each element of the `batches_solutions` list represents the solutions in one batch. batches_solutions = [] # Each element of the `batches_indices` list represents the solutions' indices in one batch. @@ -1723,7 +2051,8 @@ def cal_pop_fitness(self): # For each batch, get its indices and call the fitness function. for batch_idx in range(num_batches): batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * self.fitness_batch_size + batch_last_index = (batch_idx + 1) * \ + self.fitness_batch_size batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] batch_solutions = self.population[batch_indices, :] @@ -1732,34 +2061,41 @@ def cal_pop_fitness(self): for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") - raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + self.logger.error( + f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + raise TypeError( + f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") - raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + self.logger.error( + f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + raise ValueError( + f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") for index, fitness in zip(batch_indices, batch_fitness): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error(f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") - raise ValueError(f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") + self.logger.error( + f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") + raise ValueError( + f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") pop_fitness = numpy.array(pop_fitness) return pop_fitness def run(self): - """ Runs the genetic algorithm. This is the main method in which the genetic algorithm is evolved through a number of generations. """ if self.valid_parameters == False: - self.logger.error("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - raise Exception("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") + self.logger.error( + "Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") + raise Exception( + "Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. + # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. # self.best_solutions: Holds the best solution in each generation. @@ -1793,7 +2129,8 @@ def run(self): # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. self.last_generation_fitness = self.cal_pop_fitness() - best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) + best_solution, best_solution_fitness, best_match_idx = self.best_solution( + pop_fitness=self.last_generation_fitness) # Appending the best solution in the initial population to the best_solutions list. if self.save_best_solutions: @@ -1805,46 +2142,59 @@ def run(self): # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. self.best_solutions_fitness.append(best_solution_fitness) - + # Appending the solutions in the current generation to the solutions list. if self.save_solutions: # self.solutions.extend(self.population.copy()) population_as_list = self.population.copy() - population_as_list = [list(item) for item in population_as_list] + population_as_list = [list(item) + for item in population_as_list] self.solutions.extend(population_as_list) self.solutions_fitness.extend(self.last_generation_fitness) # Selecting the best parents in the population for mating. if callable(self.parent_selection_type): - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, - self.num_parents_mating, + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + self.num_parents_mating, self) if not type(self.last_generation_parents) is numpy.ndarray: - self.logger.error(f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") - raise TypeError(f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") + self.logger.error( + f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") + raise TypeError( + f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") if not type(self.last_generation_parents_indices) is numpy.ndarray: - self.logger.error(f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") - raise TypeError(f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") + self.logger.error( + f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") + raise TypeError( + f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") else: - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, num_parents=self.num_parents_mating) # Validate the output of the parent selection step: self.select_parents() if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): if self.last_generation_parents.shape[0] != self.num_parents_mating: - self.logger.error(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") - raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") + self.logger.error( + f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") + raise ValueError( + f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") elif self.last_generation_parents.shape[1] != self.num_genes: - self.logger.error(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") - raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") + self.logger.error( + f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") + raise ValueError( + f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") if self.last_generation_parents_indices.ndim != 1: - self.logger.error(f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") - raise ValueError(f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") + self.logger.error( + f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") + raise ValueError( + f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") elif len(self.last_generation_parents_indices) != self.num_parents_mating: - self.logger.error(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") - raise ValueError(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") + self.logger.error( + f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") + raise ValueError( + f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") if not (self.on_parents is None): self.on_parents(self, self.last_generation_parents) @@ -1852,43 +2202,56 @@ def run(self): # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. if self.crossover_type is None: if self.keep_elitism == 0: - num_parents_to_keep = self.num_parents_mating if self.keep_parents == -1 else self.keep_parents + num_parents_to_keep = self.num_parents_mating if self.keep_parents == - \ + 1 else self.keep_parents if self.num_offspring <= num_parents_to_keep: - self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] + self.last_generation_offspring_crossover = self.last_generation_parents[ + 0:self.num_offspring] else: - self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) + self.last_generation_offspring_crossover = numpy.concatenate( + (self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) else: # The steady_state_selection() function is called to select the best solutions (i.e. elitism). The keep_elitism parameter defines the number of these solutions. # The steady_state_selection() function is still called here even if its output may not be used given that the condition of the next if statement is True. The reason is that it will be used later. - self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, + self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, num_parents=self.keep_elitism) if self.num_offspring <= self.keep_elitism: - self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] + self.last_generation_offspring_crossover = self.last_generation_parents[ + 0:self.num_offspring] else: - self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) + self.last_generation_offspring_crossover = numpy.concatenate( + (self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) else: # Generating offspring using crossover. if callable(self.crossover_type): self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, - (self.num_offspring, self.num_genes), + (self.num_offspring, + self.num_genes), self) if not type(self.last_generation_offspring_crossover) is numpy.ndarray: - self.logger.error(f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") - raise TypeError(f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") + self.logger.error( + f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") + raise TypeError( + f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") else: self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, offspring_size=(self.num_offspring, self.num_genes)) if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: - self.logger.error(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") - raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") + self.logger.error( + f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") + raise ValueError( + f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: - self.logger.error(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") - raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") + self.logger.error( + f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") + raise ValueError( + f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. if not (self.on_crossover is None): - self.on_crossover(self, self.last_generation_offspring_crossover) + self.on_crossover( + self, self.last_generation_offspring_crossover) # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. if self.mutation_type is None: @@ -1896,21 +2259,28 @@ def run(self): else: # Adding some variations to the offspring using mutation. if callable(self.mutation_type): - self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, + self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, self) if not type(self.last_generation_offspring_mutation) is numpy.ndarray: - self.logger.error(f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") - raise TypeError(f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") + self.logger.error( + f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") + raise TypeError( + f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") else: - self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover) + self.last_generation_offspring_mutation = self.mutation( + self.last_generation_offspring_crossover) if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: - self.logger.error(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") - raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") + self.logger.error( + f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") + raise ValueError( + f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: - self.logger.error(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") - raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") + self.logger.error( + f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") + raise ValueError( + f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. if not (self.on_mutation is None): @@ -1923,26 +2293,32 @@ def run(self): self.population = self.last_generation_offspring_mutation elif (self.keep_parents == -1): # Creating the new population based on the parents and offspring. - self.population[0:self.last_generation_parents.shape[0], :] = self.last_generation_parents + self.population[0:self.last_generation_parents.shape[0], + :] = self.last_generation_parents self.population[self.last_generation_parents.shape[0]:, :] = self.last_generation_offspring_mutation elif (self.keep_parents > 0): - parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, + parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, num_parents=self.keep_parents) - self.population[0:parents_to_keep.shape[0], :] = parents_to_keep - self.population[parents_to_keep.shape[0]:, :] = self.last_generation_offspring_mutation + self.population[0:parents_to_keep.shape[0], + :] = parents_to_keep + self.population[parents_to_keep.shape[0]:, + :] = self.last_generation_offspring_mutation else: - self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, + self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, num_parents=self.keep_elitism) - self.population[0:self.last_generation_elitism.shape[0], :] = self.last_generation_elitism + self.population[0:self.last_generation_elitism.shape[0], + :] = self.last_generation_elitism self.population[self.last_generation_elitism.shape[0]:, :] = self.last_generation_offspring_mutation - self.generations_completed = generation + 1 # The generations_completed attribute holds the number of the last completed generation. + # The generations_completed attribute holds the number of the last completed generation. + self.generations_completed = generation + 1 self.previous_generation_fitness = self.last_generation_fitness.copy() # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. self.last_generation_fitness = self.cal_pop_fitness() - best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) + best_solution, best_solution_fitness, best_match_idx = self.best_solution( + pop_fitness=self.last_generation_fitness) # Appending the best solution in the current generation to the best_solutions list. if self.save_best_solutions: @@ -1985,12 +2361,15 @@ def run(self): self.solutions_fitness.extend(self.last_generation_fitness) # Save the fitness value of the best solution. - _, best_solution_fitness, _ = self.best_solution(pop_fitness=self.last_generation_fitness) + _, best_solution_fitness, _ = self.best_solution( + pop_fitness=self.last_generation_fitness) self.best_solutions_fitness.append(best_solution_fitness) - self.best_solution_generation = numpy.where(numpy.array(self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] + self.best_solution_generation = numpy.where(numpy.array( + self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] # After the run() method completes, the run_completed flag is changed from False to True. - self.run_completed = True # Set to True only after the run() method completes gracefully. + # Set to True only after the run() method completes gracefully. + self.run_completed = True if not (self.on_stop is None): self.on_stop(self, self.last_generation_fitness) @@ -2002,7 +2381,6 @@ def run(self): # self.solutions = numpy.array(self.solutions) def best_solution(self, pop_fitness=None): - """ Returns information about the best solution found by the genetic algorithm. Accepts the following parameters: @@ -2023,14 +2401,19 @@ def best_solution(self, pop_fitness=None): # This successfully verifies the 'pop_fitness' parameter. pass else: - self.logger.error(f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") - raise ValueError(f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") + self.logger.error( + f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") + raise ValueError( + f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") else: - self.logger.error(f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") - raise ValueError(f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") + self.logger.error( + f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") + raise ValueError( + f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") # Return the index of the best solution that has the best fitness value. - best_match_idx = numpy.where(pop_fitness == numpy.max(pop_fitness))[0][0] + best_match_idx = numpy.where( + pop_fitness == numpy.max(pop_fitness))[0][0] best_solution = self.population[best_match_idx, :].copy() best_solution_fitness = pop_fitness[best_match_idx] @@ -2038,7 +2421,6 @@ def best_solution(self, pop_fitness=None): return best_solution, best_solution_fitness, best_match_idx def save(self, filename): - """ Saves the genetic algorithm instance: -filename: Name of the file to save the instance. No extension is needed. @@ -2049,12 +2431,12 @@ def save(self, filename): file.write(cloudpickle_serialized_object) cloudpickle.dump(self, file) - def summary(self, - line_length=70, - fill_character=" ", - line_character="-", - line_character2="=", - columns_equal_len=False, + def summary(self, + line_length=70, + fill_character=" ", + line_character="-", + line_character2="=", + columns_equal_len=False, print_step_parameters=True, print_parameters_summary=True): """ @@ -2074,7 +2456,8 @@ def summary(self, def fill_message(msg, line_length=line_length, fill_character=fill_character): num_spaces = int((line_length - len(msg))/2) num_spaces = int(num_spaces / len(fill_character)) - msg = "{spaces}{msg}{spaces}".format(msg=msg, spaces=fill_character * num_spaces) + msg = "{spaces}{msg}{spaces}".format( + msg=msg, spaces=fill_character * num_spaces) return msg def line_separator(line_length=line_length, line_character=line_character): @@ -2085,10 +2468,12 @@ def create_row(columns, line_length=line_length, fill_character=fill_character, filled_columns = [] if split_percentages == None: split_percentages = [int(100/len(columns))] * 3 - columns_lengths = [int((split_percentages[idx] * line_length) / 100) for idx in range(len(split_percentages))] + columns_lengths = [int((split_percentages[idx] * line_length) / 100) + for idx in range(len(split_percentages))] for column_idx, column in enumerate(columns): current_column_length = len(column) - extra_characters = columns_lengths[column_idx] - current_column_length + extra_characters = columns_lengths[column_idx] - \ + current_column_length filled_column = column + fill_character * extra_characters filled_column = column + fill_character * extra_characters filled_columns.append(filled_column) @@ -2223,21 +2608,30 @@ def print_params_summary(): self.logger.info(m) summary_output = summary_output + m + "\n" - lifecycle_steps = ["on_start()", "Fitness Function", "On Fitness", "Parent Selection", "On Parents", "Crossover", "On Crossover", "Mutation", "On Mutation", "On Generation", "On Stop"] - lifecycle_functions = [self.on_start, self.fitness_func, self.on_fitness, self.select_parents, self.on_parents, self.crossover, self.on_crossover, self.mutation, self.on_mutation, self.on_generation, self.on_stop] - lifecycle_functions = [getattr(lifecycle_func, '__name__', "None") for lifecycle_func in lifecycle_functions] - lifecycle_functions = [lifecycle_func + "()" if lifecycle_func != "None" else "None" for lifecycle_func in lifecycle_functions] - lifecycle_output = ["None", "(1)", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", "None", "None"] - lifecycle_step_parameters = [None, print_fitness_params, None, print_parent_selection_params, None, print_crossover_params, None, print_mutation_params, None, print_on_generation_params, None] + lifecycle_steps = ["on_start()", "Fitness Function", "On Fitness", "Parent Selection", "On Parents", + "Crossover", "On Crossover", "Mutation", "On Mutation", "On Generation", "On Stop"] + lifecycle_functions = [self.on_start, self.fitness_func, self.on_fitness, self.select_parents, self.on_parents, + self.crossover, self.on_crossover, self.mutation, self.on_mutation, self.on_generation, self.on_stop] + lifecycle_functions = [getattr( + lifecycle_func, '__name__', "None") for lifecycle_func in lifecycle_functions] + lifecycle_functions = [lifecycle_func + "()" if lifecycle_func != + "None" else "None" for lifecycle_func in lifecycle_functions] + lifecycle_output = ["None", "(1)", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", + f"({self.num_parents_mating}, {self.num_genes})", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", "None", "None"] + lifecycle_step_parameters = [None, print_fitness_params, None, print_parent_selection_params, None, + print_crossover_params, None, print_mutation_params, None, print_on_generation_params, None] if not columns_equal_len: - max_lengthes = [max(list(map(len, lifecycle_steps))), max(list(map(len, lifecycle_functions))), max(list(map(len, lifecycle_output)))] - split_percentages = [int((column_len / sum(max_lengthes)) * 100) for column_len in max_lengthes] + max_lengthes = [max(list(map(len, lifecycle_steps))), max( + list(map(len, lifecycle_functions))), max(list(map(len, lifecycle_output)))] + split_percentages = [ + int((column_len / sum(max_lengthes)) * 100) for column_len in max_lengthes] else: split_percentages = None header_columns = ["Step", "Handler", "Output Shape"] - header_row = create_row(header_columns, split_percentages=split_percentages) + header_row = create_row( + header_columns, split_percentages=split_percentages) m = header_row self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2246,10 +2640,12 @@ def print_params_summary(): summary_output = summary_output + m + "\n" for lifecycle_idx in range(len(lifecycle_steps)): - lifecycle_column = [lifecycle_steps[lifecycle_idx], lifecycle_functions[lifecycle_idx], lifecycle_output[lifecycle_idx]] + lifecycle_column = [lifecycle_steps[lifecycle_idx], + lifecycle_functions[lifecycle_idx], lifecycle_output[lifecycle_idx]] if lifecycle_column[1] == "None": continue - lifecycle_row = create_row(lifecycle_column, split_percentages=split_percentages) + lifecycle_row = create_row( + lifecycle_column, split_percentages=split_percentages) m = lifecycle_row self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2270,8 +2666,8 @@ def print_params_summary(): summary_output = summary_output + m + "\n" return summary_output -def load(filename): +def load(filename): """ Reads a saved instance of the genetic algorithm: -filename: Name of the file to read the instance. No extension is needed. @@ -2282,7 +2678,8 @@ def load(filename): with open(filename + ".pkl", 'rb') as file: ga_in = cloudpickle.load(file) except FileNotFoundError: - raise FileNotFoundError(f"Error reading the file {filename}. Please check your inputs.") + raise FileNotFoundError( + f"Error reading the file {filename}. Please check your inputs.") except: # raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).") raise BaseException("Error loading the file.") From 6188034a3b638df926dd0a6c879b2104342d9d88 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 6 May 2023 13:57:20 -0400 Subject: [PATCH 046/220] Test using Python 3.5 --- .github/workflows/mainpython35.yml | 37 ++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 .github/workflows/mainpython35.yml diff --git a/.github/workflows/mainpython35.yml b/.github/workflows/mainpython35.yml new file mode 100644 index 0000000..a575787 --- /dev/null +++ b/.github/workflows/mainpython35.yml @@ -0,0 +1,37 @@ +name: Testing PyGAD using PyTest + +on: + push: + branches: + - github-actions + # - master + +jobs: + job_id_1: + runs-on: ubuntu-latest + name: PyTest Workflow Job + + steps: + - name: Checkout Pre-Built Action + uses: actions/checkout@v3 + + - name: Setup Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.5' + + - name: Build PyGAD from the Repository + run: | + python3 -m pip install --upgrade build + python3 -m build + + - name: Install PyGAD after Building the .whl File + run: | + find ./dist/*.whl | xargs pip install + + - name: Install PyTest + run: pip install pytest + + - name: Run the Tests by Calling PyTest + run: | + pytest From 3ef1a9d5aab420dc46508ae32a0ab62f97f26a44 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 6 May 2023 14:00:28 -0400 Subject: [PATCH 047/220] Test using Python 3.5 --- .github/workflows/mainpython35.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/mainpython35.yml b/.github/workflows/mainpython35.yml index a575787..8b0bed5 100644 --- a/.github/workflows/mainpython35.yml +++ b/.github/workflows/mainpython35.yml @@ -15,10 +15,10 @@ jobs: - name: Checkout Pre-Built Action uses: actions/checkout@v3 - - name: Setup Python 3.10 + - name: Setup Python 3.5.9 uses: actions/setup-python@v4 with: - python-version: '3.5' + python-version: '3.5.9' - name: Build PyGAD from the Repository run: | From 9f3665b826025d958348a4c934d31f44bba2e43d Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 6 May 2023 14:02:41 -0400 Subject: [PATCH 048/220] Test using Python 3.5 --- .github/workflows/mainpython35.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/mainpython35.yml b/.github/workflows/mainpython35.yml index 8b0bed5..3667030 100644 --- a/.github/workflows/mainpython35.yml +++ b/.github/workflows/mainpython35.yml @@ -8,17 +8,17 @@ on: jobs: job_id_1: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 name: PyTest Workflow Job steps: - name: Checkout Pre-Built Action uses: actions/checkout@v3 - - name: Setup Python 3.5.9 + - name: Setup Python 3.5 uses: actions/setup-python@v4 with: - python-version: '3.5.9' + python-version: '3.5' - name: Build PyGAD from the Repository run: | From d49758fa2d19d57c0845937f55a79995f9f29821 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 6 May 2023 14:04:00 -0400 Subject: [PATCH 049/220] Test using Python 3.6 --- .github/workflows/mainpython35.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/mainpython35.yml b/.github/workflows/mainpython35.yml index 3667030..235508d 100644 --- a/.github/workflows/mainpython35.yml +++ b/.github/workflows/mainpython35.yml @@ -15,10 +15,10 @@ jobs: - name: Checkout Pre-Built Action uses: actions/checkout@v3 - - name: Setup Python 3.5 + - name: Setup Python 3.6 uses: actions/setup-python@v4 with: - python-version: '3.5' + python-version: '3.6' - name: Build PyGAD from the Repository run: | From f1b697d113dea0585ac78cbd0a9de6892c5aabb9 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 6 May 2023 14:05:07 -0400 Subject: [PATCH 050/220] Test using Python 3.7 --- .github/workflows/mainpython35.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/mainpython35.yml b/.github/workflows/mainpython35.yml index 235508d..061ada0 100644 --- a/.github/workflows/mainpython35.yml +++ b/.github/workflows/mainpython35.yml @@ -15,10 +15,10 @@ jobs: - name: Checkout Pre-Built Action uses: actions/checkout@v3 - - name: Setup Python 3.6 + - name: Setup Python 3.7 uses: actions/setup-python@v4 with: - python-version: '3.6' + python-version: '3.7' - name: Build PyGAD from the Repository run: | From f54cc16dd472e74957bc36ea8ca5a37e9af93d53 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 6 May 2023 14:11:50 -0400 Subject: [PATCH 051/220] Test using different Python versions --- .../workflows/{main.yml => main_py310.yml} | 0 .github/workflows/main_py311.yml | 37 +++++++++++++++++++ .../{mainpython35.yml => main_py37.yml} | 0 .github/workflows/main_py38.yml | 37 +++++++++++++++++++ .github/workflows/main_py39.yml | 37 +++++++++++++++++++ 5 files changed, 111 insertions(+) rename .github/workflows/{main.yml => main_py310.yml} (100%) create mode 100644 .github/workflows/main_py311.yml rename .github/workflows/{mainpython35.yml => main_py37.yml} (100%) create mode 100644 .github/workflows/main_py38.yml create mode 100644 .github/workflows/main_py39.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main_py310.yml similarity index 100% rename from .github/workflows/main.yml rename to .github/workflows/main_py310.yml diff --git a/.github/workflows/main_py311.yml b/.github/workflows/main_py311.yml new file mode 100644 index 0000000..37b2085 --- /dev/null +++ b/.github/workflows/main_py311.yml @@ -0,0 +1,37 @@ +name: Testing PyGAD using PyTest + +on: + push: + branches: + - github-actions + # - master + +jobs: + job_id_1: + runs-on: ubuntu-latest + name: PyTest Workflow Job + + steps: + - name: Checkout Pre-Built Action + uses: actions/checkout@v3 + + - name: Setup Python 3.11 + uses: actions/setup-python@v4 + with: + python-version: '3.11' + + - name: Build PyGAD from the Repository + run: | + python3 -m pip install --upgrade build + python3 -m build + + - name: Install PyGAD after Building the .whl File + run: | + find ./dist/*.whl | xargs pip install + + - name: Install PyTest + run: pip install pytest + + - name: Run the Tests by Calling PyTest + run: | + pytest diff --git a/.github/workflows/mainpython35.yml b/.github/workflows/main_py37.yml similarity index 100% rename from .github/workflows/mainpython35.yml rename to .github/workflows/main_py37.yml diff --git a/.github/workflows/main_py38.yml b/.github/workflows/main_py38.yml new file mode 100644 index 0000000..908b08e --- /dev/null +++ b/.github/workflows/main_py38.yml @@ -0,0 +1,37 @@ +name: Testing PyGAD using PyTest + +on: + push: + branches: + - github-actions + # - master + +jobs: + job_id_1: + runs-on: ubuntu-latest + name: PyTest Workflow Job + + steps: + - name: Checkout Pre-Built Action + uses: actions/checkout@v3 + + - name: Setup Python 3.8 + uses: actions/setup-python@v4 + with: + python-version: '3.8' + + - name: Build PyGAD from the Repository + run: | + python3 -m pip install --upgrade build + python3 -m build + + - name: Install PyGAD after Building the .whl File + run: | + find ./dist/*.whl | xargs pip install + + - name: Install PyTest + run: pip install pytest + + - name: Run the Tests by Calling PyTest + run: | + pytest diff --git a/.github/workflows/main_py39.yml b/.github/workflows/main_py39.yml new file mode 100644 index 0000000..0ef4f1c --- /dev/null +++ b/.github/workflows/main_py39.yml @@ -0,0 +1,37 @@ +name: Testing PyGAD using PyTest + +on: + push: + branches: + - github-actions + # - master + +jobs: + job_id_1: + runs-on: ubuntu-latest + name: PyTest Workflow Job + + steps: + - name: Checkout Pre-Built Action + uses: actions/checkout@v3 + + - name: Setup Python 3.9 + uses: actions/setup-python@v4 + with: + python-version: '3.9' + + - name: Build PyGAD from the Repository + run: | + python3 -m pip install --upgrade build + python3 -m build + + - name: Install PyGAD after Building the .whl File + run: | + find ./dist/*.whl | xargs pip install + + - name: Install PyTest + run: pip install pytest + + - name: Run the Tests by Calling PyTest + run: | + pytest From 22e353df25c3176c86dd910eafc16efb56be1a1a Mon Sep 17 00:00:00 2001 From: King Lok Chung Date: Sun, 7 May 2023 00:15:00 +0100 Subject: [PATCH 052/220] logging all exception at the except point --- pygad/pygad.py | 3239 ++++++++++++++++++++++-------------------------- 1 file changed, 1497 insertions(+), 1742 deletions(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index 8d95941..d2b1b23 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -128,678 +128,552 @@ def __init__(self, logger: Added in PyGAD 2.20.0. It accepts a logger object of the 'logging.Logger' class to log the messages. If no logger is passed, then a default logger is created to log/print the messages to the console exactly like using the 'print()' function. """ + try: + # If no logger is passed, then create a logger that logs only the messages to the console. + if logger is None: + # Create a logger named with the module name. + logger = logging.getLogger(__name__) + # Set the logger log level to 'DEBUG' to log all kinds of messages. + logger.setLevel(logging.DEBUG) + + # Clear any attached handlers to the logger from the previous runs. + # If the handlers are not cleared, then the new handler will be appended to the list of handlers. + # This makes the single log message be repeated according to the length of the list of handlers. + logger.handlers.clear() + + # Create the handlers. + stream_handler = logging.StreamHandler() + # Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger. + stream_handler.setLevel(logging.DEBUG) + + # Create the formatter that just includes the log message. + formatter = logging.Formatter('%(message)s') + + # Add the formatter to the handler. + stream_handler.setFormatter(formatter) + + # Add the handler to the logger. + logger.addHandler(stream_handler) + else: + # Validate that the passed logger is of type 'logging.Logger'. + if isinstance(logger, logging.Logger): + pass + else: + raise TypeError( + f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") - # If no logger is passed, then create a logger that logs only the messages to the console. - if logger is None: - # Create a logger named with the module name. - logger = logging.getLogger(__name__) - # Set the logger log level to 'DEBUG' to log all kinds of messages. - logger.setLevel(logging.DEBUG) - - # Clear any attached handlers to the logger from the previous runs. - # If the handlers are not cleared, then the new handler will be appended to the list of handlers. - # This makes the single log message be repeated according to the length of the list of handlers. - logger.handlers.clear() - - # Create the handlers. - stream_handler = logging.StreamHandler() - # Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger. - stream_handler.setLevel(logging.DEBUG) - - # Create the formatter that just includes the log message. - formatter = logging.Formatter('%(message)s') - - # Add the formatter to the handler. - stream_handler.setFormatter(formatter) + # Create the 'self.logger' attribute to hold the logger. + # Instead of using 'print()', use 'self.logger.info()' + self.logger = logger - # Add the handler to the logger. - logger.addHandler(stream_handler) - else: - # Validate that the passed logger is of type 'logging.Logger'. - if isinstance(logger, logging.Logger): + self.random_seed = random_seed + if random_seed is None: pass else: + numpy.random.seed(self.random_seed) + random.seed(self.random_seed) + + # If suppress_warnings is bool and its valud is False, then print warning messages. + if type(suppress_warnings) is bool: + self.suppress_warnings = suppress_warnings + else: + self.valid_parameters = False + raise TypeError( - f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") + f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") - # Create the 'self.logger' attribute to hold the logger. - # Instead of using 'print()', use 'self.logger.info()' - self.logger = logger + # Validating mutation_by_replacement + if not (type(mutation_by_replacement) is bool): + self.valid_parameters = False + + raise TypeError( + f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") - self.random_seed = random_seed - if random_seed is None: - pass - else: - numpy.random.seed(self.random_seed) - random.seed(self.random_seed) + self.mutation_by_replacement = mutation_by_replacement - # If suppress_warnings is bool and its valud is False, then print warning messages. - if type(suppress_warnings) is bool: - self.suppress_warnings = suppress_warnings - else: - self.valid_parameters = False - self.logger.error( - f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") - raise TypeError( - f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") - - # Validating mutation_by_replacement - if not (type(mutation_by_replacement) is bool): - self.valid_parameters = False - self.logger.error( - f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") - raise TypeError( - f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") - - self.mutation_by_replacement = mutation_by_replacement - - # Validate allow_duplicate_genes - if not (type(allow_duplicate_genes) is bool): - self.valid_parameters = False - self.logger.error( - f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") - raise TypeError( - f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") - - self.allow_duplicate_genes = allow_duplicate_genes - - # Validate gene_space - self.gene_space_nested = False - if type(gene_space) is type(None): - pass - elif type(gene_space) is range: - if len(gene_space) == 0: + # Validate allow_duplicate_genes + if not (type(allow_duplicate_genes) is bool): self.valid_parameters = False - self.logger.error( - "'gene_space' cannot be empty (i.e. its length must be >= 0).") - raise ValueError( - "'gene_space' cannot be empty (i.e. its length must be >= 0).") - elif type(gene_space) in [list, numpy.ndarray]: - if len(gene_space) == 0: - self.valid_parameters = False - self.logger.error( - "'gene_space' cannot be empty (i.e. its length must be >= 0).") - raise ValueError( - "'gene_space' cannot be empty (i.e. its length must be >= 0).") - else: - for index, el in enumerate(gene_space): - if type(el) in [numpy.ndarray, list, tuple, range]: - if len(el) == 0: - self.valid_parameters = False - self.logger.error( - f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") - raise ValueError( - f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") - else: - for val in el: - if not (type(val) in [type(None)] + GA.supported_int_float_types): - self.logger.error( - f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") - raise TypeError( - f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") - self.gene_space_nested = True - elif type(el) == type(None): - pass - # self.gene_space_nested = True - elif type(el) is dict: - if len(el.items()) == 2: - if ('low' in el.keys()) and ('high' in el.keys()): - pass - else: + + raise TypeError( + f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") + + self.allow_duplicate_genes = allow_duplicate_genes + + # Validate gene_space + self.gene_space_nested = False + if type(gene_space) is type(None): + pass + elif type(gene_space) is range: + if len(gene_space) == 0: + self.valid_parameters = False + + raise ValueError( + "'gene_space' cannot be empty (i.e. its length must be >= 0).") + elif type(gene_space) in [list, numpy.ndarray]: + if len(gene_space) == 0: + self.valid_parameters = False + + raise ValueError( + "'gene_space' cannot be empty (i.e. its length must be >= 0).") + else: + for index, el in enumerate(gene_space): + if type(el) in [numpy.ndarray, list, tuple, range]: + if len(el) == 0: self.valid_parameters = False - self.logger.error( - f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") raise ValueError( - f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") - elif len(el.items()) == 3: - if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): - pass + f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") + else: + for val in el: + if not (type(val) in [type(None)] + GA.supported_int_float_types): + raise TypeError( + f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") + self.gene_space_nested = True + elif type(el) == type(None): + pass + # self.gene_space_nested = True + elif type(el) is dict: + if len(el.items()) == 2: + if ('low' in el.keys()) and ('high' in el.keys()): + pass + else: + self.valid_parameters = False + raise ValueError( + f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + elif len(el.items()) == 3: + if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): + pass + else: + self.valid_parameters = False + raise ValueError( + f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") else: self.valid_parameters = False - self.logger.error( - f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + raise ValueError( - f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") - else: + f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") + self.gene_space_nested = True + elif not (type(el) in GA.supported_int_float_types): self.valid_parameters = False - self.logger.error( - f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") - raise ValueError( - f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") - self.gene_space_nested = True - elif not (type(el) in GA.supported_int_float_types): - self.valid_parameters = False - self.logger.error( - f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") - raise TypeError( - f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") + raise TypeError( + f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") - elif type(gene_space) is dict: - if len(gene_space.items()) == 2: - if ('low' in gene_space.keys()) and ('high' in gene_space.keys()): - pass - else: - self.valid_parameters = False - self.logger.error( - f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") - raise ValueError( - f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") - elif len(gene_space.items()) == 3: - if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): - pass + elif type(gene_space) is dict: + if len(gene_space.items()) == 2: + if ('low' in gene_space.keys()) and ('high' in gene_space.keys()): + pass + else: + self.valid_parameters = False + raise ValueError( + f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + elif len(gene_space.items()) == 3: + if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): + pass + else: + self.valid_parameters = False + raise ValueError( + f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") else: self.valid_parameters = False - self.logger.error( - f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") raise ValueError( - f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") + else: self.valid_parameters = False - self.logger.error( - f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") - raise ValueError( - f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") + raise TypeError( + f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") - else: - self.valid_parameters = False - self.logger.error( - f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") - raise TypeError( - f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") - - self.gene_space = gene_space - - # Validate init_range_low and init_range_high - if type(init_range_low) in GA.supported_int_float_types: - if type(init_range_high) in GA.supported_int_float_types: - self.init_range_low = init_range_low - self.init_range_high = init_range_high + self.gene_space = gene_space + + # Validate init_range_low and init_range_high + if type(init_range_low) in GA.supported_int_float_types: + if type(init_range_high) in GA.supported_int_float_types: + self.init_range_low = init_range_low + self.init_range_high = init_range_high + else: + self.valid_parameters = False + raise ValueError( + f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") else: self.valid_parameters = False - self.logger.error( - f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") raise ValueError( - f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") - else: - self.valid_parameters = False - self.logger.error( - f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") - raise ValueError( - f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") - - # Validate random_mutation_min_val and random_mutation_max_val - if type(random_mutation_min_val) in GA.supported_int_float_types: - if type(random_mutation_max_val) in GA.supported_int_float_types: - if random_mutation_min_val == random_mutation_max_val: - if not self.suppress_warnings: - warnings.warn( - "The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") + f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") + + # Validate random_mutation_min_val and random_mutation_max_val + if type(random_mutation_min_val) in GA.supported_int_float_types: + if type(random_mutation_max_val) in GA.supported_int_float_types: + if random_mutation_min_val == random_mutation_max_val: + if not self.suppress_warnings: + warnings.warn( + "The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") + else: + self.valid_parameters = False + raise TypeError( + f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") else: self.valid_parameters = False - self.logger.error( - f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") raise TypeError( - f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") - else: - self.valid_parameters = False - self.logger.error( - f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") - raise TypeError( - f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") - self.random_mutation_min_val = random_mutation_min_val - self.random_mutation_max_val = random_mutation_max_val - - # Validate gene_type - if gene_type in GA.supported_int_float_types: - self.gene_type = [gene_type, None] - self.gene_type_single = True - # A single data type of float with precision. - elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): - self.gene_type = gene_type - self.gene_type_single = True - # A single data type of int with precision. - elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): - self.gene_type_single = False - self.logger.error( - f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") - raise ValueError( - f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") - elif type(gene_type) in [list, tuple, numpy.ndarray]: - if num_genes is None: - if initial_population is None: - self.valid_parameters = False - self.logger.error( - "When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - raise TypeError( - "When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - elif not len(gene_type) == len(initial_population[0]): + f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") + self.random_mutation_min_val = random_mutation_min_val + self.random_mutation_max_val = random_mutation_max_val + + # Validate gene_type + if gene_type in GA.supported_int_float_types: + self.gene_type = [gene_type, None] + self.gene_type_single = True + # A single data type of float with precision. + elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): + self.gene_type = gene_type + self.gene_type_single = True + # A single data type of int with precision. + elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): + self.gene_type_single = False + raise ValueError( + f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") + elif type(gene_type) in [list, tuple, numpy.ndarray]: + if num_genes is None: + if initial_population is None: + self.valid_parameters = False + raise TypeError( + "When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") + elif not len(gene_type) == len(initial_population[0]): + self.valid_parameters = False + raise ValueError( + f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") + elif not len(gene_type) == num_genes: self.valid_parameters = False - self.logger.error( - f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") raise ValueError( - f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") - elif not len(gene_type) == num_genes: - self.valid_parameters = False - self.logger.error( - f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") - raise ValueError( - f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") - for gene_type_idx, gene_type_val in enumerate(gene_type): - if gene_type_val in GA.supported_float_types: - # If the gene type is float and no precision is passed, set it to None. - gene_type[gene_type_idx] = [gene_type_val, None] - elif gene_type_val in GA.supported_int_types: - gene_type[gene_type_idx] = [gene_type_val, None] - elif type(gene_type_val) in [list, tuple, numpy.ndarray]: - # A float type is expected in a list/tuple/numpy.ndarray of length 2. - if len(gene_type_val) == 2: - if gene_type_val[0] in GA.supported_float_types: - if type(gene_type_val[1]) in GA.supported_int_types: - pass + f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") + for gene_type_idx, gene_type_val in enumerate(gene_type): + if gene_type_val in GA.supported_float_types: + # If the gene type is float and no precision is passed, set it to None. + gene_type[gene_type_idx] = [gene_type_val, None] + elif gene_type_val in GA.supported_int_types: + gene_type[gene_type_idx] = [gene_type_val, None] + elif type(gene_type_val) in [list, tuple, numpy.ndarray]: + # A float type is expected in a list/tuple/numpy.ndarray of length 2. + if len(gene_type_val) == 2: + if gene_type_val[0] in GA.supported_float_types: + if type(gene_type_val[1]) in GA.supported_int_types: + pass + else: + self.valid_parameters = False + raise TypeError( + f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") else: self.valid_parameters = False - self.logger.error( - f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") raise TypeError( - f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") + f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") else: self.valid_parameters = False - self.logger.error( - f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") - raise TypeError( - f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") + raise ValueError( + f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") else: self.valid_parameters = False - self.logger.error( - f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") raise ValueError( - f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") - else: - self.valid_parameters = False - self.logger.error( - f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") - raise ValueError( - f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") - self.gene_type = gene_type - self.gene_type_single = False - else: - self.valid_parameters = False - self.logger.error( - f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") - raise ValueError( - f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") - - # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. - self.gene_space_unpacked = self.unpack_gene_space() - - # Build the initial population - if initial_population is None: - if (sol_per_pop is None) or (num_genes is None): + f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") + self.gene_type = gene_type + self.gene_type_single = False + else: self.valid_parameters = False - self.logger.error("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") - raise TypeError("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") - elif (type(sol_per_pop) is int) and (type(num_genes) is int): - # Validating the number of solutions in the population (sol_per_pop) - if sol_per_pop <= 0: - self.valid_parameters = False - self.logger.error( - f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") - raise ValueError( - f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") - # Validating the number of gene. - if (num_genes <= 0): + raise ValueError( + f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") + + # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. + self.gene_space_unpacked = self.unpack_gene_space() + + # Build the initial population + if initial_population is None: + if (sol_per_pop is None) or (num_genes is None): self.valid_parameters = False - self.logger.error( - f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") - raise ValueError( - f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") - # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. - # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. - self.num_genes = num_genes # Number of genes in the solution. - - # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. - if self.gene_space_nested: - if len(gene_space) != self.num_genes: + raise TypeError("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") + elif (type(sol_per_pop) is int) and (type(num_genes) is int): + # Validating the number of solutions in the population (sol_per_pop) + if sol_per_pop <= 0: self.valid_parameters = False - self.logger.error( - f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") raise ValueError( - f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") - - # Number of solutions in the population. - self.sol_per_pop = sol_per_pop - self.initialize_population(self.init_range_low, - self.init_range_high, - allow_duplicate_genes, - True, - self.gene_type) - else: - self.valid_parameters = False - self.logger.error( - f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") - raise TypeError( - f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") - elif not type(initial_population) in [list, tuple, numpy.ndarray]: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") - raise TypeError( - f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") - elif numpy.array(initial_population).ndim != 2: - self.valid_parameters = False - self.logger.error( - f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") - raise ValueError( - f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") - else: - # Validate the type of each value in the 'initial_population' parameter. - for row_idx in range(len(initial_population)): - for col_idx in range(len(initial_population[0])): - if type(initial_population[row_idx][col_idx]) in GA.supported_int_float_types: - pass - else: + f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + # Validating the number of gene. + if (num_genes <= 0): self.valid_parameters = False - self.logger.error( - f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") - raise TypeError( - f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") - - # Forcing the initial_population array to have the data type assigned to the gene_type parameter. - if self.gene_type_single == True: - if self.gene_type[1] == None: - self.initial_population = numpy.array(initial_population, - dtype=self.gene_type[0]) + raise ValueError( + f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") + # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. + # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. + self.num_genes = num_genes # Number of genes in the solution. + + # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. + if self.gene_space_nested: + if len(gene_space) != self.num_genes: + self.valid_parameters = False + raise ValueError( + f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + + # Number of solutions in the population. + self.sol_per_pop = sol_per_pop + self.initialize_population(self.init_range_low, + self.init_range_high, + allow_duplicate_genes, + True, + self.gene_type) else: - # This block is reached only for non-integer data types (i.e. float). - self.initial_population = numpy.round(numpy.array(initial_population, - dtype=self.gene_type[0]), - self.gene_type[1]) - else: - initial_population = numpy.array(initial_population) - self.initial_population = numpy.zeros(shape=(initial_population.shape[0], - initial_population.shape[1]), - dtype=object) - for gene_idx in range(initial_population.shape[1]): - if self.gene_type[gene_idx][1] is None: - self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], - dtype=self.gene_type[gene_idx][0]) - else: - # This block is reached only for non-integer data types (i.e. float). - self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], - dtype=self.gene_type[gene_idx][0]), - self.gene_type[gene_idx][1]) - - # Check if duplicates are allowed. If not, then solve any exisiting duplicates in the passed initial population. - if self.allow_duplicate_genes == False: - for initial_solution_idx, initial_solution in enumerate(self.initial_population): - if self.gene_space is None: - self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=initial_solution, - min_val=self.init_range_low, - max_val=self.init_range_high, - mutation_by_replacement=self.mutation_by_replacement, - gene_type=self.gene_type, - num_trials=10) - else: - self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=initial_solution, - gene_type=self.gene_type, - num_trials=10) - - # A NumPy array holding the initial population. - self.population = self.initial_population.copy() - # Number of genes in the solution. - self.num_genes = self.initial_population.shape[1] - # Number of solutions in the population. - self.sol_per_pop = self.initial_population.shape[0] - # The population size. - self.pop_size = (self.sol_per_pop, self.num_genes) - - # Round initial_population and population - self.initial_population = self.round_genes(self.initial_population) - self.population = self.round_genes(self.population) - - # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. - if self.gene_space_nested: - if len(gene_space) != self.num_genes: + self.valid_parameters = False + raise TypeError( + f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") + elif not type(initial_population) in [list, tuple, numpy.ndarray]: + self.valid_parameters = False + raise TypeError( + f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") + elif numpy.array(initial_population).ndim != 2: self.valid_parameters = False - self.logger.error( - f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") raise ValueError( - f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") - - # Validating the number of parents to be selected for mating (num_parents_mating) - if num_parents_mating <= 0: - self.valid_parameters = False - self.logger.error( - f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") - raise ValueError( - f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") - - # Validating the number of parents to be selected for mating: num_parents_mating - if (num_parents_mating > self.sol_per_pop): - self.valid_parameters = False - self.logger.error( - f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") - raise ValueError( - f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") - - self.num_parents_mating = num_parents_mating - - # crossover: Refers to the method that applies the crossover operator based on the selected type of crossover in the crossover_type property. - # Validating the crossover type: crossover_type - if (crossover_type is None): - self.crossover = None - elif inspect.ismethod(crossover_type): - # Check if the crossover_type is a method that accepts 4 paramaters. - if (crossover_type.__code__.co_argcount == 4): - # The crossover method assigned to the crossover_type parameter is validated. - self.crossover = crossover_type + f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") else: + # Validate the type of each value in the 'initial_population' parameter. + for row_idx in range(len(initial_population)): + for col_idx in range(len(initial_population[0])): + if type(initial_population[row_idx][col_idx]) in GA.supported_int_float_types: + pass + else: + self.valid_parameters = False + raise TypeError( + f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") + + # Forcing the initial_population array to have the data type assigned to the gene_type parameter. + if self.gene_type_single == True: + if self.gene_type[1] == None: + self.initial_population = numpy.array(initial_population, + dtype=self.gene_type[0]) + else: + # This block is reached only for non-integer data types (i.e. float). + self.initial_population = numpy.round(numpy.array(initial_population, + dtype=self.gene_type[0]), + self.gene_type[1]) + else: + initial_population = numpy.array(initial_population) + self.initial_population = numpy.zeros(shape=(initial_population.shape[0], + initial_population.shape[1]), + dtype=object) + for gene_idx in range(initial_population.shape[1]): + if self.gene_type[gene_idx][1] is None: + self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], + dtype=self.gene_type[gene_idx][0]) + else: + # This block is reached only for non-integer data types (i.e. float). + self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], + dtype=self.gene_type[gene_idx][0]), + self.gene_type[gene_idx][1]) + + # Check if duplicates are allowed. If not, then solve any exisiting duplicates in the passed initial population. + if self.allow_duplicate_genes == False: + for initial_solution_idx, initial_solution in enumerate(self.initial_population): + if self.gene_space is None: + self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=initial_solution, + min_val=self.init_range_low, + max_val=self.init_range_high, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + else: + self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=initial_solution, + gene_type=self.gene_type, + num_trials=10) + + # A NumPy array holding the initial population. + self.population = self.initial_population.copy() + # Number of genes in the solution. + self.num_genes = self.initial_population.shape[1] + # Number of solutions in the population. + self.sol_per_pop = self.initial_population.shape[0] + # The population size. + self.pop_size = (self.sol_per_pop, self.num_genes) + + # Round initial_population and population + self.initial_population = self.round_genes(self.initial_population) + self.population = self.round_genes(self.population) + + # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. + if self.gene_space_nested: + if len(gene_space) != self.num_genes: + self.valid_parameters = False + raise ValueError( + f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + + # Validating the number of parents to be selected for mating (num_parents_mating) + if num_parents_mating <= 0: self.valid_parameters = False - self.logger.error( - f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") raise ValueError( - f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") - elif callable(crossover_type): - # Check if the crossover_type is a function that accepts 2 paramaters. - if (crossover_type.__code__.co_argcount == 3): - # The crossover function assigned to the crossover_type parameter is validated. - self.crossover = crossover_type - else: + f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + + # Validating the number of parents to be selected for mating: num_parents_mating + if (num_parents_mating > self.sol_per_pop): self.valid_parameters = False - self.logger.error( - f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") raise ValueError( - f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") - elif not (type(crossover_type) is str): - self.valid_parameters = False - self.logger.error( - f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") - raise TypeError( - f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") - else: # type crossover_type is str - crossover_type = crossover_type.lower() - if (crossover_type == "single_point"): - self.crossover = self.single_point_crossover - elif (crossover_type == "two_points"): - self.crossover = self.two_points_crossover - elif (crossover_type == "uniform"): - self.crossover = self.uniform_crossover - elif (crossover_type == "scattered"): - self.crossover = self.scattered_crossover - else: + f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") + + self.num_parents_mating = num_parents_mating + + # crossover: Refers to the method that applies the crossover operator based on the selected type of crossover in the crossover_type property. + # Validating the crossover type: crossover_type + if (crossover_type is None): + self.crossover = None + elif inspect.ismethod(crossover_type): + # Check if the crossover_type is a method that accepts 4 paramaters. + if (crossover_type.__code__.co_argcount == 4): + # The crossover method assigned to the crossover_type parameter is validated. + self.crossover = crossover_type + else: + self.valid_parameters = False + raise ValueError( + f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + elif callable(crossover_type): + # Check if the crossover_type is a function that accepts 2 paramaters. + if (crossover_type.__code__.co_argcount == 3): + # The crossover function assigned to the crossover_type parameter is validated. + self.crossover = crossover_type + else: + self.valid_parameters = False + raise ValueError( + f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + elif not (type(crossover_type) is str): self.valid_parameters = False - self.logger.error( - f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") raise TypeError( - f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") + f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") + else: # type crossover_type is str + crossover_type = crossover_type.lower() + if (crossover_type == "single_point"): + self.crossover = self.single_point_crossover + elif (crossover_type == "two_points"): + self.crossover = self.two_points_crossover + elif (crossover_type == "uniform"): + self.crossover = self.uniform_crossover + elif (crossover_type == "scattered"): + self.crossover = self.scattered_crossover + else: + self.valid_parameters = False + raise TypeError( + f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") - self.crossover_type = crossover_type + self.crossover_type = crossover_type - # Calculate the value of crossover_probability - if crossover_probability is None: - self.crossover_probability = None - elif type(crossover_probability) in GA.supported_int_float_types: - if crossover_probability >= 0 and crossover_probability <= 1: - self.crossover_probability = crossover_probability - else: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") - raise ValueError( - f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") - else: - self.valid_parameters = False - self.logger.error( - f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") - raise TypeError( - f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") - - # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. - # Validating the mutation type: mutation_type - # "adaptive" mutation is supported starting from PyGAD 2.10.0 - if mutation_type is None: - self.mutation = None - elif inspect.ismethod(mutation_type): - # Check if the mutation_type is a method that accepts 3 paramater. - if (mutation_type.__code__.co_argcount == 3): - # The mutation method assigned to the mutation_type parameter is validated. - self.mutation = mutation_type - else: - self.valid_parameters = False - self.logger.error( - f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") - raise ValueError( - f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") - elif callable(mutation_type): - # Check if the mutation_type is a function that accepts 2 paramater. - if (mutation_type.__code__.co_argcount == 2): - # The mutation function assigned to the mutation_type parameter is validated. - self.mutation = mutation_type + # Calculate the value of crossover_probability + if crossover_probability is None: + self.crossover_probability = None + elif type(crossover_probability) in GA.supported_int_float_types: + if crossover_probability >= 0 and crossover_probability <= 1: + self.crossover_probability = crossover_probability + else: + self.valid_parameters = False + raise ValueError( + f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") else: self.valid_parameters = False - self.logger.error( - f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") - raise ValueError( - f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") - elif not (type(mutation_type) is str): - self.valid_parameters = False - self.logger.error( - f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") - raise TypeError( - f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") - else: # type mutation_type is str - mutation_type = mutation_type.lower() - if (mutation_type == "random"): - self.mutation = self.random_mutation - elif (mutation_type == "swap"): - self.mutation = self.swap_mutation - elif (mutation_type == "scramble"): - self.mutation = self.scramble_mutation - elif (mutation_type == "inversion"): - self.mutation = self.inversion_mutation - elif (mutation_type == "adaptive"): - self.mutation = self.adaptive_mutation - else: + raise TypeError( + f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") + + # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. + # Validating the mutation type: mutation_type + # "adaptive" mutation is supported starting from PyGAD 2.10.0 + if mutation_type is None: + self.mutation = None + elif inspect.ismethod(mutation_type): + # Check if the mutation_type is a method that accepts 3 paramater. + if (mutation_type.__code__.co_argcount == 3): + # The mutation method assigned to the mutation_type parameter is validated. + self.mutation = mutation_type + else: + self.valid_parameters = False + raise ValueError( + f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + elif callable(mutation_type): + # Check if the mutation_type is a function that accepts 2 paramater. + if (mutation_type.__code__.co_argcount == 2): + # The mutation function assigned to the mutation_type parameter is validated. + self.mutation = mutation_type + else: + self.valid_parameters = False + raise ValueError( + f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + elif not (type(mutation_type) is str): self.valid_parameters = False - self.logger.error( - f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") raise TypeError( - f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") - - self.mutation_type = mutation_type - - # Calculate the value of mutation_probability - if not (self.mutation_type is None): - if mutation_probability is None: - self.mutation_probability = None - elif (mutation_type != "adaptive"): - # Mutation probability is fixed not adaptive. - if type(mutation_probability) in GA.supported_int_float_types: - if mutation_probability >= 0 and mutation_probability <= 1: - self.mutation_probability = mutation_probability - else: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") - raise ValueError( - f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") + f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") + else: # type mutation_type is str + mutation_type = mutation_type.lower() + if (mutation_type == "random"): + self.mutation = self.random_mutation + elif (mutation_type == "swap"): + self.mutation = self.swap_mutation + elif (mutation_type == "scramble"): + self.mutation = self.scramble_mutation + elif (mutation_type == "inversion"): + self.mutation = self.inversion_mutation + elif (mutation_type == "adaptive"): + self.mutation = self.adaptive_mutation else: self.valid_parameters = False - self.logger.error( - f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") raise TypeError( - f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") - else: - # Mutation probability is adaptive not fixed. - if type(mutation_probability) in [list, tuple, numpy.ndarray]: - if len(mutation_probability) == 2: - for el in mutation_probability: - if type(el) in GA.supported_int_float_types: - if el >= 0 and el <= 1: - pass - else: - self.valid_parameters = False - self.logger.error( - f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") - raise ValueError( - f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") - else: - self.valid_parameters = False - self.logger.error( - f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") - raise TypeError( - f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") - if mutation_probability[0] < mutation_probability[1]: - if not self.suppress_warnings: - warnings.warn( - f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") - self.mutation_probability = mutation_probability + f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") + + self.mutation_type = mutation_type + + # Calculate the value of mutation_probability + if not (self.mutation_type is None): + if mutation_probability is None: + self.mutation_probability = None + elif (mutation_type != "adaptive"): + # Mutation probability is fixed not adaptive. + if type(mutation_probability) in GA.supported_int_float_types: + if mutation_probability >= 0 and mutation_probability <= 1: + self.mutation_probability = mutation_probability + else: + self.valid_parameters = False + raise ValueError( + f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") else: self.valid_parameters = False - self.logger.error( - f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") - raise ValueError( - f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") + raise TypeError( + f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") else: - self.valid_parameters = False - self.logger.error( - f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") - raise TypeError( - f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") - else: - pass - - # Calculate the value of mutation_num_genes - if not (self.mutation_type is None): - if mutation_num_genes is None: - # The mutation_num_genes parameter does not exist. Checking whether adaptive mutation is used. - if (mutation_type != "adaptive"): - # The percent of genes to mutate is fixed not adaptive. - if mutation_percent_genes == 'default'.lower(): - mutation_percent_genes = 10 - # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes = numpy.uint32( - (mutation_percent_genes*self.num_genes)/100) - # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. - if mutation_num_genes == 0: - if self.mutation_probability is None: + # Mutation probability is adaptive not fixed. + if type(mutation_probability) in [list, tuple, numpy.ndarray]: + if len(mutation_probability) == 2: + for el in mutation_probability: + if type(el) in GA.supported_int_float_types: + if el >= 0 and el <= 1: + pass + else: + self.valid_parameters = False + raise ValueError( + f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") + else: + self.valid_parameters = False + raise TypeError( + f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") + if mutation_probability[0] < mutation_probability[1]: if not self.suppress_warnings: warnings.warn( - f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") - mutation_num_genes = 1 - - elif type(mutation_percent_genes) in GA.supported_int_float_types: - if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): + f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") + self.mutation_probability = mutation_probability + else: self.valid_parameters = False - self.logger.error( - f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") raise ValueError( - f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") - else: - # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. - if mutation_percent_genes == 'default'.lower(): - mutation_percent_genes = 10 + f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") + else: + self.valid_parameters = False + raise TypeError( + f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + else: + pass + # Calculate the value of mutation_num_genes + if not (self.mutation_type is None): + if mutation_num_genes is None: + # The mutation_num_genes parameter does not exist. Checking whether adaptive mutation is used. + if (mutation_type != "adaptive"): + # The percent of genes to mutate is fixed not adaptive. + if mutation_percent_genes == 'default'.lower(): + mutation_percent_genes = 10 # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. mutation_num_genes = numpy.uint32( (mutation_percent_genes*self.num_genes)/100) @@ -810,763 +684,661 @@ def __init__(self, warnings.warn( f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes = 1 + + elif type(mutation_percent_genes) in GA.supported_int_float_types: + if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): + self.valid_parameters = False + raise ValueError( + f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + else: + # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. + if mutation_percent_genes == 'default'.lower(): + mutation_percent_genes = 10 + + # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. + mutation_num_genes = numpy.uint32( + (mutation_percent_genes*self.num_genes)/100) + # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. + if mutation_num_genes == 0: + if self.mutation_probability is None: + if not self.suppress_warnings: + warnings.warn( + f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") + mutation_num_genes = 1 + else: + self.valid_parameters = False + raise TypeError( + f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") + else: + # The percent of genes to mutate is adaptive not fixed. + if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: + if len(mutation_percent_genes) == 2: + mutation_num_genes = numpy.zeros_like( + mutation_percent_genes, dtype=numpy.uint32) + for idx, el in enumerate(mutation_percent_genes): + if type(el) in GA.supported_int_float_types: + if (el <= 0 or el > 100): + self.valid_parameters = False + raise ValueError( + f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + else: + self.valid_parameters = False + raise TypeError( + f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. + # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. + mutation_num_genes[idx] = numpy.uint32( + (mutation_percent_genes[idx]*self.num_genes)/100) + # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. + if mutation_num_genes[idx] == 0: + if not self.suppress_warnings: + warnings.warn( + f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") + mutation_num_genes[idx] = 1 + if mutation_percent_genes[0] < mutation_percent_genes[1]: + if not self.suppress_warnings: + warnings.warn( + f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.") + # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. + else: + self.valid_parameters = False + raise ValueError( + f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") + else: + if self.mutation_probability is None: + self.valid_parameters = False + raise TypeError( + f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") + # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. + elif (mutation_type != "adaptive"): + # Number of genes to mutate is fixed not adaptive. + if type(mutation_num_genes) in GA.supported_int_types: + if (mutation_num_genes <= 0): + self.valid_parameters = False + raise ValueError( + f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") + elif (mutation_num_genes > self.num_genes): + self.valid_parameters = False + raise ValueError( + f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") else: self.valid_parameters = False - self.logger.error( - f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") raise TypeError( - f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") + f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") else: - # The percent of genes to mutate is adaptive not fixed. - if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: - if len(mutation_percent_genes) == 2: - mutation_num_genes = numpy.zeros_like( - mutation_percent_genes, dtype=numpy.uint32) - for idx, el in enumerate(mutation_percent_genes): - if type(el) in GA.supported_int_float_types: - if (el <= 0 or el > 100): + # Number of genes to mutate is adaptive not fixed. + if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: + if len(mutation_num_genes) == 2: + for el in mutation_num_genes: + if type(el) in GA.supported_int_types: + if (el <= 0): self.valid_parameters = False - self.logger.error( - f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") raise ValueError( - f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") + elif (el > self.num_genes): + self.valid_parameters = False + raise ValueError( + f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") else: self.valid_parameters = False - self.logger.error( - f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") raise TypeError( - f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") - # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. - # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes[idx] = numpy.uint32( - (mutation_percent_genes[idx]*self.num_genes)/100) - # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. - if mutation_num_genes[idx] == 0: - if not self.suppress_warnings: - warnings.warn( - f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") - mutation_num_genes[idx] = 1 - if mutation_percent_genes[0] < mutation_percent_genes[1]: + f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. + if mutation_num_genes[0] < mutation_num_genes[1]: if not self.suppress_warnings: warnings.warn( - f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.") - # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. + f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") + # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. else: self.valid_parameters = False - self.logger.error( - f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") raise ValueError( - f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") + f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") else: - if self.mutation_probability is None: - self.valid_parameters = False - self.logger.error( - f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") - raise TypeError( - f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") - # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. - elif (mutation_type != "adaptive"): - # Number of genes to mutate is fixed not adaptive. - if type(mutation_num_genes) in GA.supported_int_types: - if (mutation_num_genes <= 0): - self.valid_parameters = False - self.logger.error( - f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") - raise ValueError( - f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") - elif (mutation_num_genes > self.num_genes): self.valid_parameters = False - self.logger.error( - f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") - raise ValueError( - f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") + raise TypeError( + f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") + else: + pass + + # Validating mutation_by_replacement and mutation_type + if self.mutation_type != "random" and self.mutation_by_replacement: + if not self.suppress_warnings: + warnings.warn( + f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") + + # Check if crossover and mutation are both disabled. + if (self.mutation_type is None) and (self.crossover_type is None): + if not self.suppress_warnings: + warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") + + # select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute. + # Validating the selected type of parent selection: parent_selection_type + if inspect.ismethod(parent_selection_type): + # Check if the parent_selection_type is a method that accepts 4 paramaters. + if (parent_selection_type.__code__.co_argcount == 4): + # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. + # The parent selection method assigned to the parent_selection_type parameter is validated. + self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error( - f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") - raise TypeError( - f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") - else: - # Number of genes to mutate is adaptive not fixed. - if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: - if len(mutation_num_genes) == 2: - for el in mutation_num_genes: - if type(el) in GA.supported_int_types: - if (el <= 0): - self.valid_parameters = False - self.logger.error( - f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") - raise ValueError( - f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") - elif (el > self.num_genes): - self.valid_parameters = False - self.logger.error( - f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") - raise ValueError( - f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") - else: - self.valid_parameters = False - self.logger.error( - f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") - raise TypeError( - f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") - # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. - if mutation_num_genes[0] < mutation_num_genes[1]: - if not self.suppress_warnings: - warnings.warn( - f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") - # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. - else: - self.valid_parameters = False - self.logger.error( - f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") - raise ValueError( - f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") + raise ValueError( + f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + elif callable(parent_selection_type): + # Check if the parent_selection_type is a function that accepts 3 paramaters. + if (parent_selection_type.__code__.co_argcount == 3): + # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. + # The parent selection function assigned to the parent_selection_type parameter is validated. + self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error( - f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") - raise TypeError( - f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") - else: - pass - - # Validating mutation_by_replacement and mutation_type - if self.mutation_type != "random" and self.mutation_by_replacement: - if not self.suppress_warnings: - warnings.warn( - f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") - - # Check if crossover and mutation are both disabled. - if (self.mutation_type is None) and (self.crossover_type is None): - if not self.suppress_warnings: - warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") - - # select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute. - # Validating the selected type of parent selection: parent_selection_type - if inspect.ismethod(parent_selection_type): - # Check if the parent_selection_type is a method that accepts 4 paramaters. - if (parent_selection_type.__code__.co_argcount == 4): - # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. - # The parent selection method assigned to the parent_selection_type parameter is validated. - self.select_parents = parent_selection_type - else: + raise ValueError( + f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + elif not (type(parent_selection_type) is str): self.valid_parameters = False - self.logger.error( - f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") - raise ValueError( - f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") - elif callable(parent_selection_type): - # Check if the parent_selection_type is a function that accepts 3 paramaters. - if (parent_selection_type.__code__.co_argcount == 3): - # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. - # The parent selection function assigned to the parent_selection_type parameter is validated. - self.select_parents = parent_selection_type + + raise TypeError( + f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") else: + parent_selection_type = parent_selection_type.lower() + if (parent_selection_type == "sss"): + self.select_parents = self.steady_state_selection + elif (parent_selection_type == "rws"): + self.select_parents = self.roulette_wheel_selection + elif (parent_selection_type == "sus"): + self.select_parents = self.stochastic_universal_selection + elif (parent_selection_type == "random"): + self.select_parents = self.random_selection + elif (parent_selection_type == "tournament"): + self.select_parents = self.tournament_selection + elif (parent_selection_type == "rank"): + self.select_parents = self.rank_selection + else: + self.valid_parameters = False + raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n") + + # For tournament selection, validate the K value. + if (parent_selection_type == "tournament"): + if (K_tournament > self.sol_per_pop): + K_tournament = self.sol_per_pop + if not self.suppress_warnings: + warnings.warn( + f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") + elif (K_tournament <= 0): + self.valid_parameters = False + raise ValueError( + f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") + + self.K_tournament = K_tournament + + # Validating the number of parents to keep in the next population: keep_parents + if not (type(keep_parents) in GA.supported_int_types): self.valid_parameters = False - self.logger.error( - f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") - raise ValueError( - f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") - elif not (type(parent_selection_type) is str): - self.valid_parameters = False - self.logger.error( - f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") - raise TypeError( - f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") - else: - parent_selection_type = parent_selection_type.lower() - if (parent_selection_type == "sss"): - self.select_parents = self.steady_state_selection - elif (parent_selection_type == "rws"): - self.select_parents = self.roulette_wheel_selection - elif (parent_selection_type == "sus"): - self.select_parents = self.stochastic_universal_selection - elif (parent_selection_type == "random"): - self.select_parents = self.random_selection - elif (parent_selection_type == "tournament"): - self.select_parents = self.tournament_selection - elif (parent_selection_type == "rank"): - self.select_parents = self.rank_selection - else: + raise TypeError( + f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") + elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): self.valid_parameters = False - self.logger.error(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n") - raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n") + raise ValueError( + f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") + + self.keep_parents = keep_parents - # For tournament selection, validate the K value. - if (parent_selection_type == "tournament"): - if (K_tournament > self.sol_per_pop): - K_tournament = self.sol_per_pop + if parent_selection_type == "sss" and self.keep_parents == 0: if not self.suppress_warnings: warnings.warn( - f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") - elif (K_tournament <= 0): + "The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") + + # Validating the number of elitism to keep in the next population: keep_elitism + if not (type(keep_elitism) in GA.supported_int_types): self.valid_parameters = False - self.logger.error( - f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") - raise ValueError( - f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") - - self.K_tournament = K_tournament - - # Validating the number of parents to keep in the next population: keep_parents - if not (type(keep_parents) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error( - f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") - raise TypeError( - f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") - elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): - self.valid_parameters = False - self.logger.error( - f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") - raise ValueError( - f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") - - self.keep_parents = keep_parents - - if parent_selection_type == "sss" and self.keep_parents == 0: - if not self.suppress_warnings: - warnings.warn( - "The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") - - # Validating the number of elitism to keep in the next population: keep_elitism - if not (type(keep_elitism) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error( - f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") - raise TypeError( - f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") - elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): - self.valid_parameters = False - self.logger.error( - f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") - raise ValueError( - f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") - - self.keep_elitism = keep_elitism - - # Validate keep_parents. - if self.keep_elitism == 0: - # Keep all parents in the next population. - if (self.keep_parents == -1): - self.num_offspring = self.sol_per_pop - self.num_parents_mating - # Keep no parents in the next population. - elif (self.keep_parents == 0): - self.num_offspring = self.sol_per_pop - # Keep the specified number of parents in the next population. - elif (self.keep_parents > 0): - self.num_offspring = self.sol_per_pop - self.keep_parents - else: - self.num_offspring = self.sol_per_pop - self.keep_elitism - - # Check if the fitness_func is a method. - # In PyGAD 2.19.0, a method can be passed to the fitness function. If function is passed, then it accepts 2 parameters. If method, then it accepts 3 parameters. - # In PyGAD 2.20.0, a new parameter is passed referring to the instance of the `pygad.GA` class. So, the function accepts 3 parameters and the method accepts 4 parameters. - if inspect.ismethod(fitness_func): - # If the fitness is calculated through a method, not a function, then there is a fourth 'self` paramaters. - if (fitness_func.__code__.co_argcount == 4): - self.fitness_func = fitness_func - else: + raise TypeError( + f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") + elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): self.valid_parameters = False - self.logger.error( - f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") raise ValueError( - f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") - elif callable(fitness_func): - # Check if the fitness function accepts 2 paramaters. - if (fitness_func.__code__.co_argcount == 3): - self.fitness_func = fitness_func + f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") + + self.keep_elitism = keep_elitism + + # Validate keep_parents. + if self.keep_elitism == 0: + # Keep all parents in the next population. + if (self.keep_parents == -1): + self.num_offspring = self.sol_per_pop - self.num_parents_mating + # Keep no parents in the next population. + elif (self.keep_parents == 0): + self.num_offspring = self.sol_per_pop + # Keep the specified number of parents in the next population. + elif (self.keep_parents > 0): + self.num_offspring = self.sol_per_pop - self.keep_parents else: - self.valid_parameters = False - self.logger.error( - f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") - raise ValueError( - f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") - raise TypeError( - f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") - - if fitness_batch_size is None: - pass - elif not (type(fitness_batch_size) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error( - f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") - raise TypeError( - f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") - elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") - raise ValueError( - f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") - - self.fitness_batch_size = fitness_batch_size - - # Check if the on_start exists. - if not (on_start is None): - if inspect.ismethod(on_start): - # Check if the on_start method accepts 2 paramaters. - if (on_start.__code__.co_argcount == 2): - self.on_start = on_start + self.num_offspring = self.sol_per_pop - self.keep_elitism + + # Check if the fitness_func is a method. + # In PyGAD 2.19.0, a method can be passed to the fitness function. If function is passed, then it accepts 2 parameters. If method, then it accepts 3 parameters. + # In PyGAD 2.20.0, a new parameter is passed referring to the instance of the `pygad.GA` class. So, the function accepts 3 parameters and the method accepts 4 parameters. + if inspect.ismethod(fitness_func): + # If the fitness is calculated through a method, not a function, then there is a fourth 'self` paramaters. + if (fitness_func.__code__.co_argcount == 4): + self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error( - f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") raise ValueError( - f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") - # Check if the on_start is a function. - elif callable(on_start): - # Check if the on_start function accepts only a single paramater. - if (on_start.__code__.co_argcount == 1): - self.on_start = on_start + f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + elif callable(fitness_func): + # Check if the fitness function accepts 2 paramaters. + if (fitness_func.__code__.co_argcount == 3): + self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error( - f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") raise ValueError( - f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") + raise TypeError( - f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") - else: - self.on_start = None - - # Check if the on_fitness exists. - if not (on_fitness is None): - # Check if the on_fitness is a method. - if inspect.ismethod(on_fitness): - # Check if the on_fitness method accepts 3 paramaters. - if (on_fitness.__code__.co_argcount == 3): - self.on_fitness = on_fitness - else: - self.valid_parameters = False - self.logger.error( - f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") - # Check if the on_fitness is a function. - elif callable(on_fitness): - # Check if the on_fitness function accepts 2 paramaters. - if (on_fitness.__code__.co_argcount == 2): - self.on_fitness = on_fitness - else: - self.valid_parameters = False - self.logger.error( - f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") - else: + f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") + + if fitness_batch_size is None: + pass + elif not (type(fitness_batch_size) in GA.supported_int_types): self.valid_parameters = False - self.logger.error( - f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") raise TypeError( - f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") - else: - self.on_fitness = None - - # Check if the on_parents exists. - if not (on_parents is None): - # Check if the on_parents is a method. - if inspect.ismethod(on_parents): - # Check if the on_parents method accepts 3 paramaters. - if (on_parents.__code__.co_argcount == 3): - self.on_parents = on_parents + f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") + elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: + self.valid_parameters = False + raise ValueError( + f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") + + self.fitness_batch_size = fitness_batch_size + + # Check if the on_start exists. + if not (on_start is None): + if inspect.ismethod(on_start): + # Check if the on_start method accepts 2 paramaters. + if (on_start.__code__.co_argcount == 2): + self.on_start = on_start + else: + self.valid_parameters = False + raise ValueError( + f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + # Check if the on_start is a function. + elif callable(on_start): + # Check if the on_start function accepts only a single paramater. + if (on_start.__code__.co_argcount == 1): + self.on_start = on_start + else: + self.valid_parameters = False + raise ValueError( + f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") - # Check if the on_parents is a function. - elif callable(on_parents): - # Check if the on_parents function accepts 2 paramaters. - if (on_parents.__code__.co_argcount == 2): - self.on_parents = on_parents + + raise TypeError( + f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") + else: + self.on_start = None + + # Check if the on_fitness exists. + if not (on_fitness is None): + # Check if the on_fitness is a method. + if inspect.ismethod(on_fitness): + # Check if the on_fitness method accepts 3 paramaters. + if (on_fitness.__code__.co_argcount == 3): + self.on_fitness = on_fitness + else: + self.valid_parameters = False + raise ValueError( + f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + # Check if the on_fitness is a function. + elif callable(on_fitness): + # Check if the on_fitness function accepts 2 paramaters. + if (on_fitness.__code__.co_argcount == 2): + self.on_fitness = on_fitness + else: + self.valid_parameters = False + raise ValueError( + f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + raise TypeError( + f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") else: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") - raise TypeError( - f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") - else: - self.on_parents = None - - # Check if the on_crossover exists. - if not (on_crossover is None): - # Check if the on_crossover is a method. - if inspect.ismethod(on_crossover): - # Check if the on_crossover method accepts 3 paramaters. - if (on_crossover.__code__.co_argcount == 3): - self.on_crossover = on_crossover + self.on_fitness = None + + # Check if the on_parents exists. + if not (on_parents is None): + # Check if the on_parents is a method. + if inspect.ismethod(on_parents): + # Check if the on_parents method accepts 3 paramaters. + if (on_parents.__code__.co_argcount == 3): + self.on_parents = on_parents + else: + self.valid_parameters = False + raise ValueError( + f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + # Check if the on_parents is a function. + elif callable(on_parents): + # Check if the on_parents function accepts 2 paramaters. + if (on_parents.__code__.co_argcount == 2): + self.on_parents = on_parents + else: + self.valid_parameters = False + raise ValueError( + f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") - # Check if the on_crossover is a function. - elif callable(on_crossover): - # Check if the on_crossover function accepts 2 paramaters. - if (on_crossover.__code__.co_argcount == 2): - self.on_crossover = on_crossover + raise TypeError( + f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") + else: + self.on_parents = None + + # Check if the on_crossover exists. + if not (on_crossover is None): + # Check if the on_crossover is a method. + if inspect.ismethod(on_crossover): + # Check if the on_crossover method accepts 3 paramaters. + if (on_crossover.__code__.co_argcount == 3): + self.on_crossover = on_crossover + else: + self.valid_parameters = False + raise ValueError( + f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + # Check if the on_crossover is a function. + elif callable(on_crossover): + # Check if the on_crossover function accepts 2 paramaters. + if (on_crossover.__code__.co_argcount == 2): + self.on_crossover = on_crossover + else: + self.valid_parameters = False + raise ValueError( + f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + + raise TypeError( + f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") else: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") - raise TypeError( - f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") - else: - self.on_crossover = None - - # Check if the on_mutation exists. - if not (on_mutation is None): - # Check if the on_mutation is a method. - if inspect.ismethod(on_mutation): - # Check if the on_mutation method accepts 3 paramaters. - if (on_mutation.__code__.co_argcount == 3): - self.on_mutation = on_mutation + self.on_crossover = None + + # Check if the on_mutation exists. + if not (on_mutation is None): + # Check if the on_mutation is a method. + if inspect.ismethod(on_mutation): + # Check if the on_mutation method accepts 3 paramaters. + if (on_mutation.__code__.co_argcount == 3): + self.on_mutation = on_mutation + else: + self.valid_parameters = False + raise ValueError( + f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + # Check if the on_mutation is a function. + elif callable(on_mutation): + # Check if the on_mutation function accepts 2 paramaters. + if (on_mutation.__code__.co_argcount == 2): + self.on_mutation = on_mutation + else: + self.valid_parameters = False + raise ValueError( + f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") - # Check if the on_mutation is a function. - elif callable(on_mutation): - # Check if the on_mutation function accepts 2 paramaters. - if (on_mutation.__code__.co_argcount == 2): - self.on_mutation = on_mutation + raise TypeError( + f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") + else: + self.on_mutation = None + + # Check if the on_generation exists. + if not (on_generation is None): + # Check if the on_generation is a method. + if inspect.ismethod(on_generation): + # Check if the on_generation method accepts 2 paramaters. + if (on_generation.__code__.co_argcount == 2): + self.on_generation = on_generation + else: + self.valid_parameters = False + raise ValueError( + f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + # Check if the on_generation is a function. + elif callable(on_generation): + # Check if the on_generation function accepts only a single paramater. + if (on_generation.__code__.co_argcount == 1): + self.on_generation = on_generation + else: + self.valid_parameters = False + raise ValueError( + f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + raise TypeError( + f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") else: - self.valid_parameters = False - self.logger.error( - f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") - raise TypeError( - f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") - else: - self.on_mutation = None - - # Check if the on_generation exists. - if not (on_generation is None): - # Check if the on_generation is a method. - if inspect.ismethod(on_generation): - # Check if the on_generation method accepts 2 paramaters. - if (on_generation.__code__.co_argcount == 2): - self.on_generation = on_generation + self.on_generation = None + + # Check if the on_stop exists. + if not (on_stop is None): + # Check if the on_stop is a method. + if inspect.ismethod(on_stop): + # Check if the on_stop method accepts 3 paramaters. + if (on_stop.__code__.co_argcount == 3): + self.on_stop = on_stop + else: + self.valid_parameters = False + raise ValueError( + f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + # Check if the on_stop is a function. + elif callable(on_stop): + # Check if the on_stop function accepts 2 paramaters. + if (on_stop.__code__.co_argcount == 2): + self.on_stop = on_stop + else: + self.valid_parameters = False + raise ValueError( + f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error( - f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") - # Check if the on_generation is a function. - elif callable(on_generation): - # Check if the on_generation function accepts only a single paramater. - if (on_generation.__code__.co_argcount == 1): - self.on_generation = on_generation + raise TypeError( + f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") + else: + self.on_stop = None + + # Validate delay_after_gen + if type(delay_after_gen) in GA.supported_int_float_types: + if delay_after_gen >= 0.0: + self.delay_after_gen = delay_after_gen else: self.valid_parameters = False - self.logger.error( - f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") raise ValueError( - f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") else: self.valid_parameters = False - self.logger.error( - f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") raise TypeError( - f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") - else: - self.on_generation = None - - # Check if the on_stop exists. - if not (on_stop is None): - # Check if the on_stop is a method. - if inspect.ismethod(on_stop): - # Check if the on_stop method accepts 3 paramaters. - if (on_stop.__code__.co_argcount == 3): - self.on_stop = on_stop - else: - self.valid_parameters = False - self.logger.error( - f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") - # Check if the on_stop is a function. - elif callable(on_stop): - # Check if the on_stop function accepts 2 paramaters. - if (on_stop.__code__.co_argcount == 2): - self.on_stop = on_stop - else: - self.valid_parameters = False - self.logger.error( - f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") - raise ValueError( - f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") + + # Validate save_best_solutions + if type(save_best_solutions) is bool: + if save_best_solutions == True: + if not self.suppress_warnings: + warnings.warn( + "Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") else: self.valid_parameters = False - self.logger.error( - f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") raise TypeError( - f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") - else: - self.on_stop = None + f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") - # Validate delay_after_gen - if type(delay_after_gen) in GA.supported_int_float_types: - if delay_after_gen >= 0.0: - self.delay_after_gen = delay_after_gen + # Validate save_solutions + if type(save_solutions) is bool: + if save_solutions == True: + if not self.suppress_warnings: + warnings.warn( + "Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") else: self.valid_parameters = False - self.logger.error( - f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") - raise ValueError( - f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") - else: - self.valid_parameters = False - self.logger.error( - f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") - raise TypeError( - f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") - - # Validate save_best_solutions - if type(save_best_solutions) is bool: - if save_best_solutions == True: - if not self.suppress_warnings: - warnings.warn( - "Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") - else: - self.valid_parameters = False - self.logger.error( - f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") - raise TypeError( - f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") - - # Validate save_solutions - if type(save_solutions) is bool: - if save_solutions == True: - if not self.suppress_warnings: - warnings.warn( - "Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") - else: - self.valid_parameters = False - self.logger.error( - f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") - raise TypeError( - f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") - - self.stop_criteria = [] - self.supported_stop_words = ["reach", "saturate"] - if stop_criteria is None: - # None: Stop after passing through all generations. - self.stop_criteria = None - elif type(stop_criteria) is str: - # reach_{target_fitness}: Stop if the target fitness value is reached. - # saturate_{num_generations}: Stop if the fitness value does not change (saturates) for the given number of generations. - criterion = stop_criteria.split("_") - if len(criterion) == 2: - stop_word = criterion[0] - number = criterion[1] - - if stop_word in self.supported_stop_words: - pass - else: - self.valid_parameters = False - self.logger.error( - f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") - raise ValueError( - f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") + raise TypeError( + f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") + + self.stop_criteria = [] + self.supported_stop_words = ["reach", "saturate"] + if stop_criteria is None: + # None: Stop after passing through all generations. + self.stop_criteria = None + elif type(stop_criteria) is str: + # reach_{target_fitness}: Stop if the target fitness value is reached. + # saturate_{num_generations}: Stop if the fitness value does not change (saturates) for the given number of generations. + criterion = stop_criteria.split("_") + if len(criterion) == 2: + stop_word = criterion[0] + number = criterion[1] + + if stop_word in self.supported_stop_words: + pass + else: + self.valid_parameters = False + raise ValueError( + f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") + + if number.replace(".", "").isnumeric(): + number = float(number) + else: + self.valid_parameters = False + raise ValueError( + f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + + self.stop_criteria.append([stop_word, number]) - if number.replace(".", "").isnumeric(): - number = float(number) else: self.valid_parameters = False - self.logger.error( - f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") raise ValueError( - f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") + + elif type(stop_criteria) in [list, tuple, numpy.ndarray]: + # Remove duplicate criterira by converting the list to a set then back to a list. + stop_criteria = list(set(stop_criteria)) + for idx, val in enumerate(stop_criteria): + if type(val) is str: + criterion = val.split("_") + if len(criterion) == 2: + stop_word = criterion[0] + number = criterion[1] + + if stop_word in self.supported_stop_words: + pass + else: + self.valid_parameters = False + raise ValueError( + f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") - self.stop_criteria.append([stop_word, number]) + if number.replace(".", "").isnumeric(): + number = float(number) + else: + self.valid_parameters = False + raise ValueError( + f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") - else: - self.valid_parameters = False - self.logger.error( - f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") - raise ValueError( - f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") - - elif type(stop_criteria) in [list, tuple, numpy.ndarray]: - # Remove duplicate criterira by converting the list to a set then back to a list. - stop_criteria = list(set(stop_criteria)) - for idx, val in enumerate(stop_criteria): - if type(val) is str: - criterion = val.split("_") - if len(criterion) == 2: - stop_word = criterion[0] - number = criterion[1] - - if stop_word in self.supported_stop_words: - pass - else: - self.valid_parameters = False - self.logger.error( - f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") - raise ValueError( - f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") + self.stop_criteria.append([stop_word, number]) - if number.replace(".", "").isnumeric(): - number = float(number) else: self.valid_parameters = False - self.logger.error( - f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") raise ValueError( - f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") - - self.stop_criteria.append([stop_word, number]) - + f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") else: self.valid_parameters = False - self.logger.error( - f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") - raise ValueError( - f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") - else: - self.valid_parameters = False - self.logger.error( - f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") - raise TypeError( - f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") - else: - self.valid_parameters = False - self.logger.error( - f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") - raise TypeError( - f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") - - if parallel_processing is None: - self.parallel_processing = None - elif type(parallel_processing) in GA.supported_int_types: - if parallel_processing > 0: - self.parallel_processing = ["thread", parallel_processing] + raise TypeError( + f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") else: self.valid_parameters = False - self.logger.error( - f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") - raise ValueError( - f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") - elif type(parallel_processing) in [list, tuple]: - if len(parallel_processing) == 2: - if type(parallel_processing[0]) is str: - if parallel_processing[0] in ["process", "thread"]: - if (type(parallel_processing[1]) in GA.supported_int_types and parallel_processing[1] > 0) or (parallel_processing[1] == 0) or (parallel_processing[1] is None): - if parallel_processing[1] == 0: - # If the number of processes/threads is 0, this means no parallel processing is used. It is equivelant to setting parallel_processing=None. - self.parallel_processing = None + raise TypeError( + f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") + + if parallel_processing is None: + self.parallel_processing = None + elif type(parallel_processing) in GA.supported_int_types: + if parallel_processing > 0: + self.parallel_processing = ["thread", parallel_processing] + else: + self.valid_parameters = False + raise ValueError( + f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") + elif type(parallel_processing) in [list, tuple]: + if len(parallel_processing) == 2: + if type(parallel_processing[0]) is str: + if parallel_processing[0] in ["process", "thread"]: + if (type(parallel_processing[1]) in GA.supported_int_types and parallel_processing[1] > 0) or (parallel_processing[1] == 0) or (parallel_processing[1] is None): + if parallel_processing[1] == 0: + # If the number of processes/threads is 0, this means no parallel processing is used. It is equivelant to setting parallel_processing=None. + self.parallel_processing = None + else: + # Whether the second value is None or a positive integer. + self.parallel_processing = parallel_processing else: - # Whether the second value is None or a positive integer. - self.parallel_processing = parallel_processing + self.valid_parameters = False + raise TypeError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") else: self.valid_parameters = False - self.logger.error( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") - raise TypeError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") + raise ValueError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") else: self.valid_parameters = False - self.logger.error( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") - raise ValueError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") + raise TypeError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") else: self.valid_parameters = False - self.logger.error( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") - raise TypeError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") + raise ValueError( + f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") else: self.valid_parameters = False - self.logger.error( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") - raise ValueError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") - else: - self.valid_parameters = False - self.logger.error(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") - raise ValueError(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") - - # Set the `run_completed` property to False. It is set to `True` only after the `run()` method is complete. - self.run_completed = False - - # The number of completed generations. - self.generations_completed = 0 - - # At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid. - # Set to True when all the parameters passed in the GA class constructor are valid. - self.valid_parameters = True - - # Parameters of the genetic algorithm. - self.num_generations = abs(num_generations) - self.parent_selection_type = parent_selection_type - - # Parameters of the mutation operation. - self.mutation_percent_genes = mutation_percent_genes - self.mutation_num_genes = mutation_num_genes - - # Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object. - # A list holding the fitness value of the best solution for each generation. - self.best_solutions_fitness = [] - - # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. - self.best_solution_generation = -1 - - self.save_best_solutions = save_best_solutions - self.best_solutions = [] # Holds the best solution in each generation. - - self.save_solutions = save_solutions - self.solutions = [] # Holds the solutions in each generation. - # Holds the fitness of the solutions in each generation. - self.solutions_fitness = [] - - # A list holding the fitness values of all solutions in the last generation. - self.last_generation_fitness = None - # A list holding the parents of the last generation. - self.last_generation_parents = None - # A list holding the offspring after applying crossover in the last generation. - self.last_generation_offspring_crossover = None - # A list holding the offspring after applying mutation in the last generation. - self.last_generation_offspring_mutation = None - # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. - self.previous_generation_fitness = None - # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. - self.last_generation_elitism = None - # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. - self.last_generation_elitism_indices = None + raise ValueError(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") + + # Set the `run_completed` property to False. It is set to `True` only after the `run()` method is complete. + self.run_completed = False + + # The number of completed generations. + self.generations_completed = 0 + + # At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid. + # Set to True when all the parameters passed in the GA class constructor are valid. + self.valid_parameters = True + + # Parameters of the genetic algorithm. + self.num_generations = abs(num_generations) + self.parent_selection_type = parent_selection_type + + # Parameters of the mutation operation. + self.mutation_percent_genes = mutation_percent_genes + self.mutation_num_genes = mutation_num_genes + + # Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object. + # A list holding the fitness value of the best solution for each generation. + self.best_solutions_fitness = [] + + # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. + self.best_solution_generation = -1 + + self.save_best_solutions = save_best_solutions + self.best_solutions = [] # Holds the best solution in each generation. + + self.save_solutions = save_solutions + self.solutions = [] # Holds the solutions in each generation. + # Holds the fitness of the solutions in each generation. + self.solutions_fitness = [] + + # A list holding the fitness values of all solutions in the last generation. + self.last_generation_fitness = None + # A list holding the parents of the last generation. + self.last_generation_parents = None + # A list holding the offspring after applying crossover in the last generation. + self.last_generation_offspring_crossover = None + # A list holding the offspring after applying mutation in the last generation. + self.last_generation_offspring_mutation = None + # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. + self.previous_generation_fitness = None + # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. + self.last_generation_elitism = None + # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. + self.last_generation_elitism_indices = None + except Exception as e: + self.logger.exception(e) + exit(-1) def round_genes(self, solutions): for gene_idx in range(self.num_genes): @@ -1863,211 +1635,101 @@ def cal_pop_fitness(self): It returns: -fitness: An array of the calculated fitness values. """ - - if self.valid_parameters == False: - self.logger.error( - "ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") - raise Exception( - "ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") - - # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' - # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. - if self.last_generation_parents is not None: - last_generation_parents_as_list = [ - list(gen_parent) for gen_parent in self.last_generation_parents] - - # 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism' - # It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. - if self.last_generation_elitism is not None: - last_generation_elitism_as_list = [ - list(gen_elitism) for gen_elitism in self.last_generation_elitism] - - pop_fitness = ["undefined"] * len(self.population) - if self.parallel_processing is None: - # Calculating the fitness value of each solution in the current population. - for sol_idx, sol in enumerate(self.population): - # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. - # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. - # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. - # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. - # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1))): - # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1))): - if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): - solution_idx = self.solutions.index(list(sol)) - fitness = self.solutions_fitness[solution_idx] - elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): - # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. - # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index( - list(sol)) - # Use the returned elitism index to return its index in the last population. - elitism_idx = self.last_generation_elitism_indices[elitism_idx] - # Use the elitism's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[elitism_idx] - # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. - # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. - # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): - elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. - # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. - # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index( - list(sol)) - # Use the returned parent index to return its index in the last population. - parent_idx = self.last_generation_parents_indices[parent_idx] - # Use the parent's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[parent_idx] - else: - # Check if batch processing is used. If not, then calculate this missing fitness value. - if self.fitness_batch_size in [1, None]: - fitness = self.fitness_func(self, sol, sol_idx) - if type(fitness) in GA.supported_int_float_types: - pass - else: - self.logger.error( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - raise ValueError( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + try: + if self.valid_parameters == False: + raise Exception( + "ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") + + # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' + # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. + if self.last_generation_parents is not None: + last_generation_parents_as_list = [ + list(gen_parent) for gen_parent in self.last_generation_parents] + + # 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism' + # It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. + if self.last_generation_elitism is not None: + last_generation_elitism_as_list = [ + list(gen_elitism) for gen_elitism in self.last_generation_elitism] + + pop_fitness = ["undefined"] * len(self.population) + if self.parallel_processing is None: + # Calculating the fitness value of each solution in the current population. + for sol_idx, sol in enumerate(self.population): + # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. + # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. + # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. + # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. + # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1))): + # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1))): + if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): + solution_idx = self.solutions.index(list(sol)) + fitness = self.solutions_fitness[solution_idx] + elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): + # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. + # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. + elitism_idx = last_generation_elitism_as_list.index( + list(sol)) + # Use the returned elitism index to return its index in the last population. + elitism_idx = self.last_generation_elitism_indices[elitism_idx] + # Use the elitism's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[elitism_idx] + # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. + # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. + # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): + elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): + # Index of the parent in the 'self.last_generation_parents' array. + # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. + # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] + parent_idx = last_generation_parents_as_list.index( + list(sol)) + # Use the returned parent index to return its index in the last population. + parent_idx = self.last_generation_parents_indices[parent_idx] + # Use the parent's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[parent_idx] else: - # Reaching this point means that batch processing is in effect to calculate the fitness values. - # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. - continue - - # This is only executed if the fitness value was already calculated. - pop_fitness[sol_idx] = fitness - - if self.fitness_batch_size not in [1, None]: - # Reaching this block means that batch fitness calculation is used. - - # Indices of the solutions to calculate their fitness. - solutions_indices = numpy.where( - numpy.array(pop_fitness) == "undefined")[0] - # Number of batches. - num_batches = int(numpy.ceil( - len(solutions_indices) / self.fitness_batch_size)) - # For each batch, get its indices and call the fitness function. - for batch_idx in range(num_batches): - batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * \ - self.fitness_batch_size - batch_indices = solutions_indices[batch_first_index:batch_last_index] - batch_solutions = self.population[batch_indices, :] - - batch_fitness = self.fitness_func( - self, batch_solutions, batch_indices) - if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error( - f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") - raise TypeError( - f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") - elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error( - f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") - raise ValueError( - f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") - - for index, fitness in zip(batch_indices, batch_fitness): - if type(fitness) in GA.supported_int_float_types: - pop_fitness[index] = fitness + # Check if batch processing is used. If not, then calculate this missing fitness value. + if self.fitness_batch_size in [1, None]: + fitness = self.fitness_func(self, sol, sol_idx) + if type(fitness) in GA.supported_int_float_types: + pass + else: + + raise ValueError( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: - self.logger.error( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - raise ValueError( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - else: - # Calculating the fitness value of each solution in the current population. - for sol_idx, sol in enumerate(self.population): - # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. - # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. - # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. - # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. - if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): - solution_idx = self.solutions.index(list(sol)) - fitness = self.solutions_fitness[solution_idx] - pop_fitness[sol_idx] = fitness - elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): - # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. - # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index( - list(sol)) - # Use the returned elitism index to return its index in the last population. - elitism_idx = self.last_generation_elitism_indices[elitism_idx] - # Use the elitism's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[elitism_idx] - - pop_fitness[sol_idx] = fitness - # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. - # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. - # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): - elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. - # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. - # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index( - list(sol)) - # Use the returned parent index to return its index in the last population. - parent_idx = self.last_generation_parents_indices[parent_idx] - # Use the parent's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[parent_idx] + # Reaching this point means that batch processing is in effect to calculate the fitness values. + # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. + continue + # This is only executed if the fitness value was already calculated. pop_fitness[sol_idx] = fitness - # Decide which class to use based on whether the user selected "process" or "thread" - if self.parallel_processing[0] == "process": - ExecutorClass = concurrent.futures.ProcessPoolExecutor - else: - ExecutorClass = concurrent.futures.ThreadPoolExecutor - - # We can use a with statement to ensure threads are cleaned up promptly (https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example) - with ExecutorClass(max_workers=self.parallel_processing[1]) as executor: - solutions_to_submit_indices = [] - solutions_to_submit = [] - for sol_idx, sol in enumerate(self.population): - # The "undefined" value means that the fitness of this solution must be calculated. - if pop_fitness[sol_idx] == "undefined": - solutions_to_submit.append(sol.copy()) - solutions_to_submit_indices.append(sol_idx) - - # Check if batch processing is used. If not, then calculate the fitness value for individual solutions. - if self.fitness_batch_size in [1, None]: - for index, fitness in zip(solutions_to_submit_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), solutions_to_submit, solutions_to_submit_indices)): - if type(fitness) in GA.supported_int_float_types: - pop_fitness[index] = fitness - else: - self.logger.error( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - raise ValueError( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") - else: - # Reaching this block means that batch processing is used. The fitness values are calculated in batches. + if self.fitness_batch_size not in [1, None]: + # Reaching this block means that batch fitness calculation is used. + # Indices of the solutions to calculate their fitness. + solutions_indices = numpy.where( + numpy.array(pop_fitness) == "undefined")[0] # Number of batches. num_batches = int(numpy.ceil( - len(solutions_to_submit_indices) / self.fitness_batch_size)) - # Each element of the `batches_solutions` list represents the solutions in one batch. - batches_solutions = [] - # Each element of the `batches_indices` list represents the solutions' indices in one batch. - batches_indices = [] + len(solutions_indices) / self.fitness_batch_size)) # For each batch, get its indices and call the fitness function. for batch_idx in range(num_batches): batch_first_index = batch_idx * self.fitness_batch_size batch_last_index = (batch_idx + 1) * \ self.fitness_batch_size - batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] + batch_indices = solutions_indices[batch_first_index:batch_last_index] batch_solutions = self.population[batch_indices, :] - batches_solutions.append(batch_solutions) - batches_indices.append(batch_indices) - - for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): + batch_fitness = self.fitness_func( + self, batch_solutions, batch_indices) if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error( - f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + raise TypeError( f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error( - f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + raise ValueError( f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") @@ -2075,310 +1737,402 @@ def cal_pop_fitness(self): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error( - f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") + raise ValueError( - f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + else: + # Calculating the fitness value of each solution in the current population. + for sol_idx, sol in enumerate(self.population): + # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. + # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. + # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. + # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. + if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): + solution_idx = self.solutions.index(list(sol)) + fitness = self.solutions_fitness[solution_idx] + pop_fitness[sol_idx] = fitness + elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): + # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. + # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. + elitism_idx = last_generation_elitism_as_list.index( + list(sol)) + # Use the returned elitism index to return its index in the last population. + elitism_idx = self.last_generation_elitism_indices[elitism_idx] + # Use the elitism's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[elitism_idx] + + pop_fitness[sol_idx] = fitness + # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. + # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. + # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): + elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): + # Index of the parent in the 'self.last_generation_parents' array. + # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. + # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] + parent_idx = last_generation_parents_as_list.index( + list(sol)) + # Use the returned parent index to return its index in the last population. + parent_idx = self.last_generation_parents_indices[parent_idx] + # Use the parent's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[parent_idx] + + pop_fitness[sol_idx] = fitness + + # Decide which class to use based on whether the user selected "process" or "thread" + if self.parallel_processing[0] == "process": + ExecutorClass = concurrent.futures.ProcessPoolExecutor + else: + ExecutorClass = concurrent.futures.ThreadPoolExecutor + + # We can use a with statement to ensure threads are cleaned up promptly (https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example) + with ExecutorClass(max_workers=self.parallel_processing[1]) as executor: + solutions_to_submit_indices = [] + solutions_to_submit = [] + for sol_idx, sol in enumerate(self.population): + # The "undefined" value means that the fitness of this solution must be calculated. + if pop_fitness[sol_idx] == "undefined": + solutions_to_submit.append(sol.copy()) + solutions_to_submit_indices.append(sol_idx) + + # Check if batch processing is used. If not, then calculate the fitness value for individual solutions. + if self.fitness_batch_size in [1, None]: + for index, fitness in zip(solutions_to_submit_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), solutions_to_submit, solutions_to_submit_indices)): + if type(fitness) in GA.supported_int_float_types: + pop_fitness[index] = fitness + else: + + raise ValueError( + f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + else: + # Reaching this block means that batch processing is used. The fitness values are calculated in batches. + + # Number of batches. + num_batches = int(numpy.ceil( + len(solutions_to_submit_indices) / self.fitness_batch_size)) + # Each element of the `batches_solutions` list represents the solutions in one batch. + batches_solutions = [] + # Each element of the `batches_indices` list represents the solutions' indices in one batch. + batches_indices = [] + # For each batch, get its indices and call the fitness function. + for batch_idx in range(num_batches): + batch_first_index = batch_idx * self.fitness_batch_size + batch_last_index = (batch_idx + 1) * \ + self.fitness_batch_size + batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] + batch_solutions = self.population[batch_indices, :] + + batches_solutions.append(batch_solutions) + batches_indices.append(batch_indices) + + for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): + if type(batch_fitness) not in [list, tuple, numpy.ndarray]: + + raise TypeError( + f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + elif len(numpy.array(batch_fitness)) != len(batch_indices): + + raise ValueError( + f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") - pop_fitness = numpy.array(pop_fitness) + for index, fitness in zip(batch_indices, batch_fitness): + if type(fitness) in GA.supported_int_float_types: + pop_fitness[index] = fitness + else: + + raise ValueError( + f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") + pop_fitness = numpy.array(pop_fitness) + except Exception as ex: + self.logger.exception(ex) + exit(-1) return pop_fitness def run(self): """ Runs the genetic algorithm. This is the main method in which the genetic algorithm is evolved through a number of generations. """ + try: + if self.valid_parameters == False: + + raise Exception( + "Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") + + # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. + # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. + + # self.best_solutions: Holds the best solution in each generation. + if type(self.best_solutions) is numpy.ndarray: + self.best_solutions = list(self.best_solutions) + # self.best_solutions_fitness: A list holding the fitness value of the best solution for each generation. + if type(self.best_solutions_fitness) is numpy.ndarray: + self.best_solutions_fitness = list(self.best_solutions_fitness) + # self.solutions: Holds the solutions in each generation. + if type(self.solutions) is numpy.ndarray: + self.solutions = list(self.solutions) + # self.solutions_fitness: Holds the fitness of the solutions in each generation. + if type(self.solutions_fitness) is numpy.ndarray: + self.solutions_fitness = list(self.solutions_fitness) + + if not (self.on_start is None): + self.on_start(self) + + stop_run = False + + # To continue from where we stopped, the first generation index should start from the value of the 'self.generations_completed' parameter. + if self.generations_completed != 0 and type(self.generations_completed) in GA.supported_int_types: + # If the 'self.generations_completed' parameter is not '0', then this means we continue execution. + generation_first_idx = self.generations_completed + generation_last_idx = self.num_generations + self.generations_completed + else: + # If the 'self.generations_completed' parameter is '0', then stat from scratch. + generation_first_idx = 0 + generation_last_idx = self.num_generations - if self.valid_parameters == False: - self.logger.error( - "Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - raise Exception( - "Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - - # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. - # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. - - # self.best_solutions: Holds the best solution in each generation. - if type(self.best_solutions) is numpy.ndarray: - self.best_solutions = list(self.best_solutions) - # self.best_solutions_fitness: A list holding the fitness value of the best solution for each generation. - if type(self.best_solutions_fitness) is numpy.ndarray: - self.best_solutions_fitness = list(self.best_solutions_fitness) - # self.solutions: Holds the solutions in each generation. - if type(self.solutions) is numpy.ndarray: - self.solutions = list(self.solutions) - # self.solutions_fitness: Holds the fitness of the solutions in each generation. - if type(self.solutions_fitness) is numpy.ndarray: - self.solutions_fitness = list(self.solutions_fitness) - - if not (self.on_start is None): - self.on_start(self) - - stop_run = False - - # To continue from where we stopped, the first generation index should start from the value of the 'self.generations_completed' parameter. - if self.generations_completed != 0 and type(self.generations_completed) in GA.supported_int_types: - # If the 'self.generations_completed' parameter is not '0', then this means we continue execution. - generation_first_idx = self.generations_completed - generation_last_idx = self.num_generations + self.generations_completed - else: - # If the 'self.generations_completed' parameter is '0', then stat from scratch. - generation_first_idx = 0 - generation_last_idx = self.num_generations - - # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. - self.last_generation_fitness = self.cal_pop_fitness() - - best_solution, best_solution_fitness, best_match_idx = self.best_solution( - pop_fitness=self.last_generation_fitness) - - # Appending the best solution in the initial population to the best_solutions list. - if self.save_best_solutions: - self.best_solutions.append(best_solution) + # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. + self.last_generation_fitness = self.cal_pop_fitness() - for generation in range(generation_first_idx, generation_last_idx): - if not (self.on_fitness is None): - self.on_fitness(self, self.last_generation_fitness) + best_solution, best_solution_fitness, best_match_idx = self.best_solution( + pop_fitness=self.last_generation_fitness) - # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. - self.best_solutions_fitness.append(best_solution_fitness) + # Appending the best solution in the initial population to the best_solutions list. + if self.save_best_solutions: + self.best_solutions.append(best_solution) - # Appending the solutions in the current generation to the solutions list. - if self.save_solutions: - # self.solutions.extend(self.population.copy()) - population_as_list = self.population.copy() - population_as_list = [list(item) - for item in population_as_list] - self.solutions.extend(population_as_list) + for generation in range(generation_first_idx, generation_last_idx): + if not (self.on_fitness is None): + self.on_fitness(self, self.last_generation_fitness) + + # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. + self.best_solutions_fitness.append(best_solution_fitness) + + # Appending the solutions in the current generation to the solutions list. + if self.save_solutions: + # self.solutions.extend(self.population.copy()) + population_as_list = self.population.copy() + population_as_list = [list(item) + for item in population_as_list] + self.solutions.extend(population_as_list) + + self.solutions_fitness.extend(self.last_generation_fitness) + + # Selecting the best parents in the population for mating. + if callable(self.parent_selection_type): + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + self.num_parents_mating, + self) + if not type(self.last_generation_parents) is numpy.ndarray: + + raise TypeError( + f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") + if not type(self.last_generation_parents_indices) is numpy.ndarray: + + raise TypeError( + f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") + else: + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + num_parents=self.num_parents_mating) - self.solutions_fitness.extend(self.last_generation_fitness) + # Validate the output of the parent selection step: self.select_parents() + if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): + if self.last_generation_parents.shape[0] != self.num_parents_mating: + + raise ValueError( + f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") + elif self.last_generation_parents.shape[1] != self.num_genes: + + raise ValueError( + f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") - # Selecting the best parents in the population for mating. - if callable(self.parent_selection_type): - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, - self.num_parents_mating, - self) - if not type(self.last_generation_parents) is numpy.ndarray: - self.logger.error( - f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") - raise TypeError( - f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") - if not type(self.last_generation_parents_indices) is numpy.ndarray: - self.logger.error( - f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") - raise TypeError( - f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") - else: - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, - num_parents=self.num_parents_mating) - - # Validate the output of the parent selection step: self.select_parents() - if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): - if self.last_generation_parents.shape[0] != self.num_parents_mating: - self.logger.error( - f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") + if self.last_generation_parents_indices.ndim != 1: + raise ValueError( - f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") - elif self.last_generation_parents.shape[1] != self.num_genes: - self.logger.error( - f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") + f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") + elif len(self.last_generation_parents_indices) != self.num_parents_mating: + raise ValueError( - f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") - - if self.last_generation_parents_indices.ndim != 1: - self.logger.error( - f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") - raise ValueError( - f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") - elif len(self.last_generation_parents_indices) != self.num_parents_mating: - self.logger.error( - f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") - raise ValueError( - f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") - - if not (self.on_parents is None): - self.on_parents(self, self.last_generation_parents) - - # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. - if self.crossover_type is None: - if self.keep_elitism == 0: - num_parents_to_keep = self.num_parents_mating if self.keep_parents == - \ - 1 else self.keep_parents - if self.num_offspring <= num_parents_to_keep: - self.last_generation_offspring_crossover = self.last_generation_parents[ - 0:self.num_offspring] + f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") + + if not (self.on_parents is None): + self.on_parents(self, self.last_generation_parents) + + # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. + if self.crossover_type is None: + if self.keep_elitism == 0: + num_parents_to_keep = self.num_parents_mating if self.keep_parents == - \ + 1 else self.keep_parents + if self.num_offspring <= num_parents_to_keep: + self.last_generation_offspring_crossover = self.last_generation_parents[ + 0:self.num_offspring] + else: + self.last_generation_offspring_crossover = numpy.concatenate( + (self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) else: - self.last_generation_offspring_crossover = numpy.concatenate( - (self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) + # The steady_state_selection() function is called to select the best solutions (i.e. elitism). The keep_elitism parameter defines the number of these solutions. + # The steady_state_selection() function is still called here even if its output may not be used given that the condition of the next if statement is True. The reason is that it will be used later. + self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_elitism) + if self.num_offspring <= self.keep_elitism: + self.last_generation_offspring_crossover = self.last_generation_parents[ + 0:self.num_offspring] + else: + self.last_generation_offspring_crossover = numpy.concatenate( + (self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) else: - # The steady_state_selection() function is called to select the best solutions (i.e. elitism). The keep_elitism parameter defines the number of these solutions. - # The steady_state_selection() function is still called here even if its output may not be used given that the condition of the next if statement is True. The reason is that it will be used later. - self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_elitism) - if self.num_offspring <= self.keep_elitism: - self.last_generation_offspring_crossover = self.last_generation_parents[ - 0:self.num_offspring] + # Generating offspring using crossover. + if callable(self.crossover_type): + self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, + (self.num_offspring, + self.num_genes), + self) + if not type(self.last_generation_offspring_crossover) is numpy.ndarray: + + raise TypeError( + f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") else: - self.last_generation_offspring_crossover = numpy.concatenate( - (self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) - else: - # Generating offspring using crossover. - if callable(self.crossover_type): - self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, - (self.num_offspring, - self.num_genes), - self) - if not type(self.last_generation_offspring_crossover) is numpy.ndarray: - self.logger.error( - f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") - raise TypeError( - f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") - else: - self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, - offspring_size=(self.num_offspring, self.num_genes)) - if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): - if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: - self.logger.error( - f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") - raise ValueError( - f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") - elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: - self.logger.error( - f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") - raise ValueError( - f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") + self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, + offspring_size=(self.num_offspring, self.num_genes)) + if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): + if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: + + raise ValueError( + f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") + elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: + + raise ValueError( + f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") - # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. - if not (self.on_crossover is None): - self.on_crossover( - self, self.last_generation_offspring_crossover) + # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. + if not (self.on_crossover is None): + self.on_crossover( + self, self.last_generation_offspring_crossover) - # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. - if self.mutation_type is None: - self.last_generation_offspring_mutation = self.last_generation_offspring_crossover - else: - # Adding some variations to the offspring using mutation. - if callable(self.mutation_type): - self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, - self) - if not type(self.last_generation_offspring_mutation) is numpy.ndarray: - self.logger.error( - f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") - raise TypeError( - f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") + # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. + if self.mutation_type is None: + self.last_generation_offspring_mutation = self.last_generation_offspring_crossover else: - self.last_generation_offspring_mutation = self.mutation( - self.last_generation_offspring_crossover) - - if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): - if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: - self.logger.error( - f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") - raise ValueError( - f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") - elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: - self.logger.error( - f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") - raise ValueError( - f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") - - # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. - if not (self.on_mutation is None): - self.on_mutation(self, self.last_generation_offspring_mutation) - - # Update the population attribute according to the offspring generated. - if self.keep_elitism == 0: - # If the keep_elitism parameter is 0, then the keep_parents parameter will be used to decide if the parents are kept in the next generation. - if (self.keep_parents == 0): - self.population = self.last_generation_offspring_mutation - elif (self.keep_parents == -1): - # Creating the new population based on the parents and offspring. - self.population[0:self.last_generation_parents.shape[0], - :] = self.last_generation_parents - self.population[self.last_generation_parents.shape[0]:, :] = self.last_generation_offspring_mutation - elif (self.keep_parents > 0): - parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_parents) - self.population[0:parents_to_keep.shape[0], - :] = parents_to_keep - self.population[parents_to_keep.shape[0]:, - :] = self.last_generation_offspring_mutation - else: - self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_elitism) - self.population[0:self.last_generation_elitism.shape[0], - :] = self.last_generation_elitism - self.population[self.last_generation_elitism.shape[0]:, :] = self.last_generation_offspring_mutation - - # The generations_completed attribute holds the number of the last completed generation. - self.generations_completed = generation + 1 - - self.previous_generation_fitness = self.last_generation_fitness.copy() - # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. - self.last_generation_fitness = self.cal_pop_fitness() - - best_solution, best_solution_fitness, best_match_idx = self.best_solution( - pop_fitness=self.last_generation_fitness) + # Adding some variations to the offspring using mutation. + if callable(self.mutation_type): + self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, + self) + if not type(self.last_generation_offspring_mutation) is numpy.ndarray: + + raise TypeError( + f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") + else: + self.last_generation_offspring_mutation = self.mutation( + self.last_generation_offspring_crossover) - # Appending the best solution in the current generation to the best_solutions list. - if self.save_best_solutions: - self.best_solutions.append(best_solution) + if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): + if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: + + raise ValueError( + f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") + elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: + + raise ValueError( + f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") - # If the on_generation attribute is not None, then cal the callback function after the generation. - if not (self.on_generation is None): - r = self.on_generation(self) - if type(r) is str and r.lower() == "stop": - # Before aborting the loop, save the fitness value of the best solution. - # _, best_solution_fitness, _ = self.best_solution() - self.best_solutions_fitness.append(best_solution_fitness) - break + # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. + if not (self.on_mutation is None): + self.on_mutation(self, self.last_generation_offspring_mutation) - if not self.stop_criteria is None: - for criterion in self.stop_criteria: - if criterion[0] == "reach": - if max(self.last_generation_fitness) >= criterion[1]: - stop_run = True - break - elif criterion[0] == "saturate": - criterion[1] = int(criterion[1]) - if (self.generations_completed >= criterion[1]): - if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0: + # Update the population attribute according to the offspring generated. + if self.keep_elitism == 0: + # If the keep_elitism parameter is 0, then the keep_parents parameter will be used to decide if the parents are kept in the next generation. + if (self.keep_parents == 0): + self.population = self.last_generation_offspring_mutation + elif (self.keep_parents == -1): + # Creating the new population based on the parents and offspring. + self.population[0:self.last_generation_parents.shape[0], + :] = self.last_generation_parents + self.population[self.last_generation_parents.shape[0]:, :] = self.last_generation_offspring_mutation + elif (self.keep_parents > 0): + parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_parents) + self.population[0:parents_to_keep.shape[0], + :] = parents_to_keep + self.population[parents_to_keep.shape[0]:, + :] = self.last_generation_offspring_mutation + else: + self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_elitism) + self.population[0:self.last_generation_elitism.shape[0], + :] = self.last_generation_elitism + self.population[self.last_generation_elitism.shape[0]:, :] = self.last_generation_offspring_mutation + + # The generations_completed attribute holds the number of the last completed generation. + self.generations_completed = generation + 1 + + self.previous_generation_fitness = self.last_generation_fitness.copy() + # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. + self.last_generation_fitness = self.cal_pop_fitness() + + best_solution, best_solution_fitness, best_match_idx = self.best_solution( + pop_fitness=self.last_generation_fitness) + + # Appending the best solution in the current generation to the best_solutions list. + if self.save_best_solutions: + self.best_solutions.append(best_solution) + + # If the on_generation attribute is not None, then cal the callback function after the generation. + if not (self.on_generation is None): + r = self.on_generation(self) + if type(r) is str and r.lower() == "stop": + # Before aborting the loop, save the fitness value of the best solution. + # _, best_solution_fitness, _ = self.best_solution() + self.best_solutions_fitness.append(best_solution_fitness) + break + + if not self.stop_criteria is None: + for criterion in self.stop_criteria: + if criterion[0] == "reach": + if max(self.last_generation_fitness) >= criterion[1]: stop_run = True break + elif criterion[0] == "saturate": + criterion[1] = int(criterion[1]) + if (self.generations_completed >= criterion[1]): + if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0: + stop_run = True + break + + if stop_run: + break - if stop_run: - break - - time.sleep(self.delay_after_gen) + time.sleep(self.delay_after_gen) - # Save the fitness of the last generation. - if self.save_solutions: - # self.solutions.extend(self.population.copy()) - population_as_list = self.population.copy() - population_as_list = [list(item) for item in population_as_list] - self.solutions.extend(population_as_list) + # Save the fitness of the last generation. + if self.save_solutions: + # self.solutions.extend(self.population.copy()) + population_as_list = self.population.copy() + population_as_list = [list(item) for item in population_as_list] + self.solutions.extend(population_as_list) - self.solutions_fitness.extend(self.last_generation_fitness) + self.solutions_fitness.extend(self.last_generation_fitness) - # Save the fitness value of the best solution. - _, best_solution_fitness, _ = self.best_solution( - pop_fitness=self.last_generation_fitness) - self.best_solutions_fitness.append(best_solution_fitness) + # Save the fitness value of the best solution. + _, best_solution_fitness, _ = self.best_solution( + pop_fitness=self.last_generation_fitness) + self.best_solutions_fitness.append(best_solution_fitness) - self.best_solution_generation = numpy.where(numpy.array( - self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] - # After the run() method completes, the run_completed flag is changed from False to True. - # Set to True only after the run() method completes gracefully. - self.run_completed = True + self.best_solution_generation = numpy.where(numpy.array( + self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] + # After the run() method completes, the run_completed flag is changed from False to True. + # Set to True only after the run() method completes gracefully. + self.run_completed = True - if not (self.on_stop is None): - self.on_stop(self, self.last_generation_fitness) + if not (self.on_stop is None): + self.on_stop(self, self.last_generation_fitness) - # Converting the 'best_solutions' list into a NumPy array. - self.best_solutions = numpy.array(self.best_solutions) + # Converting the 'best_solutions' list into a NumPy array. + self.best_solutions = numpy.array(self.best_solutions) - # Converting the 'solutions' list into a NumPy array. - # self.solutions = numpy.array(self.solutions) + # Converting the 'solutions' list into a NumPy array. + # self.solutions = numpy.array(self.solutions) + except Exception as ex: + self.logger.exception(ex) + exit(-1) def best_solution(self, pop_fitness=None): """ @@ -2390,33 +2144,34 @@ def best_solution(self, pop_fitness=None): -best_solution_fitness: Fitness value of the best solution. -best_match_idx: Index of the best solution in the current population. """ - - if pop_fitness is None: - # If the 'pop_fitness' parameter is not passed, then we have to call the 'cal_pop_fitness()' method to calculate the fitness of all solutions in the lastest population. - pop_fitness = self.cal_pop_fitness() - # Verify the type of the 'pop_fitness' parameter. - elif type(pop_fitness) in [tuple, list, numpy.ndarray]: - # Verify that the length of the passed population fitness matches the length of the 'self.population' attribute. - if len(pop_fitness) == len(self.population): - # This successfully verifies the 'pop_fitness' parameter. - pass + try: + if pop_fitness is None: + # If the 'pop_fitness' parameter is not passed, then we have to call the 'cal_pop_fitness()' method to calculate the fitness of all solutions in the lastest population. + pop_fitness = self.cal_pop_fitness() + # Verify the type of the 'pop_fitness' parameter. + elif type(pop_fitness) in [tuple, list, numpy.ndarray]: + # Verify that the length of the passed population fitness matches the length of the 'self.population' attribute. + if len(pop_fitness) == len(self.population): + # This successfully verifies the 'pop_fitness' parameter. + pass + else: + + raise ValueError( + f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") else: - self.logger.error( - f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") + raise ValueError( - f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") - else: - self.logger.error( - f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") - raise ValueError( - f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") + f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") - # Return the index of the best solution that has the best fitness value. - best_match_idx = numpy.where( - pop_fitness == numpy.max(pop_fitness))[0][0] + # Return the index of the best solution that has the best fitness value. + best_match_idx = numpy.where( + pop_fitness == numpy.max(pop_fitness))[0][0] - best_solution = self.population[best_match_idx, :].copy() - best_solution_fitness = pop_fitness[best_match_idx] + best_solution = self.population[best_match_idx, :].copy() + best_solution_fitness = pop_fitness[best_match_idx] + except Exception as ex: + self.logger.exception(ex) + exit(-1) return best_solution, best_solution_fitness, best_match_idx From 61809c7c590a242bc3f4d4ee63b5e03ad24fcea8 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Mon, 8 May 2023 23:44:01 -0400 Subject: [PATCH 053/220] USe f-string instead of format() --- pygad/cnn/cnn.py | 22 +- pygad/gann/gann.py | 18 +- pygad/helper/unique.py | 10 +- pygad/nn/nn.py | 24 +- pygad/pygad.py | 3100 +++++++++++++++++++-------------------- pygad/visualize/plot.py | 16 +- 6 files changed, 1581 insertions(+), 1609 deletions(-) diff --git a/pygad/cnn/cnn.py b/pygad/cnn/cnn.py index 0e78a52..d0141ae 100644 --- a/pygad/cnn/cnn.py +++ b/pygad/cnn/cnn.py @@ -84,7 +84,7 @@ def layers_weights(model, initial=True): elif initial == False: network_weights.append(layer.trained_weights) else: - raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + raise ValueError(f"Unexpected value to the 'initial' parameter: {initial}.") # Go to the previous layer. layer = layer.previous_layer @@ -164,7 +164,7 @@ def layers_weights_as_vector(model, initial=True): # vector = pygad.nn.DenseLayer.to_vector(array=layer.trained_weights) network_weights.extend(vector) else: - raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + raise ValueError(f"Unexpected value to the 'initial' parameter: {initial}.") # Go to the previous layer. layer = layer.previous_layer @@ -214,7 +214,7 @@ def __init__(self, input_shape): # If the input sample has less than 2 dimensions, then an exception is raised. if len(input_shape) < 2: - raise ValueError("The Input2D class creates an input layer for data inputs with at least 2 dimensions but ({num_dim}) dimensions found.".format(num_dim=len(input_shape))) + raise ValueError(f"The Input2D class creates an input layer for data inputs with at least 2 dimensions but ({len(input_shape)}) dimensions found.") # If the input sample has exactly 2 dimensions, the third dimension is set to 1. elif len(input_shape) == 2: input_shape = (input_shape[0], input_shape[1], 1) @@ -261,7 +261,7 @@ def __init__(self, num_filters, kernel_size, previous_layer, activation_function elif (activation_function == "softmax"): raise ValueError("The softmax activation function cannot be used in a conv layer.") else: - raise ValueError("The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.".format(activation_function=activation_function, supported_activation_functions=supported_activation_functions)) + raise ValueError(f"The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.") # The activation function used in the current layer. self.activation_function = activation_function @@ -445,7 +445,7 @@ def __init__(self, pool_size, previous_layer, stride=2): """ if not (type(pool_size) is int): - raise ValueError("The expected type of the pool_size is int but {pool_size_type} found.".format(pool_size_type=type(pool_size))) + raise ValueError(f"The expected type of the pool_size is int but {type(pool_size)} found.") if pool_size <= 0: raise ValueError("The passed value to the pool_size parameter cannot be <= 0.") @@ -642,7 +642,7 @@ def __init__(self, num_neurons, previous_layer, activation_function="relu"): elif (activation_function == "softmax"): self.activation = softmax else: - raise ValueError("The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.".format(activation_function=activation_function, supported_activation_functions=supported_activation_functions)) + raise ValueError(f"The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.") self.activation_function = activation_function @@ -652,7 +652,7 @@ def __init__(self, num_neurons, previous_layer, activation_function="relu"): self.previous_layer = previous_layer if type(self.previous_layer.layer_output_size) in [list, tuple, numpy.ndarray] and len(self.previous_layer.layer_output_size) > 1: - raise ValueError("The input to the dense layer must be of type int but {sh} found.".format(sh=type(self.previous_layer.layer_output_size))) + raise ValueError(f"The input to the dense layer must be of type int but {type(self.previous_layer.layer_output_size)} found.") # Initializing the weights of the layer. self.initial_weights = numpy.random.uniform(low=-0.1, high=0.1, @@ -741,13 +741,13 @@ def train(self, train_inputs, train_outputs): raise ValueError("The training data input has {num_dims} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample.".format(num_dims=train_inputs.ndim)) if (train_inputs.shape[0] != len(train_outputs)): - raise ValueError("Mismatch between the number of input samples and number of labels: {num_samples_inputs} != {num_samples_outputs}.".format(num_samples_inputs=train_inputs.shape[0], num_samples_outputs=len(train_outputs))) + raise ValueError(f"Mismatch between the number of input samples and number of labels: {train_inputs.shape[0]} != {len(train_outputs)}.") network_predictions = [] network_error = 0 for epoch in range(self.epochs): - print("Epoch {epoch}".format(epoch=epoch)) + print(f"Epoch {epoch}") for sample_idx in range(train_inputs.shape[0]): # print("Sample {sample_idx}".format(sample_idx=sample_idx)) self.feed_sample(train_inputs[sample_idx, :]) @@ -797,7 +797,7 @@ def feed_sample(self, sample): pass else: print("Other") - raise TypeError("The layer of type {layer_type} is not supported yet.".format(layer_type=type(layer))) + raise TypeError("The layer of type {type(layer)} is not supported yet.") last_layer_outputs = layer.layer_output return self.network_layers[-1].layer_output @@ -828,7 +828,7 @@ def predict(self, data_inputs): """ if (data_inputs.ndim != 4): - raise ValueError("The data input has {num_dims} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample.".format(num_dims=data_inputs.ndim)) + raise ValueError("The data input has {data_inputs.ndim} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample.") predictions = [] for sample in data_inputs: diff --git a/pygad/gann/gann.py b/pygad/gann/gann.py index 69eea98..4037e5a 100644 --- a/pygad/gann/gann.py +++ b/pygad/gann/gann.py @@ -26,8 +26,8 @@ def validate_network_parameters(num_neurons_input, # Validating the number of solutions within the population. if not (num_solutions is None): if num_solutions < 2: - raise ValueError("num_solutions: The number of solutions within the population must be at least 2. The current value is {num_solutions}.".format(num_solutions=num_solutions)) - + raise ValueError(f"num_solutions: The number of solutions within the population must be at least 2. The current value is {num_solutions}.") + # Validating the number of neurons in the input layer. if num_neurons_input is int and num_neurons_input <= 0: raise ValueError("num_neurons_input: The number of neurons in the input layer must be > 0.") @@ -35,13 +35,13 @@ def validate_network_parameters(num_neurons_input, # Validating the number of neurons in the output layer. if num_neurons_output is int and num_neurons_output <= 0: raise ValueError("num_neurons_output: The number of neurons in the output layer must be > 0.") - + # Validating the type of the 'num_neurons_hidden_layers' parameter which is expected to be list or tuple. if not (type(num_neurons_hidden_layers) in [list, tuple]): - raise TypeError("num_neurons_hidden_layers: A list or a tuple is expected but {hidden_layers_neurons_type} found.".format(hidden_layers_neurons_type=type(num_neurons_hidden_layers))) - + raise TypeError(f"num_neurons_hidden_layers: A list or a tuple is expected but {type(num_neurons_hidden_layers)} found.") + # Frequently used error messages. - unexpected_output_activation_value = "Output activation function: The activation function of the output layer is passed as a string not {activation_type}." + unexpected_output_activation_value = f"Output activation function: The activation function of the output layer is passed as a string not {type(output_activation)}." unexpected_activation_value = "Activation function: The supported values for the activation function are {supported_activations} but an unexpected value is found:\n{activations}" unexpected_activation_type = "Activation Function: A list, tuple, or a string is expected but {activations_type} found." length_mismatch = "Hidden activation functions: When passing the activation function(s) as a list or a tuple, its length must match the length of the 'num_neurons_hidden_layers' parameter but a mismatch is found:\n{mismatched_lengths}" @@ -51,7 +51,7 @@ def validate_network_parameters(num_neurons_input, # Validating the output layer activation function. if not (type(output_activation) is str): - raise ValueError(unexpected_output_activation_value.format(activation_type=type(output_activation))) + raise ValueError(unexpected_output_activation_value) if not (output_activation in supported_activations): #activation_type raise ValueError(unexpected_activation_value.format(activations=output_activation, supported_activations=supported_activations)) @@ -81,9 +81,9 @@ def validate_network_parameters(num_neurons_input, else: raise TypeError(unexpected_activation_type.format(activations_type=type(hidden_activations))) else: # In case there are no hidden layers (num_hidden_layers == 0) - print("WARNING: There are no hidden layers however a value is assigned to the parameter 'hidden_activations'. It will be reset to [].".format(hidden_activations=hidden_activations)) + print("WARNING: There are no hidden layers however a value is assigned to the parameter 'hidden_activations'. It will be reset to [].") hidden_activations = [] - + # If the value passed to the 'hidden_activations' parameter is actually a list, then its elements are checked to make sure the listed name(s) of the activation function(s) are supported. for act in hidden_activations: if not (act in supported_activations): diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py index ad4bd8a..af381fe 100644 --- a/pygad/helper/unique.py +++ b/pygad/helper/unique.py @@ -92,7 +92,7 @@ def solve_duplicate_genes_randomly(self, if temp_val in new_solution and trial_index == (num_trials - 1): num_unsolved_duplicates = num_unsolved_duplicates + 1 - if not self.suppress_warnings: warnings.warn("Failed to find a unique value for gene with index {gene_idx} whose value is {gene_value}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.".format(gene_idx=duplicate_index, gene_value=solution[duplicate_index])) + if not self.suppress_warnings: warnings.warn(f"Failed to find a unique value for gene with index {duplicate_index} whose value is {solution[duplicate_index]}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.") elif temp_val in new_solution: continue else: @@ -105,7 +105,7 @@ def solve_duplicate_genes_randomly(self, # self.logger.info("not_unique_indices INSIDE", not_unique_indices) return new_solution, not_unique_indices, num_unsolved_duplicates - + def solve_duplicate_genes_by_space(self, solution, gene_type, @@ -230,7 +230,7 @@ def unique_int_gene_from_range(self, all_gene_values = numpy.asarray(all_gene_values, gene_type[gene_index][0]) - values_to_select_from = list(set(all_gene_values) - set(solution)) + values_to_select_from = list(set(list(all_gene_values)) - set(solution)) if len(values_to_select_from) == 0: # If there is no values, then keep the current gene value. @@ -274,7 +274,7 @@ def unique_genes_by_space(self, if temp_val in new_solution and trial_index == (num_trials - 1): # self.logger.info("temp_val, duplicate_index", temp_val, duplicate_index, new_solution) num_unsolved_duplicates = num_unsolved_duplicates + 1 - if not self.suppress_warnings: warnings.warn("Failed to find a unique value for gene with index {gene_idx} whose value is {gene_value}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.".format(gene_idx=duplicate_index, gene_value=new_solution[duplicate_index])) + if not self.suppress_warnings: warnings.warn(f"Failed to find a unique value for gene with index {duplicate_index} whose value is {new_solution[duplicate_index]}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.") elif temp_val in new_solution: continue else: @@ -451,7 +451,7 @@ def unique_gene_by_space(self, # If the gene space has only 1 value, then select it. The old and new values of the gene are identical. if len(curr_gene_space) == 1: value_from_space = curr_gene_space[0] - if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but the space of the gene with index {gene_idx} has only a single value. Thus, duplicates are possible.".format(gene_idx=gene_idx)) + if not self.suppress_warnings: warnings.warn(f"You set 'allow_duplicate_genes=False' but the space of the gene with index {gene_idx} has only a single value. Thus, duplicates are possible.") # If the gene space has more than 1 value, then select a new one that is different from the current value. else: values_to_select_from = list(set(curr_gene_space) - set(solution)) diff --git a/pygad/nn/nn.py b/pygad/nn/nn.py index 04933d7..023c4e2 100644 --- a/pygad/nn/nn.py +++ b/pygad/nn/nn.py @@ -27,7 +27,7 @@ def layers_weights(last_layer, initial=True): elif initial == False: network_weights.append(layer.trained_weights) else: - raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + raise ValueError(f"Unexpected value to the 'initial' parameter: {initial}.") # Go to the previous layer. layer = layer.previous_layer @@ -64,7 +64,7 @@ def layers_weights_as_vector(last_layer, initial=True): # vector = DenseLayer.to_vector(array=layer.trained_weights) network_weights.extend(vector) else: - raise ValueError("Unexpected value to the 'initial' parameter: {initial}.".format(initial=initial)) + raise ValueError(f"Unexpected value to the 'initial' parameter: {initial}.") # Go to the previous layer. layer = layer.previous_layer @@ -207,8 +207,8 @@ def train(num_epochs, """ if not (problem_type in ["classification", "regression"]): - raise ValueError("The value of the problem_type parameter can be either classification or regression but {problem_type_val} found.".format(problem_type_val=problem_type)) - + raise ValueError(f"The value of the problem_type parameter can be either classification or regression but {problem_type} found.") + # To fetch the initial weights of the layer, the 'initial' argument is set to True. weights = layers_weights(last_layer, initial=True) activations = layers_activations(last_layer) @@ -294,14 +294,14 @@ def predict(last_layer, data_inputs, problem_type="classification"): Returns the predictions of all samples. """ if not (problem_type in ["classification", "regression"]): - raise ValueError("The value of the problem_type parameter can be either classification or regression but {problem_type_val} found.".format(problem_type_val=problem_type)) - + raise ValueError(f"The value of the problem_type parameter can be either classification or regression but {problem_type} found.") + # To fetch the trained weights of the layer, the 'initial' argument is set to False. weights = layers_weights(last_layer, initial=False) activations = layers_activations(last_layer) if len(weights) != len(activations): - raise TypeError("The length of layers {num_layers} is not equal to the number of activations functions {num_activations} and they must be equal.".format(num_layers=len(weights), num_activations=len(activations))) + raise TypeError(f"The length of layers {len(weights)} is not equal to the number of activations functions {len(activations)} and they must be equal.") predictions = [] for sample_idx in range(data_inputs.shape[0]): @@ -337,7 +337,7 @@ def to_vector(array): Example: weights_vector = nn.DenseLayer.to_vector(array=array) """ if not (type(array) is numpy.ndarray): - raise TypeError("An input of type numpy.ndarray is expected but an input of type {in_type} found.".format(in_type=type(array))) + raise TypeError(f"An input of type numpy.ndarray is expected but an input of type {type(array)} found.") return numpy.reshape(array, newshape=(array.size)) def to_array(vector, shape): @@ -352,11 +352,11 @@ def to_array(vector, shape): Example: weights_matrix = nn.DenseLayer.to_array(vector=vector, shape=shape) """ if not (type(vector) is numpy.ndarray): - raise TypeError("An input of type numpy.ndarray is expected but an input of type {in_type} found.".format(in_type=type(vector))) + raise TypeError(f"An input of type numpy.ndarray is expected but an input of type {type(vector)} found.") if vector.ndim > 1: - raise ValueError("A 1D NumPy array is expected but an array of {ndim} dimensions found.".format(ndim=vector.ndim)) + raise ValueError(f"A 1D NumPy array is expected but an array of {vector.ndim} dimensions found.") if vector.size != functools.reduce(lambda x,y:x*y, shape, 1): # (operator.mul == lambda x,y:x*y - raise ValueError("Mismatch between the vector length and the array shape. A vector of length {vector_length} cannot be converted into a array of shape ({array_shape}).".format(vector_length=vector.size, array_shape=shape)) + raise ValueError(f"Mismatch between the vector length and the array shape. A vector of length {vector.size} cannot be converted into a array of shape ({shape}).") return numpy.reshape(vector, newshape=shape) class InputLayer: @@ -381,7 +381,7 @@ def __init__(self, num_neurons, previous_layer, activation_function="sigmoid"): supported_activation_functions = ("sigmoid", "relu", "softmax", "None") if not (activation_function in supported_activation_functions): - raise ValueError("The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.".format(activation_function=activation_function, supported_activation_functions=supported_activation_functions)) + raise ValueError(f"The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.") self.activation_function = activation_function if previous_layer is None: diff --git a/pygad/pygad.py b/pygad/pygad.py index b7382cc..152fef2 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -10,23 +10,25 @@ from pygad import helper from pygad import visualize -class GA(utils.parent_selection.ParentSelection, - utils.crossover.Crossover, - utils.mutation.Mutation, +class GA(utils.parent_selection.ParentSelection, + utils.crossover.Crossover, + utils.mutation.Mutation, helper.unique.Unique, visualize.plot.Plot): - supported_int_types = [int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64] - supported_float_types = [float, numpy.float16, numpy.float32, numpy.float64] + supported_int_types = [int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, + numpy.uint, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64] + supported_float_types = [ + float, numpy.float16, numpy.float32, numpy.float64] supported_int_float_types = supported_int_types + supported_float_types - def __init__(self, - num_generations, - num_parents_mating, + def __init__(self, + num_generations, + num_parents_mating, fitness_func, fitness_batch_size=None, initial_population=None, - sol_per_pop=None, + sol_per_pop=None, num_genes=None, init_range_low=-4, init_range_high=4, @@ -61,7 +63,6 @@ def __init__(self, parallel_processing=None, random_seed=None, logger=None): - """ The constructor of the GA class accepts all parameters required to create an instance of the GA class. It validates such parameters. @@ -127,1179 +128,1100 @@ def __init__(self, logger: Added in PyGAD 2.20.0. It accepts a logger object of the 'logging.Logger' class to log the messages. If no logger is passed, then a default logger is created to log/print the messages to the console exactly like using the 'print()' function. """ + try: + # If no logger is passed, then create a logger that logs only the messages to the console. + if logger is None: + # Create a logger named with the module name. + logger = logging.getLogger(__name__) + # Set the logger log level to 'DEBUG' to log all kinds of messages. + logger.setLevel(logging.DEBUG) + + # Clear any attached handlers to the logger from the previous runs. + # If the handlers are not cleared, then the new handler will be appended to the list of handlers. + # This makes the single log message be repeated according to the length of the list of handlers. + logger.handlers.clear() + + # Create the handlers. + stream_handler = logging.StreamHandler() + # Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger. + stream_handler.setLevel(logging.DEBUG) + + # Create the formatter that just includes the log message. + formatter = logging.Formatter('%(message)s') + + # Add the formatter to the handler. + stream_handler.setFormatter(formatter) + + # Add the handler to the logger. + logger.addHandler(stream_handler) + else: + # Validate that the passed logger is of type 'logging.Logger'. + if isinstance(logger, logging.Logger): + pass + else: + raise TypeError(f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") - # If no logger is passed, then create a logger that logs only the messages to the console. - if logger is None: - # Create a logger named with the module name. - logger = logging.getLogger(__name__) - # Set the logger log level to 'DEBUG' to log all kinds of messages. - logger.setLevel(logging.DEBUG) - - # Clear any attached handlers to the logger from the previous runs. - # If the handlers are not cleared, then the new handler will be appended to the list of handlers. - # This makes the single log message be repeated according to the length of the list of handlers. - logger.handlers.clear() - - # Create the handlers. - stream_handler = logging.StreamHandler() - # Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger. - stream_handler.setLevel(logging.DEBUG) - - # Create the formatter that just includes the log message. - formatter = logging.Formatter('%(message)s') - - # Add the formatter to the handler. - stream_handler.setFormatter(formatter) + # Create the 'self.logger' attribute to hold the logger. + # Instead of using 'print()', use 'self.logger.info()' + self.logger = logger - # Add the handler to the logger. - logger.addHandler(stream_handler) - else: - # Validate that the passed logger is of type 'logging.Logger'. - if isinstance(logger, logging.Logger): + self.random_seed = random_seed + if random_seed is None: pass else: - raise TypeError("The expected type of the 'logger' parameter is 'logging.Logger' but {logger_type} found.".format(logger_type=type(logger))) - - # Create the 'self.logger' attribute to hold the logger. - # Instead of using 'print()', use 'self.logger.info()' - self.logger = logger + numpy.random.seed(self.random_seed) + random.seed(self.random_seed) - self.random_seed = random_seed - if random_seed is None: - pass - else: - numpy.random.seed(self.random_seed) - random.seed(self.random_seed) + # If suppress_warnings is bool and its valud is False, then print warning messages. + if type(suppress_warnings) is bool: + self.suppress_warnings = suppress_warnings + else: + self.valid_parameters = False + raise TypeError(f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") - # If suppress_warnings is bool and its valud is False, then print warning messages. - if type(suppress_warnings) is bool: - self.suppress_warnings = suppress_warnings - else: - self.valid_parameters = False - self.logger.error("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) - raise TypeError("The expected type of the 'suppress_warnings' parameter is bool but {suppress_warnings_type} found.".format(suppress_warnings_type=type(suppress_warnings))) - - # Validating mutation_by_replacement - if not (type(mutation_by_replacement) is bool): - self.valid_parameters = False - self.logger.error("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) - raise TypeError("The expected type of the 'mutation_by_replacement' parameter is bool but {mutation_by_replacement_type} found.".format(mutation_by_replacement_type=type(mutation_by_replacement))) - - self.mutation_by_replacement = mutation_by_replacement - - # Validate allow_duplicate_genes - if not (type(allow_duplicate_genes) is bool): - self.valid_parameters = False - self.logger.error("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) - raise TypeError("The expected type of the 'allow_duplicate_genes' parameter is bool but {allow_duplicate_genes_type} found.".format(allow_duplicate_genes_type=type(allow_duplicate_genes))) - - self.allow_duplicate_genes = allow_duplicate_genes - - # Validate gene_space - self.gene_space_nested = False - if type(gene_space) is type(None): - pass - elif type(gene_space) is range: - if len(gene_space) == 0: + # Validating mutation_by_replacement + if not (type(mutation_by_replacement) is bool): self.valid_parameters = False - self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") - raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") - elif type(gene_space) in [list, numpy.ndarray]: - if len(gene_space) == 0: + raise TypeError(f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") + + self.mutation_by_replacement = mutation_by_replacement + + # Validate allow_duplicate_genes + if not (type(allow_duplicate_genes) is bool): self.valid_parameters = False - self.logger.error("'gene_space' cannot be empty (i.e. its length must be >= 0).") - raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") - else: - for index, el in enumerate(gene_space): - if type(el) in [numpy.ndarray, list, tuple, range]: - if len(el) == 0: - self.valid_parameters = False - self.logger.error("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) - raise ValueError("The element indexed {index} of 'gene_space' with type {el_type} cannot be empty (i.e. its length must be >= 0).".format(index=index, el_type=type(el))) - else: - for val in el: - if not (type(val) in [type(None)] + GA.supported_int_float_types): - self.logger.error("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) - raise TypeError("All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {typ} found.".format(val=val, typ=type(val))) - self.gene_space_nested = True - elif type(el) == type(None): - pass - # self.gene_space_nested = True - elif type(el) is dict: - if len(el.items()) == 2: - if ('low' in el.keys()) and ('high' in el.keys()): - pass - else: + raise TypeError(f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") + + self.allow_duplicate_genes = allow_duplicate_genes + + # Validate gene_space + self.gene_space_nested = False + if type(gene_space) is type(None): + pass + elif type(gene_space) is range: + if len(gene_space) == 0: + self.valid_parameters = False + raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") + elif type(gene_space) in [list, numpy.ndarray]: + if len(gene_space) == 0: + self.valid_parameters = False + raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") + else: + for index, el in enumerate(gene_space): + if type(el) in [numpy.ndarray, list, tuple, range]: + if len(el) == 0: self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - elif len(el.items()) == 3: - if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): - pass + raise ValueError(f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") + else: + for val in el: + if not (type(val) in [type(None)] + GA.supported_int_float_types): + raise TypeError(f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") + self.gene_space_nested = True + elif type(el) == type(None): + pass + # self.gene_space_nested = True + elif type(el) is dict: + if len(el.items()) == 2: + if ('low' in el.keys()) and ('high' in el.keys()): + pass + else: + self.valid_parameters = False + raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") + elif len(el.items()) == 3: + if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): + pass + else: + self.valid_parameters = False + raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") else: self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=el.keys())) - else: + raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") + self.gene_space_nested = True + elif not (type(el) in GA.supported_int_float_types): self.valid_parameters = False - self.logger.error("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) - raise ValueError("When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(el.items()))) - self.gene_space_nested = True - elif not (type(el) in GA.supported_int_float_types): - self.valid_parameters = False - self.logger.error("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) - raise TypeError("Unexpected type {el_type} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.".format(index=index, el_type=type(el))) + raise TypeError(f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") - elif type(gene_space) is dict: - if len(gene_space.items()) == 2: - if ('low' in gene_space.keys()) and ('high' in gene_space.keys()): - pass - else: - self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - elif len(gene_space.items()) == 3: - if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): - pass + elif type(gene_space) is dict: + if len(gene_space.items()) == 2: + if ('low' in gene_space.keys()) and ('high' in gene_space.keys()): + pass + else: + self.valid_parameters = False + raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") + elif len(gene_space.items()) == 3: + if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): + pass + else: + self.valid_parameters = False + raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") else: self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) - raise ValueError("When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space_dict_keys}".format(gene_space_dict_keys=gene_space.keys())) + raise ValueError(f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") + else: self.valid_parameters = False - self.logger.error("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) - raise ValueError("When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({num_items}) items found.".format(num_items=len(gene_space.items()))) + raise TypeError(f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") - else: - self.valid_parameters = False - self.logger.error("The expected type of 'gene_space' is list, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) - raise TypeError("The expected type of 'gene_space' is list, range, or numpy.ndarray but {gene_space_type} found.".format(gene_space_type=type(gene_space))) - - self.gene_space = gene_space + self.gene_space = gene_space - # Validate init_range_low and init_range_high - if type(init_range_low) in GA.supported_int_float_types: - if type(init_range_high) in GA.supported_int_float_types: - self.init_range_low = init_range_low - self.init_range_high = init_range_high + # Validate init_range_low and init_range_high + if type(init_range_low) in GA.supported_int_float_types: + if type(init_range_high) in GA.supported_int_float_types: + self.init_range_low = init_range_low + self.init_range_high = init_range_high + else: + self.valid_parameters = False + raise ValueError(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") else: self.valid_parameters = False - self.logger.error("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) - raise ValueError("The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high_value}) of type {init_range_high_type} found.".format(init_range_high_value=init_range_high, init_range_high_type=type(init_range_high))) - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) - raise ValueError("The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low_value}) of type {init_range_low_type} found.".format(init_range_low_value=init_range_low, init_range_low_type=type(init_range_low))) - - # Validate random_mutation_min_val and random_mutation_max_val - if type(random_mutation_min_val) in GA.supported_int_float_types: - if type(random_mutation_max_val) in GA.supported_int_float_types: - if random_mutation_min_val == random_mutation_max_val: - if not self.suppress_warnings: warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") + raise ValueError(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") + + # Validate random_mutation_min_val and random_mutation_max_val + if type(random_mutation_min_val) in GA.supported_int_float_types: + if type(random_mutation_max_val) in GA.supported_int_float_types: + if random_mutation_min_val == random_mutation_max_val: + if not self.suppress_warnings: + warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") + else: + self.valid_parameters = False + raise TypeError(f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") else: self.valid_parameters = False - self.logger.error("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) - raise TypeError("The expected type of the 'random_mutation_max_val' parameter is numeric but {random_mutation_max_val_type} found.".format(random_mutation_max_val_type=type(random_mutation_max_val))) - else: - self.valid_parameters = False - self.logger.error("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) - raise TypeError("The expected type of the 'random_mutation_min_val' parameter is numeric but {random_mutation_min_val_type} found.".format(random_mutation_min_val_type=type(random_mutation_min_val))) - self.random_mutation_min_val = random_mutation_min_val - self.random_mutation_max_val = random_mutation_max_val - - # Validate gene_type - if gene_type in GA.supported_int_float_types: - self.gene_type = [gene_type, None] - self.gene_type_single = True - # A single data type of float with precision. - elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): - self.gene_type = gene_type - self.gene_type_single = True - # A single data type of int with precision. - elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): - self.gene_type_single = False - self.logger.error("Integers cannot have precision. Please use the integer data type directly instead of {gene_type_val}.".format(gene_type_val=gene_type)) - raise ValueError("Integers cannot have precision. Please use the integer data type directly instead of {gene_type_val}.".format(gene_type_val=gene_type)) - elif type(gene_type) in [list, tuple, numpy.ndarray]: - if num_genes is None: - if initial_population is None: - self.valid_parameters = False - self.logger.error("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - elif not len(gene_type) == len(initial_population[0]): + raise TypeError(f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") + self.random_mutation_min_val = random_mutation_min_val + self.random_mutation_max_val = random_mutation_max_val + + # Validate gene_type + if gene_type in GA.supported_int_float_types: + self.gene_type = [gene_type, None] + self.gene_type_single = True + # A single data type of float with precision. + elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): + self.gene_type = gene_type + self.gene_type_single = True + # A single data type of int with precision. + elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): + self.gene_type_single = False + raise ValueError(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") + elif type(gene_type) in [list, tuple, numpy.ndarray]: + if num_genes is None: + if initial_population is None: + self.valid_parameters = False + raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") + elif not len(gene_type) == len(initial_population[0]): + self.valid_parameters = False + raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") + elif not len(gene_type) == num_genes: self.valid_parameters = False - self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) - raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != number of genes ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=len(initial_population[0]))) - elif not len(gene_type) == num_genes: - self.valid_parameters = False - self.logger.error("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) - raise ValueError("When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type_val} with len(gene_type) ({len_gene_type}) != len(num_genes) ({num_genes}) found.".format(gene_type_val=gene_type, len_gene_type=len(gene_type), num_genes=num_genes)) - for gene_type_idx, gene_type_val in enumerate(gene_type): - if gene_type_val in GA.supported_float_types: - # If the gene type is float and no precision is passed, set it to None. - gene_type[gene_type_idx] = [gene_type_val, None] - elif gene_type_val in GA.supported_int_types: - gene_type[gene_type_idx] = [gene_type_val, None] - elif type(gene_type_val) in [list, tuple, numpy.ndarray]: - # A float type is expected in a list/tuple/numpy.ndarray of length 2. - if len(gene_type_val) == 2: - if gene_type_val[0] in GA.supported_float_types: - if type(gene_type_val[1]) in GA.supported_int_types: - pass + raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") + for gene_type_idx, gene_type_val in enumerate(gene_type): + if gene_type_val in GA.supported_float_types: + # If the gene type is float and no precision is passed, set it to None. + gene_type[gene_type_idx] = [gene_type_val, None] + elif gene_type_val in GA.supported_int_types: + gene_type[gene_type_idx] = [gene_type_val, None] + elif type(gene_type_val) in [list, tuple, numpy.ndarray]: + # A float type is expected in a list/tuple/numpy.ndarray of length 2. + if len(gene_type_val) == 2: + if gene_type_val[0] in GA.supported_float_types: + if type(gene_type_val[1]) in GA.supported_int_types: + pass + else: + self.valid_parameters = False + raise TypeError(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") else: self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) - raise TypeError("In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_precision_val} with type {gene_type_type}.".format(gene_type_val=gene_type_val, gene_type_precision_val=gene_type_val[1], gene_type_type=gene_type_val[0], gene_type_idx=gene_type_idx)) + raise TypeError( + f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") else: self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) - raise TypeError("In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.".format(gene_type=gene_type_val, gene_type_idx=gene_type_idx)) + raise ValueError(f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") else: self.valid_parameters = False - self.logger.error("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) - raise ValueError("In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {gene_type_type} with length {gene_type_length} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx, gene_type_length=len(gene_type_val))) - else: - self.valid_parameters = False - self.logger.error("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) - raise ValueError("When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {gene_type_type} found at index {gene_type_idx}.".format(gene_type_val=gene_type_val, gene_type_type=type(gene_type_val), gene_type_idx=gene_type_idx)) - self.gene_type = gene_type - self.gene_type_single = False - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) - raise ValueError("The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type_val}) of type {gene_type_type} found.".format(gene_type_val=gene_type, gene_type_type=type(gene_type))) + raise ValueError(f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") + self.gene_type = gene_type + self.gene_type_single = False + else: + self.valid_parameters = False + raise ValueError(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") - # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. - self.gene_space_unpacked = self.unpack_gene_space() + # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. + self.gene_space_unpacked = self.unpack_gene_space() - # Build the initial population - if initial_population is None: - if (sol_per_pop is None) or (num_genes is None): - self.valid_parameters = False - self.logger.error("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") - raise TypeError("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") - elif (type(sol_per_pop) is int) and (type(num_genes) is int): - # Validating the number of solutions in the population (sol_per_pop) - if sol_per_pop <= 0: + # Build the initial population + if initial_population is None: + if (sol_per_pop is None) or (num_genes is None): self.valid_parameters = False - self.logger.error("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) - raise ValueError("The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(sol_per_pop=sol_per_pop)) - # Validating the number of gene. - if (num_genes <= 0): - self.valid_parameters = False - self.logger.error("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) - raise ValueError("The number of genes cannot be <= 0 but ({num_genes}) found.\n".format(num_genes=num_genes)) - # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. - # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. - self.num_genes = num_genes # Number of genes in the solution. - - # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. - if self.gene_space_nested: - if len(gene_space) != self.num_genes: + raise TypeError("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assinging the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.") + elif (type(sol_per_pop) is int) and (type(num_genes) is int): + # Validating the number of solutions in the population (sol_per_pop) + if sol_per_pop <= 0: self.valid_parameters = False - self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) - raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({num_genes})".format(len_gene_space=len(gene_space), num_genes=self.num_genes)) - - self.sol_per_pop = sol_per_pop # Number of solutions in the population. - self.initialize_population(self.init_range_low, - self.init_range_high, - allow_duplicate_genes, - True, - self.gene_type) - else: - self.valid_parameters = False - self.logger.error("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) - raise TypeError("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes))) - elif not type(initial_population) in [list, tuple, numpy.ndarray]: - self.valid_parameters = False - self.logger.error("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) - raise TypeError("The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {initial_population_type} found.".format(initial_population_type=type(initial_population))) - elif numpy.array(initial_population).ndim != 2: - self.valid_parameters = False - self.logger.error("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) - raise ValueError("A 2D list is expected to the initail_population parameter but a ({initial_population_ndim}-D) list found.".format(initial_population_ndim=numpy.array(initial_population).ndim)) - else: - # Validate the type of each value in the 'initial_population' parameter. - for row_idx in range(len(initial_population)): - for col_idx in range(len(initial_population[0])): - if type(initial_population[row_idx][col_idx]) in GA.supported_int_float_types: - pass - else: + raise ValueError(f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + # Validating the number of gene. + if (num_genes <= 0): self.valid_parameters = False - self.logger.error("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) - raise TypeError("The values in the initial population can be integers or floats but the value ({value}) of type {value_type} found.".format(value=initial_population[row_idx][col_idx], value_type=type(initial_population[row_idx][col_idx]))) - - # Forcing the initial_population array to have the data type assigned to the gene_type parameter. - if self.gene_type_single == True: - if self.gene_type[1] == None: - self.initial_population = numpy.array(initial_population, - dtype=self.gene_type[0]) + raise ValueError(f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") + # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. + # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. + self.num_genes = num_genes # Number of genes in the solution. + + # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. + if self.gene_space_nested: + if len(gene_space) != self.num_genes: + self.valid_parameters = False + raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + + # Number of solutions in the population. + self.sol_per_pop = sol_per_pop + self.initialize_population(self.init_range_low, + self.init_range_high, + allow_duplicate_genes, + True, + self.gene_type) else: - # This block is reached only for non-integer data types (i.e. float). - self.initial_population = numpy.round(numpy.array(initial_population, - dtype=self.gene_type[0]), - self.gene_type[1]) + self.valid_parameters = False + raise TypeError(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") + elif not type(initial_population) in [list, tuple, numpy.ndarray]: + self.valid_parameters = False + raise TypeError(f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") + elif numpy.array(initial_population).ndim != 2: + self.valid_parameters = False + raise ValueError(f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") else: - initial_population = numpy.array(initial_population) - self.initial_population = numpy.zeros(shape=(initial_population.shape[0], - initial_population.shape[1]), - dtype=object) - for gene_idx in range(initial_population.shape[1]): - if self.gene_type[gene_idx][1] is None: - self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], - dtype=self.gene_type[gene_idx][0]) + # Validate the type of each value in the 'initial_population' parameter. + for row_idx in range(len(initial_population)): + for col_idx in range(len(initial_population[0])): + if type(initial_population[row_idx][col_idx]) in GA.supported_int_float_types: + pass + else: + self.valid_parameters = False + raise TypeError(f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") + + # Forcing the initial_population array to have the data type assigned to the gene_type parameter. + if self.gene_type_single == True: + if self.gene_type[1] == None: + self.initial_population = numpy.array(initial_population, + dtype=self.gene_type[0]) else: # This block is reached only for non-integer data types (i.e. float). - self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], - dtype=self.gene_type[gene_idx][0]), - self.gene_type[gene_idx][1]) - - # Check if duplicates are allowed. If not, then solve any exisiting duplicates in the passed initial population. - if self.allow_duplicate_genes == False: - for initial_solution_idx, initial_solution in enumerate(self.initial_population): - if self.gene_space is None: - self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=initial_solution, - min_val=self.init_range_low, - max_val=self.init_range_high, - mutation_by_replacement=self.mutation_by_replacement, - gene_type=self.gene_type, - num_trials=10) - else: - self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=initial_solution, - gene_type=self.gene_type, - num_trials=10) - - self.population = self.initial_population.copy() # A NumPy array holding the initial population. - self.num_genes = self.initial_population.shape[1] # Number of genes in the solution. - self.sol_per_pop = self.initial_population.shape[0] # Number of solutions in the population. - self.pop_size = (self.sol_per_pop,self.num_genes) # The population size. - - # Round initial_population and population - self.initial_population = self.round_genes(self.initial_population) - self.population = self.round_genes(self.population) - - # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. - if self.gene_space_nested: - if len(gene_space) != self.num_genes: - self.valid_parameters = False - self.logger.error("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) - raise ValueError("When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len_gene_space}) != num_genes ({len_num_genes})".format(len_gene_space=len(gene_space), len_num_genes=self.num_genes)) - - # Validating the number of parents to be selected for mating (num_parents_mating) - if num_parents_mating <= 0: - self.valid_parameters = False - self.logger.error("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) - raise ValueError("The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n".format(num_parents_mating=num_parents_mating)) - - # Validating the number of parents to be selected for mating: num_parents_mating - if (num_parents_mating > self.sol_per_pop): - self.valid_parameters = False - self.logger.error("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) - raise ValueError("The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n".format(num_parents_mating=num_parents_mating, sol_per_pop=self.sol_per_pop)) - - self.num_parents_mating = num_parents_mating - - # crossover: Refers to the method that applies the crossover operator based on the selected type of crossover in the crossover_type property. - # Validating the crossover type: crossover_type - if (crossover_type is None): - self.crossover = None - elif inspect.ismethod(crossover_type): - # Check if the crossover_type is a method that accepts 4 paramaters. - if (crossover_type.__code__.co_argcount == 4): - # The crossover method assigned to the crossover_type parameter is validated. - self.crossover = crossover_type - else: + self.initial_population = numpy.round(numpy.array(initial_population, + dtype=self.gene_type[0]), + self.gene_type[1]) + else: + initial_population = numpy.array(initial_population) + self.initial_population = numpy.zeros(shape=(initial_population.shape[0], + initial_population.shape[1]), + dtype=object) + for gene_idx in range(initial_population.shape[1]): + if self.gene_type[gene_idx][1] is None: + self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx], + dtype=self.gene_type[gene_idx][0]) + else: + # This block is reached only for non-integer data types (i.e. float). + self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx], + dtype=self.gene_type[gene_idx][0]), + self.gene_type[gene_idx][1]) + + # Check if duplicates are allowed. If not, then solve any exisiting duplicates in the passed initial population. + if self.allow_duplicate_genes == False: + for initial_solution_idx, initial_solution in enumerate(self.initial_population): + if self.gene_space is None: + self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=initial_solution, + min_val=self.init_range_low, + max_val=self.init_range_high, + mutation_by_replacement=self.mutation_by_replacement, + gene_type=self.gene_type, + num_trials=10) + else: + self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=initial_solution, + gene_type=self.gene_type, + num_trials=10) + + # A NumPy array holding the initial population. + self.population = self.initial_population.copy() + # Number of genes in the solution. + self.num_genes = self.initial_population.shape[1] + # Number of solutions in the population. + self.sol_per_pop = self.initial_population.shape[0] + # The population size. + self.pop_size = (self.sol_per_pop, self.num_genes) + + # Round initial_population and population + self.initial_population = self.round_genes(self.initial_population) + self.population = self.round_genes(self.population) + + # In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes. + if self.gene_space_nested: + if len(gene_space) != self.num_genes: + self.valid_parameters = False + raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + + # Validating the number of parents to be selected for mating (num_parents_mating) + if num_parents_mating <= 0: self.valid_parameters = False - self.logger.error("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - raise ValueError("When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - elif callable(crossover_type): - # Check if the crossover_type is a function that accepts 2 paramaters. - if (crossover_type.__code__.co_argcount == 3): - # The crossover function assigned to the crossover_type parameter is validated. - self.crossover = crossover_type - else: + raise ValueError(f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") + + # Validating the number of parents to be selected for mating: num_parents_mating + if (num_parents_mating > self.sol_per_pop): self.valid_parameters = False - self.logger.error("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - raise ValueError("When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=crossover_type.__code__.co_name, argcount=crossover_type.__code__.co_argcount)) - elif not (type(crossover_type) is str): - self.valid_parameters = False - self.logger.error("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) - raise TypeError("The expected type of the 'crossover_type' parameter is either callable or str but {crossover_type} found.".format(crossover_type=type(crossover_type))) - else: # type crossover_type is str - crossover_type = crossover_type.lower() - if (crossover_type == "single_point"): - self.crossover = self.single_point_crossover - elif (crossover_type == "two_points"): - self.crossover = self.two_points_crossover - elif (crossover_type == "uniform"): - self.crossover = self.uniform_crossover - elif (crossover_type == "scattered"): - self.crossover = self.scattered_crossover - else: + raise ValueError(f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") + + self.num_parents_mating = num_parents_mating + + # crossover: Refers to the method that applies the crossover operator based on the selected type of crossover in the crossover_type property. + # Validating the crossover type: crossover_type + if (crossover_type is None): + self.crossover = None + elif inspect.ismethod(crossover_type): + # Check if the crossover_type is a method that accepts 4 paramaters. + if (crossover_type.__code__.co_argcount == 4): + # The crossover method assigned to the crossover_type parameter is validated. + self.crossover = crossover_type + else: + self.valid_parameters = False + raise ValueError(f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + elif callable(crossover_type): + # Check if the crossover_type is a function that accepts 2 paramaters. + if (crossover_type.__code__.co_argcount == 3): + # The crossover function assigned to the crossover_type parameter is validated. + self.crossover = crossover_type + else: + self.valid_parameters = False + raise ValueError(f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") + elif not (type(crossover_type) is str): self.valid_parameters = False - self.logger.error("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) - raise TypeError("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n".format(crossover_type=crossover_type)) + raise TypeError(f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") + else: # type crossover_type is str + crossover_type = crossover_type.lower() + if (crossover_type == "single_point"): + self.crossover = self.single_point_crossover + elif (crossover_type == "two_points"): + self.crossover = self.two_points_crossover + elif (crossover_type == "uniform"): + self.crossover = self.uniform_crossover + elif (crossover_type == "scattered"): + self.crossover = self.scattered_crossover + else: + self.valid_parameters = False + raise TypeError(f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") - self.crossover_type = crossover_type + self.crossover_type = crossover_type - # Calculate the value of crossover_probability - if crossover_probability is None: - self.crossover_probability = None - elif type(crossover_probability) in GA.supported_int_float_types: - if crossover_probability >= 0 and crossover_probability <= 1: - self.crossover_probability = crossover_probability - else: - self.valid_parameters = False - self.logger.error("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) - raise ValueError("The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability_value}) found.".format(crossover_probability_value=crossover_probability)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) - raise TypeError("Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability_value}) of type {crossover_probability_type} found.".format(crossover_probability_value=crossover_probability, crossover_probability_type=type(crossover_probability))) - - # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. - # Validating the mutation type: mutation_type - # "adaptive" mutation is supported starting from PyGAD 2.10.0 - if mutation_type is None: - self.mutation = None - elif inspect.ismethod(mutation_type): - # Check if the mutation_type is a method that accepts 3 paramater. - if (mutation_type.__code__.co_argcount == 3): - # The mutation method assigned to the mutation_type parameter is validated. - self.mutation = mutation_type - else: - self.valid_parameters = False - self.logger.error("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - raise ValueError("When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - elif callable(mutation_type): - # Check if the mutation_type is a function that accepts 2 paramater. - if (mutation_type.__code__.co_argcount == 2): - # The mutation function assigned to the mutation_type parameter is validated. - self.mutation = mutation_type + # Calculate the value of crossover_probability + if crossover_probability is None: + self.crossover_probability = None + elif type(crossover_probability) in GA.supported_int_float_types: + if crossover_probability >= 0 and crossover_probability <= 1: + self.crossover_probability = crossover_probability + else: + self.valid_parameters = False + raise ValueError(f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") else: self.valid_parameters = False - self.logger.error("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - raise ValueError("When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=mutation_type.__code__.co_name, argcount=mutation_type.__code__.co_argcount)) - elif not (type(mutation_type) is str): - self.valid_parameters = False - self.logger.error("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) - raise TypeError("The expected type of the 'mutation_type' parameter is either callable or str but {mutation_type} found.".format(mutation_type=type(mutation_type))) - else: # type mutation_type is str - mutation_type = mutation_type.lower() - if (mutation_type == "random"): - self.mutation = self.random_mutation - elif (mutation_type == "swap"): - self.mutation = self.swap_mutation - elif (mutation_type == "scramble"): - self.mutation = self.scramble_mutation - elif (mutation_type == "inversion"): - self.mutation = self.inversion_mutation - elif (mutation_type == "adaptive"): - self.mutation = self.adaptive_mutation - else: + raise TypeError(f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") + + # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. + # Validating the mutation type: mutation_type + # "adaptive" mutation is supported starting from PyGAD 2.10.0 + if mutation_type is None: + self.mutation = None + elif inspect.ismethod(mutation_type): + # Check if the mutation_type is a method that accepts 3 paramater. + if (mutation_type.__code__.co_argcount == 3): + # The mutation method assigned to the mutation_type parameter is validated. + self.mutation = mutation_type + else: + self.valid_parameters = False + raise ValueError(f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + elif callable(mutation_type): + # Check if the mutation_type is a function that accepts 2 paramater. + if (mutation_type.__code__.co_argcount == 2): + # The mutation function assigned to the mutation_type parameter is validated. + self.mutation = mutation_type + else: + self.valid_parameters = False + raise ValueError(f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") + elif not (type(mutation_type) is str): self.valid_parameters = False - self.logger.error("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) - raise TypeError("Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n".format(mutation_type=mutation_type)) - - self.mutation_type = mutation_type - - # Calculate the value of mutation_probability - if not (self.mutation_type is None): - if mutation_probability is None: - self.mutation_probability = None - elif (mutation_type != "adaptive"): - # Mutation probability is fixed not adaptive. - if type(mutation_probability) in GA.supported_int_float_types: - if mutation_probability >= 0 and mutation_probability <= 1: - self.mutation_probability = mutation_probability + raise TypeError(f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") + else: # type mutation_type is str + mutation_type = mutation_type.lower() + if (mutation_type == "random"): + self.mutation = self.random_mutation + elif (mutation_type == "swap"): + self.mutation = self.swap_mutation + elif (mutation_type == "scramble"): + self.mutation = self.scramble_mutation + elif (mutation_type == "inversion"): + self.mutation = self.inversion_mutation + elif (mutation_type == "adaptive"): + self.mutation = self.adaptive_mutation + else: + self.valid_parameters = False + raise TypeError(f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") + + self.mutation_type = mutation_type + + # Calculate the value of mutation_probability + if not (self.mutation_type is None): + if mutation_probability is None: + self.mutation_probability = None + elif (mutation_type != "adaptive"): + # Mutation probability is fixed not adaptive. + if type(mutation_probability) in GA.supported_int_float_types: + if mutation_probability >= 0 and mutation_probability <= 1: + self.mutation_probability = mutation_probability + else: + self.valid_parameters = False + raise ValueError(f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") else: self.valid_parameters = False - self.logger.error("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) - raise ValueError("The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=mutation_probability)) + raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") else: - self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - raise TypeError("Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - else: - # Mutation probability is adaptive not fixed. - if type(mutation_probability) in [list, tuple, numpy.ndarray]: - if len(mutation_probability) == 2: - for el in mutation_probability: - if type(el) in GA.supported_int_float_types: - if el >= 0 and el <= 1: - pass + # Mutation probability is adaptive not fixed. + if type(mutation_probability) in [list, tuple, numpy.ndarray]: + if len(mutation_probability) == 2: + for el in mutation_probability: + if type(el) in GA.supported_int_float_types: + if el >= 0 and el <= 1: + pass + else: + self.valid_parameters = False + raise ValueError(f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") else: self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) - raise ValueError("The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability_value}) found.".format(mutation_probability_value=el)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=el, mutation_probability_type=type(el))) - if mutation_probability[0] < mutation_probability[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_probability' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_probability[0], second_el=mutation_probability[1])) - self.mutation_probability = mutation_probability + raise TypeError(f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") + if mutation_probability[0] < mutation_probability[1]: + if not self.suppress_warnings: + warnings.warn(f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") + self.mutation_probability = mutation_probability + else: + self.valid_parameters = False + raise ValueError(f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") else: self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({mutation_probability_length}) element(s) found.".format(mutation_probability_length=len(mutation_probability))) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - raise TypeError("Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability_value}) of type {mutation_probability_type} found.".format(mutation_probability_value=mutation_probability, mutation_probability_type=type(mutation_probability))) - else: - pass - - # Calculate the value of mutation_num_genes - if not (self.mutation_type is None): - if mutation_num_genes is None: - # The mutation_num_genes parameter does not exist. Checking whether adaptive mutation is used. - if (mutation_type != "adaptive"): - # The percent of genes to mutate is fixed not adaptive. - if mutation_percent_genes == 'default'.lower(): - mutation_percent_genes = 10 - # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) - # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. - if mutation_num_genes == 0: - if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) - mutation_num_genes = 1 - - elif type(mutation_percent_genes) in GA.supported_int_float_types: - if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): - self.valid_parameters = False - self.logger.error("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - raise ValueError("The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - else: - # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. - if mutation_percent_genes == 'default'.lower(): - mutation_percent_genes = 10 + raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") + else: + pass + # Calculate the value of mutation_num_genes + if not (self.mutation_type is None): + if mutation_num_genes is None: + # The mutation_num_genes parameter does not exist. Checking whether adaptive mutation is used. + if (mutation_type != "adaptive"): + # The percent of genes to mutate is fixed not adaptive. + if mutation_percent_genes == 'default'.lower(): + mutation_percent_genes = 10 # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes = numpy.uint32((mutation_percent_genes*self.num_genes)/100) + mutation_num_genes = numpy.uint32( + (mutation_percent_genes*self.num_genes)/100) # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes == 0: if self.mutation_probability is None: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate (mutation_percent_genes={mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes, mutation_num=mutation_num_genes)) + if not self.suppress_warnings: + warnings.warn( + f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes = 1 + + elif type(mutation_percent_genes) in GA.supported_int_float_types: + if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): + self.valid_parameters = False + raise ValueError(f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + else: + # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. + if mutation_percent_genes == 'default'.lower(): + mutation_percent_genes = 10 + + # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. + mutation_num_genes = numpy.uint32( + (mutation_percent_genes*self.num_genes)/100) + # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. + if mutation_num_genes == 0: + if self.mutation_probability is None: + if not self.suppress_warnings: + warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") + mutation_num_genes = 1 + else: + self.valid_parameters = False + raise TypeError(f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") + else: + # The percent of genes to mutate is adaptive not fixed. + if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: + if len(mutation_percent_genes) == 2: + mutation_num_genes = numpy.zeros_like( + mutation_percent_genes, dtype=numpy.uint32) + for idx, el in enumerate(mutation_percent_genes): + if type(el) in GA.supported_int_float_types: + if (el <= 0 or el > 100): + self.valid_parameters = False + raise ValueError(f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") + else: + self.valid_parameters = False + raise TypeError(f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. + # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. + mutation_num_genes[idx] = numpy.uint32( + (mutation_percent_genes[idx]*self.num_genes)/100) + # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. + if mutation_num_genes[idx] == 0: + if not self.suppress_warnings: + warnings.warn(f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") + mutation_num_genes[idx] = 1 + if mutation_percent_genes[0] < mutation_percent_genes[1]: + if not self.suppress_warnings: + warnings.warn(f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.") + # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. + else: + self.valid_parameters = False + raise ValueError(f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") + else: + if self.mutation_probability is None: + self.valid_parameters = False + raise TypeError(f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") + # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. + elif (mutation_type != "adaptive"): + # Number of genes to mutate is fixed not adaptive. + if type(mutation_num_genes) in GA.supported_int_types: + if (mutation_num_genes <= 0): + self.valid_parameters = False + raise ValueError(f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") + elif (mutation_num_genes > self.num_genes): + self.valid_parameters = False + raise ValueError(f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") else: self.valid_parameters = False - self.logger.error("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) - raise TypeError("Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=mutation_percent_genes, mutation_percent_genes_type=type(mutation_percent_genes))) + raise TypeError(f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") else: - # The percent of genes to mutate is adaptive not fixed. - if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: - if len(mutation_percent_genes) == 2: - mutation_num_genes = numpy.zeros_like(mutation_percent_genes, dtype=numpy.uint32) - for idx, el in enumerate(mutation_percent_genes): - if type(el) in GA.supported_int_float_types: - if (el <= 0 or el > 100): + # Number of genes to mutate is adaptive not fixed. + if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: + if len(mutation_num_genes) == 2: + for el in mutation_num_genes: + if type(el) in GA.supported_int_types: + if (el <= 0): self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) - raise ValueError("The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n".format(mutation_percent_genes=mutation_percent_genes)) + raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") + elif (el > self.num_genes): + self.valid_parameters = False + raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") else: self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({mutation_percent_genes_value}) of type {mutation_percent_genes_type} found.".format(mutation_percent_genes_value=el, mutation_percent_genes_type=type(el))) - # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. - # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. - mutation_num_genes[idx] = numpy.uint32((mutation_percent_genes[idx]*self.num_genes)/100) - # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. - if mutation_num_genes[idx] == 0: - if not self.suppress_warnings: warnings.warn("The percentage of genes to mutate ({mutation_percent}) resutled in selecting ({mutation_num}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.".format(mutation_percent=mutation_percent_genes[idx], mutation_num=mutation_num_genes[idx])) - mutation_num_genes[idx] = 1 - if mutation_percent_genes[0] < mutation_percent_genes[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_percent_genes' parameter is ({first_el}) which is smaller than the second element ({second_el}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.".format(first_el=mutation_percent_genes[0], second_el=mutation_percent_genes[1])) - # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. + raise TypeError(f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") + # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. + if mutation_num_genes[0] < mutation_num_genes[1]: + if not self.suppress_warnings: + warnings.warn(f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") + # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. else: self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({mutation_percent_genes_length}) element(s) found.".format(mutation_percent_genes_length=len(mutation_percent_genes))) + raise ValueError(f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") else: - if self.mutation_probability is None: - self.valid_parameters = False - self.logger.error("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) - raise TypeError("Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes_value}) found.".format(mutation_percent_genes_value=mutation_percent_genes)) - # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. - elif (mutation_type != "adaptive"): - # Number of genes to mutate is fixed not adaptive. - if type(mutation_num_genes) in GA.supported_int_types: - if (mutation_num_genes <= 0): - self.valid_parameters = False - self.logger.error("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) - raise ValueError("The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes=mutation_num_genes)) - elif (mutation_num_genes > self.num_genes): self.valid_parameters = False - self.logger.error("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) - raise ValueError("The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({num_genes}).\n".format(mutation_num_genes=mutation_num_genes, num_genes=self.num_genes)) + raise TypeError(f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") + else: + pass + + # Validating mutation_by_replacement and mutation_type + if self.mutation_type != "random" and self.mutation_by_replacement: + if not self.suppress_warnings: + warnings.warn(f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") + + # Check if crossover and mutation are both disabled. + if (self.mutation_type is None) and (self.crossover_type is None): + if not self.suppress_warnings: + warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") + + # select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute. + # Validating the selected type of parent selection: parent_selection_type + if inspect.ismethod(parent_selection_type): + # Check if the parent_selection_type is a method that accepts 4 paramaters. + if (parent_selection_type.__code__.co_argcount == 4): + # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. + # The parent selection method assigned to the parent_selection_type parameter is validated. + self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - raise TypeError("The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.\n".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - else: - # Number of genes to mutate is adaptive not fixed. - if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: - if len(mutation_num_genes) == 2: - for el in mutation_num_genes: - if type(el) in GA.supported_int_types: - if (el <= 0): - self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) - raise ValueError("The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({mutation_num_genes_value}) found. If you do not want to use mutation, please set mutation_type=None\n".format(mutation_num_genes_value=el)) - elif (el > self.num_genes): - self.valid_parameters = False - self.logger.error("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) - raise ValueError("The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({num_genes}) but ({mutation_num_genes_value}) found.\n".format(mutation_num_genes_value=el, num_genes=self.num_genes)) - else: - self.valid_parameters = False - self.logger.error("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) - raise TypeError("Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=el, mutation_num_genes_type=type(el))) - # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. - if mutation_num_genes[0] < mutation_num_genes[1]: - if not self.suppress_warnings: warnings.warn("The first element in the 'mutation_num_genes' parameter is {first_el} which is smaller than the second element {second_el}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.".format(first_el=mutation_num_genes[0], second_el=mutation_num_genes[1])) - # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. - else: - self.valid_parameters = False - self.logger.error("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) - raise ValueError("When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({mutation_num_genes_length}) element(s) found.".format(mutation_num_genes_length=len(mutation_num_genes))) + raise ValueError(f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + elif callable(parent_selection_type): + # Check if the parent_selection_type is a function that accepts 3 paramaters. + if (parent_selection_type.__code__.co_argcount == 3): + # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. + # The parent selection function assigned to the parent_selection_type parameter is validated. + self.select_parents = parent_selection_type else: self.valid_parameters = False - self.logger.error("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - raise TypeError("Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes_value}) of type {mutation_num_genes_type} found.".format(mutation_num_genes_value=mutation_num_genes, mutation_num_genes_type=type(mutation_num_genes))) - else: - pass - - # Validating mutation_by_replacement and mutation_type - if self.mutation_type != "random" and self.mutation_by_replacement: - if not self.suppress_warnings: warnings.warn("The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mut_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.".format(mut_type=mutation_type)) - - # Check if crossover and mutation are both disabled. - if (self.mutation_type is None) and (self.crossover_type is None): - if not self.suppress_warnings: warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.") - - # select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute. - # Validating the selected type of parent selection: parent_selection_type - if inspect.ismethod(parent_selection_type): - # Check if the parent_selection_type is a method that accepts 4 paramaters. - if (parent_selection_type.__code__.co_argcount == 4): - # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. - # The parent selection method assigned to the parent_selection_type parameter is validated. - self.select_parents = parent_selection_type - else: + raise ValueError(f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") + elif not (type(parent_selection_type) is str): self.valid_parameters = False - self.logger.error("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - raise ValueError("When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - elif callable(parent_selection_type): - # Check if the parent_selection_type is a function that accepts 3 paramaters. - if (parent_selection_type.__code__.co_argcount == 3): - # population: Added in PyGAD 2.16.0. It should used only to support custom parent selection functions. Otherwise, it should be left to None to retirve the population by self.population. - # The parent selection function assigned to the parent_selection_type parameter is validated. - self.select_parents = parent_selection_type + + raise TypeError(f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") else: + parent_selection_type = parent_selection_type.lower() + if (parent_selection_type == "sss"): + self.select_parents = self.steady_state_selection + elif (parent_selection_type == "rws"): + self.select_parents = self.roulette_wheel_selection + elif (parent_selection_type == "sus"): + self.select_parents = self.stochastic_universal_selection + elif (parent_selection_type == "random"): + self.select_parents = self.random_selection + elif (parent_selection_type == "tournament"): + self.select_parents = self.tournament_selection + elif (parent_selection_type == "rank"): + self.select_parents = self.rank_selection + else: + self.valid_parameters = False + raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n") + + # For tournament selection, validate the K value. + if (parent_selection_type == "tournament"): + if (K_tournament > self.sol_per_pop): + K_tournament = self.sol_per_pop + if not self.suppress_warnings: + warnings.warn(f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") + elif (K_tournament <= 0): + self.valid_parameters = False + raise ValueError(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") + + self.K_tournament = K_tournament + + # Validating the number of parents to keep in the next population: keep_parents + if not (type(keep_parents) in GA.supported_int_types): self.valid_parameters = False - self.logger.error("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - raise ValueError("When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=parent_selection_type.__code__.co_name, argcount=parent_selection_type.__code__.co_argcount)) - elif not (type(parent_selection_type) is str): - self.valid_parameters = False - self.logger.error("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) - raise TypeError("The expected type of the 'parent_selection_type' parameter is either callable or str but {parent_selection_type} found.".format(parent_selection_type=type(parent_selection_type))) - else: - parent_selection_type = parent_selection_type.lower() - if (parent_selection_type == "sss"): - self.select_parents = self.steady_state_selection - elif (parent_selection_type == "rws"): - self.select_parents = self.roulette_wheel_selection - elif (parent_selection_type == "sus"): - self.select_parents = self.stochastic_universal_selection - elif (parent_selection_type == "random"): - self.select_parents = self.random_selection - elif (parent_selection_type == "tournament"): - self.select_parents = self.tournament_selection - elif (parent_selection_type == "rank"): - self.select_parents = self.rank_selection - else: + raise TypeError(f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") + elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): self.valid_parameters = False - self.logger.error("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) - raise TypeError("Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n".format(parent_selection_type=parent_selection_type)) - - # For tournament selection, validate the K value. - if(parent_selection_type == "tournament"): - if (K_tournament > self.sol_per_pop): - K_tournament = self.sol_per_pop - if not self.suppress_warnings: warnings.warn("K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n".format(K_tournament=K_tournament, sol_per_pop=self.sol_per_pop)) - elif (K_tournament <= 0): + raise ValueError(f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") + + self.keep_parents = keep_parents + + if parent_selection_type == "sss" and self.keep_parents == 0: + if not self.suppress_warnings: + warnings.warn("The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") + + # Validating the number of elitism to keep in the next population: keep_elitism + if not (type(keep_elitism) in GA.supported_int_types): self.valid_parameters = False - self.logger.error("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) - raise ValueError("K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n".format(K_tournament=K_tournament)) - - self.K_tournament = K_tournament - - # Validating the number of parents to keep in the next population: keep_parents - if not (type(keep_parents) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) - raise TypeError("Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {keep_parents_type} found but an integer is expected.".format(keep_parents=keep_parents, keep_parents_type=type(keep_parents))) - elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): - self.valid_parameters = False - self.logger.error("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) - raise ValueError("Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.".format(keep_parents=keep_parents)) - - self.keep_parents = keep_parents - - if parent_selection_type == "sss" and self.keep_parents == 0: - if not self.suppress_warnings: warnings.warn("The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") - - # Validating the number of elitism to keep in the next population: keep_elitism - if not (type(keep_elitism) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) - raise TypeError("Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {keep_elitism_type} found but an integer is expected.".format(keep_elitism=keep_elitism, keep_elitism_type=type(keep_elitism))) - elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): - self.valid_parameters = False - self.logger.error("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) - raise ValueError("Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.".format(keep_elitism=keep_elitism)) - - self.keep_elitism = keep_elitism - - # Validate keep_parents. - if self.keep_elitism == 0: - if (self.keep_parents == -1): # Keep all parents in the next population. - self.num_offspring = self.sol_per_pop - self.num_parents_mating - elif (self.keep_parents == 0): # Keep no parents in the next population. - self.num_offspring = self.sol_per_pop - elif (self.keep_parents > 0): # Keep the specified number of parents in the next population. - self.num_offspring = self.sol_per_pop - self.keep_parents - else: - self.num_offspring = self.sol_per_pop - self.keep_elitism - - # Check if the fitness_func is a method. - # In PyGAD 2.19.0, a method can be passed to the fitness function. If function is passed, then it accepts 2 parameters. If method, then it accepts 3 parameters. - # In PyGAD 2.20.0, a new parameter is passed referring to the instance of the `pygad.GA` class. So, the function accepts 3 parameters and the method accepts 4 parameters. - if inspect.ismethod(fitness_func): - # If the fitness is calculated through a method, not a function, then there is a fourth 'self` paramaters. - if (fitness_func.__code__.co_argcount == 4): - self.fitness_func = fitness_func - else: + raise TypeError(f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") + elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): self.valid_parameters = False - self.logger.error("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - raise ValueError("In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - elif callable(fitness_func): - # Check if the fitness function accepts 2 paramaters. - if (fitness_func.__code__.co_argcount == 3): - self.fitness_func = fitness_func + raise ValueError(f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") + + self.keep_elitism = keep_elitism + + # Validate keep_parents. + if self.keep_elitism == 0: + # Keep all parents in the next population. + if (self.keep_parents == -1): + self.num_offspring = self.sol_per_pop - self.num_parents_mating + # Keep no parents in the next population. + elif (self.keep_parents == 0): + self.num_offspring = self.sol_per_pop + # Keep the specified number of parents in the next population. + elif (self.keep_parents > 0): + self.num_offspring = self.sol_per_pop - self.keep_parents else: - self.valid_parameters = False - self.logger.error("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - raise ValueError("In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount)) - else: - self.valid_parameters = False - self.logger.error("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) - raise TypeError("The value assigned to the fitness_func parameter is expected to be of type function but {fitness_func_type} found.".format(fitness_func_type=type(fitness_func))) - - if fitness_batch_size is None: - pass - elif not (type(fitness_batch_size) in GA.supported_int_types): - self.valid_parameters = False - self.logger.error("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) - raise TypeError("The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {fitness_batch_size_type} found.".format(fitness_batch_size=fitness_batch_size, fitness_batch_size_type=type(fitness_batch_size))) - elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: - self.valid_parameters = False - self.logger.error("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) - raise ValueError("The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({sol_per_pop}).\nBut the value ({fitness_batch_size}) found.".format(fitness_batch_size=fitness_batch_size, sol_per_pop=self.sol_per_pop)) - - self.fitness_batch_size = fitness_batch_size - - # Check if the on_start exists. - if not (on_start is None): - if inspect.ismethod(on_start): - # Check if the on_start method accepts 2 paramaters. - if (on_start.__code__.co_argcount == 2): - self.on_start = on_start + self.num_offspring = self.sol_per_pop - self.keep_elitism + + # Check if the fitness_func is a method. + # In PyGAD 2.19.0, a method can be passed to the fitness function. If function is passed, then it accepts 2 parameters. If method, then it accepts 3 parameters. + # In PyGAD 2.20.0, a new parameter is passed referring to the instance of the `pygad.GA` class. So, the function accepts 3 parameters and the method accepts 4 parameters. + if inspect.ismethod(fitness_func): + # If the fitness is calculated through a method, not a function, then there is a fourth 'self` paramaters. + if (fitness_func.__code__.co_argcount == 4): + self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - raise ValueError("The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - # Check if the on_start is a function. - elif callable(on_start): - # Check if the on_start function accepts only a single paramater. - if (on_start.__code__.co_argcount == 1): - self.on_start = on_start + raise ValueError(f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") + elif callable(fitness_func): + # Check if the fitness function accepts 2 paramaters. + if (fitness_func.__code__.co_argcount == 3): + self.fitness_func = fitness_func else: self.valid_parameters = False - self.logger.error("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) - raise ValueError("The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_start.__code__.co_name, argcount=on_start.__code__.co_argcount)) + raise ValueError(f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) - raise TypeError("The value assigned to the on_start parameter is expected to be of type function but {on_start_type} found.".format(on_start_type=type(on_start))) - else: - self.on_start = None - - # Check if the on_fitness exists. - if not (on_fitness is None): - # Check if the on_fitness is a method. - if inspect.ismethod(on_fitness): - # Check if the on_fitness method accepts 3 paramaters. - if (on_fitness.__code__.co_argcount == 3): - self.on_fitness = on_fitness - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - raise ValueError("The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - # Check if the on_fitness is a function. - elif callable(on_fitness): - # Check if the on_fitness function accepts 2 paramaters. - if (on_fitness.__code__.co_argcount == 2): - self.on_fitness = on_fitness - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - raise ValueError("The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_fitness.__code__.co_name, argcount=on_fitness.__code__.co_argcount)) - else: + + raise TypeError(f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") + + if fitness_batch_size is None: + pass + elif not (type(fitness_batch_size) in GA.supported_int_types): self.valid_parameters = False - self.logger.error("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) - raise TypeError("The value assigned to the on_fitness parameter is expected to be of type function but {on_fitness_type} found.".format(on_fitness_type=type(on_fitness))) - else: - self.on_fitness = None - - # Check if the on_parents exists. - if not (on_parents is None): - # Check if the on_parents is a method. - if inspect.ismethod(on_parents): - # Check if the on_parents method accepts 3 paramaters. - if (on_parents.__code__.co_argcount == 3): - self.on_parents = on_parents + raise TypeError(f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") + elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: + self.valid_parameters = False + raise ValueError(f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") + + self.fitness_batch_size = fitness_batch_size + + # Check if the on_start exists. + if not (on_start is None): + if inspect.ismethod(on_start): + # Check if the on_start method accepts 2 paramaters. + if (on_start.__code__.co_argcount == 2): + self.on_start = on_start + else: + self.valid_parameters = False + raise ValueError(f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") + # Check if the on_start is a function. + elif callable(on_start): + # Check if the on_start function accepts only a single paramater. + if (on_start.__code__.co_argcount == 1): + self.on_start = on_start + else: + self.valid_parameters = False + raise ValueError(f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - raise ValueError("The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - # Check if the on_parents is a function. - elif callable(on_parents): - # Check if the on_parents function accepts 2 paramaters. - if (on_parents.__code__.co_argcount == 2): - self.on_parents = on_parents + + raise TypeError(f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") + else: + self.on_start = None + + # Check if the on_fitness exists. + if not (on_fitness is None): + # Check if the on_fitness is a method. + if inspect.ismethod(on_fitness): + # Check if the on_fitness method accepts 3 paramaters. + if (on_fitness.__code__.co_argcount == 3): + self.on_fitness = on_fitness + else: + self.valid_parameters = False + raise ValueError(f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") + # Check if the on_fitness is a function. + elif callable(on_fitness): + # Check if the on_fitness function accepts 2 paramaters. + if (on_fitness.__code__.co_argcount == 2): + self.on_fitness = on_fitness + else: + self.valid_parameters = False + raise ValueError(f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) - raise ValueError("The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_parents.__code__.co_name, argcount=on_parents.__code__.co_argcount)) + raise TypeError(f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) - raise TypeError("The value assigned to the on_parents parameter is expected to be of type function but {on_parents_type} found.".format(on_parents_type=type(on_parents))) - else: - self.on_parents = None - - # Check if the on_crossover exists. - if not (on_crossover is None): - # Check if the on_crossover is a method. - if inspect.ismethod(on_crossover): - # Check if the on_crossover method accepts 3 paramaters. - if (on_crossover.__code__.co_argcount == 3): - self.on_crossover = on_crossover + self.on_fitness = None + + # Check if the on_parents exists. + if not (on_parents is None): + # Check if the on_parents is a method. + if inspect.ismethod(on_parents): + # Check if the on_parents method accepts 3 paramaters. + if (on_parents.__code__.co_argcount == 3): + self.on_parents = on_parents + else: + self.valid_parameters = False + raise ValueError(f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") + # Check if the on_parents is a function. + elif callable(on_parents): + # Check if the on_parents function accepts 2 paramaters. + if (on_parents.__code__.co_argcount == 2): + self.on_parents = on_parents + else: + self.valid_parameters = False + raise ValueError(f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - raise ValueError("The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - # Check if the on_crossover is a function. - elif callable(on_crossover): - # Check if the on_crossover function accepts 2 paramaters. - if (on_crossover.__code__.co_argcount == 2): - self.on_crossover = on_crossover + raise TypeError(f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") + else: + self.on_parents = None + + # Check if the on_crossover exists. + if not (on_crossover is None): + # Check if the on_crossover is a method. + if inspect.ismethod(on_crossover): + # Check if the on_crossover method accepts 3 paramaters. + if (on_crossover.__code__.co_argcount == 3): + self.on_crossover = on_crossover + else: + self.valid_parameters = False + raise ValueError(f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") + # Check if the on_crossover is a function. + elif callable(on_crossover): + # Check if the on_crossover function accepts 2 paramaters. + if (on_crossover.__code__.co_argcount == 2): + self.on_crossover = on_crossover + else: + self.valid_parameters = False + raise ValueError(f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) - raise ValueError("The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_crossover.__code__.co_name, argcount=on_crossover.__code__.co_argcount)) + raise TypeError(f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) - raise TypeError("The value assigned to the on_crossover parameter is expected to be of type function but {on_crossover_type} found.".format(on_crossover_type=type(on_crossover))) - else: - self.on_crossover = None - - # Check if the on_mutation exists. - if not (on_mutation is None): - # Check if the on_mutation is a method. - if inspect.ismethod(on_mutation): - # Check if the on_mutation method accepts 3 paramaters. - if (on_mutation.__code__.co_argcount == 3): - self.on_mutation = on_mutation + self.on_crossover = None + + # Check if the on_mutation exists. + if not (on_mutation is None): + # Check if the on_mutation is a method. + if inspect.ismethod(on_mutation): + # Check if the on_mutation method accepts 3 paramaters. + if (on_mutation.__code__.co_argcount == 3): + self.on_mutation = on_mutation + else: + self.valid_parameters = False + raise ValueError(f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") + # Check if the on_mutation is a function. + elif callable(on_mutation): + # Check if the on_mutation function accepts 2 paramaters. + if (on_mutation.__code__.co_argcount == 2): + self.on_mutation = on_mutation + else: + self.valid_parameters = False + raise ValueError(f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - raise ValueError("The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - # Check if the on_mutation is a function. - elif callable(on_mutation): - # Check if the on_mutation function accepts 2 paramaters. - if (on_mutation.__code__.co_argcount == 2): - self.on_mutation = on_mutation + raise TypeError(f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") + else: + self.on_mutation = None + + # Check if the on_generation exists. + if not (on_generation is None): + # Check if the on_generation is a method. + if inspect.ismethod(on_generation): + # Check if the on_generation method accepts 2 paramaters. + if (on_generation.__code__.co_argcount == 2): + self.on_generation = on_generation + else: + self.valid_parameters = False + raise ValueError(f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") + # Check if the on_generation is a function. + elif callable(on_generation): + # Check if the on_generation function accepts only a single paramater. + if (on_generation.__code__.co_argcount == 1): + self.on_generation = on_generation + else: + self.valid_parameters = False + raise ValueError(f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) - raise ValueError("The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_mutation.__code__.co_name, argcount=on_mutation.__code__.co_argcount)) + raise TypeError(f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") else: - self.valid_parameters = False - self.logger.error("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) - raise TypeError("The value assigned to the on_mutation parameter is expected to be of type function but {on_mutation_type} found.".format(on_mutation_type=type(on_mutation))) - else: - self.on_mutation = None - - # Check if the on_generation exists. - if not (on_generation is None): - # Check if the on_generation is a method. - if inspect.ismethod(on_generation): - # Check if the on_generation method accepts 2 paramaters. - if (on_generation.__code__.co_argcount == 2): - self.on_generation = on_generation + self.on_generation = None + + # Check if the on_stop exists. + if not (on_stop is None): + # Check if the on_stop is a method. + if inspect.ismethod(on_stop): + # Check if the on_stop method accepts 3 paramaters. + if (on_stop.__code__.co_argcount == 3): + self.on_stop = on_stop + else: + self.valid_parameters = False + raise ValueError(f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") + # Check if the on_stop is a function. + elif callable(on_stop): + # Check if the on_stop function accepts 2 paramaters. + if (on_stop.__code__.co_argcount == 2): + self.on_stop = on_stop + else: + self.valid_parameters = False + raise ValueError(f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False - self.logger.error("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - raise ValueError("The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - # Check if the on_generation is a function. - elif callable(on_generation): - # Check if the on_generation function accepts only a single paramater. - if (on_generation.__code__.co_argcount == 1): - self.on_generation = on_generation + raise TypeError(f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") + else: + self.on_stop = None + + # Validate delay_after_gen + if type(delay_after_gen) in GA.supported_int_float_types: + if delay_after_gen >= 0.0: + self.delay_after_gen = delay_after_gen else: self.valid_parameters = False - self.logger.error("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) - raise ValueError("The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_generation.__code__.co_name, argcount=on_generation.__code__.co_argcount)) + raise ValueError(f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") else: self.valid_parameters = False - self.logger.error("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) - raise TypeError("The value assigned to the on_generation parameter is expected to be of type function but {on_generation_type} found.".format(on_generation_type=type(on_generation))) - else: - self.on_generation = None - - # Check if the on_stop exists. - if not (on_stop is None): - # Check if the on_stop is a method. - if inspect.ismethod(on_stop): - # Check if the on_stop method accepts 3 paramaters. - if (on_stop.__code__.co_argcount == 3): - self.on_stop = on_stop - else: - self.valid_parameters = False - self.logger.error("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - raise ValueError("The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - # Check if the on_stop is a function. - elif callable(on_stop): - # Check if the on_stop function accepts 2 paramaters. - if (on_stop.__code__.co_argcount == 2): - self.on_stop = on_stop - else: - self.valid_parameters = False - self.logger.error("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) - raise ValueError("The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=on_stop.__code__.co_name, argcount=on_stop.__code__.co_argcount)) + raise TypeError(f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") + + # Validate save_best_solutions + if type(save_best_solutions) is bool: + if save_best_solutions == True: + if not self.suppress_warnings: + warnings.warn("Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") else: self.valid_parameters = False - self.logger.error("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) - raise TypeError("The value assigned to the 'on_stop' parameter is expected to be of type function but {on_stop_type} found.".format(on_stop_type=type(on_stop))) - else: - self.on_stop = None + raise TypeError(f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") - # Validate delay_after_gen - if type(delay_after_gen) in GA.supported_int_float_types: - if delay_after_gen >= 0.0: - self.delay_after_gen = delay_after_gen + # Validate save_solutions + if type(save_solutions) is bool: + if save_solutions == True: + if not self.suppress_warnings: + warnings.warn("Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") else: self.valid_parameters = False - self.logger.error("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) - raise ValueError("The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {delay_after_gen_type}.".format(delay_after_gen=delay_after_gen, delay_after_gen_type=type(delay_after_gen))) - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) - raise TypeError("The value passed to the 'delay_after_gen' parameter must be of type int or float but {delay_after_gen_type} found.".format(delay_after_gen_type=type(delay_after_gen))) - - # Validate save_best_solutions - if type(save_best_solutions) is bool: - if save_best_solutions == True: - if not self.suppress_warnings: warnings.warn("Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) - raise TypeError("The value passed to the 'save_best_solutions' parameter must be of type bool but {save_best_solutions_type} found.".format(save_best_solutions_type=type(save_best_solutions))) - - # Validate save_solutions - if type(save_solutions) is bool: - if save_solutions == True: - if not self.suppress_warnings: warnings.warn("Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") - else: - self.valid_parameters = False - self.logger.error("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) - raise TypeError("The value passed to the 'save_solutions' parameter must be of type bool but {save_solutions_type} found.".format(save_solutions_type=type(save_solutions))) - - self.stop_criteria = [] - self.supported_stop_words = ["reach", "saturate"] - if stop_criteria is None: - # None: Stop after passing through all generations. - self.stop_criteria = None - elif type(stop_criteria) is str: - # reach_{target_fitness}: Stop if the target fitness value is reached. - # saturate_{num_generations}: Stop if the fitness value does not change (saturates) for the given number of generations. - criterion = stop_criteria.split("_") - if len(criterion) == 2: - stop_word = criterion[0] - number = criterion[1] - - if stop_word in self.supported_stop_words: - pass - else: - self.valid_parameters = False - self.logger.error("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - raise ValueError("In the 'stop_criteria' parameter, the supported stop words are '{supported_stop_words}' but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + raise TypeError(f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") + + self.stop_criteria = [] + self.supported_stop_words = ["reach", "saturate"] + if stop_criteria is None: + # None: Stop after passing through all generations. + self.stop_criteria = None + elif type(stop_criteria) is str: + # reach_{target_fitness}: Stop if the target fitness value is reached. + # saturate_{num_generations}: Stop if the fitness value does not change (saturates) for the given number of generations. + criterion = stop_criteria.split("_") + if len(criterion) == 2: + stop_word = criterion[0] + number = criterion[1] + + if stop_word in self.supported_stop_words: + pass + else: + self.valid_parameters = False + raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") + + if number.replace(".", "").isnumeric(): + number = float(number) + else: + self.valid_parameters = False + raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + + self.stop_criteria.append([stop_word, number]) - if number.replace(".", "").isnumeric(): - number = float(number) else: self.valid_parameters = False - self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - - self.stop_criteria.append([stop_word, number]) + raise ValueError(f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") + + elif type(stop_criteria) in [list, tuple, numpy.ndarray]: + # Remove duplicate criterira by converting the list to a set then back to a list. + stop_criteria = list(set(stop_criteria)) + for idx, val in enumerate(stop_criteria): + if type(val) is str: + criterion = val.split("_") + if len(criterion) == 2: + stop_word = criterion[0] + number = criterion[1] + + if stop_word in self.supported_stop_words: + pass + else: + self.valid_parameters = False + raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") - else: - self.valid_parameters = False - self.logger.error("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) - raise ValueError("For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.".format(stop_criteria=stop_criteria)) - - elif type(stop_criteria) in [list, tuple, numpy.ndarray]: - # Remove duplicate criterira by converting the list to a set then back to a list. - stop_criteria = list(set(stop_criteria)) - for idx, val in enumerate(stop_criteria): - if type(val) is str: - criterion = val.split("_") - if len(criterion) == 2: - stop_word = criterion[0] - number = criterion[1] - - if stop_word in self.supported_stop_words: - pass - else: - self.valid_parameters = False - self.logger.error("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) - raise ValueError("In the 'stop_criteria' parameter, the supported stop words are {supported_stop_words} but '{stop_word}' found.".format(supported_stop_words=self.supported_stop_words, stop_word=stop_word)) + if number.replace(".", "").isnumeric(): + number = float(number) + else: + self.valid_parameters = False + raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") + + self.stop_criteria.append([stop_word, number]) - if number.replace(".", "").isnumeric(): - number = float(number) else: self.valid_parameters = False - self.logger.error("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - raise ValueError("The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({stop_val}) of type {stop_val_type} found.".format(stop_val=number, stop_val_type=type(number))) - - self.stop_criteria.append([stop_word, number]) - + raise ValueError(f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") else: self.valid_parameters = False - self.logger.error("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) - raise ValueError("The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {stop_criteria} found.".format(stop_criteria=criterion)) - else: - self.valid_parameters = False - self.logger.error("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) - raise TypeError("When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({stop_criteria_val}) of type {stop_criteria_val_type} found at index {stop_criteria_val_idx}.".format(stop_criteria_val=val, stop_criteria_val_type=type(val), stop_criteria_val_idx=idx)) - else: - self.valid_parameters = False - self.logger.error("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) - raise TypeError("The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria_val}) of type {stop_criteria_type} found.".format(stop_criteria_val=stop_criteria, stop_criteria_type=type(stop_criteria))) - - if parallel_processing is None: - self.parallel_processing = None - elif type(parallel_processing) in GA.supported_int_types: - if parallel_processing > 0: - self.parallel_processing = ["thread", parallel_processing] + raise TypeError(f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") else: self.valid_parameters = False - self.logger.error("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) - raise ValueError("When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing_value}) found.".format(parallel_processing_value=parallel_processing)) - elif type(parallel_processing) in [list, tuple]: - if len(parallel_processing) == 2: - if type(parallel_processing[0]) is str: - if parallel_processing[0] in ["process", "thread"]: - if (type(parallel_processing[1]) in GA.supported_int_types and parallel_processing[1] > 0) or (parallel_processing[1] == 0) or (parallel_processing[1] is None): - if parallel_processing[1] == 0: - # If the number of processes/threads is 0, this means no parallel processing is used. It is equivelant to setting parallel_processing=None. - self.parallel_processing = None + raise TypeError(f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") + + if parallel_processing is None: + self.parallel_processing = None + elif type(parallel_processing) in GA.supported_int_types: + if parallel_processing > 0: + self.parallel_processing = ["thread", parallel_processing] + else: + self.valid_parameters = False + raise ValueError(f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") + elif type(parallel_processing) in [list, tuple]: + if len(parallel_processing) == 2: + if type(parallel_processing[0]) is str: + if parallel_processing[0] in ["process", "thread"]: + if (type(parallel_processing[1]) in GA.supported_int_types and parallel_processing[1] > 0) or (parallel_processing[1] == 0) or (parallel_processing[1] is None): + if parallel_processing[1] == 0: + # If the number of processes/threads is 0, this means no parallel processing is used. It is equivelant to setting parallel_processing=None. + self.parallel_processing = None + else: + # Whether the second value is None or a positive integer. + self.parallel_processing = parallel_processing else: - # Whether the second value is None or a positive integer. - self.parallel_processing = parallel_processing + self.valid_parameters = False + raise TypeError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) - raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({second_value}) of type {second_value_type} found.".format(second_value=parallel_processing[1], second_value_type=type(parallel_processing[1]))) + raise ValueError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) - raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({first_value}) found.".format(first_value=parallel_processing[0])) + raise TypeError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) - raise TypeError("When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({first_value}) of type {first_value_type} found.".format(first_value=parallel_processing[0], first_value_type=type(parallel_processing[0]))) + raise ValueError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") else: self.valid_parameters = False - self.logger.error("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) - raise ValueError("When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({num_elements}) found.".format(num_elements=len(parallel_processing))) - else: - self.valid_parameters = False - self.logger.error("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) - raise ValueError("Unexpected value ({parallel_processing_value}) of type ({parallel_processing_type}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.".format(parallel_processing_value=parallel_processing, parallel_processing_type=type(parallel_processing))) - - # Set the `run_completed` property to False. It is set to `True` only after the `run()` method is complete. - self.run_completed = False - - # The number of completed generations. - self.generations_completed = 0 - - # At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid. - self.valid_parameters = True # Set to True when all the parameters passed in the GA class constructor are valid. - - # Parameters of the genetic algorithm. - self.num_generations = abs(num_generations) - self.parent_selection_type = parent_selection_type - - # Parameters of the mutation operation. - self.mutation_percent_genes = mutation_percent_genes - self.mutation_num_genes = mutation_num_genes - - # Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object. - self.best_solutions_fitness = [] # A list holding the fitness value of the best solution for each generation. - - self.best_solution_generation = -1 # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. - - self.save_best_solutions = save_best_solutions - self.best_solutions = [] # Holds the best solution in each generation. - - self.save_solutions = save_solutions - self.solutions = [] # Holds the solutions in each generation. - self.solutions_fitness = [] # Holds the fitness of the solutions in each generation. - - self.last_generation_fitness = None # A list holding the fitness values of all solutions in the last generation. - self.last_generation_parents = None # A list holding the parents of the last generation. - self.last_generation_offspring_crossover = None # A list holding the offspring after applying crossover in the last generation. - self.last_generation_offspring_mutation = None # A list holding the offspring after applying mutation in the last generation. - self.previous_generation_fitness = None # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. - self.last_generation_elitism = None # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. - self.last_generation_elitism_indices = None # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. + raise ValueError(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") + + # Set the `run_completed` property to False. It is set to `True` only after the `run()` method is complete. + self.run_completed = False + + # The number of completed generations. + self.generations_completed = 0 + + # At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid. + # Set to True when all the parameters passed in the GA class constructor are valid. + self.valid_parameters = True + + # Parameters of the genetic algorithm. + self.num_generations = abs(num_generations) + self.parent_selection_type = parent_selection_type + + # Parameters of the mutation operation. + self.mutation_percent_genes = mutation_percent_genes + self.mutation_num_genes = mutation_num_genes + + # Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object. + # A list holding the fitness value of the best solution for each generation. + self.best_solutions_fitness = [] + + # The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1. + self.best_solution_generation = -1 + + self.save_best_solutions = save_best_solutions + self.best_solutions = [] # Holds the best solution in each generation. + + self.save_solutions = save_solutions + self.solutions = [] # Holds the solutions in each generation. + # Holds the fitness of the solutions in each generation. + self.solutions_fitness = [] + + # A list holding the fitness values of all solutions in the last generation. + self.last_generation_fitness = None + # A list holding the parents of the last generation. + self.last_generation_parents = None + # A list holding the offspring after applying crossover in the last generation. + self.last_generation_offspring_crossover = None + # A list holding the offspring after applying mutation in the last generation. + self.last_generation_offspring_mutation = None + # Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2. + self.previous_generation_fitness = None + # Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value. + self.last_generation_elitism = None + # Added in PyGAD 2.19.0. A NumPy array holding the indices of the elitism of the current generation. It works only if the 'keep_elitism' parameter has a non-zero value. + self.last_generation_elitism_indices = None + except Exception as e: + self.logger.exception(e) + exit(-1) def round_genes(self, solutions): for gene_idx in range(self.num_genes): if self.gene_type_single: if not self.gene_type[1] is None: - solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], + solutions[:, gene_idx] = numpy.round(solutions[:, gene_idx], self.gene_type[1]) else: if not self.gene_type[gene_idx][1] is None: - solutions[:, gene_idx] = numpy.round(numpy.asarray(solutions[:, gene_idx], - dtype=self.gene_type[gene_idx][0]), + solutions[:, gene_idx] = numpy.round(numpy.asarray(solutions[:, gene_idx], + dtype=self.gene_type[gene_idx][0]), self.gene_type[gene_idx][1]) return solutions - def initialize_population(self, - low, - high, - allow_duplicate_genes, - mutation_by_replacement, + def initialize_population(self, + low, + high, + allow_duplicate_genes, + mutation_by_replacement, gene_type): - """ Creates an initial population randomly as a NumPy array. The array is saved in the instance attribute named 'population'. @@ -1313,24 +1235,26 @@ def initialize_population(self, """ # Population size = (number of chromosomes, number of genes per chromosome) - self.pop_size = (self.sol_per_pop,self.num_genes) # The population will have sol_per_pop chromosome where each chromosome has num_genes genes. + # The population will have sol_per_pop chromosome where each chromosome has num_genes genes. + self.pop_size = (self.sol_per_pop, self.num_genes) if self.gene_space is None: # Creating the initial population randomly. if self.gene_type_single == True: - self.population = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. + self.population = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=self.pop_size), + dtype=self.gene_type[0]) # A NumPy array holding the initial population. else: # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) + self.population = numpy.zeros( + shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): # A vector of all values of this single gene across all solutions in the population. - gene_values = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=self.pop_size[0]), + gene_values = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) # Adding the current gene values to the population. self.population[:, gene_idx] = gene_values @@ -1339,7 +1263,7 @@ def initialize_population(self, for solution_idx in range(self.population.shape[0]): # self.logger.info("Before", self.population[solution_idx]) self.population[solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=self.population[solution_idx], - min_val=low, + min_val=low, max_val=high, mutation_by_replacement=True, gene_type=gene_type, @@ -1349,25 +1273,25 @@ def initialize_population(self, elif self.gene_space_nested: if self.gene_type_single == True: # Reaching this block means: - # 1) gene_space is nested (gene_space_nested is True). - # 2) gene_type is not nested (gene_type_single is True). - self.population = numpy.zeros(shape=self.pop_size, + # 1) gene_space is nested (gene_space_nested is True). + # 2) gene_type is not nested (gene_type_single is True). + self.population = numpy.zeros(shape=self.pop_size, dtype=self.gene_type[0]) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): if self.gene_space[gene_idx] is None: - # The following commented code replace the None value with a single number that will not change again. + # The following commented code replace the None value with a single number that will not change again. # This means the gene value will be the same across all solutions. # self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - # high=high, + # high=high, # size=1), dtype=self.gene_type[0])[0] # self.population[sol_idx, gene_idx] = list(self.gene_space[gene_idx]).copy() # The above problem is solved by keeping the None value in the gene_space parameter. This forces PyGAD to generate this value for each solution. self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + high=high, + size=1), dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in [numpy.ndarray, list, tuple, range]: # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. @@ -1378,47 +1302,56 @@ def initialize_population(self, # We copy the gene_space to a temp variable to keep its original value. # In the next for loop, the gene_space is changed. # Later, the gene_space is restored to its original value using the temp variable. - temp_gene_space = list(self.gene_space[gene_idx]).copy() + temp_gene_space = list( + self.gene_space[gene_idx]).copy() for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), dtype=self.gene_type[0])[0] # Find the difference between the current gene space and the current values in the solution. - unique_gene_values = list(set(self.gene_space[gene_idx]).difference(set(self.population[sol_idx, :gene_idx]))) + unique_gene_values = list(set(self.gene_space[gene_idx]).difference( + set(self.population[sol_idx, :gene_idx]))) if len(unique_gene_values) > 0: - self.population[sol_idx, gene_idx] = random.choice(unique_gene_values) + self.population[sol_idx, gene_idx] = random.choice( + unique_gene_values) else: # If there is no unique values, then we have to select a duplicate value. - self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = random.choice( + self.gene_space[gene_idx]) - self.population[sol_idx, gene_idx] = self.gene_type[0](self.population[sol_idx, gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[0]( + self.population[sol_idx, gene_idx]) # Restore the gene_space from the temp_gene_space variable. - self.gene_space[gene_idx] = list(temp_gene_space).copy() + self.gene_space[gene_idx] = list( + temp_gene_space).copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], + stop=self.gene_space[ + gene_idx]['high'], step=self.gene_space[gene_idx]['step']), size=1), dtype=self.gene_type[0])[0] else: self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[gene_idx]['high'], + high=self.gene_space[ + gene_idx]['high'], size=1), dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + self.population[sol_idx, + gene_idx] = self.gene_space[gene_idx] else: # There is no more options. pass else: # Reaching this block means: - # 1) gene_space is nested (gene_space_nested is True). - # 2) gene_type is nested (gene_type_single is False). - self.population = numpy.zeros(shape=self.pop_size, + # 1) gene_space is nested (gene_space_nested is True). + # 2) gene_type is nested (gene_type_single is False). + self.population = numpy.zeros(shape=self.pop_size, dtype=object) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): @@ -1427,59 +1360,66 @@ def initialize_population(self, # We copy the gene_space to a temp variable to keep its original value. # In the next for loop, the gene_space is changed. # Later, the gene_space is restored to its original value using the temp variable. - temp_gene_space = list(self.gene_space[gene_idx]).copy() + temp_gene_space = list( + self.gene_space[gene_idx]).copy() # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) - self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0](self.population[sol_idx, gene_idx]) + self.population[sol_idx, gene_idx] = random.choice( + self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0]( + self.population[sol_idx, gene_idx]) # Restore the gene_space from the temp_gene_space variable. self.gene_space[gene_idx] = temp_gene_space.copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[gene_idx]['high'], + stop=self.gene_space[ + gene_idx]['high'], step=self.gene_space[gene_idx]['step']), size=1), dtype=self.gene_type[gene_idx][0])[0] else: self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[gene_idx]['high'], - size=1), + high=self.gene_space[ + gene_idx]['high'], + size=1), dtype=self.gene_type[gene_idx][0])[0] elif type(self.gene_space[gene_idx]) == type(None): temp_gene_value = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + high=high, + size=1), dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = temp_gene_value.copy() + self.population[sol_idx, + gene_idx] = temp_gene_value.copy() elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] + self.population[sol_idx, + gene_idx] = self.gene_space[gene_idx] else: - # There is no more options. + # There is no more options. pass else: # Handle the non-nested gene_space. It can be assigned a numeric value, list, numpy.ndarray, or a dict. if self.gene_type_single == True: # Reaching this block means: - # 1) gene_space is not nested (gene_space_nested is False). - # 2) gene_type is not nested (gene_type_single is True). + # 1) gene_space is not nested (gene_space_nested is False). + # 2) gene_type is not nested (gene_type_single is True). # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. for idx, curr_gene_space in enumerate(self.gene_space): if curr_gene_space is None: - self.gene_space[idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), + self.gene_space[idx] = numpy.asarray(numpy.random.uniform(low=low, + high=high, + size=1), dtype=self.gene_type[0])[0] - + # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. if type(self.gene_space) is dict: if 'step' in self.gene_space.keys(): @@ -1492,20 +1432,20 @@ def initialize_population(self, self.population = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], high=self.gene_space['high'], size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. + dtype=self.gene_type[0]) # A NumPy array holding the initial population. else: self.population = numpy.asarray(numpy.random.choice(self.gene_space, size=self.pop_size), - dtype=self.gene_type[0]) # A NumPy array holding the initial population. + dtype=self.gene_type[0]) # A NumPy array holding the initial population. else: # Reaching this block means: - # 1) gene_space is not nested (gene_space_nested is False). - # 2) gene_type is nested (gene_type_single is False). + # 1) gene_space is not nested (gene_space_nested is False). + # 2) gene_type is nested (gene_type_single is False). # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. if type(self.gene_space) is dict: # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, + self.population = numpy.zeros(shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): @@ -1518,24 +1458,25 @@ def initialize_population(self, size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) else: - gene_values = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], - high=self.gene_space['high'], - size=self.pop_size[0]), + gene_values = numpy.asarray(numpy.random.uniform(low=self.gene_space['low'], + high=self.gene_space['high'], + size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) # Adding the current gene values to the population. self.population[:, gene_idx] = gene_values - + else: # Reaching this block means that the gene_space is not None or dict. # It can be either range, numpy.ndarray, or list. # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros(shape=self.pop_size, dtype=object) + self.population = numpy.zeros( + shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): # A vector of all values of this single gene across all solutions in the population. - gene_values = numpy.asarray(numpy.random.choice(self.gene_space, - size=self.pop_size[0]), + gene_values = numpy.asarray(numpy.random.choice(self.gene_space, + size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) # Adding the current gene values to the population. self.population[:, gene_idx] = gene_values @@ -1552,457 +1493,473 @@ def initialize_population(self, self.initial_population = self.population.copy() def cal_pop_fitness(self): - """ Calculating the fitness values of batches of solutions in the current population. It returns: -fitness: An array of the calculated fitness values. """ - - if self.valid_parameters == False: - self.logger.error("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") - raise Exception("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") - - # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' - # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. - if self.last_generation_parents is not None: - last_generation_parents_as_list = [list(gen_parent) for gen_parent in self.last_generation_parents] - - # 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism' - # It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. - if self.last_generation_elitism is not None: - last_generation_elitism_as_list = [list(gen_elitism) for gen_elitism in self.last_generation_elitism] - - pop_fitness = ["undefined"] * len(self.population) - if self.parallel_processing is None: - # Calculating the fitness value of each solution in the current population. - for sol_idx, sol in enumerate(self.population): - # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. - # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. - # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. - # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. - # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1))): - # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1))): - if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): - solution_idx = self.solutions.index(list(sol)) - fitness = self.solutions_fitness[solution_idx] - elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): - # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. - # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index(list(sol)) - # Use the returned elitism index to return its index in the last population. - elitism_idx = self.last_generation_elitism_indices[elitism_idx] - # Use the elitism's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[elitism_idx] - # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. - # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. - # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): - elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. - # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. - # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index(list(sol)) - # Use the returned parent index to return its index in the last population. - parent_idx = self.last_generation_parents_indices[parent_idx] - # Use the parent's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[parent_idx] - else: - # Check if batch processing is used. If not, then calculate this missing fitness value. - if self.fitness_batch_size in [1, None]: - fitness = self.fitness_func(self, sol, sol_idx) - if type(fitness) in GA.supported_int_float_types: - pass - else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) + try: + if self.valid_parameters == False: + raise Exception("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") + + # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' + # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. + if self.last_generation_parents is not None: + last_generation_parents_as_list = [ + list(gen_parent) for gen_parent in self.last_generation_parents] + + # 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism' + # It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. + if self.last_generation_elitism is not None: + last_generation_elitism_as_list = [ + list(gen_elitism) for gen_elitism in self.last_generation_elitism] + + pop_fitness = ["undefined"] * len(self.population) + if self.parallel_processing is None: + # Calculating the fitness value of each solution in the current population. + for sol_idx, sol in enumerate(self.population): + # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. + # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. + # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. + # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. + # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1))): + # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1))): + if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): + solution_idx = self.solutions.index(list(sol)) + fitness = self.solutions_fitness[solution_idx] + elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): + # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. + # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. + elitism_idx = last_generation_elitism_as_list.index( + list(sol)) + # Use the returned elitism index to return its index in the last population. + elitism_idx = self.last_generation_elitism_indices[elitism_idx] + # Use the elitism's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[elitism_idx] + # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. + # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. + # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): + elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): + # Index of the parent in the 'self.last_generation_parents' array. + # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. + # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] + parent_idx = last_generation_parents_as_list.index( + list(sol)) + # Use the returned parent index to return its index in the last population. + parent_idx = self.last_generation_parents_indices[parent_idx] + # Use the parent's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[parent_idx] else: - # Reaching this point means that batch processing is in effect to calculate the fitness values. - # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. - continue - - # This is only executed if the fitness value was already calculated. - pop_fitness[sol_idx] = fitness - - if self.fitness_batch_size not in [1, None]: - # Reaching this block means that batch fitness calculation is used. - - # Indices of the solutions to calculate their fitness. - solutions_indices = numpy.where(numpy.array(pop_fitness) == "undefined")[0] - # Number of batches. - num_batches = int(numpy.ceil(len(solutions_indices) / self.fitness_batch_size)) - # For each batch, get its indices and call the fitness function. - for batch_idx in range(num_batches): - batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * self.fitness_batch_size - batch_indices = solutions_indices[batch_first_index:batch_last_index] - batch_solutions = self.population[batch_indices, :] - - batch_fitness = self.fitness_func(self, batch_solutions, batch_indices) - if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - - for index, fitness in zip(batch_indices, batch_fitness): - if type(fitness) in GA.supported_int_float_types: - pop_fitness[index] = fitness + # Check if batch processing is used. If not, then calculate this missing fitness value. + if self.fitness_batch_size in [1, None]: + fitness = self.fitness_func(self, sol, sol_idx) + if type(fitness) in GA.supported_int_float_types: + pass + else: + + raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - else: - # Calculating the fitness value of each solution in the current population. - for sol_idx, sol in enumerate(self.population): - # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. - # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. - # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. - # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. - if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): - solution_idx = self.solutions.index(list(sol)) - fitness = self.solutions_fitness[solution_idx] - pop_fitness[sol_idx] = fitness - elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): - # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. - # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index(list(sol)) - # Use the returned elitism index to return its index in the last population. - elitism_idx = self.last_generation_elitism_indices[elitism_idx] - # Use the elitism's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[elitism_idx] + # Reaching this point means that batch processing is in effect to calculate the fitness values. + # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. + continue + # This is only executed if the fitness value was already calculated. pop_fitness[sol_idx] = fitness - # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. - # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. - # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): - elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): - # Index of the parent in the 'self.last_generation_parents' array. - # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. - # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index(list(sol)) - # Use the returned parent index to return its index in the last population. - parent_idx = self.last_generation_parents_indices[parent_idx] - # Use the parent's index to return its pre-calculated fitness value. - fitness = self.previous_generation_fitness[parent_idx] - pop_fitness[sol_idx] = fitness - - # Decide which class to use based on whether the user selected "process" or "thread" - if self.parallel_processing[0] == "process": - ExecutorClass = concurrent.futures.ProcessPoolExecutor - else: - ExecutorClass = concurrent.futures.ThreadPoolExecutor - - # We can use a with statement to ensure threads are cleaned up promptly (https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example) - with ExecutorClass(max_workers=self.parallel_processing[1]) as executor: - solutions_to_submit_indices = [] - solutions_to_submit = [] - for sol_idx, sol in enumerate(self.population): - # The "undefined" value means that the fitness of this solution must be calculated. - if pop_fitness[sol_idx] == "undefined": - solutions_to_submit.append(sol.copy()) - solutions_to_submit_indices.append(sol_idx) - - # Check if batch processing is used. If not, then calculate the fitness value for individual solutions. - if self.fitness_batch_size in [1, None]: - for index, fitness in zip(solutions_to_submit_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), solutions_to_submit, solutions_to_submit_indices)): - if type(fitness) in GA.supported_int_float_types: - pop_fitness[index] = fitness - else: - self.logger.error("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - else: - # Reaching this block means that batch processing is used. The fitness values are calculated in batches. + if self.fitness_batch_size not in [1, None]: + # Reaching this block means that batch fitness calculation is used. + # Indices of the solutions to calculate their fitness. + solutions_indices = numpy.where( + numpy.array(pop_fitness) == "undefined")[0] # Number of batches. - num_batches = int(numpy.ceil(len(solutions_to_submit_indices) / self.fitness_batch_size)) - # Each element of the `batches_solutions` list represents the solutions in one batch. - batches_solutions = [] - # Each element of the `batches_indices` list represents the solutions' indices in one batch. - batches_indices = [] + num_batches = int(numpy.ceil( + len(solutions_indices) / self.fitness_batch_size)) # For each batch, get its indices and call the fitness function. for batch_idx in range(num_batches): batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * self.fitness_batch_size - batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] + batch_last_index = (batch_idx + 1) * \ + self.fitness_batch_size + batch_indices = solutions_indices[batch_first_index:batch_last_index] batch_solutions = self.population[batch_indices, :] - batches_solutions.append(batch_solutions) - batches_indices.append(batch_indices) - - for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): + batch_fitness = self.fitness_func( + self, batch_solutions, batch_indices) if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - self.logger.error("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) - raise TypeError("Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {batch_fitness_type}.".format(batch_fitness=batch_fitness, batch_fitness_type=type(batch_fitness))) + + raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): - self.logger.error("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) - raise ValueError("There is a mismatch between the number of solutions passed to the fitness function ({batch_indices_len}) and the number of fitness values returned ({batch_fitness_len}). They must match.".format(batch_fitness_len=len(batch_fitness), batch_indices_len=len(batch_indices))) + + raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") for index, fitness in zip(batch_indices, batch_fitness): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - self.logger.error("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - raise ValueError("The fitness function should return a number but the value ({fit_val}) of type {fit_type} found.".format(fit_val=fitness, fit_type=type(fitness))) - - pop_fitness = numpy.array(pop_fitness) + + raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + else: + # Calculating the fitness value of each solution in the current population. + for sol_idx, sol in enumerate(self.population): + # Check if the `save_solutions` parameter is `True` and whether the solution already exists in the `solutions` list. If so, use its fitness rather than calculating it again. + # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow. + # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list. + # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'. + if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions): + solution_idx = self.solutions.index(list(sol)) + fitness = self.solutions_fitness[solution_idx] + pop_fitness[sol_idx] = fitness + elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): + # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. + # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. + elitism_idx = last_generation_elitism_as_list.index( + list(sol)) + # Use the returned elitism index to return its index in the last population. + elitism_idx = self.last_generation_elitism_indices[elitism_idx] + # Use the elitism's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[elitism_idx] + + pop_fitness[sol_idx] = fitness + # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function. + # We cannot use the `numpy.where()` function directly because it does not support the `axis` parameter. This is why the `numpy.all()` function is used to match the solutions on axis=1. + # elif (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0): + elif ((self.keep_parents == -1) or (self.keep_parents > 0)) and (self.last_generation_parents is not None) and (len(self.last_generation_parents) > 0) and (list(sol) in last_generation_parents_as_list): + # Index of the parent in the 'self.last_generation_parents' array. + # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. + # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] + parent_idx = last_generation_parents_as_list.index( + list(sol)) + # Use the returned parent index to return its index in the last population. + parent_idx = self.last_generation_parents_indices[parent_idx] + # Use the parent's index to return its pre-calculated fitness value. + fitness = self.previous_generation_fitness[parent_idx] + + pop_fitness[sol_idx] = fitness + + # Decide which class to use based on whether the user selected "process" or "thread" + if self.parallel_processing[0] == "process": + ExecutorClass = concurrent.futures.ProcessPoolExecutor + else: + ExecutorClass = concurrent.futures.ThreadPoolExecutor + + # We can use a with statement to ensure threads are cleaned up promptly (https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example) + with ExecutorClass(max_workers=self.parallel_processing[1]) as executor: + solutions_to_submit_indices = [] + solutions_to_submit = [] + for sol_idx, sol in enumerate(self.population): + # The "undefined" value means that the fitness of this solution must be calculated. + if pop_fitness[sol_idx] == "undefined": + solutions_to_submit.append(sol.copy()) + solutions_to_submit_indices.append(sol_idx) + + # Check if batch processing is used. If not, then calculate the fitness value for individual solutions. + if self.fitness_batch_size in [1, None]: + for index, fitness in zip(solutions_to_submit_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), solutions_to_submit, solutions_to_submit_indices)): + if type(fitness) in GA.supported_int_float_types: + pop_fitness[index] = fitness + else: + raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") + else: + # Reaching this block means that batch processing is used. The fitness values are calculated in batches. + + # Number of batches. + num_batches = int(numpy.ceil( + len(solutions_to_submit_indices) / self.fitness_batch_size)) + # Each element of the `batches_solutions` list represents the solutions in one batch. + batches_solutions = [] + # Each element of the `batches_indices` list represents the solutions' indices in one batch. + batches_indices = [] + # For each batch, get its indices and call the fitness function. + for batch_idx in range(num_batches): + batch_first_index = batch_idx * self.fitness_batch_size + batch_last_index = (batch_idx + 1) * \ + self.fitness_batch_size + batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] + batch_solutions = self.population[batch_indices, :] + + batches_solutions.append(batch_solutions) + batches_indices.append(batch_indices) + + for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): + if type(batch_fitness) not in [list, tuple, numpy.ndarray]: + raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") + elif len(numpy.array(batch_fitness)) != len(batch_indices): + + raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") + + for index, fitness in zip(batch_indices, batch_fitness): + if type(fitness) in GA.supported_int_float_types: + pop_fitness[index] = fitness + else: + raise ValueError(f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") + pop_fitness = numpy.array(pop_fitness) + except Exception as ex: + self.logger.exception(ex) + exit(-1) return pop_fitness def run(self): - """ Runs the genetic algorithm. This is the main method in which the genetic algorithm is evolved through a number of generations. """ + try: + if self.valid_parameters == False: + raise Exception("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") + + # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. + # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. + + # self.best_solutions: Holds the best solution in each generation. + if type(self.best_solutions) is numpy.ndarray: + self.best_solutions = list(self.best_solutions) + # self.best_solutions_fitness: A list holding the fitness value of the best solution for each generation. + if type(self.best_solutions_fitness) is numpy.ndarray: + self.best_solutions_fitness = list(self.best_solutions_fitness) + # self.solutions: Holds the solutions in each generation. + if type(self.solutions) is numpy.ndarray: + self.solutions = list(self.solutions) + # self.solutions_fitness: Holds the fitness of the solutions in each generation. + if type(self.solutions_fitness) is numpy.ndarray: + self.solutions_fitness = list(self.solutions_fitness) + + if not (self.on_start is None): + self.on_start(self) + + stop_run = False + + # To continue from where we stopped, the first generation index should start from the value of the 'self.generations_completed' parameter. + if self.generations_completed != 0 and type(self.generations_completed) in GA.supported_int_types: + # If the 'self.generations_completed' parameter is not '0', then this means we continue execution. + generation_first_idx = self.generations_completed + generation_last_idx = self.num_generations + self.generations_completed + else: + # If the 'self.generations_completed' parameter is '0', then stat from scratch. + generation_first_idx = 0 + generation_last_idx = self.num_generations - if self.valid_parameters == False: - self.logger.error("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - raise Exception("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") - - # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. - # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. - - # self.best_solutions: Holds the best solution in each generation. - if type(self.best_solutions) is numpy.ndarray: - self.best_solutions = list(self.best_solutions) - # self.best_solutions_fitness: A list holding the fitness value of the best solution for each generation. - if type(self.best_solutions_fitness) is numpy.ndarray: - self.best_solutions_fitness = list(self.best_solutions_fitness) - # self.solutions: Holds the solutions in each generation. - if type(self.solutions) is numpy.ndarray: - self.solutions = list(self.solutions) - # self.solutions_fitness: Holds the fitness of the solutions in each generation. - if type(self.solutions_fitness) is numpy.ndarray: - self.solutions_fitness = list(self.solutions_fitness) - - if not (self.on_start is None): - self.on_start(self) - - stop_run = False - - # To continue from where we stopped, the first generation index should start from the value of the 'self.generations_completed' parameter. - if self.generations_completed != 0 and type(self.generations_completed) in GA.supported_int_types: - # If the 'self.generations_completed' parameter is not '0', then this means we continue execution. - generation_first_idx = self.generations_completed - generation_last_idx = self.num_generations + self.generations_completed - else: - # If the 'self.generations_completed' parameter is '0', then stat from scratch. - generation_first_idx = 0 - generation_last_idx = self.num_generations - - # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. - self.last_generation_fitness = self.cal_pop_fitness() - - best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) - - # Appending the best solution in the initial population to the best_solutions list. - if self.save_best_solutions: - self.best_solutions.append(best_solution) + # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. + self.last_generation_fitness = self.cal_pop_fitness() - for generation in range(generation_first_idx, generation_last_idx): - if not (self.on_fitness is None): - self.on_fitness(self, self.last_generation_fitness) + best_solution, best_solution_fitness, best_match_idx = self.best_solution( + pop_fitness=self.last_generation_fitness) - # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. - self.best_solutions_fitness.append(best_solution_fitness) - - # Appending the solutions in the current generation to the solutions list. - if self.save_solutions: - # self.solutions.extend(self.population.copy()) - population_as_list = self.population.copy() - population_as_list = [list(item) for item in population_as_list] - self.solutions.extend(population_as_list) - - self.solutions_fitness.extend(self.last_generation_fitness) + # Appending the best solution in the initial population to the best_solutions list. + if self.save_best_solutions: + self.best_solutions.append(best_solution) - # Selecting the best parents in the population for mating. - if callable(self.parent_selection_type): - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, - self.num_parents_mating, - self) - if not type(self.last_generation_parents) is numpy.ndarray: - self.logger.error("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) - raise TypeError("The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {last_generation_parents_type} found.".format(last_generation_parents_type=type(self.last_generation_parents))) - if not type(self.last_generation_parents_indices) is numpy.ndarray: - self.logger.error("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) - raise TypeError("The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {last_generation_parents_indices_type} found.".format(last_generation_parents_indices_type=type(self.last_generation_parents_indices))) - else: - self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, - num_parents=self.num_parents_mating) - - # Validate the output of the parent selection step: self.select_parents() - if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): - if self.last_generation_parents.shape[0] != self.num_parents_mating: - self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) - raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. It is expected to select ({num_parents_mating}) parents but ({num_parents_mating_selected}) selected.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_parents_mating=self.num_parents_mating, num_parents_mating_selected=self.last_generation_parents.shape[0])) - elif self.last_generation_parents.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) - raise ValueError("Size mismatch between the size of the selected parents {parents_size_actual} and the expected size {parents_size_expected}. Parents are expected to have ({num_genes}) genes but ({num_genes_selected}) produced.".format(parents_size_actual=self.last_generation_parents.shape, parents_size_expected=(self.num_parents_mating, self.num_genes), num_genes=self.num_genes, num_genes_selected=self.last_generation_parents.shape[1])) - - if self.last_generation_parents_indices.ndim != 1: - self.logger.error("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) - raise ValueError("The iterable holding the selected parents indices is expected to have 1 dimension but ({parents_indices_ndim}) found.".format(parents_indices_ndim=len(self.last_generation_parents_indices))) - elif len(self.last_generation_parents_indices) != self.num_parents_mating: - self.logger.error("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) - raise ValueError("The iterable holding the selected parents indices is expected to have ({num_parents_mating}) values but ({num_parents_mating_selected}) found.".format(num_parents_mating=self.num_parents_mating, num_parents_mating_selected=len(self.last_generation_parents_indices))) - - if not (self.on_parents is None): - self.on_parents(self, self.last_generation_parents) - - # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. - if self.crossover_type is None: - if self.keep_elitism == 0: - num_parents_to_keep = self.num_parents_mating if self.keep_parents == -1 else self.keep_parents - if self.num_offspring <= num_parents_to_keep: - self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] - else: - self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) + for generation in range(generation_first_idx, generation_last_idx): + if not (self.on_fitness is None): + self.on_fitness(self, self.last_generation_fitness) + + # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. + self.best_solutions_fitness.append(best_solution_fitness) + + # Appending the solutions in the current generation to the solutions list. + if self.save_solutions: + # self.solutions.extend(self.population.copy()) + population_as_list = self.population.copy() + population_as_list = [list(item) + for item in population_as_list] + self.solutions.extend(population_as_list) + + self.solutions_fitness.extend(self.last_generation_fitness) + + # Selecting the best parents in the population for mating. + if callable(self.parent_selection_type): + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + self.num_parents_mating, + self) + if not type(self.last_generation_parents) is numpy.ndarray: + raise TypeError(f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") + if not type(self.last_generation_parents_indices) is numpy.ndarray: + raise TypeError(f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") else: - # The steady_state_selection() function is called to select the best solutions (i.e. elitism). The keep_elitism parameter defines the number of these solutions. - # The steady_state_selection() function is still called here even if its output may not be used given that the condition of the next if statement is True. The reason is that it will be used later. - self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_elitism) - if self.num_offspring <= self.keep_elitism: - self.last_generation_offspring_crossover = self.last_generation_parents[0:self.num_offspring] + self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, + num_parents=self.num_parents_mating) + + # Validate the output of the parent selection step: self.select_parents() + if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): + if self.last_generation_parents.shape[0] != self.num_parents_mating: + raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") + elif self.last_generation_parents.shape[1] != self.num_genes: + raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") + + if self.last_generation_parents_indices.ndim != 1: + raise ValueError(f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") + elif len(self.last_generation_parents_indices) != self.num_parents_mating: + raise ValueError(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") + + if not (self.on_parents is None): + self.on_parents(self, self.last_generation_parents) + + # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. + if self.crossover_type is None: + if self.keep_elitism == 0: + num_parents_to_keep = self.num_parents_mating if self.keep_parents == - \ + 1 else self.keep_parents + if self.num_offspring <= num_parents_to_keep: + self.last_generation_offspring_crossover = self.last_generation_parents[ + 0:self.num_offspring] + else: + self.last_generation_offspring_crossover = numpy.concatenate( + (self.last_generation_parents, self.population[0:(self.num_offspring - self.last_generation_parents.shape[0])])) else: - self.last_generation_offspring_crossover = numpy.concatenate((self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) - else: - # Generating offspring using crossover. - if callable(self.crossover_type): - self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, - (self.num_offspring, self.num_genes), - self) - if not type(self.last_generation_offspring_crossover) is numpy.ndarray: - self.logger.error("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) - raise TypeError("The output of the crossover step is expected to be of type (numpy.ndarray) but {last_generation_offspring_crossover_type} found.".format(last_generation_offspring_crossover_type=type(self.last_generation_offspring_crossover))) + # The steady_state_selection() function is called to select the best solutions (i.e. elitism). The keep_elitism parameter defines the number of these solutions. + # The steady_state_selection() function is still called here even if its output may not be used given that the condition of the next if statement is True. The reason is that it will be used later. + self.last_generation_elitism, _ = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_elitism) + if self.num_offspring <= self.keep_elitism: + self.last_generation_offspring_crossover = self.last_generation_parents[ + 0:self.num_offspring] + else: + self.last_generation_offspring_crossover = numpy.concatenate( + (self.last_generation_elitism, self.population[0:(self.num_offspring - self.last_generation_elitism.shape[0])])) else: - self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, - offspring_size=(self.num_offspring, self.num_genes)) - if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): - if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: - self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) - raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_crossover.shape[0])) - elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) - raise ValueError("Size mismatch between the crossover output {crossover_actual_size} and the expected crossover output {crossover_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(crossover_actual_size=self.last_generation_offspring_crossover.shape, crossover_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_crossover.shape[1])) - - # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. - if not (self.on_crossover is None): - self.on_crossover(self, self.last_generation_offspring_crossover) - - # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. - if self.mutation_type is None: - self.last_generation_offspring_mutation = self.last_generation_offspring_crossover - else: - # Adding some variations to the offspring using mutation. - if callable(self.mutation_type): - self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, - self) - if not type(self.last_generation_offspring_mutation) is numpy.ndarray: - self.logger.error("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) - raise TypeError("The output of the mutation step is expected to be of type (numpy.ndarray) but {last_generation_offspring_mutation_type} found.".format(last_generation_offspring_mutation_type=type(self.last_generation_offspring_mutation))) + # Generating offspring using crossover. + if callable(self.crossover_type): + self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, + (self.num_offspring, + self.num_genes), + self) + if not type(self.last_generation_offspring_crossover) is numpy.ndarray: + raise TypeError(f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") + else: + self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, + offspring_size=(self.num_offspring, self.num_genes)) + if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): + if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: + raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") + elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: + raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") + + # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. + if not (self.on_crossover is None): + self.on_crossover( + self, self.last_generation_offspring_crossover) + + # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. + if self.mutation_type is None: + self.last_generation_offspring_mutation = self.last_generation_offspring_crossover else: - self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover) - - if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): - if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: - self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) - raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected to produce ({num_offspring}) offspring but ({num_offspring_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_offspring=self.num_offspring, num_offspring_produced=self.last_generation_offspring_mutation.shape[0])) - elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: - self.logger.error("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) - raise ValueError("Size mismatch between the mutation output {mutation_actual_size} and the expected mutation output {mutation_expected_size}. It is expected that the offspring has ({num_genes}) genes but ({num_genes_produced}) produced.".format(mutation_actual_size=self.last_generation_offspring_mutation.shape, mutation_expected_size=(self.num_offspring, self.num_genes), num_genes=self.num_genes, num_genes_produced=self.last_generation_offspring_mutation.shape[1])) - - # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. - if not (self.on_mutation is None): - self.on_mutation(self, self.last_generation_offspring_mutation) - - # Update the population attribute according to the offspring generated. - if self.keep_elitism == 0: - # If the keep_elitism parameter is 0, then the keep_parents parameter will be used to decide if the parents are kept in the next generation. - if (self.keep_parents == 0): - self.population = self.last_generation_offspring_mutation - elif (self.keep_parents == -1): - # Creating the new population based on the parents and offspring. - self.population[0:self.last_generation_parents.shape[0], :] = self.last_generation_parents - self.population[self.last_generation_parents.shape[0]:, :] = self.last_generation_offspring_mutation - elif (self.keep_parents > 0): - parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_parents) - self.population[0:parents_to_keep.shape[0], :] = parents_to_keep - self.population[parents_to_keep.shape[0]:, :] = self.last_generation_offspring_mutation - else: - self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, - num_parents=self.keep_elitism) - self.population[0:self.last_generation_elitism.shape[0], :] = self.last_generation_elitism - self.population[self.last_generation_elitism.shape[0]:, :] = self.last_generation_offspring_mutation - - self.generations_completed = generation + 1 # The generations_completed attribute holds the number of the last completed generation. + # Adding some variations to the offspring using mutation. + if callable(self.mutation_type): + self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, + self) + if not type(self.last_generation_offspring_mutation) is numpy.ndarray: + raise TypeError(f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") + else: + self.last_generation_offspring_mutation = self.mutation( + self.last_generation_offspring_crossover) - self.previous_generation_fitness = self.last_generation_fitness.copy() - # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. - self.last_generation_fitness = self.cal_pop_fitness() + if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): + if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: + raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") + elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: + raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") - best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) + # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. + if not (self.on_mutation is None): + self.on_mutation(self, self.last_generation_offspring_mutation) - # Appending the best solution in the current generation to the best_solutions list. - if self.save_best_solutions: - self.best_solutions.append(best_solution) - - # If the on_generation attribute is not None, then cal the callback function after the generation. - if not (self.on_generation is None): - r = self.on_generation(self) - if type(r) is str and r.lower() == "stop": - # Before aborting the loop, save the fitness value of the best solution. - # _, best_solution_fitness, _ = self.best_solution() - self.best_solutions_fitness.append(best_solution_fitness) - break - - if not self.stop_criteria is None: - for criterion in self.stop_criteria: - if criterion[0] == "reach": - if max(self.last_generation_fitness) >= criterion[1]: - stop_run = True - break - elif criterion[0] == "saturate": - criterion[1] = int(criterion[1]) - if (self.generations_completed >= criterion[1]): - if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0: + # Update the population attribute according to the offspring generated. + if self.keep_elitism == 0: + # If the keep_elitism parameter is 0, then the keep_parents parameter will be used to decide if the parents are kept in the next generation. + if (self.keep_parents == 0): + self.population = self.last_generation_offspring_mutation + elif (self.keep_parents == -1): + # Creating the new population based on the parents and offspring. + self.population[0:self.last_generation_parents.shape[0], + :] = self.last_generation_parents + self.population[self.last_generation_parents.shape[0]:, :] = self.last_generation_offspring_mutation + elif (self.keep_parents > 0): + parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_parents) + self.population[0:parents_to_keep.shape[0], + :] = parents_to_keep + self.population[parents_to_keep.shape[0]:, + :] = self.last_generation_offspring_mutation + else: + self.last_generation_elitism, self.last_generation_elitism_indices = self.steady_state_selection(self.last_generation_fitness, + num_parents=self.keep_elitism) + self.population[0:self.last_generation_elitism.shape[0], + :] = self.last_generation_elitism + self.population[self.last_generation_elitism.shape[0]:, :] = self.last_generation_offspring_mutation + + # The generations_completed attribute holds the number of the last completed generation. + self.generations_completed = generation + 1 + + self.previous_generation_fitness = self.last_generation_fitness.copy() + # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. + self.last_generation_fitness = self.cal_pop_fitness() + + best_solution, best_solution_fitness, best_match_idx = self.best_solution( + pop_fitness=self.last_generation_fitness) + + # Appending the best solution in the current generation to the best_solutions list. + if self.save_best_solutions: + self.best_solutions.append(best_solution) + + # If the on_generation attribute is not None, then cal the callback function after the generation. + if not (self.on_generation is None): + r = self.on_generation(self) + if type(r) is str and r.lower() == "stop": + # Before aborting the loop, save the fitness value of the best solution. + # _, best_solution_fitness, _ = self.best_solution() + self.best_solutions_fitness.append(best_solution_fitness) + break + + if not self.stop_criteria is None: + for criterion in self.stop_criteria: + if criterion[0] == "reach": + if max(self.last_generation_fitness) >= criterion[1]: stop_run = True break + elif criterion[0] == "saturate": + criterion[1] = int(criterion[1]) + if (self.generations_completed >= criterion[1]): + if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0: + stop_run = True + break + + if stop_run: + break - if stop_run: - break - - time.sleep(self.delay_after_gen) + time.sleep(self.delay_after_gen) - # Save the fitness of the last generation. - if self.save_solutions: - # self.solutions.extend(self.population.copy()) - population_as_list = self.population.copy() - population_as_list = [list(item) for item in population_as_list] - self.solutions.extend(population_as_list) + # Save the fitness of the last generation. + if self.save_solutions: + # self.solutions.extend(self.population.copy()) + population_as_list = self.population.copy() + population_as_list = [list(item) for item in population_as_list] + self.solutions.extend(population_as_list) - self.solutions_fitness.extend(self.last_generation_fitness) + self.solutions_fitness.extend(self.last_generation_fitness) - # Save the fitness value of the best solution. - _, best_solution_fitness, _ = self.best_solution(pop_fitness=self.last_generation_fitness) - self.best_solutions_fitness.append(best_solution_fitness) + # Save the fitness value of the best solution. + _, best_solution_fitness, _ = self.best_solution( + pop_fitness=self.last_generation_fitness) + self.best_solutions_fitness.append(best_solution_fitness) - self.best_solution_generation = numpy.where(numpy.array(self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] - # After the run() method completes, the run_completed flag is changed from False to True. - self.run_completed = True # Set to True only after the run() method completes gracefully. + self.best_solution_generation = numpy.where(numpy.array( + self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0] + # After the run() method completes, the run_completed flag is changed from False to True. + # Set to True only after the run() method completes gracefully. + self.run_completed = True - if not (self.on_stop is None): - self.on_stop(self, self.last_generation_fitness) + if not (self.on_stop is None): + self.on_stop(self, self.last_generation_fitness) - # Converting the 'best_solutions' list into a NumPy array. - self.best_solutions = numpy.array(self.best_solutions) + # Converting the 'best_solutions' list into a NumPy array. + self.best_solutions = numpy.array(self.best_solutions) - # Converting the 'solutions' list into a NumPy array. - # self.solutions = numpy.array(self.solutions) + # Converting the 'solutions' list into a NumPy array. + # self.solutions = numpy.array(self.solutions) + except Exception as ex: + self.logger.exception(ex) + exit(-1) def best_solution(self, pop_fitness=None): - """ Returns information about the best solution found by the genetic algorithm. Accepts the following parameters: @@ -2012,33 +1969,34 @@ def best_solution(self, pop_fitness=None): -best_solution_fitness: Fitness value of the best solution. -best_match_idx: Index of the best solution in the current population. """ - - if pop_fitness is None: - # If the 'pop_fitness' parameter is not passed, then we have to call the 'cal_pop_fitness()' method to calculate the fitness of all solutions in the lastest population. - pop_fitness = self.cal_pop_fitness() - # Verify the type of the 'pop_fitness' parameter. - elif type(pop_fitness) in [tuple, list, numpy.ndarray]: - # Verify that the length of the passed population fitness matches the length of the 'self.population' attribute. - if len(pop_fitness) == len(self.population): - # This successfully verifies the 'pop_fitness' parameter. - pass + try: + if pop_fitness is None: + # If the 'pop_fitness' parameter is not passed, then we have to call the 'cal_pop_fitness()' method to calculate the fitness of all solutions in the lastest population. + pop_fitness = self.cal_pop_fitness() + # Verify the type of the 'pop_fitness' parameter. + elif type(pop_fitness) in [tuple, list, numpy.ndarray]: + # Verify that the length of the passed population fitness matches the length of the 'self.population' attribute. + if len(pop_fitness) == len(self.population): + # This successfully verifies the 'pop_fitness' parameter. + pass + else: + raise ValueError(f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") else: - self.logger.error("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) - raise ValueError("The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({pop_fitness_length}) must match the length of the 'self.population' attribute ({population_length}).".format(pop_fitness_length=len(pop_fitness), population_length=len(self.population))) - else: - self.logger.error("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) - raise ValueError("The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({pop_fitness_type}) found.".format(pop_fitness_type=type(pop_fitness))) + raise ValueError(f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") - # Return the index of the best solution that has the best fitness value. - best_match_idx = numpy.where(pop_fitness == numpy.max(pop_fitness))[0][0] + # Return the index of the best solution that has the best fitness value. + best_match_idx = numpy.where( + pop_fitness == numpy.max(pop_fitness))[0][0] - best_solution = self.population[best_match_idx, :].copy() - best_solution_fitness = pop_fitness[best_match_idx] + best_solution = self.population[best_match_idx, :].copy() + best_solution_fitness = pop_fitness[best_match_idx] + except Exception as ex: + self.logger.exception(ex) + exit(-1) return best_solution, best_solution_fitness, best_match_idx def save(self, filename): - """ Saves the genetic algorithm instance: -filename: Name of the file to save the instance. No extension is needed. @@ -2049,12 +2007,12 @@ def save(self, filename): file.write(cloudpickle_serialized_object) cloudpickle.dump(self, file) - def summary(self, - line_length=70, - fill_character=" ", - line_character="-", - line_character2="=", - columns_equal_len=False, + def summary(self, + line_length=70, + fill_character=" ", + line_character="-", + line_character2="=", + columns_equal_len=False, print_step_parameters=True, print_parameters_summary=True): """ @@ -2074,7 +2032,8 @@ def summary(self, def fill_message(msg, line_length=line_length, fill_character=fill_character): num_spaces = int((line_length - len(msg))/2) num_spaces = int(num_spaces / len(fill_character)) - msg = "{spaces}{msg}{spaces}".format(msg=msg, spaces=fill_character * num_spaces) + msg = "{spaces}{msg}{spaces}".format( + msg=msg, spaces=fill_character * num_spaces) return msg def line_separator(line_length=line_length, line_character=line_character): @@ -2085,10 +2044,12 @@ def create_row(columns, line_length=line_length, fill_character=fill_character, filled_columns = [] if split_percentages == None: split_percentages = [int(100/len(columns))] * 3 - columns_lengths = [int((split_percentages[idx] * line_length) / 100) for idx in range(len(split_percentages))] + columns_lengths = [int((split_percentages[idx] * line_length) / 100) + for idx in range(len(split_percentages))] for column_idx, column in enumerate(columns): current_column_length = len(column) - extra_characters = columns_lengths[column_idx] - current_column_length + extra_characters = columns_lengths[column_idx] - \ + current_column_length filled_column = column + fill_character * extra_characters filled_column = column + fill_character * extra_characters filled_columns.append(filled_column) @@ -2097,72 +2058,72 @@ def create_row(columns, line_length=line_length, fill_character=fill_character, def print_parent_selection_params(): nonlocal summary_output - m = "Number of Parents: {num_parents_mating}".format(num_parents_mating=self.num_parents_mating) + m = f"Number of Parents: {self.num_parents_mating}" self.logger.info(m) summary_output = summary_output + m + "\n" if self.parent_selection_type == "tournament": - m = "K Tournament: {K_tournament}".format(K_tournament=self.K_tournament) + m = f"K Tournament: {self.K_tournament}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_fitness_params(): nonlocal summary_output if not self.fitness_batch_size is None: - m = "Fitness batch size: {fitness_batch_size}".format(fitness_batch_size=self.fitness_batch_size) + m = f"Fitness batch size: {self.fitness_batch_size}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_crossover_params(): nonlocal summary_output if not self.crossover_probability is None: - m = "Crossover probability: {crossover_probability}".format(crossover_probability=self.crossover_probability) + m = f"Crossover probability: {self.crossover_probability}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_mutation_params(): nonlocal summary_output if not self.mutation_probability is None: - m = "Mutation Probability: {mutation_probability}".format(mutation_probability=self.mutation_probability) + m = f"Mutation Probability: {self.mutation_probability}" self.logger.info(m) summary_output = summary_output + m + "\n" if self.mutation_percent_genes == "default": - m = "Mutation Percentage: {mutation_percent_genes}".format(mutation_percent_genes=self.mutation_percent_genes) + m = f"Mutation Percentage: {self.mutation_percent_genes}" self.logger.info(m) summary_output = summary_output + m + "\n" # Number of mutation genes is already showed above. - m = "Mutation Genes: {mutation_num_genes}".format(mutation_num_genes=self.mutation_num_genes) + m = f"Mutation Genes: {self.mutation_num_genes}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Random Mutation Range: ({random_mutation_min_val}, {random_mutation_max_val})".format(random_mutation_min_val=self.random_mutation_min_val, random_mutation_max_val=self.random_mutation_max_val) + m = f"Random Mutation Range: ({self.random_mutation_min_val}, {self.random_mutation_max_val})" self.logger.info(m) summary_output = summary_output + m + "\n" if not self.gene_space is None: - m = "Gene Space: {gene_space}".format(gene_space=self.gene_space) + m = f"Gene Space: {self.gene_space}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Mutation by Replacement: {mutation_by_replacement}".format(mutation_by_replacement=self.mutation_by_replacement) + m = f"Mutation by Replacement: {self.mutation_by_replacement}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Allow Duplicated Genes: {allow_duplicate_genes}".format(allow_duplicate_genes=self.allow_duplicate_genes) + m = f"Allow Duplicated Genes: {self.allow_duplicate_genes}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_on_generation_params(): nonlocal summary_output if not self.stop_criteria is None: - m = "Stop Criteria: {stop_criteria}".format(stop_criteria=self.stop_criteria) + m = f"Stop Criteria: {self.stop_criteria}" self.logger.info(m) summary_output = summary_output + m + "\n" def print_params_summary(): nonlocal summary_output - m = "Population Size: ({sol_per_pop}, {num_genes})".format(sol_per_pop=self.sol_per_pop, num_genes=self.num_genes) + m = f"Population Size: ({self.sol_per_pop}, {self.num_genes})" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Number of Generations: {num_generations}".format(num_generations=self.num_generations) + m = f"Number of Generations: {self.num_generations}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Initial Population Range: ({init_range_low}, {init_range_high})".format(init_range_low=self.init_range_low, init_range_high=self.init_range_high) + m = f"Initial Population Range: ({self.init_range_low}, {self.init_range_high})" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2173,14 +2134,14 @@ def print_params_summary(): print_parent_selection_params() if self.keep_elitism != 0: - m = "Keep Elitism: {keep_elitism}".format(keep_elitism=self.keep_elitism) + m = f"Keep Elitism: {self.keep_elitism}" self.logger.info(m) summary_output = summary_output + m + "\n" else: - m = "Keep Parents: {keep_parents}".format(keep_parents=self.keep_parents) + m = f"Keep Parents: {self.keep_parents}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Gene DType: {gene_type}".format(gene_type=self.gene_type) + m = f"Gene DType: {self.gene_type}" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2191,7 +2152,7 @@ def print_params_summary(): print_mutation_params() if self.delay_after_gen != 0: - m = "Post-Generation Delay: {delay_after_gen}".format(delay_after_gen=self.delay_after_gen) + m = f"Post-Generation Delay: {self.delay_after_gen}" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2199,17 +2160,17 @@ def print_params_summary(): print_on_generation_params() if not self.parallel_processing is None: - m = "Parallel Processing: {parallel_processing}".format(parallel_processing=self.parallel_processing) + m = f"Parallel Processing: {self.parallel_processing}" self.logger.info(m) summary_output = summary_output + m + "\n" if not self.random_seed is None: - m = "Random Seed: {random_seed}".format(random_seed=self.random_seed) + m = f"Random Seed: {self.random_seed}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Save Best Solutions: {save_best_solutions}".format(save_best_solutions=self.save_best_solutions) + m = f"Save Best Solutions: {self.save_best_solutions}" self.logger.info(m) summary_output = summary_output + m + "\n" - m = "Save Solutions: {save_solutions}".format(save_solutions=self.save_solutions) + m = f"Save Solutions: {self.save_solutions}" self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2223,21 +2184,30 @@ def print_params_summary(): self.logger.info(m) summary_output = summary_output + m + "\n" - lifecycle_steps = ["on_start()", "Fitness Function", "On Fitness", "Parent Selection", "On Parents", "Crossover", "On Crossover", "Mutation", "On Mutation", "On Generation", "On Stop"] - lifecycle_functions = [self.on_start, self.fitness_func, self.on_fitness, self.select_parents, self.on_parents, self.crossover, self.on_crossover, self.mutation, self.on_mutation, self.on_generation, self.on_stop] - lifecycle_functions = [getattr(lifecycle_func, '__name__', "None") for lifecycle_func in lifecycle_functions] - lifecycle_functions = [lifecycle_func + "()" if lifecycle_func != "None" else "None" for lifecycle_func in lifecycle_functions] - lifecycle_output = ["None", "(1)", "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "({num_parents_mating}, {num_genes})".format(num_parents_mating=self.num_parents_mating, num_genes=self.num_genes), "None", "None", "None"] - lifecycle_step_parameters = [None, print_fitness_params, None, print_parent_selection_params, None, print_crossover_params, None, print_mutation_params, None, print_on_generation_params, None] + lifecycle_steps = ["on_start()", "Fitness Function", "On Fitness", "Parent Selection", "On Parents", + "Crossover", "On Crossover", "Mutation", "On Mutation", "On Generation", "On Stop"] + lifecycle_functions = [self.on_start, self.fitness_func, self.on_fitness, self.select_parents, self.on_parents, + self.crossover, self.on_crossover, self.mutation, self.on_mutation, self.on_generation, self.on_stop] + lifecycle_functions = [getattr( + lifecycle_func, '__name__', "None") for lifecycle_func in lifecycle_functions] + lifecycle_functions = [lifecycle_func + "()" if lifecycle_func != + "None" else "None" for lifecycle_func in lifecycle_functions] + lifecycle_output = ["None", "(1)", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", + f"({self.num_parents_mating}, {self.num_genes})", "None", f"({self.num_parents_mating}, {self.num_genes})", "None", "None", "None"] + lifecycle_step_parameters = [None, print_fitness_params, None, print_parent_selection_params, None, + print_crossover_params, None, print_mutation_params, None, print_on_generation_params, None] if not columns_equal_len: - max_lengthes = [max(list(map(len, lifecycle_steps))), max(list(map(len, lifecycle_functions))), max(list(map(len, lifecycle_output)))] - split_percentages = [int((column_len / sum(max_lengthes)) * 100) for column_len in max_lengthes] + max_lengthes = [max(list(map(len, lifecycle_steps))), max( + list(map(len, lifecycle_functions))), max(list(map(len, lifecycle_output)))] + split_percentages = [ + int((column_len / sum(max_lengthes)) * 100) for column_len in max_lengthes] else: split_percentages = None header_columns = ["Step", "Handler", "Output Shape"] - header_row = create_row(header_columns, split_percentages=split_percentages) + header_row = create_row( + header_columns, split_percentages=split_percentages) m = header_row self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2246,10 +2216,12 @@ def print_params_summary(): summary_output = summary_output + m + "\n" for lifecycle_idx in range(len(lifecycle_steps)): - lifecycle_column = [lifecycle_steps[lifecycle_idx], lifecycle_functions[lifecycle_idx], lifecycle_output[lifecycle_idx]] + lifecycle_column = [lifecycle_steps[lifecycle_idx], + lifecycle_functions[lifecycle_idx], lifecycle_output[lifecycle_idx]] if lifecycle_column[1] == "None": continue - lifecycle_row = create_row(lifecycle_column, split_percentages=split_percentages) + lifecycle_row = create_row( + lifecycle_column, split_percentages=split_percentages) m = lifecycle_row self.logger.info(m) summary_output = summary_output + m + "\n" @@ -2270,8 +2242,8 @@ def print_params_summary(): summary_output = summary_output + m + "\n" return summary_output -def load(filename): +def load(filename): """ Reads a saved instance of the genetic algorithm: -filename: Name of the file to read the instance. No extension is needed. @@ -2282,8 +2254,8 @@ def load(filename): with open(filename + ".pkl", 'rb') as file: ga_in = cloudpickle.load(file) except FileNotFoundError: - raise FileNotFoundError("Error reading the file {filename}. Please check your inputs.".format(filename=filename)) + raise FileNotFoundError(f"Error reading the file {filename}. Please check your inputs.") except: # raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).") raise BaseException("Error loading the file.") - return ga_in + return ga_in \ No newline at end of file diff --git a/pygad/visualize/plot.py b/pygad/visualize/plot.py index a79f848..9d6f70b 100644 --- a/pygad/visualize/plot.py +++ b/pygad/visualize/plot.py @@ -56,8 +56,8 @@ def plot_fitness(self, """ if self.generations_completed < 1: - self.logger.error("The plot_fitness() (i.e. plot_result()) method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) - raise RuntimeError("The plot_fitness() (i.e. plot_result()) method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + self.logger.error("The plot_fitness() (i.e. plot_result()) method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.") + raise RuntimeError("The plot_fitness() (i.e. plot_result()) method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.") fig = matplotlib.pyplot.figure() if plot_type == "plot": @@ -104,8 +104,8 @@ def plot_new_solution_rate(self, """ if self.generations_completed < 1: - self.logger.error("The plot_new_solution_rate() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) - raise RuntimeError("The plot_new_solution_rate() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + self.logger.error("The plot_new_solution_rate() method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.") + raise RuntimeError("The plot_new_solution_rate() method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.") if self.save_solutions == False: self.logger.error("The plot_new_solution_rate() method works only when save_solutions=True in the constructor of the pygad.GA class.") @@ -181,8 +181,8 @@ def plot_genes(self, """ if self.generations_completed < 1: - self.logger.error("The plot_genes() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) - raise RuntimeError("The plot_genes() method can only be called after completing at least 1 generation but ({generations_completed}) is completed.".format(generations_completed=self.generations_completed)) + self.logger.error("The plot_genes() method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.") + raise RuntimeError("The plot_genes() method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.") if type(solutions) is str: if solutions == 'all': @@ -198,8 +198,8 @@ def plot_genes(self, self.logger.error("The plot_genes() method with solutions='best' can only be called if 'save_best_solutions=True' in the pygad.GA class constructor.") raise RuntimeError("The plot_genes() method with solutions='best' can only be called if 'save_best_solutions=True' in the pygad.GA class constructor.") else: - self.logger.error("The solutions parameter can be either 'all' or 'best' but {solutions} found.".format(solutions=solutions)) - raise RuntimeError("The solutions parameter can be either 'all' or 'best' but {solutions} found.".format(solutions=solutions)) + self.logger.error("The solutions parameter can be either 'all' or 'best' but {solutions} found.") + raise RuntimeError("The solutions parameter can be either 'all' or 'best' but {solutions} found.") else: self.logger.error("The solutions parameter must be a string but {solutions_type} found.".format(solutions_type=type(solutions))) raise RuntimeError("The solutions parameter must be a string but {solutions_type} found.".format(solutions_type=type(solutions))) From 4e834a851a10c3809962905143ae3739273954d2 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 9 May 2023 00:02:26 -0400 Subject: [PATCH 054/220] Merge --- pygad/pygad.py | 749 ------------------------------------------------- 1 file changed, 749 deletions(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index 2ecdc94..9839c05 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -159,12 +159,7 @@ def __init__(self, if isinstance(logger, logging.Logger): pass else: -<<<<<<< HEAD raise TypeError(f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") -======= - raise TypeError( - f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Create the 'self.logger' attribute to hold the logger. # Instead of using 'print()', use 'self.logger.info()' @@ -182,37 +177,19 @@ def __init__(self, self.suppress_warnings = suppress_warnings else: self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") -======= - - raise TypeError( - f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Validating mutation_by_replacement if not (type(mutation_by_replacement) is bool): self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") -======= - - raise TypeError( - f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.mutation_by_replacement = mutation_by_replacement # Validate allow_duplicate_genes if not (type(allow_duplicate_genes) is bool): self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") -======= - - raise TypeError( - f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.allow_duplicate_genes = allow_duplicate_genes @@ -223,43 +200,21 @@ def __init__(self, elif type(gene_space) is range: if len(gene_space) == 0: self.valid_parameters = False -<<<<<<< HEAD raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") elif type(gene_space) in [list, numpy.ndarray]: if len(gene_space) == 0: self.valid_parameters = False raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).") -======= - - raise ValueError( - "'gene_space' cannot be empty (i.e. its length must be >= 0).") - elif type(gene_space) in [list, numpy.ndarray]: - if len(gene_space) == 0: - self.valid_parameters = False - - raise ValueError( - "'gene_space' cannot be empty (i.e. its length must be >= 0).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: for index, el in enumerate(gene_space): if type(el) in [numpy.ndarray, list, tuple, range]: if len(el) == 0: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") else: for val in el: if not (type(val) in [type(None)] + GA.supported_int_float_types): raise TypeError(f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") -======= - raise ValueError( - f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).") - else: - for val in el: - if not (type(val) in [type(None)] + GA.supported_int_float_types): - raise TypeError( - f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.gene_space_nested = True elif type(el) == type(None): pass @@ -270,18 +225,12 @@ def __init__(self, pass else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") -======= - raise ValueError( - f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif len(el.items()) == 3: if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()): pass else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") else: self.valid_parameters = False @@ -290,20 +239,6 @@ def __init__(self, elif not (type(el) in GA.supported_int_float_types): self.valid_parameters = False raise TypeError(f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") -======= - raise ValueError( - f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}") - else: - self.valid_parameters = False - - raise ValueError( - f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.") - self.gene_space_nested = True - elif not (type(el) in GA.supported_int_float_types): - self.valid_parameters = False - raise TypeError( - f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif type(gene_space) is dict: if len(gene_space.items()) == 2: @@ -311,18 +246,12 @@ def __init__(self, pass else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") -======= - raise ValueError( - f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif len(gene_space.items()) == 3: if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()): pass else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") else: self.valid_parameters = False @@ -331,19 +260,6 @@ def __init__(self, else: self.valid_parameters = False raise TypeError(f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") -======= - raise ValueError( - f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}") - else: - self.valid_parameters = False - raise ValueError( - f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.") - - else: - self.valid_parameters = False - raise TypeError( - f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.gene_space = gene_space @@ -354,26 +270,16 @@ def __init__(self, self.init_range_high = init_range_high else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") else: self.valid_parameters = False raise ValueError(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") -======= - raise ValueError( - f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") - else: - self.valid_parameters = False - raise ValueError( - f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Validate random_mutation_min_val and random_mutation_max_val if type(random_mutation_min_val) in GA.supported_int_float_types: if type(random_mutation_max_val) in GA.supported_int_float_types: if random_mutation_min_val == random_mutation_max_val: if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") else: self.valid_parameters = False @@ -381,18 +287,6 @@ def __init__(self, else: self.valid_parameters = False raise TypeError(f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") -======= - warnings.warn( - "The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") - else: - self.valid_parameters = False - raise TypeError( - f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") - else: - self.valid_parameters = False - raise TypeError( - f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.random_mutation_min_val = random_mutation_min_val self.random_mutation_max_val = random_mutation_max_val @@ -407,17 +301,11 @@ def __init__(self, # A single data type of int with precision. elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None): self.gene_type_single = False -<<<<<<< HEAD raise ValueError(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") -======= - raise ValueError( - f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif type(gene_type) in [list, tuple, numpy.ndarray]: if num_genes is None: if initial_population is None: self.valid_parameters = False -<<<<<<< HEAD raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") elif not len(gene_type) == len(initial_population[0]): self.valid_parameters = False @@ -425,18 +313,6 @@ def __init__(self, elif not len(gene_type) == num_genes: self.valid_parameters = False raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") -======= - raise TypeError( - "When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") - elif not len(gene_type) == len(initial_population[0]): - self.valid_parameters = False - raise ValueError( - f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.") - elif not len(gene_type) == num_genes: - self.valid_parameters = False - raise ValueError( - f"When the parameter 'gene_type' is nested, then it can be either [float, int] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 for gene_type_idx, gene_type_val in enumerate(gene_type): if gene_type_val in GA.supported_float_types: # If the gene type is float and no precision is passed, set it to None. @@ -451,41 +327,22 @@ def __init__(self, pass else: self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") -======= - raise TypeError( - f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.valid_parameters = False raise TypeError( f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.") else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") else: self.valid_parameters = False raise ValueError(f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") -======= - raise ValueError( - f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.") - else: - self.valid_parameters = False - raise ValueError( - f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.gene_type = gene_type self.gene_type_single = False else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") -======= - raise ValueError( - f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. self.gene_space_unpacked = self.unpack_gene_space() @@ -499,21 +356,11 @@ def __init__(self, # Validating the number of solutions in the population (sol_per_pop) if sol_per_pop <= 0: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") # Validating the number of gene. if (num_genes <= 0): self.valid_parameters = False raise ValueError(f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") -======= - raise ValueError( - f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") - # Validating the number of gene. - if (num_genes <= 0): - self.valid_parameters = False - raise ValueError( - f"The number of genes cannot be <= 0 but ({num_genes}) found.\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created. # Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible. self.num_genes = num_genes # Number of genes in the solution. @@ -522,12 +369,7 @@ def __init__(self, if self.gene_space_nested: if len(gene_space) != self.num_genes: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") -======= - raise ValueError( - f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Number of solutions in the population. self.sol_per_pop = sol_per_pop @@ -538,7 +380,6 @@ def __init__(self, self.gene_type) else: self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") elif not type(initial_population) in [list, tuple, numpy.ndarray]: self.valid_parameters = False @@ -546,18 +387,6 @@ def __init__(self, elif numpy.array(initial_population).ndim != 2: self.valid_parameters = False raise ValueError(f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") -======= - raise TypeError( - f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") - elif not type(initial_population) in [list, tuple, numpy.ndarray]: - self.valid_parameters = False - raise TypeError( - f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") - elif numpy.array(initial_population).ndim != 2: - self.valid_parameters = False - raise ValueError( - f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # Validate the type of each value in the 'initial_population' parameter. for row_idx in range(len(initial_population)): @@ -566,12 +395,7 @@ def __init__(self, pass else: self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") -======= - raise TypeError( - f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Forcing the initial_population array to have the data type assigned to the gene_type parameter. if self.gene_type_single == True: @@ -630,32 +454,17 @@ def __init__(self, if self.gene_space_nested: if len(gene_space) != self.num_genes: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") -======= - raise ValueError( - f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Validating the number of parents to be selected for mating (num_parents_mating) if num_parents_mating <= 0: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") -======= - raise ValueError( - f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Validating the number of parents to be selected for mating: num_parents_mating if (num_parents_mating > self.sol_per_pop): self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") -======= - raise ValueError( - f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.num_parents_mating = num_parents_mating @@ -670,12 +479,7 @@ def __init__(self, self.crossover = crossover_type else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"When 'crossover_type' is assigned to a method, then this crossover method must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The selected parents.\n3) The size of the offspring to be produced.\n4) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif callable(crossover_type): # Check if the crossover_type is a function that accepts 2 paramaters. if (crossover_type.__code__.co_argcount == 3): @@ -683,19 +487,10 @@ def __init__(self, self.crossover = crossover_type else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") elif not (type(crossover_type) is str): self.valid_parameters = False raise TypeError(f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") -======= - raise ValueError( - f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {crossover_type.__code__.co_argcount} parameter(s).") - elif not (type(crossover_type) is str): - self.valid_parameters = False - raise TypeError( - f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # type crossover_type is str crossover_type = crossover_type.lower() if (crossover_type == "single_point"): @@ -708,12 +503,7 @@ def __init__(self, self.crossover = self.scattered_crossover else: self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") -======= - raise TypeError( - f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.crossover_type = crossover_type @@ -725,19 +515,10 @@ def __init__(self, self.crossover_probability = crossover_probability else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") else: self.valid_parameters = False raise TypeError(f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") -======= - raise ValueError( - f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.") - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property. # Validating the mutation type: mutation_type @@ -751,12 +532,7 @@ def __init__(self, self.mutation = mutation_type else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"When 'mutation_type' is assigned to a method, then it must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The offspring to be mutated.\n3) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif callable(mutation_type): # Check if the mutation_type is a function that accepts 2 paramater. if (mutation_type.__code__.co_argcount == 2): @@ -764,19 +540,10 @@ def __init__(self, self.mutation = mutation_type else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") elif not (type(mutation_type) is str): self.valid_parameters = False raise TypeError(f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") -======= - raise ValueError( - f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {mutation_type.__code__.co_argcount} parameter(s).") - elif not (type(mutation_type) is str): - self.valid_parameters = False - raise TypeError( - f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # type mutation_type is str mutation_type = mutation_type.lower() if (mutation_type == "random"): @@ -791,12 +558,7 @@ def __init__(self, self.mutation = self.adaptive_mutation else: self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") -======= - raise TypeError( - f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.mutation_type = mutation_type @@ -811,19 +573,10 @@ def __init__(self, self.mutation_probability = mutation_probability else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") else: self.valid_parameters = False raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") -======= - raise ValueError( - f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.") - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # Mutation probability is adaptive not fixed. if type(mutation_probability) in [list, tuple, numpy.ndarray]: @@ -834,7 +587,6 @@ def __init__(self, pass else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") else: self.valid_parameters = False @@ -849,27 +601,6 @@ def __init__(self, else: self.valid_parameters = False raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") -======= - raise ValueError( - f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.") - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.") - if mutation_probability[0] < mutation_probability[1]: - if not self.suppress_warnings: - warnings.warn( - f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") - self.mutation_probability = mutation_probability - else: - self.valid_parameters = False - raise ValueError( - f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.") - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: pass @@ -895,12 +626,7 @@ def __init__(self, elif type(mutation_percent_genes) in GA.supported_int_float_types: if (mutation_percent_genes <= 0 or mutation_percent_genes > 100): self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") -======= - raise ValueError( - f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10. if mutation_percent_genes == 'default'.lower(): @@ -913,21 +639,11 @@ def __init__(self, if mutation_num_genes == 0: if self.mutation_probability is None: if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes = 1 else: self.valid_parameters = False raise TypeError(f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") -======= - warnings.warn( - f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") - mutation_num_genes = 1 - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # The percent of genes to mutate is adaptive not fixed. if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]: @@ -938,19 +654,10 @@ def __init__(self, if type(el) in GA.supported_int_float_types: if (el <= 0 or el > 100): self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") else: self.valid_parameters = False raise TypeError(f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") -======= - raise ValueError( - f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n") - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated. # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated. mutation_num_genes[idx] = numpy.uint32( @@ -958,7 +665,6 @@ def __init__(self, # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1. if mutation_num_genes[idx] == 0: if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn(f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") mutation_num_genes[idx] = 1 if mutation_percent_genes[0] < mutation_percent_genes[1]: @@ -972,32 +678,12 @@ def __init__(self, if self.mutation_probability is None: self.valid_parameters = False raise TypeError(f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") -======= - warnings.warn( - f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.") - mutation_num_genes[idx] = 1 - if mutation_percent_genes[0] < mutation_percent_genes[1]: - if not self.suppress_warnings: - warnings.warn( - f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.") - # At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Eveyrthing is OK. - else: - self.valid_parameters = False - raise ValueError( - f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.") - else: - if self.mutation_probability is None: - self.valid_parameters = False - raise TypeError( - f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # The mutation_num_genes parameter exists. Checking whether adaptive mutation is used. elif (mutation_type != "adaptive"): # Number of genes to mutate is fixed not adaptive. if type(mutation_num_genes) in GA.supported_int_types: if (mutation_num_genes <= 0): self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") elif (mutation_num_genes > self.num_genes): self.valid_parameters = False @@ -1005,18 +691,6 @@ def __init__(self, else: self.valid_parameters = False raise TypeError(f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") -======= - raise ValueError( - f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n") - elif (mutation_num_genes > self.num_genes): - self.valid_parameters = False - raise ValueError( - f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n") - else: - self.valid_parameters = False - raise TypeError( - f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # Number of genes to mutate is adaptive not fixed. if type(mutation_num_genes) in [list, tuple, numpy.ndarray]: @@ -1025,7 +699,6 @@ def __init__(self, if type(el) in GA.supported_int_types: if (el <= 0): self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") elif (el > self.num_genes): self.valid_parameters = False @@ -1044,44 +717,13 @@ def __init__(self, else: self.valid_parameters = False raise TypeError(f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") -======= - raise ValueError( - f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n") - elif (el > self.num_genes): - self.valid_parameters = False - raise ValueError( - f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n") - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.") - # At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated. - if mutation_num_genes[0] < mutation_num_genes[1]: - if not self.suppress_warnings: - warnings.warn( - f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high qualitiy solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.") - # At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Eveyrthing is OK. - else: - self.valid_parameters = False - raise ValueError( - f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.") - else: - self.valid_parameters = False - raise TypeError( - f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: pass # Validating mutation_by_replacement and mutation_type if self.mutation_type != "random" and self.mutation_by_replacement: if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn(f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") -======= - warnings.warn( - f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if crossover and mutation are both disabled. if (self.mutation_type is None) and (self.crossover_type is None): @@ -1098,12 +740,7 @@ def __init__(self, self.select_parents = parent_selection_type else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"When 'parent_selection_type' is assigned to a method, then it must accept 4 parameters:\n1) Expected to be the 'self' object.\n2) The fitness values of the current population.\n3) The number of parents needed.\n4) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif callable(parent_selection_type): # Check if the parent_selection_type is a function that accepts 3 paramaters. if (parent_selection_type.__code__.co_argcount == 3): @@ -1112,21 +749,11 @@ def __init__(self, self.select_parents = parent_selection_type else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") elif not (type(parent_selection_type) is str): self.valid_parameters = False raise TypeError(f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") -======= - raise ValueError( - f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {parent_selection_type.__code__.co_argcount} parameter(s).") - elif not (type(parent_selection_type) is str): - self.valid_parameters = False - - raise TypeError( - f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: parent_selection_type = parent_selection_type.lower() if (parent_selection_type == "sss"): @@ -1150,66 +777,34 @@ def __init__(self, if (K_tournament > self.sol_per_pop): K_tournament = self.sol_per_pop if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn(f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") elif (K_tournament <= 0): self.valid_parameters = False raise ValueError(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") -======= - warnings.warn( - f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n") - elif (K_tournament <= 0): - self.valid_parameters = False - raise ValueError( - f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.K_tournament = K_tournament # Validating the number of parents to keep in the next population: keep_parents if not (type(keep_parents) in GA.supported_int_types): self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): self.valid_parameters = False raise ValueError(f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") -======= - raise TypeError( - f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.") - elif (keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1): - self.valid_parameters = False - raise ValueError( - f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.keep_parents = keep_parents if parent_selection_type == "sss" and self.keep_parents == 0: if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn("The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") -======= - warnings.warn( - "The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Validating the number of elitism to keep in the next population: keep_elitism if not (type(keep_elitism) in GA.supported_int_types): self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): self.valid_parameters = False raise ValueError(f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") -======= - raise TypeError( - f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.") - elif (keep_elitism > self.sol_per_pop or keep_elitism < 0): - self.valid_parameters = False - raise ValueError( - f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.keep_elitism = keep_elitism @@ -1236,51 +831,27 @@ def __init__(self, self.fitness_func = fitness_func else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 4 parameters\n1) Expected to be the 'self' object.\n2) The instance of the 'pygad.GA' class.\n3) A solution to calculate its fitness value.\n4) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif callable(fitness_func): # Check if the fitness function accepts 2 paramaters. if (fitness_func.__code__.co_argcount == 3): self.fitness_func = fitness_func else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") -======= - raise ValueError( - f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {fitness_func.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - - raise TypeError( - f"The value assigned to the fitness_func parameter is expected to be of type function but {type(fitness_func)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 if fitness_batch_size is None: pass elif not (type(fitness_batch_size) in GA.supported_int_types): self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: self.valid_parameters = False raise ValueError(f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") -======= - raise TypeError( - f"The value assigned to the fitness_batch_size parameter is expected to be integer but the value ({fitness_batch_size}) of type {type(fitness_batch_size)} found.") - elif fitness_batch_size <= 0 or fitness_batch_size > self.sol_per_pop: - self.valid_parameters = False - raise ValueError( - f"The value assigned to the fitness_batch_size parameter must be:\n1) Greater than 0.\n2) Less than or equal to sol_per_pop ({self.sol_per_pop}).\nBut the value ({fitness_batch_size}) found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.fitness_batch_size = fitness_batch_size @@ -1292,12 +863,7 @@ def __init__(self, self.on_start = on_start else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"The method assigned to the on_start parameter must accept only 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if the on_start is a function. elif callable(on_start): # Check if the on_start function accepts only a single paramater. @@ -1305,21 +871,11 @@ def __init__(self, self.on_start = on_start else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") -======= - raise ValueError( - f"The function assigned to the on_start parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_start.__code__.co_name}' accepts {on_start.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - - raise TypeError( - f"The value assigned to the on_start parameter is expected to be of type function but {type(on_start)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.on_start = None @@ -1332,12 +888,7 @@ def __init__(self, self.on_fitness = on_fitness else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"The method assigned to the on_fitness parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.3) The fitness values of all solutions.\nThe passed method named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if the on_fitness is a function. elif callable(on_fitness): # Check if the on_fitness function accepts 2 paramaters. @@ -1345,19 +896,10 @@ def __init__(self, self.on_fitness = on_fitness else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") -======= - raise ValueError( - f"The function assigned to the on_fitness parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_fitness.__code__.co_name}' accepts {on_fitness.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - raise TypeError( - f"The value assigned to the on_fitness parameter is expected to be of type function but {type(on_fitness)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.on_fitness = None @@ -1370,12 +912,7 @@ def __init__(self, self.on_parents = on_parents else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"The method assigned to the on_parents parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n3) The fitness values of all solutions.\nThe passed method named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if the on_parents is a function. elif callable(on_parents): # Check if the on_parents function accepts 2 paramaters. @@ -1383,19 +920,10 @@ def __init__(self, self.on_parents = on_parents else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") -======= - raise ValueError( - f"The function assigned to the on_parents parameter must accept 2 parameters representing the instance of the genetic algorithm and the fitness values of all solutions.\nThe passed function named '{on_parents.__code__.co_name}' accepts {on_parents.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - raise TypeError( - f"The value assigned to the on_parents parameter is expected to be of type function but {type(on_parents)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.on_parents = None @@ -1408,12 +936,7 @@ def __init__(self, self.on_crossover = on_crossover else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"The method assigned to the on_crossover parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring generated using crossover.\nThe passed method named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if the on_crossover is a function. elif callable(on_crossover): # Check if the on_crossover function accepts 2 paramaters. @@ -1421,20 +944,10 @@ def __init__(self, self.on_crossover = on_crossover else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") -======= - raise ValueError( - f"The function assigned to the on_crossover parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring generated using crossover.\nThe passed function named '{on_crossover.__code__.co_name}' accepts {on_crossover.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - - raise TypeError( - f"The value assigned to the on_crossover parameter is expected to be of type function but {type(on_crossover)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.on_crossover = None @@ -1447,12 +960,7 @@ def __init__(self, self.on_mutation = on_mutation else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"The method assigned to the on_mutation parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) The offspring after applying the mutation operation.\nThe passed method named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if the on_mutation is a function. elif callable(on_mutation): # Check if the on_mutation function accepts 2 paramaters. @@ -1460,19 +968,10 @@ def __init__(self, self.on_mutation = on_mutation else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") -======= - raise ValueError( - f"The function assigned to the on_mutation parameter must accept 2 parameters representing the instance of the genetic algorithm and the offspring after applying the mutation operation.\nThe passed function named '{on_mutation.__code__.co_name}' accepts {on_mutation.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - raise TypeError( - f"The value assigned to the on_mutation parameter is expected to be of type function but {type(on_mutation)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.on_mutation = None @@ -1485,12 +984,7 @@ def __init__(self, self.on_generation = on_generation else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"The method assigned to the on_generation parameter must accept 2 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\nThe passed method named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if the on_generation is a function. elif callable(on_generation): # Check if the on_generation function accepts only a single paramater. @@ -1498,19 +992,10 @@ def __init__(self, self.on_generation = on_generation else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") -======= - raise ValueError( - f"The function assigned to the on_generation parameter must accept only 1 parameter representing the instance of the genetic algorithm.\nThe passed function named '{on_generation.__code__.co_name}' accepts {on_generation.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - raise TypeError( - f"The value assigned to the on_generation parameter is expected to be of type function but {type(on_generation)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.on_generation = None @@ -1523,12 +1008,7 @@ def __init__(self, self.on_stop = on_stop else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") -======= - raise ValueError( - f"The method assigned to the on_stop parameter must accept 3 parameters:\n1) Expected to be the 'self' object.\n2) The instance of the genetic algorithm.\n2) A list of the fitness values of the solutions in the last population.\nThe passed method named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Check if the on_stop is a function. elif callable(on_stop): # Check if the on_stop function accepts 2 paramaters. @@ -1536,19 +1016,10 @@ def __init__(self, self.on_stop = on_stop else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") else: self.valid_parameters = False raise TypeError(f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") -======= - raise ValueError( - f"The function assigned to the on_stop parameter must accept 2 parameters representing the instance of the genetic algorithm and a list of the fitness values of the solutions in the last population.\nThe passed function named '{on_stop.__code__.co_name}' accepts {on_stop.__code__.co_argcount} parameter(s).") - else: - self.valid_parameters = False - raise TypeError( - f"The value assigned to the 'on_stop' parameter is expected to be of type function but {type(on_stop)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.on_stop = None @@ -1558,55 +1029,28 @@ def __init__(self, self.delay_after_gen = delay_after_gen else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") else: self.valid_parameters = False raise TypeError(f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") -======= - raise ValueError( - f"The value passed to the 'delay_after_gen' parameter must be a non-negative number. The value passed is ({delay_after_gen}) of type {type(delay_after_gen)}.") - else: - self.valid_parameters = False - raise TypeError( - f"The value passed to the 'delay_after_gen' parameter must be of type int or float but {type(delay_after_gen)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Validate save_best_solutions if type(save_best_solutions) is bool: if save_best_solutions == True: if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn("Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") else: self.valid_parameters = False raise TypeError(f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") -======= - warnings.warn( - "Use the 'save_best_solutions' parameter with caution as it may cause memory overflow when either the number of generations or number of genes is large.") - else: - self.valid_parameters = False - raise TypeError( - f"The value passed to the 'save_best_solutions' parameter must be of type bool but {type(save_best_solutions)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Validate save_solutions if type(save_solutions) is bool: if save_solutions == True: if not self.suppress_warnings: -<<<<<<< HEAD warnings.warn("Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") else: self.valid_parameters = False raise TypeError(f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") -======= - warnings.warn( - "Use the 'save_solutions' parameter with caution as it may cause memory overflow when either the number of generations, number of genes, or number of solutions in population is large.") - else: - self.valid_parameters = False - raise TypeError( - f"The value passed to the 'save_solutions' parameter must be of type bool but {type(save_solutions)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.stop_criteria = [] self.supported_stop_words = ["reach", "saturate"] @@ -1625,34 +1069,19 @@ def __init__(self, pass else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") -======= - raise ValueError( - f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 if number.replace(".", "").isnumeric(): number = float(number) else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") -======= - raise ValueError( - f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.stop_criteria.append([stop_word, number]) else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") -======= - raise ValueError( - f"For format of a single criterion in the 'stop_criteria' parameter is 'word_number' but '{stop_criteria}' found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif type(stop_criteria) in [list, tuple, numpy.ndarray]: # Remove duplicate criterira by converting the list to a set then back to a list. @@ -1668,29 +1097,18 @@ def __init__(self, pass else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") -======= - raise ValueError( - f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 if number.replace(".", "").isnumeric(): number = float(number) else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") -======= - raise ValueError( - f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 self.stop_criteria.append([stop_word, number]) else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") else: self.valid_parameters = False @@ -1698,18 +1116,6 @@ def __init__(self, else: self.valid_parameters = False raise TypeError(f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") -======= - raise ValueError( - f"The format of a single criterion in the 'stop_criteria' parameter is 'word_number' but {criterion} found.") - else: - self.valid_parameters = False - raise TypeError( - f"When the 'stop_criteria' parameter is assigned a tuple/list/numpy.ndarray, then its elements must be strings but the value ({val}) of type {type(val)} found at index {idx}.") - else: - self.valid_parameters = False - raise TypeError( - f"The expected value of the 'stop_criteria' is a single string or a list/tuple/numpy.ndarray of strings but the value ({stop_criteria}) of type {type(stop_criteria)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 if parallel_processing is None: self.parallel_processing = None @@ -1718,12 +1124,7 @@ def __init__(self, self.parallel_processing = ["thread", parallel_processing] else: self.valid_parameters = False -<<<<<<< HEAD raise ValueError(f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") -======= - raise ValueError( - f"When the 'parallel_processing' parameter is assigned an integer, then the integer must be positive but the value ({parallel_processing}) found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 elif type(parallel_processing) in [list, tuple]: if len(parallel_processing) == 2: if type(parallel_processing[0]) is str: @@ -1737,7 +1138,6 @@ def __init__(self, self.parallel_processing = parallel_processing else: self.valid_parameters = False -<<<<<<< HEAD raise TypeError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") else: self.valid_parameters = False @@ -1748,22 +1148,6 @@ def __init__(self, else: self.valid_parameters = False raise ValueError(f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") -======= - raise TypeError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the second element must be an integer but the value ({parallel_processing[1]}) of type {type(parallel_processing[1])} found.") - else: - self.valid_parameters = False - raise ValueError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the value of the first element must be either 'process' or 'thread' but the value ({parallel_processing[0]}) found.") - else: - self.valid_parameters = False - raise TypeError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then the first element must be of type 'str' but the value ({parallel_processing[0]}) of type {type(parallel_processing[0])} found.") - else: - self.valid_parameters = False - raise ValueError( - f"When a list or tuple is assigned to the 'parallel_processing' parameter, then it must have 2 elements but ({len(parallel_processing)}) found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.valid_parameters = False raise ValueError(f"Unexpected value ({parallel_processing}) of type ({type(parallel_processing)}) assigned to the 'parallel_processing' parameter. The accepted values for this parameter are:\n1) None: (Default) It means no parallel processing is used.\n2) A positive integer referring to the number of threads to be used (i.e. threads, not processes, are used.\n3) list/tuple: If a list or a tuple of exactly 2 elements is assigned, then:\n\t*1) The first element can be either 'process' or 'thread' to specify whether processes or threads are used, respectively.\n\t*2) The second element can be:\n\t\t**1) A positive integer to select the maximum number of processes or threads to be used.\n\t\t**2) 0 to indicate that parallel processing is not used. This is identical to setting 'parallel_processing=None'.\n\t\t**3) None to use the default value as calculated by the concurrent.futures module.") @@ -2116,12 +1500,7 @@ def cal_pop_fitness(self): """ try: if self.valid_parameters == False: -<<<<<<< HEAD raise Exception("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") -======= - raise Exception( - "ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents' # It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'. @@ -2177,13 +1556,7 @@ def cal_pop_fitness(self): if type(fitness) in GA.supported_int_float_types: pass else: - -<<<<<<< HEAD raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") -======= - raise ValueError( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # Reaching this point means that batch processing is in effect to calculate the fitness values. # Do not continue the loop as no fitness is calculated. The fitness will be calculated later in batch mode. @@ -2212,32 +1585,15 @@ def cal_pop_fitness(self): batch_fitness = self.fitness_func( self, batch_solutions, batch_indices) if type(batch_fitness) not in [list, tuple, numpy.ndarray]: - -<<<<<<< HEAD raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): - raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") -======= - raise TypeError( - f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") - elif len(numpy.array(batch_fitness)) != len(batch_indices): - - raise ValueError( - f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 for index, fitness in zip(batch_indices, batch_fitness): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: - -<<<<<<< HEAD raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") -======= - raise ValueError( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # Calculating the fitness value of each solution in the current population. for sol_idx, sol in enumerate(self.population): @@ -2298,13 +1654,7 @@ def cal_pop_fitness(self): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: -<<<<<<< HEAD raise ValueError(f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") -======= - - raise ValueError( - f"The fitness function should return a number but the value {fitness} of type {type(fitness)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: # Reaching this block means that batch processing is used. The fitness values are calculated in batches. @@ -2328,32 +1678,16 @@ def cal_pop_fitness(self): for batch_indices, batch_fitness in zip(batches_indices, executor.map(self.fitness_func, [self]*len(solutions_to_submit_indices), batches_solutions, batches_indices)): if type(batch_fitness) not in [list, tuple, numpy.ndarray]: -<<<<<<< HEAD raise TypeError(f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") elif len(numpy.array(batch_fitness)) != len(batch_indices): raise ValueError(f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") -======= - - raise TypeError( - f"Expected to receive a list, tuple, or numpy.ndarray from the fitness function but the value ({batch_fitness}) of type {type(batch_fitness)}.") - elif len(numpy.array(batch_fitness)) != len(batch_indices): - - raise ValueError( - f"There is a mismatch between the number of solutions passed to the fitness function ({len(batch_indices)}) and the number of fitness values returned ({len(batch_fitness)}). They must match.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 for index, fitness in zip(batch_indices, batch_fitness): if type(fitness) in GA.supported_int_float_types: pop_fitness[index] = fitness else: -<<<<<<< HEAD raise ValueError(f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") -======= - - raise ValueError( - f"The fitness function should return a number but the value ({fitness}) of type {type(fitness)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 pop_fitness = numpy.array(pop_fitness) except Exception as ex: @@ -2367,13 +1701,7 @@ def run(self): """ try: if self.valid_parameters == False: -<<<<<<< HEAD raise Exception("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") -======= - - raise Exception( - "Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Starting from PyGAD 2.18.0, the 4 properties (best_solutions, best_solutions_fitness, solutions, and solutions_fitness) are no longer reset with each call to the run() method. Instead, they are extended. # For example, if there are 50 generations and the user set save_best_solutions=True, then the length of the 2 properties best_solutions and best_solutions_fitness will be 50 after the first call to the run() method, then 100 after the second call, 150 after the third, and so on. @@ -2439,19 +1767,9 @@ def run(self): self.num_parents_mating, self) if not type(self.last_generation_parents) is numpy.ndarray: -<<<<<<< HEAD raise TypeError(f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") if not type(self.last_generation_parents_indices) is numpy.ndarray: raise TypeError(f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") -======= - - raise TypeError( - f"The type of the iterable holding the selected parents is expected to be (numpy.ndarray) but {type(self.last_generation_parents)} found.") - if not type(self.last_generation_parents_indices) is numpy.ndarray: - - raise TypeError( - f"The type of the iterable holding the selected parents' indices is expected to be (numpy.ndarray) but {type(self.last_generation_parents_indices)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.last_generation_parents, self.last_generation_parents_indices = self.select_parents(self.last_generation_fitness, num_parents=self.num_parents_mating) @@ -2459,7 +1777,6 @@ def run(self): # Validate the output of the parent selection step: self.select_parents() if self.last_generation_parents.shape != (self.num_parents_mating, self.num_genes): if self.last_generation_parents.shape[0] != self.num_parents_mating: -<<<<<<< HEAD raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") elif self.last_generation_parents.shape[1] != self.num_genes: raise ValueError(f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") @@ -2468,24 +1785,6 @@ def run(self): raise ValueError(f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") elif len(self.last_generation_parents_indices) != self.num_parents_mating: raise ValueError(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") -======= - - raise ValueError( - f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. It is expected to select ({self.num_parents_mating}) parents but ({self.last_generation_parents.shape[0]}) selected.") - elif self.last_generation_parents.shape[1] != self.num_genes: - - raise ValueError( - f"Size mismatch between the size of the selected parents {self.last_generation_parents.shape} and the expected size {(self.num_parents_mating, self.num_genes)}. Parents are expected to have ({self.num_genes}) genes but ({self.last_generation_parents.shape[1]}) produced.") - - if self.last_generation_parents_indices.ndim != 1: - - raise ValueError( - f"The iterable holding the selected parents indices is expected to have 1 dimension but ({len(self.last_generation_parents_indices)}) found.") - elif len(self.last_generation_parents_indices) != self.num_parents_mating: - - raise ValueError( - f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 if not (self.on_parents is None): self.on_parents(self, self.last_generation_parents) @@ -2520,31 +1819,15 @@ def run(self): self.num_genes), self) if not type(self.last_generation_offspring_crossover) is numpy.ndarray: -<<<<<<< HEAD raise TypeError(f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") -======= - - raise TypeError( - f"The output of the crossover step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_crossover)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents, offspring_size=(self.num_offspring, self.num_genes)) if self.last_generation_offspring_crossover.shape != (self.num_offspring, self.num_genes): if self.last_generation_offspring_crossover.shape[0] != self.num_offspring: -<<<<<<< HEAD raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: raise ValueError(f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") -======= - - raise ValueError( - f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_crossover.shape[0]}) produced.") - elif self.last_generation_offspring_crossover.shape[1] != self.num_genes: - - raise ValueError( - f"Size mismatch between the crossover output {self.last_generation_offspring_crossover.shape} and the expected crossover output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_crossover.shape[1]}) produced.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. if not (self.on_crossover is None): @@ -2560,32 +1843,16 @@ def run(self): self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, self) if not type(self.last_generation_offspring_mutation) is numpy.ndarray: -<<<<<<< HEAD raise TypeError(f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") -======= - - raise TypeError( - f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 else: self.last_generation_offspring_mutation = self.mutation( self.last_generation_offspring_crossover) if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: -<<<<<<< HEAD raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: raise ValueError(f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") -======= - - raise ValueError( - f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected to produce ({self.num_offspring}) offspring but ({self.last_generation_offspring_mutation.shape[0]}) produced.") - elif self.last_generation_offspring_mutation.shape[1] != self.num_genes: - - raise ValueError( - f"Size mismatch between the mutation output {self.last_generation_offspring_mutation.shape} and the expected mutation output {(self.num_offspring, self.num_genes)}. It is expected that the offspring has ({self.num_genes}) genes but ({self.last_generation_offspring_mutation.shape[1]}) produced.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. if not (self.on_mutation is None): @@ -2709,19 +1976,9 @@ def best_solution(self, pop_fitness=None): # This successfully verifies the 'pop_fitness' parameter. pass else: -<<<<<<< HEAD raise ValueError(f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") else: raise ValueError(f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") -======= - - raise ValueError( - f"The length of the list/tuple/numpy.ndarray passed to the 'pop_fitness' parameter ({len(pop_fitness)}) must match the length of the 'self.population' attribute ({len(self.population)}).") - else: - - raise ValueError( - f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 # Return the index of the best solution that has the best fitness value. best_match_idx = numpy.where( @@ -2981,7 +2238,6 @@ def print_params_summary(): summary_output = summary_output + m + "\n" return summary_output - def load(filename): """ Reads a saved instance of the genetic algorithm: @@ -2993,12 +2249,7 @@ def load(filename): with open(filename + ".pkl", 'rb') as file: ga_in = cloudpickle.load(file) except FileNotFoundError: -<<<<<<< HEAD raise FileNotFoundError(f"Error reading the file {filename}. Please check your inputs.") -======= - raise FileNotFoundError( - f"Error reading the file {filename}. Please check your inputs.") ->>>>>>> 977acdaf553eed3f8c0f01e720a1756c2b6d79a9 except: # raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).") raise BaseException("Error loading the file.") From f1aa930871aac692d10dfdf0ba1e7a52ca4fa05b Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 9 May 2023 12:05:47 -0400 Subject: [PATCH 055/220] Use exist() from sys lib --- pygad/pygad.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index 9839c05..01dc850 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -9,6 +9,7 @@ from pygad import utils from pygad import helper from pygad import visualize +import sys class GA(utils.parent_selection.ParentSelection, utils.crossover.Crossover, @@ -1201,7 +1202,7 @@ def __init__(self, self.last_generation_elitism_indices = None except Exception as e: self.logger.exception(e) - exit(-1) + sys.exit(-1) def round_genes(self, solutions): for gene_idx in range(self.num_genes): From ee02e2fd8b304ee4ec0cd16798b1293eb03eae80 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 9 May 2023 16:17:12 -0400 Subject: [PATCH 056/220] Use logging module --- pygad/cnn/cnn.py | 338 ++++++++++++++++++++++++++++++++++++--------- pygad/gann/gann.py | 3 +- 2 files changed, 276 insertions(+), 65 deletions(-) diff --git a/pygad/cnn/cnn.py b/pygad/cnn/cnn.py index d0141ae..1bd9033 100644 --- a/pygad/cnn/cnn.py +++ b/pygad/cnn/cnn.py @@ -1,5 +1,6 @@ import numpy import functools +import logging """ Convolutional neural network implementation using NumPy @@ -84,14 +85,18 @@ def layers_weights(model, initial=True): elif initial == False: network_weights.append(layer.trained_weights) else: - raise ValueError(f"Unexpected value to the 'initial' parameter: {initial}.") + msg = f"Unexpected value to the 'initial' parameter: {initial}." + model.logger.error(msg) + raise ValueError(msg) # Go to the previous layer. layer = layer.previous_layer # If the first layer in the network is not an input layer (i.e. an instance of the Input2D class), raise an error. if not (type(layer) is Input2D): - raise TypeError("The first layer in the network architecture must be an input layer.") + msg = "The first layer in the network architecture must be an input layer." + model.logger.error(msg) + raise TypeError(msg) # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). @@ -131,7 +136,9 @@ def layers_weights_as_matrix(model, vector_weights): # If the first layer in the network is not an input layer (i.e. an instance of the Input2D class), raise an error. if not (type(layer) is Input2D): - raise TypeError("The first layer in the network architecture must be an input layer.") + msg = "The first layer in the network architecture must be an input layer." + model.logger.error(msg) + raise TypeError(msg) # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). @@ -164,14 +171,18 @@ def layers_weights_as_vector(model, initial=True): # vector = pygad.nn.DenseLayer.to_vector(array=layer.trained_weights) network_weights.extend(vector) else: - raise ValueError(f"Unexpected value to the 'initial' parameter: {initial}.") + msg = f"Unexpected value to the 'initial' parameter: {initial}." + model.logger.error(msg) + raise ValueError(msg) # Go to the previous layer. layer = layer.previous_layer # If the first layer in the network is not an input layer (i.e. an instance of the Input2D class), raise an error. if not (type(layer) is Input2D): - raise TypeError("The first layer in the network architecture must be an input layer.") + msg = "The first layer in the network architecture must be an input layer." + model.logger.error(msg) + raise TypeError(msg) # Currently, the weights of the layers are in the reverse order. In other words, the weights of the first layer are at the last index of the 'network_weights' list while the weights of the last layer are at the first index. # Reversing the 'network_weights' list to order the layers' weights according to their location in the network architecture (i.e. the weights of the first layer appears at index 0 of the list). @@ -199,40 +210,91 @@ def update_layers_trained_weights(model, final_weights): # Go to the previous layer. layer = layer.previous_layer -class Input2D: + +class CustomLogger: + + def __init__(self): + # Create a logger named with the module name. + logger = logging.getLogger(__name__) + # Set the logger log level to 'DEBUG' to log all kinds of messages. + logger.setLevel(logging.DEBUG) + + # Clear any attached handlers to the logger from the previous runs. + # If the handlers are not cleared, then the new handler will be appended to the list of handlers. + # This makes the single log message be repeated according to the length of the list of handlers. + logger.handlers.clear() + + # Create the handlers. + stream_handler = logging.StreamHandler() + # Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger. + stream_handler.setLevel(logging.DEBUG) + + # Create the formatter that just includes the log message. + formatter = logging.Formatter('%(message)s') + + # Add the formatter to the handler. + stream_handler.setFormatter(formatter) + + # Add the handler to the logger. + logger.addHandler(stream_handler) + + # Create the 'self.logger' attribute to hold the logger. + # Instead of using 'print()', use 'self.logger.info()' + self.logger = logger + +class Input2D(CustomLogger): """ Implementing the input layer of a CNN. The CNN architecture must start with an input layer. """ - def __init__(self, input_shape): + def __init__(self, + input_shape, + logger=None): """ input_shape: Shape of the input sample to the CNN. """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + # If the input sample has less than 2 dimensions, then an exception is raised. if len(input_shape) < 2: - raise ValueError(f"The Input2D class creates an input layer for data inputs with at least 2 dimensions but ({len(input_shape)}) dimensions found.") + msg = f"The Input2D class creates an input layer for data inputs with at least 2 dimensions but ({len(input_shape)}) dimensions found." + self.logger.error(msg) + raise ValueError(msg) # If the input sample has exactly 2 dimensions, the third dimension is set to 1. elif len(input_shape) == 2: input_shape = (input_shape[0], input_shape[1], 1) for dim_idx, dim in enumerate(input_shape): if dim <= 0: - raise ValueError("The dimension size of the inputs cannot be <= 0. Please pass a valid value to the 'input_size' parameter.") + msg = "The dimension size of the inputs cannot be <= 0. Please pass a valid value to the 'input_size' parameter." + self.logger.error(msg) + raise ValueError(msg) self.input_shape = input_shape # Shape of the input sample. self.layer_output_size = input_shape # Shape of the output from the current layer. For an input layer, it is the same as the shape of the input sample. -class Conv2D: +class Conv2D(CustomLogger): """ Implementing the convolution layer. """ - def __init__(self, num_filters, kernel_size, previous_layer, activation_function=None): + def __init__(self, + num_filters, + kernel_size, + previous_layer, + activation_function=None, + logger=None): """ num_filters: Number of filters in the convolution layer. @@ -241,13 +303,25 @@ def __init__(self, num_filters, kernel_size, previous_layer, activation_function activation_function=None: The name of the activation function to be used in the conv layer. If None, then no activation function is applied besides the convolution operation. The activation function can be applied by a separate layer. """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + if num_filters <= 0: - raise ValueError("Number of filters cannot be <= 0. Please pass a valid value to the 'num_filters' parameter.") + msg = "Number of filters cannot be <= 0. Please pass a valid value to the 'num_filters' parameter." + self.logger.error(msg) + raise ValueError(msg) # Number of filters in the conv layer. self.num_filters = num_filters if kernel_size <= 0: - raise ValueError("The kernel size cannot be <= 0. Please pass a valid value to the 'kernel_size' parameter.") + msg = "The kernel size cannot be <= 0. Please pass a valid value to the 'kernel_size' parameter." + self.logger.error(msg) + raise ValueError(msg) # Kernel size of each filter. self.kernel_size = kernel_size @@ -259,15 +333,21 @@ def __init__(self, num_filters, kernel_size, previous_layer, activation_function elif (activation_function == "sigmoid"): self.activation = sigmoid elif (activation_function == "softmax"): - raise ValueError("The softmax activation function cannot be used in a conv layer.") + msg = "The softmax activation function cannot be used in a conv layer." + self.logger.error(msg) + raise ValueError(msg) else: - raise ValueError(f"The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.") + msg = f"The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions." + self.logger.error(msg) + raise ValueError(msg) # The activation function used in the current layer. self.activation_function = activation_function if previous_layer is None: - raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + msg = "The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter." + self.logger.error(msg) + raise TypeError(msg) # A reference to the layer that preceeds the current layer in the network architecture. self.previous_layer = previous_layer @@ -353,24 +433,36 @@ def conv(self, input2D): """ if len(input2D.shape) != len(self.initial_weights.shape) - 1: # Check if there is a match in the number of dimensions between the image and the filters. - raise ValueError("Number of dimensions in the conv filter and the input do not match.") + msg = "Number of dimensions in the conv filter and the input do not match." + self.logger.error(msg) + raise ValueError(msg) if len(input2D.shape) > 2 or len(self.initial_weights.shape) > 3: # Check if number of image channels matches the filter depth. if input2D.shape[-1] != self.initial_weights.shape[-1]: - raise ValueError("Number of channels in both the input and the filter must match.") + msg = "Number of channels in both the input and the filter must match." + self.logger.error(msg) + raise ValueError(msg) if self.initial_weights.shape[1] != self.initial_weights.shape[2]: # Check if filter dimensions are equal. - raise ValueError('A filter must be a square matrix. I.e. number of rows and columns must match.') + msg = 'A filter must be a square matrix. I.e. number of rows and columns must match.' + self.logger.error(msg) + raise ValueError(msg) if self.initial_weights.shape[1]%2==0: # Check if filter diemnsions are odd. - raise ValueError('A filter must have an odd size. I.e. number of rows and columns must be odd.') + msg = 'A filter must have an odd size. I.e. number of rows and columns must be odd.' + self.logger.error(msg) + raise ValueError(msg) self.layer_output = self.conv_(input2D, self.trained_weights) -class AveragePooling2D: +class AveragePooling2D(CustomLogger): """ Implementing the average pooling layer. """ - def __init__(self, pool_size, previous_layer, stride=2): + def __init__(self, + pool_size, + previous_layer, + stride=2, + logger=None): """ pool_size: Pool size. @@ -378,19 +470,35 @@ def __init__(self, pool_size, previous_layer, stride=2): stride=2: Stride """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + if not (type(pool_size) is int): - raise ValueError("The expected type of the pool_size is int but {pool_size_type} found.".format(pool_size_type=type(pool_size))) + msg = "The expected type of the pool_size is int but {pool_size_type} found.".format(pool_size_type=type(pool_size)) + self.logger.error(msg) + raise ValueError(msg) if pool_size <= 0: - raise ValueError("The passed value to the pool_size parameter cannot be <= 0.") + msg = "The passed value to the pool_size parameter cannot be <= 0." + self.logger.error(msg) + raise ValueError(msg) self.pool_size = pool_size if stride <= 0: - raise ValueError("The passed value to the stride parameter cannot be <= 0.") + msg = "The passed value to the stride parameter cannot be <= 0." + self.logger.error(msg) + raise ValueError(msg) self.stride = stride if previous_layer is None: - raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + msg = "The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter." + self.logger.error(msg) + raise TypeError(msg) # A reference to the layer that preceeds the current layer in the network architecture. self.previous_layer = previous_layer @@ -430,33 +538,53 @@ def average_pooling(self, input2D): self.layer_output = pool_out -class MaxPooling2D: +class MaxPooling2D(CustomLogger): """ Similar to the AveragePooling2D class except that it implements max pooling. """ - def __init__(self, pool_size, previous_layer, stride=2): + def __init__(self, + pool_size, + previous_layer, + stride=2, + logger=None): """ pool_size: Pool size. previous_layer: Reference to the previous layer in the CNN architecture. stride=2: Stride """ - + + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + if not (type(pool_size) is int): - raise ValueError(f"The expected type of the pool_size is int but {type(pool_size)} found.") + msg = f"The expected type of the pool_size is int but {type(pool_size)} found." + self.logger.error(msg) + raise ValueError(msg) if pool_size <= 0: - raise ValueError("The passed value to the pool_size parameter cannot be <= 0.") + msg = "The passed value to the pool_size parameter cannot be <= 0." + self.logger.error(msg) + raise ValueError(msg) self.pool_size = pool_size if stride <= 0: - raise ValueError("The passed value to the stride parameter cannot be <= 0.") + msg = "The passed value to the stride parameter cannot be <= 0." + self.logger.error(msg) + raise ValueError(msg) self.stride = stride if previous_layer is None: - raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + msg = "The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter." + self.logger.error(msg) + raise TypeError(msg) # A reference to the layer that preceeds the current layer in the network architecture. self.previous_layer = previous_layer @@ -496,20 +624,32 @@ def max_pooling(self, input2D): self.layer_output = pool_out -class ReLU: +class ReLU(CustomLogger): """ Implementing the ReLU layer. """ - def __init__(self, previous_layer): + def __init__(self, + previous_layer, + logger=None): """ previous_layer: Reference to the previous layer. """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + if previous_layer is None: - raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + msg = "The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter." + self.logger.error(msg) + raise TypeError(msg) # A reference to the layer that preceeds the current layer in the network architecture. self.previous_layer = previous_layer @@ -536,20 +676,32 @@ def relu_layer(self, layer_input): self.layer_output_size = layer_input.size self.layer_output = relu(layer_input) -class Sigmoid: +class Sigmoid(CustomLogger): """ Implementing the sigmoid layer. """ - def __init__(self, previous_layer): + def __init__(self, + previous_layer, + logger=None): """ previous_layer: Reference to the previous layer. """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + if previous_layer is None: - raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + msg = "The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter." + self.logger.error(msg) + raise TypeError(msg) # A reference to the layer that preceeds the current layer in the network architecture. self.previous_layer = previous_layer @@ -575,20 +727,32 @@ def sigmoid_layer(self, layer_input): self.layer_output_size = layer_input.size self.layer_output = sigmoid(layer_input) -class Flatten: +class Flatten(CustomLogger): """ Implementing the flatten layer. """ - def __init__(self, previous_layer): + def __init__(self, + previous_layer, + logger=None): """ previous_layer: Reference to the previous layer. """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + if previous_layer is None: - raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + msg = "The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter." + self.logger.error(msg) + raise TypeError(msg) # A reference to the layer that preceeds the current layer in the network architecture. self.previous_layer = previous_layer @@ -614,22 +778,37 @@ def flatten(self, input2D): self.layer_output_size = input2D.size self.layer_output = numpy.ravel(input2D) -class Dense: +class Dense(CustomLogger): """ Implementing the input dense (fully connected) layer of a CNN. """ - def __init__(self, num_neurons, previous_layer, activation_function="relu"): + def __init__(self, + num_neurons, + previous_layer, + activation_function="relu", + logger=None): """ num_neurons: Number of neurons in the dense layer. previous_layer: Reference to the previous layer. activation_function: Name of the activation function to be used in the current layer. + logger=None: Reference to the instance of the logging.Logger class. """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + if num_neurons <= 0: - raise ValueError("Number of neurons cannot be <= 0. Please pass a valid value to the 'num_neurons' parameter.") + msg = "Number of neurons cannot be <= 0. Please pass a valid value to the 'num_neurons' parameter." + self.logger.error(msg) + raise ValueError(msg) # Number of neurons in the dense layer. self.num_neurons = num_neurons @@ -642,17 +821,23 @@ def __init__(self, num_neurons, previous_layer, activation_function="relu"): elif (activation_function == "softmax"): self.activation = softmax else: - raise ValueError(f"The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions.") + msg = f"The specified activation function '{activation_function}' is not among the supported activation functions {supported_activation_functions}. Please use one of the supported functions." + self.logger.error(msg) + raise ValueError(msg) self.activation_function = activation_function if previous_layer is None: - raise TypeError("The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter.") + msg = "The previous layer cannot be of Type 'None'. Please pass a valid layer to the 'previous_layer' parameter." + self.logger.error(msg) + raise TypeError(msg) # A reference to the layer that preceeds the current layer in the network architecture. self.previous_layer = previous_layer - + if type(self.previous_layer.layer_output_size) in [list, tuple, numpy.ndarray] and len(self.previous_layer.layer_output_size) > 1: - raise ValueError(f"The input to the dense layer must be of type int but {type(self.previous_layer.layer_output_size)} found.") + msg = f"The input to the dense layer must be of type int but {type(self.previous_layer.layer_output_size)} found." + self.logger.error(msg) + raise ValueError(msg) # Initializing the weights of the layer. self.initial_weights = numpy.random.uniform(low=-0.1, high=0.1, @@ -682,26 +867,41 @@ def dense_layer(self, layer_input): """ if self.trained_weights is None: - raise TypeError("The weights of the dense layer cannot be of Type 'None'.") + msg = "The weights of the dense layer cannot be of Type 'None'." + self.logger.error(msg) + raise TypeError(msg) sop = numpy.matmul(layer_input, self.trained_weights) self.layer_output = self.activation(sop) -class Model: +class Model(CustomLogger): """ Creating a CNN model. """ - def __init__(self, last_layer, epochs=10, learning_rate=0.01): + def __init__(self, + last_layer, + epochs=10, + learning_rate=0.01, + logger=None): """ last_layer: A reference to the last layer in the CNN architecture. epochs=10: Number of epochs. learning_rate=0.01: Learning rate. + logger=None: Reference to the instance of the logging.Logger class. """ + super().__init__() + + # If logger is None, then the CustomLogger.logger is created. + if logger is None: + pass + else: + self.logger = logger + self.last_layer = last_layer self.epochs = epochs self.learning_rate = learning_rate @@ -728,7 +928,7 @@ def get_layers(self): return network_layers def train(self, train_inputs, train_outputs): - + """ Trains the CNN model. It is important to note that no learning algorithm is used for training the CNN. Just the learning rate is used for making some changes which is better than leaving the weights unchanged. @@ -738,16 +938,20 @@ def train(self, train_inputs, train_outputs): """ if (train_inputs.ndim != 4): - raise ValueError("The training data input has {num_dims} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample.".format(num_dims=train_inputs.ndim)) + msg = f"The training data input has {train_inputs.ndim} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample." + self.logger.error(msg) + raise ValueError(msg) if (train_inputs.shape[0] != len(train_outputs)): - raise ValueError(f"Mismatch between the number of input samples and number of labels: {train_inputs.shape[0]} != {len(train_outputs)}.") + msg = f"Mismatch between the number of input samples and number of labels: {train_inputs.shape[0]} != {len(train_outputs)}." + self.logger.error(msg) + raise ValueError(msg) network_predictions = [] network_error = 0 for epoch in range(self.epochs): - print(f"Epoch {epoch}") + self.logger.info(f"Epoch {epoch}") for sample_idx in range(train_inputs.shape[0]): # print("Sample {sample_idx}".format(sample_idx=sample_idx)) self.feed_sample(train_inputs[sample_idx, :]) @@ -755,8 +959,10 @@ def train(self, train_inputs, train_outputs): try: predicted_label = numpy.where(numpy.max(self.last_layer.layer_output) == self.last_layer.layer_output)[0][0] except IndexError: - print(self.last_layer.layer_output) - raise IndexError("Index out of range") + self.logger.info(self.last_layer.layer_output) + msg = "Index out of range" + self.logger.error(msg) + raise IndexError(msg) network_predictions.append(predicted_label) network_error = network_error + abs(predicted_label - train_outputs[sample_idx]) @@ -796,8 +1002,10 @@ def feed_sample(self, sample): elif type(layer) is Input2D: pass else: - print("Other") - raise TypeError("The layer of type {type(layer)} is not supported yet.") + # self.logger.info("Other") + msg = "The layer of type {type(layer)} is not supported." + self.logger.error(msg) + raise TypeError(msg) last_layer_outputs = layer.layer_output return self.network_layers[-1].layer_output @@ -828,7 +1036,9 @@ def predict(self, data_inputs): """ if (data_inputs.ndim != 4): - raise ValueError("The data input has {data_inputs.ndim} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample.") + msg = "The data input has {data_inputs.ndim} but it must have 4 dimensions. The first dimension is the number of training samples, the second & third dimensions represent the width and height of the sample, and the fourth dimension represents the number of channels in the sample." + self.logger.error(msg) + raise ValueError(msg) predictions = [] for sample in data_inputs: @@ -843,7 +1053,7 @@ def summary(self): Prints a summary of the CNN architecture. """ - print("\n----------Network Architecture----------") + self.logger.info("\n----------Network Architecture----------") for layer in self.network_layers: - print(type(layer)) - print("----------------------------------------\n") + self.logger.info(type(layer)) + self.logger.info("----------------------------------------\n") diff --git a/pygad/gann/gann.py b/pygad/gann/gann.py index 4037e5a..2c3c5a7 100644 --- a/pygad/gann/gann.py +++ b/pygad/gann/gann.py @@ -1,4 +1,5 @@ from ..nn import nn +import warnings def validate_network_parameters(num_neurons_input, num_neurons_output, @@ -81,7 +82,7 @@ def validate_network_parameters(num_neurons_input, else: raise TypeError(unexpected_activation_type.format(activations_type=type(hidden_activations))) else: # In case there are no hidden layers (num_hidden_layers == 0) - print("WARNING: There are no hidden layers however a value is assigned to the parameter 'hidden_activations'. It will be reset to [].") + warnings.warn("WARNING: There are no hidden layers however a value is assigned to the parameter 'hidden_activations'. It will be reset to [].") hidden_activations = [] # If the value passed to the 'hidden_activations' parameter is actually a list, then its elements are checked to make sure the listed name(s) of the activation function(s) are supported. From 4de21aecec631fda6d48c2703e7de7059f61a88a Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 9 May 2023 17:28:57 -0400 Subject: [PATCH 057/220] Return outputs from callbacks --- pygad/pygad.py | 94 ++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 84 insertions(+), 10 deletions(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index 01dc850..9352169 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -1693,7 +1693,7 @@ def cal_pop_fitness(self): pop_fitness = numpy.array(pop_fitness) except Exception as ex: self.logger.exception(ex) - exit(-1) + sys.exit(-1) return pop_fitness def run(self): @@ -1738,8 +1738,7 @@ def run(self): # Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute. self.last_generation_fitness = self.cal_pop_fitness() - best_solution, best_solution_fitness, best_match_idx = self.best_solution( - pop_fitness=self.last_generation_fitness) + best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness) # Appending the best solution in the initial population to the best_solutions list. if self.save_best_solutions: @@ -1747,7 +1746,20 @@ def run(self): for generation in range(generation_first_idx, generation_last_idx): if not (self.on_fitness is None): - self.on_fitness(self, self.last_generation_fitness) + on_fitness_output = self.on_fitness(self, + self.last_generation_fitness) + + if on_fitness_output is None: + pass + else: + if type(on_fitness_output) in [tuple, list, numpy.ndarray, range]: + on_fitness_output = numpy.array(on_fitness_output) + if on_fitness_output.shape == self.last_generation_fitness.shape: + self.last_generation_fitness = on_fitness_output + else: + raise ValueError(f"Size mismatch between the output of on_fitness() {on_fitness_output.shape} and the expected fitness output {self.last_generation_fitness.shape}.") + else: + raise ValueError(f"The output of on_fitness() is expected to be tuple/list/range/numpy.ndarray but {type(on_fitness_output)} found.") # Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute. self.best_solutions_fitness.append(best_solution_fitness) @@ -1788,7 +1800,45 @@ def run(self): raise ValueError(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.") if not (self.on_parents is None): - self.on_parents(self, self.last_generation_parents) + on_parents_output = self.on_parents(self, + self.last_generation_parents) + + if on_parents_output is None: + pass + elif type(on_parents_output) in [list, tuple, numpy.ndarray]: + if len(on_parents_output) == 2: + on_parents_selected_parents, on_parents_selected_parents_indices = on_parents_output + else: + raise ValueError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray of length 2 but {type(on_parents_output)} of length {len(on_parents_output)} found.") + + # Validate the parents. + if on_parents_selected_parents is None: + raise ValueError("The returned outputs of on_parents() cannot be None but the first output is None.") + else: + if type(on_parents_selected_parents) in [tuple, list, numpy.ndarray]: + on_parents_selected_parents = numpy.array(on_parents_selected_parents) + if on_parents_selected_parents.shape == self.last_generation_parents.shape: + self.last_generation_parents = on_parents_selected_parents + else: + raise ValueError(f"Size mismatch between the parents retrned by on_parents() {on_parents_selected_parents.shape} and the expected parents shape {self.last_generation_parents.shape}.") + else: + raise ValueError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray but the first output type is {type(on_parents_selected_parents)}.") + + # Validate the parents indices. + if on_parents_selected_parents_indices is None: + raise ValueError("The returned outputs of on_parents() cannot be None but the second output is None.") + else: + if type(on_parents_selected_parents_indices) in [tuple, list, numpy.ndarray, range]: + on_parents_selected_parents_indices = numpy.array(on_parents_selected_parents_indices) + if on_parents_selected_parents_indices.shape == self.last_generation_parents_indices.shape: + self.last_generation_parents_indices = on_parents_selected_parents_indices + else: + raise ValueError(f"Size mismatch between the parents indices returned by on_parents() {on_parents_selected_parents_indices.shape} and the expected crossover output {self.last_generation_parents_indices.shape}.") + else: + raise ValueError(f"The output of on_parents() is expected to be tuple/list/range/numpy.ndarray but the second output type is {type(on_parents_selected_parents_indices)}.") + + else: + raise TypeError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray but {type(on_parents_output)} found.") # If self.crossover_type=None, then no crossover is applied and thus no offspring will be created in the next generations. The next generation will use the solutions in the current population. if self.crossover_type is None: @@ -1832,8 +1882,19 @@ def run(self): # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None. if not (self.on_crossover is None): - self.on_crossover( - self, self.last_generation_offspring_crossover) + on_crossover_output = self.on_crossover(self, + self.last_generation_offspring_crossover) + if on_crossover_output is None: + pass + else: + if type(on_crossover_output) in [tuple, list, numpy.ndarray]: + on_crossover_output = numpy.array(on_crossover_output) + if on_crossover_output.shape == self.last_generation_offspring_crossover.shape: + self.last_generation_offspring_crossover = on_crossover_output + else: + raise ValueError(f"Size mismatch between the output of on_crossover() {on_crossover_output.shape} and the expected crossover output {self.last_generation_offspring_crossover.shape}.") + else: + raise ValueError(f"The output of on_crossover() is expected to be tuple/list/numpy.ndarray but {type(on_crossover_output)} found.") # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation. if self.mutation_type is None: @@ -1857,7 +1918,20 @@ def run(self): # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None. if not (self.on_mutation is None): - self.on_mutation(self, self.last_generation_offspring_mutation) + on_mutation_output = self.on_mutation(self, + self.last_generation_offspring_mutation) + + if on_mutation_output is None: + pass + else: + if type(on_mutation_output) in [tuple, list, numpy.ndarray]: + on_mutation_output = numpy.array(on_mutation_output) + if on_mutation_output.shape == self.last_generation_offspring_mutation.shape: + self.last_generation_offspring_mutation = on_mutation_output + else: + raise ValueError(f"Size mismatch between the output of on_mutation() {on_mutation_output.shape} and the expected mutation output {self.last_generation_offspring_mutation.shape}.") + else: + raise ValueError(f"The output of on_mutation() is expected to be tuple/list/numpy.ndarray but {type(on_mutation_output)} found.") # Update the population attribute according to the offspring generated. if self.keep_elitism == 0: @@ -1954,7 +2028,7 @@ def run(self): # self.solutions = numpy.array(self.solutions) except Exception as ex: self.logger.exception(ex) - exit(-1) + sys.exit(-1) def best_solution(self, pop_fitness=None): """ @@ -1989,7 +2063,7 @@ def best_solution(self, pop_fitness=None): best_solution_fitness = pop_fitness[best_match_idx] except Exception as ex: self.logger.exception(ex) - exit(-1) + sys.exit(-1) return best_solution, best_solution_fitness, best_match_idx From 63e83122040b2ead0f3a57eaa88e3d418df50628 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 9 May 2023 17:31:21 -0400 Subject: [PATCH 058/220] Change workflows names --- .github/workflows/main_py310.yml | 2 +- .github/workflows/main_py311.yml | 2 +- .github/workflows/main_py37.yml | 2 +- .github/workflows/main_py38.yml | 2 +- .github/workflows/main_py39.yml | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/main_py310.yml b/.github/workflows/main_py310.yml index 7e45fa9..7260720 100644 --- a/.github/workflows/main_py310.yml +++ b/.github/workflows/main_py310.yml @@ -1,4 +1,4 @@ -name: Testing PyGAD using PyTest +name: Python 3.10 Testing PyGAD using PyTest on: push: diff --git a/.github/workflows/main_py311.yml b/.github/workflows/main_py311.yml index 37b2085..5c37789 100644 --- a/.github/workflows/main_py311.yml +++ b/.github/workflows/main_py311.yml @@ -1,4 +1,4 @@ -name: Testing PyGAD using PyTest +name: Python 3.11 Testing PyGAD using PyTest on: push: diff --git a/.github/workflows/main_py37.yml b/.github/workflows/main_py37.yml index 061ada0..77e5a9f 100644 --- a/.github/workflows/main_py37.yml +++ b/.github/workflows/main_py37.yml @@ -1,4 +1,4 @@ -name: Testing PyGAD using PyTest +name: Python 3.7 Testing PyGAD using PyTest on: push: diff --git a/.github/workflows/main_py38.yml b/.github/workflows/main_py38.yml index 908b08e..7839f18 100644 --- a/.github/workflows/main_py38.yml +++ b/.github/workflows/main_py38.yml @@ -1,4 +1,4 @@ -name: Testing PyGAD using PyTest +name: Python 3.8 Testing PyGAD using PyTest on: push: diff --git a/.github/workflows/main_py39.yml b/.github/workflows/main_py39.yml index 0ef4f1c..b34d7ba 100644 --- a/.github/workflows/main_py39.yml +++ b/.github/workflows/main_py39.yml @@ -1,4 +1,4 @@ -name: Testing PyGAD using PyTest +name: Python 3.9 Testing PyGAD using PyTest on: push: From 464bfd619cac2f17d954e087a315b855630dc86d Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Wed, 10 May 2023 10:23:17 -0400 Subject: [PATCH 059/220] Batch fitness for adaptive mutation --- pygad/pygad.py | 18 ++++++------------ pygad/utils/mutation.py | 35 +++++++++++++++++++++++++++++++++-- 2 files changed, 39 insertions(+), 14 deletions(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index 9352169..95a59cd 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -1531,8 +1531,7 @@ def cal_pop_fitness(self): elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list): # Return the index of the elitism from the elitism array 'self.last_generation_elitism'. # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array. - elitism_idx = last_generation_elitism_as_list.index( - list(sol)) + elitism_idx = last_generation_elitism_as_list.index(list(sol)) # Use the returned elitism index to return its index in the last population. elitism_idx = self.last_generation_elitism_indices[elitism_idx] # Use the elitism's index to return its pre-calculated fitness value. @@ -1544,8 +1543,7 @@ def cal_pop_fitness(self): # Index of the parent in the 'self.last_generation_parents' array. # This is not its index within the population. It is just its index in the 'self.last_generation_parents' array. # parent_idx = numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0][0] - parent_idx = last_generation_parents_as_list.index( - list(sol)) + parent_idx = last_generation_parents_as_list.index(list(sol)) # Use the returned parent index to return its index in the last population. parent_idx = self.last_generation_parents_indices[parent_idx] # Use the parent's index to return its pre-calculated fitness value. @@ -1573,13 +1571,11 @@ def cal_pop_fitness(self): solutions_indices = numpy.where( numpy.array(pop_fitness) == "undefined")[0] # Number of batches. - num_batches = int(numpy.ceil( - len(solutions_indices) / self.fitness_batch_size)) + num_batches = int(numpy.ceil(len(solutions_indices) / self.fitness_batch_size)) # For each batch, get its indices and call the fitness function. for batch_idx in range(num_batches): batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * \ - self.fitness_batch_size + batch_last_index = (batch_idx + 1) * self.fitness_batch_size batch_indices = solutions_indices[batch_first_index:batch_last_index] batch_solutions = self.population[batch_indices, :] @@ -1660,8 +1656,7 @@ def cal_pop_fitness(self): # Reaching this block means that batch processing is used. The fitness values are calculated in batches. # Number of batches. - num_batches = int(numpy.ceil( - len(solutions_to_submit_indices) / self.fitness_batch_size)) + num_batches = int(numpy.ceil(len(solutions_to_submit_indices) / self.fitness_batch_size)) # Each element of the `batches_solutions` list represents the solutions in one batch. batches_solutions = [] # Each element of the `batches_indices` list represents the solutions' indices in one batch. @@ -1669,8 +1664,7 @@ def cal_pop_fitness(self): # For each batch, get its indices and call the fitness function. for batch_idx in range(num_batches): batch_first_index = batch_idx * self.fitness_batch_size - batch_last_index = (batch_idx + 1) * \ - self.fitness_batch_size + batch_last_index = (batch_idx + 1) * self.fitness_batch_size batch_indices = solutions_to_submit_indices[batch_first_index:batch_last_index] batch_solutions = self.population[batch_indices, :] diff --git a/pygad/utils/mutation.py b/pygad/utils/mutation.py index 326ba6b..39a1a5d 100644 --- a/pygad/utils/mutation.py +++ b/pygad/utils/mutation.py @@ -438,8 +438,39 @@ def adaptive_mutation_population_fitness(self, offspring): fitness[:self.last_generation_parents.shape[0]] = self.last_generation_fitness[self.last_generation_parents_indices] - for idx in range(len(parents_to_keep), fitness.shape[0]): - fitness[idx] = self.fitness_func(self, temp_population[idx], None) + first_idx = len(parents_to_keep) + last_idx = fitness.shape[0] + fitness[first_idx:last_idx] = [0]*(last_idx - first_idx) + + if self.fitness_batch_size in [1, None]: + # Calculate the fitness for each individual solution. + for idx in range(first_idx, last_idx): + fitness[idx] = self.fitness_func(self, + temp_population[idx], + None) + else: + # Calculate the fitness for batch of solutions. + + # Number of batches. + num_batches = int(numpy.ceil((last_idx - first_idx) / self.fitness_batch_size)) + + for batch_idx in range(num_batches): + # The index of the first solution in the current batch. + batch_first_index = first_idx + batch_idx * self.fitness_batch_size + # The index of the last solution in the current batch. + if batch_idx == (num_batches - 1): + batch_last_index = last_idx + else: + batch_last_index = first_idx + (batch_idx + 1) * self.fitness_batch_size + + # Calculate the fitness values for the batch. + fitness_temp = self.fitness_func(self, + temp_population[batch_first_index:batch_last_index], + None) + # Insert the fitness of each solution at the proper index. + for idx in range(batch_first_index, batch_last_index): + fitness[idx] = fitness_temp[idx - batch_first_index] + average_fitness = numpy.mean(fitness) return average_fitness, fitness[len(parents_to_keep):] From b91a20641b7cafb0072fccf67eda4a29bb2f8aed Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Wed, 10 May 2023 11:32:03 -0400 Subject: [PATCH 060/220] Update example --- docs/source/README_pygad_ReadTheDocs.rst | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/source/README_pygad_ReadTheDocs.rst b/docs/source/README_pygad_ReadTheDocs.rst index 1ab5505..c1e6d2f 100644 --- a/docs/source/README_pygad_ReadTheDocs.rst +++ b/docs/source/README_pygad_ReadTheDocs.rst @@ -913,7 +913,7 @@ It returns the following: - ``best_match_idx``: Index of the best solution in the current population. -.. _plotfitness-1: +.. _plotfitness: ``plot_fitness()`` ------------------ @@ -946,7 +946,7 @@ and higher, this method accepts the following parameters: 8. ``save_dir``: Directory to save the figure. -.. _plotnewsolutionrate-1: +.. _plotnewsolutionrate: ``plot_new_solution_rate()`` ---------------------------- @@ -979,7 +979,7 @@ This method accepts the following parameters: 8. ``save_dir``: Directory to save the figure. -.. _plotgenes-1: +.. _plotgenes: ``plot_genes()`` ---------------- @@ -1229,7 +1229,7 @@ The next step is to import PyGAD as follows: The ``pygad.GA`` class holds the implementation of all methods for running the genetic algorithm. -.. _create-an-instance-of-the-pygadga-class-1: +.. _create-an-instance-of-the-pygadga-class: Create an Instance of the ``pygad.GA`` Class -------------------------------------------- @@ -2403,6 +2403,8 @@ The function should return 2 outputs: 2. The indices of the selected parents inside the population. It is a 1D list with length equal to the number of selected parents. +The outputs must be of type ``numpy.ndarray``. + Here is a template for building a custom parent selection function. .. code:: python @@ -2427,7 +2429,7 @@ parents are selected. The number of parents is equal to the value in the for parent_num in range(num_parents): parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() - return parents, fitness_sorted[:num_parents] + return parents, numpy.array(fitness_sorted[:num_parents]) Finally, the defined function is assigned to the ``parent_selection_type`` parameter as in the next code. @@ -2474,7 +2476,7 @@ previous 3 user-defined functions instead of the built-in functions. for parent_num in range(num_parents): parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() - return parents, fitness_sorted[:num_parents] + return parents, numpy.array(fitness_sorted[:num_parents]) def crossover_func(parents, offspring_size, ga_instance): @@ -3004,7 +3006,7 @@ methods. The ``plot_fitness()`` method shows the fitness value for each generation. -.. _plottypeplot-1: +.. _plottypeplot: ``plot_type="plot"`` ~~~~~~~~~~~~~~~~~~~~ @@ -3021,7 +3023,7 @@ line connecting the fitness values across all generations: .. figure:: https://user-images.githubusercontent.com/16560492/122472609-d02f5280-cf8e-11eb-88a7-f9366ff6e7c6.png :alt: -.. _plottypescatter-1: +.. _plottypescatter: ``plot_type="scatter"`` ~~~~~~~~~~~~~~~~~~~~~~~ @@ -3037,7 +3039,7 @@ these dots can be changed using the ``linewidth`` parameter. .. figure:: https://user-images.githubusercontent.com/16560492/122473159-75e2c180-cf8f-11eb-942d-31279b286dbd.png :alt: -.. _plottypebar-1: +.. _plottypebar: ``plot_type="bar"`` ~~~~~~~~~~~~~~~~~~~ @@ -3393,8 +3395,6 @@ parameter: given the value 0, this means do not use parallel processing. This is identical to ``parallel_processing=None``. -.. _examples-1: - Examples -------- From b4bfb9ab630c027d4e874eae740bd53b7ff3aa12 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Wed, 10 May 2023 12:40:06 -0400 Subject: [PATCH 061/220] Test adaptive mutation --- tests/test_adaptive_mutation.py | 222 ++++++++++++++++++++ tests/test_gene_space.py | 50 ++++- tests/test_number_fitness_function_calls.py | 2 + 3 files changed, 273 insertions(+), 1 deletion(-) create mode 100644 tests/test_adaptive_mutation.py diff --git a/tests/test_adaptive_mutation.py b/tests/test_adaptive_mutation.py new file mode 100644 index 0000000..9b427df --- /dev/null +++ b/tests/test_adaptive_mutation.py @@ -0,0 +1,222 @@ +import pygad +import random +import numpy + +num_generations = 1 + +initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]] + +def output_adaptive_mutation(gene_space=None, + gene_type=float, + num_genes=10, + mutation_by_replacement=False, + random_mutation_min_val=-1, + random_mutation_max_val=1, + init_range_low=-4, + init_range_high=4, + initial_population=None, + mutation_probability=[0.2, 0.1], + fitness_batch_size=None, + mutation_type="adaptive"): + + def fitness_func_single(ga, solution, idx): + return random.random() + + def fitness_func_batch(ga, soluions, idxs): + return numpy.random.uniform(size=len(soluions)) + + if fitness_batch_size in [1, None]: + fitness_func = fitness_func_single + else: + fitness_func = fitness_func_batch + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=5, + fitness_func=fitness_func, + sol_per_pop=10, + num_genes=num_genes, + gene_space=gene_space, + gene_type=gene_type, + initial_population=initial_population, + init_range_low=init_range_low, + init_range_high=init_range_high, + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val, + allow_duplicate_genes=True, + mutation_by_replacement=mutation_by_replacement, + save_solutions=True, + mutation_probability=mutation_probability, + mutation_type=mutation_type, + suppress_warnings=True, + fitness_batch_size=fitness_batch_size, + random_seed=1) + + ga_instance.run() + + return None, ga_instance + +def test_adaptive_mutation(): + result, ga_instance = output_adaptive_mutation() + + # assert result == True + +def test_adaptive_mutation_gene_space(): + result, ga_instance = output_adaptive_mutation(gene_space=range(10)) + + # assert result == True + +def test_adaptive_mutation_int_gene_type(): + result, ga_instance = output_adaptive_mutation(gene_type=int) + + # assert result == True + +def test_adaptive_mutation_gene_space_gene_type(): + result, ga_instance = output_adaptive_mutation(gene_space={"low": 0, "high": 10}, + gene_type=[float, 2]) + + # assert result == True + +def test_adaptive_mutation_nested_gene_space(): + result, ga_instance = output_adaptive_mutation(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]]) + # assert result == True + +def test_adaptive_mutation_nested_gene_type(): + result, ga_instance = output_adaptive_mutation(gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + # assert result == True + +def test_adaptive_mutation_nested_gene_space_nested_gene_type(): + result, ga_instance = output_adaptive_mutation(gene_space=[[0, 1, 2, 3, 4], + numpy.arange(5, 10), + range(10, 15), + {"low": 15, "high": 20}, + {"low": 20, "high": 30, "step": 2}, + None, + numpy.arange(30, 35), + numpy.arange(35, 40), + numpy.arange(40, 45), + [45, 46, 47, 48, 49]], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + # assert result == True + +def test_adaptive_mutation_initial_population(): + global initial_population + result, ga_instance = output_adaptive_mutation(initial_population=initial_population) + + # assert result == True + +def test_adaptive_mutation_initial_population_nested_gene_type(): + global initial_population + result, ga_instance = output_adaptive_mutation(initial_population=initial_population, + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + + # assert result == True + +def test_adaptive_mutation_fitness_batch_size_1(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=1) + +def test_adaptive_mutation_fitness_batch_size_2(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=2) + +def test_adaptive_mutation_fitness_batch_size_3(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=3) + +def test_adaptive_mutation_fitness_batch_size_4(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=4) + +def test_adaptive_mutation_fitness_batch_size_5(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=5) + +def test_adaptive_mutation_fitness_batch_size_6(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=6) + +def test_adaptive_mutation_fitness_batch_size_7(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=7) + +def test_adaptive_mutation_fitness_batch_size_8(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=8) + +def test_adaptive_mutation_fitness_batch_size_9(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=9) + +def test_adaptive_mutation_fitness_batch_size_10(): + result, ga_instance = output_adaptive_mutation(fitness_batch_size=10) + +if __name__ == "__main__": + print() + test_adaptive_mutation() + print() + + test_adaptive_mutation_int_gene_type() + print() + + test_adaptive_mutation_gene_space() + print() + + test_adaptive_mutation_gene_space_gene_type() + print() + + test_adaptive_mutation_nested_gene_space() + print() + + test_adaptive_mutation_nested_gene_type() + print() + + test_adaptive_mutation_initial_population() + print() + + test_adaptive_mutation_initial_population_nested_gene_type() + print() + + test_adaptive_mutation_fitness_batch_size_1() + print() + + test_adaptive_mutation_fitness_batch_size_1() + print() + + test_adaptive_mutation_fitness_batch_size_2() + print() + + test_adaptive_mutation_fitness_batch_size_3() + print() + + test_adaptive_mutation_fitness_batch_size_4() + print() + + test_adaptive_mutation_fitness_batch_size_5() + print() + + test_adaptive_mutation_fitness_batch_size_6() + print() + + test_adaptive_mutation_fitness_batch_size_7() + print() + + test_adaptive_mutation_fitness_batch_size_8() + print() + + test_adaptive_mutation_fitness_batch_size_9() + print() + + test_adaptive_mutation_fitness_batch_size_10() + print() + diff --git a/tests/test_gene_space.py b/tests/test_gene_space.py index 063acf7..7a375f2 100644 --- a/tests/test_gene_space.py +++ b/tests/test_gene_space.py @@ -30,6 +30,8 @@ def number_respect_gene_space(gene_space=None, random_mutation_max_val=1, init_range_low=-4, init_range_high=4, + mutation_type="random", + mutation_percent_genes="default", initial_population=None): def fitness_func(ga, solution, idx): @@ -50,6 +52,8 @@ def fitness_func(ga, solution, idx): allow_duplicate_genes=True, mutation_by_replacement=mutation_by_replacement, save_solutions=True, + mutation_type=mutation_type, + mutation_percent_genes=mutation_percent_genes, suppress_warnings=True, random_seed=2) @@ -77,6 +81,7 @@ def fitness_func(ga, solution, idx): if val >= ga_instance.gene_space[gene_idx]["low"] and val < ga_instance.gene_space[gene_idx]["high"]: pass else: + print(gene_idx, val, current_gene_space) num_outside += 1 else: gene_space_values = numpy.arange(ga_instance.gene_space[gene_idx]["low"], @@ -378,6 +383,43 @@ def test_nested_gene_space_mix_initial_population_single_gene_type(): assert num_outside == 0 +def test_nested_gene_space_single_gene_type_adaptive_mutation(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + numpy.arange(0, 10), + range(0, 10), + {"low": 0, "high": 10}, + {"low": 0, "high": 10}, + range(0, 10), + numpy.arange(0, 10), + numpy.arange(0, 10), + {"low": 0, "high": 10}, + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], + # Due to rounding the genes, a gene at index 4 will have a value of 10 (outside the dict range) if [float, 2] is used. + gene_type=[float, 4], + mutation_percent_genes=[70, 50], + mutation_type="adaptive") + # print(ga_instance.population) + + assert num_outside == 0 + +def test_nested_gene_space_nested_gene_type_adaptive_mutation(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], + numpy.arange(0, 10), + range(0, 10), + {"low": 0, "high": 10}, + {"low": 0, "high": 10}, + range(0, 10), + numpy.arange(0, 10), + numpy.arange(0, 10), + {"low": 0, "high": 10}, + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], + gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]], + mutation_percent_genes=[70, 50], + mutation_type="adaptive") + # print(ga_instance.population) + + assert num_outside == 0 + if __name__ == "__main__": print() test_gene_space_range() @@ -446,4 +488,10 @@ def test_nested_gene_space_mix_initial_population_single_gene_type(): print() test_nested_gene_space_mix_initial_population_single_gene_type() - print() \ No newline at end of file + print() + + test_nested_gene_space_single_gene_type_adaptive_mutation() + print() + + test_nested_gene_space_nested_gene_type_adaptive_mutation() + print() diff --git a/tests/test_number_fitness_function_calls.py b/tests/test_number_fitness_function_calls.py index c628606..2910c7b 100644 --- a/tests/test_number_fitness_function_calls.py +++ b/tests/test_number_fitness_function_calls.py @@ -9,6 +9,8 @@ sol_per_pop = 10 num_parents_mating = 5 +# TODO: Calculate the number when fitness_batch_size is used. + def number_calls_fitness_function(keep_elitism=1, keep_parents=-1, mutation_type="random", From 0869b365d7b15f0a5088b5d55804d53bc891594d Mon Sep 17 00:00:00 2001 From: Tasos Papadopoulos Date: Sat, 13 May 2023 00:53:59 +0300 Subject: [PATCH 062/220] Fix typo --- pygad/pygad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pygad/pygad.py b/pygad/pygad.py index 95a59cd..6a0ef4e 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -387,7 +387,7 @@ def __init__(self, raise TypeError(f"The value assigned to the 'initial_population' parameter is expected to by of type list, tuple, or ndarray but {type(initial_population)} found.") elif numpy.array(initial_population).ndim != 2: self.valid_parameters = False - raise ValueError(f"A 2D list is expected to the initail_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") + raise ValueError(f"A 2D list is expected to the initial_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.") else: # Validate the type of each value in the 'initial_population' parameter. for row_idx in range(len(initial_population)): From 72ab5f7a7d105eaa0acff2f67407f502b32b5fae Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 13 Jun 2023 15:56:05 -0400 Subject: [PATCH 063/220] Fix rank selection --- pygad/utils/parent_selection.py | 390 ++++++++++++++++++-------------- 1 file changed, 214 insertions(+), 176 deletions(-) diff --git a/pygad/utils/parent_selection.py b/pygad/utils/parent_selection.py index 976e130..519c281 100644 --- a/pygad/utils/parent_selection.py +++ b/pygad/utils/parent_selection.py @@ -7,187 +7,225 @@ class ParentSelection: def steady_state_selection(self, fitness, num_parents): - """ - Selects the parents using the steady-state selection technique. Later, these parents will mate to produce the offspring. - It accepts 2 parameters: - -fitness: The fitness values of the solutions in the current population. - -num_parents: The number of parents to be selected. - It returns an array of the selected parents. - """ - - fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) - fitness_sorted.reverse() - # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. - if self.gene_type_single == True: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) - else: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) - for parent_num in range(num_parents): - parents[parent_num, :] = self.population[fitness_sorted[parent_num], :].copy() - - return parents, numpy.array(fitness_sorted[:num_parents]) - + """ + Selects the parents using the steady-state selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + fitness_sorted.reverse() + + # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + for parent_num in range(num_parents): + parents[parent_num, :] = self.population[fitness_sorted[parent_num], :].copy() + + return parents, numpy.array(fitness_sorted[:num_parents]) + def rank_selection(self, fitness, num_parents): - - """ - Selects the parents using the rank selection technique. Later, these parents will mate to produce the offspring. - It accepts 2 parameters: - -fitness: The fitness values of the solutions in the current population. - -num_parents: The number of parents to be selected. - It returns an array of the selected parents. - """ - - fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) - fitness_sorted.reverse() - # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. - if self.gene_type_single == True: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) - else: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) - for parent_num in range(num_parents): - parents[parent_num, :] = self.population[fitness_sorted[parent_num], :].copy() - - return parents, numpy.array(fitness_sorted[:num_parents]) - + + """ + Selects the parents using the rank selection technique. Later, these parents will mate to produce the offspring. + Rank selection gives a rank from 1 to N (number of solutions) to each solution based on its fitness. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + # This has the index of each solution in the population. + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + + # Rank the solutions based on their fitness. The worst is gives the rank 1. The best has the rank N. + rank = numpy.arange(1, self.sol_per_pop+1) + + probs = rank / numpy.sum(rank) + + probs_start, probs_end, parents = self.wheel_cumulative_probs(probs=probs.copy(), + num_parents=num_parents) + + parents_indices = [] + + for parent_num in range(num_parents): + rand_prob = numpy.random.rand() + for idx in range(probs.shape[0]): + if (rand_prob >= probs_start[idx] and rand_prob < probs_end[idx]): + # The variable idx has the rank of solution but not its index in the population. + # Return the correct index of the solution. + mapped_idx = fitness_sorted[idx] + parents[parent_num, :] = self.population[mapped_idx, :].copy() + parents_indices.append(mapped_idx) + break + + return parents, numpy.array(parents_indices) + def random_selection(self, fitness, num_parents): - """ - Selects the parents randomly. Later, these parents will mate to produce the offspring. - It accepts 2 parameters: - -fitness: The fitness values of the solutions in the current population. - -num_parents: The number of parents to be selected. - It returns an array of the selected parents. - """ - - if self.gene_type_single == True: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) - else: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) - - rand_indices = numpy.random.randint(low=0.0, high=fitness.shape[0], size=num_parents) - - for parent_num in range(num_parents): - parents[parent_num, :] = self.population[rand_indices[parent_num], :].copy() - - return parents, rand_indices - + """ + Selects the parents randomly. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + rand_indices = numpy.random.randint(low=0.0, high=fitness.shape[0], size=num_parents) + + for parent_num in range(num_parents): + parents[parent_num, :] = self.population[rand_indices[parent_num], :].copy() + + return parents, rand_indices + def tournament_selection(self, fitness, num_parents): - - """ - Selects the parents using the tournament selection technique. Later, these parents will mate to produce the offspring. - It accepts 2 parameters: - -fitness: The fitness values of the solutions in the current population. - -num_parents: The number of parents to be selected. - It returns an array of the selected parents. - """ - - if self.gene_type_single == True: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) - else: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) - - parents_indices = [] - - for parent_num in range(num_parents): - rand_indices = numpy.random.randint(low=0.0, high=len(fitness), size=self.K_tournament) - K_fitnesses = fitness[rand_indices] - selected_parent_idx = numpy.where(K_fitnesses == numpy.max(K_fitnesses))[0][0] - parents_indices.append(rand_indices[selected_parent_idx]) - parents[parent_num, :] = self.population[rand_indices[selected_parent_idx], :].copy() - - return parents, numpy.array(parents_indices) + + """ + Selects the parents using the tournament selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + parents_indices = [] + + for parent_num in range(num_parents): + rand_indices = numpy.random.randint(low=0.0, high=len(fitness), size=self.K_tournament) + K_fitnesses = fitness[rand_indices] + selected_parent_idx = numpy.where(K_fitnesses == numpy.max(K_fitnesses))[0][0] + parents_indices.append(rand_indices[selected_parent_idx]) + parents[parent_num, :] = self.population[rand_indices[selected_parent_idx], :].copy() + + return parents, numpy.array(parents_indices) def roulette_wheel_selection(self, fitness, num_parents): - """ - Selects the parents using the roulette wheel selection technique. Later, these parents will mate to produce the offspring. - It accepts 2 parameters: - -fitness: The fitness values of the solutions in the current population. - -num_parents: The number of parents to be selected. - It returns an array of the selected parents. - """ - - fitness_sum = numpy.sum(fitness) - if fitness_sum == 0: - self.logger.error("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") - raise ZeroDivisionError("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") - probs = fitness / fitness_sum - probs_start = numpy.zeros(probs.shape, dtype=float) # An array holding the start values of the ranges of probabilities. - probs_end = numpy.zeros(probs.shape, dtype=float) # An array holding the end values of the ranges of probabilities. - - curr = 0.0 - - # Calculating the probabilities of the solutions to form a roulette wheel. - for _ in range(probs.shape[0]): - min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0] - probs_start[min_probs_idx] = curr - curr = curr + probs[min_probs_idx] - probs_end[min_probs_idx] = curr - probs[min_probs_idx] = 99999999999 - - # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. - if self.gene_type_single == True: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) - else: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) - - parents_indices = [] - - for parent_num in range(num_parents): - rand_prob = numpy.random.rand() - for idx in range(probs.shape[0]): - if (rand_prob >= probs_start[idx] and rand_prob < probs_end[idx]): - parents[parent_num, :] = self.population[idx, :].copy() - parents_indices.append(idx) - break - return parents, numpy.array(parents_indices) - + """ + Selects the parents using the roulette wheel selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + fitness_sum = numpy.sum(fitness) + if fitness_sum == 0: + self.logger.error("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + raise ZeroDivisionError("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + + probs = fitness / fitness_sum + + probs_start, probs_end, parents = self.wheel_cumulative_probs(probs=probs.copy(), + num_parents=num_parents) + + parents_indices = [] + + for parent_num in range(num_parents): + rand_prob = numpy.random.rand() + for idx in range(probs.shape[0]): + if (rand_prob >= probs_start[idx] and rand_prob < probs_end[idx]): + parents[parent_num, :] = self.population[idx, :].copy() + parents_indices.append(idx) + break + + return parents, numpy.array(parents_indices) + + def wheel_cumulative_probs(self, probs, num_parents): + """ + A helper function to calculate the wheel probabilities for these 2 methods: + 1) roulette_wheel_selection + 2) rank_selection + It accepts a single 1D array representing the probabilities of selecting each solution. + It returns 2 1D arrays: + 1) probs_start has the start of each range. + 2) probs_start has the end of each range. + It also returns an empty array for the parents. + """ + + probs_start = numpy.zeros(probs.shape, dtype=float) # An array holding the start values of the ranges of probabilities. + probs_end = numpy.zeros(probs.shape, dtype=float) # An array holding the end values of the ranges of probabilities. + + curr = 0.0 + + # Calculating the probabilities of the solutions to form a roulette wheel. + for _ in range(probs.shape[0]): + min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0] + probs_start[min_probs_idx] = curr + curr = curr + probs[min_probs_idx] + probs_end[min_probs_idx] = curr + probs[min_probs_idx] = 99999999999 + + # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + return probs_start, probs_end, parents + def stochastic_universal_selection(self, fitness, num_parents): - - """ - Selects the parents using the stochastic universal selection technique. Later, these parents will mate to produce the offspring. - It accepts 2 parameters: - -fitness: The fitness values of the solutions in the current population. - -num_parents: The number of parents to be selected. - It returns an array of the selected parents. - """ - - fitness_sum = numpy.sum(fitness) - if fitness_sum == 0: - self.logger.error("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") - raise ZeroDivisionError("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") - probs = fitness / fitness_sum - probs_start = numpy.zeros(probs.shape, dtype=float) # An array holding the start values of the ranges of probabilities. - probs_end = numpy.zeros(probs.shape, dtype=float) # An array holding the end values of the ranges of probabilities. - - curr = 0.0 - - # Calculating the probabilities of the solutions to form a roulette wheel. - for _ in range(probs.shape[0]): - min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0] - probs_start[min_probs_idx] = curr - curr = curr + probs[min_probs_idx] - probs_end[min_probs_idx] = curr - probs[min_probs_idx] = 99999999999 - - pointers_distance = 1.0 / self.num_parents_mating # Distance between different pointers. - first_pointer = numpy.random.uniform(low=0.0, - high=pointers_distance, - size=1)[0] # Location of the first pointer. - - # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. - if self.gene_type_single == True: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) - else: - parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) - - parents_indices = [] - - for parent_num in range(num_parents): - rand_pointer = first_pointer + parent_num*pointers_distance - for idx in range(probs.shape[0]): - if (rand_pointer >= probs_start[idx] and rand_pointer < probs_end[idx]): - parents[parent_num, :] = self.population[idx, :].copy() - parents_indices.append(idx) - break - return parents, numpy.array(parents_indices) + + """ + Selects the parents using the stochastic universal selection technique. Later, these parents will mate to produce the offspring. + It accepts 2 parameters: + -fitness: The fitness values of the solutions in the current population. + -num_parents: The number of parents to be selected. + It returns an array of the selected parents. + """ + + fitness_sum = numpy.sum(fitness) + if fitness_sum == 0: + self.logger.error("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + raise ZeroDivisionError("Cannot proceed because the sum of fitness values is zero. Cannot divide by zero.") + probs = fitness / fitness_sum + probs_start = numpy.zeros(probs.shape, dtype=float) # An array holding the start values of the ranges of probabilities. + probs_end = numpy.zeros(probs.shape, dtype=float) # An array holding the end values of the ranges of probabilities. + + curr = 0.0 + + # Calculating the probabilities of the solutions to form a roulette wheel. + for _ in range(probs.shape[0]): + min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0] + probs_start[min_probs_idx] = curr + curr = curr + probs[min_probs_idx] + probs_end[min_probs_idx] = curr + probs[min_probs_idx] = 99999999999 + + pointers_distance = 1.0 / self.num_parents_mating # Distance between different pointers. + first_pointer = numpy.random.uniform(low=0.0, + high=pointers_distance, + size=1)[0] # Location of the first pointer. + + # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation. + if self.gene_type_single == True: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=self.gene_type[0]) + else: + parents = numpy.empty((num_parents, self.population.shape[1]), dtype=object) + + parents_indices = [] + + for parent_num in range(num_parents): + rand_pointer = first_pointer + parent_num*pointers_distance + for idx in range(probs.shape[0]): + if (rand_pointer >= probs_start[idx] and rand_pointer < probs_end[idx]): + parents[parent_num, :] = self.population[idx, :].copy() + parents_indices.append(idx) + break + + return parents, numpy.array(parents_indices) From 83a16355bdf91d524cb180aca20d553ee57ec5e7 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 13 Jun 2023 17:07:30 -0400 Subject: [PATCH 064/220] More tests for mutation --- tests/test_crossover_mutation.py | 67 ++++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) diff --git a/tests/test_crossover_mutation.py b/tests/test_crossover_mutation.py index f65a795..8be66c9 100644 --- a/tests/test_crossover_mutation.py +++ b/tests/test_crossover_mutation.py @@ -154,6 +154,64 @@ def test_zero_crossover_probability_zero_mutation_probability(): assert result == True +def test_random_mutation_manual_call(): + result, ga_instance = output_crossover_mutation(mutation_type="random", + random_mutation_min_val=888, + random_mutation_max_val=999) + ga_instance.mutation_num_genes = 9 + + temp_offspring = numpy.array(initial_population[0:1]) + offspring = ga_instance.random_mutation(offspring=temp_offspring.copy()) + + comp = offspring - temp_offspring + comp_sorted = sorted(comp.copy()) + comp_sorted = numpy.abs(numpy.unique(comp_sorted)) + + # The other 1 added to include the last value in the range. + assert len(comp_sorted) in range(1, 1 + 1 + ga_instance.mutation_num_genes) + assert comp_sorted[0] == 0 + +def test_random_mutation_manual_call2(): + result, ga_instance = output_crossover_mutation(mutation_type="random", + random_mutation_min_val=888, + random_mutation_max_val=999) + ga_instance.mutation_num_genes = 10 + + temp_offspring = numpy.array(initial_population[0:1]) + offspring = ga_instance.random_mutation(offspring=temp_offspring.copy()) + + comp = offspring - temp_offspring + comp_sorted = sorted(comp.copy()) + comp_sorted = numpy.abs(numpy.unique(comp_sorted)) + + # The other 1 added to include the last value in the range. + assert len(comp_sorted) in range(1, 1 + 1 + ga_instance.mutation_num_genes) + # assert comp_sorted[0] == 0 + +def test_random_mutation_manual_call3(): + random_mutation_min_val = 888 + random_mutation_max_val = 999 + result, ga_instance = output_crossover_mutation(mutation_type="random", + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val, + mutation_by_replacement=True) + ga_instance.mutation_num_genes = 10 + + temp_offspring = numpy.array(initial_population[0:1]) + offspring = ga_instance.random_mutation(offspring=temp_offspring.copy()) + + comp = offspring + comp_sorted = sorted(comp.copy()) + comp_sorted = numpy.abs(numpy.unique(comp)) + + # The other 1 added to include the last value in the range. + # assert len(comp_sorted) in range(1, 1 + 1 + ga_instance.mutation_num_genes) + # assert comp_sorted[0] == 0 + + value_space = list(range(random_mutation_min_val, random_mutation_max_val)) + for value in comp_sorted: + assert value in value_space + if __name__ == "__main__": print() test_no_crossover_no_mutation() @@ -186,3 +244,12 @@ def test_zero_crossover_probability_zero_mutation_probability(): test_zero_crossover_probability_zero_mutation_probability() print() + test_random_mutation_manual_call() + print() + + test_random_mutation_manual_call2() + print() + + test_random_mutation_manual_call3() + print() + From 31a3975cce88022c48433e324550e7ef92d81953 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Wed, 14 Jun 2023 15:32:43 -0400 Subject: [PATCH 065/220] More updates --- pygad/helper/unique.py | 12 +- pygad/kerasga/kerasga.py | 4 +- pygad/pygad.py | 240 +++++++++++++----- pygad/utils/mutation.py | 122 ++++++--- tests/test_crossover_mutation.py | 29 ++- tests/test_gene_space.py | 120 ++++++++- .../test_gene_space_allow_duplicate_genes.py | 66 ++--- 7 files changed, 451 insertions(+), 142 deletions(-) diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py index af381fe..1a0ba63 100644 --- a/pygad/helper/unique.py +++ b/pygad/helper/unique.py @@ -603,11 +603,15 @@ def find_two_duplicates(self, return None, gene def unpack_gene_space(self, + range_min, + range_max, num_values_from_inf_range=100): """ Unpack the gene_space for the purpose of selecting a value that solves the duplicates. This is by replacing each range by a list of values. It accepts: + range_min: The range minimum value. + range_min: The range maximum value. num_values_from_inf_range: For infinite range of float values, a fixed number of values equal to num_values_from_inf_range is selected using the numpy.linspace() function. It returns the unpacked gene space. """ @@ -662,8 +666,8 @@ def unpack_gene_space(self, gene_space_unpacked[space_idx] = [space] elif space is None: # Randomly generate the value using the mutation range. - gene_space_unpacked[space_idx] = numpy.arange(start=self.random_mutation_min_val, - stop=self.random_mutation_max_val) + gene_space_unpacked[space_idx] = numpy.arange(start=range_min, + stop=range_max) elif type(space) is range: # Convert the range to a list. gene_space_unpacked[space_idx] = list(space) @@ -720,8 +724,8 @@ def unpack_gene_space(self, none_indices = numpy.where(numpy.array(gene_space_unpacked[space_idx]) == None)[0] if len(none_indices) > 0: for idx in none_indices: - random_value = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + random_value = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] gene_space_unpacked[space_idx][idx] = random_value diff --git a/pygad/kerasga/kerasga.py b/pygad/kerasga/kerasga.py index 0e1b618..93491a0 100644 --- a/pygad/kerasga/kerasga.py +++ b/pygad/kerasga/kerasga.py @@ -37,13 +37,13 @@ def model_weights_as_matrix(model, weights_vector): return weights_matrix -def predict(model, solution, data): +def predict(model, solution, data, verbose=1): # Fetch the parameters of the best solution. solution_weights = model_weights_as_matrix(model=model, weights_vector=solution) _model = tensorflow.keras.models.clone_model(model) _model.set_weights(solution_weights) - predictions = _model.predict(data) + predictions = _model(data) return predictions diff --git a/pygad/pygad.py b/pygad/pygad.py index 95a59cd..01ace61 100644 --- a/pygad/pygad.py +++ b/pygad/pygad.py @@ -264,32 +264,78 @@ def __init__(self, self.gene_space = gene_space + # Validate init_range_low and init_range_high + # if type(init_range_low) in GA.supported_int_float_types: + # if type(init_range_high) in GA.supported_int_float_types: + # self.init_range_low = init_range_low + # self.init_range_high = init_range_high + # else: + # self.valid_parameters = False + # raise ValueError(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") + # else: + # self.valid_parameters = False + # raise ValueError(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") + # Validate init_range_low and init_range_high if type(init_range_low) in GA.supported_int_float_types: if type(init_range_high) in GA.supported_int_float_types: - self.init_range_low = init_range_low - self.init_range_high = init_range_high + if init_range_low == init_range_high: + if not self.suppress_warnings: + warnings.warn("The values of the 2 parameters 'init_range_low' and 'init_range_high' are equal and this might return the same value for some genes in the initial population.") else: self.valid_parameters = False - raise ValueError(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.") - else: - self.valid_parameters = False - raise ValueError(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.") + raise TypeError(f"Type mismatch between the 2 parameters 'init_range_low' {type(init_range_low)} and 'init_range_high' {type(init_range_high)}.") + elif type(init_range_low) in [list, tuple, numpy.ndarray]: + # The self.num_genes attribute is not created yet. + # if len(init_range_low) == self.num_genes: + # pass + # else: + # self.valid_parameters = False + # raise ValueError(f"The length of the 'init_range_low' parameter is {len(init_range_low)} which is different from the number of genes {self.num_genes}.") + + # Get the number of genes before validating the num_genes parameter. + if num_genes is None: + if initial_population is None: + self.valid_parameters = False + raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.") + elif not len(init_range_low) == len(initial_population[0]): + self.valid_parameters = False + raise ValueError(f"The length of the 'init_range_low' parameter is {len(init_range_low)} which is different from the number of genes {len(initial_population[0])}.") + elif not len(init_range_low) == num_genes: + self.valid_parameters = False + raise ValueError(f"The length of the 'init_range_low' parameter is {len(init_range_low)} which is different from the number of genes {num_genes}.") - # Validate random_mutation_min_val and random_mutation_max_val - if type(random_mutation_min_val) in GA.supported_int_float_types: - if type(random_mutation_max_val) in GA.supported_int_float_types: - if random_mutation_min_val == random_mutation_max_val: - if not self.suppress_warnings: - warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this causes a fixed change to all genes.") + if type(init_range_high) in [list, tuple, numpy.ndarray]: + if len(init_range_low) == len(init_range_high): + pass + else: + self.valid_parameters = False + raise ValueError(f"Size mismatch between the 2 parameters 'init_range_low' {len(init_range_low)} and 'init_range_high' {len(init_range_high)}.") + + # Validate the values in init_range_low + for val in init_range_low: + if type(val) in GA.supported_int_float_types: + pass + else: + self.valid_parameters = False + raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'init_range_low' parameter, its elements must be numeric but the value {val} of type {type(val)} found.") + + # Validate the values in init_range_high + for val in init_range_high: + if type(val) in GA.supported_int_float_types: + pass + else: + self.valid_parameters = False + raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'init_range_high' parameter, its elements must be numeric but the value {val} of type {type(val)} found.") else: self.valid_parameters = False - raise TypeError(f"The expected type of the 'random_mutation_max_val' parameter is numeric but {type(random_mutation_max_val)} found.") + raise TypeError(f"Type mismatch between the 2 parameters 'init_range_low' {type(init_range_low)} and 'init_range_high' {type(init_range_high)}. Both of them can be either numeric or iterable (list/tuple/numpy.ndarray).") else: self.valid_parameters = False - raise TypeError(f"The expected type of the 'random_mutation_min_val' parameter is numeric but {type(random_mutation_min_val)} found.") - self.random_mutation_min_val = random_mutation_min_val - self.random_mutation_max_val = random_mutation_max_val + raise TypeError(f"The expected type of the 'init_range_low' parameter is numeric or list/tuple/numpy.ndarray but {type(init_range_low)} found.") + + self.init_range_low = init_range_low + self.init_range_high = init_range_high # Validate gene_type if gene_type in GA.supported_int_float_types: @@ -304,6 +350,7 @@ def __init__(self, self.gene_type_single = False raise ValueError(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.") elif type(gene_type) in [list, tuple, numpy.ndarray]: + # Get the number of genes before validating the num_genes parameter. if num_genes is None: if initial_population is None: self.valid_parameters = False @@ -346,7 +393,8 @@ def __init__(self, raise ValueError(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.") # Call the unpack_gene_space() method in the pygad.helper.unique.Unique class. - self.gene_space_unpacked = self.unpack_gene_space() + self.gene_space_unpacked = self.unpack_gene_space(range_min=self.init_range_low, + range_max=self.init_range_high) # Build the initial population if initial_population is None: @@ -374,11 +422,11 @@ def __init__(self, # Number of solutions in the population. self.sol_per_pop = sol_per_pop - self.initialize_population(self.init_range_low, - self.init_range_high, - allow_duplicate_genes, - True, - self.gene_type) + self.initialize_population(low=self.init_range_low, + high=self.init_range_high, + allow_duplicate_genes=allow_duplicate_genes, + mutation_by_replacement=True, + gene_type=self.gene_type) else: self.valid_parameters = False raise TypeError(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.") @@ -457,6 +505,53 @@ def __init__(self, self.valid_parameters = False raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})") + # Validate random_mutation_min_val and random_mutation_max_val + if type(random_mutation_min_val) in GA.supported_int_float_types: + if type(random_mutation_max_val) in GA.supported_int_float_types: + if random_mutation_min_val == random_mutation_max_val: + if not self.suppress_warnings: + warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this might cause a fixed mutation to some genes.") + else: + self.valid_parameters = False + raise TypeError(f"Type mismatch between the 2 parameters 'random_mutation_min_val' {type(random_mutation_min_val)} and 'random_mutation_max_val' {type(random_mutation_max_val)}.") + elif type(random_mutation_min_val) in [list, tuple, numpy.ndarray]: + if len(random_mutation_min_val) == self.num_genes: + pass + else: + self.valid_parameters = False + raise ValueError(f"The length of the 'random_mutation_min_val' parameter is {len(random_mutation_min_val)} which is different from the number of genes {self.num_genes}.") + if type(random_mutation_max_val) in [list, tuple, numpy.ndarray]: + if len(random_mutation_min_val) == len(random_mutation_max_val): + pass + else: + self.valid_parameters = False + raise ValueError(f"Size mismatch between the 2 parameters 'random_mutation_min_val' {len(random_mutation_min_val)} and 'random_mutation_max_val' {len(random_mutation_max_val)}.") + + # Validate the values in random_mutation_min_val + for val in random_mutation_min_val: + if type(val) in GA.supported_int_float_types: + pass + else: + self.valid_parameters = False + raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'random_mutation_min_val' parameter, its elements must be numeric but the value {val} of type {type(val)} found.") + + # Validate the values in random_mutation_max_val + for val in random_mutation_max_val: + if type(val) in GA.supported_int_float_types: + pass + else: + self.valid_parameters = False + raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'random_mutation_max_val' parameter, its elements must be numeric but the value {val} of type {type(val)} found.") + else: + self.valid_parameters = False + raise TypeError(f"Type mismatch between the 2 parameters 'random_mutation_min_val' {type(random_mutation_min_val)} and 'random_mutation_max_val' {type(random_mutation_max_val)}.") + else: + self.valid_parameters = False + raise TypeError(f"The expected type of the 'random_mutation_min_val' parameter is numeric or list/tuple/numpy.ndarray but {type(random_mutation_min_val)} found.") + + self.random_mutation_min_val = random_mutation_min_val + self.random_mutation_max_val = random_mutation_max_val + # Validating the number of parents to be selected for mating (num_parents_mating) if num_parents_mating <= 0: self.valid_parameters = False @@ -1252,9 +1347,17 @@ def initialize_population(self, shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): + + if type(self.init_range_low) in self.supported_int_float_types: + range_min = self.init_range_low + range_max = self.init_range_high + else: + range_min = self.init_range_low[gene_idx] + range_max = self.init_range_high[gene_idx] + # A vector of all values of this single gene across all solutions in the population. - gene_values = numpy.asarray(numpy.random.uniform(low=low, - high=high, + gene_values = numpy.asarray(numpy.random.uniform(low=range_min, + high=range_max, size=self.pop_size[0]), dtype=self.gene_type[gene_idx][0]) # Adding the current gene values to the population. @@ -1280,6 +1383,14 @@ def initialize_population(self, dtype=self.gene_type[0]) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): + + if type(self.init_range_low) in self.supported_int_float_types: + range_min = self.init_range_low + range_max = self.init_range_high + else: + range_min = self.init_range_low[gene_idx] + range_max = self.init_range_high[gene_idx] + if self.gene_space[gene_idx] is None: # The following commented code replace the None value with a single number that will not change again. @@ -1290,8 +1401,8 @@ def initialize_population(self, # self.population[sol_idx, gene_idx] = list(self.gene_space[gene_idx]).copy() # The above problem is solved by keeping the None value in the gene_space parameter. This forces PyGAD to generate this value for each solution. - self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, + self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=range_min, + high=range_max, size=1), dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in [numpy.ndarray, list, tuple, range]: @@ -1308,16 +1419,15 @@ def initialize_population(self, for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=range_min, + high=range_max, size=1), dtype=self.gene_type[0])[0] # Find the difference between the current gene space and the current values in the solution. unique_gene_values = list(set(self.gene_space[gene_idx]).difference( set(self.population[sol_idx, :gene_idx]))) if len(unique_gene_values) > 0: - self.population[sol_idx, gene_idx] = random.choice( - unique_gene_values) + self.population[sol_idx, gene_idx] = random.choice(unique_gene_values) else: # If there is no unique values, then we have to select a duplicate value. self.population[sol_idx, gene_idx] = random.choice( @@ -1331,20 +1441,17 @@ def initialize_population(self, elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[ - gene_idx]['high'], + stop=self.gene_space[gene_idx]['high'], step=self.gene_space[gene_idx]['step']), size=1), dtype=self.gene_type[0])[0] else: self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[ - gene_idx]['high'], + high=self.gene_space[gene_idx]['high'], size=1), dtype=self.gene_type[0])[0] elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, - gene_idx] = self.gene_space[gene_idx] + self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] else: # There is no more options. pass @@ -1356,53 +1463,54 @@ def initialize_population(self, dtype=object) for sol_idx in range(self.sol_per_pop): for gene_idx in range(self.num_genes): + + if type(self.init_range_low) in self.supported_int_float_types: + range_min = self.init_range_low + range_max = self.init_range_high + else: + range_min = self.init_range_low[gene_idx] + range_max = self.init_range_high[gene_idx] + if type(self.gene_space[gene_idx]) in [numpy.ndarray, list, tuple, range]: # Convert to list because tuple and range do not have copy(). # We copy the gene_space to a temp variable to keep its original value. # In the next for loop, the gene_space is changed. # Later, the gene_space is restored to its original value using the temp variable. - temp_gene_space = list( - self.gene_space[gene_idx]).copy() + temp_gene_space = list(self.gene_space[gene_idx]).copy() # Check if the gene space has None values. If any, then replace it with randomly generated values according to the 3 attributes init_range_low, init_range_high, and gene_type. for idx, val in enumerate(self.gene_space[gene_idx]): if val is None: - self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, + self.gene_space[gene_idx][idx] = numpy.asarray(numpy.random.uniform(low=range_min, + high=range_max, size=1), dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, gene_idx] = random.choice( - self.gene_space[gene_idx]) - self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0]( - self.population[sol_idx, gene_idx]) + self.population[sol_idx, gene_idx] = random.choice(self.gene_space[gene_idx]) + self.population[sol_idx, gene_idx] = self.gene_type[gene_idx][0](self.population[sol_idx, gene_idx]) # Restore the gene_space from the temp_gene_space variable. self.gene_space[gene_idx] = temp_gene_space.copy() elif type(self.gene_space[gene_idx]) is dict: if 'step' in self.gene_space[gene_idx].keys(): self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.choice(numpy.arange(start=self.gene_space[gene_idx]['low'], - stop=self.gene_space[ - gene_idx]['high'], + stop=self.gene_space[gene_idx]['high'], step=self.gene_space[gene_idx]['step']), size=1), dtype=self.gene_type[gene_idx][0])[0] else: self.population[sol_idx, gene_idx] = numpy.asarray(numpy.random.uniform(low=self.gene_space[gene_idx]['low'], - high=self.gene_space[ - gene_idx]['high'], + high=self.gene_space[gene_idx]['high'], size=1), dtype=self.gene_type[gene_idx][0])[0] elif type(self.gene_space[gene_idx]) == type(None): - temp_gene_value = numpy.asarray(numpy.random.uniform(low=low, - high=high, + temp_gene_value = numpy.asarray(numpy.random.uniform(low=range_min, + high=range_max, size=1), dtype=self.gene_type[gene_idx][0])[0] - self.population[sol_idx, - gene_idx] = temp_gene_value.copy() + self.population[sol_idx, gene_idx] = temp_gene_value.copy() elif type(self.gene_space[gene_idx]) in GA.supported_int_float_types: - self.population[sol_idx, - gene_idx] = self.gene_space[gene_idx] + self.population[sol_idx, gene_idx] = self.gene_space[gene_idx] else: # There is no more options. pass @@ -1414,12 +1522,20 @@ def initialize_population(self, # 2) gene_type is not nested (gene_type_single is True). # Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes. - for idx, curr_gene_space in enumerate(self.gene_space): + for gene_idx, curr_gene_space in enumerate(self.gene_space): + + if type(self.init_range_low) in self.supported_int_float_types: + range_min = self.init_range_low + range_max = self.init_range_high + else: + range_min = self.init_range_low[gene_idx] + range_max = self.init_range_high[gene_idx] + if curr_gene_space is None: - self.gene_space[idx] = numpy.asarray(numpy.random.uniform(low=low, - high=high, - size=1), - dtype=self.gene_type[0])[0] + self.gene_space[gene_idx] = numpy.asarray(numpy.random.uniform(low=range_min, + high=range_max, + size=1), + dtype=self.gene_type[0])[0] # Creating the initial population by randomly selecting the genes' values from the values inside the 'gene_space' parameter. if type(self.gene_space) is dict: @@ -1471,8 +1587,7 @@ def initialize_population(self, # It can be either range, numpy.ndarray, or list. # Create an empty population of dtype=object to support storing mixed data types within the same array. - self.population = numpy.zeros( - shape=self.pop_size, dtype=object) + self.population = numpy.zeros(shape=self.pop_size, dtype=object) # Loop through the genes, randomly generate the values of a single gene across the entire population, and add the values of each gene to the population. for gene_idx in range(self.num_genes): # A vector of all values of this single gene across all solutions in the population. @@ -1901,8 +2016,7 @@ def run(self): if not type(self.last_generation_offspring_mutation) is numpy.ndarray: raise TypeError(f"The output of the mutation step is expected to be of type (numpy.ndarray) but {type(self.last_generation_offspring_mutation)} found.") else: - self.last_generation_offspring_mutation = self.mutation( - self.last_generation_offspring_crossover) + self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover) if self.last_generation_offspring_mutation.shape != (self.num_offspring, self.num_genes): if self.last_generation_offspring_mutation.shape[0] != self.num_offspring: diff --git a/pygad/utils/mutation.py b/pygad/utils/mutation.py index 39a1a5d..6a9f6a9 100644 --- a/pygad/utils/mutation.py +++ b/pygad/utils/mutation.py @@ -53,6 +53,13 @@ def mutation_by_space(self, offspring): mutation_indices = numpy.array(random.sample(range(0, self.num_genes), self.mutation_num_genes)) for gene_idx in mutation_indices: + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + if self.gene_space_nested: # Returning the current gene space from the 'gene_space' attribute. if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: @@ -65,8 +72,8 @@ def mutation_by_space(self, offspring): value_from_space = curr_gene_space # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. elif curr_gene_space is None: - rand_val = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + rand_val = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val @@ -125,8 +132,8 @@ def mutation_by_space(self, offspring): if value_from_space is None: # TODO: Return index 0. # TODO: Check if this if statement is necessary. - value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + value_from_space = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] # Assinging the selected value from the space to the gene. @@ -163,6 +170,14 @@ def mutation_probs_by_space(self, offspring): for offspring_idx in range(offspring.shape[0]): probs = numpy.random.random(size=offspring.shape[1]) for gene_idx in range(offspring.shape[1]): + + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + if probs[gene_idx] <= self.mutation_probability: if self.gene_space_nested: # Returning the current gene space from the 'gene_space' attribute. @@ -176,8 +191,8 @@ def mutation_probs_by_space(self, offspring): value_from_space = curr_gene_space # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. elif curr_gene_space is None: - rand_val = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + rand_val = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val @@ -260,9 +275,17 @@ def mutation_randomly(self, offspring): for offspring_idx in range(offspring.shape[0]): mutation_indices = numpy.array(random.sample(range(0, self.num_genes), self.mutation_num_genes)) for gene_idx in mutation_indices: + + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + # Generating a random value. - random_value = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + random_value = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: @@ -293,8 +316,8 @@ def mutation_randomly(self, offspring): if self.allow_duplicate_genes == False: offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, + min_val=range_min, + max_val=range_max, mutation_by_replacement=self.mutation_by_replacement, gene_type=self.gene_type, num_trials=10) @@ -314,10 +337,18 @@ def mutation_probs_randomly(self, offspring): for offspring_idx in range(offspring.shape[0]): probs = numpy.random.random(size=offspring.shape[1]) for gene_idx in range(offspring.shape[1]): + + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + if probs[gene_idx] <= self.mutation_probability: # Generating a random value. - random_value = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + random_value = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: @@ -348,8 +379,8 @@ def mutation_probs_randomly(self, offspring): if self.allow_duplicate_genes == False: offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, + min_val=range_min, + max_val=range_max, mutation_by_replacement=self.mutation_by_replacement, gene_type=self.gene_type, num_trials=10) @@ -532,6 +563,13 @@ def adaptive_mutation_by_space(self, offspring): mutation_indices = numpy.array(random.sample(range(0, self.num_genes), adaptive_mutation_num_genes)) for gene_idx in mutation_indices: + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + if self.gene_space_nested: # Returning the current gene space from the 'gene_space' attribute. if type(self.gene_space[gene_idx]) in [numpy.ndarray, list]: @@ -544,8 +582,8 @@ def adaptive_mutation_by_space(self, offspring): value_from_space = curr_gene_space # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. elif curr_gene_space is None: - rand_val = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + rand_val = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val @@ -600,8 +638,8 @@ def adaptive_mutation_by_space(self, offspring): if value_from_space is None: - value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + value_from_space = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] # Assinging the selected value from the space to the gene. @@ -646,9 +684,17 @@ def adaptive_mutation_randomly(self, offspring): adaptive_mutation_num_genes = self.mutation_num_genes[1] mutation_indices = numpy.array(random.sample(range(0, self.num_genes), adaptive_mutation_num_genes)) for gene_idx in mutation_indices: + + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + # Generating a random value. - random_value = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + random_value = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: @@ -678,8 +724,8 @@ def adaptive_mutation_randomly(self, offspring): if self.allow_duplicate_genes == False: offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, + min_val=range_min, + max_val=range_max, mutation_by_replacement=self.mutation_by_replacement, gene_type=self.gene_type, num_trials=10) @@ -710,6 +756,14 @@ def adaptive_mutation_probs_by_space(self, offspring): probs = numpy.random.random(size=offspring.shape[1]) for gene_idx in range(offspring.shape[1]): + + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + if probs[gene_idx] <= adaptive_mutation_probability: if self.gene_space_nested: # Returning the current gene space from the 'gene_space' attribute. @@ -723,8 +777,8 @@ def adaptive_mutation_probs_by_space(self, offspring): value_from_space = curr_gene_space # If the gene space is None, apply mutation by adding a random value between the range defined by the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val'. elif curr_gene_space is None: - rand_val = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + rand_val = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] if self.mutation_by_replacement: value_from_space = rand_val @@ -778,8 +832,8 @@ def adaptive_mutation_probs_by_space(self, offspring): value_from_space = random.choice(values_to_select_from) if value_from_space is None: - value_from_space = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + value_from_space = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] # Assinging the selected value from the space to the gene. @@ -825,10 +879,18 @@ def adaptive_mutation_probs_randomly(self, offspring): probs = numpy.random.random(size=offspring.shape[1]) for gene_idx in range(offspring.shape[1]): + + if type(self.random_mutation_min_val) in self.supported_int_float_types: + range_min = self.random_mutation_min_val + range_max = self.random_mutation_max_val + else: + range_min = self.random_mutation_min_val[gene_idx] + range_max = self.random_mutation_max_val[gene_idx] + if probs[gene_idx] <= adaptive_mutation_probability: # Generating a random value. - random_value = numpy.random.uniform(low=self.random_mutation_min_val, - high=self.random_mutation_max_val, + random_value = numpy.random.uniform(low=range_min, + high=range_max, size=1)[0] # If the mutation_by_replacement attribute is True, then the random value replaces the current gene value. if self.mutation_by_replacement: @@ -858,8 +920,8 @@ def adaptive_mutation_probs_randomly(self, offspring): if self.allow_duplicate_genes == False: offspring[offspring_idx], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[offspring_idx], - min_val=self.random_mutation_min_val, - max_val=self.random_mutation_max_val, + min_val=range_min, + max_val=range_max, mutation_by_replacement=self.mutation_by_replacement, gene_type=self.gene_type, num_trials=10) diff --git a/tests/test_crossover_mutation.py b/tests/test_crossover_mutation.py index 8be66c9..757d912 100644 --- a/tests/test_crossover_mutation.py +++ b/tests/test_crossover_mutation.py @@ -189,6 +189,7 @@ def test_random_mutation_manual_call2(): # assert comp_sorted[0] == 0 def test_random_mutation_manual_call3(): + # Use random_mutation_min_val & random_mutation_max_val as numbers. random_mutation_min_val = 888 random_mutation_max_val = 999 result, ga_instance = output_crossover_mutation(mutation_type="random", @@ -204,14 +205,31 @@ def test_random_mutation_manual_call3(): comp_sorted = sorted(comp.copy()) comp_sorted = numpy.abs(numpy.unique(comp)) - # The other 1 added to include the last value in the range. - # assert len(comp_sorted) in range(1, 1 + 1 + ga_instance.mutation_num_genes) - # assert comp_sorted[0] == 0 - value_space = list(range(random_mutation_min_val, random_mutation_max_val)) for value in comp_sorted: assert value in value_space +def test_random_mutation_manual_call4(): + # Use random_mutation_min_val & random_mutation_max_val as lists. + random_mutation_min_val = [888]*10 + random_mutation_max_val = [999]*10 + result, ga_instance = output_crossover_mutation(mutation_type="random", + random_mutation_min_val=random_mutation_min_val, + random_mutation_max_val=random_mutation_max_val, + mutation_by_replacement=True) + ga_instance.mutation_num_genes = 10 + + temp_offspring = numpy.array(initial_population[0:1]) + offspring = ga_instance.random_mutation(offspring=temp_offspring.copy()) + + comp = offspring + comp_sorted = sorted(comp.copy()) + comp_sorted = numpy.abs(numpy.unique(comp)) + + value_space = list(range(random_mutation_min_val[0], random_mutation_max_val[0])) + for value in comp_sorted: + assert value in value_space + if __name__ == "__main__": print() test_no_crossover_no_mutation() @@ -253,3 +271,6 @@ def test_random_mutation_manual_call3(): test_random_mutation_manual_call3() print() + test_random_mutation_manual_call4() + print() + diff --git a/tests/test_gene_space.py b/tests/test_gene_space.py index 7a375f2..3a1b21f 100644 --- a/tests/test_gene_space.py +++ b/tests/test_gene_space.py @@ -65,12 +65,37 @@ def fitness_func(ga, solution, idx): num_outside = 0 if ga_instance.gene_space_nested == True: for gene_idx in range(ga_instance.num_genes): + + if type(ga_instance.init_range_low) in ga_instance.supported_int_float_types: + range_min_init = ga_instance.init_range_low + range_max_init = ga_instance.init_range_high + else: + range_min_init = ga_instance.init_range_low[gene_idx] + range_max_init = ga_instance.init_range_high[gene_idx] + if type(ga_instance.random_mutation_min_val) in ga_instance.supported_int_float_types: + range_min_mutation = ga_instance.random_mutation_min_val + range_max_mutation = ga_instance.random_mutation_max_val + else: + range_min_mutation = ga_instance.random_mutation_min_val[gene_idx] + range_max_mutation = ga_instance.random_mutation_max_val[gene_idx] + all_gene_values = ga_instance.solutions[:, gene_idx] if type(ga_instance.gene_space[gene_idx]) in [list, tuple, range, numpy.ndarray]: current_gene_space = list(ga_instance.gene_space[gene_idx]) - for val in all_gene_values: - if val in current_gene_space: - # print(val, current_gene_space) + # print("current_gene_space", current_gene_space) + for val_idx, val in enumerate(all_gene_values): + if None in current_gene_space: + if (val in current_gene_space) or (val >= range_min_init and val < range_max_init) or (val >= range_min_mutation and val < range_max_mutation): + pass + else: + # print("###########") + # print(gene_idx, val) + # print(current_gene_space) + # print(range_min_mutation, range_max_mutation) + # print("\n\n") + num_outside += 1 + elif val in current_gene_space: + # print("val, current_gene_space", val, current_gene_space) pass else: # print(gene_idx, val, current_gene_space) @@ -98,14 +123,51 @@ def fitness_func(ga, solution, idx): pass else: num_outside += 1 + elif ga_instance.gene_space[gene_idx] is None: + for val in all_gene_values: + # print(val) + if (val >= range_min_init and val < range_max_init) or (val >= range_min_mutation and val < range_max_mutation): + pass + else: + # print("###########") + # print(gene_idx, val) + # print(ga_instance.gene_space[gene_idx]) + # print(range_min_init, range_max_init) + # print(range_min_mutation, range_max_mutation) + # print("\n\n") + num_outside += 1 else: for gene_idx in range(ga_instance.num_genes): + + if type(ga_instance.init_range_low) in ga_instance.supported_int_float_types: + range_min_init = ga_instance.init_range_low + range_max_init = ga_instance.init_range_high + else: + range_min_init = ga_instance.init_range_low[gene_idx] + range_max_init = ga_instance.init_range_high[gene_idx] + if type(ga_instance.random_mutation_min_val) in ga_instance.supported_int_float_types: + range_min_mutation = ga_instance.random_mutation_min_val + range_max_mutation = ga_instance.random_mutation_max_val + else: + range_min_mutation = ga_instance.random_mutation_min_val[gene_idx] + range_max_mutation = ga_instance.random_mutation_max_val[gene_idx] + all_gene_values = ga_instance.solutions[:, gene_idx] # print("all_gene_values", gene_idx, all_gene_values) if type(ga_instance.gene_space) in [list, tuple, range, numpy.ndarray]: current_gene_space = list(ga_instance.gene_space) for val in all_gene_values: - if val in current_gene_space: + if None in current_gene_space: + if (val in current_gene_space) or (val >= range_min_init and val < range_max_init) or (val >= range_min_mutation and val < range_max_mutation): + pass + else: + # print("###########") + # print(gene_idx, val) + # print(current_gene_space) + # print(range_min_mutation, range_max_mutation) + # print("\n\n") + num_outside += 1 + elif val in current_gene_space: pass else: num_outside += 1 @@ -144,6 +206,11 @@ def test_gene_space_list(): assert num_outside == 0 +def test_gene_space_list_None(): + num_outside, _ = number_respect_gene_space(gene_space=[30, None, 40, 50, None, 60, 70, None, None, None]) + + assert num_outside == 0 + def test_gene_space_numpy(): num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10)))) @@ -318,6 +385,38 @@ def test_nested_gene_space_list2(): assert num_outside == 0 +def test_nested_gene_space_list3_None(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, None], + [1, 2], + [2, None], + [3, 4], + [None, 5], + None, + [None, 7], + [None, None], + [8, 9], + None], + mutation_by_replacement=True) + + assert num_outside == 0 + +def test_nested_gene_space_list4_None_custom_mutation_range(): + num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, None], + [1, 2], + [2, None], + [3, 4], + [None, 5], + None, + [None, 7], + [None, None], + [8, 9], + None], + random_mutation_min_val=20, + random_mutation_max_val=40, + mutation_by_replacement=True) + + assert num_outside == 0 + def test_nested_gene_space_mix(): num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4], numpy.arange(5, 10), @@ -329,7 +428,8 @@ def test_nested_gene_space_mix(): numpy.arange(35, 40), numpy.arange(40, 45), [45, 46, 47, 48, 49]], - gene_type=int) + gene_type=int, + mutation_by_replacement=True) assert num_outside == 0 @@ -344,7 +444,7 @@ def test_nested_gene_space_mix_nested_gene_type(): numpy.arange(35, 40), numpy.arange(40, 45), [45, 46, 47, 48, 49]], - gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) + gene_type=[int, float, numpy.float64, [float, 3], int, numpy.int16, [numpy.float32, 1], int, float, [float, 3]]) # print(ga_instance.population) assert num_outside == 0 @@ -434,6 +534,8 @@ def test_nested_gene_space_nested_gene_type_adaptive_mutation(): test_gene_space_list() print() + test_gene_space_list_None() + print() test_gene_space_list_nested_gene_type() print() @@ -478,6 +580,12 @@ def test_nested_gene_space_nested_gene_type_adaptive_mutation(): test_nested_gene_space_list2() print() + test_nested_gene_space_list3_None() + print() + + test_nested_gene_space_list4_None_custom_mutation_range() + print() + test_nested_gene_space_mix() print() diff --git a/tests/test_gene_space_allow_duplicate_genes.py b/tests/test_gene_space_allow_duplicate_genes.py index 2a65f1a..4d78dea 100644 --- a/tests/test_gene_space_allow_duplicate_genes.py +++ b/tests/test_gene_space_allow_duplicate_genes.py @@ -379,39 +379,39 @@ def test_nested_gene_space_mix_initial_population_single_gene_type(): assert num_outside == 0 if __name__ == "__main__": - # print() - # test_gene_space_range() - # print() - # test_gene_space_range_nested_gene_type() - # print() - - # test_gene_space_numpy_arange() - # print() - # test_gene_space_numpy_arange_nested_gene_type() - # print() - - # test_gene_space_list() - # print() - # test_gene_space_list_nested_gene_type() - # print() - - # test_gene_space_list_single_value() - # print() - # test_gene_space_list_single_value_nested_gene_type() - # print() - - # test_gene_space_numpy() - # print() - # test_gene_space_numpy_nested_gene_type() - # print() - - # test_gene_space_dict_without_step() - # print() - # test_gene_space_dict_without_step_nested_gene_type() - # print() - - # test_gene_space_dict_with_step() - # print() + print() + test_gene_space_range() + print() + test_gene_space_range_nested_gene_type() + print() + + test_gene_space_numpy_arange() + print() + test_gene_space_numpy_arange_nested_gene_type() + print() + + test_gene_space_list() + print() + test_gene_space_list_nested_gene_type() + print() + + test_gene_space_list_single_value() + print() + test_gene_space_list_single_value_nested_gene_type() + print() + + test_gene_space_numpy() + print() + test_gene_space_numpy_nested_gene_type() + print() + + test_gene_space_dict_without_step() + print() + test_gene_space_dict_without_step_nested_gene_type() + print() + + test_gene_space_dict_with_step() + print() test_gene_space_dict_with_step_nested_gene_type() print() From 0fe7e245239d4e7e630f8ffcc4cdb97d8ba6ecb1 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Wed, 14 Jun 2023 15:38:13 -0400 Subject: [PATCH 066/220] Keras prediction --- pygad/kerasga/kerasga.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pygad/kerasga/kerasga.py b/pygad/kerasga/kerasga.py index 93491a0..3e6ae0f 100644 --- a/pygad/kerasga/kerasga.py +++ b/pygad/kerasga/kerasga.py @@ -44,6 +44,7 @@ def predict(model, solution, data, verbose=1): _model = tensorflow.keras.models.clone_model(model) _model.set_weights(solution_weights) predictions = _model(data) + predictions = predictions.numpy() return predictions From 0b5ab78745f783c116123dafdc9deec15b6b0176 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Sat, 17 Jun 2023 17:27:22 -0400 Subject: [PATCH 067/220] Accept data as a generator --- pygad/kerasga/kerasga.py | 67 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 64 insertions(+), 3 deletions(-) diff --git a/pygad/kerasga/kerasga.py b/pygad/kerasga/kerasga.py index 3e6ae0f..cda2c4b 100644 --- a/pygad/kerasga/kerasga.py +++ b/pygad/kerasga/kerasga.py @@ -3,6 +3,20 @@ import tensorflow.keras def model_weights_as_vector(model): + """ + Reshapes the Keras model weight as a vector. + + Parameters + ---------- + model : TYPE + The Keras model. + + Returns + ------- + TYPE + The weights as a 1D vector. + + """ weights_vector = [] for layer in model.layers: # model.get_weights(): @@ -15,6 +29,22 @@ def model_weights_as_vector(model): return numpy.array(weights_vector) def model_weights_as_matrix(model, weights_vector): + """ + Reshapes the PyGAD 1D solution as a Keras weight matrix. + + Parameters + ---------- + model : TYPE + The Keras model. + weights_vector : TYPE + The PyGAD solution as a 1D vector. + + Returns + ------- + weights_matrix : TYPE + The Keras weights as a matrix. + + """ weights_matrix = [] start = 0 @@ -37,14 +67,45 @@ def model_weights_as_matrix(model, weights_vector): return weights_matrix -def predict(model, solution, data, verbose=1): +def predict(model, + solution, + data, + batch_size=None, + verbose=0, + steps=None): + """ + Use the PyGAD's solution to make predictions using the Keras model. + + Parameters + ---------- + model : TYPE + The Keras model. + solution : TYPE + A single PyGAD solution as 1D vector. + data : TYPE + The data or a generator. + batch_size : TYPE, optional + The batch size (i.e. number of samples per step or batch). The default is None. Check documentation of the Keras Model.predict() method for more information. + verbose : TYPE, optional + Verbosity mode. The default is 0. Check documentation of the Keras Model.predict() method for more information. + steps : TYPE, optional + The total number of steps (batches of samples). The default is None. Check documentation of the Keras Model.predict() method for more information. + + Returns + ------- + predictions : TYPE + The Keras model predictions. + + """ # Fetch the parameters of the best solution. solution_weights = model_weights_as_matrix(model=model, weights_vector=solution) _model = tensorflow.keras.models.clone_model(model) _model.set_weights(solution_weights) - predictions = _model(data) - predictions = predictions.numpy() + predictions = _model.predict(x=data, + batch_size=batch_size, + verbose=verbose, + steps=steps) return predictions From ee643a961d60afdde47cd2e6830c37ec3770bf54 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Mon, 19 Jun 2023 15:24:40 -0400 Subject: [PATCH 068/220] More examples --- examples/KerasGA/XOR_classification.py | 86 ++++++ examples/KerasGA/cancer_dataset.py | 95 +++++++ examples/KerasGA/cancer_dataset_generator.py | 89 ++++++ examples/KerasGA/image_classification_CNN.py | 90 ++++++ .../KerasGA/image_classification_Dense.py | 82 ++++++ 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examples/data/Skin_Cancer_Dataset/malignant/99.jpg create mode 100644 examples/data/dataset_features.npy create mode 100644 examples/data/dataset_inputs.npy create mode 100644 examples/data/dataset_outputs.npy create mode 100644 examples/data/outputs.npy diff --git a/examples/KerasGA/XOR_classification.py b/examples/KerasGA/XOR_classification.py new file mode 100644 index 0000000..da3c752 --- /dev/null +++ b/examples/KerasGA/XOR_classification.py @@ -0,0 +1,86 @@ +import tensorflow.keras +import pygad.kerasga +import numpy +import pygad + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + bce = tensorflow.keras.losses.BinaryCrossentropy() + solution_fitness = 1.0 / (bce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Build the keras model using the functional API. +input_layer = tensorflow.keras.layers.Input(2) +dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer) +output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer) + +model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +# Create an instance of the pygad.kerasga.KerasGA class to build the initial population. +keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + +# XOR problem inputs +data_inputs = numpy.array([[0.0, 0.0], + [0.0, 1.0], + [1.0, 0.0], + [1.0, 1.0]]) + +# XOR problem outputs +data_outputs = numpy.array([[1.0, 0.0], + [0.0, 1.0], + [0.0, 1.0], + [1.0, 0.0]]) + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 250 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = keras_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) +print("Predictions : \n", predictions) + +# Calculate the binary crossentropy for the trained model. +bce = tensorflow.keras.losses.BinaryCrossentropy() +print("Binary Crossentropy : ", bce(data_outputs, predictions).numpy()) + +# Calculate the classification accuracy for the trained model. +ba = tensorflow.keras.metrics.BinaryAccuracy() +ba.update_state(data_outputs, predictions) +accuracy = ba.result().numpy() +print("Accuracy : ", accuracy) + +# model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) + +# _ = model.fit(x, y, verbose=0) +# r = model.predict(data_inputs) diff --git a/examples/KerasGA/cancer_dataset.py b/examples/KerasGA/cancer_dataset.py new file mode 100644 index 0000000..5aceae6 --- /dev/null +++ b/examples/KerasGA/cancer_dataset.py @@ -0,0 +1,95 @@ +import tensorflow as tf +import tensorflow.keras +import pygad.kerasga +import pygad +import numpy + +def fitness_func(ga_instanse, solution, sol_idx): + global train_data, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=train_data) + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + +def on_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(ga_instance.last_generation_fitness)[1])) + +# The dataset path. +dataset_path = r'../data/Skin_Cancer_Dataset' + +num_classes = 2 +img_size = 224 + +# Create a simple CNN. This does not gurantee high classification accuracy. +model = tf.keras.models.Sequential() +model.add(tf.keras.layers.Input(shape=(img_size, img_size, 3))) +model.add(tf.keras.layers.Conv2D(32, (3,3), activation="relu", padding="same")) +model.add(tf.keras.layers.MaxPooling2D((2, 2))) +model.add(tf.keras.layers.Flatten()) +model.add(tf.keras.layers.Dropout(rate=0.2)) +model.add(tf.keras.layers.Dense(num_classes, activation="softmax")) + +# Create an instance of the pygad.kerasga.KerasGA class to build the initial population. +keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + +train_data = tf.keras.utils.image_dataset_from_directory( + directory=dataset_path, + image_size=(img_size, img_size), + label_mode="categorical", + batch_size=32 +) + +# Get the dataset labels. +# train_data.class_indices +data_outputs = numpy.array([]) +for x, y in train_data: + data_outputs = numpy.concatenate([data_outputs, numpy.argmax(y.numpy(), axis=-1)]) +data_outputs = tf.keras.utils.to_categorical(data_outputs) + +# Check the documentation for more information about the parameters: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +initial_population = keras_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=10, + num_parents_mating=5, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=on_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness) +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=train_data) +# print("Predictions : \n", predictions) + +# Calculate the categorical crossentropy for the trained model. +cce = tensorflow.keras.losses.CategoricalCrossentropy() +print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) + +# Calculate the classification accuracy for the trained model. +ca = tensorflow.keras.metrics.CategoricalAccuracy() +ca.update_state(data_outputs, predictions) +accuracy = ca.result().numpy() +print("Accuracy : ", accuracy) + +# model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) + +# _ = model.fit(x, y, verbose=0) +# r = model.predict(data_inputs) diff --git a/examples/KerasGA/cancer_dataset_generator.py b/examples/KerasGA/cancer_dataset_generator.py new file mode 100644 index 0000000..3f8afeb --- /dev/null +++ b/examples/KerasGA/cancer_dataset_generator.py @@ -0,0 +1,89 @@ +import tensorflow as tf +import tensorflow.keras +import pygad.kerasga +import pygad + +def fitness_func(ga_instanse, solution, sol_idx): + global train_generator, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=train_generator) + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + +def on_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(ga_instance.last_generation_fitness)[1])) + +# The dataset path. +dataset_path = r'../data/Skin_Cancer_Dataset' + +num_classes = 2 +img_size = 224 + +# Create a simple CNN. This does not gurantee high classification accuracy. +model = tf.keras.models.Sequential() +model.add(tf.keras.layers.Input(shape=(img_size, img_size, 3))) +model.add(tf.keras.layers.Conv2D(32, (3,3), activation="relu", padding="same")) +model.add(tf.keras.layers.MaxPooling2D((2, 2))) +model.add(tf.keras.layers.Flatten()) +model.add(tf.keras.layers.Dropout(rate=0.2)) +model.add(tf.keras.layers.Dense(num_classes, activation="softmax")) + +# Create an instance of the pygad.kerasga.KerasGA class to build the initial population. +keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + +data_generator = tf.keras.preprocessing.image.ImageDataGenerator() +train_generator = data_generator.flow_from_directory(dataset_path, + class_mode='categorical', + target_size=(224, 224), + batch_size=32, + shuffle=False) +# train_generator.class_indices +data_outputs = tf.keras.utils.to_categorical(train_generator.labels) + +# Check the documentation for more information about the parameters: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +initial_population = keras_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=10, + num_parents_mating=5, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=on_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness) +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=train_generator) +# print("Predictions : \n", predictions) + +# Calculate the categorical crossentropy for the trained model. +cce = tensorflow.keras.losses.CategoricalCrossentropy() +print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) + +# Calculate the classification accuracy for the trained model. +ca = tensorflow.keras.metrics.CategoricalAccuracy() +ca.update_state(data_outputs, predictions) +accuracy = ca.result().numpy() +print("Accuracy : ", accuracy) + +# model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) + +# _ = model.fit(x, y, verbose=0) +# r = model.predict(data_inputs) diff --git a/examples/KerasGA/image_classification_CNN.py b/examples/KerasGA/image_classification_CNN.py new file mode 100644 index 0000000..a20ab03 --- /dev/null +++ b/examples/KerasGA/image_classification_CNN.py @@ -0,0 +1,90 @@ +import tensorflow.keras +import pygad.kerasga +import numpy +import pygad + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Build the keras model using the functional API. +input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3)) +conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5, + kernel_size=7, + activation="relu")(input_layer) +max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5), + strides=5)(conv_layer1) +conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3, + kernel_size=3, + activation="relu")(max_pool1) +flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2) +dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer) +output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) + +model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +# Create an instance of the pygad.kerasga.KerasGA class to build the initial population. +keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + +# Data inputs +data_inputs = numpy.load("../data/dataset_inputs.npy") + +# Data outputs +data_outputs = numpy.load("../data/dataset_outputs.npy") +data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 200 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = keras_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) +# print("Predictions : \n", predictions) + +# Calculate the categorical crossentropy for the trained model. +cce = tensorflow.keras.losses.CategoricalCrossentropy() +print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) + +# Calculate the classification accuracy for the trained model. +ca = tensorflow.keras.metrics.CategoricalAccuracy() +ca.update_state(data_outputs, predictions) +accuracy = ca.result().numpy() +print("Accuracy : ", accuracy) + +# model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) + +# _ = model.fit(x, y, verbose=0) +# r = model.predict(data_inputs) diff --git a/examples/KerasGA/image_classification_Dense.py b/examples/KerasGA/image_classification_Dense.py new file mode 100644 index 0000000..aee14f5 --- /dev/null +++ b/examples/KerasGA/image_classification_Dense.py @@ -0,0 +1,82 @@ +import tensorflow.keras +import pygad.kerasga +import numpy +import pygad + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Build the keras model using the functional API. +input_layer = tensorflow.keras.layers.Input(360) +dense_layer = tensorflow.keras.layers.Dense(50, activation="relu")(input_layer) +output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) + +model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +# Create an instance of the pygad.kerasga.KerasGA class to build the initial population. +keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + +# Data inputs +data_inputs = numpy.load("../data/dataset_features.npy") + +# Data outputs +data_outputs = numpy.load("../data/outputs.npy") +data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 100 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = keras_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +# Fetch the parameters of the best solution. +predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) +# print("Predictions : \n", predictions) + +# Calculate the categorical crossentropy for the trained model. +cce = tensorflow.keras.losses.CategoricalCrossentropy() +print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) + +# Calculate the classification accuracy for the trained model. +ca = tensorflow.keras.metrics.CategoricalAccuracy() +ca.update_state(data_outputs, predictions) +accuracy = ca.result().numpy() +print("Accuracy : ", accuracy) + +# model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) + +# _ = model.fit(x, y, verbose=0) +# r = model.predict(data_inputs) diff --git a/examples/KerasGA/regression_example.py b/examples/KerasGA/regression_example.py new file mode 100644 index 0000000..36b78fb --- /dev/null +++ b/examples/KerasGA/regression_example.py @@ -0,0 +1,79 @@ +import tensorflow.keras +import pygad.kerasga +import numpy +import pygad + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + mae = tensorflow.keras.losses.MeanAbsoluteError() + abs_error = mae(data_outputs, predictions).numpy() + 0.00000001 + solution_fitness = 1.0 / abs_error + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Create the Keras model. +input_layer = tensorflow.keras.layers.Input(3) +dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) +dense_layer1.trainable = False +output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) + +model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + +# Data inputs +data_inputs = numpy.array([[0.02, 0.1, 0.15], + [0.7, 0.6, 0.8], + [1.5, 1.2, 1.7], + [3.2, 2.9, 3.1]]) + +# Data outputs +data_outputs = numpy.array([[0.1], + [0.6], + [1.3], + [2.5]]) + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 250 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = keras_ga.population_weights # Initial population of network weights + +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) +print("Predictions : \n", predictions) + +mae = tensorflow.keras.losses.MeanAbsoluteError() +abs_error = mae(data_outputs, predictions).numpy() +print("Absolute Error : ", abs_error) + +# model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) + +# _ = model.fit(x, y, verbose=0) +# r = model.predict(data_inputs) diff --git a/examples/TorchGA/XOR_classification.py b/examples/TorchGA/XOR_classification.py new file mode 100644 index 0000000..6cedb9c --- /dev/null +++ b/examples/TorchGA/XOR_classification.py @@ -0,0 +1,86 @@ +import torch +import pygad.torchga +import pygad + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Create the PyTorch model. +input_layer = torch.nn.Linear(2, 4) +relu_layer = torch.nn.ReLU() +dense_layer = torch.nn.Linear(4, 2) +output_layer = torch.nn.Softmax(1) + +model = torch.nn.Sequential(input_layer, + relu_layer, + dense_layer, + output_layer) +# print(model) + +# Create an instance of the pygad.torchga.TorchGA class to build the initial population. +torch_ga = pygad.torchga.TorchGA(model=model, + num_solutions=10) + +loss_function = torch.nn.BCELoss() + +# XOR problem inputs +data_inputs = torch.tensor([[0.0, 0.0], + [0.0, 1.0], + [1.0, 0.0], + [1.0, 1.0]]) + +# XOR problem outputs +data_outputs = torch.tensor([[1.0, 0.0], + [0.0, 1.0], + [0.0, 1.0], + [1.0, 0.0]]) + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 250 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = torch_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + parallel_processing=3, + on_generation=callback_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) +print("Predictions : \n", predictions.detach().numpy()) + +# Calculate the binary crossentropy for the trained model. +print("Binary Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) + +# Calculate the classification accuracy of the trained model. +a = torch.max(predictions, axis=1) +b = torch.max(data_outputs, axis=1) +accuracy = torch.true_divide(torch.sum(a.indices == b.indices), len(data_outputs)) +print("Accuracy : ", accuracy.detach().numpy()) diff --git a/examples/TorchGA/image_classification_CNN.py b/examples/TorchGA/image_classification_CNN.py new file mode 100644 index 0000000..be26e00 --- /dev/null +++ b/examples/TorchGA/image_classification_CNN.py @@ -0,0 +1,94 @@ +import torch +import pygad.torchga +import pygad +import numpy + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Build the PyTorch model. +input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7) +relu_layer1 = torch.nn.ReLU() +max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5) + +conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3) +relu_layer2 = torch.nn.ReLU() + +flatten_layer1 = torch.nn.Flatten() +# The value 768 is pre-computed by tracing the sizes of the layers' outputs. +dense_layer1 = torch.nn.Linear(in_features=768, out_features=15) +relu_layer3 = torch.nn.ReLU() + +dense_layer2 = torch.nn.Linear(in_features=15, out_features=4) +output_layer = torch.nn.Softmax(1) + +model = torch.nn.Sequential(input_layer, + relu_layer1, + max_pool1, + conv_layer2, + relu_layer2, + flatten_layer1, + dense_layer1, + relu_layer3, + dense_layer2, + output_layer) + +# Create an instance of the pygad.torchga.TorchGA class to build the initial population. +torch_ga = pygad.torchga.TorchGA(model=model, + num_solutions=10) + +loss_function = torch.nn.CrossEntropyLoss() + +# Data inputs +data_inputs = torch.from_numpy(numpy.load("../data/dataset_inputs.npy")).float() +data_inputs = data_inputs.reshape((data_inputs.shape[0], data_inputs.shape[3], data_inputs.shape[1], data_inputs.shape[2])) + +# Data outputs +data_outputs = torch.from_numpy(numpy.load("../data/dataset_outputs.npy")).long() + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 200 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = torch_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) +# print("Predictions : \n", predictions) + +# Calculate the crossentropy for the trained model. +print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) + +# Calculate the classification accuracy for the trained model. +accuracy = torch.true_divide(torch.sum(torch.max(predictions, axis=1).indices == data_outputs), len(data_outputs)) +print("Accuracy : ", accuracy.detach().numpy()) diff --git a/examples/TorchGA/image_classification_Dense.py b/examples/TorchGA/image_classification_Dense.py new file mode 100644 index 0000000..cedb169 --- /dev/null +++ b/examples/TorchGA/image_classification_Dense.py @@ -0,0 +1,80 @@ +import torch +import pygad.torchga +import pygad +import numpy + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Build the PyTorch model using the functional API. +input_layer = torch.nn.Linear(360, 50) +relu_layer = torch.nn.ReLU() +dense_layer = torch.nn.Linear(50, 4) +output_layer = torch.nn.Softmax(1) + +model = torch.nn.Sequential(input_layer, + relu_layer, + dense_layer, + output_layer) + +# Create an instance of the pygad.torchga.TorchGA class to build the initial population. +torch_ga = pygad.torchga.TorchGA(model=model, + num_solutions=10) + +loss_function = torch.nn.CrossEntropyLoss() + +# Data inputs +data_inputs = torch.from_numpy(numpy.load("../data/dataset_features.npy")).float() + +# Data outputs +data_outputs = torch.from_numpy(numpy.load("../data/outputs.npy")).long() +# The next 2 lines are equivelant to this Keras function to perform 1-hot encoding: tensorflow.keras.utils.to_categorical(data_outputs) +# temp_outs = numpy.zeros((data_outputs.shape[0], numpy.unique(data_outputs).size), dtype=numpy.uint8) +# temp_outs[numpy.arange(data_outputs.shape[0]), numpy.uint8(data_outputs)] = 1 + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 200 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = torch_ga.population_weights # Initial population of network weights. + +# Create an instance of the pygad.GA class +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +# Start the genetic algorithm evolution. +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) +# print("Predictions : \n", predictions) + +# Calculate the crossentropy loss of the trained model. +print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) + +# Calculate the classification accuracy for the trained model. +accuracy = torch.true_divide(torch.sum(torch.max(predictions, axis=1).indices == data_outputs), len(data_outputs)) +print("Accuracy : ", accuracy.detach().numpy()) diff --git a/examples/TorchGA/regression_example.py b/examples/TorchGA/regression_example.py new file mode 100644 index 0000000..4735e85 --- /dev/null +++ b/examples/TorchGA/regression_example.py @@ -0,0 +1,76 @@ +import torch +import pygad.torchga +import pygad + +def fitness_func(ga_instanse, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001 + + solution_fitness = 1.0 / abs_error + + return solution_fitness + +def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +# Create the PyTorch model. +input_layer = torch.nn.Linear(3, 2) +relu_layer = torch.nn.ReLU() +output_layer = torch.nn.Linear(2, 1) + +model = torch.nn.Sequential(input_layer, + relu_layer, + output_layer) +# print(model) + +# Create an instance of the pygad.torchga.TorchGA class to build the initial population. +torch_ga = pygad.torchga.TorchGA(model=model, + num_solutions=10) + +loss_function = torch.nn.L1Loss() + +# Data inputs +data_inputs = torch.tensor([[0.02, 0.1, 0.15], + [0.7, 0.6, 0.8], + [1.5, 1.2, 1.7], + [3.2, 2.9, 3.1]]) + +# Data outputs +data_outputs = torch.tensor([[0.1], + [0.6], + [1.3], + [2.5]]) + +# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class +num_generations = 250 # Number of generations. +num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. +initial_population = torch_ga.population_weights # Initial population of network weights + +ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +ga_instance.run() + +# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. +ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + +# Returning the details of the best solution. +solution, solution_fitness, solution_idx = ga_instance.best_solution() +print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) +print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) +print("Predictions : \n", predictions.detach().numpy()) + +abs_error = loss_function(predictions, data_outputs) +print("Absolute Error : ", abs_error.detach().numpy()) diff --git a/examples/example_clustering_2.py b/examples/clustering/example_clustering_2.py similarity index 97% rename from examples/example_clustering_2.py rename to examples/clustering/example_clustering_2.py index fa14bb7..877e318 100644 --- a/examples/example_clustering_2.py +++ b/examples/clustering/example_clustering_2.py @@ -1,122 +1,122 @@ -import numpy -import matplotlib.pyplot -import pygad - -cluster1_num_samples = 10 -cluster1_x1_start = 0 -cluster1_x1_end = 5 -cluster1_x2_start = 2 -cluster1_x2_end = 6 -cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start -cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start - -cluster2_num_samples = 10 -cluster2_x1_start = 10 -cluster2_x1_end = 15 -cluster2_x2_start = 8 -cluster2_x2_end = 12 -cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start -cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start - -c1 = numpy.array([cluster1_x1, cluster1_x2]).T -c2 = numpy.array([cluster2_x1, cluster2_x2]).T - -data = numpy.concatenate((c1, c2), axis=0) - -matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) -matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) -matplotlib.pyplot.title("Optimal Clustering") -matplotlib.pyplot.show() - -def euclidean_distance(X, Y): - """ - Calculate the euclidean distance between X and Y. It accepts: - :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. - :Y should be of size f. In other words, it is a single sample. - - Returns a vector of N elements with the distances between the N samples and the Y. - """ - - return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) - -def cluster_data(solution, solution_idx): - """ - Clusters the data based on the current solution. - """ - - global num_cluster, data - feature_vector_length = data.shape[1] - cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. - all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. - clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. - clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. - - for clust_idx in range(num_clusters): - # Return the current cluster center. - cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) - # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. - cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) - all_clusters_dists.append(numpy.array(cluster_center_dists)) - - cluster_centers = numpy.array(cluster_centers) - all_clusters_dists = numpy.array(all_clusters_dists) - - # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. - # In other words, the array holds the sample's cluster index. - cluster_indices = numpy.argmin(all_clusters_dists, axis=0) - for clust_idx in range(num_clusters): - clusters.append(numpy.where(cluster_indices == clust_idx)[0]) - # Calculate the sum of distances for the cluster. - if len(clusters[clust_idx]) == 0: - # In case the cluster is empty (i.e. has zero samples). - clusters_sum_dist.append(0) - else: - # When the cluster is not empty (i.e. has at least 1 sample). - clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) - # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) - - clusters_sum_dist = numpy.array(clusters_sum_dist) - - return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist - -def fitness_func(ga_instance, solution, solution_idx): - _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) - - # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. - fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) - - return fitness - -num_clusters = 2 -num_genes = num_clusters * data.shape[1] - -ga_instance = pygad.GA(num_generations=100, - sol_per_pop=10, - num_parents_mating=5, - init_range_low=-6, - init_range_high=20, - keep_parents=2, - num_genes=num_genes, - fitness_func=fitness_func, - suppress_warnings=True) - -ga_instance.run() - -best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() -print("Best solution is {bs}".format(bs=best_solution)) -print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) -print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) - -cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) - -for cluster_idx in range(num_clusters): - cluster_x = data[clusters[cluster_idx], 0] - cluster_y = data[clusters[cluster_idx], 1] - matplotlib.pyplot.scatter(cluster_x, cluster_y) - matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) -matplotlib.pyplot.title("Clustering using PyGAD") -matplotlib.pyplot.show() +import numpy +import matplotlib.pyplot +import pygad + +cluster1_num_samples = 10 +cluster1_x1_start = 0 +cluster1_x1_end = 5 +cluster1_x2_start = 2 +cluster1_x2_end = 6 +cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start +cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start + +cluster2_num_samples = 10 +cluster2_x1_start = 10 +cluster2_x1_end = 15 +cluster2_x2_start = 8 +cluster2_x2_end = 12 +cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start +cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start + +c1 = numpy.array([cluster1_x1, cluster1_x2]).T +c2 = numpy.array([cluster2_x1, cluster2_x2]).T + +data = numpy.concatenate((c1, c2), axis=0) + +matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) +matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) +matplotlib.pyplot.title("Optimal Clustering") +matplotlib.pyplot.show() + +def euclidean_distance(X, Y): + """ + Calculate the euclidean distance between X and Y. It accepts: + :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. + :Y should be of size f. In other words, it is a single sample. + + Returns a vector of N elements with the distances between the N samples and the Y. + """ + + return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) + +def cluster_data(solution, solution_idx): + """ + Clusters the data based on the current solution. + """ + + global num_cluster, data + feature_vector_length = data.shape[1] + cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. + all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. + clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. + clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. + + for clust_idx in range(num_clusters): + # Return the current cluster center. + cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) + # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. + cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) + all_clusters_dists.append(numpy.array(cluster_center_dists)) + + cluster_centers = numpy.array(cluster_centers) + all_clusters_dists = numpy.array(all_clusters_dists) + + # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. + # In other words, the array holds the sample's cluster index. + cluster_indices = numpy.argmin(all_clusters_dists, axis=0) + for clust_idx in range(num_clusters): + clusters.append(numpy.where(cluster_indices == clust_idx)[0]) + # Calculate the sum of distances for the cluster. + if len(clusters[clust_idx]) == 0: + # In case the cluster is empty (i.e. has zero samples). + clusters_sum_dist.append(0) + else: + # When the cluster is not empty (i.e. has at least 1 sample). + clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) + # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) + + clusters_sum_dist = numpy.array(clusters_sum_dist) + + return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist + +def fitness_func(ga_instance, solution, solution_idx): + _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) + + # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. + fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) + + return fitness + +num_clusters = 2 +num_genes = num_clusters * data.shape[1] + +ga_instance = pygad.GA(num_generations=100, + sol_per_pop=10, + num_parents_mating=5, + init_range_low=-6, + init_range_high=20, + keep_parents=2, + num_genes=num_genes, + fitness_func=fitness_func, + suppress_warnings=True) + +ga_instance.run() + +best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() +print("Best solution is {bs}".format(bs=best_solution)) +print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) +print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) + +cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) + +for cluster_idx in range(num_clusters): + cluster_x = data[clusters[cluster_idx], 0] + cluster_y = data[clusters[cluster_idx], 1] + matplotlib.pyplot.scatter(cluster_x, cluster_y) + matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) +matplotlib.pyplot.title("Clustering using PyGAD") +matplotlib.pyplot.show() diff --git a/examples/example_clustering_3.py b/examples/clustering/example_clustering_3.py similarity index 97% rename from examples/example_clustering_3.py rename to examples/clustering/example_clustering_3.py index 08e3dd7..608d54b 100644 --- a/examples/example_clustering_3.py +++ b/examples/clustering/example_clustering_3.py @@ -1,134 +1,134 @@ -import numpy -import matplotlib.pyplot -import pygad - -cluster1_num_samples = 20 -cluster1_x1_start = 0 -cluster1_x1_end = 5 -cluster1_x2_start = 2 -cluster1_x2_end = 6 -cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start -cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) -cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start - -cluster2_num_samples = 20 -cluster2_x1_start = 4 -cluster2_x1_end = 12 -cluster2_x2_start = 14 -cluster2_x2_end = 18 -cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start -cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) -cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start - -cluster3_num_samples = 20 -cluster3_x1_start = 12 -cluster3_x1_end = 18 -cluster3_x2_start = 8 -cluster3_x2_end = 11 -cluster3_x1 = numpy.random.random(size=(cluster3_num_samples)) -cluster3_x1 = cluster3_x1 * (cluster3_x1_end - cluster3_x1_start) + cluster3_x1_start -cluster3_x2 = numpy.random.random(size=(cluster3_num_samples)) -cluster3_x2 = cluster3_x2 * (cluster3_x2_end - cluster3_x2_start) + cluster3_x2_start - -c1 = numpy.array([cluster1_x1, cluster1_x2]).T -c2 = numpy.array([cluster2_x1, cluster2_x2]).T -c3 = numpy.array([cluster3_x1, cluster3_x2]).T - -data = numpy.concatenate((c1, c2, c3), axis=0) - -matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) -matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) -matplotlib.pyplot.scatter(cluster3_x1, cluster3_x2) -matplotlib.pyplot.title("Optimal Clustering") -matplotlib.pyplot.show() - -def euclidean_distance(X, Y): - """ - Calculate the euclidean distance between X and Y. It accepts: - :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. - :Y should be of size f. In other words, it is a single sample. - - Returns a vector of N elements with the distances between the N samples and the Y. - """ - - return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) - -def cluster_data(solution, solution_idx): - """ - Clusters the data based on the current solution. - """ - - global num_clusters, feature_vector_length, data - cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. - all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. - clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. - clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. - - for clust_idx in range(num_clusters): - # Return the current cluster center. - cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) - # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. - cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) - all_clusters_dists.append(numpy.array(cluster_center_dists)) - - cluster_centers = numpy.array(cluster_centers) - all_clusters_dists = numpy.array(all_clusters_dists) - - # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. - # In other words, the array holds the sample's cluster index. - cluster_indices = numpy.argmin(all_clusters_dists, axis=0) - for clust_idx in range(num_clusters): - clusters.append(numpy.where(cluster_indices == clust_idx)[0]) - # Calculate the sum of distances for the cluster. - if len(clusters[clust_idx]) == 0: - # In case the cluster is empty (i.e. has zero samples). - clusters_sum_dist.append(0) - else: - # When the cluster is not empty (i.e. has at least 1 sample). - clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) - # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) - - clusters_sum_dist = numpy.array(clusters_sum_dist) - - return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist - -def fitness_func(ga_instance, solution, solution_idx): - _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) - - # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. - fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) - - return fitness - -num_clusters = 3 -feature_vector_length = data.shape[1] -num_genes = num_clusters * feature_vector_length - -ga_instance = pygad.GA(num_generations=100, - sol_per_pop=10, - init_range_low=0, - init_range_high=20, - num_parents_mating=5, - keep_parents=2, - num_genes=num_genes, - fitness_func=fitness_func, - suppress_warnings=True) - -ga_instance.run() - -best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() -print("Best solution is {bs}".format(bs=best_solution)) -print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) -print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) - -cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) - -for cluster_idx in range(num_clusters): - cluster_x = data[clusters[cluster_idx], 0] - cluster_y = data[clusters[cluster_idx], 1] - matplotlib.pyplot.scatter(cluster_x, cluster_y) - matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) -matplotlib.pyplot.title("Clustering using PyGAD") -matplotlib.pyplot.show() +import numpy +import matplotlib.pyplot +import pygad + +cluster1_num_samples = 20 +cluster1_x1_start = 0 +cluster1_x1_end = 5 +cluster1_x2_start = 2 +cluster1_x2_end = 6 +cluster1_x1 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x1 = cluster1_x1 * (cluster1_x1_end - cluster1_x1_start) + cluster1_x1_start +cluster1_x2 = numpy.random.random(size=(cluster1_num_samples)) +cluster1_x2 = cluster1_x2 * (cluster1_x2_end - cluster1_x2_start) + cluster1_x2_start + +cluster2_num_samples = 20 +cluster2_x1_start = 4 +cluster2_x1_end = 12 +cluster2_x2_start = 14 +cluster2_x2_end = 18 +cluster2_x1 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x1 = cluster2_x1 * (cluster2_x1_end - cluster2_x1_start) + cluster2_x1_start +cluster2_x2 = numpy.random.random(size=(cluster2_num_samples)) +cluster2_x2 = cluster2_x2 * (cluster2_x2_end - cluster2_x2_start) + cluster2_x2_start + +cluster3_num_samples = 20 +cluster3_x1_start = 12 +cluster3_x1_end = 18 +cluster3_x2_start = 8 +cluster3_x2_end = 11 +cluster3_x1 = numpy.random.random(size=(cluster3_num_samples)) +cluster3_x1 = cluster3_x1 * (cluster3_x1_end - cluster3_x1_start) + cluster3_x1_start +cluster3_x2 = numpy.random.random(size=(cluster3_num_samples)) +cluster3_x2 = cluster3_x2 * (cluster3_x2_end - cluster3_x2_start) + cluster3_x2_start + +c1 = numpy.array([cluster1_x1, cluster1_x2]).T +c2 = numpy.array([cluster2_x1, cluster2_x2]).T +c3 = numpy.array([cluster3_x1, cluster3_x2]).T + +data = numpy.concatenate((c1, c2, c3), axis=0) + +matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2) +matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2) +matplotlib.pyplot.scatter(cluster3_x1, cluster3_x2) +matplotlib.pyplot.title("Optimal Clustering") +matplotlib.pyplot.show() + +def euclidean_distance(X, Y): + """ + Calculate the euclidean distance between X and Y. It accepts: + :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample. + :Y should be of size f. In other words, it is a single sample. + + Returns a vector of N elements with the distances between the N samples and the Y. + """ + + return numpy.sqrt(numpy.sum(numpy.power(X - Y, 2), axis=1)) + +def cluster_data(solution, solution_idx): + """ + Clusters the data based on the current solution. + """ + + global num_clusters, feature_vector_length, data + cluster_centers = [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample. + all_clusters_dists = [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples. + clusters = [] # A list with C elements where each element holds the indices of the samples within a cluster. + clusters_sum_dist = [] # A list with C elements where each element represents the sum of distances of the samples with a cluster. + + for clust_idx in range(num_clusters): + # Return the current cluster center. + cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)]) + # Calculate the distance (e.g. euclidean) between the current cluster center and all samples. + cluster_center_dists = euclidean_distance(data, cluster_centers[clust_idx]) + all_clusters_dists.append(numpy.array(cluster_center_dists)) + + cluster_centers = numpy.array(cluster_centers) + all_clusters_dists = numpy.array(all_clusters_dists) + + # A 1D array that, for each sample, holds the index of the cluster with the smallest distance. + # In other words, the array holds the sample's cluster index. + cluster_indices = numpy.argmin(all_clusters_dists, axis=0) + for clust_idx in range(num_clusters): + clusters.append(numpy.where(cluster_indices == clust_idx)[0]) + # Calculate the sum of distances for the cluster. + if len(clusters[clust_idx]) == 0: + # In case the cluster is empty (i.e. has zero samples). + clusters_sum_dist.append(0) + else: + # When the cluster is not empty (i.e. has at least 1 sample). + clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]])) + # clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx]))) + + clusters_sum_dist = numpy.array(clusters_sum_dist) + + return cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist + +def fitness_func(ga_instance, solution, solution_idx): + _, _, _, _, clusters_sum_dist = cluster_data(solution, solution_idx) + + # The tiny value 0.00000001 is added to the denominator in case the average distance is 0. + fitness = 1.0 / (numpy.sum(clusters_sum_dist) + 0.00000001) + + return fitness + +num_clusters = 3 +feature_vector_length = data.shape[1] +num_genes = num_clusters * feature_vector_length + +ga_instance = pygad.GA(num_generations=100, + sol_per_pop=10, + init_range_low=0, + init_range_high=20, + num_parents_mating=5, + keep_parents=2, + num_genes=num_genes, + fitness_func=fitness_func, + suppress_warnings=True) + +ga_instance.run() + +best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution() +print("Best solution is {bs}".format(bs=best_solution)) +print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness)) +print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation)) + +cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx) + +for cluster_idx in range(num_clusters): + cluster_x = data[clusters[cluster_idx], 0] + cluster_y = data[clusters[cluster_idx], 1] + matplotlib.pyplot.scatter(cluster_x, cluster_y) + matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5) +matplotlib.pyplot.title("Clustering using PyGAD") +matplotlib.pyplot.show() diff --git a/examples/data/Skin_Cancer_Dataset/benign/1.jpg b/examples/data/Skin_Cancer_Dataset/benign/1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..60cc36ea0c1e2bb7df7d29f56c3a81df516b0da2 GIT binary patch literal 48625 zcmbq)^-mp4(C)##xNC8DcXuf6R$LG64u#_GP@uTW!5xZ2ahHR;yUXqS-J9G$;G6wn zvzg43Y&O}QndjNh<y`&!A!ctzqE+@Cbi>ljK>+>-`2RygLP0~o z0AS(Z|9J(_Aprju7ZMr<8VUdb0|ANt6$*n5Af^gU{vDIuIXDqU{C6LPTK&uwESBju zhs$*crG$D?!RIOf8R}m!IutrU1n}PaLV65b89RTkvDkWOqsR_jdqy9{g$rhM6=T7` zH%;Tifg(zr!mLV^uew$sq)cppRSV!ySJP|H$};0^cTK9;UmxbVI5*C?e`l$!KHkN4 z8+{e6=AC0IfUY?03Kw88%tbBn6~O>tHeIosDeE^o;YVJ<1ms3sn9Xf^ z$jG~d5q-yW)ol?>T`S+MCia)Th|9zqUL(%37*A((9EtjfS8V+Re0vDfa9;H_8@be^ z0dDId7M{QJ-T;2P8^?7Om-0umly~8>JFCftdu=!HJfArD%eKehNzR6wsYJG~^EAJX zwf6ihC_MiKLQz=mFvTa7d9Mu`0gU($$D4#OW>H`i?pEx!4jpfL`du5IY#?iKI7)m1 z>t?sA+E3S2qH{tp&~*=_ijxDFZ!wkDHMv7{>_kv0rG`Hdf?|C%2B_viEFSK zpwsjWRS;7o0Ms-9C6%JAC+9q$FI9e1R!pMatFS>EwJXj)GLw&X0{Q1H=zC;H9rdm5JW4PFn&MV}<*uE;1p{0RILKG)l! 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index 0000000000000000000000000000000000000000..3523b07b18e2056a6c84570ea79cf9d5a215d217 GIT binary patch literal 15824 zcmeIyF=_%q7(n4oCppD*qp*!BL?vJ Date: Mon, 19 Jun 2023 16:39:29 -0400 Subject: [PATCH 069/220] Reformat documentation --- ...ADME_pygad_cnn_ReadTheDocs.rst => cnn.rst} | 1496 +-- ..._pygad_gacnn_ReadTheDocs.rst => gacnn.rst} | 1324 +-- ...ME_pygad_gann_ReadTheDocs.rst => gann.rst} | 2524 ++--- docs/source/index.rst | 16 +- ...ad_kerasga_ReadTheDocs.rst => kerasga.rst} | 1942 ++-- ...README_pygad_nn_ReadTheDocs.rst => nn.rst} | 1952 ++-- ...README_pygad_ReadTheDocs.rst => pygad.rst} | 9252 ++++++++--------- docs/source/{Footer.rst => releases.rst} | 4092 ++++---- ...ad_torchga_ReadTheDocs.rst => torchga.rst} | 1892 ++-- examples/KerasGA/XOR_classification.py | 4 +- examples/KerasGA/image_classification_CNN.py | 4 +- .../KerasGA/image_classification_Dense.py | 4 +- examples/KerasGA/regression_example.py | 4 +- examples/TorchGA/XOR_classification.py | 4 +- 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a/docs/source/README_pygad_cnn_ReadTheDocs.rst +++ b/docs/source/cnn.rst @@ -1,748 +1,748 @@ -.. _pygadcnn-module: - -``pygad.cnn`` Module -==================== - -This section of the PyGAD's library documentation discusses the -**pygad.cnn** module. - -Using the **pygad.cnn** module, convolutional neural networks (CNNs) are -created. The purpose of this module is to only implement the **forward -pass** of a convolutional neural network without using a training -algorithm. The **pygad.cnn** module builds the network layers, -implements the activations functions, trains the network, makes -predictions, and more. - -Later, the **pygad.gacnn** module is used to train the **pygad.cnn** -network using the genetic algorithm built in the **pygad** module. - -Supported Layers -================ - -Each layer supported by the **pygad.cnn** module has a corresponding -class. The layers and their classes are: - -1. **Input**: Implemented using the ``pygad.cnn.Input2D`` class. - -2. **Convolution**: Implemented using the ``pygad.cnn.Conv2D`` class. - -3. **Max Pooling**: Implemented using the ``pygad.cnn.MaxPooling2D`` - class. - -4. **Average Pooling**: Implemented using the - ``pygad.cnn.AveragePooling2D`` class. - -5. **Flatten**: Implemented using the ``pygad.cnn.Flatten`` class. - -6. **ReLU**: Implemented using the ``pygad.cnn.ReLU`` class. - -7. **Sigmoid**: Implemented using the ``pygad.cnn.Sigmoid`` class. - -8. **Dense** (Fully Connected): Implemented using the - ``pygad.cnn.Dense`` class. - -In the future, more layers will be added. - -Except for the input layer, all of listed layers has 4 instance -attributes that do the same function which are: - -1. ``previous_layer``: A reference to the previous layer in the CNN - architecture. - -2. ``layer_input_size``: The size of the input to the layer. - -3. ``layer_output_size``: The size of the output from the layer. - -4. ``layer_output``: The latest output generated from the layer. It - default to ``None``. - -In addition to such attributes, the layers may have some additional -attributes. The next subsections discuss such layers. - -.. _pygadcnninput2d-class: - -``pygad.cnn.Input2D`` Class ---------------------------- - -The ``pygad.cnn.Input2D`` class creates the input layer for the -convolutional neural network. For each network, there is only a single -input layer. The network architecture must start with an input layer. - -This class has no methods or class attributes. All it has is a -constructor that accepts a parameter named ``input_shape`` representing -the shape of the input. - -The instances from the ``Input2D`` class has the following attributes: - -1. ``input_shape``: The shape of the input to the pygad.cnn. - -2. ``layer_output_size`` - -Here is an example of building an input layer with shape -``(50, 50, 3)``. - -.. code:: python - - input_layer = pygad.cnn.Input2D(input_shape=(50, 50, 3)) - -Here is how to access the attributes within the instance of the -``pygad.cnn.Input2D`` class. - -.. code:: python - - input_shape = input_layer.input_shape - layer_output_size = input_layer.layer_output_size - - print("Input2D Input shape =", input_shape) - print("Input2D Output shape =", layer_output_size) - -This is everything about the input layer. - -.. _pygadcnnconv2d-class: - -``pygad.cnn.Conv2D`` Class --------------------------- - -Using the ``pygad.cnn.Conv2D`` class, convolution (conv) layers can be -created. To create a convolution layer, just create a new instance of -the class. The constructor accepts the following parameters: - -- ``num_filters``: Number of filters. - -- ``kernel_size``: Filter kernel size. - -- ``previous_layer``: A reference to the previous layer. Using the - ``previous_layer`` attribute, a linked list is created that connects - all network layers. For more information about this attribute, please - check the **previous_layer** attribute section of the ``pygad.nn`` - module documentation. - -- ``activation_function=None``: A string representing the activation - function to be used in this layer. Defaults to ``None`` which means - no activation function is applied while applying the convolution - layer. An activation layer can be added separately in this case. The - supported activation functions in the conv layer are ``relu`` and - ``sigmoid``. - -Within the constructor, the accepted parameters are used as instance -attributes. Besides the parameters, some new instance attributes are -created which are: - -- ``filter_bank_size``: Size of the filter bank in this layer. - -- ``initial_weights``: The initial weights for the conv layer. - -- ``trained_weights``: The trained weights of the conv layer. This - attribute is initialized by the value in the ``initial_weights`` - attribute. - -- ``layer_input_size`` - -- ``layer_output_size`` - -- ``layer_output`` - -Here is an example for creating a conv layer with 2 filters and a kernel -size of 3. Note that the ``previous_layer`` parameter is assigned to the -input layer ``input_layer``. - -.. code:: python - - conv_layer = pygad.cnn.Conv2D(num_filters=2, - kernel_size=3, - previous_layer=input_layer, - activation_function=None) - -Here is how to access some attributes in the dense layer: - -.. code:: python - - filter_bank_size = conv_layer.filter_bank_size - conv_initail_weights = conv_layer.initial_weights - - print("Filter bank size attributes =", filter_bank_size) - print("Initial weights of the conv layer :", conv_initail_weights) - -Because ``conv_layer`` holds a reference to the input layer, then the -number of input neurons can be accessed. - -.. code:: python - - input_layer = conv_layer.previous_layer - input_shape = input_layer.num_neurons - - print("Input shape =", input_shape) - -Here is another conv layer where its ``previous_layer`` attribute points -to the previously created conv layer and it uses the ``ReLU`` activation -function. - -.. code:: python - - conv_layer2 = pygad.cnn.Conv2D(num_filters=2, - kernel_size=3, - previous_layer=conv_layer, - activation_function="relu") - -Because ``conv_layer2`` holds a reference to ``conv_layer`` in its -``previous_layer`` attribute, then the attributes in ``conv_layer`` can -be accessed. - -.. code:: python - - conv_layer = conv_layer2.previous_layer - filter_bank_size = conv_layer.filter_bank_size - - print("Filter bank size attributes =", filter_bank_size) - -After getting the reference to ``conv_layer``, we can use it to access -the number of input neurons. - -.. code:: python - - conv_layer = conv_layer2.previous_layer - input_layer = conv_layer.previous_layer - input_shape = input_layer.num_neurons - - print("Input shape =", input_shape) - -.. _pygadcnnmaxpooling2d-class: - -``pygad.cnn.MaxPooling2D`` Class --------------------------------- - -The ``pygad.cnn.MaxPooling2D`` class builds a max pooling layer for the -CNN architecture. The constructor of this class accepts the following -parameter: - -- ``pool_size``: Size of the window. - -- ``previous_layer``: A reference to the previous layer in the CNN - architecture. - -- ``stride=2``: A stride that default to 2. - -Within the constructor, the accepted parameters are used as instance -attributes. Besides the parameters, some new instance attributes are -created which are: - -- ``layer_input_size`` - -- ``layer_output_size`` - -- ``layer_output`` - -.. _pygadcnnaveragepooling2d-class: - -``pygad.cnn.AveragePooling2D`` Class ------------------------------------- - -The ``pygad.cnn.AveragePooling2D`` class is similar to the -``pygad.cnn.MaxPooling2D`` class except that it applies the max pooling -operation rather than average pooling. - -.. _pygadcnnflatten-class: - -``pygad.cnn.Flatten`` Class ---------------------------- - -The ``pygad.cnn.Flatten`` class implements the flatten layer which -converts the output of the previous layer into a 1D vector. The -constructor accepts only the ``previous_layer`` parameter. - -The following instance attributes exist: - -- ``previous_layer`` - -- ``layer_input_size`` - -- ``layer_output_size`` - -- ``layer_output`` - -.. _pygadcnnrelu-class: - -``pygad.cnn.ReLU`` Class ------------------------- - -The ``pygad.cnn.ReLU`` class implements the ReLU layer which applies the -ReLU activation function to the output of the previous layer. - -The constructor accepts only the ``previous_layer`` parameter. - -The following instance attributes exist: - -- ``previous_layer`` - -- ``layer_input_size`` - -- ``layer_output_size`` - -- ``layer_output`` - -.. _pygadcnnsigmoid-class: - -``pygad.cnn.Sigmoid`` Class ---------------------------- - -The ``pygad.cnn.Sigmoid`` class is similar to the ``pygad.cnn.ReLU`` -class except that it applies the sigmoid function rather than the ReLU -function. - -.. _pygadcnndense-class: - -``pygad.cnn.Dense`` Class -------------------------- - -The ``pygad.cnn.Dense`` class implement the dense layer. Its constructor -accepts the following parameters: - -- ``num_neurons``: Number of neurons in the dense layer. - -- ``previous_layer``: A reference to the previous layer. - -- ``activation_function``: A string representing the activation - function to be used in this layer. Defaults to ``"sigmoid"``. - Currently, the supported activation functions in the dense layer are - ``"sigmoid"``, ``"relu"``, and ``softmax``. - -Within the constructor, the accepted parameters are used as instance -attributes. Besides the parameters, some new instance attributes are -created which are: - -- ``initial_weights``: The initial weights for the dense layer. - -- ``trained_weights``: The trained weights of the dense layer. This - attribute is initialized by the value in the ``initial_weights`` - attribute. - -- ``layer_input_size`` - -- ``layer_output_size`` - -- ``layer_output`` - -.. _pygadcnnmodel-class: - -``pygad.cnn.Model`` Class -========================= - -An instance of the ``pygad.cnn.Model`` class represents a CNN model. The -constructor of this class accepts the following parameters: - -- ``last_layer``: A reference to the last layer in the CNN architecture - (i.e. dense layer). - -- ``epochs=10``: Number of epochs. - -- ``learning_rate=0.01``: Learning rate. - -Within the constructor, the accepted parameters are used as instance -attributes. Besides the parameters, a new instance attribute named -``network_layers`` is created which holds a list with references to the -CNN layers. Such a list is returned using the ``get_layers()`` method in -the ``pygad.cnn.Model`` class. - -There are a number of methods in the ``pygad.cnn.Model`` class which -serves in training, testing, and retrieving information about the model. -These methods are discussed in the next subsections. - -.. _getlayers: - -``get_layers()`` ----------------- - -Creates a list of all layers in the CNN model. It accepts no parameters. - -``train()`` ------------ - -Trains the CNN model. - -Accepts the following parameters: - -- ``train_inputs``: Training data inputs. - -- ``train_outputs``: Training data outputs. - -This method trains the CNN model according to the number of epochs -specified in the constructor of the ``pygad.cnn.Model`` class. - -It is important to note that no learning algorithm is used for training -the pygad.cnn. Just the learning rate is used for making some changes -which is better than leaving the weights unchanged. - -.. _feedsample: - -``feed_sample()`` ------------------ - -Feeds a sample in the CNN layers and returns results of the last layer -in the pygad.cnn. - -.. _updateweights: - -``update_weights()`` --------------------- - -Updates the CNN weights using the learning rate. It is important to note -that no learning algorithm is used for training the pygad.cnn. Just the -learning rate is used for making some changes which is better than -leaving the weights unchanged. - -``predict()`` -------------- - -Uses the trained CNN for making predictions. - -Accepts the following parameter: - -- ``data_inputs``: The inputs to predict their label. - -It returns a list holding the samples predictions. - -``summary()`` -------------- - -Prints a summary of the CNN architecture. - -Supported Activation Functions -============================== - -The supported activation functions in the convolution layer are: - -1. Sigmoid: Implemented using the ``pygad.cnn.sigmoid()`` function. - -2. Rectified Linear Unit (ReLU): Implemented using the - ``pygad.cnn.relu()`` function. - -The dense layer supports these functions besides the ``softmax`` -function implemented in the ``pygad.cnn.softmax()`` function. - -Steps to Build a Neural Network -=============================== - -This section discusses how to use the ``pygad.cnn`` module for building -a neural network. The summary of the steps are as follows: - -- Reading the Data - -- Building the CNN Architecture - -- Building Model - -- Model Summary - -- Training the CNN - -- Making Predictions - -- Calculating Some Statistics - -Reading the Data ----------------- - -Before building the network architecture, the first thing to do is to -prepare the data that will be used for training the network. - -In this example, 4 classes of the **Fruits360** dataset are used for -preparing the training data. The 4 classes are: - -1. `Apple - Braeburn `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/apple - -2. `Lemon - Meyer `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/lemon - -3. `Mango `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/mango - -4. `Raspberry `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/raspberry - -Just 20 samples from each of the 4 classes are saved into a NumPy array -available in the -`dataset_inputs.npy `__ -file: -https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy - -The shape of this array is ``(80, 100, 100, 3)`` where the shape of the -single image is ``(100, 100, 3)``. - -The -`dataset_outputs.npy `__ -file -(https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy) -has the class labels for the 4 classes: - -1. `Apple - Braeburn `__: - Class label is **0** - -2. `Lemon - Meyer `__: - Class label is **1** - -3. `Mango `__: - Class label is **2** - -4. `Raspberry `__: - Class label is **3** - -Simply, download and reach the 2 files to return the NumPy arrays -according to the next 2 lines: - -.. code:: python - - train_inputs = numpy.load("dataset_inputs.npy") - train_outputs = numpy.load("dataset_outputs.npy") - -After the data is prepared, next is to create the network architecture. - -Building the Network Architecture ---------------------------------- - -The input layer is created by instantiating the ``pygad.cnn.Input2D`` -class according to the next code. A network can only have a single input -layer. - -.. code:: python - - import pygad.cnn - sample_shape = train_inputs.shape[1:] - - input_layer = pygad.cnn.Input2D(input_shape=sample_shape) - -After the input layer is created, next is to create a number of layers -layers according to the next code. Normally, the last dense layer is -regarded as the output layer. Note that the output layer has a number of -neurons equal to the number of classes in the dataset which is 4. - -.. code:: python - - conv_layer1 = pygad.cnn.Conv2D(num_filters=2, - kernel_size=3, - previous_layer=input_layer, - activation_function=None) - relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1) - average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, - previous_layer=relu_layer1, - stride=2) - - conv_layer2 = pygad.cnn.Conv2D(num_filters=3, - kernel_size=3, - previous_layer=average_pooling_layer, - activation_function=None) - relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2) - max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2, - previous_layer=relu_layer2, - stride=2) - - conv_layer3 = pygad.cnn.Conv2D(num_filters=1, - kernel_size=3, - previous_layer=max_pooling_layer, - activation_function=None) - relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3) - pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, - previous_layer=relu_layer3, - stride=2) - - flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer) - dense_layer1 = pygad.cnn.Dense(num_neurons=100, - previous_layer=flatten_layer, - activation_function="relu") - dense_layer2 = pygad.cnn.Dense(num_neurons=4, - previous_layer=dense_layer1, - activation_function="softmax") - -After the network architecture is prepared, the next step is to create a -CNN model. - -Building Model --------------- - -The CNN model is created as an instance of the ``pygad.cnn.Model`` -class. Here is an example. - -.. code:: python - - model = pygad.cnn.Model(last_layer=dense_layer2, - epochs=5, - learning_rate=0.01) - -After the model is created, a summary of the model architecture can be -printed. - -Model Summary -------------- - -The ``summary()`` method in the ``pygad.cnn.Model`` class prints a -summary of the CNN model. - -.. code:: python - - model.summary() - -.. code:: python - - ----------Network Architecture---------- - - - - - - - - - - - - - ---------------------------------------- - -Training the Network --------------------- - -After the model and the data are prepared, then the model can be trained -using the the ``pygad.cnn.train()`` method. - -.. code:: python - - model.train(train_inputs=train_inputs, - train_outputs=train_outputs) - -After training the network, the next step is to make predictions. - -Making Predictions ------------------- - -The ``pygad.cnn.predict()`` method uses the trained network for making -predictions. Here is an example. - -.. code:: python - - predictions = model.predict(data_inputs=train_inputs) - -It is not expected to have high accuracy in the predictions because no -training algorithm is used. - -Calculating Some Statistics ---------------------------- - -Based on the predictions the network made, some statistics can be -calculated such as the number of correct and wrong predictions in -addition to the classification accuracy. - -.. code:: python - - num_wrong = numpy.where(predictions != train_outputs)[0] - num_correct = train_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/train_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -It is very important to note that it is not expected that the -classification accuracy is high because no training algorithm is used. -Please check the documentation of the ``pygad.gacnn`` module for -training the CNN using the genetic algorithm. - -Examples -======== - -This section gives the complete code of some examples that build neural -networks using ``pygad.cnn``. Each subsection builds a different -network. - -Image Classification --------------------- - -This example is discussed in the **Steps to Build a Convolutional Neural -Network** section and its complete code is listed below. - -Remember to either download or create the -`dataset_features.npy `__ -and -`dataset_outputs.npy `__ -files before running this code. - -.. code:: python - - import numpy - import pygad.cnn - - """ - Convolutional neural network implementation using NumPy - A tutorial that helps to get started (Building Convolutional Neural Network using NumPy from Scratch) available in these links: - https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad - https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a - https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html - It is also translated into Chinese: http://m.aliyun.com/yunqi/articles/585741 - """ - - train_inputs = numpy.load("dataset_inputs.npy") - train_outputs = numpy.load("dataset_outputs.npy") - - sample_shape = train_inputs.shape[1:] - num_classes = 4 - - input_layer = pygad.cnn.Input2D(input_shape=sample_shape) - conv_layer1 = pygad.cnn.Conv2D(num_filters=2, - kernel_size=3, - previous_layer=input_layer, - activation_function=None) - relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1) - average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, - previous_layer=relu_layer1, - stride=2) - - conv_layer2 = pygad.cnn.Conv2D(num_filters=3, - kernel_size=3, - previous_layer=average_pooling_layer, - activation_function=None) - relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2) - max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2, - previous_layer=relu_layer2, - stride=2) - - conv_layer3 = pygad.cnn.Conv2D(num_filters=1, - kernel_size=3, - previous_layer=max_pooling_layer, - activation_function=None) - relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3) - pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, - previous_layer=relu_layer3, - stride=2) - - flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer) - dense_layer1 = pygad.cnn.Dense(num_neurons=100, - previous_layer=flatten_layer, - activation_function="relu") - dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes, - previous_layer=dense_layer1, - activation_function="softmax") - - model = pygad.cnn.Model(last_layer=dense_layer2, - epochs=1, - learning_rate=0.01) - - model.summary() - - model.train(train_inputs=train_inputs, - train_outputs=train_outputs) - - predictions = model.predict(data_inputs=train_inputs) - print(predictions) - - num_wrong = numpy.where(predictions != train_outputs)[0] - num_correct = train_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/train_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) +.. _pygadcnn-module: + +``pygad.cnn`` Module +==================== + +This section of the PyGAD's library documentation discusses the +**pygad.cnn** module. + +Using the **pygad.cnn** module, convolutional neural networks (CNNs) are +created. The purpose of this module is to only implement the **forward +pass** of a convolutional neural network without using a training +algorithm. The **pygad.cnn** module builds the network layers, +implements the activations functions, trains the network, makes +predictions, and more. + +Later, the **pygad.gacnn** module is used to train the **pygad.cnn** +network using the genetic algorithm built in the **pygad** module. + +Supported Layers +================ + +Each layer supported by the **pygad.cnn** module has a corresponding +class. The layers and their classes are: + +1. **Input**: Implemented using the ``pygad.cnn.Input2D`` class. + +2. **Convolution**: Implemented using the ``pygad.cnn.Conv2D`` class. + +3. **Max Pooling**: Implemented using the ``pygad.cnn.MaxPooling2D`` + class. + +4. **Average Pooling**: Implemented using the + ``pygad.cnn.AveragePooling2D`` class. + +5. **Flatten**: Implemented using the ``pygad.cnn.Flatten`` class. + +6. **ReLU**: Implemented using the ``pygad.cnn.ReLU`` class. + +7. **Sigmoid**: Implemented using the ``pygad.cnn.Sigmoid`` class. + +8. **Dense** (Fully Connected): Implemented using the + ``pygad.cnn.Dense`` class. + +In the future, more layers will be added. + +Except for the input layer, all of listed layers has 4 instance +attributes that do the same function which are: + +1. ``previous_layer``: A reference to the previous layer in the CNN + architecture. + +2. ``layer_input_size``: The size of the input to the layer. + +3. ``layer_output_size``: The size of the output from the layer. + +4. ``layer_output``: The latest output generated from the layer. It + default to ``None``. + +In addition to such attributes, the layers may have some additional +attributes. The next subsections discuss such layers. + +.. _pygadcnninput2d-class: + +``pygad.cnn.Input2D`` Class +--------------------------- + +The ``pygad.cnn.Input2D`` class creates the input layer for the +convolutional neural network. For each network, there is only a single +input layer. The network architecture must start with an input layer. + +This class has no methods or class attributes. All it has is a +constructor that accepts a parameter named ``input_shape`` representing +the shape of the input. + +The instances from the ``Input2D`` class has the following attributes: + +1. ``input_shape``: The shape of the input to the pygad.cnn. + +2. ``layer_output_size`` + +Here is an example of building an input layer with shape +``(50, 50, 3)``. + +.. code:: python + + input_layer = pygad.cnn.Input2D(input_shape=(50, 50, 3)) + +Here is how to access the attributes within the instance of the +``pygad.cnn.Input2D`` class. + +.. code:: python + + input_shape = input_layer.input_shape + layer_output_size = input_layer.layer_output_size + + print("Input2D Input shape =", input_shape) + print("Input2D Output shape =", layer_output_size) + +This is everything about the input layer. + +.. _pygadcnnconv2d-class: + +``pygad.cnn.Conv2D`` Class +-------------------------- + +Using the ``pygad.cnn.Conv2D`` class, convolution (conv) layers can be +created. To create a convolution layer, just create a new instance of +the class. The constructor accepts the following parameters: + +- ``num_filters``: Number of filters. + +- ``kernel_size``: Filter kernel size. + +- ``previous_layer``: A reference to the previous layer. Using the + ``previous_layer`` attribute, a linked list is created that connects + all network layers. For more information about this attribute, please + check the **previous_layer** attribute section of the ``pygad.nn`` + module documentation. + +- ``activation_function=None``: A string representing the activation + function to be used in this layer. Defaults to ``None`` which means + no activation function is applied while applying the convolution + layer. An activation layer can be added separately in this case. The + supported activation functions in the conv layer are ``relu`` and + ``sigmoid``. + +Within the constructor, the accepted parameters are used as instance +attributes. Besides the parameters, some new instance attributes are +created which are: + +- ``filter_bank_size``: Size of the filter bank in this layer. + +- ``initial_weights``: The initial weights for the conv layer. + +- ``trained_weights``: The trained weights of the conv layer. This + attribute is initialized by the value in the ``initial_weights`` + attribute. + +- ``layer_input_size`` + +- ``layer_output_size`` + +- ``layer_output`` + +Here is an example for creating a conv layer with 2 filters and a kernel +size of 3. Note that the ``previous_layer`` parameter is assigned to the +input layer ``input_layer``. + +.. code:: python + + conv_layer = pygad.cnn.Conv2D(num_filters=2, + kernel_size=3, + previous_layer=input_layer, + activation_function=None) + +Here is how to access some attributes in the dense layer: + +.. code:: python + + filter_bank_size = conv_layer.filter_bank_size + conv_initail_weights = conv_layer.initial_weights + + print("Filter bank size attributes =", filter_bank_size) + print("Initial weights of the conv layer :", conv_initail_weights) + +Because ``conv_layer`` holds a reference to the input layer, then the +number of input neurons can be accessed. + +.. code:: python + + input_layer = conv_layer.previous_layer + input_shape = input_layer.num_neurons + + print("Input shape =", input_shape) + +Here is another conv layer where its ``previous_layer`` attribute points +to the previously created conv layer and it uses the ``ReLU`` activation +function. + +.. code:: python + + conv_layer2 = pygad.cnn.Conv2D(num_filters=2, + kernel_size=3, + previous_layer=conv_layer, + activation_function="relu") + +Because ``conv_layer2`` holds a reference to ``conv_layer`` in its +``previous_layer`` attribute, then the attributes in ``conv_layer`` can +be accessed. + +.. code:: python + + conv_layer = conv_layer2.previous_layer + filter_bank_size = conv_layer.filter_bank_size + + print("Filter bank size attributes =", filter_bank_size) + +After getting the reference to ``conv_layer``, we can use it to access +the number of input neurons. + +.. code:: python + + conv_layer = conv_layer2.previous_layer + input_layer = conv_layer.previous_layer + input_shape = input_layer.num_neurons + + print("Input shape =", input_shape) + +.. _pygadcnnmaxpooling2d-class: + +``pygad.cnn.MaxPooling2D`` Class +-------------------------------- + +The ``pygad.cnn.MaxPooling2D`` class builds a max pooling layer for the +CNN architecture. The constructor of this class accepts the following +parameter: + +- ``pool_size``: Size of the window. + +- ``previous_layer``: A reference to the previous layer in the CNN + architecture. + +- ``stride=2``: A stride that default to 2. + +Within the constructor, the accepted parameters are used as instance +attributes. Besides the parameters, some new instance attributes are +created which are: + +- ``layer_input_size`` + +- ``layer_output_size`` + +- ``layer_output`` + +.. _pygadcnnaveragepooling2d-class: + +``pygad.cnn.AveragePooling2D`` Class +------------------------------------ + +The ``pygad.cnn.AveragePooling2D`` class is similar to the +``pygad.cnn.MaxPooling2D`` class except that it applies the max pooling +operation rather than average pooling. + +.. _pygadcnnflatten-class: + +``pygad.cnn.Flatten`` Class +--------------------------- + +The ``pygad.cnn.Flatten`` class implements the flatten layer which +converts the output of the previous layer into a 1D vector. The +constructor accepts only the ``previous_layer`` parameter. + +The following instance attributes exist: + +- ``previous_layer`` + +- ``layer_input_size`` + +- ``layer_output_size`` + +- ``layer_output`` + +.. _pygadcnnrelu-class: + +``pygad.cnn.ReLU`` Class +------------------------ + +The ``pygad.cnn.ReLU`` class implements the ReLU layer which applies the +ReLU activation function to the output of the previous layer. + +The constructor accepts only the ``previous_layer`` parameter. + +The following instance attributes exist: + +- ``previous_layer`` + +- ``layer_input_size`` + +- ``layer_output_size`` + +- ``layer_output`` + +.. _pygadcnnsigmoid-class: + +``pygad.cnn.Sigmoid`` Class +--------------------------- + +The ``pygad.cnn.Sigmoid`` class is similar to the ``pygad.cnn.ReLU`` +class except that it applies the sigmoid function rather than the ReLU +function. + +.. _pygadcnndense-class: + +``pygad.cnn.Dense`` Class +------------------------- + +The ``pygad.cnn.Dense`` class implement the dense layer. Its constructor +accepts the following parameters: + +- ``num_neurons``: Number of neurons in the dense layer. + +- ``previous_layer``: A reference to the previous layer. + +- ``activation_function``: A string representing the activation + function to be used in this layer. Defaults to ``"sigmoid"``. + Currently, the supported activation functions in the dense layer are + ``"sigmoid"``, ``"relu"``, and ``softmax``. + +Within the constructor, the accepted parameters are used as instance +attributes. Besides the parameters, some new instance attributes are +created which are: + +- ``initial_weights``: The initial weights for the dense layer. + +- ``trained_weights``: The trained weights of the dense layer. This + attribute is initialized by the value in the ``initial_weights`` + attribute. + +- ``layer_input_size`` + +- ``layer_output_size`` + +- ``layer_output`` + +.. _pygadcnnmodel-class: + +``pygad.cnn.Model`` Class +========================= + +An instance of the ``pygad.cnn.Model`` class represents a CNN model. The +constructor of this class accepts the following parameters: + +- ``last_layer``: A reference to the last layer in the CNN architecture + (i.e. dense layer). + +- ``epochs=10``: Number of epochs. + +- ``learning_rate=0.01``: Learning rate. + +Within the constructor, the accepted parameters are used as instance +attributes. Besides the parameters, a new instance attribute named +``network_layers`` is created which holds a list with references to the +CNN layers. Such a list is returned using the ``get_layers()`` method in +the ``pygad.cnn.Model`` class. + +There are a number of methods in the ``pygad.cnn.Model`` class which +serves in training, testing, and retrieving information about the model. +These methods are discussed in the next subsections. + +.. _getlayers: + +``get_layers()`` +---------------- + +Creates a list of all layers in the CNN model. It accepts no parameters. + +``train()`` +----------- + +Trains the CNN model. + +Accepts the following parameters: + +- ``train_inputs``: Training data inputs. + +- ``train_outputs``: Training data outputs. + +This method trains the CNN model according to the number of epochs +specified in the constructor of the ``pygad.cnn.Model`` class. + +It is important to note that no learning algorithm is used for training +the pygad.cnn. Just the learning rate is used for making some changes +which is better than leaving the weights unchanged. + +.. _feedsample: + +``feed_sample()`` +----------------- + +Feeds a sample in the CNN layers and returns results of the last layer +in the pygad.cnn. + +.. _updateweights: + +``update_weights()`` +-------------------- + +Updates the CNN weights using the learning rate. It is important to note +that no learning algorithm is used for training the pygad.cnn. Just the +learning rate is used for making some changes which is better than +leaving the weights unchanged. + +``predict()`` +------------- + +Uses the trained CNN for making predictions. + +Accepts the following parameter: + +- ``data_inputs``: The inputs to predict their label. + +It returns a list holding the samples predictions. + +``summary()`` +------------- + +Prints a summary of the CNN architecture. + +Supported Activation Functions +============================== + +The supported activation functions in the convolution layer are: + +1. Sigmoid: Implemented using the ``pygad.cnn.sigmoid()`` function. + +2. Rectified Linear Unit (ReLU): Implemented using the + ``pygad.cnn.relu()`` function. + +The dense layer supports these functions besides the ``softmax`` +function implemented in the ``pygad.cnn.softmax()`` function. + +Steps to Build a Neural Network +=============================== + +This section discusses how to use the ``pygad.cnn`` module for building +a neural network. The summary of the steps are as follows: + +- Reading the Data + +- Building the CNN Architecture + +- Building Model + +- Model Summary + +- Training the CNN + +- Making Predictions + +- Calculating Some Statistics + +Reading the Data +---------------- + +Before building the network architecture, the first thing to do is to +prepare the data that will be used for training the network. + +In this example, 4 classes of the **Fruits360** dataset are used for +preparing the training data. The 4 classes are: + +1. `Apple + Braeburn `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/apple + +2. `Lemon + Meyer `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/lemon + +3. `Mango `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/mango + +4. `Raspberry `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/raspberry + +Just 20 samples from each of the 4 classes are saved into a NumPy array +available in the +`dataset_inputs.npy `__ +file: +https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy + +The shape of this array is ``(80, 100, 100, 3)`` where the shape of the +single image is ``(100, 100, 3)``. + +The +`dataset_outputs.npy `__ +file +(https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy) +has the class labels for the 4 classes: + +1. `Apple + Braeburn `__: + Class label is **0** + +2. `Lemon + Meyer `__: + Class label is **1** + +3. `Mango `__: + Class label is **2** + +4. `Raspberry `__: + Class label is **3** + +Simply, download and reach the 2 files to return the NumPy arrays +according to the next 2 lines: + +.. code:: python + + train_inputs = numpy.load("dataset_inputs.npy") + train_outputs = numpy.load("dataset_outputs.npy") + +After the data is prepared, next is to create the network architecture. + +Building the Network Architecture +--------------------------------- + +The input layer is created by instantiating the ``pygad.cnn.Input2D`` +class according to the next code. A network can only have a single input +layer. + +.. code:: python + + import pygad.cnn + sample_shape = train_inputs.shape[1:] + + input_layer = pygad.cnn.Input2D(input_shape=sample_shape) + +After the input layer is created, next is to create a number of layers +layers according to the next code. Normally, the last dense layer is +regarded as the output layer. Note that the output layer has a number of +neurons equal to the number of classes in the dataset which is 4. + +.. code:: python + + conv_layer1 = pygad.cnn.Conv2D(num_filters=2, + kernel_size=3, + previous_layer=input_layer, + activation_function=None) + relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1) + average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, + previous_layer=relu_layer1, + stride=2) + + conv_layer2 = pygad.cnn.Conv2D(num_filters=3, + kernel_size=3, + previous_layer=average_pooling_layer, + activation_function=None) + relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2) + max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2, + previous_layer=relu_layer2, + stride=2) + + conv_layer3 = pygad.cnn.Conv2D(num_filters=1, + kernel_size=3, + previous_layer=max_pooling_layer, + activation_function=None) + relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3) + pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, + previous_layer=relu_layer3, + stride=2) + + flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer) + dense_layer1 = pygad.cnn.Dense(num_neurons=100, + previous_layer=flatten_layer, + activation_function="relu") + dense_layer2 = pygad.cnn.Dense(num_neurons=4, + previous_layer=dense_layer1, + activation_function="softmax") + +After the network architecture is prepared, the next step is to create a +CNN model. + +Building Model +-------------- + +The CNN model is created as an instance of the ``pygad.cnn.Model`` +class. Here is an example. + +.. code:: python + + model = pygad.cnn.Model(last_layer=dense_layer2, + epochs=5, + learning_rate=0.01) + +After the model is created, a summary of the model architecture can be +printed. + +Model Summary +------------- + +The ``summary()`` method in the ``pygad.cnn.Model`` class prints a +summary of the CNN model. + +.. code:: python + + model.summary() + +.. code:: python + + ----------Network Architecture---------- + + + + + + + + + + + + + ---------------------------------------- + +Training the Network +-------------------- + +After the model and the data are prepared, then the model can be trained +using the the ``pygad.cnn.train()`` method. + +.. code:: python + + model.train(train_inputs=train_inputs, + train_outputs=train_outputs) + +After training the network, the next step is to make predictions. + +Making Predictions +------------------ + +The ``pygad.cnn.predict()`` method uses the trained network for making +predictions. Here is an example. + +.. code:: python + + predictions = model.predict(data_inputs=train_inputs) + +It is not expected to have high accuracy in the predictions because no +training algorithm is used. + +Calculating Some Statistics +--------------------------- + +Based on the predictions the network made, some statistics can be +calculated such as the number of correct and wrong predictions in +addition to the classification accuracy. + +.. code:: python + + num_wrong = numpy.where(predictions != train_outputs)[0] + num_correct = train_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/train_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +It is very important to note that it is not expected that the +classification accuracy is high because no training algorithm is used. +Please check the documentation of the ``pygad.gacnn`` module for +training the CNN using the genetic algorithm. + +Examples +======== + +This section gives the complete code of some examples that build neural +networks using ``pygad.cnn``. Each subsection builds a different +network. + +Image Classification +-------------------- + +This example is discussed in the **Steps to Build a Convolutional Neural +Network** section and its complete code is listed below. + +Remember to either download or create the +`dataset_features.npy `__ +and +`dataset_outputs.npy `__ +files before running this code. + +.. code:: python + + import numpy + import pygad.cnn + + """ + Convolutional neural network implementation using NumPy + A tutorial that helps to get started (Building Convolutional Neural Network using NumPy from Scratch) available in these links: + https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad + https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a + https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html + It is also translated into Chinese: http://m.aliyun.com/yunqi/articles/585741 + """ + + train_inputs = numpy.load("dataset_inputs.npy") + train_outputs = numpy.load("dataset_outputs.npy") + + sample_shape = train_inputs.shape[1:] + num_classes = 4 + + input_layer = pygad.cnn.Input2D(input_shape=sample_shape) + conv_layer1 = pygad.cnn.Conv2D(num_filters=2, + kernel_size=3, + previous_layer=input_layer, + activation_function=None) + relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1) + average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, + previous_layer=relu_layer1, + stride=2) + + conv_layer2 = pygad.cnn.Conv2D(num_filters=3, + kernel_size=3, + previous_layer=average_pooling_layer, + activation_function=None) + relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2) + max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2, + previous_layer=relu_layer2, + stride=2) + + conv_layer3 = pygad.cnn.Conv2D(num_filters=1, + kernel_size=3, + previous_layer=max_pooling_layer, + activation_function=None) + relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3) + pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, + previous_layer=relu_layer3, + stride=2) + + flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer) + dense_layer1 = pygad.cnn.Dense(num_neurons=100, + previous_layer=flatten_layer, + activation_function="relu") + dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes, + previous_layer=dense_layer1, + activation_function="softmax") + + model = pygad.cnn.Model(last_layer=dense_layer2, + epochs=1, + learning_rate=0.01) + + model.summary() + + model.train(train_inputs=train_inputs, + train_outputs=train_outputs) + + predictions = model.predict(data_inputs=train_inputs) + print(predictions) + + num_wrong = numpy.where(predictions != train_outputs)[0] + num_correct = train_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/train_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) diff --git a/docs/source/README_pygad_gacnn_ReadTheDocs.rst b/docs/source/gacnn.rst similarity index 97% rename from docs/source/README_pygad_gacnn_ReadTheDocs.rst rename to docs/source/gacnn.rst index 39bbc6e..c9b6336 100644 --- a/docs/source/README_pygad_gacnn_ReadTheDocs.rst +++ b/docs/source/gacnn.rst @@ -1,662 +1,662 @@ -.. _pygadgacnn-module: - -``pygad.gacnn`` Module -====================== - -This section of the PyGAD's library documentation discusses the -**pygad.gacnn** module. - -The ``pygad.gacnn`` module trains convolutional neural networks using -the genetic algorithm. It makes use of the 2 modules ``pygad`` and -``pygad.cnn``. - -.. _pygadgacnngacnn-class: - -``pygad.gacnn.GACNN`` Class -=========================== - -The ``pygad.gacnn`` module has a class named ``pygad.gacnn.GACNN`` for -training convolutional neural networks (CNNs) using the genetic -algorithm. The constructor, methods, function, and attributes within the -class are discussed in this section. - -.. _init: - -``__init__()`` --------------- - -In order to train a CNN using the genetic algorithm, the first thing to -do is to create an instance of the ``pygad.gacnn.GACNN`` class. - -The ``pygad.gacnn.GACNN`` class constructor accepts the following -parameters: - -- ``model``: model: An instance of the pygad.cnn.Model class - representing the architecture of all solutions in the population. - -- ``num_solutions``: Number of CNNs (i.e. solutions) in the population. - Based on the value passed to this parameter, a number of identical - CNNs are created where their parameters are optimized using the - genetic algorithm. - -Instance Attributes -------------------- - -All the parameters in the ``pygad.gacnn.GACNN`` class constructor are -used as instance attributes. Besides such attributes, there is an extra -attribute added to the instances from the ``pygad.gacnn.GACNN`` class -which is: - -- ``population_networks``: A list holding references to all the - solutions (i.e. CNNs) used in the population. - -Methods in the GACNN Class --------------------------- - -This section discusses the methods available for instances of the -``pygad.gacnn.GACNN`` class. - -.. _createpopulation: - -``create_population()`` -~~~~~~~~~~~~~~~~~~~~~~~ - -The ``create_population()`` method creates the initial population of the -genetic algorithm as a list of CNNs (i.e. solutions). All the networks -are copied from the CNN model passed to constructor of the GACNN class. - -The list of networks is assigned to the ``population_networks`` -attribute of the instance. - -.. _updatepopulationtrainedweights: - -``update_population_trained_weights()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The ``update_population_trained_weights()`` method updates the -``trained_weights`` attribute of the layers of each network (check the -documentation of the ``pygad.cnn`` module) for more information) -according to the weights passed in the ``population_trained_weights`` -parameter. - -Accepts the following parameters: - -- ``population_trained_weights``: A list holding the trained weights of - all networks as matrices. Such matrices are to be assigned to the - ``trained_weights`` attribute of all layers of all networks. - -.. _functions-in-the-pygadgacnn-module: - -Functions in the ``pygad.gacnn`` Module -======================================= - -This section discusses the functions in the ``pygad.gacnn`` module. - -.. _pygadgacnnpopulationasvectors: - -``pygad.gacnn.population_as_vectors()`` ----------------------------------------- - -Accepts the population as a list of references to the -``pygad.cnn.Model`` class and returns a list holding all weights of the -layers of each solution (i.e. network) in the population as a vector. - -For example, if the population has 6 solutions (i.e. networks), this -function accepts references to such networks and returns a list with 6 -vectors, one for each network (i.e. solution). Each vector holds the -weights for all layers for a single network. - -Accepts the following parameters: - -- ``population_networks``: A list holding references to the - ``pygad.cnn.Model`` class of the networks used in the population. - -Returns a list holding the weights vectors for all solutions (i.e. -networks). - -.. _pygadgacnnpopulationasmatrices: - -``pygad.gacnn.population_as_matrices()`` ----------------------------------------- - -Accepts the population as both networks and weights vectors and returns -the weights of all layers of each solution (i.e. network) in the -population as a matrix. - -For example, if the population has 6 solutions (i.e. networks), this -function returns a list with 6 matrices, one for each network holding -its weights for all layers. - -Accepts the following parameters: - -- ``population_networks``: A list holding references to the - ``pygad.cnn.Model`` class of the networks used in the population. - -- ``population_vectors``: A list holding the weights of all networks as - vectors. Such vectors are to be converted into matrices. - -Returns a list holding the weights matrices for all solutions (i.e. -networks). - -Steps to Build and Train CNN using Genetic Algorithm -==================================================== - -The steps to use this project for building and training a neural network -using the genetic algorithm are as follows: - -- Prepare the training data. - -- Create an instance of the ``pygad.gacnn.GACNN`` class. - -- Fetch the population weights as vectors. - -- Prepare the fitness function. - -- Prepare the generation callback function. - -- Create an instance of the ``pygad.GA`` class. - -- Run the created instance of the ``pygad.GA`` class. - -- Plot the Fitness Values - -- Information about the best solution. - -- Making predictions using the trained weights. - -- Calculating some statistics. - -Let's start covering all of these steps. - -Prepare the Training Data -------------------------- - -Before building and training neural networks, the training data (input -and output) is to be prepared. The inputs and the outputs of the -training data are NumPy arrays. - -The data used in this example is available as 2 files: - -1. `dataset_inputs.npy `__: - Data inputs. - https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy - -2. `dataset_outputs.npy `__: - Class labels. - https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy - -The data consists of 4 classes of images. The image shape is -``(100, 100, 3)`` and there are 20 images per class. For more -information about the dataset, check the **Reading the Data** section of -the ``pygad.cnn`` module. - -Simply download these 2 files and read them according to the next code. - -.. code:: python - - import numpy - - train_inputs = numpy.load("dataset_inputs.npy") - train_outputs = numpy.load("dataset_outputs.npy") - -For the output array, each element must be a single number representing -the class label of the sample. The class labels must start at ``0``. So, -if there are 80 samples, then the shape of the output array is ``(80)``. -If there are 5 classes in the data, then the values of all the 200 -elements in the output array must range from 0 to 4 inclusive. -Generally, the class labels start from ``0`` to ``N-1`` where ``N`` is -the number of classes. - -Note that the project only supports that each sample is assigned to only -one class. - -Building the Network Architecture ---------------------------------- - -Here is an example for a CNN architecture. - -.. code:: python - - import pygad.cnn - - input_layer = pygad.cnn.Input2D(input_shape=(80, 80, 3)) - conv_layer = pygad.cnn.Conv2D(num_filters=2, - kernel_size=3, - previous_layer=input_layer, - activation_function="relu") - average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=5, - previous_layer=conv_layer, - stride=3) - - flatten_layer = pygad.cnn.Flatten(previous_layer=average_pooling_layer) - dense_layer = pygad.cnn.Dense(num_neurons=4, - previous_layer=flatten_layer, - activation_function="softmax") - -After the network architecture is prepared, the next step is to create a -CNN model. - -Building Model --------------- - -The CNN model is created as an instance of the ``pygad.cnn.Model`` -class. Here is an example. - -.. code:: python - - model = pygad.cnn.Model(last_layer=dense_layer, - epochs=5, - learning_rate=0.01) - -After the model is created, a summary of the model architecture can be -printed. - -Model Summary -------------- - -The ``summary()`` method in the ``pygad.cnn.Model`` class prints a -summary of the CNN model. - -.. code:: python - - model.summary() - -.. code:: python - - ----------Network Architecture---------- - - - - - ---------------------------------------- - -The next step is to create an instance of the ``pygad.gacnn.GACNN`` -class. - -.. _create-an-instance-of-the-pygadgacnngacnn-class: - -Create an Instance of the ``pygad.gacnn.GACNN`` Class ------------------------------------------------------ - -After preparing the input data and building the CNN model, an instance -of the ``pygad.gacnn.GACNN`` class is created by passing the appropriate -parameters. - -Here is an example where the ``num_solutions`` parameter is set to 4 -which means the genetic algorithm population will have 6 solutions (i.e. -networks). All of these 6 CNNs will have the same architectures as -specified by the ``model`` parameter. - -.. code:: python - - import pygad.gacnn - - GACNN_instance = pygad.gacnn.GACNN(model=model, - num_solutions=4) - -After creating the instance of the ``pygad.gacnn.GACNN`` class, next is -to fetch the weights of the population as a list of vectors. - -Fetch the Population Weights as Vectors ---------------------------------------- - -For the genetic algorithm, the parameters (i.e. genes) of each solution -are represented as a single vector. - -For this task, the weights of each CNN must be available as a single -vector. In other words, the weights of all layers of a CNN must be -grouped into a vector. - -To create a list holding the population weights as vectors, one for each -network, the ``pygad.gacnn.population_as_vectors()`` function is used. - -.. code:: python - - population_vectors = gacnn.population_as_vectors(population_networks=GACNN_instance.population_networks) - -Such population of vectors is used as the initial population. - -.. code:: python - - initial_population = population_vectors.copy() - -After preparing the population weights as a set of vectors, next is to -prepare 2 functions which are: - -1. Fitness function. - -2. Callback function after each generation. - -Prepare the Fitness Function ----------------------------- - -The PyGAD library works by allowing the users to customize the genetic -algorithm for their own problems. Because the problems differ in how the -fitness values are calculated, then PyGAD allows the user to use a -custom function as a maximization fitness function. This function must -accept 2 positional parameters representing the following: - -- The solution. - -- The solution index in the population. - -The fitness function must return a single number representing the -fitness. The higher the fitness value, the better the solution. - -Here is the implementation of the fitness function for training a CNN. - -It uses the ``pygad.cnn.predict()`` function to predict the class labels -based on the current solution's weights. The ``pygad.cnn.predict()`` -function uses the trained weights available in the ``trained_weights`` -attribute of each layer of the network for making predictions. - -Based on such predictions, the classification accuracy is calculated. -This accuracy is used as the fitness value of the solution. Finally, the -fitness value is returned. - -.. code:: python - - def fitness_func(ga_instance, solution, sol_idx): - global GACNN_instance, data_inputs, data_outputs - - predictions = GACNN_instance.population_networks[sol_idx].predict(data_inputs=data_inputs) - correct_predictions = numpy.where(predictions == data_outputs)[0].size - solution_fitness = (correct_predictions/data_outputs.size)*100 - - return solution_fitness - -Prepare the Generation Callback Function ----------------------------------------- - -After each generation of the genetic algorithm, the fitness function -will be called to calculate the fitness value of each solution. Within -the fitness function, the ``pygad.cnn.predict()`` function is used for -predicting the outputs based on the current solution's -``trained_weights`` attribute. Thus, it is required that such an -attribute is updated by weights evolved by the genetic algorithm after -each generation. - -PyGAD has a parameter accepted by the ``pygad.GA`` class constructor -named ``on_generation``. It could be assigned to a function that is -called after each generation. The function must accept a single -parameter representing the instance of the ``pygad.GA`` class. - -This callback function can be used to update the ``trained_weights`` -attribute of layers of each network in the population. - -Here is the implementation for a function that updates the -``trained_weights`` attribute of the layers of the population networks. - -It works by converting the current population from the vector form to -the matric form using the ``pygad.gacnn.population_as_matrices()`` -function. It accepts the population as vectors and returns it as -matrices. - -The population matrices are then passed to the -``update_population_trained_weights()`` method in the ``pygad.gacnn`` -module to update the ``trained_weights`` attribute of all layers for all -solutions within the population. - -.. code:: python - - def callback_generation(ga_instance): - global GACNN_instance, last_fitness - - population_matrices = gacnn.population_as_matrices(population_networks=GACNN_instance.population_networks, population_vectors=ga_instance.population) - GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - -After preparing the fitness and callback function, next is to create an -instance of the ``pygad.GA`` class. - -.. _create-an-instance-of-the-pygadga-class: - -Create an Instance of the ``pygad.GA`` Class --------------------------------------------- - -Once the parameters of the genetic algorithm are prepared, an instance -of the ``pygad.GA`` class can be created. Here is an example where the -number of generations is 10. - -.. code:: python - - import pygad - - num_parents_mating = 4 - - num_generations = 10 - - mutation_percent_genes = 5 - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - mutation_percent_genes=mutation_percent_genes, - on_generation=callback_generation) - -The last step for training the neural networks using the genetic -algorithm is calling the ``run()`` method. - -.. _run-the-created-instance-of-the-pygadga-class: - -Run the Created Instance of the ``pygad.GA`` Class --------------------------------------------------- - -By calling the ``run()`` method from the ``pygad.GA`` instance, the -genetic algorithm will iterate through the number of generations -specified in its ``num_generations`` parameter. - -.. code:: python - - ga_instance.run() - -Plot the Fitness Values ------------------------ - -After the ``run()`` method completes, the ``plot_fitness()`` method can -be called to show how the fitness values evolve by generation. - -.. code:: python - - ga_instance.plot_fitness() - -.. figure:: https://user-images.githubusercontent.com/16560492/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png - :alt: - -Information about the Best Solution ------------------------------------ - -The following information about the best solution in the last population -is returned using the ``best_solution()`` method in the ``pygad.GA`` -class. - -- Solution - -- Fitness value of the solution - -- Index of the solution within the population - -Here is how such information is returned. - -.. code:: python - - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - -.. code:: - - ... - Fitness value of the best solution = 83.75 - Index of the best solution : 0 - Best fitness value reached after 4 generations. - -Making Predictions using the Trained Weights --------------------------------------------- - -The ``pygad.cnn.predict()`` function can be used to make predictions -using the trained network. As printed, the network is able to predict -the labels correctly. - -.. code:: python - - predictions = pygad.cnn.predict(last_layer=GANN_instance.population_networks[solution_idx], data_inputs=data_inputs) - print("Predictions of the trained network : {predictions}".format(predictions=predictions)) - -Calculating Some Statistics ---------------------------- - -Based on the predictions the network made, some statistics can be -calculated such as the number of correct and wrong predictions in -addition to the classification accuracy. - -.. code:: python - - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -.. code:: - - Number of correct classifications : 67. - Number of wrong classifications : 13. - Classification accuracy : 83.75. - -Examples -======== - -This section gives the complete code of some examples that build and -train neural networks using the genetic algorithm. Each subsection -builds a different network. - -Image Classification --------------------- - -This example is discussed in the **Steps to Build and Train CNN using -Genetic Algorithm** section that builds the an image classifier. Its -complete code is listed below. - -.. code:: python - - import numpy - import pygad.cnn - import pygad.gacnn - import pygad - - """ - Convolutional neural network implementation using NumPy - A tutorial that helps to get started (Building Convolutional Neural Network using NumPy from Scratch) available in these links: - https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad - https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a - https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html - It is also translated into Chinese: http://m.aliyun.com/yunqi/articles/585741 - """ - - def fitness_func(ga_instance, solution, sol_idx): - global GACNN_instance, data_inputs, data_outputs - - predictions = GACNN_instance.population_networks[sol_idx].predict(data_inputs=data_inputs) - correct_predictions = numpy.where(predictions == data_outputs)[0].size - solution_fitness = (correct_predictions/data_outputs.size)*100 - - return solution_fitness - - def callback_generation(ga_instance): - global GACNN_instance, last_fitness - - population_matrices = pygad.gacnn.population_as_matrices(population_networks=GACNN_instance.population_networks, - population_vectors=ga_instance.population) - - GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solutions_fitness)) - - data_inputs = numpy.load("dataset_inputs.npy") - data_outputs = numpy.load("dataset_outputs.npy") - - sample_shape = data_inputs.shape[1:] - num_classes = 4 - - data_inputs = data_inputs - data_outputs = data_outputs - - input_layer = pygad.cnn.Input2D(input_shape=sample_shape) - conv_layer1 = pygad.cnn.Conv2D(num_filters=2, - kernel_size=3, - previous_layer=input_layer, - activation_function="relu") - average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=5, - previous_layer=conv_layer1, - stride=3) - - flatten_layer = pygad.cnn.Flatten(previous_layer=average_pooling_layer) - dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes, - previous_layer=flatten_layer, - activation_function="softmax") - - model = pygad.cnn.Model(last_layer=dense_layer2, - epochs=1, - learning_rate=0.01) - - model.summary() - - - GACNN_instance = pygad.gacnn.GACNN(model=model, - num_solutions=4) - - # GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. - # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. - population_vectors = pygad.gacnn.population_as_vectors(population_networks=GACNN_instance.population_networks) - - # To prepare the initial population, there are 2 ways: - # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. - # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. - initial_population = population_vectors.copy() - - num_parents_mating = 2 # Number of solutions to be selected as parents in the mating pool. - - num_generations = 10 # Number of generations. - - mutation_percent_genes = 0.1 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - mutation_percent_genes=mutation_percent_genes, - on_generation=callback_generation) - - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness() - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - - # Predicting the outputs of the data using the best solution. - predictions = GACNN_instance.population_networks[solution_idx].predict(data_inputs=data_inputs) - print("Predictions of the trained network : {predictions}".format(predictions=predictions)) - - # Calculating some statistics - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) +.. _pygadgacnn-module: + +``pygad.gacnn`` Module +====================== + +This section of the PyGAD's library documentation discusses the +**pygad.gacnn** module. + +The ``pygad.gacnn`` module trains convolutional neural networks using +the genetic algorithm. It makes use of the 2 modules ``pygad`` and +``pygad.cnn``. + +.. _pygadgacnngacnn-class: + +``pygad.gacnn.GACNN`` Class +=========================== + +The ``pygad.gacnn`` module has a class named ``pygad.gacnn.GACNN`` for +training convolutional neural networks (CNNs) using the genetic +algorithm. The constructor, methods, function, and attributes within the +class are discussed in this section. + +.. _init: + +``__init__()`` +-------------- + +In order to train a CNN using the genetic algorithm, the first thing to +do is to create an instance of the ``pygad.gacnn.GACNN`` class. + +The ``pygad.gacnn.GACNN`` class constructor accepts the following +parameters: + +- ``model``: model: An instance of the pygad.cnn.Model class + representing the architecture of all solutions in the population. + +- ``num_solutions``: Number of CNNs (i.e. solutions) in the population. + Based on the value passed to this parameter, a number of identical + CNNs are created where their parameters are optimized using the + genetic algorithm. + +Instance Attributes +------------------- + +All the parameters in the ``pygad.gacnn.GACNN`` class constructor are +used as instance attributes. Besides such attributes, there is an extra +attribute added to the instances from the ``pygad.gacnn.GACNN`` class +which is: + +- ``population_networks``: A list holding references to all the + solutions (i.e. CNNs) used in the population. + +Methods in the GACNN Class +-------------------------- + +This section discusses the methods available for instances of the +``pygad.gacnn.GACNN`` class. + +.. _createpopulation: + +``create_population()`` +~~~~~~~~~~~~~~~~~~~~~~~ + +The ``create_population()`` method creates the initial population of the +genetic algorithm as a list of CNNs (i.e. solutions). All the networks +are copied from the CNN model passed to constructor of the GACNN class. + +The list of networks is assigned to the ``population_networks`` +attribute of the instance. + +.. _updatepopulationtrainedweights: + +``update_population_trained_weights()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``update_population_trained_weights()`` method updates the +``trained_weights`` attribute of the layers of each network (check the +documentation of the ``pygad.cnn`` module) for more information) +according to the weights passed in the ``population_trained_weights`` +parameter. + +Accepts the following parameters: + +- ``population_trained_weights``: A list holding the trained weights of + all networks as matrices. Such matrices are to be assigned to the + ``trained_weights`` attribute of all layers of all networks. + +.. _functions-in-the-pygadgacnn-module: + +Functions in the ``pygad.gacnn`` Module +======================================= + +This section discusses the functions in the ``pygad.gacnn`` module. + +.. _pygadgacnnpopulationasvectors: + +``pygad.gacnn.population_as_vectors()`` +---------------------------------------- + +Accepts the population as a list of references to the +``pygad.cnn.Model`` class and returns a list holding all weights of the +layers of each solution (i.e. network) in the population as a vector. + +For example, if the population has 6 solutions (i.e. networks), this +function accepts references to such networks and returns a list with 6 +vectors, one for each network (i.e. solution). Each vector holds the +weights for all layers for a single network. + +Accepts the following parameters: + +- ``population_networks``: A list holding references to the + ``pygad.cnn.Model`` class of the networks used in the population. + +Returns a list holding the weights vectors for all solutions (i.e. +networks). + +.. _pygadgacnnpopulationasmatrices: + +``pygad.gacnn.population_as_matrices()`` +---------------------------------------- + +Accepts the population as both networks and weights vectors and returns +the weights of all layers of each solution (i.e. network) in the +population as a matrix. + +For example, if the population has 6 solutions (i.e. networks), this +function returns a list with 6 matrices, one for each network holding +its weights for all layers. + +Accepts the following parameters: + +- ``population_networks``: A list holding references to the + ``pygad.cnn.Model`` class of the networks used in the population. + +- ``population_vectors``: A list holding the weights of all networks as + vectors. Such vectors are to be converted into matrices. + +Returns a list holding the weights matrices for all solutions (i.e. +networks). + +Steps to Build and Train CNN using Genetic Algorithm +==================================================== + +The steps to use this project for building and training a neural network +using the genetic algorithm are as follows: + +- Prepare the training data. + +- Create an instance of the ``pygad.gacnn.GACNN`` class. + +- Fetch the population weights as vectors. + +- Prepare the fitness function. + +- Prepare the generation callback function. + +- Create an instance of the ``pygad.GA`` class. + +- Run the created instance of the ``pygad.GA`` class. + +- Plot the Fitness Values + +- Information about the best solution. + +- Making predictions using the trained weights. + +- Calculating some statistics. + +Let's start covering all of these steps. + +Prepare the Training Data +------------------------- + +Before building and training neural networks, the training data (input +and output) is to be prepared. The inputs and the outputs of the +training data are NumPy arrays. + +The data used in this example is available as 2 files: + +1. `dataset_inputs.npy `__: + Data inputs. + https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy + +2. `dataset_outputs.npy `__: + Class labels. + https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy + +The data consists of 4 classes of images. The image shape is +``(100, 100, 3)`` and there are 20 images per class. For more +information about the dataset, check the **Reading the Data** section of +the ``pygad.cnn`` module. + +Simply download these 2 files and read them according to the next code. + +.. code:: python + + import numpy + + train_inputs = numpy.load("dataset_inputs.npy") + train_outputs = numpy.load("dataset_outputs.npy") + +For the output array, each element must be a single number representing +the class label of the sample. The class labels must start at ``0``. So, +if there are 80 samples, then the shape of the output array is ``(80)``. +If there are 5 classes in the data, then the values of all the 200 +elements in the output array must range from 0 to 4 inclusive. +Generally, the class labels start from ``0`` to ``N-1`` where ``N`` is +the number of classes. + +Note that the project only supports that each sample is assigned to only +one class. + +Building the Network Architecture +--------------------------------- + +Here is an example for a CNN architecture. + +.. code:: python + + import pygad.cnn + + input_layer = pygad.cnn.Input2D(input_shape=(80, 80, 3)) + conv_layer = pygad.cnn.Conv2D(num_filters=2, + kernel_size=3, + previous_layer=input_layer, + activation_function="relu") + average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=5, + previous_layer=conv_layer, + stride=3) + + flatten_layer = pygad.cnn.Flatten(previous_layer=average_pooling_layer) + dense_layer = pygad.cnn.Dense(num_neurons=4, + previous_layer=flatten_layer, + activation_function="softmax") + +After the network architecture is prepared, the next step is to create a +CNN model. + +Building Model +-------------- + +The CNN model is created as an instance of the ``pygad.cnn.Model`` +class. Here is an example. + +.. code:: python + + model = pygad.cnn.Model(last_layer=dense_layer, + epochs=5, + learning_rate=0.01) + +After the model is created, a summary of the model architecture can be +printed. + +Model Summary +------------- + +The ``summary()`` method in the ``pygad.cnn.Model`` class prints a +summary of the CNN model. + +.. code:: python + + model.summary() + +.. code:: python + + ----------Network Architecture---------- + + + + + ---------------------------------------- + +The next step is to create an instance of the ``pygad.gacnn.GACNN`` +class. + +.. _create-an-instance-of-the-pygadgacnngacnn-class: + +Create an Instance of the ``pygad.gacnn.GACNN`` Class +----------------------------------------------------- + +After preparing the input data and building the CNN model, an instance +of the ``pygad.gacnn.GACNN`` class is created by passing the appropriate +parameters. + +Here is an example where the ``num_solutions`` parameter is set to 4 +which means the genetic algorithm population will have 6 solutions (i.e. +networks). All of these 6 CNNs will have the same architectures as +specified by the ``model`` parameter. + +.. code:: python + + import pygad.gacnn + + GACNN_instance = pygad.gacnn.GACNN(model=model, + num_solutions=4) + +After creating the instance of the ``pygad.gacnn.GACNN`` class, next is +to fetch the weights of the population as a list of vectors. + +Fetch the Population Weights as Vectors +--------------------------------------- + +For the genetic algorithm, the parameters (i.e. genes) of each solution +are represented as a single vector. + +For this task, the weights of each CNN must be available as a single +vector. In other words, the weights of all layers of a CNN must be +grouped into a vector. + +To create a list holding the population weights as vectors, one for each +network, the ``pygad.gacnn.population_as_vectors()`` function is used. + +.. code:: python + + population_vectors = gacnn.population_as_vectors(population_networks=GACNN_instance.population_networks) + +Such population of vectors is used as the initial population. + +.. code:: python + + initial_population = population_vectors.copy() + +After preparing the population weights as a set of vectors, next is to +prepare 2 functions which are: + +1. Fitness function. + +2. Callback function after each generation. + +Prepare the Fitness Function +---------------------------- + +The PyGAD library works by allowing the users to customize the genetic +algorithm for their own problems. Because the problems differ in how the +fitness values are calculated, then PyGAD allows the user to use a +custom function as a maximization fitness function. This function must +accept 2 positional parameters representing the following: + +- The solution. + +- The solution index in the population. + +The fitness function must return a single number representing the +fitness. The higher the fitness value, the better the solution. + +Here is the implementation of the fitness function for training a CNN. + +It uses the ``pygad.cnn.predict()`` function to predict the class labels +based on the current solution's weights. The ``pygad.cnn.predict()`` +function uses the trained weights available in the ``trained_weights`` +attribute of each layer of the network for making predictions. + +Based on such predictions, the classification accuracy is calculated. +This accuracy is used as the fitness value of the solution. Finally, the +fitness value is returned. + +.. code:: python + + def fitness_func(ga_instance, solution, sol_idx): + global GACNN_instance, data_inputs, data_outputs + + predictions = GACNN_instance.population_networks[sol_idx].predict(data_inputs=data_inputs) + correct_predictions = numpy.where(predictions == data_outputs)[0].size + solution_fitness = (correct_predictions/data_outputs.size)*100 + + return solution_fitness + +Prepare the Generation Callback Function +---------------------------------------- + +After each generation of the genetic algorithm, the fitness function +will be called to calculate the fitness value of each solution. Within +the fitness function, the ``pygad.cnn.predict()`` function is used for +predicting the outputs based on the current solution's +``trained_weights`` attribute. Thus, it is required that such an +attribute is updated by weights evolved by the genetic algorithm after +each generation. + +PyGAD has a parameter accepted by the ``pygad.GA`` class constructor +named ``on_generation``. It could be assigned to a function that is +called after each generation. The function must accept a single +parameter representing the instance of the ``pygad.GA`` class. + +This callback function can be used to update the ``trained_weights`` +attribute of layers of each network in the population. + +Here is the implementation for a function that updates the +``trained_weights`` attribute of the layers of the population networks. + +It works by converting the current population from the vector form to +the matric form using the ``pygad.gacnn.population_as_matrices()`` +function. It accepts the population as vectors and returns it as +matrices. + +The population matrices are then passed to the +``update_population_trained_weights()`` method in the ``pygad.gacnn`` +module to update the ``trained_weights`` attribute of all layers for all +solutions within the population. + +.. code:: python + + def callback_generation(ga_instance): + global GACNN_instance, last_fitness + + population_matrices = gacnn.population_as_matrices(population_networks=GACNN_instance.population_networks, population_vectors=ga_instance.population) + GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + +After preparing the fitness and callback function, next is to create an +instance of the ``pygad.GA`` class. + +.. _create-an-instance-of-the-pygadga-class: + +Create an Instance of the ``pygad.GA`` Class +-------------------------------------------- + +Once the parameters of the genetic algorithm are prepared, an instance +of the ``pygad.GA`` class can be created. Here is an example where the +number of generations is 10. + +.. code:: python + + import pygad + + num_parents_mating = 4 + + num_generations = 10 + + mutation_percent_genes = 5 + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + mutation_percent_genes=mutation_percent_genes, + on_generation=callback_generation) + +The last step for training the neural networks using the genetic +algorithm is calling the ``run()`` method. + +.. _run-the-created-instance-of-the-pygadga-class: + +Run the Created Instance of the ``pygad.GA`` Class +-------------------------------------------------- + +By calling the ``run()`` method from the ``pygad.GA`` instance, the +genetic algorithm will iterate through the number of generations +specified in its ``num_generations`` parameter. + +.. code:: python + + ga_instance.run() + +Plot the Fitness Values +----------------------- + +After the ``run()`` method completes, the ``plot_fitness()`` method can +be called to show how the fitness values evolve by generation. + +.. code:: python + + ga_instance.plot_fitness() + +.. figure:: https://user-images.githubusercontent.com/16560492/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png + :alt: + +Information about the Best Solution +----------------------------------- + +The following information about the best solution in the last population +is returned using the ``best_solution()`` method in the ``pygad.GA`` +class. + +- Solution + +- Fitness value of the solution + +- Index of the solution within the population + +Here is how such information is returned. + +.. code:: python + + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +.. code:: + + ... + Fitness value of the best solution = 83.75 + Index of the best solution : 0 + Best fitness value reached after 4 generations. + +Making Predictions using the Trained Weights +-------------------------------------------- + +The ``pygad.cnn.predict()`` function can be used to make predictions +using the trained network. As printed, the network is able to predict +the labels correctly. + +.. code:: python + + predictions = pygad.cnn.predict(last_layer=GANN_instance.population_networks[solution_idx], data_inputs=data_inputs) + print("Predictions of the trained network : {predictions}".format(predictions=predictions)) + +Calculating Some Statistics +--------------------------- + +Based on the predictions the network made, some statistics can be +calculated such as the number of correct and wrong predictions in +addition to the classification accuracy. + +.. code:: python + + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +.. code:: + + Number of correct classifications : 67. + Number of wrong classifications : 13. + Classification accuracy : 83.75. + +Examples +======== + +This section gives the complete code of some examples that build and +train neural networks using the genetic algorithm. Each subsection +builds a different network. + +Image Classification +-------------------- + +This example is discussed in the **Steps to Build and Train CNN using +Genetic Algorithm** section that builds the an image classifier. Its +complete code is listed below. + +.. code:: python + + import numpy + import pygad.cnn + import pygad.gacnn + import pygad + + """ + Convolutional neural network implementation using NumPy + A tutorial that helps to get started (Building Convolutional Neural Network using NumPy from Scratch) available in these links: + https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad + https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a + https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html + It is also translated into Chinese: http://m.aliyun.com/yunqi/articles/585741 + """ + + def fitness_func(ga_instance, solution, sol_idx): + global GACNN_instance, data_inputs, data_outputs + + predictions = GACNN_instance.population_networks[sol_idx].predict(data_inputs=data_inputs) + correct_predictions = numpy.where(predictions == data_outputs)[0].size + solution_fitness = (correct_predictions/data_outputs.size)*100 + + return solution_fitness + + def callback_generation(ga_instance): + global GACNN_instance, last_fitness + + population_matrices = pygad.gacnn.population_as_matrices(population_networks=GACNN_instance.population_networks, + population_vectors=ga_instance.population) + + GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solutions_fitness)) + + data_inputs = numpy.load("dataset_inputs.npy") + data_outputs = numpy.load("dataset_outputs.npy") + + sample_shape = data_inputs.shape[1:] + num_classes = 4 + + data_inputs = data_inputs + data_outputs = data_outputs + + input_layer = pygad.cnn.Input2D(input_shape=sample_shape) + conv_layer1 = pygad.cnn.Conv2D(num_filters=2, + kernel_size=3, + previous_layer=input_layer, + activation_function="relu") + average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=5, + previous_layer=conv_layer1, + stride=3) + + flatten_layer = pygad.cnn.Flatten(previous_layer=average_pooling_layer) + dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes, + previous_layer=flatten_layer, + activation_function="softmax") + + model = pygad.cnn.Model(last_layer=dense_layer2, + epochs=1, + learning_rate=0.01) + + model.summary() + + + GACNN_instance = pygad.gacnn.GACNN(model=model, + num_solutions=4) + + # GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. + # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. + population_vectors = pygad.gacnn.population_as_vectors(population_networks=GACNN_instance.population_networks) + + # To prepare the initial population, there are 2 ways: + # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. + # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. + initial_population = population_vectors.copy() + + num_parents_mating = 2 # Number of solutions to be selected as parents in the mating pool. + + num_generations = 10 # Number of generations. + + mutation_percent_genes = 0.1 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + mutation_percent_genes=mutation_percent_genes, + on_generation=callback_generation) + + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness() + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + + # Predicting the outputs of the data using the best solution. + predictions = GACNN_instance.population_networks[solution_idx].predict(data_inputs=data_inputs) + print("Predictions of the trained network : {predictions}".format(predictions=predictions)) + + # Calculating some statistics + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) diff --git a/docs/source/README_pygad_gann_ReadTheDocs.rst b/docs/source/gann.rst similarity index 97% rename from docs/source/README_pygad_gann_ReadTheDocs.rst rename to docs/source/gann.rst index ac95e61..8b243e7 100644 --- a/docs/source/README_pygad_gann_ReadTheDocs.rst +++ b/docs/source/gann.rst @@ -1,1262 +1,1262 @@ -.. _pygadgann-module: - -``pygad.gann`` Module -===================== - -This section of the PyGAD's library documentation discusses the -**pygad.gann** module. - -The ``pygad.gann`` module trains neural networks (for either -classification or regression) using the genetic algorithm. It makes use -of the 2 modules ``pygad`` and ``pygad.nn``. - -.. _pygadganngann-class: - -``pygad.gann.GANN`` Class -========================= - -The ``pygad.gann`` module has a class named ``pygad.gann.GANN`` for -training neural networks using the genetic algorithm. The constructor, -methods, function, and attributes within the class are discussed in this -section. - -.. _init: - -``__init__()`` --------------- - -In order to train a neural network using the genetic algorithm, the -first thing to do is to create an instance of the ``pygad.gann.GANN`` -class. - -The ``pygad.gann.GANN`` class constructor accepts the following -parameters: - -- ``num_solutions``: Number of neural networks (i.e. solutions) in the - population. Based on the value passed to this parameter, a number of - identical neural networks are created where their parameters are - optimized using the genetic algorithm. - -- ``num_neurons_input``: Number of neurons in the input layer. - -- ``num_neurons_output``: Number of neurons in the output layer. - -- ``num_neurons_hidden_layers=[]``: A list holding the number of - neurons in the hidden layer(s). If empty ``[]``, then no hidden - layers are used. For each ``int`` value it holds, then a hidden layer - is created with a number of hidden neurons specified by the - corresponding ``int`` value. For example, - ``num_neurons_hidden_layers=[10]`` creates a single hidden layer with - **10** neurons. ``num_neurons_hidden_layers=[10, 5]`` creates 2 - hidden layers with 10 neurons for the first and 5 neurons for the - second hidden layer. - -- ``output_activation="softmax"``: The name of the activation function - of the output layer which defaults to ``"softmax"``. - -- ``hidden_activations="relu"``: The name(s) of the activation - function(s) of the hidden layer(s). It defaults to ``"relu"``. If - passed as a string, this means the specified activation function will - be used across all the hidden layers. If passed as a list, then it - must have the same length as the length of the - ``num_neurons_hidden_layers`` list. An exception is raised if their - lengths are different. When ``hidden_activations`` is a list, a - one-to-one mapping between the ``num_neurons_hidden_layers`` and - ``hidden_activations`` lists occurs. - -In order to validate the parameters passed to the ``pygad.gann.GANN`` -class constructor, the ``pygad.gann.validate_network_parameters()`` -function is called. - -Instance Attributes -------------------- - -All the parameters in the ``pygad.gann.GANN`` class constructor are used -as instance attributes. Besides such attributes, there are other -attributes added to the instances from the ``pygad.gann.GANN`` class -which are: - -- ``parameters_validated``: If ``True``, then the parameters passed to - the GANN class constructor are valid. Its initial value is ``False``. - -- ``population_networks``: A list holding references to all the - solutions (i.e. neural networks) used in the population. - -Methods in the GANN Class -------------------------- - -This section discusses the methods available for instances of the -``pygad.gann.GANN`` class. - -.. _createpopulation: - -``create_population()`` -~~~~~~~~~~~~~~~~~~~~~~~ - -The ``create_population()`` method creates the initial population of the -genetic algorithm as a list of neural networks (i.e. solutions). For -each network to be created, the ``pygad.gann.create_network()`` function -is called. - -Each element in the list holds a reference to the last (i.e. output) -layer for the network. The method does not accept any parameter and it -accesses all the required details from the ``pygad.gann.GANN`` instance. - -The method returns the list holding the references to the networks. This -list is later assigned to the ``population_networks`` attribute of the -instance. - -.. _updatepopulationtrainedweights: - -``update_population_trained_weights()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The ``update_population_trained_weights()`` method updates the -``trained_weights`` attribute of the layers of each network (check the -`documentation of the pygad.nn.DenseLayer -class `__ for -more information) according to the weights passed in the -``population_trained_weights`` parameter. - -Accepts the following parameters: - -- ``population_trained_weights``: A list holding the trained weights of - all networks as matrices. Such matrices are to be assigned to the - ``trained_weights`` attribute of all layers of all networks. - -.. _functions-in-the-pygadgann-module: - -Functions in the ``pygad.gann`` Module -====================================== - -This section discusses the functions in the ``pygad.gann`` module. - -.. _pygadgannvalidatenetworkparameters: - -``pygad.gann.validate_network_parameters()`` --------------------------------------------- - -Validates the parameters passed to the constructor of the -``pygad.gann.GANN`` class. If at least one an invalid parameter exists, -an exception is raised and the execution stops. - -The function accepts the same parameters passed to the constructor of -the ``pygad.gann.GANN`` class. Please check the documentation of such -parameters in the section discussing the class constructor. - -The reason why this function sets a default value to the -``num_solutions`` parameter is differentiating whether a population of -networks or a single network is to be created. If ``None``, then a -single network will be created. If not ``None``, then a population of -networks is to be created. - -If the value passed to the ``hidden_activations`` parameter is a string, -not a list, then a list is created by replicating the passed name of the -activation function a number of times equal to the number of hidden -layers (i.e. the length of the ``num_neurons_hidden_layers`` parameter). - -Returns a list holding the name(s) of the activation function(s) of the -hidden layer(s). - -.. _pygadganncreatenetwork: - -``pygad.gann.create_network()`` -------------------------------- - -Creates a neural network as a linked list between the input, hidden, and -output layers where the layer at index N (which is the last/output -layer) references the layer at index N-1 (which is a hidden layer) using -its previous_layer attribute. The input layer does not reference any -layer because it is the last layer in the linked list. - -In addition to the ``parameters_validated`` parameter, this function -accepts the same parameters passed to the constructor of the -``pygad.gann.GANN`` class except for the ``num_solutions`` parameter -because only a single network is created out of the ``create_network()`` -function. - -``parameters_validated``: If ``False``, then the parameters are not -validated and a call to the ``validate_network_parameters()`` function -is made. - -Returns the reference to the last layer in the network architecture -which is the output layer. Based on such a reference, all network layers -can be fetched. - -.. _pygadgannpopulationasvectors: - -``pygad.gann.population_as_vectors()`` ---------------------------------------- - -Accepts the population as networks and returns a list holding all -weights of the layers of each solution (i.e. network) in the population -as a vector. - -For example, if the population has 6 solutions (i.e. networks), this -function accepts references to such networks and returns a list with 6 -vectors, one for each network (i.e. solution). Each vector holds the -weights for all layers for a single network. - -Accepts the following parameters: - -- ``population_networks``: A list holding references to the output - (last) layers of the neural networks used in the population. - -Returns a list holding the weights vectors for all solutions (i.e. -networks). - -.. _pygadgannpopulationasmatrices: - -``pygad.gann.population_as_matrices()`` ---------------------------------------- - -Accepts the population as both networks and weights vectors and returns -the weights of all layers of each solution (i.e. network) in the -population as a matrix. - -For example, if the population has 6 solutions (i.e. networks), this -function returns a list with 6 matrices, one for each network holding -its weights for all layers. - -Accepts the following parameters: - -- ``population_networks``: A list holding references to the output - (last) layers of the neural networks used in the population. - -- ``population_vectors``: A list holding the weights of all networks as - vectors. Such vectors are to be converted into matrices. - -Returns a list holding the weights matrices for all solutions (i.e. -networks). - -Steps to Build and Train Neural Networks using Genetic Algorithm -================================================================ - -The steps to use this project for building and training a neural network -using the genetic algorithm are as follows: - -- Prepare the training data. - -- Create an instance of the ``pygad.gann.GANN`` class. - -- Fetch the population weights as vectors. - -- Prepare the fitness function. - -- Prepare the generation callback function. - -- Create an instance of the ``pygad.GA`` class. - -- Run the created instance of the ``pygad.GA`` class. - -- Plot the Fitness Values - -- Information about the best solution. - -- Making predictions using the trained weights. - -- Calculating some statistics. - -Let's start covering all of these steps. - -Prepare the Training Data -------------------------- - -Before building and training neural networks, the training data (input -and output) is to be prepared. The inputs and the outputs of the -training data are NumPy arrays. - -Here is an example of preparing the training data for the XOR problem. - -For the input array, each element must be a list representing the inputs -(i.e. features) for the sample. If there are 200 samples and each sample -has 50 features, then the shape of the inputs array is ``(200, 50)``. -The variable ``num_inputs`` holds the length of each sample which is 2 -in this example. - -.. code:: python - - data_inputs = numpy.array([[1, 1], - [1, 0], - [0, 1], - [0, 0]]) - - data_outputs = numpy.array([0, - 1, - 1, - 0]) - - num_inputs = data_inputs.shape[1] - -For the output array, each element must be a single number representing -the class label of the sample. The class labels must start at ``0``. So, -if there are 200 samples, then the shape of the output array is -``(200)``. If there are 5 classes in the data, then the values of all -the 200 elements in the output array must range from 0 to 4 inclusive. -Generally, the class labels start from ``0`` to ``N-1`` where ``N`` is -the number of classes. - -For the XOR example, there are 2 classes and thus their labels are 0 and -1. The ``num_classes`` variable is assigned to 2. - -Note that the project only supports classification problems where each -sample is assigned to only one class. - -.. _create-an-instance-of-the-pygadganngann-class: - -Create an Instance of the ``pygad.gann.GANN`` Class ---------------------------------------------------- - -After preparing the input data, an instance of the ``pygad.gann.GANN`` -class is created by passing the appropriate parameters. - -Here is an example that creates a network for the XOR problem. The -``num_solutions`` parameter is set to 6 which means the genetic -algorithm population will have 6 solutions (i.e. networks). All of these -6 neural networks will have the same architectures as specified by the -other parameters. - -The output layer has 2 neurons because there are only 2 classes (0 and -1). - -.. code:: python - - import pygad.gann - import pygad.nn - - num_solutions = 6 - GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, - num_neurons_input=num_inputs, - num_neurons_hidden_layers=[2], - num_neurons_output=2, - hidden_activations=["relu"], - output_activation="softmax") - -The architecture of the created network has the following layers: - -- An input layer with 2 neurons (i.e. inputs) - -- A single hidden layer with 2 neurons. - -- An output layer with 2 neurons (i.e. classes). - -The weights of the network are as follows: - -- Between the input and the hidden layer, there is a weights matrix of - size equal to ``(number inputs x number of hidden neurons) = (2x2)``. - -- Between the hidden and the output layer, there is a weights matrix of - size equal to - ``(number of hidden neurons x number of outputs) = (2x2)``. - -The activation function used for the output layer is ``softmax``. The -``relu`` activation function is used for the hidden layer. - -After creating the instance of the ``pygad.gann.GANN`` class next is to -fetch the weights of the population as a list of vectors. - -Fetch the Population Weights as Vectors ---------------------------------------- - -For the genetic algorithm, the parameters (i.e. genes) of each solution -are represented as a single vector. - -For the task of training the network for the XOR problem, the weights of -each network in the population are not represented as a vector but 2 -matrices each of size 2x2. - -To create a list holding the population weights as vectors, one for each -network, the ``pygad.gann.population_as_vectors()`` function is used. - -.. code:: python - - population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) - -After preparing the population weights as a set of vectors, next is to -prepare 2 functions which are: - -1. Fitness function. - -2. Callback function after each generation. - -Prepare the Fitness Function ----------------------------- - -The PyGAD library works by allowing the users to customize the genetic -algorithm for their own problems. Because the problems differ in how the -fitness values are calculated, then PyGAD allows the user to use a -custom function as a maximization fitness function. This function must -accept 2 positional parameters representing the following: - -- The solution. - -- The solution index in the population. - -The fitness function must return a single number representing the -fitness. The higher the fitness value, the better the solution. - -Here is the implementation of the fitness function for training a neural -network. It uses the ``pygad.nn.predict()`` function to predict the -class labels based on the current solution's weights. The -``pygad.nn.predict()`` function uses the trained weights available in -the ``trained_weights`` attribute of each layer of the network for -making predictions. - -Based on such predictions, the classification accuracy is calculated. -This accuracy is used as the fitness value of the solution. Finally, the -fitness value is returned. - -.. code:: python - - def fitness_func(ga_instance, solution, sol_idx): - global GANN_instance, data_inputs, data_outputs - - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], - data_inputs=data_inputs) - correct_predictions = numpy.where(predictions == data_outputs)[0].size - solution_fitness = (correct_predictions/data_outputs.size)*100 - - return solution_fitness - -Prepare the Generation Callback Function ----------------------------------------- - -After each generation of the genetic algorithm, the fitness function -will be called to calculate the fitness value of each solution. Within -the fitness function, the ``pygad.nn.predict()`` function is used for -predicting the outputs based on the current solution's -``trained_weights`` attribute. Thus, it is required that such an -attribute is updated by weights evolved by the genetic algorithm after -each generation. - -PyGAD 2.0.0 and higher has a new parameter accepted by the ``pygad.GA`` -class constructor named ``on_generation``. It could be assigned to a -function that is called after each generation. The function must accept -a single parameter representing the instance of the ``pygad.GA`` class. - -This callback function can be used to update the ``trained_weights`` -attribute of layers of each network in the population. - -Here is the implementation for a function that updates the -``trained_weights`` attribute of the layers of the population networks. - -It works by converting the current population from the vector form to -the matric form using the ``pygad.gann.population_as_matrices()`` -function. It accepts the population as vectors and returns it as -matrices. - -The population matrices are then passed to the -``update_population_trained_weights()`` method in the ``pygad.gann`` -module to update the ``trained_weights`` attribute of all layers for all -solutions within the population. - -.. code:: python - - def callback_generation(ga_instance): - global GANN_instance - - population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, population_vectors=ga_instance.population) - GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - -After preparing the fitness and callback function, next is to create an -instance of the ``pygad.GA`` class. - -.. _create-an-instance-of-the-pygadga-class: - -Create an Instance of the ``pygad.GA`` Class --------------------------------------------- - -Once the parameters of the genetic algorithm are prepared, an instance -of the ``pygad.GA`` class can be created. - -Here is an example. - -.. code:: python - - initial_population = population_vectors.copy() - - num_parents_mating = 4 - - num_generations = 500 - - mutation_percent_genes = 5 - - parent_selection_type = "sss" - - crossover_type = "single_point" - - mutation_type = "random" - - keep_parents = 1 - - init_range_low = -2 - init_range_high = 5 - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - mutation_percent_genes=mutation_percent_genes, - init_range_low=init_range_low, - init_range_high=init_range_high, - parent_selection_type=parent_selection_type, - crossover_type=crossover_type, - mutation_type=mutation_type, - keep_parents=keep_parents, - on_generation=callback_generation) - -The last step for training the neural networks using the genetic -algorithm is calling the ``run()`` method. - -.. _run-the-created-instance-of-the-pygadga-class: - -Run the Created Instance of the ``pygad.GA`` Class --------------------------------------------------- - -By calling the ``run()`` method from the ``pygad.GA`` instance, the -genetic algorithm will iterate through the number of generations -specified in its ``num_generations`` parameter. - -.. code:: python - - ga_instance.run() - -Plot the Fitness Values ------------------------ - -After the ``run()`` method completes, the ``plot_fitness()`` method can -be called to show how the fitness values evolve by generation. A fitness -value (i.e. accuracy) of 100 is reached after around 180 generations. - -.. code:: python - - ga_instance.plot_fitness() - -.. figure:: https://user-images.githubusercontent.com/16560492/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png - :alt: - -By running the code again, a different initial population is created and -thus a classification accuracy of 100 can be reached using a less number -of generations. On the other hand, a different initial population might -cause 100% accuracy to be reached using more generations or not reached -at all. - -Information about the Best Solution ------------------------------------ - -The following information about the best solution in the last population -is returned using the ``best_solution()`` method in the ``pygad.GA`` -class. - -- Solution - -- Fitness value of the solution - -- Index of the solution within the population - -Here is how such information is returned. The fitness value (i.e. -accuracy) is 100. - -.. code:: python - - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - -.. code:: - - Parameters of the best solution : [3.55081391 -3.21562011 -14.2617784 0.68044231 -1.41258145 -3.2979315 1.58136006 -7.83726169] - Fitness value of the best solution = 100.0 - Index of the best solution : 0 - -Using the ``best_solution_generation`` attribute of the instance from -the ``pygad.GA`` class, the generation number at which the **best -fitness** is reached could be fetched. According to the result, the best -fitness value is reached after 182 generations. - -.. code:: python - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - -.. code:: - - Best solution reached after 182 generations. - -Making Predictions using the Trained Weights --------------------------------------------- - -The ``pygad.nn.predict()`` function can be used to make predictions -using the trained network. As printed, the network is able to predict -the labels correctly. - -.. code:: python - - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], data_inputs=data_inputs) - print("Predictions of the trained network : {predictions}".format(predictions=predictions)) - -.. code:: - - Predictions of the trained network : [0. 1. 1. 0.] - -Calculating Some Statistics ---------------------------- - -Based on the predictions the network made, some statistics can be -calculated such as the number of correct and wrong predictions in -addition to the classification accuracy. - -.. code:: python - - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -.. code:: - - Number of correct classifications : 4 - print("Number of wrong classifications : 0 - Classification accuracy : 100 - -Examples -======== - -This section gives the complete code of some examples that build and -train neural networks using the genetic algorithm. Each subsection -builds a different network. - -XOR Classification ------------------- - -This example is discussed in the **Steps to Build and Train Neural -Networks using Genetic Algorithm** section that builds the XOR gate and -its complete code is listed below. - -.. code:: python - - import numpy - import pygad - import pygad.nn - import pygad.gann - - def fitness_func(ga_instance, solution, sol_idx): - global GANN_instance, data_inputs, data_outputs - - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], - data_inputs=data_inputs) - correct_predictions = numpy.where(predictions == data_outputs)[0].size - solution_fitness = (correct_predictions/data_outputs.size)*100 - - return solution_fitness - - def callback_generation(ga_instance): - global GANN_instance, last_fitness - - population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, - population_vectors=ga_instance.population) - - GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) - - last_fitness = ga_instance.best_solution()[1].copy() - - # Holds the fitness value of the previous generation. - last_fitness = 0 - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.array([[1, 1], - [1, 0], - [0, 1], - [0, 0]]) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.array([0, - 1, - 1, - 0]) - - # The length of the input vector for each sample (i.e. number of neurons in the input layer). - num_inputs = data_inputs.shape[1] - # The number of neurons in the output layer (i.e. number of classes). - num_classes = 2 - - # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. - num_solutions = 6 # A solution or a network can be used interchangeably. - GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, - num_neurons_input=num_inputs, - num_neurons_hidden_layers=[2], - num_neurons_output=num_classes, - hidden_activations=["relu"], - output_activation="softmax") - - # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. - # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. - population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) - - # To prepare the initial population, there are 2 ways: - # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. - # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. - initial_population = population_vectors.copy() - - num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. - - num_generations = 500 # Number of generations. - - mutation_percent_genes = 5 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. - - parent_selection_type = "sss" # Type of parent selection. - - crossover_type = "single_point" # Type of the crossover operator. - - mutation_type = "random" # Type of the mutation operator. - - keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. - - init_range_low = -2 - init_range_high = 5 - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - mutation_percent_genes=mutation_percent_genes, - init_range_low=init_range_low, - init_range_high=init_range_high, - parent_selection_type=parent_selection_type, - crossover_type=crossover_type, - mutation_type=mutation_type, - keep_parents=keep_parents, - on_generation=callback_generation) - - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness() - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - - # Predicting the outputs of the data using the best solution. - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], - data_inputs=data_inputs) - print("Predictions of the trained network : {predictions}".format(predictions=predictions)) - - # Calculating some statistics - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -Image Classification --------------------- - -In the documentation of the ``pygad.nn`` module, a neural network is -created for classifying images from the Fruits360 dataset without being -trained using an optimization algorithm. This section discusses how to -train such a classifier using the genetic algorithm with the help of the -``pygad.gann`` module. - -Please make sure that the training data files -`dataset_features.npy `__ -and -`outputs.npy `__ -are available. For downloading them, use these links: - -1. `dataset_features.npy `__: - The features - https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy - -2. `outputs.npy `__: - The class labels - https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy - -After the data is available, here is the complete code that builds and -trains a neural network using the genetic algorithm for classifying -images from 4 classes of the Fruits360 dataset. - -Because there are 4 classes, the output layer is assigned has 4 neurons -according to the ``num_neurons_output`` parameter of the -``pygad.gann.GANN`` class constructor. - -.. code:: python - - import numpy - import pygad - import pygad.nn - import pygad.gann - - def fitness_func(ga_instance, solution, sol_idx): - global GANN_instance, data_inputs, data_outputs - - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], - data_inputs=data_inputs) - correct_predictions = numpy.where(predictions == data_outputs)[0].size - solution_fitness = (correct_predictions/data_outputs.size)*100 - - return solution_fitness - - def callback_generation(ga_instance): - global GANN_instance, last_fitness - - population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, - population_vectors=ga_instance.population) - - GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) - - last_fitness = ga_instance.best_solution()[1].copy() - - # Holds the fitness value of the previous generation. - last_fitness = 0 - - # Reading the input data. - data_inputs = numpy.load("dataset_features.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy - - # Optional step of filtering the input data using the standard deviation. - features_STDs = numpy.std(a=data_inputs, axis=0) - data_inputs = data_inputs[:, features_STDs>50] - - # Reading the output data. - data_outputs = numpy.load("outputs.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy - - # The length of the input vector for each sample (i.e. number of neurons in the input layer). - num_inputs = data_inputs.shape[1] - # The number of neurons in the output layer (i.e. number of classes). - num_classes = 4 - - # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. - num_solutions = 8 # A solution or a network can be used interchangeably. - GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, - num_neurons_input=num_inputs, - num_neurons_hidden_layers=[150, 50], - num_neurons_output=num_classes, - hidden_activations=["relu", "relu"], - output_activation="softmax") - - # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. - # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. - population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) - - # To prepare the initial population, there are 2 ways: - # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. - # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. - initial_population = population_vectors.copy() - - num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. - - num_generations = 500 # Number of generations. - - mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. - - parent_selection_type = "sss" # Type of parent selection. - - crossover_type = "single_point" # Type of the crossover operator. - - mutation_type = "random" # Type of the mutation operator. - - keep_parents = -1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - mutation_percent_genes=mutation_percent_genes, - parent_selection_type=parent_selection_type, - crossover_type=crossover_type, - mutation_type=mutation_type, - keep_parents=keep_parents, - on_generation=callback_generation) - - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness() - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - - # Predicting the outputs of the data using the best solution. - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], - data_inputs=data_inputs) - print("Predictions of the trained network : {predictions}".format(predictions=predictions)) - - # Calculating some statistics - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -After training completes, here are the outputs of the print statements. -The number of wrong classifications is only 1 and the accuracy is -99.949%. This accuracy is reached after 482 generations. - -.. code:: - - Fitness value of the best solution = 99.94903160040775 - Index of the best solution : 0 - Best fitness value reached after 482 generations. - Number of correct classifications : 1961. - Number of wrong classifications : 1. - Classification accuracy : 99.94903160040775. - -The next figure shows how fitness value evolves by generation. - -.. figure:: https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png - :alt: - -Regression Example 1 --------------------- - -To train a neural network for regression, follow these instructions: - -1. Set the ``output_activation`` parameter in the constructor of the - ``pygad.gann.GANN`` class to ``"None"``. It is possible to use the - ReLU function if all outputs are nonnegative. - -.. code:: python - - GANN_instance = pygad.gann.GANN(... - output_activation="None") - -1. Wherever the ``pygad.nn.predict()`` function is used, set the - ``problem_type`` parameter to ``"regression"``. - -.. code:: python - - predictions = pygad.nn.predict(..., - problem_type="regression") - -1. Design the fitness function to calculate the error (e.g. mean - absolute error). - -.. code:: python - - def fitness_func(ga_instance, solution, sol_idx): - ... - - predictions = pygad.nn.predict(..., - problem_type="regression") - - solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) - - return solution_fitness - -The next code builds a complete example for building a neural network -for regression. - -.. code:: python - - import numpy - import pygad - import pygad.nn - import pygad.gann - - def fitness_func(ga_instance, solution, sol_idx): - global GANN_instance, data_inputs, data_outputs - - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], - data_inputs=data_inputs, problem_type="regression") - solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) - - return solution_fitness - - def callback_generation(ga_instance): - global GANN_instance, last_fitness - - population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, - population_vectors=ga_instance.population) - - GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) - - last_fitness = ga_instance.best_solution()[1].copy() - - # Holds the fitness value of the previous generation. - last_fitness = 0 - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.array([[2, 5, -3, 0.1], - [8, 15, 20, 13]]) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.array([0.1, - 1.5]) - - # The length of the input vector for each sample (i.e. number of neurons in the input layer). - num_inputs = data_inputs.shape[1] - - # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. - num_solutions = 6 # A solution or a network can be used interchangeably. - GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, - num_neurons_input=num_inputs, - num_neurons_hidden_layers=[2], - num_neurons_output=1, - hidden_activations=["relu"], - output_activation="None") - - # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. - # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. - population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) - - # To prepare the initial population, there are 2 ways: - # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. - # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. - initial_population = population_vectors.copy() - - num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. - - num_generations = 500 # Number of generations. - - mutation_percent_genes = 5 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. - - parent_selection_type = "sss" # Type of parent selection. - - crossover_type = "single_point" # Type of the crossover operator. - - mutation_type = "random" # Type of the mutation operator. - - keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. - - init_range_low = -1 - init_range_high = 1 - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - mutation_percent_genes=mutation_percent_genes, - init_range_low=init_range_low, - init_range_high=init_range_high, - parent_selection_type=parent_selection_type, - crossover_type=crossover_type, - mutation_type=mutation_type, - keep_parents=keep_parents, - on_generation=callback_generation) - - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness() - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - - # Predicting the outputs of the data using the best solution. - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], - data_inputs=data_inputs, - problem_type="regression") - print("Predictions of the trained network : {predictions}".format(predictions=predictions)) - - # Calculating some statistics - abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) - print("Absolute error : {abs_error}.".format(abs_error=abs_error)) - -The next figure shows how the fitness value changes for the generations -used. - -.. figure:: https://user-images.githubusercontent.com/16560492/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png - :alt: - -Regression Example 2 - Fish Weight Prediction ---------------------------------------------- - -This example uses the Fish Market Dataset available at Kaggle -(https://www.kaggle.com/aungpyaeap/fish-market). Simply download the CSV -dataset from `this -link `__ -(https://www.kaggle.com/aungpyaeap/fish-market/download). The dataset is -also available at the `GitHub project of the pygad.gann -module `__: -https://github.com/ahmedfgad/NeuralGenetic - -Using the Pandas library, the dataset is read using the ``read_csv()`` -function. - -.. code:: python - - data = numpy.array(pandas.read_csv("Fish.csv")) - -The last 5 columns in the dataset are used as inputs and the **Weight** -column is used as output. - -.. code:: python - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) # Fish Weight - -Note how the activation function at the last layer is set to ``"None"``. -Moreover, the ``problem_type`` parameter in the ``pygad.nn.train()`` and -``pygad.nn.predict()`` functions is set to ``"regression"``. Remember to -design an appropriate fitness function for the regression problem. In -this example, the fitness value is calculated based on the mean absolute -error. - -.. code:: python - - solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) - -Here is the complete code. - -.. code:: python - - import numpy - import pygad - import pygad.nn - import pygad.gann - import pandas - - def fitness_func(ga_instance, solution, sol_idx): - global GANN_instance, data_inputs, data_outputs - - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], - data_inputs=data_inputs, problem_type="regression") - solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) - - return solution_fitness - - def callback_generation(ga_instance): - global GANN_instance, last_fitness - - population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, - population_vectors=ga_instance.population) - - GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) - - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) - - last_fitness = ga_instance.best_solution()[1].copy() - - # Holds the fitness value of the previous generation. - last_fitness = 0 - - data = numpy.array(pandas.read_csv("Fish.csv")) - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) - - # The length of the input vector for each sample (i.e. number of neurons in the input layer). - num_inputs = data_inputs.shape[1] - - # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. - num_solutions = 6 # A solution or a network can be used interchangeably. - GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, - num_neurons_input=num_inputs, - num_neurons_hidden_layers=[2], - num_neurons_output=1, - hidden_activations=["relu"], - output_activation="None") - - # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. - # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. - population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) - - # To prepare the initial population, there are 2 ways: - # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. - # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. - initial_population = population_vectors.copy() - - num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. - - num_generations = 500 # Number of generations. - - mutation_percent_genes = 5 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. - - parent_selection_type = "sss" # Type of parent selection. - - crossover_type = "single_point" # Type of the crossover operator. - - mutation_type = "random" # Type of the mutation operator. - - keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. - - init_range_low = -1 - init_range_high = 1 - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - mutation_percent_genes=mutation_percent_genes, - init_range_low=init_range_low, - init_range_high=init_range_high, - parent_selection_type=parent_selection_type, - crossover_type=crossover_type, - mutation_type=mutation_type, - keep_parents=keep_parents, - on_generation=callback_generation) - - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness() - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - - # Predicting the outputs of the data using the best solution. - predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], - data_inputs=data_inputs, - problem_type="regression") - print("Predictions of the trained network : {predictions}".format(predictions=predictions)) - - # Calculating some statistics - abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) - print("Absolute error : {abs_error}.".format(abs_error=abs_error)) - -The next figure shows how the fitness value changes for the 500 -generations used. - -.. figure:: https://user-images.githubusercontent.com/16560492/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png - :alt: +.. _pygadgann-module: + +``pygad.gann`` Module +===================== + +This section of the PyGAD's library documentation discusses the +**pygad.gann** module. + +The ``pygad.gann`` module trains neural networks (for either +classification or regression) using the genetic algorithm. It makes use +of the 2 modules ``pygad`` and ``pygad.nn``. + +.. _pygadganngann-class: + +``pygad.gann.GANN`` Class +========================= + +The ``pygad.gann`` module has a class named ``pygad.gann.GANN`` for +training neural networks using the genetic algorithm. The constructor, +methods, function, and attributes within the class are discussed in this +section. + +.. _init: + +``__init__()`` +-------------- + +In order to train a neural network using the genetic algorithm, the +first thing to do is to create an instance of the ``pygad.gann.GANN`` +class. + +The ``pygad.gann.GANN`` class constructor accepts the following +parameters: + +- ``num_solutions``: Number of neural networks (i.e. solutions) in the + population. Based on the value passed to this parameter, a number of + identical neural networks are created where their parameters are + optimized using the genetic algorithm. + +- ``num_neurons_input``: Number of neurons in the input layer. + +- ``num_neurons_output``: Number of neurons in the output layer. + +- ``num_neurons_hidden_layers=[]``: A list holding the number of + neurons in the hidden layer(s). If empty ``[]``, then no hidden + layers are used. For each ``int`` value it holds, then a hidden layer + is created with a number of hidden neurons specified by the + corresponding ``int`` value. For example, + ``num_neurons_hidden_layers=[10]`` creates a single hidden layer with + **10** neurons. ``num_neurons_hidden_layers=[10, 5]`` creates 2 + hidden layers with 10 neurons for the first and 5 neurons for the + second hidden layer. + +- ``output_activation="softmax"``: The name of the activation function + of the output layer which defaults to ``"softmax"``. + +- ``hidden_activations="relu"``: The name(s) of the activation + function(s) of the hidden layer(s). It defaults to ``"relu"``. If + passed as a string, this means the specified activation function will + be used across all the hidden layers. If passed as a list, then it + must have the same length as the length of the + ``num_neurons_hidden_layers`` list. An exception is raised if their + lengths are different. When ``hidden_activations`` is a list, a + one-to-one mapping between the ``num_neurons_hidden_layers`` and + ``hidden_activations`` lists occurs. + +In order to validate the parameters passed to the ``pygad.gann.GANN`` +class constructor, the ``pygad.gann.validate_network_parameters()`` +function is called. + +Instance Attributes +------------------- + +All the parameters in the ``pygad.gann.GANN`` class constructor are used +as instance attributes. Besides such attributes, there are other +attributes added to the instances from the ``pygad.gann.GANN`` class +which are: + +- ``parameters_validated``: If ``True``, then the parameters passed to + the GANN class constructor are valid. Its initial value is ``False``. + +- ``population_networks``: A list holding references to all the + solutions (i.e. neural networks) used in the population. + +Methods in the GANN Class +------------------------- + +This section discusses the methods available for instances of the +``pygad.gann.GANN`` class. + +.. _createpopulation: + +``create_population()`` +~~~~~~~~~~~~~~~~~~~~~~~ + +The ``create_population()`` method creates the initial population of the +genetic algorithm as a list of neural networks (i.e. solutions). For +each network to be created, the ``pygad.gann.create_network()`` function +is called. + +Each element in the list holds a reference to the last (i.e. output) +layer for the network. The method does not accept any parameter and it +accesses all the required details from the ``pygad.gann.GANN`` instance. + +The method returns the list holding the references to the networks. This +list is later assigned to the ``population_networks`` attribute of the +instance. + +.. _updatepopulationtrainedweights: + +``update_population_trained_weights()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``update_population_trained_weights()`` method updates the +``trained_weights`` attribute of the layers of each network (check the +`documentation of the pygad.nn.DenseLayer +class `__ for +more information) according to the weights passed in the +``population_trained_weights`` parameter. + +Accepts the following parameters: + +- ``population_trained_weights``: A list holding the trained weights of + all networks as matrices. Such matrices are to be assigned to the + ``trained_weights`` attribute of all layers of all networks. + +.. _functions-in-the-pygadgann-module: + +Functions in the ``pygad.gann`` Module +====================================== + +This section discusses the functions in the ``pygad.gann`` module. + +.. _pygadgannvalidatenetworkparameters: + +``pygad.gann.validate_network_parameters()`` +-------------------------------------------- + +Validates the parameters passed to the constructor of the +``pygad.gann.GANN`` class. If at least one an invalid parameter exists, +an exception is raised and the execution stops. + +The function accepts the same parameters passed to the constructor of +the ``pygad.gann.GANN`` class. Please check the documentation of such +parameters in the section discussing the class constructor. + +The reason why this function sets a default value to the +``num_solutions`` parameter is differentiating whether a population of +networks or a single network is to be created. If ``None``, then a +single network will be created. If not ``None``, then a population of +networks is to be created. + +If the value passed to the ``hidden_activations`` parameter is a string, +not a list, then a list is created by replicating the passed name of the +activation function a number of times equal to the number of hidden +layers (i.e. the length of the ``num_neurons_hidden_layers`` parameter). + +Returns a list holding the name(s) of the activation function(s) of the +hidden layer(s). + +.. _pygadganncreatenetwork: + +``pygad.gann.create_network()`` +------------------------------- + +Creates a neural network as a linked list between the input, hidden, and +output layers where the layer at index N (which is the last/output +layer) references the layer at index N-1 (which is a hidden layer) using +its previous_layer attribute. The input layer does not reference any +layer because it is the last layer in the linked list. + +In addition to the ``parameters_validated`` parameter, this function +accepts the same parameters passed to the constructor of the +``pygad.gann.GANN`` class except for the ``num_solutions`` parameter +because only a single network is created out of the ``create_network()`` +function. + +``parameters_validated``: If ``False``, then the parameters are not +validated and a call to the ``validate_network_parameters()`` function +is made. + +Returns the reference to the last layer in the network architecture +which is the output layer. Based on such a reference, all network layers +can be fetched. + +.. _pygadgannpopulationasvectors: + +``pygad.gann.population_as_vectors()`` +--------------------------------------- + +Accepts the population as networks and returns a list holding all +weights of the layers of each solution (i.e. network) in the population +as a vector. + +For example, if the population has 6 solutions (i.e. networks), this +function accepts references to such networks and returns a list with 6 +vectors, one for each network (i.e. solution). Each vector holds the +weights for all layers for a single network. + +Accepts the following parameters: + +- ``population_networks``: A list holding references to the output + (last) layers of the neural networks used in the population. + +Returns a list holding the weights vectors for all solutions (i.e. +networks). + +.. _pygadgannpopulationasmatrices: + +``pygad.gann.population_as_matrices()`` +--------------------------------------- + +Accepts the population as both networks and weights vectors and returns +the weights of all layers of each solution (i.e. network) in the +population as a matrix. + +For example, if the population has 6 solutions (i.e. networks), this +function returns a list with 6 matrices, one for each network holding +its weights for all layers. + +Accepts the following parameters: + +- ``population_networks``: A list holding references to the output + (last) layers of the neural networks used in the population. + +- ``population_vectors``: A list holding the weights of all networks as + vectors. Such vectors are to be converted into matrices. + +Returns a list holding the weights matrices for all solutions (i.e. +networks). + +Steps to Build and Train Neural Networks using Genetic Algorithm +================================================================ + +The steps to use this project for building and training a neural network +using the genetic algorithm are as follows: + +- Prepare the training data. + +- Create an instance of the ``pygad.gann.GANN`` class. + +- Fetch the population weights as vectors. + +- Prepare the fitness function. + +- Prepare the generation callback function. + +- Create an instance of the ``pygad.GA`` class. + +- Run the created instance of the ``pygad.GA`` class. + +- Plot the Fitness Values + +- Information about the best solution. + +- Making predictions using the trained weights. + +- Calculating some statistics. + +Let's start covering all of these steps. + +Prepare the Training Data +------------------------- + +Before building and training neural networks, the training data (input +and output) is to be prepared. The inputs and the outputs of the +training data are NumPy arrays. + +Here is an example of preparing the training data for the XOR problem. + +For the input array, each element must be a list representing the inputs +(i.e. features) for the sample. If there are 200 samples and each sample +has 50 features, then the shape of the inputs array is ``(200, 50)``. +The variable ``num_inputs`` holds the length of each sample which is 2 +in this example. + +.. code:: python + + data_inputs = numpy.array([[1, 1], + [1, 0], + [0, 1], + [0, 0]]) + + data_outputs = numpy.array([0, + 1, + 1, + 0]) + + num_inputs = data_inputs.shape[1] + +For the output array, each element must be a single number representing +the class label of the sample. The class labels must start at ``0``. So, +if there are 200 samples, then the shape of the output array is +``(200)``. If there are 5 classes in the data, then the values of all +the 200 elements in the output array must range from 0 to 4 inclusive. +Generally, the class labels start from ``0`` to ``N-1`` where ``N`` is +the number of classes. + +For the XOR example, there are 2 classes and thus their labels are 0 and +1. The ``num_classes`` variable is assigned to 2. + +Note that the project only supports classification problems where each +sample is assigned to only one class. + +.. _create-an-instance-of-the-pygadganngann-class: + +Create an Instance of the ``pygad.gann.GANN`` Class +--------------------------------------------------- + +After preparing the input data, an instance of the ``pygad.gann.GANN`` +class is created by passing the appropriate parameters. + +Here is an example that creates a network for the XOR problem. The +``num_solutions`` parameter is set to 6 which means the genetic +algorithm population will have 6 solutions (i.e. networks). All of these +6 neural networks will have the same architectures as specified by the +other parameters. + +The output layer has 2 neurons because there are only 2 classes (0 and +1). + +.. code:: python + + import pygad.gann + import pygad.nn + + num_solutions = 6 + GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, + num_neurons_input=num_inputs, + num_neurons_hidden_layers=[2], + num_neurons_output=2, + hidden_activations=["relu"], + output_activation="softmax") + +The architecture of the created network has the following layers: + +- An input layer with 2 neurons (i.e. inputs) + +- A single hidden layer with 2 neurons. + +- An output layer with 2 neurons (i.e. classes). + +The weights of the network are as follows: + +- Between the input and the hidden layer, there is a weights matrix of + size equal to ``(number inputs x number of hidden neurons) = (2x2)``. + +- Between the hidden and the output layer, there is a weights matrix of + size equal to + ``(number of hidden neurons x number of outputs) = (2x2)``. + +The activation function used for the output layer is ``softmax``. The +``relu`` activation function is used for the hidden layer. + +After creating the instance of the ``pygad.gann.GANN`` class next is to +fetch the weights of the population as a list of vectors. + +Fetch the Population Weights as Vectors +--------------------------------------- + +For the genetic algorithm, the parameters (i.e. genes) of each solution +are represented as a single vector. + +For the task of training the network for the XOR problem, the weights of +each network in the population are not represented as a vector but 2 +matrices each of size 2x2. + +To create a list holding the population weights as vectors, one for each +network, the ``pygad.gann.population_as_vectors()`` function is used. + +.. code:: python + + population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) + +After preparing the population weights as a set of vectors, next is to +prepare 2 functions which are: + +1. Fitness function. + +2. Callback function after each generation. + +Prepare the Fitness Function +---------------------------- + +The PyGAD library works by allowing the users to customize the genetic +algorithm for their own problems. Because the problems differ in how the +fitness values are calculated, then PyGAD allows the user to use a +custom function as a maximization fitness function. This function must +accept 2 positional parameters representing the following: + +- The solution. + +- The solution index in the population. + +The fitness function must return a single number representing the +fitness. The higher the fitness value, the better the solution. + +Here is the implementation of the fitness function for training a neural +network. It uses the ``pygad.nn.predict()`` function to predict the +class labels based on the current solution's weights. The +``pygad.nn.predict()`` function uses the trained weights available in +the ``trained_weights`` attribute of each layer of the network for +making predictions. + +Based on such predictions, the classification accuracy is calculated. +This accuracy is used as the fitness value of the solution. Finally, the +fitness value is returned. + +.. code:: python + + def fitness_func(ga_instance, solution, sol_idx): + global GANN_instance, data_inputs, data_outputs + + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], + data_inputs=data_inputs) + correct_predictions = numpy.where(predictions == data_outputs)[0].size + solution_fitness = (correct_predictions/data_outputs.size)*100 + + return solution_fitness + +Prepare the Generation Callback Function +---------------------------------------- + +After each generation of the genetic algorithm, the fitness function +will be called to calculate the fitness value of each solution. Within +the fitness function, the ``pygad.nn.predict()`` function is used for +predicting the outputs based on the current solution's +``trained_weights`` attribute. Thus, it is required that such an +attribute is updated by weights evolved by the genetic algorithm after +each generation. + +PyGAD 2.0.0 and higher has a new parameter accepted by the ``pygad.GA`` +class constructor named ``on_generation``. It could be assigned to a +function that is called after each generation. The function must accept +a single parameter representing the instance of the ``pygad.GA`` class. + +This callback function can be used to update the ``trained_weights`` +attribute of layers of each network in the population. + +Here is the implementation for a function that updates the +``trained_weights`` attribute of the layers of the population networks. + +It works by converting the current population from the vector form to +the matric form using the ``pygad.gann.population_as_matrices()`` +function. It accepts the population as vectors and returns it as +matrices. + +The population matrices are then passed to the +``update_population_trained_weights()`` method in the ``pygad.gann`` +module to update the ``trained_weights`` attribute of all layers for all +solutions within the population. + +.. code:: python + + def callback_generation(ga_instance): + global GANN_instance + + population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, population_vectors=ga_instance.population) + GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + +After preparing the fitness and callback function, next is to create an +instance of the ``pygad.GA`` class. + +.. _create-an-instance-of-the-pygadga-class: + +Create an Instance of the ``pygad.GA`` Class +-------------------------------------------- + +Once the parameters of the genetic algorithm are prepared, an instance +of the ``pygad.GA`` class can be created. + +Here is an example. + +.. code:: python + + initial_population = population_vectors.copy() + + num_parents_mating = 4 + + num_generations = 500 + + mutation_percent_genes = 5 + + parent_selection_type = "sss" + + crossover_type = "single_point" + + mutation_type = "random" + + keep_parents = 1 + + init_range_low = -2 + init_range_high = 5 + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + mutation_percent_genes=mutation_percent_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + parent_selection_type=parent_selection_type, + crossover_type=crossover_type, + mutation_type=mutation_type, + keep_parents=keep_parents, + on_generation=callback_generation) + +The last step for training the neural networks using the genetic +algorithm is calling the ``run()`` method. + +.. _run-the-created-instance-of-the-pygadga-class: + +Run the Created Instance of the ``pygad.GA`` Class +-------------------------------------------------- + +By calling the ``run()`` method from the ``pygad.GA`` instance, the +genetic algorithm will iterate through the number of generations +specified in its ``num_generations`` parameter. + +.. code:: python + + ga_instance.run() + +Plot the Fitness Values +----------------------- + +After the ``run()`` method completes, the ``plot_fitness()`` method can +be called to show how the fitness values evolve by generation. A fitness +value (i.e. accuracy) of 100 is reached after around 180 generations. + +.. code:: python + + ga_instance.plot_fitness() + +.. figure:: https://user-images.githubusercontent.com/16560492/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png + :alt: + +By running the code again, a different initial population is created and +thus a classification accuracy of 100 can be reached using a less number +of generations. On the other hand, a different initial population might +cause 100% accuracy to be reached using more generations or not reached +at all. + +Information about the Best Solution +----------------------------------- + +The following information about the best solution in the last population +is returned using the ``best_solution()`` method in the ``pygad.GA`` +class. + +- Solution + +- Fitness value of the solution + +- Index of the solution within the population + +Here is how such information is returned. The fitness value (i.e. +accuracy) is 100. + +.. code:: python + + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +.. code:: + + Parameters of the best solution : [3.55081391 -3.21562011 -14.2617784 0.68044231 -1.41258145 -3.2979315 1.58136006 -7.83726169] + Fitness value of the best solution = 100.0 + Index of the best solution : 0 + +Using the ``best_solution_generation`` attribute of the instance from +the ``pygad.GA`` class, the generation number at which the **best +fitness** is reached could be fetched. According to the result, the best +fitness value is reached after 182 generations. + +.. code:: python + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + +.. code:: + + Best solution reached after 182 generations. + +Making Predictions using the Trained Weights +-------------------------------------------- + +The ``pygad.nn.predict()`` function can be used to make predictions +using the trained network. As printed, the network is able to predict +the labels correctly. + +.. code:: python + + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], data_inputs=data_inputs) + print("Predictions of the trained network : {predictions}".format(predictions=predictions)) + +.. code:: + + Predictions of the trained network : [0. 1. 1. 0.] + +Calculating Some Statistics +--------------------------- + +Based on the predictions the network made, some statistics can be +calculated such as the number of correct and wrong predictions in +addition to the classification accuracy. + +.. code:: python + + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +.. code:: + + Number of correct classifications : 4 + print("Number of wrong classifications : 0 + Classification accuracy : 100 + +Examples +======== + +This section gives the complete code of some examples that build and +train neural networks using the genetic algorithm. Each subsection +builds a different network. + +XOR Classification +------------------ + +This example is discussed in the **Steps to Build and Train Neural +Networks using Genetic Algorithm** section that builds the XOR gate and +its complete code is listed below. + +.. code:: python + + import numpy + import pygad + import pygad.nn + import pygad.gann + + def fitness_func(ga_instance, solution, sol_idx): + global GANN_instance, data_inputs, data_outputs + + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], + data_inputs=data_inputs) + correct_predictions = numpy.where(predictions == data_outputs)[0].size + solution_fitness = (correct_predictions/data_outputs.size)*100 + + return solution_fitness + + def callback_generation(ga_instance): + global GANN_instance, last_fitness + + population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, + population_vectors=ga_instance.population) + + GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) + + last_fitness = ga_instance.best_solution()[1].copy() + + # Holds the fitness value of the previous generation. + last_fitness = 0 + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.array([[1, 1], + [1, 0], + [0, 1], + [0, 0]]) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.array([0, + 1, + 1, + 0]) + + # The length of the input vector for each sample (i.e. number of neurons in the input layer). + num_inputs = data_inputs.shape[1] + # The number of neurons in the output layer (i.e. number of classes). + num_classes = 2 + + # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. + num_solutions = 6 # A solution or a network can be used interchangeably. + GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, + num_neurons_input=num_inputs, + num_neurons_hidden_layers=[2], + num_neurons_output=num_classes, + hidden_activations=["relu"], + output_activation="softmax") + + # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. + # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. + population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) + + # To prepare the initial population, there are 2 ways: + # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. + # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. + initial_population = population_vectors.copy() + + num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. + + num_generations = 500 # Number of generations. + + mutation_percent_genes = 5 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. + + parent_selection_type = "sss" # Type of parent selection. + + crossover_type = "single_point" # Type of the crossover operator. + + mutation_type = "random" # Type of the mutation operator. + + keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. + + init_range_low = -2 + init_range_high = 5 + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + mutation_percent_genes=mutation_percent_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + parent_selection_type=parent_selection_type, + crossover_type=crossover_type, + mutation_type=mutation_type, + keep_parents=keep_parents, + on_generation=callback_generation) + + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness() + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + + # Predicting the outputs of the data using the best solution. + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], + data_inputs=data_inputs) + print("Predictions of the trained network : {predictions}".format(predictions=predictions)) + + # Calculating some statistics + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +Image Classification +-------------------- + +In the documentation of the ``pygad.nn`` module, a neural network is +created for classifying images from the Fruits360 dataset without being +trained using an optimization algorithm. This section discusses how to +train such a classifier using the genetic algorithm with the help of the +``pygad.gann`` module. + +Please make sure that the training data files +`dataset_features.npy `__ +and +`outputs.npy `__ +are available. For downloading them, use these links: + +1. `dataset_features.npy `__: + The features + https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy + +2. `outputs.npy `__: + The class labels + https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy + +After the data is available, here is the complete code that builds and +trains a neural network using the genetic algorithm for classifying +images from 4 classes of the Fruits360 dataset. + +Because there are 4 classes, the output layer is assigned has 4 neurons +according to the ``num_neurons_output`` parameter of the +``pygad.gann.GANN`` class constructor. + +.. code:: python + + import numpy + import pygad + import pygad.nn + import pygad.gann + + def fitness_func(ga_instance, solution, sol_idx): + global GANN_instance, data_inputs, data_outputs + + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], + data_inputs=data_inputs) + correct_predictions = numpy.where(predictions == data_outputs)[0].size + solution_fitness = (correct_predictions/data_outputs.size)*100 + + return solution_fitness + + def callback_generation(ga_instance): + global GANN_instance, last_fitness + + population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, + population_vectors=ga_instance.population) + + GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) + + last_fitness = ga_instance.best_solution()[1].copy() + + # Holds the fitness value of the previous generation. + last_fitness = 0 + + # Reading the input data. + data_inputs = numpy.load("dataset_features.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy + + # Optional step of filtering the input data using the standard deviation. + features_STDs = numpy.std(a=data_inputs, axis=0) + data_inputs = data_inputs[:, features_STDs>50] + + # Reading the output data. + data_outputs = numpy.load("outputs.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy + + # The length of the input vector for each sample (i.e. number of neurons in the input layer). + num_inputs = data_inputs.shape[1] + # The number of neurons in the output layer (i.e. number of classes). + num_classes = 4 + + # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. + num_solutions = 8 # A solution or a network can be used interchangeably. + GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, + num_neurons_input=num_inputs, + num_neurons_hidden_layers=[150, 50], + num_neurons_output=num_classes, + hidden_activations=["relu", "relu"], + output_activation="softmax") + + # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. + # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. + population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) + + # To prepare the initial population, there are 2 ways: + # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. + # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. + initial_population = population_vectors.copy() + + num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. + + num_generations = 500 # Number of generations. + + mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. + + parent_selection_type = "sss" # Type of parent selection. + + crossover_type = "single_point" # Type of the crossover operator. + + mutation_type = "random" # Type of the mutation operator. + + keep_parents = -1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + mutation_percent_genes=mutation_percent_genes, + parent_selection_type=parent_selection_type, + crossover_type=crossover_type, + mutation_type=mutation_type, + keep_parents=keep_parents, + on_generation=callback_generation) + + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness() + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + + # Predicting the outputs of the data using the best solution. + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], + data_inputs=data_inputs) + print("Predictions of the trained network : {predictions}".format(predictions=predictions)) + + # Calculating some statistics + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +After training completes, here are the outputs of the print statements. +The number of wrong classifications is only 1 and the accuracy is +99.949%. This accuracy is reached after 482 generations. + +.. code:: + + Fitness value of the best solution = 99.94903160040775 + Index of the best solution : 0 + Best fitness value reached after 482 generations. + Number of correct classifications : 1961. + Number of wrong classifications : 1. + Classification accuracy : 99.94903160040775. + +The next figure shows how fitness value evolves by generation. + +.. figure:: https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png + :alt: + +Regression Example 1 +-------------------- + +To train a neural network for regression, follow these instructions: + +1. Set the ``output_activation`` parameter in the constructor of the + ``pygad.gann.GANN`` class to ``"None"``. It is possible to use the + ReLU function if all outputs are nonnegative. + +.. code:: python + + GANN_instance = pygad.gann.GANN(... + output_activation="None") + +1. Wherever the ``pygad.nn.predict()`` function is used, set the + ``problem_type`` parameter to ``"regression"``. + +.. code:: python + + predictions = pygad.nn.predict(..., + problem_type="regression") + +1. Design the fitness function to calculate the error (e.g. mean + absolute error). + +.. code:: python + + def fitness_func(ga_instance, solution, sol_idx): + ... + + predictions = pygad.nn.predict(..., + problem_type="regression") + + solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) + + return solution_fitness + +The next code builds a complete example for building a neural network +for regression. + +.. code:: python + + import numpy + import pygad + import pygad.nn + import pygad.gann + + def fitness_func(ga_instance, solution, sol_idx): + global GANN_instance, data_inputs, data_outputs + + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], + data_inputs=data_inputs, problem_type="regression") + solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) + + return solution_fitness + + def callback_generation(ga_instance): + global GANN_instance, last_fitness + + population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, + population_vectors=ga_instance.population) + + GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) + + last_fitness = ga_instance.best_solution()[1].copy() + + # Holds the fitness value of the previous generation. + last_fitness = 0 + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.array([[2, 5, -3, 0.1], + [8, 15, 20, 13]]) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.array([0.1, + 1.5]) + + # The length of the input vector for each sample (i.e. number of neurons in the input layer). + num_inputs = data_inputs.shape[1] + + # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. + num_solutions = 6 # A solution or a network can be used interchangeably. + GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, + num_neurons_input=num_inputs, + num_neurons_hidden_layers=[2], + num_neurons_output=1, + hidden_activations=["relu"], + output_activation="None") + + # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. + # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. + population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) + + # To prepare the initial population, there are 2 ways: + # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. + # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. + initial_population = population_vectors.copy() + + num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. + + num_generations = 500 # Number of generations. + + mutation_percent_genes = 5 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. + + parent_selection_type = "sss" # Type of parent selection. + + crossover_type = "single_point" # Type of the crossover operator. + + mutation_type = "random" # Type of the mutation operator. + + keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. + + init_range_low = -1 + init_range_high = 1 + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + mutation_percent_genes=mutation_percent_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + parent_selection_type=parent_selection_type, + crossover_type=crossover_type, + mutation_type=mutation_type, + keep_parents=keep_parents, + on_generation=callback_generation) + + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness() + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + + # Predicting the outputs of the data using the best solution. + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], + data_inputs=data_inputs, + problem_type="regression") + print("Predictions of the trained network : {predictions}".format(predictions=predictions)) + + # Calculating some statistics + abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) + print("Absolute error : {abs_error}.".format(abs_error=abs_error)) + +The next figure shows how the fitness value changes for the generations +used. + +.. figure:: https://user-images.githubusercontent.com/16560492/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png + :alt: + +Regression Example 2 - Fish Weight Prediction +--------------------------------------------- + +This example uses the Fish Market Dataset available at Kaggle +(https://www.kaggle.com/aungpyaeap/fish-market). Simply download the CSV +dataset from `this +link `__ +(https://www.kaggle.com/aungpyaeap/fish-market/download). The dataset is +also available at the `GitHub project of the pygad.gann +module `__: +https://github.com/ahmedfgad/NeuralGenetic + +Using the Pandas library, the dataset is read using the ``read_csv()`` +function. + +.. code:: python + + data = numpy.array(pandas.read_csv("Fish.csv")) + +The last 5 columns in the dataset are used as inputs and the **Weight** +column is used as output. + +.. code:: python + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) # Fish Weight + +Note how the activation function at the last layer is set to ``"None"``. +Moreover, the ``problem_type`` parameter in the ``pygad.nn.train()`` and +``pygad.nn.predict()`` functions is set to ``"regression"``. Remember to +design an appropriate fitness function for the regression problem. In +this example, the fitness value is calculated based on the mean absolute +error. + +.. code:: python + + solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) + +Here is the complete code. + +.. code:: python + + import numpy + import pygad + import pygad.nn + import pygad.gann + import pandas + + def fitness_func(ga_instance, solution, sol_idx): + global GANN_instance, data_inputs, data_outputs + + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx], + data_inputs=data_inputs, problem_type="regression") + solution_fitness = 1.0/numpy.mean(numpy.abs(predictions - data_outputs)) + + return solution_fitness + + def callback_generation(ga_instance): + global GANN_instance, last_fitness + + population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, + population_vectors=ga_instance.population) + + GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices) + + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness)) + + last_fitness = ga_instance.best_solution()[1].copy() + + # Holds the fitness value of the previous generation. + last_fitness = 0 + + data = numpy.array(pandas.read_csv("Fish.csv")) + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) + + # The length of the input vector for each sample (i.e. number of neurons in the input layer). + num_inputs = data_inputs.shape[1] + + # Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched. + num_solutions = 6 # A solution or a network can be used interchangeably. + GANN_instance = pygad.gann.GANN(num_solutions=num_solutions, + num_neurons_input=num_inputs, + num_neurons_hidden_layers=[2], + num_neurons_output=1, + hidden_activations=["relu"], + output_activation="None") + + # population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably. + # If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed. + population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks) + + # To prepare the initial population, there are 2 ways: + # 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population. + # 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless. + initial_population = population_vectors.copy() + + num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool. + + num_generations = 500 # Number of generations. + + mutation_percent_genes = 5 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists. + + parent_selection_type = "sss" # Type of parent selection. + + crossover_type = "single_point" # Type of the crossover operator. + + mutation_type = "random" # Type of the mutation operator. + + keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing. + + init_range_low = -1 + init_range_high = 1 + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + mutation_percent_genes=mutation_percent_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + parent_selection_type=parent_selection_type, + crossover_type=crossover_type, + mutation_type=mutation_type, + keep_parents=keep_parents, + on_generation=callback_generation) + + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness() + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + + # Predicting the outputs of the data using the best solution. + predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx], + data_inputs=data_inputs, + problem_type="regression") + print("Predictions of the trained network : {predictions}".format(predictions=predictions)) + + # Calculating some statistics + abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) + print("Absolute error : {abs_error}.".format(abs_error=abs_error)) + +The next figure shows how the fitness value changes for the 500 +generations used. + +.. figure:: https://user-images.githubusercontent.com/16560492/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png + :alt: diff --git a/docs/source/index.rst b/docs/source/index.rst index b346229..3bd6ad6 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -247,7 +247,7 @@ pygad Module :maxdepth: 4 :caption: pygad Module TOC - README_pygad_ReadTheDocs.rst + pygad.rst @@ -262,7 +262,7 @@ pygad.nn Module :maxdepth: 4 :caption: pygad.nn Module TOC - README_pygad_nn_ReadTheDocs.rst + nn.rst @@ -278,7 +278,7 @@ pygad.gann Module :maxdepth: 4 :caption: pygad.gann Module TOC - README_pygad_gann_ReadTheDocs.rst + gann.rst @@ -298,7 +298,7 @@ pygad.cnn Module :maxdepth: 4 :caption: pygad.cnn Module TOC - README_pygad_cnn_ReadTheDocs.rst + cnn.rst @@ -318,7 +318,7 @@ pygad.gacnn Module :maxdepth: 4 :caption: pygad.gacnn Module TOC - README_pygad_gacnn_ReadTheDocs.rst + gacnn.rst @@ -333,7 +333,7 @@ pygad.kerasga Module :maxdepth: 4 :caption: pygad.kerasga Module TOC - README_pygad_kerasga_ReadTheDocs.rst + kerasga.rst @@ -348,7 +348,7 @@ pygad.torchga Module :maxdepth: 4 :caption: pygad.torchga Module TOC - README_pygad_torchga_ReadTheDocs.rst + torchga.rst @@ -363,7 +363,7 @@ More Information :maxdepth: 4 :caption: More Information - Footer.rst + releases.rst diff --git a/docs/source/README_pygad_kerasga_ReadTheDocs.rst b/docs/source/kerasga.rst similarity index 97% rename from docs/source/README_pygad_kerasga_ReadTheDocs.rst rename to docs/source/kerasga.rst index 9b82467..a602468 100644 --- a/docs/source/README_pygad_kerasga_ReadTheDocs.rst +++ b/docs/source/kerasga.rst @@ -1,971 +1,971 @@ -.. _pygadkerasga-module: - -``pygad.kerasga`` Module -======================== - -This section of the PyGAD's library documentation discusses the -`pygad.kerasga `__ -module. - -The ``pygad.kerarsga`` module has helper a class and 2 functions to -train Keras models using the genetic algorithm (PyGAD). The Keras model -can be built either using the `Sequential -Model `__ or the `Functional -API `__. - -The contents of this module are: - -1. ``KerasGA``: A class for creating an initial population of all - parameters in the Keras model. - -2. ``model_weights_as_vector()``: A function to reshape the Keras model - weights to a single vector. - -3. ``model_weights_as_matrix()``: A function to restore the Keras model - weights from a vector. - -4. ``predict()``: A function to make predictions based on the Keras - model and a solution. - -More details are given in the next sections. - -Steps Summary -============= - -The summary of the steps used to train a Keras model using PyGAD is as -follows: - -1. Create a Keras model. - -2. Create an instance of the ``pygad.kerasga.KerasGA`` class. - -3. Prepare the training data. - -4. Build the fitness function. - -5. Create an instance of the ``pygad.GA`` class. - -6. Run the genetic algorithm. - -Create Keras Model -================== - -Before discussing training a Keras model using PyGAD, the first thing to -do is to create the Keras model. - -According to the `Keras library -documentation `__, there are 3 ways to -build a Keras model: - -1. `Sequential Model `__ - -2. `Functional API `__ - -3. `Model Subclassing `__ - -PyGAD supports training the models created either using the Sequential -Model or the Functional API. - -Here is an example of a model created using the Sequential Model. - -.. code:: python - - import tensorflow.keras - - input_layer = tensorflow.keras.layers.Input(3) - dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu") - output_layer = tensorflow.keras.layers.Dense(1, activation="linear") - - model = tensorflow.keras.Sequential() - model.add(input_layer) - model.add(dense_layer1) - model.add(output_layer) - -This is the same model created using the Functional API. - -.. code:: python - - input_layer = tensorflow.keras.layers.Input(3) - dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) - output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - -Feel free to add the layers of your choice. - -.. _pygadkerasgakerasga-class: - -``pygad.kerasga.KerasGA`` Class -=============================== - -The ``pygad.kerasga`` module has a class named ``KerasGA`` for creating -an initial population for the genetic algorithm based on a Keras model. -The constructor, methods, and attributes within the class are discussed -in this section. - -.. _init: - -``__init__()`` --------------- - -The ``pygad.kerasga.KerasGA`` class constructor accepts the following -parameters: - -- ``model``: An instance of the Keras model. - -- ``num_solutions``: Number of solutions in the population. Each - solution has different parameters of the model. - -Instance Attributes -------------------- - -All parameters in the ``pygad.kerasga.KerasGA`` class constructor are -used as instance attributes in addition to adding a new attribute called -``population_weights``. - -Here is a list of all instance attributes: - -- ``model`` - -- ``num_solutions`` - -- ``population_weights``: A nested list holding the weights of all - solutions in the population. - -Methods in the ``KerasGA`` Class --------------------------------- - -This section discusses the methods available for instances of the -``pygad.kerasga.KerasGA`` class. - -.. _createpopulation: - -``create_population()`` -~~~~~~~~~~~~~~~~~~~~~~~ - -The ``create_population()`` method creates the initial population of the -genetic algorithm as a list of solutions where each solution represents -different model parameters. The list of networks is assigned to the -``population_weights`` attribute of the instance. - -.. _functions-in-the-pygadkerasga-module: - -Functions in the ``pygad.kerasga`` Module -========================================= - -This section discusses the functions in the ``pygad.kerasga`` module. - -.. _pygadkerasgamodelweightsasvector: - -``pygad.kerasga.model_weights_as_vector()`` --------------------------------------------- - -The ``model_weights_as_vector()`` function accepts a single parameter -named ``model`` representing the Keras model. It returns a vector -holding all model weights. The reason for representing the model weights -as a vector is that the genetic algorithm expects all parameters of any -solution to be in a 1D vector form. - -This function filters the layers based on the ``trainable`` attribute to -see whether the layer weights are trained or not. For each layer, if its -``trainable=False``, then its weights will not be evolved using the -genetic algorithm. Otherwise, it will be represented in the chromosome -and evolved. - -The function accepts the following parameters: - -- ``model``: The Keras model. - -It returns a 1D vector holding the model weights. - -.. _pygadkerasgamodelweightsasmatrix: - -``pygad.kerasga.model_weights_as_matrix()`` -------------------------------------------- - -The ``model_weights_as_matrix()`` function accepts the following -parameters: - -1. ``model``: The Keras model. - -2. ``weights_vector``: The model parameters as a vector. - -It returns the restored model weights after reshaping the vector. - -.. _pygadkerasgapredict: - -``pygad.kerasga.predict()`` ---------------------------- - -The ``predict()`` function makes a prediction based on a solution. It -accepts the following parameters: - -1. ``model``: The Keras model. - -2. ``solution``: The solution evolved. - -3. ``data``: The test data inputs. - -It returns the predictions for the data samples. - -Examples -======== - -This section gives the complete code of some examples that build and -train a Keras model using PyGAD. Each subsection builds a different -network. - -Example 1: Regression Example ------------------------------ - -The next code builds a simple Keras model for regression. The next -subsections discuss each part in the code. - -.. code:: python - - import tensorflow.keras - import pygad.kerasga - import numpy - import pygad - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, keras_ga, model - - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - - mae = tensorflow.keras.losses.MeanAbsoluteError() - abs_error = mae(data_outputs, predictions).numpy() + 0.00000001 - solution_fitness = 1.0/abs_error - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - input_layer = tensorflow.keras.layers.Input(3) - dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) - output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - - keras_ga = pygad.kerasga.KerasGA(model=model, - num_solutions=10) - - # Data inputs - data_inputs = numpy.array([[0.02, 0.1, 0.15], - [0.7, 0.6, 0.8], - [1.5, 1.2, 1.7], - [3.2, 2.9, 3.1]]) - - # Data outputs - data_outputs = numpy.array([[0.1], - [0.6], - [1.3], - [2.5]]) - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 250 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = keras_ga.population_weights # Initial population of network weights - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Make prediction based on the best solution. - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - print("Predictions : \n", predictions) - - mae = tensorflow.keras.losses.MeanAbsoluteError() - abs_error = mae(data_outputs, predictions).numpy() - print("Absolute Error : ", abs_error) - -Create a Keras Model -~~~~~~~~~~~~~~~~~~~~ - -According to the steps mentioned previously, the first step is to create -a Keras model. Here is the code that builds the model using the -Functional API. - -.. code:: python - - import tensorflow.keras - - input_layer = tensorflow.keras.layers.Input(3) - dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) - output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - -The model can also be build using the Keras Sequential Model API. - -.. code:: python - - input_layer = tensorflow.keras.layers.Input(3) - dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu") - output_layer = tensorflow.keras.layers.Dense(1, activation="linear") - - model = tensorflow.keras.Sequential() - model.add(input_layer) - model.add(dense_layer1) - model.add(output_layer) - -.. _create-an-instance-of-the-pygadkerasgakerasga-class: - -Create an Instance of the ``pygad.kerasga.KerasGA`` Class -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The second step is to create an instance of the -``pygad.kerasga.KerasGA`` class. There are 10 solutions per population. -Change this number according to your needs. - -.. code:: python - - import pygad.kerasga - - keras_ga = pygad.kerasga.KerasGA(model=model, - num_solutions=10) - -.. _prepare-the-training-data-1: - -Prepare the Training Data -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The third step is to prepare the training data inputs and outputs. Here -is an example where there are 4 samples. Each sample has 3 inputs and 1 -output. - -.. code:: python - - import numpy - - # Data inputs - data_inputs = numpy.array([[0.02, 0.1, 0.15], - [0.7, 0.6, 0.8], - [1.5, 1.2, 1.7], - [3.2, 2.9, 3.1]]) - - # Data outputs - data_outputs = numpy.array([[0.1], - [0.6], - [1.3], - [2.5]]) - -Build the Fitness Function -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The fourth step is to build the fitness function. This function must -accept 2 parameters representing the solution and its index within the -population. - -The next fitness function returns the model predictions based on the -current solution using the ``predict()`` function. Then, it calculates -the mean absolute error (MAE) of the Keras model based on the parameters -in the solution. The reciprocal of the MAE is used as the fitness value. -Feel free to use any other loss function to calculate the fitness value. - -.. code:: python - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, keras_ga, model - - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - - mae = tensorflow.keras.losses.MeanAbsoluteError() - abs_error = mae(data_outputs, predictions).numpy() + 0.00000001 - solution_fitness = 1.0/abs_error - - return solution_fitness - -.. _create-an-instance-of-the-pygadga-class: - -Create an Instance of the ``pygad.GA`` Class -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The fifth step is to instantiate the ``pygad.GA`` class. Note how the -``initial_population`` parameter is assigned to the initial weights of -the Keras models. - -For more information, please check the `parameters this class -accepts `__. - -.. code:: python - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 250 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = keras_ga.population_weights # Initial population of network weights - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - -Run the Genetic Algorithm -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The sixth and last step is to run the genetic algorithm by calling the -``run()`` method. - -.. code:: python - - ga_instance.run() - -After the PyGAD completes its execution, then there is a figure that -shows how the fitness value changes by generation. Call the -``plot_fitness()`` method to show the figure. - -.. code:: python - - ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) - -Here is the figure. - -.. figure:: https://user-images.githubusercontent.com/16560492/93722638-ac261880-fb98-11ea-95d3-e773deb034f4.png - :alt: - -To get information about the best solution found by PyGAD, use the -``best_solution()`` method. - -.. code:: python - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - -.. code:: python - - Fitness value of the best solution = 72.77768757825352 - Index of the best solution : 0 - -The next code makes prediction using the ``predict()`` function to -return the model predictions based on the best solution. - -.. code:: python - - # Fetch the parameters of the best solution. - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - print("Predictions : \n", predictions) - -.. code:: python - - Predictions : - [[0.09935353] - [0.63082725] - [1.2765523 ] - [2.4999595 ]] - -The next code measures the trained model error. - -.. code:: python - - mae = tensorflow.keras.losses.MeanAbsoluteError() - abs_error = mae(data_outputs, predictions).numpy() - print("Absolute Error : ", abs_error) - -.. code:: - - Absolute Error : 0.013740465 - -Example 2: XOR Binary Classification ------------------------------------- - -The next code creates a Keras model to build the XOR binary -classification problem. Let's highlight the changes compared to the -previous example. - -.. code:: python - - import tensorflow.keras - import pygad.kerasga - import numpy - import pygad - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, keras_ga, model - - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - - bce = tensorflow.keras.losses.BinaryCrossentropy() - solution_fitness = 1.0 / (bce(data_outputs, predictions).numpy() + 0.00000001) - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - # Build the keras model using the functional API. - input_layer = tensorflow.keras.layers.Input(2) - dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer) - output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - - # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. - keras_ga = pygad.kerasga.KerasGA(model=model, - num_solutions=10) - - # XOR problem inputs - data_inputs = numpy.array([[0, 0], - [0, 1], - [1, 0], - [1, 1]]) - - # XOR problem outputs - data_outputs = numpy.array([[1, 0], - [0, 1], - [0, 1], - [1, 0]]) - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 250 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = keras_ga.population_weights # Initial population of network weights. - - # Create an instance of the pygad.GA class - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - # Start the genetic algorithm evolution. - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Make predictions based on the best solution. - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - print("Predictions : \n", predictions) - - # Calculate the binary crossentropy for the trained model. - bce = tensorflow.keras.losses.BinaryCrossentropy() - print("Binary Crossentropy : ", bce(data_outputs, predictions).numpy()) - - # Calculate the classification accuracy for the trained model. - ba = tensorflow.keras.metrics.BinaryAccuracy() - ba.update_state(data_outputs, predictions) - accuracy = ba.result().numpy() - print("Accuracy : ", accuracy) - -Compared to the previous regression example, here are the changes: - -- The Keras model is changed according to the nature of the problem. - Now, it has 2 inputs and 2 outputs with an in-between hidden layer of - 4 neurons. - -.. code:: python - - # Build the keras model using the functional API. - input_layer = tensorflow.keras.layers.Input(2) - dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer) - output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - -- The train data is changed. Note that the output of each sample is a - 1D vector of 2 values, 1 for each class. - -.. code:: python - - # XOR problem inputs - data_inputs = numpy.array([[0, 0], - [0, 1], - [1, 0], - [1, 1]]) - - # XOR problem outputs - data_outputs = numpy.array([[1, 0], - [0, 1], - [0, 1], - [1, 0]]) - -- The fitness value is calculated based on the binary cross entropy. - -.. code:: python - - bce = tensorflow.keras.losses.BinaryCrossentropy() - solution_fitness = 1.0 / (bce(data_outputs, predictions).numpy() + 0.00000001) - -After the previous code completes, the next figure shows how the fitness -value change by generation. - -.. figure:: https://user-images.githubusercontent.com/16560492/93722639-b811da80-fb98-11ea-8951-f13a7a266c04.png - :alt: - -Here is some information about the trained model. Its fitness value is -``739.24``, loss is ``0.0013527311`` and accuracy is 100%. - -.. code:: python - - Fitness value of the best solution = 739.2397344644013 - Index of the best solution : 7 - - Predictions : - [[9.9694413e-01 3.0558957e-03] - [5.0176249e-04 9.9949825e-01] - [1.8470541e-03 9.9815291e-01] - [9.9999976e-01 2.0538971e-07]] - - Binary Crossentropy : 0.0013527311 - - Accuracy : 1.0 - -Example 3: Image Multi-Class Classification (Dense Layers) ----------------------------------------------------------- - -Here is the code. - -.. code:: python - - import tensorflow.keras - import pygad.kerasga - import numpy - import pygad - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, keras_ga, model - - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - - cce = tensorflow.keras.losses.CategoricalCrossentropy() - solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - # Build the keras model using the functional API. - input_layer = tensorflow.keras.layers.Input(360) - dense_layer = tensorflow.keras.layers.Dense(50, activation="relu")(input_layer) - output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - - # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. - keras_ga = pygad.kerasga.KerasGA(model=model, - num_solutions=10) - - # Data inputs - data_inputs = numpy.load("dataset_features.npy") - - # Data outputs - data_outputs = numpy.load("outputs.npy") - data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 100 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = keras_ga.population_weights # Initial population of network weights. - - # Create an instance of the pygad.GA class - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - # Start the genetic algorithm evolution. - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Make predictions based on the best solution. - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - # print("Predictions : \n", predictions) - - # Calculate the categorical crossentropy for the trained model. - cce = tensorflow.keras.losses.CategoricalCrossentropy() - print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) - - # Calculate the classification accuracy for the trained model. - ca = tensorflow.keras.metrics.CategoricalAccuracy() - ca.update_state(data_outputs, predictions) - accuracy = ca.result().numpy() - print("Accuracy : ", accuracy) - -Compared to the previous binary classification example, this example has -multiple classes (4) and thus the loss is measured using categorical -cross entropy. - -.. code:: python - - cce = tensorflow.keras.losses.CategoricalCrossentropy() - solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) - -.. _prepare-the-training-data-2: - -Prepare the Training Data -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Before building and training neural networks, the training data (input -and output) needs to be prepared. The inputs and the outputs of the -training data are NumPy arrays. - -The data used in this example is available as 2 files: - -1. `dataset_features.npy `__: - Data inputs. - https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy - -2. `outputs.npy `__: - Class labels. - https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy - -The data consists of 4 classes of images. The image shape is -``(100, 100, 3)``. The number of training samples is 1962. The feature -vector extracted from each image has a length 360. - -Simply download these 2 files and read them according to the next code. -Note that the class labels are one-hot encoded using the -``tensorflow.keras.utils.to_categorical()`` function. - -.. code:: python - - import numpy - - data_inputs = numpy.load("dataset_features.npy") - - data_outputs = numpy.load("outputs.npy") - data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) - -The next figure shows how the fitness value changes. - -.. figure:: https://user-images.githubusercontent.com/16560492/93722649-c2cc6f80-fb98-11ea-96e7-3f6ce3cfe1cf.png - :alt: - -Here are some statistics about the trained model. - -.. code:: - - Fitness value of the best solution = 4.197464252185969 - Index of the best solution : 0 - Categorical Crossentropy : 0.23823906 - Accuracy : 0.9852192 - -Example 4: Image Multi-Class Classification (Conv Layers) ---------------------------------------------------------- - -Compared to the previous example that uses only dense layers, this -example uses convolutional layers to classify the same dataset. - -Here is the complete code. - -.. code:: python - - import tensorflow.keras - import pygad.kerasga - import numpy - import pygad - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, keras_ga, model - - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - - cce = tensorflow.keras.losses.CategoricalCrossentropy() - solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - # Build the keras model using the functional API. - input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3)) - conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5, - kernel_size=7, - activation="relu")(input_layer) - max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5), - strides=5)(conv_layer1) - conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3, - kernel_size=3, - activation="relu")(max_pool1) - flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2) - dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer) - output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - - # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. - keras_ga = pygad.kerasga.KerasGA(model=model, - num_solutions=10) - - # Data inputs - data_inputs = numpy.load("dataset_inputs.npy") - - # Data outputs - data_outputs = numpy.load("dataset_outputs.npy") - data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 200 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = keras_ga.population_weights # Initial population of network weights. - - # Create an instance of the pygad.GA class - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - # Start the genetic algorithm evolution. - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Make predictions based on the best solution. - predictions = pygad.kerasga.predict(model=model, - solution=solution, - data=data_inputs) - # print("Predictions : \n", predictions) - - # Calculate the categorical crossentropy for the trained model. - cce = tensorflow.keras.losses.CategoricalCrossentropy() - print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) - - # Calculate the classification accuracy for the trained model. - ca = tensorflow.keras.metrics.CategoricalAccuracy() - ca.update_state(data_outputs, predictions) - accuracy = ca.result().numpy() - print("Accuracy : ", accuracy) - -Compared to the previous example, the only change is that the -architecture uses convolutional and max-pooling layers. The shape of -each input sample is 100x100x3. - -.. code:: python - - # Build the keras model using the functional API. - input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3)) - conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5, - kernel_size=7, - activation="relu")(input_layer) - max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5), - strides=5)(conv_layer1) - conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3, - kernel_size=3, - activation="relu")(max_pool1) - flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2) - dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer) - output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) - - model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - -.. _prepare-the-training-data-3: - -Prepare the Training Data -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The data used in this example is available as 2 files: - -1. `dataset_inputs.npy `__: - Data inputs. - https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy - -2. `dataset_outputs.npy `__: - Class labels. - https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy - -The data consists of 4 classes of images. The image shape is -``(100, 100, 3)`` and there are 20 images per class for a total of 80 -training samples. For more information about the dataset, check the -`Reading the -Data `__ -section of the ``pygad.cnn`` module. - -Simply download these 2 files and read them according to the next code. -Note that the class labels are one-hot encoded using the -``tensorflow.keras.utils.to_categorical()`` function. - -.. code:: python - - import numpy - - data_inputs = numpy.load("dataset_inputs.npy") - - data_outputs = numpy.load("dataset_outputs.npy") - data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) - -The next figure shows how the fitness value changes. - -.. figure:: https://user-images.githubusercontent.com/16560492/93722654-cc55d780-fb98-11ea-8f95-7b65dc67f5c8.png - :alt: - -Here are some statistics about the trained model. The model accuracy is -75% after the 200 generations. Note that just running the code again may -give different results. - -.. code:: - - Fitness value of the best solution = 2.7462310258668805 - Index of the best solution : 0 - Categorical Crossentropy : 0.3641354 - Accuracy : 0.75 - -To improve the model performance, you can do the following: - -- Add more layers - -- Modify the existing layers. - -- Use different parameters for the layers. - -- Use different parameters for the genetic algorithm (e.g. number of - solution, number of generations, etc) +.. _pygadkerasga-module: + +``pygad.kerasga`` Module +======================== + +This section of the PyGAD's library documentation discusses the +`pygad.kerasga `__ +module. + +The ``pygad.kerarsga`` module has helper a class and 2 functions to +train Keras models using the genetic algorithm (PyGAD). The Keras model +can be built either using the `Sequential +Model `__ or the `Functional +API `__. + +The contents of this module are: + +1. ``KerasGA``: A class for creating an initial population of all + parameters in the Keras model. + +2. ``model_weights_as_vector()``: A function to reshape the Keras model + weights to a single vector. + +3. ``model_weights_as_matrix()``: A function to restore the Keras model + weights from a vector. + +4. ``predict()``: A function to make predictions based on the Keras + model and a solution. + +More details are given in the next sections. + +Steps Summary +============= + +The summary of the steps used to train a Keras model using PyGAD is as +follows: + +1. Create a Keras model. + +2. Create an instance of the ``pygad.kerasga.KerasGA`` class. + +3. Prepare the training data. + +4. Build the fitness function. + +5. Create an instance of the ``pygad.GA`` class. + +6. Run the genetic algorithm. + +Create Keras Model +================== + +Before discussing training a Keras model using PyGAD, the first thing to +do is to create the Keras model. + +According to the `Keras library +documentation `__, there are 3 ways to +build a Keras model: + +1. `Sequential Model `__ + +2. `Functional API `__ + +3. `Model Subclassing `__ + +PyGAD supports training the models created either using the Sequential +Model or the Functional API. + +Here is an example of a model created using the Sequential Model. + +.. code:: python + + import tensorflow.keras + + input_layer = tensorflow.keras.layers.Input(3) + dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu") + output_layer = tensorflow.keras.layers.Dense(1, activation="linear") + + model = tensorflow.keras.Sequential() + model.add(input_layer) + model.add(dense_layer1) + model.add(output_layer) + +This is the same model created using the Functional API. + +.. code:: python + + input_layer = tensorflow.keras.layers.Input(3) + dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) + output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +Feel free to add the layers of your choice. + +.. _pygadkerasgakerasga-class: + +``pygad.kerasga.KerasGA`` Class +=============================== + +The ``pygad.kerasga`` module has a class named ``KerasGA`` for creating +an initial population for the genetic algorithm based on a Keras model. +The constructor, methods, and attributes within the class are discussed +in this section. + +.. _init: + +``__init__()`` +-------------- + +The ``pygad.kerasga.KerasGA`` class constructor accepts the following +parameters: + +- ``model``: An instance of the Keras model. + +- ``num_solutions``: Number of solutions in the population. Each + solution has different parameters of the model. + +Instance Attributes +------------------- + +All parameters in the ``pygad.kerasga.KerasGA`` class constructor are +used as instance attributes in addition to adding a new attribute called +``population_weights``. + +Here is a list of all instance attributes: + +- ``model`` + +- ``num_solutions`` + +- ``population_weights``: A nested list holding the weights of all + solutions in the population. + +Methods in the ``KerasGA`` Class +-------------------------------- + +This section discusses the methods available for instances of the +``pygad.kerasga.KerasGA`` class. + +.. _createpopulation: + +``create_population()`` +~~~~~~~~~~~~~~~~~~~~~~~ + +The ``create_population()`` method creates the initial population of the +genetic algorithm as a list of solutions where each solution represents +different model parameters. The list of networks is assigned to the +``population_weights`` attribute of the instance. + +.. _functions-in-the-pygadkerasga-module: + +Functions in the ``pygad.kerasga`` Module +========================================= + +This section discusses the functions in the ``pygad.kerasga`` module. + +.. _pygadkerasgamodelweightsasvector: + +``pygad.kerasga.model_weights_as_vector()`` +-------------------------------------------- + +The ``model_weights_as_vector()`` function accepts a single parameter +named ``model`` representing the Keras model. It returns a vector +holding all model weights. The reason for representing the model weights +as a vector is that the genetic algorithm expects all parameters of any +solution to be in a 1D vector form. + +This function filters the layers based on the ``trainable`` attribute to +see whether the layer weights are trained or not. For each layer, if its +``trainable=False``, then its weights will not be evolved using the +genetic algorithm. Otherwise, it will be represented in the chromosome +and evolved. + +The function accepts the following parameters: + +- ``model``: The Keras model. + +It returns a 1D vector holding the model weights. + +.. _pygadkerasgamodelweightsasmatrix: + +``pygad.kerasga.model_weights_as_matrix()`` +------------------------------------------- + +The ``model_weights_as_matrix()`` function accepts the following +parameters: + +1. ``model``: The Keras model. + +2. ``weights_vector``: The model parameters as a vector. + +It returns the restored model weights after reshaping the vector. + +.. _pygadkerasgapredict: + +``pygad.kerasga.predict()`` +--------------------------- + +The ``predict()`` function makes a prediction based on a solution. It +accepts the following parameters: + +1. ``model``: The Keras model. + +2. ``solution``: The solution evolved. + +3. ``data``: The test data inputs. + +It returns the predictions for the data samples. + +Examples +======== + +This section gives the complete code of some examples that build and +train a Keras model using PyGAD. Each subsection builds a different +network. + +Example 1: Regression Example +----------------------------- + +The next code builds a simple Keras model for regression. The next +subsections discuss each part in the code. + +.. code:: python + + import tensorflow.keras + import pygad.kerasga + import numpy + import pygad + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + mae = tensorflow.keras.losses.MeanAbsoluteError() + abs_error = mae(data_outputs, predictions).numpy() + 0.00000001 + solution_fitness = 1.0/abs_error + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + input_layer = tensorflow.keras.layers.Input(3) + dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) + output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + + keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + + # Data inputs + data_inputs = numpy.array([[0.02, 0.1, 0.15], + [0.7, 0.6, 0.8], + [1.5, 1.2, 1.7], + [3.2, 2.9, 3.1]]) + + # Data outputs + data_outputs = numpy.array([[0.1], + [0.6], + [1.3], + [2.5]]) + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 250 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = keras_ga.population_weights # Initial population of network weights + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Make prediction based on the best solution. + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + print("Predictions : \n", predictions) + + mae = tensorflow.keras.losses.MeanAbsoluteError() + abs_error = mae(data_outputs, predictions).numpy() + print("Absolute Error : ", abs_error) + +Create a Keras Model +~~~~~~~~~~~~~~~~~~~~ + +According to the steps mentioned previously, the first step is to create +a Keras model. Here is the code that builds the model using the +Functional API. + +.. code:: python + + import tensorflow.keras + + input_layer = tensorflow.keras.layers.Input(3) + dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) + output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +The model can also be build using the Keras Sequential Model API. + +.. code:: python + + input_layer = tensorflow.keras.layers.Input(3) + dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu") + output_layer = tensorflow.keras.layers.Dense(1, activation="linear") + + model = tensorflow.keras.Sequential() + model.add(input_layer) + model.add(dense_layer1) + model.add(output_layer) + +.. _create-an-instance-of-the-pygadkerasgakerasga-class: + +Create an Instance of the ``pygad.kerasga.KerasGA`` Class +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The second step is to create an instance of the +``pygad.kerasga.KerasGA`` class. There are 10 solutions per population. +Change this number according to your needs. + +.. code:: python + + import pygad.kerasga + + keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + +.. _prepare-the-training-data-1: + +Prepare the Training Data +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The third step is to prepare the training data inputs and outputs. Here +is an example where there are 4 samples. Each sample has 3 inputs and 1 +output. + +.. code:: python + + import numpy + + # Data inputs + data_inputs = numpy.array([[0.02, 0.1, 0.15], + [0.7, 0.6, 0.8], + [1.5, 1.2, 1.7], + [3.2, 2.9, 3.1]]) + + # Data outputs + data_outputs = numpy.array([[0.1], + [0.6], + [1.3], + [2.5]]) + +Build the Fitness Function +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The fourth step is to build the fitness function. This function must +accept 2 parameters representing the solution and its index within the +population. + +The next fitness function returns the model predictions based on the +current solution using the ``predict()`` function. Then, it calculates +the mean absolute error (MAE) of the Keras model based on the parameters +in the solution. The reciprocal of the MAE is used as the fitness value. +Feel free to use any other loss function to calculate the fitness value. + +.. code:: python + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + mae = tensorflow.keras.losses.MeanAbsoluteError() + abs_error = mae(data_outputs, predictions).numpy() + 0.00000001 + solution_fitness = 1.0/abs_error + + return solution_fitness + +.. _create-an-instance-of-the-pygadga-class: + +Create an Instance of the ``pygad.GA`` Class +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The fifth step is to instantiate the ``pygad.GA`` class. Note how the +``initial_population`` parameter is assigned to the initial weights of +the Keras models. + +For more information, please check the `parameters this class +accepts `__. + +.. code:: python + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 250 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = keras_ga.population_weights # Initial population of network weights + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +Run the Genetic Algorithm +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The sixth and last step is to run the genetic algorithm by calling the +``run()`` method. + +.. code:: python + + ga_instance.run() + +After the PyGAD completes its execution, then there is a figure that +shows how the fitness value changes by generation. Call the +``plot_fitness()`` method to show the figure. + +.. code:: python + + ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + +Here is the figure. + +.. figure:: https://user-images.githubusercontent.com/16560492/93722638-ac261880-fb98-11ea-95d3-e773deb034f4.png + :alt: + +To get information about the best solution found by PyGAD, use the +``best_solution()`` method. + +.. code:: python + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +.. code:: python + + Fitness value of the best solution = 72.77768757825352 + Index of the best solution : 0 + +The next code makes prediction using the ``predict()`` function to +return the model predictions based on the best solution. + +.. code:: python + + # Fetch the parameters of the best solution. + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + print("Predictions : \n", predictions) + +.. code:: python + + Predictions : + [[0.09935353] + [0.63082725] + [1.2765523 ] + [2.4999595 ]] + +The next code measures the trained model error. + +.. code:: python + + mae = tensorflow.keras.losses.MeanAbsoluteError() + abs_error = mae(data_outputs, predictions).numpy() + print("Absolute Error : ", abs_error) + +.. code:: + + Absolute Error : 0.013740465 + +Example 2: XOR Binary Classification +------------------------------------ + +The next code creates a Keras model to build the XOR binary +classification problem. Let's highlight the changes compared to the +previous example. + +.. code:: python + + import tensorflow.keras + import pygad.kerasga + import numpy + import pygad + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + bce = tensorflow.keras.losses.BinaryCrossentropy() + solution_fitness = 1.0 / (bce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + # Build the keras model using the functional API. + input_layer = tensorflow.keras.layers.Input(2) + dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer) + output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + + # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. + keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + + # XOR problem inputs + data_inputs = numpy.array([[0, 0], + [0, 1], + [1, 0], + [1, 1]]) + + # XOR problem outputs + data_outputs = numpy.array([[1, 0], + [0, 1], + [0, 1], + [1, 0]]) + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 250 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = keras_ga.population_weights # Initial population of network weights. + + # Create an instance of the pygad.GA class + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + # Start the genetic algorithm evolution. + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Make predictions based on the best solution. + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + print("Predictions : \n", predictions) + + # Calculate the binary crossentropy for the trained model. + bce = tensorflow.keras.losses.BinaryCrossentropy() + print("Binary Crossentropy : ", bce(data_outputs, predictions).numpy()) + + # Calculate the classification accuracy for the trained model. + ba = tensorflow.keras.metrics.BinaryAccuracy() + ba.update_state(data_outputs, predictions) + accuracy = ba.result().numpy() + print("Accuracy : ", accuracy) + +Compared to the previous regression example, here are the changes: + +- The Keras model is changed according to the nature of the problem. + Now, it has 2 inputs and 2 outputs with an in-between hidden layer of + 4 neurons. + +.. code:: python + + # Build the keras model using the functional API. + input_layer = tensorflow.keras.layers.Input(2) + dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer) + output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +- The train data is changed. Note that the output of each sample is a + 1D vector of 2 values, 1 for each class. + +.. code:: python + + # XOR problem inputs + data_inputs = numpy.array([[0, 0], + [0, 1], + [1, 0], + [1, 1]]) + + # XOR problem outputs + data_outputs = numpy.array([[1, 0], + [0, 1], + [0, 1], + [1, 0]]) + +- The fitness value is calculated based on the binary cross entropy. + +.. code:: python + + bce = tensorflow.keras.losses.BinaryCrossentropy() + solution_fitness = 1.0 / (bce(data_outputs, predictions).numpy() + 0.00000001) + +After the previous code completes, the next figure shows how the fitness +value change by generation. + +.. figure:: https://user-images.githubusercontent.com/16560492/93722639-b811da80-fb98-11ea-8951-f13a7a266c04.png + :alt: + +Here is some information about the trained model. Its fitness value is +``739.24``, loss is ``0.0013527311`` and accuracy is 100%. + +.. code:: python + + Fitness value of the best solution = 739.2397344644013 + Index of the best solution : 7 + + Predictions : + [[9.9694413e-01 3.0558957e-03] + [5.0176249e-04 9.9949825e-01] + [1.8470541e-03 9.9815291e-01] + [9.9999976e-01 2.0538971e-07]] + + Binary Crossentropy : 0.0013527311 + + Accuracy : 1.0 + +Example 3: Image Multi-Class Classification (Dense Layers) +---------------------------------------------------------- + +Here is the code. + +.. code:: python + + import tensorflow.keras + import pygad.kerasga + import numpy + import pygad + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + # Build the keras model using the functional API. + input_layer = tensorflow.keras.layers.Input(360) + dense_layer = tensorflow.keras.layers.Dense(50, activation="relu")(input_layer) + output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + + # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. + keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + + # Data inputs + data_inputs = numpy.load("dataset_features.npy") + + # Data outputs + data_outputs = numpy.load("outputs.npy") + data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 100 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = keras_ga.population_weights # Initial population of network weights. + + # Create an instance of the pygad.GA class + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + # Start the genetic algorithm evolution. + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Make predictions based on the best solution. + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + # print("Predictions : \n", predictions) + + # Calculate the categorical crossentropy for the trained model. + cce = tensorflow.keras.losses.CategoricalCrossentropy() + print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) + + # Calculate the classification accuracy for the trained model. + ca = tensorflow.keras.metrics.CategoricalAccuracy() + ca.update_state(data_outputs, predictions) + accuracy = ca.result().numpy() + print("Accuracy : ", accuracy) + +Compared to the previous binary classification example, this example has +multiple classes (4) and thus the loss is measured using categorical +cross entropy. + +.. code:: python + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + +.. _prepare-the-training-data-2: + +Prepare the Training Data +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Before building and training neural networks, the training data (input +and output) needs to be prepared. The inputs and the outputs of the +training data are NumPy arrays. + +The data used in this example is available as 2 files: + +1. `dataset_features.npy `__: + Data inputs. + https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy + +2. `outputs.npy `__: + Class labels. + https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy + +The data consists of 4 classes of images. The image shape is +``(100, 100, 3)``. The number of training samples is 1962. The feature +vector extracted from each image has a length 360. + +Simply download these 2 files and read them according to the next code. +Note that the class labels are one-hot encoded using the +``tensorflow.keras.utils.to_categorical()`` function. + +.. code:: python + + import numpy + + data_inputs = numpy.load("dataset_features.npy") + + data_outputs = numpy.load("outputs.npy") + data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) + +The next figure shows how the fitness value changes. + +.. figure:: https://user-images.githubusercontent.com/16560492/93722649-c2cc6f80-fb98-11ea-96e7-3f6ce3cfe1cf.png + :alt: + +Here are some statistics about the trained model. + +.. code:: + + Fitness value of the best solution = 4.197464252185969 + Index of the best solution : 0 + Categorical Crossentropy : 0.23823906 + Accuracy : 0.9852192 + +Example 4: Image Multi-Class Classification (Conv Layers) +--------------------------------------------------------- + +Compared to the previous example that uses only dense layers, this +example uses convolutional layers to classify the same dataset. + +Here is the complete code. + +.. code:: python + + import tensorflow.keras + import pygad.kerasga + import numpy + import pygad + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + # Build the keras model using the functional API. + input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3)) + conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5, + kernel_size=7, + activation="relu")(input_layer) + max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5), + strides=5)(conv_layer1) + conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3, + kernel_size=3, + activation="relu")(max_pool1) + flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2) + dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer) + output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + + # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. + keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + + # Data inputs + data_inputs = numpy.load("dataset_inputs.npy") + + # Data outputs + data_outputs = numpy.load("dataset_outputs.npy") + data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 200 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = keras_ga.population_weights # Initial population of network weights. + + # Create an instance of the pygad.GA class + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + # Start the genetic algorithm evolution. + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Make predictions based on the best solution. + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=data_inputs) + # print("Predictions : \n", predictions) + + # Calculate the categorical crossentropy for the trained model. + cce = tensorflow.keras.losses.CategoricalCrossentropy() + print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) + + # Calculate the classification accuracy for the trained model. + ca = tensorflow.keras.metrics.CategoricalAccuracy() + ca.update_state(data_outputs, predictions) + accuracy = ca.result().numpy() + print("Accuracy : ", accuracy) + +Compared to the previous example, the only change is that the +architecture uses convolutional and max-pooling layers. The shape of +each input sample is 100x100x3. + +.. code:: python + + # Build the keras model using the functional API. + input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3)) + conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5, + kernel_size=7, + activation="relu")(input_layer) + max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5), + strides=5)(conv_layer1) + conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3, + kernel_size=3, + activation="relu")(max_pool1) + flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2) + dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer) + output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) + + model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) + +.. _prepare-the-training-data-3: + +Prepare the Training Data +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The data used in this example is available as 2 files: + +1. `dataset_inputs.npy `__: + Data inputs. + https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy + +2. `dataset_outputs.npy `__: + Class labels. + https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy + +The data consists of 4 classes of images. The image shape is +``(100, 100, 3)`` and there are 20 images per class for a total of 80 +training samples. For more information about the dataset, check the +`Reading the +Data `__ +section of the ``pygad.cnn`` module. + +Simply download these 2 files and read them according to the next code. +Note that the class labels are one-hot encoded using the +``tensorflow.keras.utils.to_categorical()`` function. + +.. code:: python + + import numpy + + data_inputs = numpy.load("dataset_inputs.npy") + + data_outputs = numpy.load("dataset_outputs.npy") + data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) + +The next figure shows how the fitness value changes. + +.. figure:: https://user-images.githubusercontent.com/16560492/93722654-cc55d780-fb98-11ea-8f95-7b65dc67f5c8.png + :alt: + +Here are some statistics about the trained model. The model accuracy is +75% after the 200 generations. Note that just running the code again may +give different results. + +.. code:: + + Fitness value of the best solution = 2.7462310258668805 + Index of the best solution : 0 + Categorical Crossentropy : 0.3641354 + Accuracy : 0.75 + +To improve the model performance, you can do the following: + +- Add more layers + +- Modify the existing layers. + +- Use different parameters for the layers. + +- Use different parameters for the genetic algorithm (e.g. number of + solution, number of generations, etc) diff --git a/docs/source/README_pygad_nn_ReadTheDocs.rst b/docs/source/nn.rst similarity index 97% rename from docs/source/README_pygad_nn_ReadTheDocs.rst rename to docs/source/nn.rst index 5999754..c7902ec 100644 --- a/docs/source/README_pygad_nn_ReadTheDocs.rst +++ b/docs/source/nn.rst @@ -1,976 +1,976 @@ -.. _pygadnn-module: - -``pygad.nn`` Module -=================== - -This section of the PyGAD's library documentation discusses the -**pygad.nn** module. - -Using the **pygad.nn** module, artificial neural networks are created. -The purpose of this module is to only implement the **forward pass** of -a neural network without using a training algorithm. The **pygad.nn** -module builds the network layers, implements the activations functions, -trains the network, makes predictions, and more. - -Later, the **pygad.gann** module is used to train the **pygad.nn** -network using the genetic algorithm built in the **pygad** module. - -Starting from `PyGAD -2.7.1 `__, -the **pygad.nn** module supports both classification and regression -problems. For more information, check the ``problem_type`` parameter in -the ``pygad.nn.train()`` and ``pygad.nn.predict()`` functions. - -Supported Layers -================ - -Each layer supported by the **pygad.nn** module has a corresponding -class. The layers and their classes are: - -1. **Input**: Implemented using the ``pygad.nn.InputLayer`` class. - -2. **Dense** (Fully Connected): Implemented using the - ``pygad.nn.DenseLayer`` class. - -In the future, more layers will be added. The next subsections discuss -such layers. - -.. _pygadnninputlayer-class: - -``pygad.nn.InputLayer`` Class ------------------------------ - -The ``pygad.nn.InputLayer`` class creates the input layer for the neural -network. For each network, there is only a single input layer. The -network architecture must start with an input layer. - -This class has no methods or class attributes. All it has is a -constructor that accepts a parameter named ``num_neurons`` representing -the number of neurons in the input layer. - -An instance attribute named ``num_neurons`` is created within the -constructor to keep such a number. Here is an example of building an -input layer with 20 neurons. - -.. code:: python - - input_layer = pygad.nn.InputLayer(num_neurons=20) - -Here is how the single attribute ``num_neurons`` within the instance of -the ``pygad.nn.InputLayer`` class can be accessed. - -.. code:: python - - num_input_neurons = input_layer.num_neurons - - print("Number of input neurons =", num_input_neurons) - -This is everything about the input layer. - -.. _pygadnndenselayer-class: - -``pygad.nn.DenseLayer`` Class ------------------------------ - -Using the ``pygad.nn.DenseLayer`` class, dense (fully-connected) layers -can be created. To create a dense layer, just create a new instance of -the class. The constructor accepts the following parameters: - -- ``num_neurons``: Number of neurons in the dense layer. - -- ``previous_layer``: A reference to the previous layer. Using the - ``previous_layer`` attribute, a linked list is created that connects - all network layers. - -- ``activation_function``: A string representing the activation - function to be used in this layer. Defaults to ``"sigmoid"``. - Currently, the supported values for the activation functions are - ``"sigmoid"``, ``"relu"``, ``"softmax"`` (supported in PyGAD 2.3.0 - and higher), and ``"None"`` (supported in PyGAD 2.7.0 and higher). - When a layer has its activation function set to ``"None"``, then it - means no activation function is applied. For a **regression - problem**, set the activation function of the output (last) layer to - ``"None"``. If all outputs in the regression problem are nonnegative, - then it is possible to use the ReLU function in the output layer. - -Within the constructor, the accepted parameters are used as instance -attributes. Besides the parameters, some new instance attributes are -created which are: - -- ``initial_weights``: The initial weights for the dense layer. - -- ``trained_weights``: The trained weights of the dense layer. This - attribute is initialized by the value in the ``initial_weights`` - attribute. - -Here is an example for creating a dense layer with 12 neurons. Note that -the ``previous_layer`` parameter is assigned to the input layer -``input_layer``. - -.. code:: python - - dense_layer = pygad.nn.DenseLayer(num_neurons=12, - previous_layer=input_layer, - activation_function="relu") - -Here is how to access some attributes in the dense layer: - -.. code:: python - - num_dense_neurons = dense_layer.num_neurons - dense_initail_weights = dense_layer.initial_weights - - print("Number of dense layer attributes =", num_dense_neurons) - print("Initial weights of the dense layer :", dense_initail_weights) - -Because ``dense_layer`` holds a reference to the input layer, then the -number of input neurons can be accessed. - -.. code:: python - - input_layer = dense_layer.previous_layer - num_input_neurons = input_layer.num_neurons - - print("Number of input neurons =", num_input_neurons) - -Here is another dense layer. This dense layer's ``previous_layer`` -attribute points to the previously created dense layer. - -.. code:: python - - dense_layer2 = pygad.nn.DenseLayer(num_neurons=5, - previous_layer=dense_layer, - activation_function="relu") - -Because ``dense_layer2`` holds a reference to ``dense_layer`` in its -``previous_layer`` attribute, then the number of neurons in -``dense_layer`` can be accessed. - -.. code:: python - - dense_layer = dense_layer2.previous_layer - dense_layer_neurons = dense_layer.num_neurons - - print("Number of dense neurons =", num_input_neurons) - -After getting the reference to ``dense_layer``, we can use it to access -the number of input neurons. - -.. code:: python - - dense_layer = dense_layer2.previous_layer - input_layer = dense_layer.previous_layer - num_input_neurons = input_layer.num_neurons - - print("Number of input neurons =", num_input_neurons) - -Assuming that ``dense_layer2`` is the last dense layer, then it is -regarded as the output layer. - -.. _previouslayer-attribute: - -``previous_layer`` Attribute -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The ``previous_layer`` attribute in the ``pygad.nn.DenseLayer`` class -creates a one way linked list between all the layers in the network -architecture as described by the next figure. - -The last (output) layer indexed N points to layer **N-1**, layer **N-1** -points to the layer **N-2**, the layer **N-2** points to the layer -**N-3**, and so on until reaching the end of the linked list which is -layer 1 (input layer). - -.. figure:: https://user-images.githubusercontent.com/16560492/81918975-816af880-95d7-11ea-83e3-34d14c3316db.jpg - :alt: - -The one way linked list allows returning all properties of all layers in -the network architecture by just passing the last layer in the network. -The linked list moves from the output layer towards the input layer. - -Using the ``previous_layer`` attribute of layer **N**, the layer **N-1** -can be accessed. Using the ``previous_layer`` attribute of layer -**N-1**, layer **N-2** can be accessed. The process continues until -reaching a layer that does not have a ``previous_layer`` attribute -(which is the input layer). - -The properties of the layers include the weights (initial or trained), -activation functions, and more. Here is how a ``while`` loop is used to -iterate through all the layers. The ``while`` loop stops only when the -current layer does not have a ``previous_layer`` attribute. This layer -is the input layer. - -.. code:: python - - layer = dense_layer2 - - while "previous_layer" in layer.__init__.__code__.co_varnames: - print("Number of neurons =", layer.num_neurons) - - # Go to the previous layer. - layer = layer.previous_layer - -Functions to Manipulate Neural Networks -======================================= - -There are a number of functions existing in the ``pygad.nn`` module that -helps to manipulate the neural network. - -.. _pygadnnlayersweights: - -``pygad.nn.layers_weights()`` ------------------------------ - -Creates and returns a list holding the weights matrices of all layers in -the neural network. - -Accepts the following parameters: - -- ``last_layer``: A reference to the last (output) layer in the network - architecture. - -- ``initial``: When ``True`` (default), the function returns the - **initial** weights of the layers using the layers' - ``initial_weights`` attribute. When ``False``, it returns the - **trained** weights of the layers using the layers' - ``trained_weights`` attribute. The initial weights are only needed - before network training starts. The trained weights are needed to - predict the network outputs. - -The function uses a ``while`` loop to iterate through the layers using -their ``previous_layer`` attribute. For each layer, either the initial -weights or the trained weights are returned based on where the -``initial`` parameter is ``True`` or ``False``. - -.. _pygadnnlayersweightsasvector: - -``pygad.nn.layers_weights_as_vector()`` ---------------------------------------- - -Creates and returns a list holding the weights **vectors** of all layers -in the neural network. The weights array of each layer is reshaped to -get a vector. - -This function is similar to the ``layers_weights()`` function except -that it returns the weights of each layer as a vector, not as an array. - -Accepts the following parameters: - -- ``last_layer``: A reference to the last (output) layer in the network - architecture. - -- ``initial``: When ``True`` (default), the function returns the - **initial** weights of the layers using the layers' - ``initial_weights`` attribute. When ``False``, it returns the - **trained** weights of the layers using the layers' - ``trained_weights`` attribute. The initial weights are only needed - before network training starts. The trained weights are needed to - predict the network outputs. - -The function uses a ``while`` loop to iterate through the layers using -their ``previous_layer`` attribute. For each layer, either the initial -weights or the trained weights are returned based on where the -``initial`` parameter is ``True`` or ``False``. - -.. _pygadnnlayersweightsasmatrix: - -``pygad.nn.layers_weights_as_matrix()`` ---------------------------------------- - -Converts the network weights from vectors to matrices. - -Compared to the ``layers_weights_as_vectors()`` function that only -accepts a reference to the last layer and returns the network weights as -vectors, this function accepts a reference to the last layer in addition -to a list holding the weights as vectors. Such vectors are converted -into matrices. - -Accepts the following parameters: - -- ``last_layer``: A reference to the last (output) layer in the network - architecture. - -- ``vector_weights``: The network weights as vectors where the weights - of each layer form a single vector. - -The function uses a ``while`` loop to iterate through the layers using -their ``previous_layer`` attribute. For each layer, the shape of its -weights array is returned. This shape is used to reshape the weights -vector of the layer into a matrix. - -.. _pygadnnlayersactivations: - -``pygad.nn.layers_activations()`` ---------------------------------- - -Creates and returns a list holding the names of the activation functions -of all layers in the neural network. - -Accepts the following parameter: - -- ``last_layer``: A reference to the last (output) layer in the network - architecture. - -The function uses a ``while`` loop to iterate through the layers using -their ``previous_layer`` attribute. For each layer, the name of the -activation function used is returned using the layer's -``activation_function`` attribute. - -.. _pygadnnsigmoid: - -``pygad.nn.sigmoid()`` ----------------------- - -Applies the sigmoid function and returns its result. - -Accepts the following parameters: - -- ``sop``: The input to which the sigmoid function is applied. - -.. _pygadnnrelu: - -``pygad.nn.relu()`` -------------------- - -Applies the rectified linear unit (ReLU) function and returns its -result. - -Accepts the following parameters: - -- ``sop``: The input to which the relu function is applied. - -.. _pygadnnsoftmax: - -``pygad.nn.softmax()`` ----------------------- - -Applies the softmax function and returns its result. - -Accepts the following parameters: - -- ``sop``: The input to which the softmax function is applied. - -.. _pygadnntrain: - -``pygad.nn.train()`` --------------------- - -Trains the neural network. - -Accepts the following parameters: - -- ``num_epochs``: Number of epochs. - -- ``last_layer``: Reference to the last (output) layer in the network - architecture. - -- ``data_inputs``: Data features. - -- ``data_outputs``: Data outputs. - -- ``problem_type``: The type of the problem which can be either - ``"classification"`` or ``"regression"``. Added in PyGAD 2.7.0 and - higher. - -- ``learning_rate``: Learning rate. - -For each epoch, all the data samples are fed to the network to return -their predictions. After each epoch, the weights are updated using only -the learning rate. No learning algorithm is used because the purpose of -this project is to only build the forward pass of training a neural -network. - -.. _pygadnnupdateweights: - -``pygad.nn.update_weights()`` ------------------------------ - -Calculates and returns the updated weights. Even no training algorithm -is used in this project, the weights are updated using the learning -rate. It is not the best way to update the weights but it is better than -keeping it as it is by making some small changes to the weights. - -Accepts the following parameters: - -- ``weights``: The current weights of the network. - -- ``network_error``: The network error. - -- ``learning_rate``: The learning rate. - -.. _pygadnnupdatelayerstrainedweights: - -``pygad.nn.update_layers_trained_weights()`` --------------------------------------------- - -After the network weights are trained, this function updates the -``trained_weights`` attribute of each layer by the weights calculated -after passing all the epochs (such weights are passed in the -``final_weights`` parameter) - -By just passing a reference to the last layer in the network (i.e. -output layer) in addition to the final weights, this function updates -the ``trained_weights`` attribute of all layers. - -Accepts the following parameters: - -- ``last_layer``: A reference to the last (output) layer in the network - architecture. - -- ``final_weights``: An array of weights of all layers in the network - after passing through all the epochs. - -The function uses a ``while`` loop to iterate through the layers using -their ``previous_layer`` attribute. For each layer, its -``trained_weights`` attribute is assigned the weights of the layer from -the ``final_weights`` parameter. - -.. _pygadnnpredict: - -``pygad.nn.predict()`` ----------------------- - -Uses the trained weights for predicting the samples' outputs. It returns -a list of the predicted outputs for all samples. - -Accepts the following parameters: - -- ``last_layer``: A reference to the last (output) layer in the network - architecture. - -- ``data_inputs``: Data features. - -- ``problem_type``: The type of the problem which can be either - ``"classification"`` or ``"regression"``. Added in PyGAD 2.7.0 and - higher. - -All the data samples are fed to the network to return their predictions. - -Helper Functions -================ - -There are functions in the ``pygad.nn`` module that does not directly -manipulate the neural networks. - -.. _pygadnntovector: - -``pygad.nn.to_vector()`` ------------------------- - -Converts a passed NumPy array (of any dimensionality) to its ``array`` -parameter into a 1D vector and returns the vector. - -Accepts the following parameters: - -- ``array``: The NumPy array to be converted into a 1D vector. - -.. _pygadnntoarray: - -``pygad.nn.to_array()`` ------------------------ - -Converts a passed vector to its ``vector`` parameter into a NumPy array -and returns the array. - -Accepts the following parameters: - -- ``vector``: The 1D vector to be converted into an array. - -- ``shape``: The target shape of the array. - -Supported Activation Functions -============================== - -The supported activation functions are: - -1. Sigmoid: Implemented using the ``pygad.nn.sigmoid()`` function. - -2. Rectified Linear Unit (ReLU): Implemented using the - ``pygad.nn.relu()`` function. - -3. Softmax: Implemented using the ``pygad.nn.softmax()`` function. - -Steps to Build a Neural Network -=============================== - -This section discusses how to use the ``pygad.nn`` module for building a -neural network. The summary of the steps are as follows: - -- Reading the Data - -- Building the Network Architecture - -- Training the Network - -- Making Predictions - -- Calculating Some Statistics - -Reading the Data ----------------- - -Before building the network architecture, the first thing to do is to -prepare the data that will be used for training the network. - -In this example, 4 classes of the **Fruits360** dataset are used for -preparing the training data. The 4 classes are: - -1. `Apple - Braeburn `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/apple - -2. `Lemon - Meyer `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/lemon - -3. `Mango `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/mango - -4. `Raspberry `__: - This class's data is available at - https://github.com/ahmedfgad/NumPyANN/tree/master/raspberry - -The features from such 4 classes are extracted according to the next -code. This code reads the raw images of the 4 classes of the dataset, -prepares the features and the outputs as NumPy arrays, and saves the -arrays in 2 files. - -This code extracts a feature vector from each image representing the -color histogram of the HSV space's hue channel. - -.. code:: python - - import numpy - import skimage.io, skimage.color, skimage.feature - import os - - fruits = ["apple", "raspberry", "mango", "lemon"] - # Number of samples in the datset used = 492+490+490+490=1,962 - # 360 is the length of the feature vector. - dataset_features = numpy.zeros(shape=(1962, 360)) - outputs = numpy.zeros(shape=(1962)) - - idx = 0 - class_label = 0 - for fruit_dir in fruits: - curr_dir = os.path.join(os.path.sep, fruit_dir) - all_imgs = os.listdir(os.getcwd()+curr_dir) - for img_file in all_imgs: - if img_file.endswith(".jpg"): # Ensures reading only JPG files. - fruit_data = skimage.io.imread(fname=os.path.sep.join([os.getcwd(), curr_dir, img_file]), as_gray=False) - fruit_data_hsv = skimage.color.rgb2hsv(rgb=fruit_data) - hist = numpy.histogram(a=fruit_data_hsv[:, :, 0], bins=360) - dataset_features[idx, :] = hist[0] - outputs[idx] = class_label - idx = idx + 1 - class_label = class_label + 1 - - # Saving the extracted features and the outputs as NumPy files. - numpy.save("dataset_features.npy", dataset_features) - numpy.save("outputs.npy", outputs) - -To save your time, the training data is already prepared and 2 files -created by the next code are available for download at these links: - -1. `dataset_features.npy `__: - The features - https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy - -2. `outputs.npy `__: - The class labels - https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy - -The -`outputs.npy `__ -file gives the following labels for the 4 classes: - -1. `Apple - Braeburn `__: - Class label is **0** - -2. `Lemon - Meyer `__: - Class label is **1** - -3. `Mango `__: - Class label is **2** - -4. `Raspberry `__: - Class label is **3** - -The project has 4 folders holding the images for the 4 classes. - -After the 2 files are created, then just read them to return the NumPy -arrays according to the next 2 lines: - -.. code:: python - - data_inputs = numpy.load("dataset_features.npy") - data_outputs = numpy.load("outputs.npy") - -After the data is prepared, next is to create the network architecture. - -Building the Network Architecture ---------------------------------- - -The input layer is created by instantiating the ``pygad.nn.InputLayer`` -class according to the next code. A network can only have a single input -layer. - -.. code:: python - - import pygad.nn - num_inputs = data_inputs.shape[1] - - input_layer = pygad.nn.InputLayer(num_inputs) - -After the input layer is created, next is to create a number of dense -layers according to the next code. Normally, the last dense layer is -regarded as the output layer. Note that the output layer has a number of -neurons equal to the number of classes in the dataset which is 4. - -.. code:: python - - hidden_layer = pygad.nn.DenseLayer(num_neurons=HL2_neurons, previous_layer=input_layer, activation_function="relu") - output_layer = pygad.nn.DenseLayer(num_neurons=4, previous_layer=hidden_layer2, activation_function="softmax") - -After both the data and the network architecture are prepared, the next -step is to train the network. - -Training the Network --------------------- - -Here is an example of using the ``pygad.nn.train()`` function. - -.. code:: python - - pygad.nn.train(num_epochs=10, - last_layer=output_layer, - data_inputs=data_inputs, - data_outputs=data_outputs, - learning_rate=0.01) - -After training the network, the next step is to make predictions. - -Making Predictions ------------------- - -The ``pygad.nn.predict()`` function uses the trained network for making -predictions. Here is an example. - -.. code:: python - - predictions = pygad.nn.predict(last_layer=output_layer, data_inputs=data_inputs) - -It is not expected to have high accuracy in the predictions because no -training algorithm is used. - -Calculating Some Statistics ---------------------------- - -Based on the predictions the network made, some statistics can be -calculated such as the number of correct and wrong predictions in -addition to the classification accuracy. - -.. code:: python - - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -It is very important to note that it is not expected that the -classification accuracy is high because no training algorithm is used. -Please check the documentation of the ``pygad.gann`` module for training -the network using the genetic algorithm. - -Examples -======== - -This section gives the complete code of some examples that build neural -networks using ``pygad.nn``. Each subsection builds a different network. - -XOR Classification ------------------- - -This is an example of building a network with 1 hidden layer with 2 -neurons for building a network that simulates the XOR logic gate. -Because the XOR problem has 2 classes (0 and 1), then the output layer -has 2 neurons, one for each class. - -.. code:: python - - import numpy - import pygad.nn - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.array([[1, 1], - [1, 0], - [0, 1], - [0, 0]]) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.array([0, - 1, - 1, - 0]) - - # The number of inputs (i.e. feature vector length) per sample - num_inputs = data_inputs.shape[1] - # Number of outputs per sample - num_outputs = 2 - - HL1_neurons = 2 - - # Building the network architecture. - input_layer = pygad.nn.InputLayer(num_inputs) - hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") - output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="softmax") - - # Training the network. - pygad.nn.train(num_epochs=10, - last_layer=output_layer, - data_inputs=data_inputs, - data_outputs=data_outputs, - learning_rate=0.01) - - # Using the trained network for predictions. - predictions = pygad.nn.predict(last_layer=output_layer, data_inputs=data_inputs) - - # Calculating some statistics - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -Image Classification --------------------- - -This example is discussed in the **Steps to Build a Neural Network** -section and its complete code is listed below. - -Remember to either download or create the -`dataset_features.npy `__ -and -`outputs.npy `__ -files before running this code. - -.. code:: python - - import numpy - import pygad.nn - - # Reading the data features. Check the 'extract_features.py' script for extracting the features & preparing the outputs of the dataset. - data_inputs = numpy.load("dataset_features.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy - - # Optional step for filtering the features using the standard deviation. - features_STDs = numpy.std(a=data_inputs, axis=0) - data_inputs = data_inputs[:, features_STDs > 50] - - # Reading the data outputs. Check the 'extract_features.py' script for extracting the features & preparing the outputs of the dataset. - data_outputs = numpy.load("outputs.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy - - # The number of inputs (i.e. feature vector length) per sample - num_inputs = data_inputs.shape[1] - # Number of outputs per sample - num_outputs = 4 - - HL1_neurons = 150 - HL2_neurons = 60 - - # Building the network architecture. - input_layer = pygad.nn.InputLayer(num_inputs) - hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") - hidden_layer2 = pygad.nn.DenseLayer(num_neurons=HL2_neurons, previous_layer=hidden_layer1, activation_function="relu") - output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer2, activation_function="softmax") - - # Training the network. - pygad.nn.train(num_epochs=10, - last_layer=output_layer, - data_inputs=data_inputs, - data_outputs=data_outputs, - learning_rate=0.01) - - # Using the trained network for predictions. - predictions = pygad.nn.predict(last_layer=output_layer, data_inputs=data_inputs) - - # Calculating some statistics - num_wrong = numpy.where(predictions != data_outputs)[0] - num_correct = data_outputs.size - num_wrong.size - accuracy = 100 * (num_correct/data_outputs.size) - print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) - print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) - print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) - -Regression Example 1 --------------------- - -The next code listing builds a neural network for regression. Here is -what to do to make the code works for regression: - -1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and - ``pygad.nn.predict()`` functions to the string ``"regression"``. - -.. code:: python - - pygad.nn.train(..., - problem_type="regression") - - predictions = pygad.nn.predict(..., - problem_type="regression") - -1. Set the activation function for the output layer to the string - ``"None"``. - -.. code:: python - - output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None") - -1. Calculate the prediction error according to your preferred error - function. Here is how the mean absolute error is calculated. - -.. code:: python - - abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) - print("Absolute error : {abs_error}.".format(abs_error=abs_error)) - -Here is the complete code. Yet, there is no algorithm used to train the -network and thus the network is expected to give bad results. Later, the -``pygad.gann`` module is used to train either a regression or -classification networks. - -.. code:: python - - import numpy - import pygad.nn - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.array([[2, 5, -3, 0.1], - [8, 15, 20, 13]]) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.array([0.1, - 1.5]) - - # The number of inputs (i.e. feature vector length) per sample - num_inputs = data_inputs.shape[1] - # Number of outputs per sample - num_outputs = 1 - - HL1_neurons = 2 - - # Building the network architecture. - input_layer = pygad.nn.InputLayer(num_inputs) - hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") - output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None") - - # Training the network. - pygad.nn.train(num_epochs=100, - last_layer=output_layer, - data_inputs=data_inputs, - data_outputs=data_outputs, - learning_rate=0.01, - problem_type="regression") - - # Using the trained network for predictions. - predictions = pygad.nn.predict(last_layer=output_layer, - data_inputs=data_inputs, - problem_type="regression") - - # Calculating some statistics - abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) - print("Absolute error : {abs_error}.".format(abs_error=abs_error)) - -Regression Example 2 - Fish Weight Prediction ---------------------------------------------- - -This example uses the Fish Market Dataset available at Kaggle -(https://www.kaggle.com/aungpyaeap/fish-market). Simply download the CSV -dataset from `this -link `__ -(https://www.kaggle.com/aungpyaeap/fish-market/download). The dataset is -also available at the `GitHub project of the pygad.nn -module `__: -https://github.com/ahmedfgad/NumPyANN - -Using the Pandas library, the dataset is read using the ``read_csv()`` -function. - -.. code:: python - - data = numpy.array(pandas.read_csv("Fish.csv")) - -The last 5 columns in the dataset are used as inputs and the **Weight** -column is used as output. - -.. code:: python - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) # Fish Weight - -Note how the activation function at the last layer is set to ``"None"``. -Moreover, the ``problem_type`` parameter in the ``pygad.nn.train()`` and -``pygad.nn.predict()`` functions is set to ``"regression"``. - -After the ``pygad.nn.train()`` function completes, the mean absolute -error is calculated. - -.. code:: python - - abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) - print("Absolute error : {abs_error}.".format(abs_error=abs_error)) - -Here is the complete code. - -.. code:: python - - import numpy - import pygad.nn - import pandas - - data = numpy.array(pandas.read_csv("Fish.csv")) - - # Preparing the NumPy array of the inputs. - data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) - - # Preparing the NumPy array of the outputs. - data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) # Fish Weight - - # The number of inputs (i.e. feature vector length) per sample - num_inputs = data_inputs.shape[1] - # Number of outputs per sample - num_outputs = 1 - - HL1_neurons = 2 - - # Building the network architecture. - input_layer = pygad.nn.InputLayer(num_inputs) - hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") - output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None") - - # Training the network. - pygad.nn.train(num_epochs=100, - last_layer=output_layer, - data_inputs=data_inputs, - data_outputs=data_outputs, - learning_rate=0.01, - problem_type="regression") - - # Using the trained network for predictions. - predictions = pygad.nn.predict(last_layer=output_layer, - data_inputs=data_inputs, - problem_type="regression") - - # Calculating some statistics - abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) - print("Absolute error : {abs_error}.".format(abs_error=abs_error)) +.. _pygadnn-module: + +``pygad.nn`` Module +=================== + +This section of the PyGAD's library documentation discusses the +**pygad.nn** module. + +Using the **pygad.nn** module, artificial neural networks are created. +The purpose of this module is to only implement the **forward pass** of +a neural network without using a training algorithm. The **pygad.nn** +module builds the network layers, implements the activations functions, +trains the network, makes predictions, and more. + +Later, the **pygad.gann** module is used to train the **pygad.nn** +network using the genetic algorithm built in the **pygad** module. + +Starting from `PyGAD +2.7.1 `__, +the **pygad.nn** module supports both classification and regression +problems. For more information, check the ``problem_type`` parameter in +the ``pygad.nn.train()`` and ``pygad.nn.predict()`` functions. + +Supported Layers +================ + +Each layer supported by the **pygad.nn** module has a corresponding +class. The layers and their classes are: + +1. **Input**: Implemented using the ``pygad.nn.InputLayer`` class. + +2. **Dense** (Fully Connected): Implemented using the + ``pygad.nn.DenseLayer`` class. + +In the future, more layers will be added. The next subsections discuss +such layers. + +.. _pygadnninputlayer-class: + +``pygad.nn.InputLayer`` Class +----------------------------- + +The ``pygad.nn.InputLayer`` class creates the input layer for the neural +network. For each network, there is only a single input layer. The +network architecture must start with an input layer. + +This class has no methods or class attributes. All it has is a +constructor that accepts a parameter named ``num_neurons`` representing +the number of neurons in the input layer. + +An instance attribute named ``num_neurons`` is created within the +constructor to keep such a number. Here is an example of building an +input layer with 20 neurons. + +.. code:: python + + input_layer = pygad.nn.InputLayer(num_neurons=20) + +Here is how the single attribute ``num_neurons`` within the instance of +the ``pygad.nn.InputLayer`` class can be accessed. + +.. code:: python + + num_input_neurons = input_layer.num_neurons + + print("Number of input neurons =", num_input_neurons) + +This is everything about the input layer. + +.. _pygadnndenselayer-class: + +``pygad.nn.DenseLayer`` Class +----------------------------- + +Using the ``pygad.nn.DenseLayer`` class, dense (fully-connected) layers +can be created. To create a dense layer, just create a new instance of +the class. The constructor accepts the following parameters: + +- ``num_neurons``: Number of neurons in the dense layer. + +- ``previous_layer``: A reference to the previous layer. Using the + ``previous_layer`` attribute, a linked list is created that connects + all network layers. + +- ``activation_function``: A string representing the activation + function to be used in this layer. Defaults to ``"sigmoid"``. + Currently, the supported values for the activation functions are + ``"sigmoid"``, ``"relu"``, ``"softmax"`` (supported in PyGAD 2.3.0 + and higher), and ``"None"`` (supported in PyGAD 2.7.0 and higher). + When a layer has its activation function set to ``"None"``, then it + means no activation function is applied. For a **regression + problem**, set the activation function of the output (last) layer to + ``"None"``. If all outputs in the regression problem are nonnegative, + then it is possible to use the ReLU function in the output layer. + +Within the constructor, the accepted parameters are used as instance +attributes. Besides the parameters, some new instance attributes are +created which are: + +- ``initial_weights``: The initial weights for the dense layer. + +- ``trained_weights``: The trained weights of the dense layer. This + attribute is initialized by the value in the ``initial_weights`` + attribute. + +Here is an example for creating a dense layer with 12 neurons. Note that +the ``previous_layer`` parameter is assigned to the input layer +``input_layer``. + +.. code:: python + + dense_layer = pygad.nn.DenseLayer(num_neurons=12, + previous_layer=input_layer, + activation_function="relu") + +Here is how to access some attributes in the dense layer: + +.. code:: python + + num_dense_neurons = dense_layer.num_neurons + dense_initail_weights = dense_layer.initial_weights + + print("Number of dense layer attributes =", num_dense_neurons) + print("Initial weights of the dense layer :", dense_initail_weights) + +Because ``dense_layer`` holds a reference to the input layer, then the +number of input neurons can be accessed. + +.. code:: python + + input_layer = dense_layer.previous_layer + num_input_neurons = input_layer.num_neurons + + print("Number of input neurons =", num_input_neurons) + +Here is another dense layer. This dense layer's ``previous_layer`` +attribute points to the previously created dense layer. + +.. code:: python + + dense_layer2 = pygad.nn.DenseLayer(num_neurons=5, + previous_layer=dense_layer, + activation_function="relu") + +Because ``dense_layer2`` holds a reference to ``dense_layer`` in its +``previous_layer`` attribute, then the number of neurons in +``dense_layer`` can be accessed. + +.. code:: python + + dense_layer = dense_layer2.previous_layer + dense_layer_neurons = dense_layer.num_neurons + + print("Number of dense neurons =", num_input_neurons) + +After getting the reference to ``dense_layer``, we can use it to access +the number of input neurons. + +.. code:: python + + dense_layer = dense_layer2.previous_layer + input_layer = dense_layer.previous_layer + num_input_neurons = input_layer.num_neurons + + print("Number of input neurons =", num_input_neurons) + +Assuming that ``dense_layer2`` is the last dense layer, then it is +regarded as the output layer. + +.. _previouslayer-attribute: + +``previous_layer`` Attribute +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``previous_layer`` attribute in the ``pygad.nn.DenseLayer`` class +creates a one way linked list between all the layers in the network +architecture as described by the next figure. + +The last (output) layer indexed N points to layer **N-1**, layer **N-1** +points to the layer **N-2**, the layer **N-2** points to the layer +**N-3**, and so on until reaching the end of the linked list which is +layer 1 (input layer). + +.. figure:: https://user-images.githubusercontent.com/16560492/81918975-816af880-95d7-11ea-83e3-34d14c3316db.jpg + :alt: + +The one way linked list allows returning all properties of all layers in +the network architecture by just passing the last layer in the network. +The linked list moves from the output layer towards the input layer. + +Using the ``previous_layer`` attribute of layer **N**, the layer **N-1** +can be accessed. Using the ``previous_layer`` attribute of layer +**N-1**, layer **N-2** can be accessed. The process continues until +reaching a layer that does not have a ``previous_layer`` attribute +(which is the input layer). + +The properties of the layers include the weights (initial or trained), +activation functions, and more. Here is how a ``while`` loop is used to +iterate through all the layers. The ``while`` loop stops only when the +current layer does not have a ``previous_layer`` attribute. This layer +is the input layer. + +.. code:: python + + layer = dense_layer2 + + while "previous_layer" in layer.__init__.__code__.co_varnames: + print("Number of neurons =", layer.num_neurons) + + # Go to the previous layer. + layer = layer.previous_layer + +Functions to Manipulate Neural Networks +======================================= + +There are a number of functions existing in the ``pygad.nn`` module that +helps to manipulate the neural network. + +.. _pygadnnlayersweights: + +``pygad.nn.layers_weights()`` +----------------------------- + +Creates and returns a list holding the weights matrices of all layers in +the neural network. + +Accepts the following parameters: + +- ``last_layer``: A reference to the last (output) layer in the network + architecture. + +- ``initial``: When ``True`` (default), the function returns the + **initial** weights of the layers using the layers' + ``initial_weights`` attribute. When ``False``, it returns the + **trained** weights of the layers using the layers' + ``trained_weights`` attribute. The initial weights are only needed + before network training starts. The trained weights are needed to + predict the network outputs. + +The function uses a ``while`` loop to iterate through the layers using +their ``previous_layer`` attribute. For each layer, either the initial +weights or the trained weights are returned based on where the +``initial`` parameter is ``True`` or ``False``. + +.. _pygadnnlayersweightsasvector: + +``pygad.nn.layers_weights_as_vector()`` +--------------------------------------- + +Creates and returns a list holding the weights **vectors** of all layers +in the neural network. The weights array of each layer is reshaped to +get a vector. + +This function is similar to the ``layers_weights()`` function except +that it returns the weights of each layer as a vector, not as an array. + +Accepts the following parameters: + +- ``last_layer``: A reference to the last (output) layer in the network + architecture. + +- ``initial``: When ``True`` (default), the function returns the + **initial** weights of the layers using the layers' + ``initial_weights`` attribute. When ``False``, it returns the + **trained** weights of the layers using the layers' + ``trained_weights`` attribute. The initial weights are only needed + before network training starts. The trained weights are needed to + predict the network outputs. + +The function uses a ``while`` loop to iterate through the layers using +their ``previous_layer`` attribute. For each layer, either the initial +weights or the trained weights are returned based on where the +``initial`` parameter is ``True`` or ``False``. + +.. _pygadnnlayersweightsasmatrix: + +``pygad.nn.layers_weights_as_matrix()`` +--------------------------------------- + +Converts the network weights from vectors to matrices. + +Compared to the ``layers_weights_as_vectors()`` function that only +accepts a reference to the last layer and returns the network weights as +vectors, this function accepts a reference to the last layer in addition +to a list holding the weights as vectors. Such vectors are converted +into matrices. + +Accepts the following parameters: + +- ``last_layer``: A reference to the last (output) layer in the network + architecture. + +- ``vector_weights``: The network weights as vectors where the weights + of each layer form a single vector. + +The function uses a ``while`` loop to iterate through the layers using +their ``previous_layer`` attribute. For each layer, the shape of its +weights array is returned. This shape is used to reshape the weights +vector of the layer into a matrix. + +.. _pygadnnlayersactivations: + +``pygad.nn.layers_activations()`` +--------------------------------- + +Creates and returns a list holding the names of the activation functions +of all layers in the neural network. + +Accepts the following parameter: + +- ``last_layer``: A reference to the last (output) layer in the network + architecture. + +The function uses a ``while`` loop to iterate through the layers using +their ``previous_layer`` attribute. For each layer, the name of the +activation function used is returned using the layer's +``activation_function`` attribute. + +.. _pygadnnsigmoid: + +``pygad.nn.sigmoid()`` +---------------------- + +Applies the sigmoid function and returns its result. + +Accepts the following parameters: + +- ``sop``: The input to which the sigmoid function is applied. + +.. _pygadnnrelu: + +``pygad.nn.relu()`` +------------------- + +Applies the rectified linear unit (ReLU) function and returns its +result. + +Accepts the following parameters: + +- ``sop``: The input to which the relu function is applied. + +.. _pygadnnsoftmax: + +``pygad.nn.softmax()`` +---------------------- + +Applies the softmax function and returns its result. + +Accepts the following parameters: + +- ``sop``: The input to which the softmax function is applied. + +.. _pygadnntrain: + +``pygad.nn.train()`` +-------------------- + +Trains the neural network. + +Accepts the following parameters: + +- ``num_epochs``: Number of epochs. + +- ``last_layer``: Reference to the last (output) layer in the network + architecture. + +- ``data_inputs``: Data features. + +- ``data_outputs``: Data outputs. + +- ``problem_type``: The type of the problem which can be either + ``"classification"`` or ``"regression"``. Added in PyGAD 2.7.0 and + higher. + +- ``learning_rate``: Learning rate. + +For each epoch, all the data samples are fed to the network to return +their predictions. After each epoch, the weights are updated using only +the learning rate. No learning algorithm is used because the purpose of +this project is to only build the forward pass of training a neural +network. + +.. _pygadnnupdateweights: + +``pygad.nn.update_weights()`` +----------------------------- + +Calculates and returns the updated weights. Even no training algorithm +is used in this project, the weights are updated using the learning +rate. It is not the best way to update the weights but it is better than +keeping it as it is by making some small changes to the weights. + +Accepts the following parameters: + +- ``weights``: The current weights of the network. + +- ``network_error``: The network error. + +- ``learning_rate``: The learning rate. + +.. _pygadnnupdatelayerstrainedweights: + +``pygad.nn.update_layers_trained_weights()`` +-------------------------------------------- + +After the network weights are trained, this function updates the +``trained_weights`` attribute of each layer by the weights calculated +after passing all the epochs (such weights are passed in the +``final_weights`` parameter) + +By just passing a reference to the last layer in the network (i.e. +output layer) in addition to the final weights, this function updates +the ``trained_weights`` attribute of all layers. + +Accepts the following parameters: + +- ``last_layer``: A reference to the last (output) layer in the network + architecture. + +- ``final_weights``: An array of weights of all layers in the network + after passing through all the epochs. + +The function uses a ``while`` loop to iterate through the layers using +their ``previous_layer`` attribute. For each layer, its +``trained_weights`` attribute is assigned the weights of the layer from +the ``final_weights`` parameter. + +.. _pygadnnpredict: + +``pygad.nn.predict()`` +---------------------- + +Uses the trained weights for predicting the samples' outputs. It returns +a list of the predicted outputs for all samples. + +Accepts the following parameters: + +- ``last_layer``: A reference to the last (output) layer in the network + architecture. + +- ``data_inputs``: Data features. + +- ``problem_type``: The type of the problem which can be either + ``"classification"`` or ``"regression"``. Added in PyGAD 2.7.0 and + higher. + +All the data samples are fed to the network to return their predictions. + +Helper Functions +================ + +There are functions in the ``pygad.nn`` module that does not directly +manipulate the neural networks. + +.. _pygadnntovector: + +``pygad.nn.to_vector()`` +------------------------ + +Converts a passed NumPy array (of any dimensionality) to its ``array`` +parameter into a 1D vector and returns the vector. + +Accepts the following parameters: + +- ``array``: The NumPy array to be converted into a 1D vector. + +.. _pygadnntoarray: + +``pygad.nn.to_array()`` +----------------------- + +Converts a passed vector to its ``vector`` parameter into a NumPy array +and returns the array. + +Accepts the following parameters: + +- ``vector``: The 1D vector to be converted into an array. + +- ``shape``: The target shape of the array. + +Supported Activation Functions +============================== + +The supported activation functions are: + +1. Sigmoid: Implemented using the ``pygad.nn.sigmoid()`` function. + +2. Rectified Linear Unit (ReLU): Implemented using the + ``pygad.nn.relu()`` function. + +3. Softmax: Implemented using the ``pygad.nn.softmax()`` function. + +Steps to Build a Neural Network +=============================== + +This section discusses how to use the ``pygad.nn`` module for building a +neural network. The summary of the steps are as follows: + +- Reading the Data + +- Building the Network Architecture + +- Training the Network + +- Making Predictions + +- Calculating Some Statistics + +Reading the Data +---------------- + +Before building the network architecture, the first thing to do is to +prepare the data that will be used for training the network. + +In this example, 4 classes of the **Fruits360** dataset are used for +preparing the training data. The 4 classes are: + +1. `Apple + Braeburn `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/apple + +2. `Lemon + Meyer `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/lemon + +3. `Mango `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/mango + +4. `Raspberry `__: + This class's data is available at + https://github.com/ahmedfgad/NumPyANN/tree/master/raspberry + +The features from such 4 classes are extracted according to the next +code. This code reads the raw images of the 4 classes of the dataset, +prepares the features and the outputs as NumPy arrays, and saves the +arrays in 2 files. + +This code extracts a feature vector from each image representing the +color histogram of the HSV space's hue channel. + +.. code:: python + + import numpy + import skimage.io, skimage.color, skimage.feature + import os + + fruits = ["apple", "raspberry", "mango", "lemon"] + # Number of samples in the datset used = 492+490+490+490=1,962 + # 360 is the length of the feature vector. + dataset_features = numpy.zeros(shape=(1962, 360)) + outputs = numpy.zeros(shape=(1962)) + + idx = 0 + class_label = 0 + for fruit_dir in fruits: + curr_dir = os.path.join(os.path.sep, fruit_dir) + all_imgs = os.listdir(os.getcwd()+curr_dir) + for img_file in all_imgs: + if img_file.endswith(".jpg"): # Ensures reading only JPG files. + fruit_data = skimage.io.imread(fname=os.path.sep.join([os.getcwd(), curr_dir, img_file]), as_gray=False) + fruit_data_hsv = skimage.color.rgb2hsv(rgb=fruit_data) + hist = numpy.histogram(a=fruit_data_hsv[:, :, 0], bins=360) + dataset_features[idx, :] = hist[0] + outputs[idx] = class_label + idx = idx + 1 + class_label = class_label + 1 + + # Saving the extracted features and the outputs as NumPy files. + numpy.save("dataset_features.npy", dataset_features) + numpy.save("outputs.npy", outputs) + +To save your time, the training data is already prepared and 2 files +created by the next code are available for download at these links: + +1. `dataset_features.npy `__: + The features + https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy + +2. `outputs.npy `__: + The class labels + https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy + +The +`outputs.npy `__ +file gives the following labels for the 4 classes: + +1. `Apple + Braeburn `__: + Class label is **0** + +2. `Lemon + Meyer `__: + Class label is **1** + +3. `Mango `__: + Class label is **2** + +4. `Raspberry `__: + Class label is **3** + +The project has 4 folders holding the images for the 4 classes. + +After the 2 files are created, then just read them to return the NumPy +arrays according to the next 2 lines: + +.. code:: python + + data_inputs = numpy.load("dataset_features.npy") + data_outputs = numpy.load("outputs.npy") + +After the data is prepared, next is to create the network architecture. + +Building the Network Architecture +--------------------------------- + +The input layer is created by instantiating the ``pygad.nn.InputLayer`` +class according to the next code. A network can only have a single input +layer. + +.. code:: python + + import pygad.nn + num_inputs = data_inputs.shape[1] + + input_layer = pygad.nn.InputLayer(num_inputs) + +After the input layer is created, next is to create a number of dense +layers according to the next code. Normally, the last dense layer is +regarded as the output layer. Note that the output layer has a number of +neurons equal to the number of classes in the dataset which is 4. + +.. code:: python + + hidden_layer = pygad.nn.DenseLayer(num_neurons=HL2_neurons, previous_layer=input_layer, activation_function="relu") + output_layer = pygad.nn.DenseLayer(num_neurons=4, previous_layer=hidden_layer2, activation_function="softmax") + +After both the data and the network architecture are prepared, the next +step is to train the network. + +Training the Network +-------------------- + +Here is an example of using the ``pygad.nn.train()`` function. + +.. code:: python + + pygad.nn.train(num_epochs=10, + last_layer=output_layer, + data_inputs=data_inputs, + data_outputs=data_outputs, + learning_rate=0.01) + +After training the network, the next step is to make predictions. + +Making Predictions +------------------ + +The ``pygad.nn.predict()`` function uses the trained network for making +predictions. Here is an example. + +.. code:: python + + predictions = pygad.nn.predict(last_layer=output_layer, data_inputs=data_inputs) + +It is not expected to have high accuracy in the predictions because no +training algorithm is used. + +Calculating Some Statistics +--------------------------- + +Based on the predictions the network made, some statistics can be +calculated such as the number of correct and wrong predictions in +addition to the classification accuracy. + +.. code:: python + + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +It is very important to note that it is not expected that the +classification accuracy is high because no training algorithm is used. +Please check the documentation of the ``pygad.gann`` module for training +the network using the genetic algorithm. + +Examples +======== + +This section gives the complete code of some examples that build neural +networks using ``pygad.nn``. Each subsection builds a different network. + +XOR Classification +------------------ + +This is an example of building a network with 1 hidden layer with 2 +neurons for building a network that simulates the XOR logic gate. +Because the XOR problem has 2 classes (0 and 1), then the output layer +has 2 neurons, one for each class. + +.. code:: python + + import numpy + import pygad.nn + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.array([[1, 1], + [1, 0], + [0, 1], + [0, 0]]) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.array([0, + 1, + 1, + 0]) + + # The number of inputs (i.e. feature vector length) per sample + num_inputs = data_inputs.shape[1] + # Number of outputs per sample + num_outputs = 2 + + HL1_neurons = 2 + + # Building the network architecture. + input_layer = pygad.nn.InputLayer(num_inputs) + hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") + output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="softmax") + + # Training the network. + pygad.nn.train(num_epochs=10, + last_layer=output_layer, + data_inputs=data_inputs, + data_outputs=data_outputs, + learning_rate=0.01) + + # Using the trained network for predictions. + predictions = pygad.nn.predict(last_layer=output_layer, data_inputs=data_inputs) + + # Calculating some statistics + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +Image Classification +-------------------- + +This example is discussed in the **Steps to Build a Neural Network** +section and its complete code is listed below. + +Remember to either download or create the +`dataset_features.npy `__ +and +`outputs.npy `__ +files before running this code. + +.. code:: python + + import numpy + import pygad.nn + + # Reading the data features. Check the 'extract_features.py' script for extracting the features & preparing the outputs of the dataset. + data_inputs = numpy.load("dataset_features.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy + + # Optional step for filtering the features using the standard deviation. + features_STDs = numpy.std(a=data_inputs, axis=0) + data_inputs = data_inputs[:, features_STDs > 50] + + # Reading the data outputs. Check the 'extract_features.py' script for extracting the features & preparing the outputs of the dataset. + data_outputs = numpy.load("outputs.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy + + # The number of inputs (i.e. feature vector length) per sample + num_inputs = data_inputs.shape[1] + # Number of outputs per sample + num_outputs = 4 + + HL1_neurons = 150 + HL2_neurons = 60 + + # Building the network architecture. + input_layer = pygad.nn.InputLayer(num_inputs) + hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") + hidden_layer2 = pygad.nn.DenseLayer(num_neurons=HL2_neurons, previous_layer=hidden_layer1, activation_function="relu") + output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer2, activation_function="softmax") + + # Training the network. + pygad.nn.train(num_epochs=10, + last_layer=output_layer, + data_inputs=data_inputs, + data_outputs=data_outputs, + learning_rate=0.01) + + # Using the trained network for predictions. + predictions = pygad.nn.predict(last_layer=output_layer, data_inputs=data_inputs) + + # Calculating some statistics + num_wrong = numpy.where(predictions != data_outputs)[0] + num_correct = data_outputs.size - num_wrong.size + accuracy = 100 * (num_correct/data_outputs.size) + print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) + print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) + print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) + +Regression Example 1 +-------------------- + +The next code listing builds a neural network for regression. Here is +what to do to make the code works for regression: + +1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and + ``pygad.nn.predict()`` functions to the string ``"regression"``. + +.. code:: python + + pygad.nn.train(..., + problem_type="regression") + + predictions = pygad.nn.predict(..., + problem_type="regression") + +1. Set the activation function for the output layer to the string + ``"None"``. + +.. code:: python + + output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None") + +1. Calculate the prediction error according to your preferred error + function. Here is how the mean absolute error is calculated. + +.. code:: python + + abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) + print("Absolute error : {abs_error}.".format(abs_error=abs_error)) + +Here is the complete code. Yet, there is no algorithm used to train the +network and thus the network is expected to give bad results. Later, the +``pygad.gann`` module is used to train either a regression or +classification networks. + +.. code:: python + + import numpy + import pygad.nn + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.array([[2, 5, -3, 0.1], + [8, 15, 20, 13]]) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.array([0.1, + 1.5]) + + # The number of inputs (i.e. feature vector length) per sample + num_inputs = data_inputs.shape[1] + # Number of outputs per sample + num_outputs = 1 + + HL1_neurons = 2 + + # Building the network architecture. + input_layer = pygad.nn.InputLayer(num_inputs) + hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") + output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None") + + # Training the network. + pygad.nn.train(num_epochs=100, + last_layer=output_layer, + data_inputs=data_inputs, + data_outputs=data_outputs, + learning_rate=0.01, + problem_type="regression") + + # Using the trained network for predictions. + predictions = pygad.nn.predict(last_layer=output_layer, + data_inputs=data_inputs, + problem_type="regression") + + # Calculating some statistics + abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) + print("Absolute error : {abs_error}.".format(abs_error=abs_error)) + +Regression Example 2 - Fish Weight Prediction +--------------------------------------------- + +This example uses the Fish Market Dataset available at Kaggle +(https://www.kaggle.com/aungpyaeap/fish-market). Simply download the CSV +dataset from `this +link `__ +(https://www.kaggle.com/aungpyaeap/fish-market/download). The dataset is +also available at the `GitHub project of the pygad.nn +module `__: +https://github.com/ahmedfgad/NumPyANN + +Using the Pandas library, the dataset is read using the ``read_csv()`` +function. + +.. code:: python + + data = numpy.array(pandas.read_csv("Fish.csv")) + +The last 5 columns in the dataset are used as inputs and the **Weight** +column is used as output. + +.. code:: python + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) # Fish Weight + +Note how the activation function at the last layer is set to ``"None"``. +Moreover, the ``problem_type`` parameter in the ``pygad.nn.train()`` and +``pygad.nn.predict()`` functions is set to ``"regression"``. + +After the ``pygad.nn.train()`` function completes, the mean absolute +error is calculated. + +.. code:: python + + abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) + print("Absolute error : {abs_error}.".format(abs_error=abs_error)) + +Here is the complete code. + +.. code:: python + + import numpy + import pygad.nn + import pandas + + data = numpy.array(pandas.read_csv("Fish.csv")) + + # Preparing the NumPy array of the inputs. + data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32) + + # Preparing the NumPy array of the outputs. + data_outputs = numpy.asarray(data[:, 1], dtype=numpy.float32) # Fish Weight + + # The number of inputs (i.e. feature vector length) per sample + num_inputs = data_inputs.shape[1] + # Number of outputs per sample + num_outputs = 1 + + HL1_neurons = 2 + + # Building the network architecture. + input_layer = pygad.nn.InputLayer(num_inputs) + hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu") + output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None") + + # Training the network. + pygad.nn.train(num_epochs=100, + last_layer=output_layer, + data_inputs=data_inputs, + data_outputs=data_outputs, + learning_rate=0.01, + problem_type="regression") + + # Using the trained network for predictions. + predictions = pygad.nn.predict(last_layer=output_layer, + data_inputs=data_inputs, + problem_type="regression") + + # Calculating some statistics + abs_error = numpy.mean(numpy.abs(predictions - data_outputs)) + print("Absolute error : {abs_error}.".format(abs_error=abs_error)) diff --git a/docs/source/README_pygad_ReadTheDocs.rst b/docs/source/pygad.rst similarity index 97% rename from docs/source/README_pygad_ReadTheDocs.rst rename to docs/source/pygad.rst index c1e6d2f..4e09154 100644 --- a/docs/source/README_pygad_ReadTheDocs.rst +++ b/docs/source/pygad.rst @@ -1,4626 +1,4626 @@ -``pygad`` Module -================ - -This section of the PyGAD's library documentation discusses the -``pygad`` module. - -Using the ``pygad`` module, instances of the genetic algorithm can be -created, run, saved, and loaded. - -.. _pygadga-class: - -``pygad.GA`` Class -================== - -The first module available in PyGAD is named ``pygad`` and contains a -class named ``GA`` for building the genetic algorithm. The constructor, -methods, function, and attributes within the class are discussed in this -section. - -.. _init: - -``__init__()`` --------------- - -For creating an instance of the ``pygad.GA`` class, the constructor -accepts several parameters that allow the user to customize the genetic -algorithm to different types of applications. - -The ``pygad.GA`` class constructor supports the following parameters: - -- ``num_generations``: Number of generations. - -- ``num_parents_mating``: Number of solutions to be selected as - parents. - -- ``fitness_func``: Accepts a function/method and returns the fitness - value of the solution. If a function is passed, then it must accept 3 - parameters (1. the instance of the ``pygad.GA`` class, 2. a single - solution, and 3. its index in the population). If method, then it - accepts a fourth parameter representing the method's class instance. - Check the `Preparing the fitness_func - Parameter `__ - section for information about creating such a function. - -- ``fitness_batch_size=None``: A new optional parameter called - ``fitness_batch_size`` is supported to calculate the fitness function - in batches. If it is assigned the value ``1`` or ``None`` (default), - then the normal flow is used where the fitness function is called for - each individual solution. If the ``fitness_batch_size`` parameter is - assigned a value satisfying this condition - ``1 < fitness_batch_size <= sol_per_pop``, then the solutions are - grouped into batches of size ``fitness_batch_size`` and the fitness - function is called once for each batch. Check the `Batch Fitness - Calculation `__ - section for more details and examples. Added in from `PyGAD - 2.19.0 `__. - -- ``initial_population``: A user-defined initial population. It is - useful when the user wants to start the generations with a custom - initial population. It defaults to ``None`` which means no initial - population is specified by the user. In this case, - `PyGAD `__ creates an initial - population using the ``sol_per_pop`` and ``num_genes`` parameters. An - exception is raised if the ``initial_population`` is ``None`` while - any of the 2 parameters (``sol_per_pop`` or ``num_genes``) is also - ``None``. Introduced in `PyGAD - 2.0.0 `__ - and higher. - -- ``sol_per_pop``: Number of solutions (i.e. chromosomes) within the - population. This parameter has no action if ``initial_population`` - parameter exists. - -- ``num_genes``: Number of genes in the solution/chromosome. This - parameter is not needed if the user feeds the initial population to - the ``initial_population`` parameter. - -- ``gene_type=float``: Controls the gene type. It can be assigned to a - single data type that is applied to all genes or can specify the data - type of each individual gene. It defaults to ``float`` which means - all genes are of ``float`` data type. Starting from `PyGAD - 2.9.0 `__, - the ``gene_type`` parameter can be assigned to a numeric value of any - of these types: ``int``, ``float``, and - ``numpy.int/uint/float(8-64)``. Starting from `PyGAD - 2.14.0 `__, - it can be assigned to a ``list``, ``tuple``, or a ``numpy.ndarray`` - which hold a data type for each gene (e.g. - ``gene_type=[int, float, numpy.int8]``). This helps to control the - data type of each individual gene. In `PyGAD - 2.15.0 `__, - a precision for the ``float`` data types can be specified (e.g. - ``gene_type=[float, 2]``. - -- ``init_range_low=-4``: The lower value of the random range from which - the gene values in the initial population are selected. - ``init_range_low`` defaults to ``-4``. Available in `PyGAD - 1.0.20 `__ - and higher. This parameter has no action if the - ``initial_population`` parameter exists. - -- ``init_range_high=4``: The upper value of the random range from which - the gene values in the initial population are selected. - ``init_range_high`` defaults to ``+4``. Available in `PyGAD - 1.0.20 `__ - and higher. This parameter has no action if the - ``initial_population`` parameter exists. - -- ``parent_selection_type="sss"``: The parent selection type. Supported - types are ``sss`` (for steady-state selection), ``rws`` (for roulette - wheel selection), ``sus`` (for stochastic universal selection), - ``rank`` (for rank selection), ``random`` (for random selection), and - ``tournament`` (for tournament selection). A custom parent selection - function can be passed starting from `PyGAD - 2.16.0 `__. - Check the `User-Defined Crossover, Mutation, and Parent Selection - Operators `__ - section for more details about building a user-defined parent - selection function. - -- ``keep_parents=-1``: Number of parents to keep in the current - population. ``-1`` (default) means to keep all parents in the next - population. ``0`` means keep no parents in the next population. A - value ``greater than 0`` means keeps the specified number of parents - in the next population. Note that the value assigned to - ``keep_parents`` cannot be ``< - 1`` or greater than the number of - solutions within the population ``sol_per_pop``. Starting from `PyGAD - 2.18.0 `__, - this parameter have an effect only when the ``keep_elitism`` - parameter is ``0``. Starting from `PyGAD - 2.20.0 `__, - the parents' fitness from the last generation will not be re-used if - ``keep_parents=0``. - -- ``keep_elitism=1``: Added in `PyGAD - 2.18.0 `__. - It can take the value ``0`` or a positive integer that satisfies - (``0 <= keep_elitism <= sol_per_pop``). It defaults to ``1`` which - means only the best solution in the current generation is kept in the - next generation. If assigned ``0``, this means it has no effect. If - assigned a positive integer ``K``, then the best ``K`` solutions are - kept in the next generation. It cannot be assigned a value greater - than the value assigned to the ``sol_per_pop`` parameter. If this - parameter has a value different than ``0``, then the ``keep_parents`` - parameter will have no effect. - -- ``K_tournament=3``: In case that the parent selection type is - ``tournament``, the ``K_tournament`` specifies the number of parents - participating in the tournament selection. It defaults to ``3``. - -- ``crossover_type="single_point"``: Type of the crossover operation. - Supported types are ``single_point`` (for single-point crossover), - ``two_points`` (for two points crossover), ``uniform`` (for uniform - crossover), and ``scattered`` (for scattered crossover). Scattered - crossover is supported from PyGAD - `2.9.0 `__ - and higher. It defaults to ``single_point``. A custom crossover - function can be passed starting from `PyGAD - 2.16.0 `__. - Check the `User-Defined Crossover, Mutation, and Parent Selection - Operators `__ - section for more details about creating a user-defined crossover - function. Starting from `PyGAD - 2.2.2 `__ - and higher, if ``crossover_type=None``, then the crossover step is - bypassed which means no crossover is applied and thus no offspring - will be created in the next generations. The next generation will use - the solutions in the current population. - -- ``crossover_probability=None``: The probability of selecting a parent - for applying the crossover operation. Its value must be between 0.0 - and 1.0 inclusive. For each parent, a random value between 0.0 and - 1.0 is generated. If this random value is less than or equal to the - value assigned to the ``crossover_probability`` parameter, then the - parent is selected. Added in `PyGAD - 2.5.0 `__ - and higher. - -- ``mutation_type="random"``: Type of the mutation operation. Supported - types are ``random`` (for random mutation), ``swap`` (for swap - mutation), ``inversion`` (for inversion mutation), ``scramble`` (for - scramble mutation), and ``adaptive`` (for adaptive mutation). It - defaults to ``random``. A custom mutation function can be passed - starting from `PyGAD - 2.16.0 `__. - Check the `User-Defined Crossover, Mutation, and Parent Selection - Operators `__ - section for more details about creating a user-defined mutation - function. Starting from `PyGAD - 2.2.2 `__ - and higher, if ``mutation_type=None``, then the mutation step is - bypassed which means no mutation is applied and thus no changes are - applied to the offspring created using the crossover operation. The - offspring will be used unchanged in the next generation. ``Adaptive`` - mutation is supported starting from `PyGAD - 2.10.0 `__. - For more information about adaptive mutation, go the the `Adaptive - Mutation `__ - section. For example about using adaptive mutation, check the `Use - Adaptive Mutation in - PyGAD `__ - section. - -- ``mutation_probability=None``: The probability of selecting a gene - for applying the mutation operation. Its value must be between 0.0 - and 1.0 inclusive. For each gene in a solution, a random value - between 0.0 and 1.0 is generated. If this random value is less than - or equal to the value assigned to the ``mutation_probability`` - parameter, then the gene is selected. If this parameter exists, then - there is no need for the 2 parameters ``mutation_percent_genes`` and - ``mutation_num_genes``. Added in `PyGAD - 2.5.0 `__ - and higher. - -- ``mutation_by_replacement=False``: An optional bool parameter. It - works only when the selected type of mutation is random - (``mutation_type="random"``). In this case, - ``mutation_by_replacement=True`` means replace the gene by the - randomly generated value. If False, then it has no effect and random - mutation works by adding the random value to the gene. Supported in - `PyGAD - 2.2.2 `__ - and higher. Check the changes in `PyGAD - 2.2.2 `__ - under the Release History section for an example. - -- ``mutation_percent_genes="default"``: Percentage of genes to mutate. - It defaults to the string ``"default"`` which is later translated - into the integer ``10`` which means 10% of the genes will be mutated. - It must be ``>0`` and ``<=100``. Out of this percentage, the number - of genes to mutate is deduced which is assigned to the - ``mutation_num_genes`` parameter. The ``mutation_percent_genes`` - parameter has no action if ``mutation_probability`` or - ``mutation_num_genes`` exist. Starting from `PyGAD - 2.2.2 `__ - and higher, this parameter has no action if ``mutation_type`` is - ``None``. - -- ``mutation_num_genes=None``: Number of genes to mutate which defaults - to ``None`` meaning that no number is specified. The - ``mutation_num_genes`` parameter has no action if the parameter - ``mutation_probability`` exists. Starting from `PyGAD - 2.2.2 `__ - and higher, this parameter has no action if ``mutation_type`` is - ``None``. - -- ``random_mutation_min_val=-1.0``: For ``random`` mutation, the - ``random_mutation_min_val`` parameter specifies the start value of - the range from which a random value is selected to be added to the - gene. It defaults to ``-1``. Starting from `PyGAD - 2.2.2 `__ - and higher, this parameter has no action if ``mutation_type`` is - ``None``. - -- ``random_mutation_max_val=1.0``: For ``random`` mutation, the - ``random_mutation_max_val`` parameter specifies the end value of the - range from which a random value is selected to be added to the gene. - It defaults to ``+1``. Starting from `PyGAD - 2.2.2 `__ - and higher, this parameter has no action if ``mutation_type`` is - ``None``. - -- ``gene_space=None``: It is used to specify the possible values for - each gene in case the user wants to restrict the gene values. It is - useful if the gene space is restricted to a certain range or to - discrete values. It accepts a ``list``, ``tuple``, ``range``, or - ``numpy.ndarray``. When all genes have the same global space, specify - their values as a ``list``/``tuple``/``range``/``numpy.ndarray``. For - example, ``gene_space = [0.3, 5.2, -4, 8]`` restricts the gene values - to the 4 specified values. If each gene has its own space, then the - ``gene_space`` parameter can be nested like - ``[[0.4, -5], [0.5, -3.2, 8.2, -9], ...]`` where the first sublist - determines the values for the first gene, the second sublist for the - second gene, and so on. If the nested list/tuple has a ``None`` - value, then the gene's initial value is selected randomly from the - range specified by the 2 parameters ``init_range_low`` and - ``init_range_high`` and its mutation value is selected randomly from - the range specified by the 2 parameters ``random_mutation_min_val`` - and ``random_mutation_max_val``. ``gene_space`` is added in `PyGAD - 2.5.0 `__. - Check the `Release History of PyGAD - 2.5.0 `__ - section of the documentation for more details. In `PyGAD - 2.9.0 `__, - NumPy arrays can be assigned to the ``gene_space`` parameter. In - `PyGAD - 2.11.0 `__, - the ``gene_space`` parameter itself or any of its elements can be - assigned to a dictionary to specify the lower and upper limits of the - genes. For example, ``{'low': 2, 'high': 4}`` means the minimum and - maximum values are 2 and 4, respectively. In `PyGAD - 2.15.0 `__, - a new key called ``"step"`` is supported to specify the step of - moving from the start to the end of the range specified by the 2 - existing keys ``"low"`` and ``"high"``. - -- ``on_start=None``: Accepts a function/method to be called only once - before the genetic algorithm starts its evolution. If function, then - it must accept a single parameter representing the instance of the - genetic algorithm. If method, then it must accept 2 parameters where - the second one refers to the method's object. Added in `PyGAD - 2.6.0 `__. - -- ``on_fitness=None``: Accepts a function/method to be called after - calculating the fitness values of all solutions in the population. If - function, then it must accept 2 parameters: 1) a list of all - solutions' fitness values 2) the instance of the genetic algorithm. - If method, then it must accept 3 parameters where the third one - refers to the method's object. Added in `PyGAD - 2.6.0 `__. - -- ``on_parents=None``: Accepts a function/method to be called after - selecting the parents that mates. If function, then it must accept 2 - parameters: 1) the selected parents 2) the instance of the genetic - algorithm If method, then it must accept 3 parameters where the third - one refers to the method's object. Added in `PyGAD - 2.6.0 `__. - -- ``on_crossover=None``: Accepts a function to be called each time the - crossover operation is applied. This function must accept 2 - parameters: the first one represents the instance of the genetic - algorithm and the second one represents the offspring generated using - crossover. Added in `PyGAD - 2.6.0 `__. - -- ``on_mutation=None``: Accepts a function to be called each time the - mutation operation is applied. This function must accept 2 - parameters: the first one represents the instance of the genetic - algorithm and the second one represents the offspring after applying - the mutation. Added in `PyGAD - 2.6.0 `__. - -- ``on_generation=None``: Accepts a function to be called after each - generation. This function must accept a single parameter representing - the instance of the genetic algorithm. If the function returned the - string ``stop``, then the ``run()`` method stops without completing - the other generations. Added in `PyGAD - 2.6.0 `__. - -- ``on_stop=None``: Accepts a function to be called only once exactly - before the genetic algorithm stops or when it completes all the - generations. This function must accept 2 parameters: the first one - represents the instance of the genetic algorithm and the second one - is a list of fitness values of the last population's solutions. Added - in `PyGAD - 2.6.0 `__. - -- ``delay_after_gen=0.0``: It accepts a non-negative number specifying - the time in seconds to wait after a generation completes and before - going to the next generation. It defaults to ``0.0`` which means no - delay after the generation. Available in `PyGAD - 2.4.0 `__ - and higher. - -- ``save_best_solutions=False``: When ``True``, then the best solution - after each generation is saved into an attribute named - ``best_solutions``. If ``False`` (default), then no solutions are - saved and the ``best_solutions`` attribute will be empty. Supported - in `PyGAD - 2.9.0 `__. - -- ``save_solutions=False``: If ``True``, then all solutions in each - generation are appended into an attribute called ``solutions`` which - is NumPy array. Supported in `PyGAD - 2.15.0 `__. - -- ``suppress_warnings=False``: A bool parameter to control whether the - warning messages are printed or not. It defaults to ``False``. - -- ``allow_duplicate_genes=True``: Added in `PyGAD - 2.13.0 `__. - If ``True``, then a solution/chromosome may have duplicate gene - values. If ``False``, then each gene will have a unique value in its - solution. - -- ``stop_criteria=None``: Some criteria to stop the evolution. Added in - `PyGAD - 2.15.0 `__. - Each criterion is passed as ``str`` which has a stop word. The - current 2 supported words are ``reach`` and ``saturate``. ``reach`` - stops the ``run()`` method if the fitness value is equal to or - greater than a given fitness value. An example for ``reach`` is - ``"reach_40"`` which stops the evolution if the fitness is >= 40. - ``saturate`` means stop the evolution if the fitness saturates for a - given number of consecutive generations. An example for ``saturate`` - is ``"saturate_7"`` which means stop the ``run()`` method if the - fitness does not change for 7 consecutive generations. - -- ``parallel_processing=None``: Added in `PyGAD - 2.17.0 `__. - If ``None`` (Default), this means no parallel processing is applied. - It can accept a list/tuple of 2 elements [1) Can be either - ``'process'`` or ``'thread'`` to indicate whether processes or - threads are used, respectively., 2) The number of processes or - threads to use.]. For example, - ``parallel_processing=['process', 10]`` applies parallel processing - with 10 processes. If a positive integer is assigned, then it is used - as the number of threads. For example, ``parallel_processing=5`` uses - 5 threads which is equivalent to - ``parallel_processing=["thread", 5]``. For more information, check - the `Parallel Processing in - PyGAD `__ - section. - -- ``random_seed=None``: Added in `PyGAD - 2.18.0 `__. - It defines the random seed to be used by the random function - generators (we use random functions in the NumPy and random modules). - This helps to reproduce the same results by setting the same random - seed (e.g. ``random_seed=2``). If given the value ``None``, then it - has no effect. - -- ``logger=None``: Accepts an instance of the ``logging.Logger`` class - to log the outputs. Any message is no longer printed using - ``print()`` but logged. If ``logger=None``, then a logger is created - that uses ``StreamHandler`` to logs the messages to the console. - Added in `PyGAD - 3.0.0 `__. - Check the `Logging - Outputs `__ - for more information. - -The user doesn't have to specify all of such parameters while creating -an instance of the GA class. A very important parameter you must care -about is ``fitness_func`` which defines the fitness function. - -It is OK to set the value of any of the 2 parameters ``init_range_low`` -and ``init_range_high`` to be equal, higher, or lower than the other -parameter (i.e. ``init_range_low`` is not needed to be lower than -``init_range_high``). The same holds for the ``random_mutation_min_val`` -and ``random_mutation_max_val`` parameters. - -If the 2 parameters ``mutation_type`` and ``crossover_type`` are -``None``, this disables any type of evolution the genetic algorithm can -make. As a result, the genetic algorithm cannot find a better solution -that the best solution in the initial population. - -The parameters are validated within the constructor. If at least a -parameter is not correct, an exception is thrown. - -.. _plotting-methods-in-pygadga-class: - -Plotting Methods in ``pygad.GA`` Class --------------------------------------- - -- ``plot_fitness()``: Shows how the fitness evolves by generation. - -- ``plot_genes()``: Shows how the gene value changes for each - generation. - -- ``plot_new_solution_rate()``: Shows the number of new solutions - explored in each solution. - -Class Attributes ----------------- - -- ``supported_int_types``: A list of the supported types for the - integer numbers. - -- ``supported_float_types``: A list of the supported types for the - floating-point numbers. - -- ``supported_int_float_types``: A list of the supported types for all - numbers. It just concatenates the previous 2 lists. - -.. _other-instance-attributes--methods: - -Other Instance Attributes & Methods ------------------------------------ - -All the parameters and functions passed to the ``pygad.GA`` class -constructor are used as class attributes and methods in the instances of -the ``pygad.GA`` class. In addition to such attributes, there are other -attributes and methods added to the instances of the ``pygad.GA`` class: - -The next 2 subsections list such attributes and methods. - -Other Attributes -~~~~~~~~~~~~~~~~ - -- ``generations_completed``: Holds the number of the last completed - generation. - -- ``population``: A NumPy array holding the initial population. - -- ``valid_parameters``: Set to ``True`` when all the parameters passed - in the ``GA`` class constructor are valid. - -- ``run_completed``: Set to ``True`` only after the ``run()`` method - completes gracefully. - -- ``pop_size``: The population size. - -- ``best_solutions_fitness``: A list holding the fitness values of the - best solutions for all generations. - -- ``best_solution_generation``: The generation number at which the best - fitness value is reached. It is only assigned the generation number - after the ``run()`` method completes. Otherwise, its value is -1. - -- ``best_solutions``: A NumPy array holding the best solution per each - generation. It only exists when the ``save_best_solutions`` parameter - in the ``pygad.GA`` class constructor is set to ``True``. - -- ``last_generation_fitness``: The fitness values of the solutions in - the last generation. `Added in PyGAD - 2.12.0 `__. - -- ``previous_generation_fitness``: At the end of each generation, the - fitness of the most recent population is saved in the - ``last_generation_fitness`` attribute. The fitness of the population - exactly preceding this most recent population is saved in the - ``last_generation_fitness`` attribute. This - ``previous_generation_fitness`` attribute is used to fetch the - pre-calculated fitness instead of calling the fitness function for - already explored solutions. `Added in PyGAD - 2.16.2 `__. - -- ``last_generation_parents``: The parents selected from the last - generation. `Added in PyGAD - 2.12.0 `__. - -- ``last_generation_offspring_crossover``: The offspring generated - after applying the crossover in the last generation. `Added in PyGAD - 2.12.0 `__. - -- ``last_generation_offspring_mutation``: The offspring generated after - applying the mutation in the last generation. `Added in PyGAD - 2.12.0 `__. - -- ``gene_type_single``: A flag that is set to ``True`` if the - ``gene_type`` parameter is assigned to a single data type that is - applied to all genes. If ``gene_type`` is assigned a ``list``, - ``tuple``, or ``numpy.ndarray``, then the value of - ``gene_type_single`` will be ``False``. `Added in PyGAD - 2.14.0 `__. - -- ``last_generation_parents_indices``: This attribute holds the indices - of the selected parents in the last generation. Supported in `PyGAD - 2.15.0 `__. - -- ``last_generation_elitism``: This attribute holds the elitism of the - last generation. It is effective only if the ``keep_elitism`` - parameter has a non-zero value. Supported in `PyGAD - 2.18.0 `__. - -- ``last_generation_elitism_indices``: This attribute holds the indices - of the elitism of the last generation. It is effective only if the - ``keep_elitism`` parameter has a non-zero value. Supported in `PyGAD - 2.19.0 `__. - -- ``logger``: This attribute holds the logger from the ``logging`` - module. Supported in `PyGAD - 3.0.0 `__. - -Note that the attributes with its name start with ``last_generation_`` -are updated after each generation. - -Other Methods -~~~~~~~~~~~~~ - -- ``cal_pop_fitness()``: A method that calculates the fitness values - for all solutions within the population by calling the function - passed to the ``fitness_func`` parameter for each solution. - -- ``crossover()``: Refers to the method that applies the crossover - operator based on the selected type of crossover in the - ``crossover_type`` property. - -- ``mutation()``: Refers to the method that applies the mutation - operator based on the selected type of mutation in the - ``mutation_type`` property. - -- ``select_parents()``: Refers to a method that selects the parents - based on the parent selection type specified in the - ``parent_selection_type`` attribute. - -- ``adaptive_mutation_population_fitness()``: Returns the average - fitness value used in the adaptive mutation to filter the solutions. - -- ``solve_duplicate_genes_randomly()``: Solves the duplicates in a - solution by randomly selecting new values for the duplicating genes. - -- ``solve_duplicate_genes_by_space()``: Solves the duplicates in a - solution by selecting values for the duplicating genes from the gene - space - -- ``unique_int_gene_from_range()``: Finds a unique integer value for - the gene. - -- ``unique_genes_by_space()``: Loops through all the duplicating genes - to find unique values that from their gene spaces to solve the - duplicates. For each duplicating gene, a call to the - ``unique_gene_by_space()`` is made. - -- ``unique_gene_by_space()``: Returns a unique gene value for a single - gene based on its value space to solve the duplicates. - -- ``summary()``: Prints a Keras-like summary of the PyGAD lifecycle. - This helps to have an overview of the architecture. Supported in - `PyGAD - 2.19.0 `__. - Check the `Print Lifecycle - Summary `__ - section for more details and examples. - -The next sections discuss the methods available in the ``pygad.GA`` -class. - -.. _initializepopulation: - -``initialize_population()`` ---------------------------- - -It creates an initial population randomly as a NumPy array. The array is -saved in the instance attribute named ``population``. - -Accepts the following parameters: - -- ``low``: The lower value of the random range from which the gene - values in the initial population are selected. It defaults to -4. - Available in PyGAD 1.0.20 and higher. - -- ``high``: The upper value of the random range from which the gene - values in the initial population are selected. It defaults to -4. - Available in PyGAD 1.0.20. - -This method assigns the values of the following 3 instance attributes: - -1. ``pop_size``: Size of the population. - -2. ``population``: Initially, it holds the initial population and later - updated after each generation. - -3. ``initial_population``: Keeping the initial population. - -.. _calpopfitness: - -``cal_pop_fitness()`` ---------------------- - -The ``cal_pop_fitness()`` method calculates and returns the fitness -values of the solutions in the current population. - -This function is optimized to save time by making fewer calls the -fitness function. It follows this process: - -1. If the ``save_solutions`` parameter is set to ``True``, then it - checks if the solution is already explored and saved in the - ``solutions`` instance attribute. If so, then it just retrieves its - fitness from the ``solutions_fitness`` instance attribute without - calling the fitness function. - -2. If ``save_solutions`` is set to ``False`` or if it is ``True`` but - the solution was not explored yet, then the ``cal_pop_fitness()`` - method checks if the ``keep_elitism`` parameter is set to a positive - integer. If so, then it checks if the solution is saved into the - ``last_generation_elitism`` instance attribute. If so, then it - retrieves its fitness from the ``previous_generation_fitness`` - instance attribute. - -3. If neither of the above 3 conditions apply (1. ``save_solutions`` is - set to ``False`` or 2. if it is ``True`` but the solution was not - explored yet or 3. ``keep_elitism`` is set to zero), then the - ``cal_pop_fitness()`` method checks if the ``keep_parents`` parameter - is set to ``-1`` or a positive integer. If so, then it checks if the - solution is saved into the ``last_generation_parents`` instance - attribute. If so, then it retrieves its fitness from the - ``previous_generation_fitness`` instance attribute. - -4. If neither of the above 4 conditions apply, then we have to call the - fitness function to calculate the fitness for the solution. This is - by calling the function assigned to the ``fitness_func`` parameter. - -This function takes into consideration: - -1. The ``parallel_processing`` parameter to check whether parallel - processing is in effect. - -2. The ``fitness_batch_size`` parameter to check if the fitness should - be calculated in batches of solutions. - -It returns a vector of the solutions' fitness values. - -``run()`` ---------- - -Runs the genetic algorithm. This is the main method in which the genetic -algorithm is evolved through some generations. It accepts no parameters -as it uses the instance to access all of its requirements. - -For each generation, the fitness values of all solutions within the -population are calculated according to the ``cal_pop_fitness()`` method -which internally just calls the function assigned to the -``fitness_func`` parameter in the ``pygad.GA`` class constructor for -each solution. - -According to the fitness values of all solutions, the parents are -selected using the ``select_parents()`` method. This method behaviour is -determined according to the parent selection type in the -``parent_selection_type`` parameter in the ``pygad.GA`` class -constructor - -Based on the selected parents, offspring are generated by applying the -crossover and mutation operations using the ``crossover()`` and -``mutation()`` methods. The behaviour of such 2 methods is defined -according to the ``crossover_type`` and ``mutation_type`` parameters in -the ``pygad.GA`` class constructor. - -After the generation completes, the following takes place: - -- The ``population`` attribute is updated by the new population. - -- The ``generations_completed`` attribute is assigned by the number of - the last completed generation. - -- If there is a callback function assigned to the ``on_generation`` - attribute, then it will be called. - -After the ``run()`` method completes, the following takes place: - -- The ``best_solution_generation`` is assigned the generation number at - which the best fitness value is reached. - -- The ``run_completed`` attribute is set to ``True``. - -Parent Selection Methods ------------------------- - -The ``ParentSelection`` class in the ``pygad.utils.parent_selection`` -module has several methods for selecting the parents that will mate to -produce the offspring. All of such methods accept the same parameters -which are: - -- ``fitness``: The fitness values of the solutions in the current - population. - -- ``num_parents``: The number of parents to be selected. - -All of such methods return an array of the selected parents. - -The next subsections list the supported methods for parent selection. - -.. _steadystateselection: - -``steady_state_selection()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Selects the parents using the steady-state selection technique. - -.. _rankselection: - -``rank_selection()`` -~~~~~~~~~~~~~~~~~~~~ - -Selects the parents using the rank selection technique. - -.. _randomselection: - -``random_selection()`` -~~~~~~~~~~~~~~~~~~~~~~ - -Selects the parents randomly. - -.. _tournamentselection: - -``tournament_selection()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Selects the parents using the tournament selection technique. - -.. _roulettewheelselection: - -``roulette_wheel_selection()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Selects the parents using the roulette wheel selection technique. - -.. _stochasticuniversalselection: - -``stochastic_universal_selection()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Selects the parents using the stochastic universal selection technique. - -Crossover Methods ------------------ - -The ``Crossover`` class in the ``pygad.utils.crossover`` module supports -several methods for applying crossover between the selected parents. All -of these methods accept the same parameters which are: - -- ``parents``: The parents to mate for producing the offspring. - -- ``offspring_size``: The size of the offspring to produce. - -All of such methods return an array of the produced offspring. - -The next subsections list the supported methods for crossover. - -.. _singlepointcrossover: - -``single_point_crossover()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Applies the single-point crossover. It selects a point randomly at which -crossover takes place between the pairs of parents. - -.. _twopointscrossover: - -``two_points_crossover()`` -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Applies the 2 points crossover. It selects the 2 points randomly at -which crossover takes place between the pairs of parents. - -.. _uniformcrossover: - -``uniform_crossover()`` -~~~~~~~~~~~~~~~~~~~~~~~ - -Applies the uniform crossover. For each gene, a parent out of the 2 -mating parents is selected randomly and the gene is copied from it. - -.. _scatteredcrossover: - -``scattered_crossover()`` -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Applies the scattered crossover. It randomly selects the gene from one -of the 2 parents. - -Mutation Methods ----------------- - -The ``Mutation`` class in the ``pygad.utils.mutation`` module supports -several methods for applying mutation. All of these methods accept the -same parameter which is: - -- ``offspring``: The offspring to mutate. - -All of such methods return an array of the mutated offspring. - -The next subsections list the supported methods for mutation. - -.. _randommutation: - -``random_mutation()`` -~~~~~~~~~~~~~~~~~~~~~ - -Applies the random mutation which changes the values of some genes -randomly. The number of genes is specified according to either the -``mutation_num_genes`` or the ``mutation_percent_genes`` attributes. - -For each gene, a random value is selected according to the range -specified by the 2 attributes ``random_mutation_min_val`` and -``random_mutation_max_val``. The random value is added to the selected -gene. - -.. _swapmutation: - -``swap_mutation()`` -~~~~~~~~~~~~~~~~~~~ - -Applies the swap mutation which interchanges the values of 2 randomly -selected genes. - -.. _inversionmutation: - -``inversion_mutation()`` -~~~~~~~~~~~~~~~~~~~~~~~~ - -Applies the inversion mutation which selects a subset of genes and -inverts them. - -.. _scramblemutation: - -``scramble_mutation()`` -~~~~~~~~~~~~~~~~~~~~~~~ - -Applies the scramble mutation which selects a subset of genes and -shuffles their order randomly. - -.. _adaptivemutation: - -``adaptive_mutation()`` -~~~~~~~~~~~~~~~~~~~~~~~ - -Applies the adaptive mutation which selects a subset of genes and -shuffles their order randomly. - -.. _bestsolution: - -``best_solution()`` -------------------- - -Returns information about the best solution found by the genetic -algorithm. - -It accepts the following parameters: - -- ``pop_fitness=None``: An optional parameter that accepts a list of - the fitness values of the solutions in the population. If ``None``, - then the ``cal_pop_fitness()`` method is called to calculate the - fitness values of the population. - -It returns the following: - -- ``best_solution``: Best solution in the current population. - -- ``best_solution_fitness``: Fitness value of the best solution. - -- ``best_match_idx``: Index of the best solution in the current - population. - -.. _plotfitness: - -``plot_fitness()`` ------------------- - -Previously named ``plot_result()``, this method creates, shows, and -returns a figure that summarizes how the fitness value evolves by -generation. It works only after completing at least 1 generation. - -If no generation is completed (at least 1), an exception is raised. - -Starting from `PyGAD -2.15.0 `__ -and higher, this method accepts the following parameters: - -1. ``title``: Title of the figure. - -2. ``xlabel``: X-axis label. - -3. ``ylabel``: Y-axis label. - -4. ``linewidth``: Line width of the plot. Defaults to ``3``. - -5. ``font_size``: Font size for the labels and title. Defaults to - ``14``. - -6. ``plot_type``: Type of the plot which can be either ``"plot"`` - (default), ``"scatter"``, or ``"bar"``. - -7. ``color``: Color of the plot which defaults to ``"#3870FF"``. - -8. ``save_dir``: Directory to save the figure. - -.. _plotnewsolutionrate: - -``plot_new_solution_rate()`` ----------------------------- - -The ``plot_new_solution_rate()`` method creates, shows, and returns a -figure that shows the number of new solutions explored in each -generation. This method works only when ``save_solutions=True`` in the -constructor of the ``pygad.GA`` class. It also works only after -completing at least 1 generation. - -If no generation is completed (at least 1), an exception is raised. - -This method accepts the following parameters: - -1. ``title``: Title of the figure. - -2. ``xlabel``: X-axis label. - -3. ``ylabel``: Y-axis label. - -4. ``linewidth``: Line width of the plot. Defaults to ``3``. - -5. ``font_size``: Font size for the labels and title. Defaults to - ``14``. - -6. ``plot_type``: Type of the plot which can be either ``"plot"`` - (default), ``"scatter"``, or ``"bar"``. - -7. ``color``: Color of the plot which defaults to ``"#3870FF"``. - -8. ``save_dir``: Directory to save the figure. - -.. _plotgenes: - -``plot_genes()`` ----------------- - -The ``plot_genes()`` method creates, shows, and returns a figure that -describes each gene. It has different options to create the figures -which helps to: - -1. Explore the gene value for each generation by creating a normal plot. - -2. Create a histogram for each gene. - -3. Create a boxplot. - -This is controlled by the ``graph_type`` parameter. - -It works only after completing at least 1 generation. If no generation -is completed, an exception is raised. If no generation is completed (at -least 1), an exception is raised. - -This method accepts the following parameters: - -1. ``title``: Title of the figure. - -2. ``xlabel``: X-axis label. - -3. ``ylabel``: Y-axis label. - -4. ``linewidth``: Line width of the plot. Defaults to ``3``. - -5. ``font_size``: Font size for the labels and title. Defaults to - ``14``. - -6. ``plot_type``: Type of the plot which can be either ``"plot"`` - (default), ``"scatter"``, or ``"bar"``. - -7. ``graph_type``: Type of the graph which can be either ``"plot"`` - (default), ``"boxplot"``, or ``"histogram"``. - -8. ``fill_color``: Fill color of the graph which defaults to - ``"#3870FF"``. This has no effect if ``graph_type="plot"``. - -9. ``color``: Color of the plot which defaults to ``"#3870FF"``. - -10. ``solutions``: Defaults to ``"all"`` which means use all solutions. - If ``"best"`` then only the best solutions are used. - -11. ``save_dir``: Directory to save the figure. - -An exception is raised if: - -- ``solutions="all"`` while ``save_solutions=False`` in the constructor - of the ``pygad.GA`` class. . - -- ``solutions="best"`` while ``save_best_solutions=False`` in the - constructor of the ``pygad.GA`` class. . - -``save()`` ----------- - -Saves the genetic algorithm instance - -Accepts the following parameter: - -- ``filename``: Name of the file to save the instance. No extension is - needed. - -Functions in ``pygad`` -====================== - -Besides the methods available in the ``pygad.GA`` class, this section -discusses the functions available in ``pygad``. Up to this time, there -is only a single function named ``load()``. - -.. _pygadload: - -``pygad.load()`` ----------------- - -Reads a saved instance of the genetic algorithm. This is not a method -but a function that is indented under the ``pygad`` module. So, it could -be called by the pygad module as follows: ``pygad.load(filename)``. - -Accepts the following parameter: - -- ``filename``: Name of the file holding the saved instance of the - genetic algorithm. No extension is needed. - -Returns the genetic algorithm instance. - -Steps to Use ``pygad`` -====================== - -To use the ``pygad`` module, here is a summary of the required steps: - -1. Preparing the ``fitness_func`` parameter. - -2. Preparing Other Parameters. - -3. Import ``pygad``. - -4. Create an Instance of the ``pygad.GA`` Class. - -5. Run the Genetic Algorithm. - -6. Plotting Results. - -7. Information about the Best Solution. - -8. Saving & Loading the Results. - -Let's discuss how to do each of these steps. - -.. _preparing-the-fitnessfunc-parameter: - -Preparing the ``fitness_func`` Parameter ------------------------------------------ - -Even there are some steps in the genetic algorithm pipeline that can -work the same regardless of the problem being solved, one critical step -is the calculation of the fitness value. There is no unique way of -calculating the fitness value and it changes from one problem to -another. - -PyGAD has a parameter called ``fitness_func`` that allows the user to -specify a custom function/method to use when calculating the fitness. -This function/method must be a maximization function/method so that a -solution with a high fitness value returned is selected compared to a -solution with a low value. Doing that allows the user to freely use -PyGAD to solve any problem by passing the appropriate fitness -function/method. It is very important to understand the problem well for -creating it. - -Let's discuss an example: - - | Given the following function: - | y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6 - | where (x1,x2,x3,x4,x5,x6)=(4, -2, 3.5, 5, -11, -4.7) and y=44 - | What are the best values for the 6 weights (w1 to w6)? We are going - to use the genetic algorithm to optimize this function. - -So, the task is about using the genetic algorithm to find the best -values for the 6 weight ``W1`` to ``W6``. Thinking of the problem, it is -clear that the best solution is that returning an output that is close -to the desired output ``y=44``. So, the fitness function/method should -return a value that gets higher when the solution's output is closer to -``y=44``. Here is a function that does that: - -.. code:: python - - function_inputs = [4, -2, 3.5, 5, -11, -4.7] # Function inputs. - desired_output = 44 # Function output. - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / numpy.abs(output - desired_output) - return fitness - -Such a user-defined function must accept 3 parameters: - -1. The instance of the ``pygad.GA`` class. This helps the user to fetch - any property that helps when calculating the fitness. - -2. The solution(s) to calculate the fitness value(s). Note that the - fitness function can accept multiple solutions only if the - ``fitness_batch_size`` is given a value greater than 1. - -3. The indices of the solutions in the population. The number of indices - also depends on the ``fitness_batch_size`` parameter. - -If a method is passed to the ``fitness_func`` parameter, then it accepts -a fourth parameter representing the method's instance. - -The ``__code__`` object is used to check if this function accepts the -required number of parameters. If more or fewer parameters are passed, -an exception is thrown. - -By creating this function, you did a very important step towards using -PyGAD. - -Preparing Other Parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Here is an example for preparing the other parameters: - -.. code:: python - - num_generations = 50 - num_parents_mating = 4 - - fitness_function = fitness_func - - sol_per_pop = 8 - num_genes = len(function_inputs) - - init_range_low = -2 - init_range_high = 5 - - parent_selection_type = "sss" - keep_parents = 1 - - crossover_type = "single_point" - - mutation_type = "random" - mutation_percent_genes = 10 - -.. _the-ongeneration-parameter: - -The ``on_generation`` Parameter -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -An optional parameter named ``on_generation`` is supported which allows -the user to call a function (with a single parameter) after each -generation. Here is a simple function that just prints the current -generation number and the fitness value of the best solution in the -current generation. The ``generations_completed`` attribute of the GA -class returns the number of the last completed generation. - -.. code:: python - - def on_gen(ga_instance): - print("Generation : ", ga_instance.generations_completed) - print("Fitness of the best solution :", ga_instance.best_solution()[1]) - -After being defined, the function is assigned to the ``on_generation`` -parameter of the GA class constructor. By doing that, the ``on_gen()`` -function will be called after each generation. - -.. code:: python - - ga_instance = pygad.GA(..., - on_generation=on_gen, - ...) - -After the parameters are prepared, we can import PyGAD and build an -instance of the ``pygad.GA`` class. - -Import ``pygad`` ----------------- - -The next step is to import PyGAD as follows: - -.. code:: python - - import pygad - -The ``pygad.GA`` class holds the implementation of all methods for -running the genetic algorithm. - -.. _create-an-instance-of-the-pygadga-class: - -Create an Instance of the ``pygad.GA`` Class --------------------------------------------- - -The ``pygad.GA`` class is instantiated where the previously prepared -parameters are fed to its constructor. The constructor is responsible -for creating the initial population. - -.. code:: python - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - fitness_func=fitness_function, - sol_per_pop=sol_per_pop, - num_genes=num_genes, - init_range_low=init_range_low, - init_range_high=init_range_high, - parent_selection_type=parent_selection_type, - keep_parents=keep_parents, - crossover_type=crossover_type, - mutation_type=mutation_type, - mutation_percent_genes=mutation_percent_genes) - -Run the Genetic Algorithm -------------------------- - -After an instance of the ``pygad.GA`` class is created, the next step is -to call the ``run()`` method as follows: - -.. code:: python - - ga_instance.run() - -Inside this method, the genetic algorithm evolves over some generations -by doing the following tasks: - -1. Calculating the fitness values of the solutions within the current - population. - -2. Select the best solutions as parents in the mating pool. - -3. Apply the crossover & mutation operation - -4. Repeat the process for the specified number of generations. - -Plotting Results ----------------- - -There is a method named ``plot_fitness()`` which creates a figure -summarizing how the fitness values of the solutions change with the -generations. - -.. code:: python - - ga_instance.plot_fitness() - -.. figure:: https://user-images.githubusercontent.com/16560492/78830005-93111d00-79e7-11ea-9d8e-a8d8325a6101.png - :alt: - -Information about the Best Solution ------------------------------------ - -The following information about the best solution in the last population -is returned using the ``best_solution()`` method. - -- Solution - -- Fitness value of the solution - -- Index of the solution within the population - -.. code:: python - - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - -Using the ``best_solution_generation`` attribute of the instance from -the ``pygad.GA`` class, the generation number at which the -``best fitness`` is reached could be fetched. - -.. code:: python - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - -.. _saving--loading-the-results: - -Saving & Loading the Results ----------------------------- - -After the ``run()`` method completes, it is possible to save the current -instance of the genetic algorithm to avoid losing the progress made. The -``save()`` method is available for that purpose. Just pass the file name -to it without an extension. According to the next code, a file named -``genetic.pkl`` will be created and saved in the current directory. - -.. code:: python - - filename = 'genetic' - ga_instance.save(filename=filename) - -You can also load the saved model using the ``load()`` function and -continue using it. For example, you might run the genetic algorithm for -some generations, save its current state using the ``save()`` method, -load the model using the ``load()`` function, and then call the -``run()`` method again. - -.. code:: python - - loaded_ga_instance = pygad.load(filename=filename) - -After the instance is loaded, you can use it to run any method or access -any property. - -.. code:: python - - print(loaded_ga_instance.best_solution()) - -Crossover, Mutation, and Parent Selection -========================================= - -PyGAD supports different types for selecting the parents and applying -the crossover & mutation operators. More features will be added in the -future. To ask for a new feature, please check the ``Ask for Feature`` -section. - -Supported Crossover Operations ------------------------------- - -The supported crossover operations at this time are: - -1. Single point: Implemented using the ``single_point_crossover()`` - method. - -2. Two points: Implemented using the ``two_points_crossover()`` method. - -3. Uniform: Implemented using the ``uniform_crossover()`` method. - -Supported Mutation Operations ------------------------------ - -The supported mutation operations at this time are: - -1. Random: Implemented using the ``random_mutation()`` method. - -2. Swap: Implemented using the ``swap_mutation()`` method. - -3. Inversion: Implemented using the ``inversion_mutation()`` method. - -4. Scramble: Implemented using the ``scramble_mutation()`` method. - -Supported Parent Selection Operations -------------------------------------- - -The supported parent selection techniques at this time are: - -1. Steady-state: Implemented using the ``steady_state_selection()`` - method. - -2. Roulette wheel: Implemented using the ``roulette_wheel_selection()`` - method. - -3. Stochastic universal: Implemented using the - ``stochastic_universal_selection()``\ method. - -4. Rank: Implemented using the ``rank_selection()`` method. - -5. Random: Implemented using the ``random_selection()`` method. - -6. Tournament: Implemented using the ``tournament_selection()`` method. - -Life Cycle of PyGAD -=================== - -The next figure lists the different stages in the lifecycle of an -instance of the ``pygad.GA`` class. Note that PyGAD stops when either -all generations are completed or when the function passed to the -``on_generation`` parameter returns the string ``stop``. - -.. figure:: https://user-images.githubusercontent.com/16560492/220486073-c5b6089d-81e4-44d9-a53c-385f479a7273.jpg - :alt: - -The next code implements all the callback functions to trace the -execution of the genetic algorithm. Each callback function prints its -name. - -.. code:: python - - import pygad - import numpy - - function_inputs = [4,-2,3.5,5,-11,-4.7] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - fitness_function = fitness_func - - def on_start(ga_instance): - print("on_start()") - - def on_fitness(ga_instance, population_fitness): - print("on_fitness()") - - def on_parents(ga_instance, selected_parents): - print("on_parents()") - - def on_crossover(ga_instance, offspring_crossover): - print("on_crossover()") - - def on_mutation(ga_instance, offspring_mutation): - print("on_mutation()") - - def on_generation(ga_instance): - print("on_generation()") - - def on_stop(ga_instance, last_population_fitness): - print("on_stop()") - - ga_instance = pygad.GA(num_generations=3, - num_parents_mating=5, - fitness_func=fitness_function, - sol_per_pop=10, - num_genes=len(function_inputs), - on_start=on_start, - on_fitness=on_fitness, - on_parents=on_parents, - on_crossover=on_crossover, - on_mutation=on_mutation, - on_generation=on_generation, - on_stop=on_stop) - - ga_instance.run() - -Based on the used 3 generations as assigned to the ``num_generations`` -argument, here is the output. - -.. code:: - - on_start() - - on_fitness() - on_parents() - on_crossover() - on_mutation() - on_generation() - - on_fitness() - on_parents() - on_crossover() - on_mutation() - on_generation() - - on_fitness() - on_parents() - on_crossover() - on_mutation() - on_generation() - - on_stop() - -Adaptive Mutation -================= - -In the regular genetic algorithm, the mutation works by selecting a -single fixed mutation rate for all solutions regardless of their fitness -values. So, regardless on whether this solution has high or low quality, -the same number of genes are mutated all the time. - -The pitfalls of using a constant mutation rate for all solutions are -summarized in this paper `Libelli, S. Marsili, and P. Alba. "Adaptive -mutation in genetic algorithms." Soft computing 4.2 (2000): -76-80 `__ -as follows: - - The weak point of "classical" GAs is the total randomness of - mutation, which is applied equally to all chromosomes, irrespective - of their fitness. Thus a very good chromosome is equally likely to be - disrupted by mutation as a bad one. - - On the other hand, bad chromosomes are less likely to produce good - ones through crossover, because of their lack of building blocks, - until they remain unchanged. They would benefit the most from - mutation and could be used to spread throughout the parameter space - to increase the search thoroughness. So there are two conflicting - needs in determining the best probability of mutation. - - Usually, a reasonable compromise in the case of a constant mutation - is to keep the probability low to avoid disruption of good - chromosomes, but this would prevent a high mutation rate of - low-fitness chromosomes. Thus a constant probability of mutation - would probably miss both goals and result in a slow improvement of - the population. - -According to `Libelli, S. Marsili, and P. -Alba. `__ -work, the adaptive mutation solves the problems of constant mutation. - -Adaptive mutation works as follows: - -1. Calculate the average fitness value of the population (``f_avg``). - -2. For each chromosome, calculate its fitness value (``f``). - -3. If ``ff_avg``, then this solution is regarded as a high-quality - solution and thus the mutation rate should be kept low to avoid - disrupting this high quality solution. - -In PyGAD, if ``f=f_avg``, then the solution is regarded of high quality. - -The next figure summarizes the previous steps. - -.. figure:: https://user-images.githubusercontent.com/16560492/103468973-e3c26600-4d2c-11eb-8af3-b3bb39b50540.jpg - :alt: - -This strategy is applied in PyGAD. - -Use Adaptive Mutation in PyGAD ------------------------------- - -In PyGAD 2.10.0, adaptive mutation is supported. To use it, just follow -the following 2 simple steps: - -1. In the constructor of the ``pygad.GA`` class, set - ``mutation_type="adaptive"`` to specify that the type of mutation is - adaptive. - -2. Specify the mutation rates for the low and high quality solutions - using one of these 3 parameters according to your preference: - ``mutation_probability``, ``mutation_num_genes``, and - ``mutation_percent_genes``. Please check the `documentation of each - of these - parameters `__ - for more information. - -When adaptive mutation is used, then the value assigned to any of the 3 -parameters can be of any of these data types: - -1. ``list`` - -2. ``tuple`` - -3. ``numpy.ndarray`` - -Whatever the data type used, the length of the ``list``, ``tuple``, or -the ``numpy.ndarray`` must be exactly 2. That is there are just 2 -values: - -1. The first value is the mutation rate for the low-quality solutions. - -2. The second value is the mutation rate for the high-quality solutions. - -PyGAD expects that the first value is higher than the second value and -thus a warning is printed in case the first value is lower than the -second one. - -Here are some examples to feed the mutation rates: - -.. code:: python - - # mutation_probability - mutation_probability = [0.25, 0.1] - mutation_probability = (0.35, 0.17) - mutation_probability = numpy.array([0.15, 0.05]) - - # mutation_num_genes - mutation_num_genes = [4, 2] - mutation_num_genes = (3, 1) - mutation_num_genes = numpy.array([7, 2]) - - # mutation_percent_genes - mutation_percent_genes = [25, 12] - mutation_percent_genes = (15, 8) - mutation_percent_genes = numpy.array([21, 13]) - -Assume that the average fitness is 12 and the fitness values of 2 -solutions are 15 and 7. If the mutation probabilities are specified as -follows: - -.. code:: python - - mutation_probability = [0.25, 0.1] - -Then the mutation probability of the first solution is 0.1 because its -fitness is 15 which is higher than the average fitness 12. The mutation -probability of the second solution is 0.25 because its fitness is 7 -which is lower than the average fitness 12. - -Here is an example that uses adaptive mutation. - -.. code:: python - - import pygad - import numpy - - function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs. - desired_output = 44 # Function output. - - def fitness_func(ga_instance, solution, solution_idx): - # The fitness function calulates the sum of products between each input and its corresponding weight. - output = numpy.sum(solution*function_inputs) - # The value 0.000001 is used to avoid the Inf value when the denominator numpy.abs(output - desired_output) is 0.0. - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - # Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor. - ga_instance = pygad.GA(num_generations=200, - fitness_func=fitness_func, - num_parents_mating=10, - sol_per_pop=20, - num_genes=len(function_inputs), - mutation_type="adaptive", - mutation_num_genes=(3, 1)) - - # Running the GA to optimize the parameters of the function. - ga_instance.run() - - ga_instance.plot_fitness(title="PyGAD with Adaptive Mutation", linewidth=5) - -Limit the Gene Value Range -========================== - -In `PyGAD -2.11.0 `__, -the ``gene_space`` parameter supported a new feature to allow -customizing the range of accepted values for each gene. Let's take a -quick review of the ``gene_space`` parameter to build over it. - -The ``gene_space`` parameter allows the user to feed the space of values -of each gene. This way the accepted values for each gene is retracted to -the user-defined values. Assume there is a problem that has 3 genes -where each gene has different set of values as follows: - -1. Gene 1: ``[0.4, 12, -5, 21.2]`` - -2. Gene 2: ``[-2, 0.3]`` - -3. Gene 3: ``[1.2, 63.2, 7.4]`` - -Then, the ``gene_space`` for this problem is as given below. Note that -the order is very important. - -.. code:: python - - gene_space = [[0.4, 12, -5, 21.2], - [-2, 0.3], - [1.2, 63.2, 7.4]] - -In case all genes share the same set of values, then simply feed a -single list to the ``gene_space`` parameter as follows. In this case, -all genes can only take values from this list of 6 values. - -.. code:: python - - gene_space = [33, 7, 0.5, 95. 6.3, 0.74] - -The previous example restricts the gene values to just a set of fixed -number of discrete values. In case you want to use a range of discrete -values to the gene, then you can use the ``range()`` function. For -example, ``range(1, 7)`` means the set of allowed values for the gene -are ``1, 2, 3, 4, 5, and 6``. You can also use the ``numpy.arange()`` or -``numpy.linspace()`` functions for the same purpose. - -The previous discussion only works with a range of discrete values not -continuous values. In `PyGAD -2.11.0 `__, -the ``gene_space`` parameter can be assigned a dictionary that allows -the gene to have values from a continuous range. - -Assuming you want to restrict the gene within this half-open range [1 to -5) where 1 is included and 5 is not. Then simply create a dictionary -with 2 items where the keys of the 2 items are: - -1. ``'low'``: The minimum value in the range which is 1 in the example. - -2. ``'high'``: The maximum value in the range which is 5 in the example. - -The dictionary will look like that: - -.. code:: python - - {'low': 1, - 'high': 5} - -It is not acceptable to add more than 2 items in the dictionary or use -other keys than ``'low'`` and ``'high'``. - -For a 3-gene problem, the next code creates a dictionary for each gene -to restrict its values in a continuous range. For the first gene, it can -take any floating-point value from the range that starts from 1 -(inclusive) and ends at 5 (exclusive). - -.. code:: python - - gene_space = [{'low': 1, 'high': 5}, {'low': 0.3, 'high': 1.4}, {'low': -0.2, 'high': 4.5}] - -Stop at Any Generation -====================== - -In `PyGAD -2.4.0 `__, -it is possible to stop the genetic algorithm after any generation. All -you need to do it to return the string ``"stop"`` in the callback -function ``on_generation``. When this callback function is implemented -and assigned to the ``on_generation`` parameter in the constructor of -the ``pygad.GA`` class, then the algorithm immediately stops after -completing its current generation. Let's discuss an example. - -Assume that the user wants to stop algorithm either after the 100 -generations or if a condition is met. The user may assign a value of 100 -to the ``num_generations`` parameter of the ``pygad.GA`` class -constructor. - -The condition that stops the algorithm is written in a callback function -like the one in the next code. If the fitness value of the best solution -exceeds 70, then the string ``"stop"`` is returned. - -.. code:: python - - def func_generation(ga_instance): - if ga_instance.best_solution()[1] >= 70: - return "stop" - -Stop Criteria -============= - -In `PyGAD -2.15.0 `__, -a new parameter named ``stop_criteria`` is added to the constructor of -the ``pygad.GA`` class. It helps to stop the evolution based on some -criteria. It can be assigned to one or more criterion. - -Each criterion is passed as ``str`` that consists of 2 parts: - -1. Stop word. - -2. Number. - -It takes this form: - -.. code:: python - - "word_num" - -The current 2 supported words are ``reach`` and ``saturate``. - -The ``reach`` word stops the ``run()`` method if the fitness value is -equal to or greater than a given fitness value. An example for ``reach`` -is ``"reach_40"`` which stops the evolution if the fitness is >= 40. - -``saturate`` stops the evolution if the fitness saturates for a given -number of consecutive generations. An example for ``saturate`` is -``"saturate_7"`` which means stop the ``run()`` method if the fitness -does not change for 7 consecutive generations. - -Here is an example that stops the evolution if either the fitness value -reached ``127.4`` or if the fitness saturates for ``15`` generations. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4, -2, 3.5, 8, 9, 4] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - - return fitness - - ga_instance = pygad.GA(num_generations=200, - sol_per_pop=10, - num_parents_mating=4, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - stop_criteria=["reach_127.4", "saturate_15"]) - - ga_instance.run() - print("Number of generations passed is {generations_completed}".format(generations_completed=ga_instance.generations_completed)) - -Elitism Selection -================= - -In `PyGAD -2.18.0 `__, -a new parameter called ``keep_elitism`` is supported. It accepts an -integer to define the number of elitism (i.e. best solutions) to keep in -the next generation. This parameter defaults to ``1`` which means only -the best solution is kept in the next generation. - -In the next example, the ``keep_elitism`` parameter in the constructor -of the ``pygad.GA`` class is set to 2. Thus, the best 2 solutions in -each generation are kept in the next generation. - -.. code:: python - - import numpy - import pygad - - function_inputs = [4,-2,3.5,5,-11,-4.7] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / numpy.abs(output - desired_output) - return fitness - - ga_instance = pygad.GA(num_generations=2, - num_parents_mating=3, - fitness_func=fitness_func, - num_genes=6, - sol_per_pop=5, - keep_elitism=2) - - ga_instance.run() - -The value passed to the ``keep_elitism`` parameter must satisfy 2 -conditions: - -1. It must be ``>= 0``. - -2. It must be ``<= sol_per_pop``. That is its value cannot exceed the - number of solutions in the current population. - -In the previous example, if the ``keep_elitism`` parameter is set equal -to the value passed to the ``sol_per_pop`` parameter, which is 5, then -there will be no evolution at all as in the next figure. This is because -all the 5 solutions are used as elitism in the next generation and no -offspring will be created. - -.. code:: python - - ... - - ga_instance = pygad.GA(..., - sol_per_pop=5, - keep_elitism=5) - - ga_instance.run() - -.. figure:: https://user-images.githubusercontent.com/16560492/189273225-67ffad41-97ab-45e1-9324-429705e17b20.png - :alt: - -Note that if the ``keep_elitism`` parameter is effective (i.e. is -assigned a positive integer, not zero), then the ``keep_parents`` -parameter will have no effect. Because the default value of the -``keep_elitism`` parameter is 1, then the ``keep_parents`` parameter has -no effect by default. The ``keep_parents`` parameter is only effective -when ``keep_elitism=0``. - -Random Seed -=========== - -In `PyGAD -2.18.0 `__, -a new parameter called ``random_seed`` is supported. Its value is used -as a seed for the random function generators. - -PyGAD uses random functions in these 2 libraries: - -1. NumPy - -2. random - -The ``random_seed`` parameter defaults to ``None`` which means no seed -is used. As a result, different random numbers are generated for each -run of PyGAD. - -If this parameter is assigned a proper seed, then the results will be -reproducible. In the next example, the integer 2 is used as a random -seed. - -.. code:: python - - import numpy - import pygad - - function_inputs = [4,-2,3.5,5,-11,-4.7] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / numpy.abs(output - desired_output) - return fitness - - ga_instance = pygad.GA(num_generations=2, - num_parents_mating=3, - fitness_func=fitness_func, - sol_per_pop=5, - num_genes=6, - random_seed=2) - - ga_instance.run() - best_solution, best_solution_fitness, best_match_idx = ga_instance.best_solution() - print(best_solution) - print(best_solution_fitness) - -This is the best solution found and its fitness value. - -.. code:: - - [ 2.77249188 -4.06570662 0.04196872 -3.47770796 -0.57502138 -3.22775267] - 0.04872203136549972 - -After running the code again, it will find the same result. - -.. code:: - - [ 2.77249188 -4.06570662 0.04196872 -3.47770796 -0.57502138 -3.22775267] - 0.04872203136549972 - -Continue without Loosing Progress -================================= - -In `PyGAD -2.18.0 `__, -and thanks for `Felix Bernhard `__ for -opening `this GitHub -issue `__, -the values of these 4 instance attributes are no longer reset after each -call to the ``run()`` method. - -1. ``self.best_solutions`` - -2. ``self.best_solutions_fitness`` - -3. ``self.solutions`` - -4. ``self.solutions_fitness`` - -This helps the user to continue where the last run stopped without -loosing the values of these 4 attributes. - -Now, the user can save the model by calling the ``save()`` method. - -.. code:: python - - import pygad - - def fitness_func(ga_instance, solution, solution_idx): - ... - return fitness - - ga_instance = pygad.GA(...) - - ga_instance.run() - - ga_instance.plot_fitness() - - ga_instance.save("pygad_GA") - -Then the saved model is loaded by calling the ``load()`` function. After -calling the ``run()`` method over the loaded instance, then the data -from the previous 4 attributes are not reset but extended with the new -data. - -.. code:: python - - import pygad - - def fitness_func(ga_instance, solution, solution_idx): - ... - return fitness - - loaded_ga_instance = pygad.load("pygad_GA") - - loaded_ga_instance.run() - - loaded_ga_instance.plot_fitness() - -The plot created by the ``plot_fitness()`` method will show the data -collected from both the runs. - -Note that the 2 attributes (``self.best_solutions`` and -``self.best_solutions_fitness``) only work if the -``save_best_solutions`` parameter is set to ``True``. Also, the 2 -attributes (``self.solutions`` and ``self.solutions_fitness``) only work -if the ``save_solutions`` parameter is ``True``. - -Prevent Duplicates in Gene Values -================================= - -In `PyGAD -2.13.0 `__, -a new bool parameter called ``allow_duplicate_genes`` is supported to -control whether duplicates are supported in the chromosome or not. In -other words, whether 2 or more genes might have the same exact value. - -If ``allow_duplicate_genes=True`` (which is the default case), genes may -have the same value. If ``allow_duplicate_genes=False``, then no 2 genes -will have the same value given that there are enough unique values for -the genes. - -The next code gives an example to use the ``allow_duplicate_genes`` -parameter. A callback generation function is implemented to print the -population after each generation. - -.. code:: python - - import pygad - - def fitness_func(ga_instance, solution, solution_idx): - return 0 - - def on_generation(ga): - print("Generation", ga.generations_completed) - print(ga.population) - - ga_instance = pygad.GA(num_generations=5, - sol_per_pop=5, - num_genes=4, - mutation_num_genes=3, - random_mutation_min_val=-5, - random_mutation_max_val=5, - num_parents_mating=2, - fitness_func=fitness_func, - gene_type=int, - on_generation=on_generation, - allow_duplicate_genes=False) - ga_instance.run() - -Here are the population after the 5 generations. Note how there are no -duplicate values. - -.. code:: python - - Generation 1 - [[ 2 -2 -3 3] - [ 0 1 2 3] - [ 5 -3 6 3] - [-3 1 -2 4] - [-1 0 -2 3]] - Generation 2 - [[-1 0 -2 3] - [-3 1 -2 4] - [ 0 -3 -2 6] - [-3 0 -2 3] - [ 1 -4 2 4]] - Generation 3 - [[ 1 -4 2 4] - [-3 0 -2 3] - [ 4 0 -2 1] - [-4 0 -2 -3] - [-4 2 0 3]] - Generation 4 - [[-4 2 0 3] - [-4 0 -2 -3] - [-2 5 4 -3] - [-1 2 -4 4] - [-4 2 0 -3]] - Generation 5 - [[-4 2 0 -3] - [-1 2 -4 4] - [ 3 4 -4 0] - [-1 0 2 -2] - [-4 2 -1 1]] - -The ``allow_duplicate_genes`` parameter is configured with use with the -``gene_space`` parameter. Here is an example where each of the 4 genes -has the same space of values that consists of 4 values (1, 2, 3, and 4). - -.. code:: python - - import pygad - - def fitness_func(ga_instance, solution, solution_idx): - return 0 - - def on_generation(ga): - print("Generation", ga.generations_completed) - print(ga.population) - - ga_instance = pygad.GA(num_generations=1, - sol_per_pop=5, - num_genes=4, - num_parents_mating=2, - fitness_func=fitness_func, - gene_type=int, - gene_space=[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]], - on_generation=on_generation, - allow_duplicate_genes=False) - ga_instance.run() - -Even that all the genes share the same space of values, no 2 genes -duplicate their values as provided by the next output. - -.. code:: python - - Generation 1 - [[2 3 1 4] - [2 3 1 4] - [2 4 1 3] - [2 3 1 4] - [1 3 2 4]] - Generation 2 - [[1 3 2 4] - [2 3 1 4] - [1 3 2 4] - [2 3 4 1] - [1 3 4 2]] - Generation 3 - [[1 3 4 2] - [2 3 4 1] - [1 3 4 2] - [3 1 4 2] - [3 2 4 1]] - Generation 4 - [[3 2 4 1] - [3 1 4 2] - [3 2 4 1] - [1 2 4 3] - [1 3 4 2]] - Generation 5 - [[1 3 4 2] - [1 2 4 3] - [2 1 4 3] - [1 2 4 3] - [1 2 4 3]] - -You should care of giving enough values for the genes so that PyGAD is -able to find alternatives for the gene value in case it duplicates with -another gene. - -There might be 2 duplicate genes where changing either of the 2 -duplicating genes will not solve the problem. For example, if -``gene_space=[[3, 0, 1], [4, 1, 2], [0, 2], [3, 2, 0]]`` and the -solution is ``[3 2 0 0]``, then the values of the last 2 genes -duplicate. There are no possible changes in the last 2 genes to solve -the problem. - -This problem can be solved by randomly changing one of the -non-duplicating genes that may make a room for a unique value in one the -2 duplicating genes. For example, by changing the second gene from 2 to -4, then any of the last 2 genes can take the value 2 and solve the -duplicates. The resultant gene is then ``[3 4 2 0]``. But this option is -not yet supported in PyGAD. - -User-Defined Crossover, Mutation, and Parent Selection Operators -================================================================ - -Previously, the user can select the the type of the crossover, mutation, -and parent selection operators by assigning the name of the operator to -the following parameters of the ``pygad.GA`` class's constructor: - -1. ``crossover_type`` - -2. ``mutation_type`` - -3. ``parent_selection_type`` - -This way, the user can only use the built-in functions for each of these -operators. - -Starting from `PyGAD -2.16.0 `__, -the user can create a custom crossover, mutation, and parent selection -operators and assign these functions to the above parameters. Thus, a -new operator can be plugged easily into the `PyGAD -Lifecycle `__. - -This is a sample code that does not use any custom function. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4,-2,3.5] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func) - - ga_instance.run() - ga_instance.plot_fitness() - -This section describes the expected input parameters and outputs. For -simplicity, all of these custom functions all accept the instance of the -``pygad.GA`` class as the last parameter. - -User-Defined Crossover Operator -------------------------------- - -The user-defined crossover function is a Python function that accepts 3 -parameters: - -1. The selected parents. - -2. The size of the offspring as a tuple of 2 numbers: (the offspring - size, number of genes). - -3. The instance from the ``pygad.GA`` class. This instance helps to - retrieve any property like ``population``, ``gene_type``, - ``gene_space``, etc. - -This function should return a NumPy array of shape equal to the value -passed to the second parameter. - -The next code creates a template for the user-defined crossover -operator. You can use any names for the parameters. Note how a NumPy -array is returned. - -.. code:: python - - def crossover_func(parents, offspring_size, ga_instance): - offspring = ... - ... - return numpy.array(offspring) - -As an example, the next code creates a single-point crossover function. -By randomly generating a random point (i.e. index of a gene), the -function simply uses 2 parents to produce an offspring by copying the -genes before the point from the first parent and the remaining from the -second parent. - -.. code:: python - - def crossover_func(parents, offspring_size, ga_instance): - offspring = [] - idx = 0 - while len(offspring) != offspring_size[0]: - parent1 = parents[idx % parents.shape[0], :].copy() - parent2 = parents[(idx + 1) % parents.shape[0], :].copy() - - random_split_point = numpy.random.choice(range(offspring_size[1])) - - parent1[random_split_point:] = parent2[random_split_point:] - - offspring.append(parent1) - - idx += 1 - - return numpy.array(offspring) - -To use this user-defined function, simply assign its name to the -``crossover_type`` parameter in the constructor of the ``pygad.GA`` -class. The next code gives an example. In this case, the custom function -will be called in each generation rather than calling the built-in -crossover functions defined in PyGAD. - -.. code:: python - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - crossover_type=crossover_func) - -User-Defined Mutation Operator ------------------------------- - -A user-defined mutation function/operator can be created the same way a -custom crossover operator/function is created. Simply, it is a Python -function that accepts 2 parameters: - -1. The offspring to be mutated. - -2. The instance from the ``pygad.GA`` class. This instance helps to - retrieve any property like ``population``, ``gene_type``, - ``gene_space``, etc. - -The template for the user-defined mutation function is given in the next -code. According to the user preference, the function should make some -random changes to the genes. - -.. code:: python - - def mutation_func(offspring, ga_instance): - ... - return offspring - -The next code builds the random mutation where a single gene from each -chromosome is mutated by adding a random number between 0 and 1 to the -gene's value. - -.. code:: python - - def mutation_func(offspring, ga_instance): - - for chromosome_idx in range(offspring.shape[0]): - random_gene_idx = numpy.random.choice(range(offspring.shape[1])) - - offspring[chromosome_idx, random_gene_idx] += numpy.random.random() - - return offspring - -Here is how this function is assigned to the ``mutation_type`` -parameter. - -.. code:: python - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - crossover_type=crossover_func, - mutation_type=mutation_func) - -Note that there are other things to take into consideration like: - -- Making sure that each gene conforms to the data type(s) listed in the - ``gene_type`` parameter. - -- If the ``gene_space`` parameter is used, then the new value for the - gene should conform to the values/ranges listed. - -- Mutating a number of genes that conforms to the parameters - ``mutation_percent_genes``, ``mutation_probability``, and - ``mutation_num_genes``. - -- Whether mutation happens with or without replacement based on the - ``mutation_by_replacement`` parameter. - -- The minimum and maximum values from which a random value is generated - based on the ``random_mutation_min_val`` and - ``random_mutation_max_val`` parameters. - -- Whether duplicates are allowed or not in the chromosome based on the - ``allow_duplicate_genes`` parameter. - -and more. - -It all depends on your objective from building the mutation function. -You may neglect or consider some of the considerations according to your -objective. - -User-Defined Parent Selection Operator --------------------------------------- - -No much to mention about building a user-defined parent selection -function as things are similar to building a crossover or mutation -function. Just create a Python function that accepts 3 parameters: - -1. The fitness values of the current population. - -2. The number of parents needed. - -3. The instance from the ``pygad.GA`` class. This instance helps to - retrieve any property like ``population``, ``gene_type``, - ``gene_space``, etc. - -The function should return 2 outputs: - -1. The selected parents as a NumPy array. Its shape is equal to (the - number of selected parents, ``num_genes``). Note that the number of - selected parents is equal to the value assigned to the second input - parameter. - -2. The indices of the selected parents inside the population. It is a 1D - list with length equal to the number of selected parents. - -The outputs must be of type ``numpy.ndarray``. - -Here is a template for building a custom parent selection function. - -.. code:: python - - def parent_selection_func(fitness, num_parents, ga_instance): - ... - return parents, fitness_sorted[:num_parents] - -The next code builds the steady-state parent selection where the best -parents are selected. The number of parents is equal to the value in the -``num_parents`` parameter. - -.. code:: python - - def parent_selection_func(fitness, num_parents, ga_instance): - - fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) - fitness_sorted.reverse() - - parents = numpy.empty((num_parents, ga_instance.population.shape[1])) - - for parent_num in range(num_parents): - parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() - - return parents, numpy.array(fitness_sorted[:num_parents]) - -Finally, the defined function is assigned to the -``parent_selection_type`` parameter as in the next code. - -.. code:: python - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - crossover_type=crossover_func, - mutation_type=mutation_func, - parent_selection_type=parent_selection_func) - -Example -------- - -By discussing how to customize the 3 operators, the next code uses the -previous 3 user-defined functions instead of the built-in functions. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4,-2,3.5] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - - return fitness - - def parent_selection_func(fitness, num_parents, ga_instance): - - fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) - fitness_sorted.reverse() - - parents = numpy.empty((num_parents, ga_instance.population.shape[1])) - - for parent_num in range(num_parents): - parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() - - return parents, numpy.array(fitness_sorted[:num_parents]) - - def crossover_func(parents, offspring_size, ga_instance): - - offspring = [] - idx = 0 - while len(offspring) != offspring_size[0]: - parent1 = parents[idx % parents.shape[0], :].copy() - parent2 = parents[(idx + 1) % parents.shape[0], :].copy() - - random_split_point = numpy.random.choice(range(offspring_size[1])) - - parent1[random_split_point:] = parent2[random_split_point:] - - offspring.append(parent1) - - idx += 1 - - return numpy.array(offspring) - - def mutation_func(offspring, ga_instance): - - for chromosome_idx in range(offspring.shape[0]): - random_gene_idx = numpy.random.choice(range(offspring.shape[0])) - - offspring[chromosome_idx, random_gene_idx] += numpy.random.random() - - return offspring - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - crossover_type=crossover_func, - mutation_type=mutation_func, - parent_selection_type=parent_selection_func) - - ga_instance.run() - ga_instance.plot_fitness() - -This is the same example but using methods instead of functions. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4,-2,3.5] - desired_output = 44 - - class Test: - def fitness_func(self, ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - - return fitness - - def parent_selection_func(self, fitness, num_parents, ga_instance): - - fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) - fitness_sorted.reverse() - - parents = numpy.empty((num_parents, ga_instance.population.shape[1])) - - for parent_num in range(num_parents): - parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() - - return parents, numpy.array(fitness_sorted[:num_parents]) - - def crossover_func(self, parents, offspring_size, ga_instance): - - offspring = [] - idx = 0 - while len(offspring) != offspring_size[0]: - parent1 = parents[idx % parents.shape[0], :].copy() - parent2 = parents[(idx + 1) % parents.shape[0], :].copy() - - random_split_point = numpy.random.choice(range(offspring_size[0])) - - parent1[random_split_point:] = parent2[random_split_point:] - - offspring.append(parent1) - - idx += 1 - - return numpy.array(offspring) - - def mutation_func(self, offspring, ga_instance): - - for chromosome_idx in range(offspring.shape[0]): - random_gene_idx = numpy.random.choice(range(offspring.shape[1])) - - offspring[chromosome_idx, random_gene_idx] += numpy.random.random() - - return offspring - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=Test().fitness_func, - parent_selection_type=Test().parent_selection_func, - crossover_type=Test().crossover_func, - mutation_type=Test().mutation_func) - - ga_instance.run() - ga_instance.plot_fitness() - -.. _more-about-the-genespace-parameter: - -More about the ``gene_space`` Parameter -======================================= - -The ``gene_space`` parameter customizes the space of values of each -gene. - -Assuming that all genes have the same global space which include the -values 0.3, 5.2, -4, and 8, then those values can be assigned to the -``gene_space`` parameter as a list, tuple, or range. Here is a list -assigned to this parameter. By doing that, then the gene values are -restricted to those assigned to the ``gene_space`` parameter. - -.. code:: python - - gene_space = [0.3, 5.2, -4, 8] - -If some genes have different spaces, then ``gene_space`` should accept a -nested list or tuple. In this case, the elements could be: - -1. Number (of ``int``, ``float``, or ``NumPy`` data types): A single - value to be assigned to the gene. This means this gene will have the - same value across all generations. - -2. ``list``, ``tuple``, ``numpy.ndarray``, or any range like ``range``, - ``numpy.arange()``, or ``numpy.linspace``: It holds the space for - each individual gene. But this space is usually discrete. That is - there is a set of finite values to select from. - -3. ``dict``: To sample a value for a gene from a continuous range. The - dictionary must have 2 mandatory keys which are ``"low"`` and - ``"high"`` in addition to an optional key which is ``"step"``. A - random value is returned between the values assigned to the items - with ``"low"`` and ``"high"`` keys. If the ``"step"`` exists, then - this works as the previous options (i.e. discrete set of values). - -4. ``None``: A gene with its space set to ``None`` is initialized - randomly from the range specified by the 2 parameters - ``init_range_low`` and ``init_range_high``. For mutation, its value - is mutated based on a random value from the range specified by the 2 - parameters ``random_mutation_min_val`` and - ``random_mutation_max_val``. If all elements in the ``gene_space`` - parameter are ``None``, the parameter will not have any effect. - -Assuming that a chromosome has 2 genes and each gene has a different -value space. Then the ``gene_space`` could be assigned a nested -list/tuple where each element determines the space of a gene. - -According to the next code, the space of the first gene is ``[0.4, -5]`` -which has 2 values and the space for the second gene is -``[0.5, -3.2, 8.8, -9]`` which has 4 values. - -.. code:: python - - gene_space = [[0.4, -5], [0.5, -3.2, 8.2, -9]] - -For a 2 gene chromosome, if the first gene space is restricted to the -discrete values from 0 to 4 and the second gene is restricted to the -values from 10 to 19, then it could be specified according to the next -code. - -.. code:: python - - gene_space = [range(5), range(10, 20)] - -The ``gene_space`` can also be assigned to a single range, as given -below, where the values of all genes are sampled from the same range. - -.. code:: python - - gene_space = numpy.arange(15) - -The ``gene_space`` can be assigned a dictionary to sample a value from a -continuous range. - -.. code:: python - - gene_space = {"low": 4, "high": 30} - -A step also can be assigned to the dictionary. This works as if a range -is used. - -.. code:: python - - gene_space = {"low": 4, "high": 30, "step": 2.5} - -If a ``None`` is assigned to only a single gene, then its value will be -randomly generated initially using the ``init_range_low`` and -``init_range_high`` parameters in the ``pygad.GA`` class's constructor. -During mutation, the value are sampled from the range defined by the 2 -parameters ``random_mutation_min_val`` and ``random_mutation_max_val``. -This is an example where the second gene is given a ``None`` value. - -.. code:: python - - gene_space = [range(5), None, numpy.linspace(10, 20, 300)] - -If the user did not assign the initial population to the -``initial_population`` parameter, the initial population is created -randomly based on the ``gene_space`` parameter. Moreover, the mutation -is applied based on this parameter. - -.. _more-about-the-genetype-parameter: - -More about the ``gene_type`` Parameter -====================================== - -The ``gene_type`` parameter allows the user to control the data type for -all genes at once or each individual gene. In `PyGAD -2.15.0 `__, -the ``gene_type`` parameter also supports customizing the precision for -``float`` data types. As a result, the ``gene_type`` parameter helps to: - -1. Select a data type for all genes with or without precision. - -2. Select a data type for each individual gene with or without - precision. - -Let's discuss things by examples. - -Data Type for All Genes without Precision ------------------------------------------ - -The data type for all genes can be specified by assigning the numeric -data type directly to the ``gene_type`` parameter. This is an example to -make all genes of ``int`` data types. - -.. code:: python - - gene_type=int - -Given that the supported numeric data types of PyGAD include Python's -``int`` and ``float`` in addition to all numeric types of ``NumPy``, -then any of these types can be assigned to the ``gene_type`` parameter. - -If no precision is specified for a ``float`` data type, then the -complete floating-point number is kept. - -The next code uses an ``int`` data type for all genes where the genes in -the initial and final population are only integers. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4, -2, 3.5, 8, -2] - desired_output = 2671.1234 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - gene_type=int) - - print("Initial Population") - print(ga_instance.initial_population) - - ga_instance.run() - - print("Final Population") - print(ga_instance.population) - -.. code:: python - - Initial Population - [[ 1 -1 2 0 -3] - [ 0 -2 0 -3 -1] - [ 0 -1 -1 2 0] - [-2 3 -2 3 3] - [ 0 0 2 -2 -2]] - - Final Population - [[ 1 -1 2 2 0] - [ 1 -1 2 2 0] - [ 1 -1 2 2 0] - [ 1 -1 2 2 0] - [ 1 -1 2 2 0]] - -Data Type for All Genes with Precision --------------------------------------- - -A precision can only be specified for a ``float`` data type and cannot -be specified for integers. Here is an example to use a precision of 3 -for the ``float`` data type. In this case, all genes are of type -``float`` and their maximum precision is 3. - -.. code:: python - - gene_type=[float, 3] - -The next code uses prints the initial and final population where the -genes are of type ``float`` with precision 3. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4, -2, 3.5, 8, -2] - desired_output = 2671.1234 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - - return fitness - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - gene_type=[float, 3]) - - print("Initial Population") - print(ga_instance.initial_population) - - ga_instance.run() - - print("Final Population") - print(ga_instance.population) - -.. code:: python - - Initial Population - [[-2.417 -0.487 3.623 2.457 -2.362] - [-1.231 0.079 -1.63 1.629 -2.637] - [ 0.692 -2.098 0.705 0.914 -3.633] - [ 2.637 -1.339 -1.107 -0.781 -3.896] - [-1.495 1.378 -1.026 3.522 2.379]] - - Final Population - [[ 1.714 -1.024 3.623 3.185 -2.362] - [ 0.692 -1.024 3.623 3.185 -2.362] - [ 0.692 -1.024 3.623 3.375 -2.362] - [ 0.692 -1.024 4.041 3.185 -2.362] - [ 1.714 -0.644 3.623 3.185 -2.362]] - -Data Type for each Individual Gene without Precision ----------------------------------------------------- - -In `PyGAD -2.14.0 `__, -the ``gene_type`` parameter allows customizing the gene type for each -individual gene. This is by using a ``list``/``tuple``/``numpy.ndarray`` -with number of elements equal to the number of genes. For each element, -a type is specified for the corresponding gene. - -This is an example for a 5-gene problem where different types are -assigned to the genes. - -.. code:: python - - gene_type=[int, float, numpy.float16, numpy.int8, float] - -This is a complete code that prints the initial and final population for -a custom-gene data type. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4, -2, 3.5, 8, -2] - desired_output = 2671.1234 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - gene_type=[int, float, numpy.float16, numpy.int8, float]) - - print("Initial Population") - print(ga_instance.initial_population) - - ga_instance.run() - - print("Final Population") - print(ga_instance.population) - -.. code:: python - - Initial Population - [[0 0.8615522360026828 0.7021484375 -2 3.5301821368185866] - [-3 2.648189378595294 -3.830078125 1 -0.9586271572917742] - [3 3.7729827570110714 1.2529296875 -3 1.395741994211889] - [0 1.0490687178053282 1.51953125 -2 0.7243617940450235] - [0 -0.6550158436937226 -2.861328125 -2 1.8212734549263097]] - - Final Population - [[3 3.7729827570110714 2.055 0 0.7243617940450235] - [3 3.7729827570110714 1.458 0 -0.14638754050305036] - [3 3.7729827570110714 1.458 0 0.0869406120516778] - [3 3.7729827570110714 1.458 0 0.7243617940450235] - [3 3.7729827570110714 1.458 0 -0.14638754050305036]] - -Data Type for each Individual Gene with Precision -------------------------------------------------- - -The precision can also be specified for the ``float`` data types as in -the next line where the second gene precision is 2 and last gene -precision is 1. - -.. code:: python - - gene_type=[int, [float, 2], numpy.float16, numpy.int8, [float, 1]] - -This is a complete example where the initial and final populations are -printed where the genes comply with the data types and precisions -specified. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4, -2, 3.5, 8, -2] - desired_output = 2671.1234 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - gene_type=[int, [float, 2], numpy.float16, numpy.int8, [float, 1]]) - - print("Initial Population") - print(ga_instance.initial_population) - - ga_instance.run() - - print("Final Population") - print(ga_instance.population) - -.. code:: python - - Initial Population - [[-2 -1.22 1.716796875 -1 0.2] - [-1 -1.58 -3.091796875 0 -1.3] - [3 3.35 -0.107421875 1 -3.3] - [-2 -3.58 -1.779296875 0 0.6] - [2 -3.73 2.65234375 3 -0.5]] - - Final Population - [[2 -4.22 3.47 3 -1.3] - [2 -3.73 3.47 3 -1.3] - [2 -4.22 3.47 2 -1.3] - [2 -4.58 3.47 3 -1.3] - [2 -3.73 3.47 3 -1.3]] - -Visualization in PyGAD -====================== - -This section discusses the different options to visualize the results in -PyGAD through these methods: - -1. ``plot_fitness()`` - -2. ``plot_genes()`` - -3. ``plot_new_solution_rate()`` - -In the following code, the ``save_solutions`` flag is set to ``True`` -which means all solutions are saved in the ``solutions`` attribute. The -code runs for only 10 generations. - -.. code:: python - - import pygad - import numpy - - equation_inputs = [4, -2, 3.5, 8, -2, 3.5, 8] - desired_output = 2671.1234 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=10, - num_parents_mating=5, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - gene_space=[range(1, 10), range(10, 20), range(15, 30), range(20, 40), range(25, 50), range(10, 30), range(20, 50)], - gene_type=int, - save_solutions=True) - - ga_instance.run() - -Let's explore how to visualize the results by the above mentioned -methods. - -.. _plotfitness-2: - -``plot_fitness()`` ------------------- - -The ``plot_fitness()`` method shows the fitness value for each -generation. - -.. _plottypeplot: - -``plot_type="plot"`` -~~~~~~~~~~~~~~~~~~~~ - -The simplest way to call this method is as follows leaving the -``plot_type`` with its default value ``"plot"`` to create a continuous -line connecting the fitness values across all generations: - -.. code:: python - - ga_instance.plot_fitness() - # ga_instance.plot_fitness(plot_type="plot") - -.. figure:: https://user-images.githubusercontent.com/16560492/122472609-d02f5280-cf8e-11eb-88a7-f9366ff6e7c6.png - :alt: - -.. _plottypescatter: - -``plot_type="scatter"`` -~~~~~~~~~~~~~~~~~~~~~~~ - -The ``plot_type`` can also be set to ``"scatter"`` to create a scatter -graph with each individual fitness represented as a dot. The size of -these dots can be changed using the ``linewidth`` parameter. - -.. code:: python - - ga_instance.plot_fitness(plot_type="scatter") - -.. figure:: https://user-images.githubusercontent.com/16560492/122473159-75e2c180-cf8f-11eb-942d-31279b286dbd.png - :alt: - -.. _plottypebar: - -``plot_type="bar"`` -~~~~~~~~~~~~~~~~~~~ - -The third value for the ``plot_type`` parameter is ``"bar"`` to create a -bar graph with each individual fitness represented as a bar. - -.. code:: python - - ga_instance.plot_fitness(plot_type="bar") - -.. figure:: https://user-images.githubusercontent.com/16560492/122473340-b7736c80-cf8f-11eb-89c5-4f7db3b653cc.png - :alt: - -.. _plotnewsolutionrate-2: - -``plot_new_solution_rate()`` ----------------------------- - -The ``plot_new_solution_rate()`` method presents the number of new -solutions explored in each generation. This helps to figure out if the -genetic algorithm is able to find new solutions as an indication of more -possible evolution. If no new solutions are explored, this is an -indication that no further evolution is possible. - -The ``plot_new_solution_rate()`` method accepts the same parameters as -in the ``plot_fitness()`` method with 3 possible values for -``plot_type`` parameter. - -.. _plottypeplot-2: - -``plot_type="plot"`` -~~~~~~~~~~~~~~~~~~~~ - -The default value for the ``plot_type`` parameter is ``"plot"``. - -.. code:: python - - ga_instance.plot_new_solution_rate() - # ga_instance.plot_new_solution_rate(plot_type="plot") - -The next figure shows that, for example, generation 6 has the least -number of new solutions which is 4. The number of new solutions in the -first generation is always equal to the number of solutions in the -population (i.e. the value assigned to the ``sol_per_pop`` parameter in -the constructor of the ``pygad.GA`` class) which is 10 in this example. - -.. figure:: https://user-images.githubusercontent.com/16560492/122475815-3322e880-cf93-11eb-9648-bf66f823234b.png - :alt: - -.. _plottypescatter-2: - -``plot_type="scatter"`` -~~~~~~~~~~~~~~~~~~~~~~~ - -The previous graph can be represented as scattered points by setting -``plot_type="scatter"``. - -.. code:: python - - ga_instance.plot_new_solution_rate(plot_type="scatter") - -.. figure:: https://user-images.githubusercontent.com/16560492/122476108-adec0380-cf93-11eb-80ac-7588bf90492f.png - :alt: - -.. _plottypebar-2: - -``plot_type="bar"`` -~~~~~~~~~~~~~~~~~~~ - -By setting ``plot_type="scatter"``, each value is represented as a -vertical bar. - -.. code:: python - - ga_instance.plot_new_solution_rate(plot_type="bar") - -.. figure:: https://user-images.githubusercontent.com/16560492/122476173-c2c89700-cf93-11eb-9e77-d39737cd3a96.png - :alt: - -.. _plotgenes-2: - -``plot_genes()`` ----------------- - -The ``plot_genes()`` method is the third option to visualize the PyGAD -results. This method has 3 control variables: - -1. ``graph_type="plot"``: Can be ``"plot"`` (default), ``"boxplot"``, or - ``"histogram"``. - -2. ``plot_type="plot"``: Identical to the ``plot_type`` parameter - explored in the ``plot_fitness()`` and ``plot_new_solution_rate()`` - methods. - -3. ``solutions="all"``: Can be ``"all"`` (default) or ``"best"``. - -These 3 parameters controls the style of the output figure. - -The ``graph_type`` parameter selects the type of the graph which helps -to explore the gene values as: - -1. A normal plot. - -2. A histogram. - -3. A box and whisker plot. - -The ``plot_type`` parameter works only when the type of the graph is set -to ``"plot"``. - -The ``solutions`` parameter selects whether the genes come from all -solutions in the population or from just the best solutions. - -.. _graphtypeplot: - -``graph_type="plot"`` -~~~~~~~~~~~~~~~~~~~~~ - -When ``graph_type="plot"``, then the figure creates a normal graph where -the relationship between the gene values and the generation numbers is -represented as a continuous plot, scattered points, or bars. - -.. _plottypeplot-3: - -``plot_type="plot"`` -^^^^^^^^^^^^^^^^^^^^ - -Because the default value for both ``graph_type`` and ``plot_type`` is -``"plot"``, then all of the lines below creates the same figure. This -figure is helpful to know whether a gene value lasts for more -generations as an indication of the best value for this gene. For -example, the value 16 for the gene with index 5 (at column 2 and row 2 -of the next graph) lasted for 83 generations. - -.. code:: python - - ga_instance.plot_genes() - - ga_instance.plot_genes(graph_type="plot") - - ga_instance.plot_genes(plot_type="plot") - - ga_instance.plot_genes(graph_type="plot", - plot_type="plot") - -.. figure:: https://user-images.githubusercontent.com/16560492/122477158-4a62d580-cf95-11eb-8c93-9b6e74cb814c.png - :alt: - -As the default value for the ``solutions`` parameter is ``"all"``, then -the following method calls generate the same plot. - -.. code:: python - - ga_instance.plot_genes(solutions="all") - - ga_instance.plot_genes(graph_type="plot", - solutions="all") - - ga_instance.plot_genes(plot_type="plot", - solutions="all") - - ga_instance.plot_genes(graph_type="plot", - plot_type="plot", - solutions="all") - -.. _plottypescatter-3: - -``plot_type="scatter"`` -^^^^^^^^^^^^^^^^^^^^^^^ - -The following calls of the ``plot_genes()`` method create the same -scatter plot. - -.. code:: python - - ga_instance.plot_genes(plot_type="scatter") - - ga_instance.plot_genes(graph_type="plot", - plot_type="scatter", - solutions='all') - -.. figure:: https://user-images.githubusercontent.com/16560492/122477273-73836600-cf95-11eb-828f-f357c7b0f815.png - :alt: - -.. _plottypebar-3: - -``plot_type="bar"`` -^^^^^^^^^^^^^^^^^^^ - -.. code:: python - - ga_instance.plot_genes(plot_type="bar") - - ga_instance.plot_genes(graph_type="plot", - plot_type="bar", - solutions='all') - -.. figure:: https://user-images.githubusercontent.com/16560492/122477370-99106f80-cf95-11eb-8643-865b55e6b844.png - :alt: - -.. _graphtypeboxplot: - -``graph_type="boxplot"`` -~~~~~~~~~~~~~~~~~~~~~~~~ - -By setting ``graph_type`` to ``"boxplot"``, then a box and whisker graph -is created. Now, the ``plot_type`` parameter has no effect. - -The following 2 calls of the ``plot_genes()`` method create the same -figure as the default value for the ``solutions`` parameter is -``"all"``. - -.. code:: python - - ga_instance.plot_genes(graph_type="boxplot") - - ga_instance.plot_genes(graph_type="boxplot", - solutions='all') - -.. figure:: https://user-images.githubusercontent.com/16560492/122479260-beeb4380-cf98-11eb-8f08-23707929b12c.png - :alt: - -.. _graphtypehistogram: - -``graph_type="histogram"`` -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -For ``graph_type="boxplot"``, then a histogram is created for each gene. -Similar to ``graph_type="boxplot"``, the ``plot_type`` parameter has no -effect. - -The following 2 calls of the ``plot_genes()`` method create the same -figure as the default value for the ``solutions`` parameter is -``"all"``. - -.. code:: python - - ga_instance.plot_genes(graph_type="histogram") - - ga_instance.plot_genes(graph_type="histogram", - solutions='all') - -.. figure:: https://user-images.githubusercontent.com/16560492/122477314-8007be80-cf95-11eb-9c95-da3f49204151.png - :alt: - -All the previous figures can be created for only the best solutions by -setting ``solutions="best"``. - -Parallel Processing in PyGAD -============================ - -Starting from `PyGAD -2.17.0 `__, -parallel processing becomes supported. This section explains how to use -parallel processing in PyGAD. - -According to the `PyGAD -lifecycle `__, -parallel processing can be parallelized in only 2 operations: - -1. Population fitness calculation. - -2. Mutation. - -The reason is that the calculations in these 2 operations are -independent (i.e. each solution/chromosome is handled independently from -the others) and can be distributed across different processes or -threads. - -For the mutation operation, it does not do intensive calculations on the -CPU. Its calculations are simple like flipping the values of some genes -from 0 to 1 or adding a random value to some genes. So, it does not take -much CPU processing time. Experiments proved that parallelizing the -mutation operation across the solutions increases the time instead of -reducing it. This is because running multiple processes or threads adds -overhead to manage them. Thus, parallel processing cannot be applied on -the mutation operation. - -For the population fitness calculation, parallel processing can help -make a difference and reduce the processing time. But this is -conditional on the type of calculations done in the fitness function. If -the fitness function makes intensive calculations and takes much -processing time from the CPU, then it is probably that parallel -processing will help to cut down the overall time. - -This section explains how parallel processing works in PyGAD and how to -use parallel processing in PyGAD - -How to Use Parallel Processing in PyGAD ---------------------------------------- - -Starting from `PyGAD -2.17.0 `__, -a new parameter called ``parallel_processing`` added to the constructor -of the ``pygad.GA`` class. - -.. code:: python - - import pygad - ... - ga_instance = pygad.GA(..., - parallel_processing=...) - ... - -This parameter allows the user to do the following: - -1. Enable parallel processing. - -2. Select whether processes or threads are used. - -3. Specify the number of processes or threads to be used. - -These are 3 possible values for the ``parallel_processing`` parameter: - -1. ``None``: (Default) It means no parallel processing is used. - -2. A positive integer referring to the number of threads to be used - (i.e. threads, not processes, are used. - -3. ``list``/``tuple``: If a list or a tuple of exactly 2 elements is - assigned, then: - - 1. The first element can be either ``'process'`` or ``'thread'`` to - specify whether processes or threads are used, respectively. - - 2. The second element can be: - - 1. A positive integer to select the maximum number of processes or - threads to be used - - 2. ``0`` to indicate that 0 processes or threads are used. It - means no parallel processing. This is identical to setting - ``parallel_processing=None``. - - 3. ``None`` to use the default value as calculated by the - ``concurrent.futures module``. - -These are examples of the values assigned to the ``parallel_processing`` -parameter: - -- ``parallel_processing=4``: Because the parameter is assigned a - positive integer, this means parallel processing is activated where 4 - threads are used. - -- ``parallel_processing=["thread", 5]``: Use parallel processing with 5 - threads. This is identical to ``parallel_processing=5``. - -- ``parallel_processing=["process", 8]``: Use parallel processing with - 8 processes. - -- ``parallel_processing=["process", 0]``: As the second element is - given the value 0, this means do not use parallel processing. This is - identical to ``parallel_processing=None``. - -Examples --------- - -The examples will help you know the difference between using processes -and threads. Moreover, it will give an idea when parallel processing -would make a difference and reduce the time. These are dummy examples -where the fitness function is made to always return 0. - -The first example uses 10 genes, 5 solutions in the population where -only 3 solutions mate, and 9999 generations. The fitness function uses a -``for`` loop with 100 iterations just to have some calculations. In the -constructor of the ``pygad.GA`` class, ``parallel_processing=None`` -means no parallel processing is used. - -.. code:: python - - import pygad - import time - - def fitness_func(ga_instance, solution, solution_idx): - for _ in range(99): - pass - return 0 - - ga_instance = pygad.GA(num_generations=9999, - num_parents_mating=3, - sol_per_pop=5, - num_genes=10, - fitness_func=fitness_func, - suppress_warnings=True, - parallel_processing=None) - - if __name__ == '__main__': - t1 = time.time() - - ga_instance.run() - - t2 = time.time() - print("Time is", t2-t1) - -When parallel processing is not used, the time it takes to run the -genetic algorithm is ``1.5`` seconds. - -In the comparison, let's do a second experiment where parallel -processing is used with 5 threads. In this case, it take ``5`` seconds. - -.. code:: python - - ... - ga_instance = pygad.GA(..., - parallel_processing=5) - ... - -For the third experiment, processes instead of threads are used. Also, -only 99 generations are used instead of 9999. The time it takes is -``99`` seconds. - -.. code:: python - - ... - ga_instance = pygad.GA(num_generations=99, - ..., - parallel_processing=["process", 5]) - ... - -This is the summary of the 3 experiments: - -1. No parallel processing & 9999 generations: 1.5 seconds. - -2. Parallel processing with 5 threads & 9999 generations: 5 seconds - -3. Parallel processing with 5 processes & 99 generations: 99 seconds - -Because the fitness function does not need much CPU time, the normal -processing takes the least time. Running processes for this simple -problem takes 99 compared to only 5 seconds for threads because managing -processes is much heavier than managing threads. Thus, most of the CPU -time is for swapping the processes instead of executing the code. - -In the second example, the loop makes 99999999 iterations and only 5 -generations are used. With no parallelization, it takes 22 seconds. - -.. code:: python - - import pygad - import time - - def fitness_func(ga_instance, solution, solution_idx): - for _ in range(99999999): - pass - return 0 - - ga_instance = pygad.GA(num_generations=5, - num_parents_mating=3, - sol_per_pop=5, - num_genes=10, - fitness_func=fitness_func, - suppress_warnings=True, - parallel_processing=None) - - if __name__ == '__main__': - t1 = time.time() - ga_instance.run() - t2 = time.time() - print("Time is", t2-t1) - -It takes 15 seconds when 10 processes are used. - -.. code:: python - - ... - ga_instance = pygad.GA(..., - parallel_processing=["process", 10]) - ... - -This is compared to 20 seconds when 10 threads are used. - -.. code:: python - - ... - ga_instance = pygad.GA(..., - parallel_processing=["thread", 10]) - ... - -Based on the second example, using parallel processing with 10 processes -takes the least time because there is much CPU work done. Generally, -processes are preferred over threads when most of the work in on the -CPU. Threads are preferred over processes in some situations like doing -input/output operations. - -*Before releasing* `PyGAD -2.17.0 `__\ *,* -`László -Fazekas `__ -*wrote an article to parallelize the fitness function with PyGAD. Check -it:* `How Genetic Algorithms Can Compete with Gradient Descent and -Backprop `__. - -Print Lifecycle Summary -======================= - -In `PyGAD -2.19.0 `__, -a new method called ``summary()`` is supported. It prints a Keras-like -summary of the PyGAD lifecycle showing the steps, callback functions, -parameters, etc. - -This method accepts the following parameters: - -- ``line_length=70``: An integer representing the length of the single - line in characters. - -- ``fill_character=" "``: A character to fill the lines. - -- ``line_character="-"``: A character for creating a line separator. - -- ``line_character2="="``: A secondary character to create a line - separator. - -- ``columns_equal_len=False``: The table rows are split into - equal-sized columns or split subjective to the width needed. - -- ``print_step_parameters=True``: Whether to print extra parameters - about each step inside the step. If ``print_step_parameters=False`` - and ``print_parameters_summary=True``, then the parameters of each - step are printed at the end of the table. - -- ``print_parameters_summary=True``: Whether to print parameters - summary at the end of the table. If ``print_step_parameters=False``, - then the parameters of each step are printed at the end of the table - too. - -This is a quick example to create a PyGAD example. - -.. code:: python - - import pygad - import numpy - - function_inputs = [4,-2,3.5,5,-11,-4.7] - desired_output = 44 - - def genetic_fitness(solution, solution_idx): - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - def on_gen(ga): - pass - - def on_crossover_callback(a, b): - pass - - ga_instance = pygad.GA(num_generations=100, - num_parents_mating=10, - sol_per_pop=20, - num_genes=len(function_inputs), - on_crossover=on_crossover_callback, - on_generation=on_gen, - parallel_processing=2, - stop_criteria="reach_10", - fitness_batch_size=4, - crossover_probability=0.4, - fitness_func=genetic_fitness) - -Then call the ``summary()`` method to print the summary with the default -parameters. Note that entries for the crossover and generation callback -function are created because their callback functions are implemented -through the ``on_crossover_callback()`` and ``on_gen()``, respectively. - -.. code:: python - - ga_instance.summary() - -.. code:: bash - - ---------------------------------------------------------------------- - PyGAD Lifecycle - ====================================================================== - Step Handler Output Shape - ====================================================================== - Fitness Function genetic_fitness() (1) - Fitness batch size: 4 - ---------------------------------------------------------------------- - Parent Selection steady_state_selection() (10, 6) - Number of Parents: 10 - ---------------------------------------------------------------------- - Crossover single_point_crossover() (10, 6) - Crossover probability: 0.4 - ---------------------------------------------------------------------- - On Crossover on_crossover_callback() None - ---------------------------------------------------------------------- - Mutation random_mutation() (10, 6) - Mutation Genes: 1 - Random Mutation Range: (-1.0, 1.0) - Mutation by Replacement: False - Allow Duplicated Genes: True - ---------------------------------------------------------------------- - On Generation on_gen() None - Stop Criteria: [['reach', 10.0]] - ---------------------------------------------------------------------- - ====================================================================== - Population Size: (20, 6) - Number of Generations: 100 - Initial Population Range: (-4, 4) - Keep Elitism: 1 - Gene DType: [, None] - Parallel Processing: ['thread', 2] - Save Best Solutions: False - Save Solutions: False - ====================================================================== - -We can set the ``print_step_parameters`` and -``print_parameters_summary`` parameters to ``False`` to not print the -parameters. - -.. code:: python - - ga_instance.summary(print_step_parameters=False, - print_parameters_summary=False) - -.. code:: bash - - ---------------------------------------------------------------------- - PyGAD Lifecycle - ====================================================================== - Step Handler Output Shape - ====================================================================== - Fitness Function genetic_fitness() (1) - ---------------------------------------------------------------------- - Parent Selection steady_state_selection() (10, 6) - ---------------------------------------------------------------------- - Crossover single_point_crossover() (10, 6) - ---------------------------------------------------------------------- - On Crossover on_crossover_callback() None - ---------------------------------------------------------------------- - Mutation random_mutation() (10, 6) - ---------------------------------------------------------------------- - On Generation on_gen() None - ---------------------------------------------------------------------- - ====================================================================== - -Logging Outputs -=============== - -In `PyGAD -3.0.0 `__, -the ``print()`` statement is no longer used and the outputs are printed -using the `logging `__ -module. A a new parameter called ``logger`` is supported to accept the -user-defined logger. - -.. code:: python - - import logging - - logger = ... - - ga_instance = pygad.GA(..., - logger=logger, - ...) - -The default value for this parameter is ``None``. If there is no logger -passed (i.e. ``logger=None``), then a default logger is created to log -the messages to the console exactly like how the ``print()`` statement -works. - -Some advantages of using the the -`logging `__ module -instead of the ``print()`` statement are: - -1. The user has more control over the printed messages specially if - there is a project that uses multiple modules where each module - prints its messages. A logger can organize the outputs. - -2. Using the proper ``Handler``, the user can log the output messages to - files and not only restricted to printing it to the console. So, it - is much easier to record the outputs. - -3. The format of the printed messages can be changed by customizing the - ``Formatter`` assigned to the Logger. - -This section gives some quick examples to use the ``logging`` module and -then gives an example to use the logger with PyGAD. - -Logging to the Console ----------------------- - -This is an example to create a logger to log the messages to the -console. - -.. code:: python - - import logging - - # Create a logger - logger = logging.getLogger(__name__) - - # Set the logger level to debug so that all the messages are printed. - logger.setLevel(logging.DEBUG) - - # Create a stream handler to log the messages to the console. - stream_handler = logging.StreamHandler() - - # Set the handler level to debug. - stream_handler.setLevel(logging.DEBUG) - - # Create a formatter - formatter = logging.Formatter('%(message)s') - - # Add the formatter to handler. - stream_handler.setFormatter(formatter) - - # Add the stream handler to the logger - logger.addHandler(stream_handler) - -Now, we can log messages to the console with the format specified in the -``Formatter``. - -.. code:: python - - logger.debug('Debug message.') - logger.info('Info message.') - logger.warning('Warn message.') - logger.error('Error message.') - logger.critical('Critical message.') - -The outputs are identical to those returned using the ``print()`` -statement. - -.. code:: - - Debug message. - Info message. - Warn message. - Error message. - Critical message. - -By changing the format of the output messages, we can have more -information about each message. - -.. code:: python - - formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') - -This is a sample output. - -.. code:: python - - 2023-04-03 18:46:27 DEBUG: Debug message. - 2023-04-03 18:46:27 INFO: Info message. - 2023-04-03 18:46:27 WARNING: Warn message. - 2023-04-03 18:46:27 ERROR: Error message. - 2023-04-03 18:46:27 CRITICAL: Critical message. - -Note that you may need to clear the handlers after finishing the -execution. This is to make sure no cached handlers are used in the next -run. If the cached handlers are not cleared, then the single output -message may be repeated. - -.. code:: python - - logger.handlers.clear() - -Logging to a File ------------------ - -This is another example to log the messages to a file named -``logfile.txt``. The formatter prints the following about each message: - -1. The date and time at which the message is logged. - -2. The log level. - -3. The message. - -4. The path of the file. - -5. The lone number of the log message. - -.. code:: python - - import logging - - level = logging.DEBUG - name = 'logfile.txt' - - logger = logging.getLogger(name) - logger.setLevel(level) - - file_handler = logging.FileHandler(name, 'a+', 'utf-8') - file_handler.setLevel(logging.DEBUG) - file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s - %(pathname)s:%(lineno)d', datefmt='%Y-%m-%d %H:%M:%S') - file_handler.setFormatter(file_format) - logger.addHandler(file_handler) - -This is how the outputs look like. - -.. code:: python - - 2023-04-03 18:54:03 DEBUG: Debug message. - c:\users\agad069\desktop\logger\example2.py:46 - 2023-04-03 18:54:03 INFO: Info message. - c:\users\agad069\desktop\logger\example2.py:47 - 2023-04-03 18:54:03 WARNING: Warn message. - c:\users\agad069\desktop\logger\example2.py:48 - 2023-04-03 18:54:03 ERROR: Error message. - c:\users\agad069\desktop\logger\example2.py:49 - 2023-04-03 18:54:03 CRITICAL: Critical message. - c:\users\agad069\desktop\logger\example2.py:50 - -Consider clearing the handlers if necessary. - -.. code:: python - - logger.handlers.clear() - -Log to Both the Console and a File ----------------------------------- - -This is an example to create a single Logger associated with 2 handlers: - -1. A file handler. - -2. A stream handler. - -.. code:: python - - import logging - - level = logging.DEBUG - name = 'logfile.txt' - - logger = logging.getLogger(name) - logger.setLevel(level) - - file_handler = logging.FileHandler(name,'a+','utf-8') - file_handler.setLevel(logging.DEBUG) - file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s - %(pathname)s:%(lineno)d', datefmt='%Y-%m-%d %H:%M:%S') - file_handler.setFormatter(file_format) - logger.addHandler(file_handler) - - console_handler = logging.StreamHandler() - console_handler.setLevel(logging.INFO) - console_format = logging.Formatter('%(message)s') - console_handler.setFormatter(console_format) - logger.addHandler(console_handler) - -When a log message is executed, then it is both printed to the console -and saved in the ``logfile.txt``. - -Consider clearing the handlers if necessary. - -.. code:: python - - logger.handlers.clear() - -PyGAD Example -------------- - -To use the logger in PyGAD, just create your custom logger and pass it -to the ``logger`` parameter. - -.. code:: python - - import logging - import pygad - import numpy - - level = logging.DEBUG - name = 'logfile.txt' - - logger = logging.getLogger(name) - logger.setLevel(level) - - file_handler = logging.FileHandler(name,'a+','utf-8') - file_handler.setLevel(logging.DEBUG) - file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') - file_handler.setFormatter(file_format) - logger.addHandler(file_handler) - - console_handler = logging.StreamHandler() - console_handler.setLevel(logging.INFO) - console_format = logging.Formatter('%(message)s') - console_handler.setFormatter(console_format) - logger.addHandler(console_handler) - - equation_inputs = [4, -2, 8] - desired_output = 2671.1234 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - def on_generation(ga_instance): - ga_instance.logger.info("Generation = {generation}".format(generation=ga_instance.generations_completed)) - ga_instance.logger.info("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1])) - - ga_instance = pygad.GA(num_generations=10, - sol_per_pop=40, - num_parents_mating=2, - keep_parents=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - on_generation=on_generation, - logger=logger) - ga_instance.run() - - logger.handlers.clear() - -By executing this code, the logged messages are printed to the console -and also saved in the text file. - -.. code:: python - - 2023-04-03 19:04:27 INFO: Generation = 1 - 2023-04-03 19:04:27 INFO: Fitness = 0.00038086960368076276 - 2023-04-03 19:04:27 INFO: Generation = 2 - 2023-04-03 19:04:27 INFO: Fitness = 0.00038214871408010853 - 2023-04-03 19:04:27 INFO: Generation = 3 - 2023-04-03 19:04:27 INFO: Fitness = 0.0003832795907974678 - 2023-04-03 19:04:27 INFO: Generation = 4 - 2023-04-03 19:04:27 INFO: Fitness = 0.00038398612055017196 - 2023-04-03 19:04:27 INFO: Generation = 5 - 2023-04-03 19:04:27 INFO: Fitness = 0.00038442348890867516 - 2023-04-03 19:04:27 INFO: Generation = 6 - 2023-04-03 19:04:27 INFO: Fitness = 0.0003854406039137763 - 2023-04-03 19:04:27 INFO: Generation = 7 - 2023-04-03 19:04:27 INFO: Fitness = 0.00038646083174063284 - 2023-04-03 19:04:27 INFO: Generation = 8 - 2023-04-03 19:04:27 INFO: Fitness = 0.0003875169193024936 - 2023-04-03 19:04:27 INFO: Generation = 9 - 2023-04-03 19:04:27 INFO: Fitness = 0.0003888816727311021 - 2023-04-03 19:04:27 INFO: Generation = 10 - 2023-04-03 19:04:27 INFO: Fitness = 0.000389832593101348 - -Batch Fitness Calculation -========================= - -In `PyGAD -2.19.0 `__, -a new optional parameter called ``fitness_batch_size`` is supported. A -new optional parameter called ``fitness_batch_size`` is supported to -calculate the fitness function in batches. Thanks to `Linan -Qiu `__ for opening the `GitHub issue -#136 `__. - -Its values can be: - -- ``1`` or ``None``: If the ``fitness_batch_size`` parameter is - assigned the value ``1`` or ``None`` (default), then the normal flow - is used where the fitness function is called for each individual - solution. That is if there are 15 solutions, then the fitness - function is called 15 times. - -- ``1 < fitness_batch_size <= sol_per_pop``: If the - ``fitness_batch_size`` parameter is assigned a value satisfying this - condition ``1 < fitness_batch_size <= sol_per_pop``, then the - solutions are grouped into batches of size ``fitness_batch_size`` and - the fitness function is called once for each batch. In this case, the - fitness function must return a list/tuple/numpy.ndarray with a length - equal to the number of solutions passed. - -.. _example-without-fitnessbatchsize-parameter: - -Example without ``fitness_batch_size`` Parameter ------------------------------------------------- - -This is an example where the ``fitness_batch_size`` parameter is given -the value ``None`` (which is the default value). This is equivalent to -using the value ``1``. In this case, the fitness function will be called -for each solution. This means the fitness function ``fitness_func`` will -receive only a single solution. This is an example of the passed -arguments to the fitness function: - -.. code:: - - solution: [ 2.52860734, -0.94178795, 2.97545704, 0.84131987, -3.78447118, 2.41008358] - solution_idx: 3 - -The fitness function also must return a single numeric value as the -fitness for the passed solution. - -As we have a population of ``20`` solutions, then the fitness function -is called 20 times per generation. For 5 generations, then the fitness -function is called ``20*5 = 100`` times. In PyGAD, the fitness function -is called after the last generation too and this adds additional 20 -times. So, the total number of calls to the fitness function is -``20*5 + 20 = 120``. - -Note that the ``keep_elitism`` and ``keep_parents`` parameters are set -to ``0`` to make sure no fitness values are reused and to force calling -the fitness function for each individual solution. - -.. code:: python - - import pygad - import numpy - - function_inputs = [4,-2,3.5,5,-11,-4.7] - desired_output = 44 - - number_of_calls = 0 - - def fitness_func(ga_instance, solution, solution_idx): - global number_of_calls - number_of_calls = number_of_calls + 1 - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - ga_instance = pygad.GA(num_generations=5, - num_parents_mating=10, - sol_per_pop=20, - fitness_func=fitness_func, - fitness_batch_size=None, - # fitness_batch_size=1, - num_genes=len(function_inputs), - keep_elitism=0, - keep_parents=0) - - ga_instance.run() - print(number_of_calls) - -.. code:: - - 120 - -.. _example-with-fitnessbatchsize-parameter: - -Example with ``fitness_batch_size`` Parameter ---------------------------------------------- - -This is an example where the ``fitness_batch_size`` parameter is used -and assigned the value ``4``. This means the solutions will be grouped -into batches of ``4`` solutions. The fitness function will be called -once for each patch (i.e. called once for each 4 solutions). - -This is an example of the arguments passed to it: - -.. code:: python - - solutions: - [[ 3.1129432 -0.69123589 1.93792414 2.23772968 -1.54616001 -0.53930799] - [ 3.38508121 0.19890812 1.93792414 2.23095014 -3.08955597 3.10194128] - [ 2.37079504 -0.88819803 2.97545704 1.41742256 -3.95594055 2.45028256] - [ 2.52860734 -0.94178795 2.97545704 0.84131987 -3.78447118 2.41008358]] - solutions_indices: - [16, 17, 18, 19] - -As we have 20 solutions, then there are ``20/4 = 5`` patches. As a -result, the fitness function is called only 5 times per generation -instead of 20. For each call to the fitness function, it receives a -batch of 4 solutions. - -As we have 5 generations, then the function will be called ``5*5 = 25`` -times. Given the call to the fitness function after the last generation, -then the total number of calls is ``5*5 + 5 = 30``. - -.. code:: python - - import pygad - import numpy - - function_inputs = [4,-2,3.5,5,-11,-4.7] - desired_output = 44 - - number_of_calls = 0 - - def fitness_func_batch(ga_instance, solutions, solutions_indices): - global number_of_calls - number_of_calls = number_of_calls + 1 - batch_fitness = [] - for solution in solutions: - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - batch_fitness.append(fitness) - return batch_fitness - - ga_instance = pygad.GA(num_generations=5, - num_parents_mating=10, - sol_per_pop=20, - fitness_func=fitness_func_batch, - fitness_batch_size=4, - num_genes=len(function_inputs), - keep_elitism=0, - keep_parents=0) - - ga_instance.run() - print(number_of_calls) - -.. code:: - - 30 - -When batch fitness calculation is used, then we saved ``120 - 30 = 90`` -calls to the fitness function. - -Use Functions and Methods to Build Fitness and Callbacks -======================================================== - -In PyGAD 2.19.0, it is possible to pass user-defined functions or -methods to the following parameters: - -1. ``fitness_func`` - -2. ``on_start`` - -3. ``on_fitness`` - -4. ``on_parents`` - -5. ``on_crossover`` - -6. ``on_mutation`` - -7. ``on_generation`` - -8. ``on_stop`` - -This section gives 2 examples to assign these parameters user-defined: - -1. Functions. - -2. Methods. - -Assign Functions ----------------- - -This is a dummy example where the fitness function returns a random -value. Note that the instance of the ``pygad.GA`` class is passed as the -last parameter of all functions. - -.. code:: python - - import pygad - import numpy - - def fitness_func(ga_instanse, solution, solution_idx): - return numpy.random.rand() - - def on_start(ga_instanse): - print("on_start") - - def on_fitness(ga_instanse, last_gen_fitness): - print("on_fitness") - - def on_parents(ga_instanse, last_gen_parents): - print("on_parents") - - def on_crossover(ga_instanse, last_gen_offspring): - print("on_crossover") - - def on_mutation(ga_instanse, last_gen_offspring): - print("on_mutation") - - def on_generation(ga_instanse): - print("on_generation\n") - - def on_stop(ga_instanse, last_gen_fitness): - print("on_stop") - - ga_instance = pygad.GA(num_generations=5, - num_parents_mating=4, - sol_per_pop=10, - num_genes=2, - on_start=on_start, - on_fitness=on_fitness, - on_parents=on_parents, - on_crossover=on_crossover, - on_mutation=on_mutation, - on_generation=on_generation, - on_stop=on_stop, - fitness_func=fitness_func) - - ga_instance.run() - -Assign Methods --------------- - -The next example has all the method defined inside the class ``Test``. -All of the methods accept an additional parameter representing the -method's object of the class ``Test``. - -All methods accept ``self`` as the first parameter and the instance of -the ``pygad.GA`` class as the last parameter. - -.. code:: python - - import pygad - import numpy - - class Test: - def fitness_func(self, ga_instanse, solution, solution_idx): - return numpy.random.rand() - - def on_start(self, ga_instanse): - print("on_start") - - def on_fitness(self, ga_instanse, last_gen_fitness): - print("on_fitness") - - def on_parents(self, ga_instanse, last_gen_parents): - print("on_parents") - - def on_crossover(self, ga_instanse, last_gen_offspring): - print("on_crossover") - - def on_mutation(self, ga_instanse, last_gen_offspring): - print("on_mutation") - - def on_generation(self, ga_instanse): - print("on_generation\n") - - def on_stop(self, ga_instanse, last_gen_fitness): - print("on_stop") - - ga_instance = pygad.GA(num_generations=5, - num_parents_mating=4, - sol_per_pop=10, - num_genes=2, - on_start=Test().on_start, - on_fitness=Test().on_fitness, - on_parents=Test().on_parents, - on_crossover=Test().on_crossover, - on_mutation=Test().on_mutation, - on_generation=Test().on_generation, - on_stop=Test().on_stop, - fitness_func=Test().fitness_func) - - ga_instance.run() - -.. _examples-2: - -Examples -======== - -This section gives the complete code of some examples that use -``pygad``. Each subsection builds a different example. - -Linear Model Optimization -------------------------- - -This example is discussed in the `Steps to Use -PyGAD `__ -section which optimizes a linear model. Its complete code is listed -below. - -.. code:: python - - import pygad - import numpy - - """ - Given the following function: - y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6 - where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=44 - What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function. - """ - - function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs. - desired_output = 44 # Function output. - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution*function_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - num_generations = 100 # Number of generations. - num_parents_mating = 10 # Number of solutions to be selected as parents in the mating pool. - - sol_per_pop = 20 # Number of solutions in the population. - num_genes = len(function_inputs) - - last_fitness = 0 - def on_generation(ga_instance): - global last_fitness - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1])) - print("Change = {change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness)) - last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - sol_per_pop=sol_per_pop, - num_genes=num_genes, - fitness_func=fitness_func, - on_generation=on_generation) - - # Running the GA to optimize the parameters of the function. - ga_instance.run() - - ga_instance.plot_fitness() - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness) - print("Parameters of the best solution : {solution}".format(solution=solution)) - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - prediction = numpy.sum(numpy.array(function_inputs)*solution) - print("Predicted output based on the best solution : {prediction}".format(prediction=prediction)) - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - - # Saving the GA instance. - filename = 'genetic' # The filename to which the instance is saved. The name is without extension. - ga_instance.save(filename=filename) - - # Loading the saved GA instance. - loaded_ga_instance = pygad.load(filename=filename) - loaded_ga_instance.plot_fitness() - -Reproducing Images ------------------- - -This project reproduces a single image using PyGAD by evolving pixel -values. This project works with both color and gray images. Check this -project at `GitHub `__: -https://github.com/ahmedfgad/GARI. - -For more information about this project, read this tutorial titled -`Reproducing Images using a Genetic Algorithm with -Python `__ -available at these links: - -- `Heartbeat `__: - https://heartbeat.fritz.ai/reproducing-images-using-a-genetic-algorithm-with-python-91fc701ff84 - -- `LinkedIn `__: - https://www.linkedin.com/pulse/reproducing-images-using-genetic-algorithm-python-ahmed-gad - -Project Steps -~~~~~~~~~~~~~ - -The steps to follow in order to reproduce an image are as follows: - -- Read an image - -- Prepare the fitness function - -- Create an instance of the pygad.GA class with the appropriate - parameters - -- Run PyGAD - -- Plot results - -- Calculate some statistics - -The next sections discusses the code of each of these steps. - -Read an Image -~~~~~~~~~~~~~ - -There is an image named ``fruit.jpg`` in the `GARI -project `__ which is read according -to the next code. - -.. code:: python - - import imageio - import numpy - - target_im = imageio.imread('fruit.jpg') - target_im = numpy.asarray(target_im/255, dtype=float) - -Here is the read image. - -.. figure:: https://user-images.githubusercontent.com/16560492/36948808-f0ac882e-1fe8-11e8-8d07-1307e3477fd0.jpg - :alt: - -Based on the chromosome representation used in the example, the pixel -values can be either in the 0-255, 0-1, or any other ranges. - -Note that the range of pixel values affect other parameters like the -range from which the random values are selected during mutation and also -the range of the values used in the initial population. So, be -consistent. - -Prepare the Fitness Function -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The next code creates a function that will be used as a fitness function -for calculating the fitness value for each solution in the population. -This function must be a maximization function that accepts 3 parameters -representing the instance of the ``pygad.GA`` class, a solution, and its -index. It returns a value representing the fitness value. - -.. code:: python - - import gari - - target_chromosome = gari.img2chromosome(target_im) - - def fitness_fun(ga_instance, solution, solution_idx): - fitness = numpy.sum(numpy.abs(target_chromosome-solution)) - - # Negating the fitness value to make it increasing rather than decreasing. - fitness = numpy.sum(target_chromosome) - fitness - return fitness - -The fitness value is calculated using the sum of absolute difference -between genes values in the original and reproduced chromosomes. The -``gari.img2chromosome()`` function is called before the fitness function -to represent the image as a vector because the genetic algorithm can -work with 1D chromosomes. - -The implementation of the ``gari`` module is available at the `GARI -GitHub -project `__ and -its code is listed below. - -.. code:: python - - import numpy - import functools - import operator - - def img2chromosome(img_arr): - return numpy.reshape(a=img_arr, newshape=(functools.reduce(operator.mul, img_arr.shape))) - - def chromosome2img(vector, shape): - if len(vector) != functools.reduce(operator.mul, shape): - raise ValueError("A vector of length {vector_length} into an array of shape {shape}.".format(vector_length=len(vector), shape=shape)) - - return numpy.reshape(a=vector, newshape=shape) - -.. _create-an-instance-of-the-pygadga-class-2: - -Create an Instance of the ``pygad.GA`` Class -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -It is very important to use random mutation and set the -``mutation_by_replacement`` to ``True``. Based on the range of pixel -values, the values assigned to the ``init_range_low``, -``init_range_high``, ``random_mutation_min_val``, and -``random_mutation_max_val`` parameters should be changed. - -If the image pixel values range from 0 to 255, then set -``init_range_low`` and ``random_mutation_min_val`` to 0 as they are but -change ``init_range_high`` and ``random_mutation_max_val`` to 255. - -Feel free to change the other parameters or add other parameters. Please -check the `PyGAD's documentation `__ for -the full list of parameters. - -.. code:: python - - import pygad - - ga_instance = pygad.GA(num_generations=20000, - num_parents_mating=10, - fitness_func=fitness_fun, - sol_per_pop=20, - num_genes=target_im.size, - init_range_low=0.0, - init_range_high=1.0, - mutation_percent_genes=0.01, - mutation_type="random", - mutation_by_replacement=True, - random_mutation_min_val=0.0, - random_mutation_max_val=1.0) - -Run PyGAD -~~~~~~~~~ - -Simply, call the ``run()`` method to run PyGAD. - -.. code:: python - - ga_instance.run() - -Plot Results -~~~~~~~~~~~~ - -After the ``run()`` method completes, the fitness values of all -generations can be viewed in a plot using the ``plot_fitness()`` method. - -.. code:: python - - ga_instance.plot_fitness() - -Here is the plot after 20,000 generations. - -.. figure:: https://user-images.githubusercontent.com/16560492/82232124-77762c00-992e-11ea-9fc6-14a1cd7a04ff.png - :alt: - -Calculate Some Statistics -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Here is some information about the best solution. - -.. code:: python - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - if ga_instance.best_solution_generation != -1: - print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - - result = gari.chromosome2img(solution, target_im.shape) - matplotlib.pyplot.imshow(result) - matplotlib.pyplot.title("PyGAD & GARI for Reproducing Images") - matplotlib.pyplot.show() - -Evolution by Generation -~~~~~~~~~~~~~~~~~~~~~~~ - -The solution reached after the 20,000 generations is shown below. - -.. figure:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png - :alt: - -After more generations, the result can be enhanced like what shown -below. - -.. figure:: https://user-images.githubusercontent.com/16560492/82232345-cf149780-992e-11ea-8390-bf1a57a19de7.png - :alt: - -The results can also be enhanced by changing the parameters passed to -the constructor of the ``pygad.GA`` class. - -Here is how the image is evolved from generation 0 to generation -20,000s. - -Generation 0 - -.. figure:: https://user-images.githubusercontent.com/16560492/36948589-b47276f0-1fe5-11e8-8efe-0cd1a225ea3a.png - :alt: - -Generation 1,000 - -.. figure:: https://user-images.githubusercontent.com/16560492/36948823-16f490ee-1fe9-11e8-97db-3e8905ad5440.png - :alt: - -Generation 2,500 - -.. figure:: https://user-images.githubusercontent.com/16560492/36948832-3f314b60-1fe9-11e8-8f4a-4d9a53b99f3d.png - :alt: - -Generation 4,500 - -.. figure:: https://user-images.githubusercontent.com/16560492/36948837-53d1849a-1fe9-11e8-9b36-e9e9291e347b.png - :alt: - -Generation 7,000 - -.. figure:: https://user-images.githubusercontent.com/16560492/36948852-66f1b176-1fe9-11e8-9f9b-460804e94004.png - :alt: - -Generation 8,000 - -.. figure:: https://user-images.githubusercontent.com/16560492/36948865-7fbb5158-1fe9-11e8-8c04-8ac3c1f7b1b1.png - :alt: - -Generation 20,000 - -.. figure:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png - :alt: - -Clustering ----------- - -For a 2-cluster problem, the code is available -`here `__. -For a 3-cluster problem, the code is -`here `__. -The 2 examples are using artificial samples. - -Soon a tutorial will be published at -`Paperspace `__ to explain how -clustering works using the genetic algorithm with examples in PyGAD. - -CoinTex Game Playing using PyGAD --------------------------------- - -The code is available the `CoinTex GitHub -project `__. -CoinTex is an Android game written in Python using the Kivy framework. -Find CoinTex at `Google -Play `__: -https://play.google.com/store/apps/details?id=coin.tex.cointexreactfast - -Check this `Paperspace -tutorial `__ -for how the genetic algorithm plays CoinTex: -https://blog.paperspace.com/building-agent-for-cointex-using-genetic-algorithm. -Check also this `YouTube video `__ showing -the genetic algorithm while playing CoinTex. +``pygad`` Module +================ + +This section of the PyGAD's library documentation discusses the +``pygad`` module. + +Using the ``pygad`` module, instances of the genetic algorithm can be +created, run, saved, and loaded. + +.. _pygadga-class: + +``pygad.GA`` Class +================== + +The first module available in PyGAD is named ``pygad`` and contains a +class named ``GA`` for building the genetic algorithm. The constructor, +methods, function, and attributes within the class are discussed in this +section. + +.. _init: + +``__init__()`` +-------------- + +For creating an instance of the ``pygad.GA`` class, the constructor +accepts several parameters that allow the user to customize the genetic +algorithm to different types of applications. + +The ``pygad.GA`` class constructor supports the following parameters: + +- ``num_generations``: Number of generations. + +- ``num_parents_mating``: Number of solutions to be selected as + parents. + +- ``fitness_func``: Accepts a function/method and returns the fitness + value of the solution. If a function is passed, then it must accept 3 + parameters (1. the instance of the ``pygad.GA`` class, 2. a single + solution, and 3. its index in the population). If method, then it + accepts a fourth parameter representing the method's class instance. + Check the `Preparing the fitness_func + Parameter `__ + section for information about creating such a function. + +- ``fitness_batch_size=None``: A new optional parameter called + ``fitness_batch_size`` is supported to calculate the fitness function + in batches. If it is assigned the value ``1`` or ``None`` (default), + then the normal flow is used where the fitness function is called for + each individual solution. If the ``fitness_batch_size`` parameter is + assigned a value satisfying this condition + ``1 < fitness_batch_size <= sol_per_pop``, then the solutions are + grouped into batches of size ``fitness_batch_size`` and the fitness + function is called once for each batch. Check the `Batch Fitness + Calculation `__ + section for more details and examples. Added in from `PyGAD + 2.19.0 `__. + +- ``initial_population``: A user-defined initial population. It is + useful when the user wants to start the generations with a custom + initial population. It defaults to ``None`` which means no initial + population is specified by the user. In this case, + `PyGAD `__ creates an initial + population using the ``sol_per_pop`` and ``num_genes`` parameters. An + exception is raised if the ``initial_population`` is ``None`` while + any of the 2 parameters (``sol_per_pop`` or ``num_genes``) is also + ``None``. Introduced in `PyGAD + 2.0.0 `__ + and higher. + +- ``sol_per_pop``: Number of solutions (i.e. chromosomes) within the + population. This parameter has no action if ``initial_population`` + parameter exists. + +- ``num_genes``: Number of genes in the solution/chromosome. This + parameter is not needed if the user feeds the initial population to + the ``initial_population`` parameter. + +- ``gene_type=float``: Controls the gene type. It can be assigned to a + single data type that is applied to all genes or can specify the data + type of each individual gene. It defaults to ``float`` which means + all genes are of ``float`` data type. Starting from `PyGAD + 2.9.0 `__, + the ``gene_type`` parameter can be assigned to a numeric value of any + of these types: ``int``, ``float``, and + ``numpy.int/uint/float(8-64)``. Starting from `PyGAD + 2.14.0 `__, + it can be assigned to a ``list``, ``tuple``, or a ``numpy.ndarray`` + which hold a data type for each gene (e.g. + ``gene_type=[int, float, numpy.int8]``). This helps to control the + data type of each individual gene. In `PyGAD + 2.15.0 `__, + a precision for the ``float`` data types can be specified (e.g. + ``gene_type=[float, 2]``. + +- ``init_range_low=-4``: The lower value of the random range from which + the gene values in the initial population are selected. + ``init_range_low`` defaults to ``-4``. Available in `PyGAD + 1.0.20 `__ + and higher. This parameter has no action if the + ``initial_population`` parameter exists. + +- ``init_range_high=4``: The upper value of the random range from which + the gene values in the initial population are selected. + ``init_range_high`` defaults to ``+4``. Available in `PyGAD + 1.0.20 `__ + and higher. This parameter has no action if the + ``initial_population`` parameter exists. + +- ``parent_selection_type="sss"``: The parent selection type. Supported + types are ``sss`` (for steady-state selection), ``rws`` (for roulette + wheel selection), ``sus`` (for stochastic universal selection), + ``rank`` (for rank selection), ``random`` (for random selection), and + ``tournament`` (for tournament selection). A custom parent selection + function can be passed starting from `PyGAD + 2.16.0 `__. + Check the `User-Defined Crossover, Mutation, and Parent Selection + Operators `__ + section for more details about building a user-defined parent + selection function. + +- ``keep_parents=-1``: Number of parents to keep in the current + population. ``-1`` (default) means to keep all parents in the next + population. ``0`` means keep no parents in the next population. A + value ``greater than 0`` means keeps the specified number of parents + in the next population. Note that the value assigned to + ``keep_parents`` cannot be ``< - 1`` or greater than the number of + solutions within the population ``sol_per_pop``. Starting from `PyGAD + 2.18.0 `__, + this parameter have an effect only when the ``keep_elitism`` + parameter is ``0``. Starting from `PyGAD + 2.20.0 `__, + the parents' fitness from the last generation will not be re-used if + ``keep_parents=0``. + +- ``keep_elitism=1``: Added in `PyGAD + 2.18.0 `__. + It can take the value ``0`` or a positive integer that satisfies + (``0 <= keep_elitism <= sol_per_pop``). It defaults to ``1`` which + means only the best solution in the current generation is kept in the + next generation. If assigned ``0``, this means it has no effect. If + assigned a positive integer ``K``, then the best ``K`` solutions are + kept in the next generation. It cannot be assigned a value greater + than the value assigned to the ``sol_per_pop`` parameter. If this + parameter has a value different than ``0``, then the ``keep_parents`` + parameter will have no effect. + +- ``K_tournament=3``: In case that the parent selection type is + ``tournament``, the ``K_tournament`` specifies the number of parents + participating in the tournament selection. It defaults to ``3``. + +- ``crossover_type="single_point"``: Type of the crossover operation. + Supported types are ``single_point`` (for single-point crossover), + ``two_points`` (for two points crossover), ``uniform`` (for uniform + crossover), and ``scattered`` (for scattered crossover). Scattered + crossover is supported from PyGAD + `2.9.0 `__ + and higher. It defaults to ``single_point``. A custom crossover + function can be passed starting from `PyGAD + 2.16.0 `__. + Check the `User-Defined Crossover, Mutation, and Parent Selection + Operators `__ + section for more details about creating a user-defined crossover + function. Starting from `PyGAD + 2.2.2 `__ + and higher, if ``crossover_type=None``, then the crossover step is + bypassed which means no crossover is applied and thus no offspring + will be created in the next generations. The next generation will use + the solutions in the current population. + +- ``crossover_probability=None``: The probability of selecting a parent + for applying the crossover operation. Its value must be between 0.0 + and 1.0 inclusive. For each parent, a random value between 0.0 and + 1.0 is generated. If this random value is less than or equal to the + value assigned to the ``crossover_probability`` parameter, then the + parent is selected. Added in `PyGAD + 2.5.0 `__ + and higher. + +- ``mutation_type="random"``: Type of the mutation operation. Supported + types are ``random`` (for random mutation), ``swap`` (for swap + mutation), ``inversion`` (for inversion mutation), ``scramble`` (for + scramble mutation), and ``adaptive`` (for adaptive mutation). It + defaults to ``random``. A custom mutation function can be passed + starting from `PyGAD + 2.16.0 `__. + Check the `User-Defined Crossover, Mutation, and Parent Selection + Operators `__ + section for more details about creating a user-defined mutation + function. Starting from `PyGAD + 2.2.2 `__ + and higher, if ``mutation_type=None``, then the mutation step is + bypassed which means no mutation is applied and thus no changes are + applied to the offspring created using the crossover operation. The + offspring will be used unchanged in the next generation. ``Adaptive`` + mutation is supported starting from `PyGAD + 2.10.0 `__. + For more information about adaptive mutation, go the the `Adaptive + Mutation `__ + section. For example about using adaptive mutation, check the `Use + Adaptive Mutation in + PyGAD `__ + section. + +- ``mutation_probability=None``: The probability of selecting a gene + for applying the mutation operation. Its value must be between 0.0 + and 1.0 inclusive. For each gene in a solution, a random value + between 0.0 and 1.0 is generated. If this random value is less than + or equal to the value assigned to the ``mutation_probability`` + parameter, then the gene is selected. If this parameter exists, then + there is no need for the 2 parameters ``mutation_percent_genes`` and + ``mutation_num_genes``. Added in `PyGAD + 2.5.0 `__ + and higher. + +- ``mutation_by_replacement=False``: An optional bool parameter. It + works only when the selected type of mutation is random + (``mutation_type="random"``). In this case, + ``mutation_by_replacement=True`` means replace the gene by the + randomly generated value. If False, then it has no effect and random + mutation works by adding the random value to the gene. Supported in + `PyGAD + 2.2.2 `__ + and higher. Check the changes in `PyGAD + 2.2.2 `__ + under the Release History section for an example. + +- ``mutation_percent_genes="default"``: Percentage of genes to mutate. + It defaults to the string ``"default"`` which is later translated + into the integer ``10`` which means 10% of the genes will be mutated. + It must be ``>0`` and ``<=100``. Out of this percentage, the number + of genes to mutate is deduced which is assigned to the + ``mutation_num_genes`` parameter. The ``mutation_percent_genes`` + parameter has no action if ``mutation_probability`` or + ``mutation_num_genes`` exist. Starting from `PyGAD + 2.2.2 `__ + and higher, this parameter has no action if ``mutation_type`` is + ``None``. + +- ``mutation_num_genes=None``: Number of genes to mutate which defaults + to ``None`` meaning that no number is specified. The + ``mutation_num_genes`` parameter has no action if the parameter + ``mutation_probability`` exists. Starting from `PyGAD + 2.2.2 `__ + and higher, this parameter has no action if ``mutation_type`` is + ``None``. + +- ``random_mutation_min_val=-1.0``: For ``random`` mutation, the + ``random_mutation_min_val`` parameter specifies the start value of + the range from which a random value is selected to be added to the + gene. It defaults to ``-1``. Starting from `PyGAD + 2.2.2 `__ + and higher, this parameter has no action if ``mutation_type`` is + ``None``. + +- ``random_mutation_max_val=1.0``: For ``random`` mutation, the + ``random_mutation_max_val`` parameter specifies the end value of the + range from which a random value is selected to be added to the gene. + It defaults to ``+1``. Starting from `PyGAD + 2.2.2 `__ + and higher, this parameter has no action if ``mutation_type`` is + ``None``. + +- ``gene_space=None``: It is used to specify the possible values for + each gene in case the user wants to restrict the gene values. It is + useful if the gene space is restricted to a certain range or to + discrete values. It accepts a ``list``, ``tuple``, ``range``, or + ``numpy.ndarray``. When all genes have the same global space, specify + their values as a ``list``/``tuple``/``range``/``numpy.ndarray``. For + example, ``gene_space = [0.3, 5.2, -4, 8]`` restricts the gene values + to the 4 specified values. If each gene has its own space, then the + ``gene_space`` parameter can be nested like + ``[[0.4, -5], [0.5, -3.2, 8.2, -9], ...]`` where the first sublist + determines the values for the first gene, the second sublist for the + second gene, and so on. If the nested list/tuple has a ``None`` + value, then the gene's initial value is selected randomly from the + range specified by the 2 parameters ``init_range_low`` and + ``init_range_high`` and its mutation value is selected randomly from + the range specified by the 2 parameters ``random_mutation_min_val`` + and ``random_mutation_max_val``. ``gene_space`` is added in `PyGAD + 2.5.0 `__. + Check the `Release History of PyGAD + 2.5.0 `__ + section of the documentation for more details. In `PyGAD + 2.9.0 `__, + NumPy arrays can be assigned to the ``gene_space`` parameter. In + `PyGAD + 2.11.0 `__, + the ``gene_space`` parameter itself or any of its elements can be + assigned to a dictionary to specify the lower and upper limits of the + genes. For example, ``{'low': 2, 'high': 4}`` means the minimum and + maximum values are 2 and 4, respectively. In `PyGAD + 2.15.0 `__, + a new key called ``"step"`` is supported to specify the step of + moving from the start to the end of the range specified by the 2 + existing keys ``"low"`` and ``"high"``. + +- ``on_start=None``: Accepts a function/method to be called only once + before the genetic algorithm starts its evolution. If function, then + it must accept a single parameter representing the instance of the + genetic algorithm. If method, then it must accept 2 parameters where + the second one refers to the method's object. Added in `PyGAD + 2.6.0 `__. + +- ``on_fitness=None``: Accepts a function/method to be called after + calculating the fitness values of all solutions in the population. If + function, then it must accept 2 parameters: 1) a list of all + solutions' fitness values 2) the instance of the genetic algorithm. + If method, then it must accept 3 parameters where the third one + refers to the method's object. Added in `PyGAD + 2.6.0 `__. + +- ``on_parents=None``: Accepts a function/method to be called after + selecting the parents that mates. If function, then it must accept 2 + parameters: 1) the selected parents 2) the instance of the genetic + algorithm If method, then it must accept 3 parameters where the third + one refers to the method's object. Added in `PyGAD + 2.6.0 `__. + +- ``on_crossover=None``: Accepts a function to be called each time the + crossover operation is applied. This function must accept 2 + parameters: the first one represents the instance of the genetic + algorithm and the second one represents the offspring generated using + crossover. Added in `PyGAD + 2.6.0 `__. + +- ``on_mutation=None``: Accepts a function to be called each time the + mutation operation is applied. This function must accept 2 + parameters: the first one represents the instance of the genetic + algorithm and the second one represents the offspring after applying + the mutation. Added in `PyGAD + 2.6.0 `__. + +- ``on_generation=None``: Accepts a function to be called after each + generation. This function must accept a single parameter representing + the instance of the genetic algorithm. If the function returned the + string ``stop``, then the ``run()`` method stops without completing + the other generations. Added in `PyGAD + 2.6.0 `__. + +- ``on_stop=None``: Accepts a function to be called only once exactly + before the genetic algorithm stops or when it completes all the + generations. This function must accept 2 parameters: the first one + represents the instance of the genetic algorithm and the second one + is a list of fitness values of the last population's solutions. Added + in `PyGAD + 2.6.0 `__. + +- ``delay_after_gen=0.0``: It accepts a non-negative number specifying + the time in seconds to wait after a generation completes and before + going to the next generation. It defaults to ``0.0`` which means no + delay after the generation. Available in `PyGAD + 2.4.0 `__ + and higher. + +- ``save_best_solutions=False``: When ``True``, then the best solution + after each generation is saved into an attribute named + ``best_solutions``. If ``False`` (default), then no solutions are + saved and the ``best_solutions`` attribute will be empty. Supported + in `PyGAD + 2.9.0 `__. + +- ``save_solutions=False``: If ``True``, then all solutions in each + generation are appended into an attribute called ``solutions`` which + is NumPy array. Supported in `PyGAD + 2.15.0 `__. + +- ``suppress_warnings=False``: A bool parameter to control whether the + warning messages are printed or not. It defaults to ``False``. + +- ``allow_duplicate_genes=True``: Added in `PyGAD + 2.13.0 `__. + If ``True``, then a solution/chromosome may have duplicate gene + values. If ``False``, then each gene will have a unique value in its + solution. + +- ``stop_criteria=None``: Some criteria to stop the evolution. Added in + `PyGAD + 2.15.0 `__. + Each criterion is passed as ``str`` which has a stop word. The + current 2 supported words are ``reach`` and ``saturate``. ``reach`` + stops the ``run()`` method if the fitness value is equal to or + greater than a given fitness value. An example for ``reach`` is + ``"reach_40"`` which stops the evolution if the fitness is >= 40. + ``saturate`` means stop the evolution if the fitness saturates for a + given number of consecutive generations. An example for ``saturate`` + is ``"saturate_7"`` which means stop the ``run()`` method if the + fitness does not change for 7 consecutive generations. + +- ``parallel_processing=None``: Added in `PyGAD + 2.17.0 `__. + If ``None`` (Default), this means no parallel processing is applied. + It can accept a list/tuple of 2 elements [1) Can be either + ``'process'`` or ``'thread'`` to indicate whether processes or + threads are used, respectively., 2) The number of processes or + threads to use.]. For example, + ``parallel_processing=['process', 10]`` applies parallel processing + with 10 processes. If a positive integer is assigned, then it is used + as the number of threads. For example, ``parallel_processing=5`` uses + 5 threads which is equivalent to + ``parallel_processing=["thread", 5]``. For more information, check + the `Parallel Processing in + PyGAD `__ + section. + +- ``random_seed=None``: Added in `PyGAD + 2.18.0 `__. + It defines the random seed to be used by the random function + generators (we use random functions in the NumPy and random modules). + This helps to reproduce the same results by setting the same random + seed (e.g. ``random_seed=2``). If given the value ``None``, then it + has no effect. + +- ``logger=None``: Accepts an instance of the ``logging.Logger`` class + to log the outputs. Any message is no longer printed using + ``print()`` but logged. If ``logger=None``, then a logger is created + that uses ``StreamHandler`` to logs the messages to the console. + Added in `PyGAD + 3.0.0 `__. + Check the `Logging + Outputs `__ + for more information. + +The user doesn't have to specify all of such parameters while creating +an instance of the GA class. A very important parameter you must care +about is ``fitness_func`` which defines the fitness function. + +It is OK to set the value of any of the 2 parameters ``init_range_low`` +and ``init_range_high`` to be equal, higher, or lower than the other +parameter (i.e. ``init_range_low`` is not needed to be lower than +``init_range_high``). The same holds for the ``random_mutation_min_val`` +and ``random_mutation_max_val`` parameters. + +If the 2 parameters ``mutation_type`` and ``crossover_type`` are +``None``, this disables any type of evolution the genetic algorithm can +make. As a result, the genetic algorithm cannot find a better solution +that the best solution in the initial population. + +The parameters are validated within the constructor. If at least a +parameter is not correct, an exception is thrown. + +.. _plotting-methods-in-pygadga-class: + +Plotting Methods in ``pygad.GA`` Class +-------------------------------------- + +- ``plot_fitness()``: Shows how the fitness evolves by generation. + +- ``plot_genes()``: Shows how the gene value changes for each + generation. + +- ``plot_new_solution_rate()``: Shows the number of new solutions + explored in each solution. + +Class Attributes +---------------- + +- ``supported_int_types``: A list of the supported types for the + integer numbers. + +- ``supported_float_types``: A list of the supported types for the + floating-point numbers. + +- ``supported_int_float_types``: A list of the supported types for all + numbers. It just concatenates the previous 2 lists. + +.. _other-instance-attributes--methods: + +Other Instance Attributes & Methods +----------------------------------- + +All the parameters and functions passed to the ``pygad.GA`` class +constructor are used as class attributes and methods in the instances of +the ``pygad.GA`` class. In addition to such attributes, there are other +attributes and methods added to the instances of the ``pygad.GA`` class: + +The next 2 subsections list such attributes and methods. + +Other Attributes +~~~~~~~~~~~~~~~~ + +- ``generations_completed``: Holds the number of the last completed + generation. + +- ``population``: A NumPy array holding the initial population. + +- ``valid_parameters``: Set to ``True`` when all the parameters passed + in the ``GA`` class constructor are valid. + +- ``run_completed``: Set to ``True`` only after the ``run()`` method + completes gracefully. + +- ``pop_size``: The population size. + +- ``best_solutions_fitness``: A list holding the fitness values of the + best solutions for all generations. + +- ``best_solution_generation``: The generation number at which the best + fitness value is reached. It is only assigned the generation number + after the ``run()`` method completes. Otherwise, its value is -1. + +- ``best_solutions``: A NumPy array holding the best solution per each + generation. It only exists when the ``save_best_solutions`` parameter + in the ``pygad.GA`` class constructor is set to ``True``. + +- ``last_generation_fitness``: The fitness values of the solutions in + the last generation. `Added in PyGAD + 2.12.0 `__. + +- ``previous_generation_fitness``: At the end of each generation, the + fitness of the most recent population is saved in the + ``last_generation_fitness`` attribute. The fitness of the population + exactly preceding this most recent population is saved in the + ``last_generation_fitness`` attribute. This + ``previous_generation_fitness`` attribute is used to fetch the + pre-calculated fitness instead of calling the fitness function for + already explored solutions. `Added in PyGAD + 2.16.2 `__. + +- ``last_generation_parents``: The parents selected from the last + generation. `Added in PyGAD + 2.12.0 `__. + +- ``last_generation_offspring_crossover``: The offspring generated + after applying the crossover in the last generation. `Added in PyGAD + 2.12.0 `__. + +- ``last_generation_offspring_mutation``: The offspring generated after + applying the mutation in the last generation. `Added in PyGAD + 2.12.0 `__. + +- ``gene_type_single``: A flag that is set to ``True`` if the + ``gene_type`` parameter is assigned to a single data type that is + applied to all genes. If ``gene_type`` is assigned a ``list``, + ``tuple``, or ``numpy.ndarray``, then the value of + ``gene_type_single`` will be ``False``. `Added in PyGAD + 2.14.0 `__. + +- ``last_generation_parents_indices``: This attribute holds the indices + of the selected parents in the last generation. Supported in `PyGAD + 2.15.0 `__. + +- ``last_generation_elitism``: This attribute holds the elitism of the + last generation. It is effective only if the ``keep_elitism`` + parameter has a non-zero value. Supported in `PyGAD + 2.18.0 `__. + +- ``last_generation_elitism_indices``: This attribute holds the indices + of the elitism of the last generation. It is effective only if the + ``keep_elitism`` parameter has a non-zero value. Supported in `PyGAD + 2.19.0 `__. + +- ``logger``: This attribute holds the logger from the ``logging`` + module. Supported in `PyGAD + 3.0.0 `__. + +Note that the attributes with its name start with ``last_generation_`` +are updated after each generation. + +Other Methods +~~~~~~~~~~~~~ + +- ``cal_pop_fitness()``: A method that calculates the fitness values + for all solutions within the population by calling the function + passed to the ``fitness_func`` parameter for each solution. + +- ``crossover()``: Refers to the method that applies the crossover + operator based on the selected type of crossover in the + ``crossover_type`` property. + +- ``mutation()``: Refers to the method that applies the mutation + operator based on the selected type of mutation in the + ``mutation_type`` property. + +- ``select_parents()``: Refers to a method that selects the parents + based on the parent selection type specified in the + ``parent_selection_type`` attribute. + +- ``adaptive_mutation_population_fitness()``: Returns the average + fitness value used in the adaptive mutation to filter the solutions. + +- ``solve_duplicate_genes_randomly()``: Solves the duplicates in a + solution by randomly selecting new values for the duplicating genes. + +- ``solve_duplicate_genes_by_space()``: Solves the duplicates in a + solution by selecting values for the duplicating genes from the gene + space + +- ``unique_int_gene_from_range()``: Finds a unique integer value for + the gene. + +- ``unique_genes_by_space()``: Loops through all the duplicating genes + to find unique values that from their gene spaces to solve the + duplicates. For each duplicating gene, a call to the + ``unique_gene_by_space()`` is made. + +- ``unique_gene_by_space()``: Returns a unique gene value for a single + gene based on its value space to solve the duplicates. + +- ``summary()``: Prints a Keras-like summary of the PyGAD lifecycle. + This helps to have an overview of the architecture. Supported in + `PyGAD + 2.19.0 `__. + Check the `Print Lifecycle + Summary `__ + section for more details and examples. + +The next sections discuss the methods available in the ``pygad.GA`` +class. + +.. _initializepopulation: + +``initialize_population()`` +--------------------------- + +It creates an initial population randomly as a NumPy array. The array is +saved in the instance attribute named ``population``. + +Accepts the following parameters: + +- ``low``: The lower value of the random range from which the gene + values in the initial population are selected. It defaults to -4. + Available in PyGAD 1.0.20 and higher. + +- ``high``: The upper value of the random range from which the gene + values in the initial population are selected. It defaults to -4. + Available in PyGAD 1.0.20. + +This method assigns the values of the following 3 instance attributes: + +1. ``pop_size``: Size of the population. + +2. ``population``: Initially, it holds the initial population and later + updated after each generation. + +3. ``initial_population``: Keeping the initial population. + +.. _calpopfitness: + +``cal_pop_fitness()`` +--------------------- + +The ``cal_pop_fitness()`` method calculates and returns the fitness +values of the solutions in the current population. + +This function is optimized to save time by making fewer calls the +fitness function. It follows this process: + +1. If the ``save_solutions`` parameter is set to ``True``, then it + checks if the solution is already explored and saved in the + ``solutions`` instance attribute. If so, then it just retrieves its + fitness from the ``solutions_fitness`` instance attribute without + calling the fitness function. + +2. If ``save_solutions`` is set to ``False`` or if it is ``True`` but + the solution was not explored yet, then the ``cal_pop_fitness()`` + method checks if the ``keep_elitism`` parameter is set to a positive + integer. If so, then it checks if the solution is saved into the + ``last_generation_elitism`` instance attribute. If so, then it + retrieves its fitness from the ``previous_generation_fitness`` + instance attribute. + +3. If neither of the above 3 conditions apply (1. ``save_solutions`` is + set to ``False`` or 2. if it is ``True`` but the solution was not + explored yet or 3. ``keep_elitism`` is set to zero), then the + ``cal_pop_fitness()`` method checks if the ``keep_parents`` parameter + is set to ``-1`` or a positive integer. If so, then it checks if the + solution is saved into the ``last_generation_parents`` instance + attribute. If so, then it retrieves its fitness from the + ``previous_generation_fitness`` instance attribute. + +4. If neither of the above 4 conditions apply, then we have to call the + fitness function to calculate the fitness for the solution. This is + by calling the function assigned to the ``fitness_func`` parameter. + +This function takes into consideration: + +1. The ``parallel_processing`` parameter to check whether parallel + processing is in effect. + +2. The ``fitness_batch_size`` parameter to check if the fitness should + be calculated in batches of solutions. + +It returns a vector of the solutions' fitness values. + +``run()`` +--------- + +Runs the genetic algorithm. This is the main method in which the genetic +algorithm is evolved through some generations. It accepts no parameters +as it uses the instance to access all of its requirements. + +For each generation, the fitness values of all solutions within the +population are calculated according to the ``cal_pop_fitness()`` method +which internally just calls the function assigned to the +``fitness_func`` parameter in the ``pygad.GA`` class constructor for +each solution. + +According to the fitness values of all solutions, the parents are +selected using the ``select_parents()`` method. This method behaviour is +determined according to the parent selection type in the +``parent_selection_type`` parameter in the ``pygad.GA`` class +constructor + +Based on the selected parents, offspring are generated by applying the +crossover and mutation operations using the ``crossover()`` and +``mutation()`` methods. The behaviour of such 2 methods is defined +according to the ``crossover_type`` and ``mutation_type`` parameters in +the ``pygad.GA`` class constructor. + +After the generation completes, the following takes place: + +- The ``population`` attribute is updated by the new population. + +- The ``generations_completed`` attribute is assigned by the number of + the last completed generation. + +- If there is a callback function assigned to the ``on_generation`` + attribute, then it will be called. + +After the ``run()`` method completes, the following takes place: + +- The ``best_solution_generation`` is assigned the generation number at + which the best fitness value is reached. + +- The ``run_completed`` attribute is set to ``True``. + +Parent Selection Methods +------------------------ + +The ``ParentSelection`` class in the ``pygad.utils.parent_selection`` +module has several methods for selecting the parents that will mate to +produce the offspring. All of such methods accept the same parameters +which are: + +- ``fitness``: The fitness values of the solutions in the current + population. + +- ``num_parents``: The number of parents to be selected. + +All of such methods return an array of the selected parents. + +The next subsections list the supported methods for parent selection. + +.. _steadystateselection: + +``steady_state_selection()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Selects the parents using the steady-state selection technique. + +.. _rankselection: + +``rank_selection()`` +~~~~~~~~~~~~~~~~~~~~ + +Selects the parents using the rank selection technique. + +.. _randomselection: + +``random_selection()`` +~~~~~~~~~~~~~~~~~~~~~~ + +Selects the parents randomly. + +.. _tournamentselection: + +``tournament_selection()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Selects the parents using the tournament selection technique. + +.. _roulettewheelselection: + +``roulette_wheel_selection()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Selects the parents using the roulette wheel selection technique. + +.. _stochasticuniversalselection: + +``stochastic_universal_selection()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Selects the parents using the stochastic universal selection technique. + +Crossover Methods +----------------- + +The ``Crossover`` class in the ``pygad.utils.crossover`` module supports +several methods for applying crossover between the selected parents. All +of these methods accept the same parameters which are: + +- ``parents``: The parents to mate for producing the offspring. + +- ``offspring_size``: The size of the offspring to produce. + +All of such methods return an array of the produced offspring. + +The next subsections list the supported methods for crossover. + +.. _singlepointcrossover: + +``single_point_crossover()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Applies the single-point crossover. It selects a point randomly at which +crossover takes place between the pairs of parents. + +.. _twopointscrossover: + +``two_points_crossover()`` +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Applies the 2 points crossover. It selects the 2 points randomly at +which crossover takes place between the pairs of parents. + +.. _uniformcrossover: + +``uniform_crossover()`` +~~~~~~~~~~~~~~~~~~~~~~~ + +Applies the uniform crossover. For each gene, a parent out of the 2 +mating parents is selected randomly and the gene is copied from it. + +.. _scatteredcrossover: + +``scattered_crossover()`` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Applies the scattered crossover. It randomly selects the gene from one +of the 2 parents. + +Mutation Methods +---------------- + +The ``Mutation`` class in the ``pygad.utils.mutation`` module supports +several methods for applying mutation. All of these methods accept the +same parameter which is: + +- ``offspring``: The offspring to mutate. + +All of such methods return an array of the mutated offspring. + +The next subsections list the supported methods for mutation. + +.. _randommutation: + +``random_mutation()`` +~~~~~~~~~~~~~~~~~~~~~ + +Applies the random mutation which changes the values of some genes +randomly. The number of genes is specified according to either the +``mutation_num_genes`` or the ``mutation_percent_genes`` attributes. + +For each gene, a random value is selected according to the range +specified by the 2 attributes ``random_mutation_min_val`` and +``random_mutation_max_val``. The random value is added to the selected +gene. + +.. _swapmutation: + +``swap_mutation()`` +~~~~~~~~~~~~~~~~~~~ + +Applies the swap mutation which interchanges the values of 2 randomly +selected genes. + +.. _inversionmutation: + +``inversion_mutation()`` +~~~~~~~~~~~~~~~~~~~~~~~~ + +Applies the inversion mutation which selects a subset of genes and +inverts them. + +.. _scramblemutation: + +``scramble_mutation()`` +~~~~~~~~~~~~~~~~~~~~~~~ + +Applies the scramble mutation which selects a subset of genes and +shuffles their order randomly. + +.. _adaptivemutation: + +``adaptive_mutation()`` +~~~~~~~~~~~~~~~~~~~~~~~ + +Applies the adaptive mutation which selects a subset of genes and +shuffles their order randomly. + +.. _bestsolution: + +``best_solution()`` +------------------- + +Returns information about the best solution found by the genetic +algorithm. + +It accepts the following parameters: + +- ``pop_fitness=None``: An optional parameter that accepts a list of + the fitness values of the solutions in the population. If ``None``, + then the ``cal_pop_fitness()`` method is called to calculate the + fitness values of the population. + +It returns the following: + +- ``best_solution``: Best solution in the current population. + +- ``best_solution_fitness``: Fitness value of the best solution. + +- ``best_match_idx``: Index of the best solution in the current + population. + +.. _plotfitness: + +``plot_fitness()`` +------------------ + +Previously named ``plot_result()``, this method creates, shows, and +returns a figure that summarizes how the fitness value evolves by +generation. It works only after completing at least 1 generation. + +If no generation is completed (at least 1), an exception is raised. + +Starting from `PyGAD +2.15.0 `__ +and higher, this method accepts the following parameters: + +1. ``title``: Title of the figure. + +2. ``xlabel``: X-axis label. + +3. ``ylabel``: Y-axis label. + +4. ``linewidth``: Line width of the plot. Defaults to ``3``. + +5. ``font_size``: Font size for the labels and title. Defaults to + ``14``. + +6. ``plot_type``: Type of the plot which can be either ``"plot"`` + (default), ``"scatter"``, or ``"bar"``. + +7. ``color``: Color of the plot which defaults to ``"#3870FF"``. + +8. ``save_dir``: Directory to save the figure. + +.. _plotnewsolutionrate: + +``plot_new_solution_rate()`` +---------------------------- + +The ``plot_new_solution_rate()`` method creates, shows, and returns a +figure that shows the number of new solutions explored in each +generation. This method works only when ``save_solutions=True`` in the +constructor of the ``pygad.GA`` class. It also works only after +completing at least 1 generation. + +If no generation is completed (at least 1), an exception is raised. + +This method accepts the following parameters: + +1. ``title``: Title of the figure. + +2. ``xlabel``: X-axis label. + +3. ``ylabel``: Y-axis label. + +4. ``linewidth``: Line width of the plot. Defaults to ``3``. + +5. ``font_size``: Font size for the labels and title. Defaults to + ``14``. + +6. ``plot_type``: Type of the plot which can be either ``"plot"`` + (default), ``"scatter"``, or ``"bar"``. + +7. ``color``: Color of the plot which defaults to ``"#3870FF"``. + +8. ``save_dir``: Directory to save the figure. + +.. _plotgenes: + +``plot_genes()`` +---------------- + +The ``plot_genes()`` method creates, shows, and returns a figure that +describes each gene. It has different options to create the figures +which helps to: + +1. Explore the gene value for each generation by creating a normal plot. + +2. Create a histogram for each gene. + +3. Create a boxplot. + +This is controlled by the ``graph_type`` parameter. + +It works only after completing at least 1 generation. If no generation +is completed, an exception is raised. If no generation is completed (at +least 1), an exception is raised. + +This method accepts the following parameters: + +1. ``title``: Title of the figure. + +2. ``xlabel``: X-axis label. + +3. ``ylabel``: Y-axis label. + +4. ``linewidth``: Line width of the plot. Defaults to ``3``. + +5. ``font_size``: Font size for the labels and title. Defaults to + ``14``. + +6. ``plot_type``: Type of the plot which can be either ``"plot"`` + (default), ``"scatter"``, or ``"bar"``. + +7. ``graph_type``: Type of the graph which can be either ``"plot"`` + (default), ``"boxplot"``, or ``"histogram"``. + +8. ``fill_color``: Fill color of the graph which defaults to + ``"#3870FF"``. This has no effect if ``graph_type="plot"``. + +9. ``color``: Color of the plot which defaults to ``"#3870FF"``. + +10. ``solutions``: Defaults to ``"all"`` which means use all solutions. + If ``"best"`` then only the best solutions are used. + +11. ``save_dir``: Directory to save the figure. + +An exception is raised if: + +- ``solutions="all"`` while ``save_solutions=False`` in the constructor + of the ``pygad.GA`` class. . + +- ``solutions="best"`` while ``save_best_solutions=False`` in the + constructor of the ``pygad.GA`` class. . + +``save()`` +---------- + +Saves the genetic algorithm instance + +Accepts the following parameter: + +- ``filename``: Name of the file to save the instance. No extension is + needed. + +Functions in ``pygad`` +====================== + +Besides the methods available in the ``pygad.GA`` class, this section +discusses the functions available in ``pygad``. Up to this time, there +is only a single function named ``load()``. + +.. _pygadload: + +``pygad.load()`` +---------------- + +Reads a saved instance of the genetic algorithm. This is not a method +but a function that is indented under the ``pygad`` module. So, it could +be called by the pygad module as follows: ``pygad.load(filename)``. + +Accepts the following parameter: + +- ``filename``: Name of the file holding the saved instance of the + genetic algorithm. No extension is needed. + +Returns the genetic algorithm instance. + +Steps to Use ``pygad`` +====================== + +To use the ``pygad`` module, here is a summary of the required steps: + +1. Preparing the ``fitness_func`` parameter. + +2. Preparing Other Parameters. + +3. Import ``pygad``. + +4. Create an Instance of the ``pygad.GA`` Class. + +5. Run the Genetic Algorithm. + +6. Plotting Results. + +7. Information about the Best Solution. + +8. Saving & Loading the Results. + +Let's discuss how to do each of these steps. + +.. _preparing-the-fitnessfunc-parameter: + +Preparing the ``fitness_func`` Parameter +----------------------------------------- + +Even there are some steps in the genetic algorithm pipeline that can +work the same regardless of the problem being solved, one critical step +is the calculation of the fitness value. There is no unique way of +calculating the fitness value and it changes from one problem to +another. + +PyGAD has a parameter called ``fitness_func`` that allows the user to +specify a custom function/method to use when calculating the fitness. +This function/method must be a maximization function/method so that a +solution with a high fitness value returned is selected compared to a +solution with a low value. Doing that allows the user to freely use +PyGAD to solve any problem by passing the appropriate fitness +function/method. It is very important to understand the problem well for +creating it. + +Let's discuss an example: + + | Given the following function: + | y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6 + | where (x1,x2,x3,x4,x5,x6)=(4, -2, 3.5, 5, -11, -4.7) and y=44 + | What are the best values for the 6 weights (w1 to w6)? We are going + to use the genetic algorithm to optimize this function. + +So, the task is about using the genetic algorithm to find the best +values for the 6 weight ``W1`` to ``W6``. Thinking of the problem, it is +clear that the best solution is that returning an output that is close +to the desired output ``y=44``. So, the fitness function/method should +return a value that gets higher when the solution's output is closer to +``y=44``. Here is a function that does that: + +.. code:: python + + function_inputs = [4, -2, 3.5, 5, -11, -4.7] # Function inputs. + desired_output = 44 # Function output. + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / numpy.abs(output - desired_output) + return fitness + +Such a user-defined function must accept 3 parameters: + +1. The instance of the ``pygad.GA`` class. This helps the user to fetch + any property that helps when calculating the fitness. + +2. The solution(s) to calculate the fitness value(s). Note that the + fitness function can accept multiple solutions only if the + ``fitness_batch_size`` is given a value greater than 1. + +3. The indices of the solutions in the population. The number of indices + also depends on the ``fitness_batch_size`` parameter. + +If a method is passed to the ``fitness_func`` parameter, then it accepts +a fourth parameter representing the method's instance. + +The ``__code__`` object is used to check if this function accepts the +required number of parameters. If more or fewer parameters are passed, +an exception is thrown. + +By creating this function, you did a very important step towards using +PyGAD. + +Preparing Other Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Here is an example for preparing the other parameters: + +.. code:: python + + num_generations = 50 + num_parents_mating = 4 + + fitness_function = fitness_func + + sol_per_pop = 8 + num_genes = len(function_inputs) + + init_range_low = -2 + init_range_high = 5 + + parent_selection_type = "sss" + keep_parents = 1 + + crossover_type = "single_point" + + mutation_type = "random" + mutation_percent_genes = 10 + +.. _the-ongeneration-parameter: + +The ``on_generation`` Parameter +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +An optional parameter named ``on_generation`` is supported which allows +the user to call a function (with a single parameter) after each +generation. Here is a simple function that just prints the current +generation number and the fitness value of the best solution in the +current generation. The ``generations_completed`` attribute of the GA +class returns the number of the last completed generation. + +.. code:: python + + def on_gen(ga_instance): + print("Generation : ", ga_instance.generations_completed) + print("Fitness of the best solution :", ga_instance.best_solution()[1]) + +After being defined, the function is assigned to the ``on_generation`` +parameter of the GA class constructor. By doing that, the ``on_gen()`` +function will be called after each generation. + +.. code:: python + + ga_instance = pygad.GA(..., + on_generation=on_gen, + ...) + +After the parameters are prepared, we can import PyGAD and build an +instance of the ``pygad.GA`` class. + +Import ``pygad`` +---------------- + +The next step is to import PyGAD as follows: + +.. code:: python + + import pygad + +The ``pygad.GA`` class holds the implementation of all methods for +running the genetic algorithm. + +.. _create-an-instance-of-the-pygadga-class: + +Create an Instance of the ``pygad.GA`` Class +-------------------------------------------- + +The ``pygad.GA`` class is instantiated where the previously prepared +parameters are fed to its constructor. The constructor is responsible +for creating the initial population. + +.. code:: python + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + fitness_func=fitness_function, + sol_per_pop=sol_per_pop, + num_genes=num_genes, + init_range_low=init_range_low, + init_range_high=init_range_high, + parent_selection_type=parent_selection_type, + keep_parents=keep_parents, + crossover_type=crossover_type, + mutation_type=mutation_type, + mutation_percent_genes=mutation_percent_genes) + +Run the Genetic Algorithm +------------------------- + +After an instance of the ``pygad.GA`` class is created, the next step is +to call the ``run()`` method as follows: + +.. code:: python + + ga_instance.run() + +Inside this method, the genetic algorithm evolves over some generations +by doing the following tasks: + +1. Calculating the fitness values of the solutions within the current + population. + +2. Select the best solutions as parents in the mating pool. + +3. Apply the crossover & mutation operation + +4. Repeat the process for the specified number of generations. + +Plotting Results +---------------- + +There is a method named ``plot_fitness()`` which creates a figure +summarizing how the fitness values of the solutions change with the +generations. + +.. code:: python + + ga_instance.plot_fitness() + +.. figure:: https://user-images.githubusercontent.com/16560492/78830005-93111d00-79e7-11ea-9d8e-a8d8325a6101.png + :alt: + +Information about the Best Solution +----------------------------------- + +The following information about the best solution in the last population +is returned using the ``best_solution()`` method. + +- Solution + +- Fitness value of the solution + +- Index of the solution within the population + +.. code:: python + + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +Using the ``best_solution_generation`` attribute of the instance from +the ``pygad.GA`` class, the generation number at which the +``best fitness`` is reached could be fetched. + +.. code:: python + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + +.. _saving--loading-the-results: + +Saving & Loading the Results +---------------------------- + +After the ``run()`` method completes, it is possible to save the current +instance of the genetic algorithm to avoid losing the progress made. The +``save()`` method is available for that purpose. Just pass the file name +to it without an extension. According to the next code, a file named +``genetic.pkl`` will be created and saved in the current directory. + +.. code:: python + + filename = 'genetic' + ga_instance.save(filename=filename) + +You can also load the saved model using the ``load()`` function and +continue using it. For example, you might run the genetic algorithm for +some generations, save its current state using the ``save()`` method, +load the model using the ``load()`` function, and then call the +``run()`` method again. + +.. code:: python + + loaded_ga_instance = pygad.load(filename=filename) + +After the instance is loaded, you can use it to run any method or access +any property. + +.. code:: python + + print(loaded_ga_instance.best_solution()) + +Crossover, Mutation, and Parent Selection +========================================= + +PyGAD supports different types for selecting the parents and applying +the crossover & mutation operators. More features will be added in the +future. To ask for a new feature, please check the ``Ask for Feature`` +section. + +Supported Crossover Operations +------------------------------ + +The supported crossover operations at this time are: + +1. Single point: Implemented using the ``single_point_crossover()`` + method. + +2. Two points: Implemented using the ``two_points_crossover()`` method. + +3. Uniform: Implemented using the ``uniform_crossover()`` method. + +Supported Mutation Operations +----------------------------- + +The supported mutation operations at this time are: + +1. Random: Implemented using the ``random_mutation()`` method. + +2. Swap: Implemented using the ``swap_mutation()`` method. + +3. Inversion: Implemented using the ``inversion_mutation()`` method. + +4. Scramble: Implemented using the ``scramble_mutation()`` method. + +Supported Parent Selection Operations +------------------------------------- + +The supported parent selection techniques at this time are: + +1. Steady-state: Implemented using the ``steady_state_selection()`` + method. + +2. Roulette wheel: Implemented using the ``roulette_wheel_selection()`` + method. + +3. Stochastic universal: Implemented using the + ``stochastic_universal_selection()``\ method. + +4. Rank: Implemented using the ``rank_selection()`` method. + +5. Random: Implemented using the ``random_selection()`` method. + +6. Tournament: Implemented using the ``tournament_selection()`` method. + +Life Cycle of PyGAD +=================== + +The next figure lists the different stages in the lifecycle of an +instance of the ``pygad.GA`` class. Note that PyGAD stops when either +all generations are completed or when the function passed to the +``on_generation`` parameter returns the string ``stop``. + +.. figure:: https://user-images.githubusercontent.com/16560492/220486073-c5b6089d-81e4-44d9-a53c-385f479a7273.jpg + :alt: + +The next code implements all the callback functions to trace the +execution of the genetic algorithm. Each callback function prints its +name. + +.. code:: python + + import pygad + import numpy + + function_inputs = [4,-2,3.5,5,-11,-4.7] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + fitness_function = fitness_func + + def on_start(ga_instance): + print("on_start()") + + def on_fitness(ga_instance, population_fitness): + print("on_fitness()") + + def on_parents(ga_instance, selected_parents): + print("on_parents()") + + def on_crossover(ga_instance, offspring_crossover): + print("on_crossover()") + + def on_mutation(ga_instance, offspring_mutation): + print("on_mutation()") + + def on_generation(ga_instance): + print("on_generation()") + + def on_stop(ga_instance, last_population_fitness): + print("on_stop()") + + ga_instance = pygad.GA(num_generations=3, + num_parents_mating=5, + fitness_func=fitness_function, + sol_per_pop=10, + num_genes=len(function_inputs), + on_start=on_start, + on_fitness=on_fitness, + on_parents=on_parents, + on_crossover=on_crossover, + on_mutation=on_mutation, + on_generation=on_generation, + on_stop=on_stop) + + ga_instance.run() + +Based on the used 3 generations as assigned to the ``num_generations`` +argument, here is the output. + +.. code:: + + on_start() + + on_fitness() + on_parents() + on_crossover() + on_mutation() + on_generation() + + on_fitness() + on_parents() + on_crossover() + on_mutation() + on_generation() + + on_fitness() + on_parents() + on_crossover() + on_mutation() + on_generation() + + on_stop() + +Adaptive Mutation +================= + +In the regular genetic algorithm, the mutation works by selecting a +single fixed mutation rate for all solutions regardless of their fitness +values. So, regardless on whether this solution has high or low quality, +the same number of genes are mutated all the time. + +The pitfalls of using a constant mutation rate for all solutions are +summarized in this paper `Libelli, S. Marsili, and P. Alba. "Adaptive +mutation in genetic algorithms." Soft computing 4.2 (2000): +76-80 `__ +as follows: + + The weak point of "classical" GAs is the total randomness of + mutation, which is applied equally to all chromosomes, irrespective + of their fitness. Thus a very good chromosome is equally likely to be + disrupted by mutation as a bad one. + + On the other hand, bad chromosomes are less likely to produce good + ones through crossover, because of their lack of building blocks, + until they remain unchanged. They would benefit the most from + mutation and could be used to spread throughout the parameter space + to increase the search thoroughness. So there are two conflicting + needs in determining the best probability of mutation. + + Usually, a reasonable compromise in the case of a constant mutation + is to keep the probability low to avoid disruption of good + chromosomes, but this would prevent a high mutation rate of + low-fitness chromosomes. Thus a constant probability of mutation + would probably miss both goals and result in a slow improvement of + the population. + +According to `Libelli, S. Marsili, and P. +Alba. `__ +work, the adaptive mutation solves the problems of constant mutation. + +Adaptive mutation works as follows: + +1. Calculate the average fitness value of the population (``f_avg``). + +2. For each chromosome, calculate its fitness value (``f``). + +3. If ``ff_avg``, then this solution is regarded as a high-quality + solution and thus the mutation rate should be kept low to avoid + disrupting this high quality solution. + +In PyGAD, if ``f=f_avg``, then the solution is regarded of high quality. + +The next figure summarizes the previous steps. + +.. figure:: https://user-images.githubusercontent.com/16560492/103468973-e3c26600-4d2c-11eb-8af3-b3bb39b50540.jpg + :alt: + +This strategy is applied in PyGAD. + +Use Adaptive Mutation in PyGAD +------------------------------ + +In PyGAD 2.10.0, adaptive mutation is supported. To use it, just follow +the following 2 simple steps: + +1. In the constructor of the ``pygad.GA`` class, set + ``mutation_type="adaptive"`` to specify that the type of mutation is + adaptive. + +2. Specify the mutation rates for the low and high quality solutions + using one of these 3 parameters according to your preference: + ``mutation_probability``, ``mutation_num_genes``, and + ``mutation_percent_genes``. Please check the `documentation of each + of these + parameters `__ + for more information. + +When adaptive mutation is used, then the value assigned to any of the 3 +parameters can be of any of these data types: + +1. ``list`` + +2. ``tuple`` + +3. ``numpy.ndarray`` + +Whatever the data type used, the length of the ``list``, ``tuple``, or +the ``numpy.ndarray`` must be exactly 2. That is there are just 2 +values: + +1. The first value is the mutation rate for the low-quality solutions. + +2. The second value is the mutation rate for the high-quality solutions. + +PyGAD expects that the first value is higher than the second value and +thus a warning is printed in case the first value is lower than the +second one. + +Here are some examples to feed the mutation rates: + +.. code:: python + + # mutation_probability + mutation_probability = [0.25, 0.1] + mutation_probability = (0.35, 0.17) + mutation_probability = numpy.array([0.15, 0.05]) + + # mutation_num_genes + mutation_num_genes = [4, 2] + mutation_num_genes = (3, 1) + mutation_num_genes = numpy.array([7, 2]) + + # mutation_percent_genes + mutation_percent_genes = [25, 12] + mutation_percent_genes = (15, 8) + mutation_percent_genes = numpy.array([21, 13]) + +Assume that the average fitness is 12 and the fitness values of 2 +solutions are 15 and 7. If the mutation probabilities are specified as +follows: + +.. code:: python + + mutation_probability = [0.25, 0.1] + +Then the mutation probability of the first solution is 0.1 because its +fitness is 15 which is higher than the average fitness 12. The mutation +probability of the second solution is 0.25 because its fitness is 7 +which is lower than the average fitness 12. + +Here is an example that uses adaptive mutation. + +.. code:: python + + import pygad + import numpy + + function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs. + desired_output = 44 # Function output. + + def fitness_func(ga_instance, solution, solution_idx): + # The fitness function calulates the sum of products between each input and its corresponding weight. + output = numpy.sum(solution*function_inputs) + # The value 0.000001 is used to avoid the Inf value when the denominator numpy.abs(output - desired_output) is 0.0. + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + # Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor. + ga_instance = pygad.GA(num_generations=200, + fitness_func=fitness_func, + num_parents_mating=10, + sol_per_pop=20, + num_genes=len(function_inputs), + mutation_type="adaptive", + mutation_num_genes=(3, 1)) + + # Running the GA to optimize the parameters of the function. + ga_instance.run() + + ga_instance.plot_fitness(title="PyGAD with Adaptive Mutation", linewidth=5) + +Limit the Gene Value Range +========================== + +In `PyGAD +2.11.0 `__, +the ``gene_space`` parameter supported a new feature to allow +customizing the range of accepted values for each gene. Let's take a +quick review of the ``gene_space`` parameter to build over it. + +The ``gene_space`` parameter allows the user to feed the space of values +of each gene. This way the accepted values for each gene is retracted to +the user-defined values. Assume there is a problem that has 3 genes +where each gene has different set of values as follows: + +1. Gene 1: ``[0.4, 12, -5, 21.2]`` + +2. Gene 2: ``[-2, 0.3]`` + +3. Gene 3: ``[1.2, 63.2, 7.4]`` + +Then, the ``gene_space`` for this problem is as given below. Note that +the order is very important. + +.. code:: python + + gene_space = [[0.4, 12, -5, 21.2], + [-2, 0.3], + [1.2, 63.2, 7.4]] + +In case all genes share the same set of values, then simply feed a +single list to the ``gene_space`` parameter as follows. In this case, +all genes can only take values from this list of 6 values. + +.. code:: python + + gene_space = [33, 7, 0.5, 95. 6.3, 0.74] + +The previous example restricts the gene values to just a set of fixed +number of discrete values. In case you want to use a range of discrete +values to the gene, then you can use the ``range()`` function. For +example, ``range(1, 7)`` means the set of allowed values for the gene +are ``1, 2, 3, 4, 5, and 6``. You can also use the ``numpy.arange()`` or +``numpy.linspace()`` functions for the same purpose. + +The previous discussion only works with a range of discrete values not +continuous values. In `PyGAD +2.11.0 `__, +the ``gene_space`` parameter can be assigned a dictionary that allows +the gene to have values from a continuous range. + +Assuming you want to restrict the gene within this half-open range [1 to +5) where 1 is included and 5 is not. Then simply create a dictionary +with 2 items where the keys of the 2 items are: + +1. ``'low'``: The minimum value in the range which is 1 in the example. + +2. ``'high'``: The maximum value in the range which is 5 in the example. + +The dictionary will look like that: + +.. code:: python + + {'low': 1, + 'high': 5} + +It is not acceptable to add more than 2 items in the dictionary or use +other keys than ``'low'`` and ``'high'``. + +For a 3-gene problem, the next code creates a dictionary for each gene +to restrict its values in a continuous range. For the first gene, it can +take any floating-point value from the range that starts from 1 +(inclusive) and ends at 5 (exclusive). + +.. code:: python + + gene_space = [{'low': 1, 'high': 5}, {'low': 0.3, 'high': 1.4}, {'low': -0.2, 'high': 4.5}] + +Stop at Any Generation +====================== + +In `PyGAD +2.4.0 `__, +it is possible to stop the genetic algorithm after any generation. All +you need to do it to return the string ``"stop"`` in the callback +function ``on_generation``. When this callback function is implemented +and assigned to the ``on_generation`` parameter in the constructor of +the ``pygad.GA`` class, then the algorithm immediately stops after +completing its current generation. Let's discuss an example. + +Assume that the user wants to stop algorithm either after the 100 +generations or if a condition is met. The user may assign a value of 100 +to the ``num_generations`` parameter of the ``pygad.GA`` class +constructor. + +The condition that stops the algorithm is written in a callback function +like the one in the next code. If the fitness value of the best solution +exceeds 70, then the string ``"stop"`` is returned. + +.. code:: python + + def func_generation(ga_instance): + if ga_instance.best_solution()[1] >= 70: + return "stop" + +Stop Criteria +============= + +In `PyGAD +2.15.0 `__, +a new parameter named ``stop_criteria`` is added to the constructor of +the ``pygad.GA`` class. It helps to stop the evolution based on some +criteria. It can be assigned to one or more criterion. + +Each criterion is passed as ``str`` that consists of 2 parts: + +1. Stop word. + +2. Number. + +It takes this form: + +.. code:: python + + "word_num" + +The current 2 supported words are ``reach`` and ``saturate``. + +The ``reach`` word stops the ``run()`` method if the fitness value is +equal to or greater than a given fitness value. An example for ``reach`` +is ``"reach_40"`` which stops the evolution if the fitness is >= 40. + +``saturate`` stops the evolution if the fitness saturates for a given +number of consecutive generations. An example for ``saturate`` is +``"saturate_7"`` which means stop the ``run()`` method if the fitness +does not change for 7 consecutive generations. + +Here is an example that stops the evolution if either the fitness value +reached ``127.4`` or if the fitness saturates for ``15`` generations. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4, -2, 3.5, 8, 9, 4] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + + return fitness + + ga_instance = pygad.GA(num_generations=200, + sol_per_pop=10, + num_parents_mating=4, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + stop_criteria=["reach_127.4", "saturate_15"]) + + ga_instance.run() + print("Number of generations passed is {generations_completed}".format(generations_completed=ga_instance.generations_completed)) + +Elitism Selection +================= + +In `PyGAD +2.18.0 `__, +a new parameter called ``keep_elitism`` is supported. It accepts an +integer to define the number of elitism (i.e. best solutions) to keep in +the next generation. This parameter defaults to ``1`` which means only +the best solution is kept in the next generation. + +In the next example, the ``keep_elitism`` parameter in the constructor +of the ``pygad.GA`` class is set to 2. Thus, the best 2 solutions in +each generation are kept in the next generation. + +.. code:: python + + import numpy + import pygad + + function_inputs = [4,-2,3.5,5,-11,-4.7] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / numpy.abs(output - desired_output) + return fitness + + ga_instance = pygad.GA(num_generations=2, + num_parents_mating=3, + fitness_func=fitness_func, + num_genes=6, + sol_per_pop=5, + keep_elitism=2) + + ga_instance.run() + +The value passed to the ``keep_elitism`` parameter must satisfy 2 +conditions: + +1. It must be ``>= 0``. + +2. It must be ``<= sol_per_pop``. That is its value cannot exceed the + number of solutions in the current population. + +In the previous example, if the ``keep_elitism`` parameter is set equal +to the value passed to the ``sol_per_pop`` parameter, which is 5, then +there will be no evolution at all as in the next figure. This is because +all the 5 solutions are used as elitism in the next generation and no +offspring will be created. + +.. code:: python + + ... + + ga_instance = pygad.GA(..., + sol_per_pop=5, + keep_elitism=5) + + ga_instance.run() + +.. figure:: https://user-images.githubusercontent.com/16560492/189273225-67ffad41-97ab-45e1-9324-429705e17b20.png + :alt: + +Note that if the ``keep_elitism`` parameter is effective (i.e. is +assigned a positive integer, not zero), then the ``keep_parents`` +parameter will have no effect. Because the default value of the +``keep_elitism`` parameter is 1, then the ``keep_parents`` parameter has +no effect by default. The ``keep_parents`` parameter is only effective +when ``keep_elitism=0``. + +Random Seed +=========== + +In `PyGAD +2.18.0 `__, +a new parameter called ``random_seed`` is supported. Its value is used +as a seed for the random function generators. + +PyGAD uses random functions in these 2 libraries: + +1. NumPy + +2. random + +The ``random_seed`` parameter defaults to ``None`` which means no seed +is used. As a result, different random numbers are generated for each +run of PyGAD. + +If this parameter is assigned a proper seed, then the results will be +reproducible. In the next example, the integer 2 is used as a random +seed. + +.. code:: python + + import numpy + import pygad + + function_inputs = [4,-2,3.5,5,-11,-4.7] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / numpy.abs(output - desired_output) + return fitness + + ga_instance = pygad.GA(num_generations=2, + num_parents_mating=3, + fitness_func=fitness_func, + sol_per_pop=5, + num_genes=6, + random_seed=2) + + ga_instance.run() + best_solution, best_solution_fitness, best_match_idx = ga_instance.best_solution() + print(best_solution) + print(best_solution_fitness) + +This is the best solution found and its fitness value. + +.. code:: + + [ 2.77249188 -4.06570662 0.04196872 -3.47770796 -0.57502138 -3.22775267] + 0.04872203136549972 + +After running the code again, it will find the same result. + +.. code:: + + [ 2.77249188 -4.06570662 0.04196872 -3.47770796 -0.57502138 -3.22775267] + 0.04872203136549972 + +Continue without Loosing Progress +================================= + +In `PyGAD +2.18.0 `__, +and thanks for `Felix Bernhard `__ for +opening `this GitHub +issue `__, +the values of these 4 instance attributes are no longer reset after each +call to the ``run()`` method. + +1. ``self.best_solutions`` + +2. ``self.best_solutions_fitness`` + +3. ``self.solutions`` + +4. ``self.solutions_fitness`` + +This helps the user to continue where the last run stopped without +loosing the values of these 4 attributes. + +Now, the user can save the model by calling the ``save()`` method. + +.. code:: python + + import pygad + + def fitness_func(ga_instance, solution, solution_idx): + ... + return fitness + + ga_instance = pygad.GA(...) + + ga_instance.run() + + ga_instance.plot_fitness() + + ga_instance.save("pygad_GA") + +Then the saved model is loaded by calling the ``load()`` function. After +calling the ``run()`` method over the loaded instance, then the data +from the previous 4 attributes are not reset but extended with the new +data. + +.. code:: python + + import pygad + + def fitness_func(ga_instance, solution, solution_idx): + ... + return fitness + + loaded_ga_instance = pygad.load("pygad_GA") + + loaded_ga_instance.run() + + loaded_ga_instance.plot_fitness() + +The plot created by the ``plot_fitness()`` method will show the data +collected from both the runs. + +Note that the 2 attributes (``self.best_solutions`` and +``self.best_solutions_fitness``) only work if the +``save_best_solutions`` parameter is set to ``True``. Also, the 2 +attributes (``self.solutions`` and ``self.solutions_fitness``) only work +if the ``save_solutions`` parameter is ``True``. + +Prevent Duplicates in Gene Values +================================= + +In `PyGAD +2.13.0 `__, +a new bool parameter called ``allow_duplicate_genes`` is supported to +control whether duplicates are supported in the chromosome or not. In +other words, whether 2 or more genes might have the same exact value. + +If ``allow_duplicate_genes=True`` (which is the default case), genes may +have the same value. If ``allow_duplicate_genes=False``, then no 2 genes +will have the same value given that there are enough unique values for +the genes. + +The next code gives an example to use the ``allow_duplicate_genes`` +parameter. A callback generation function is implemented to print the +population after each generation. + +.. code:: python + + import pygad + + def fitness_func(ga_instance, solution, solution_idx): + return 0 + + def on_generation(ga): + print("Generation", ga.generations_completed) + print(ga.population) + + ga_instance = pygad.GA(num_generations=5, + sol_per_pop=5, + num_genes=4, + mutation_num_genes=3, + random_mutation_min_val=-5, + random_mutation_max_val=5, + num_parents_mating=2, + fitness_func=fitness_func, + gene_type=int, + on_generation=on_generation, + allow_duplicate_genes=False) + ga_instance.run() + +Here are the population after the 5 generations. Note how there are no +duplicate values. + +.. code:: python + + Generation 1 + [[ 2 -2 -3 3] + [ 0 1 2 3] + [ 5 -3 6 3] + [-3 1 -2 4] + [-1 0 -2 3]] + Generation 2 + [[-1 0 -2 3] + [-3 1 -2 4] + [ 0 -3 -2 6] + [-3 0 -2 3] + [ 1 -4 2 4]] + Generation 3 + [[ 1 -4 2 4] + [-3 0 -2 3] + [ 4 0 -2 1] + [-4 0 -2 -3] + [-4 2 0 3]] + Generation 4 + [[-4 2 0 3] + [-4 0 -2 -3] + [-2 5 4 -3] + [-1 2 -4 4] + [-4 2 0 -3]] + Generation 5 + [[-4 2 0 -3] + [-1 2 -4 4] + [ 3 4 -4 0] + [-1 0 2 -2] + [-4 2 -1 1]] + +The ``allow_duplicate_genes`` parameter is configured with use with the +``gene_space`` parameter. Here is an example where each of the 4 genes +has the same space of values that consists of 4 values (1, 2, 3, and 4). + +.. code:: python + + import pygad + + def fitness_func(ga_instance, solution, solution_idx): + return 0 + + def on_generation(ga): + print("Generation", ga.generations_completed) + print(ga.population) + + ga_instance = pygad.GA(num_generations=1, + sol_per_pop=5, + num_genes=4, + num_parents_mating=2, + fitness_func=fitness_func, + gene_type=int, + gene_space=[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]], + on_generation=on_generation, + allow_duplicate_genes=False) + ga_instance.run() + +Even that all the genes share the same space of values, no 2 genes +duplicate their values as provided by the next output. + +.. code:: python + + Generation 1 + [[2 3 1 4] + [2 3 1 4] + [2 4 1 3] + [2 3 1 4] + [1 3 2 4]] + Generation 2 + [[1 3 2 4] + [2 3 1 4] + [1 3 2 4] + [2 3 4 1] + [1 3 4 2]] + Generation 3 + [[1 3 4 2] + [2 3 4 1] + [1 3 4 2] + [3 1 4 2] + [3 2 4 1]] + Generation 4 + [[3 2 4 1] + [3 1 4 2] + [3 2 4 1] + [1 2 4 3] + [1 3 4 2]] + Generation 5 + [[1 3 4 2] + [1 2 4 3] + [2 1 4 3] + [1 2 4 3] + [1 2 4 3]] + +You should care of giving enough values for the genes so that PyGAD is +able to find alternatives for the gene value in case it duplicates with +another gene. + +There might be 2 duplicate genes where changing either of the 2 +duplicating genes will not solve the problem. For example, if +``gene_space=[[3, 0, 1], [4, 1, 2], [0, 2], [3, 2, 0]]`` and the +solution is ``[3 2 0 0]``, then the values of the last 2 genes +duplicate. There are no possible changes in the last 2 genes to solve +the problem. + +This problem can be solved by randomly changing one of the +non-duplicating genes that may make a room for a unique value in one the +2 duplicating genes. For example, by changing the second gene from 2 to +4, then any of the last 2 genes can take the value 2 and solve the +duplicates. The resultant gene is then ``[3 4 2 0]``. But this option is +not yet supported in PyGAD. + +User-Defined Crossover, Mutation, and Parent Selection Operators +================================================================ + +Previously, the user can select the the type of the crossover, mutation, +and parent selection operators by assigning the name of the operator to +the following parameters of the ``pygad.GA`` class's constructor: + +1. ``crossover_type`` + +2. ``mutation_type`` + +3. ``parent_selection_type`` + +This way, the user can only use the built-in functions for each of these +operators. + +Starting from `PyGAD +2.16.0 `__, +the user can create a custom crossover, mutation, and parent selection +operators and assign these functions to the above parameters. Thus, a +new operator can be plugged easily into the `PyGAD +Lifecycle `__. + +This is a sample code that does not use any custom function. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4,-2,3.5] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func) + + ga_instance.run() + ga_instance.plot_fitness() + +This section describes the expected input parameters and outputs. For +simplicity, all of these custom functions all accept the instance of the +``pygad.GA`` class as the last parameter. + +User-Defined Crossover Operator +------------------------------- + +The user-defined crossover function is a Python function that accepts 3 +parameters: + +1. The selected parents. + +2. The size of the offspring as a tuple of 2 numbers: (the offspring + size, number of genes). + +3. The instance from the ``pygad.GA`` class. This instance helps to + retrieve any property like ``population``, ``gene_type``, + ``gene_space``, etc. + +This function should return a NumPy array of shape equal to the value +passed to the second parameter. + +The next code creates a template for the user-defined crossover +operator. You can use any names for the parameters. Note how a NumPy +array is returned. + +.. code:: python + + def crossover_func(parents, offspring_size, ga_instance): + offspring = ... + ... + return numpy.array(offspring) + +As an example, the next code creates a single-point crossover function. +By randomly generating a random point (i.e. index of a gene), the +function simply uses 2 parents to produce an offspring by copying the +genes before the point from the first parent and the remaining from the +second parent. + +.. code:: python + + def crossover_func(parents, offspring_size, ga_instance): + offspring = [] + idx = 0 + while len(offspring) != offspring_size[0]: + parent1 = parents[idx % parents.shape[0], :].copy() + parent2 = parents[(idx + 1) % parents.shape[0], :].copy() + + random_split_point = numpy.random.choice(range(offspring_size[1])) + + parent1[random_split_point:] = parent2[random_split_point:] + + offspring.append(parent1) + + idx += 1 + + return numpy.array(offspring) + +To use this user-defined function, simply assign its name to the +``crossover_type`` parameter in the constructor of the ``pygad.GA`` +class. The next code gives an example. In this case, the custom function +will be called in each generation rather than calling the built-in +crossover functions defined in PyGAD. + +.. code:: python + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + crossover_type=crossover_func) + +User-Defined Mutation Operator +------------------------------ + +A user-defined mutation function/operator can be created the same way a +custom crossover operator/function is created. Simply, it is a Python +function that accepts 2 parameters: + +1. The offspring to be mutated. + +2. The instance from the ``pygad.GA`` class. This instance helps to + retrieve any property like ``population``, ``gene_type``, + ``gene_space``, etc. + +The template for the user-defined mutation function is given in the next +code. According to the user preference, the function should make some +random changes to the genes. + +.. code:: python + + def mutation_func(offspring, ga_instance): + ... + return offspring + +The next code builds the random mutation where a single gene from each +chromosome is mutated by adding a random number between 0 and 1 to the +gene's value. + +.. code:: python + + def mutation_func(offspring, ga_instance): + + for chromosome_idx in range(offspring.shape[0]): + random_gene_idx = numpy.random.choice(range(offspring.shape[1])) + + offspring[chromosome_idx, random_gene_idx] += numpy.random.random() + + return offspring + +Here is how this function is assigned to the ``mutation_type`` +parameter. + +.. code:: python + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + crossover_type=crossover_func, + mutation_type=mutation_func) + +Note that there are other things to take into consideration like: + +- Making sure that each gene conforms to the data type(s) listed in the + ``gene_type`` parameter. + +- If the ``gene_space`` parameter is used, then the new value for the + gene should conform to the values/ranges listed. + +- Mutating a number of genes that conforms to the parameters + ``mutation_percent_genes``, ``mutation_probability``, and + ``mutation_num_genes``. + +- Whether mutation happens with or without replacement based on the + ``mutation_by_replacement`` parameter. + +- The minimum and maximum values from which a random value is generated + based on the ``random_mutation_min_val`` and + ``random_mutation_max_val`` parameters. + +- Whether duplicates are allowed or not in the chromosome based on the + ``allow_duplicate_genes`` parameter. + +and more. + +It all depends on your objective from building the mutation function. +You may neglect or consider some of the considerations according to your +objective. + +User-Defined Parent Selection Operator +-------------------------------------- + +No much to mention about building a user-defined parent selection +function as things are similar to building a crossover or mutation +function. Just create a Python function that accepts 3 parameters: + +1. The fitness values of the current population. + +2. The number of parents needed. + +3. The instance from the ``pygad.GA`` class. This instance helps to + retrieve any property like ``population``, ``gene_type``, + ``gene_space``, etc. + +The function should return 2 outputs: + +1. The selected parents as a NumPy array. Its shape is equal to (the + number of selected parents, ``num_genes``). Note that the number of + selected parents is equal to the value assigned to the second input + parameter. + +2. The indices of the selected parents inside the population. It is a 1D + list with length equal to the number of selected parents. + +The outputs must be of type ``numpy.ndarray``. + +Here is a template for building a custom parent selection function. + +.. code:: python + + def parent_selection_func(fitness, num_parents, ga_instance): + ... + return parents, fitness_sorted[:num_parents] + +The next code builds the steady-state parent selection where the best +parents are selected. The number of parents is equal to the value in the +``num_parents`` parameter. + +.. code:: python + + def parent_selection_func(fitness, num_parents, ga_instance): + + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + fitness_sorted.reverse() + + parents = numpy.empty((num_parents, ga_instance.population.shape[1])) + + for parent_num in range(num_parents): + parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() + + return parents, numpy.array(fitness_sorted[:num_parents]) + +Finally, the defined function is assigned to the +``parent_selection_type`` parameter as in the next code. + +.. code:: python + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + crossover_type=crossover_func, + mutation_type=mutation_func, + parent_selection_type=parent_selection_func) + +Example +------- + +By discussing how to customize the 3 operators, the next code uses the +previous 3 user-defined functions instead of the built-in functions. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4,-2,3.5] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + + return fitness + + def parent_selection_func(fitness, num_parents, ga_instance): + + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + fitness_sorted.reverse() + + parents = numpy.empty((num_parents, ga_instance.population.shape[1])) + + for parent_num in range(num_parents): + parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() + + return parents, numpy.array(fitness_sorted[:num_parents]) + + def crossover_func(parents, offspring_size, ga_instance): + + offspring = [] + idx = 0 + while len(offspring) != offspring_size[0]: + parent1 = parents[idx % parents.shape[0], :].copy() + parent2 = parents[(idx + 1) % parents.shape[0], :].copy() + + random_split_point = numpy.random.choice(range(offspring_size[1])) + + parent1[random_split_point:] = parent2[random_split_point:] + + offspring.append(parent1) + + idx += 1 + + return numpy.array(offspring) + + def mutation_func(offspring, ga_instance): + + for chromosome_idx in range(offspring.shape[0]): + random_gene_idx = numpy.random.choice(range(offspring.shape[0])) + + offspring[chromosome_idx, random_gene_idx] += numpy.random.random() + + return offspring + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + crossover_type=crossover_func, + mutation_type=mutation_func, + parent_selection_type=parent_selection_func) + + ga_instance.run() + ga_instance.plot_fitness() + +This is the same example but using methods instead of functions. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4,-2,3.5] + desired_output = 44 + + class Test: + def fitness_func(self, ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + + return fitness + + def parent_selection_func(self, fitness, num_parents, ga_instance): + + fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k]) + fitness_sorted.reverse() + + parents = numpy.empty((num_parents, ga_instance.population.shape[1])) + + for parent_num in range(num_parents): + parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy() + + return parents, numpy.array(fitness_sorted[:num_parents]) + + def crossover_func(self, parents, offspring_size, ga_instance): + + offspring = [] + idx = 0 + while len(offspring) != offspring_size[0]: + parent1 = parents[idx % parents.shape[0], :].copy() + parent2 = parents[(idx + 1) % parents.shape[0], :].copy() + + random_split_point = numpy.random.choice(range(offspring_size[0])) + + parent1[random_split_point:] = parent2[random_split_point:] + + offspring.append(parent1) + + idx += 1 + + return numpy.array(offspring) + + def mutation_func(self, offspring, ga_instance): + + for chromosome_idx in range(offspring.shape[0]): + random_gene_idx = numpy.random.choice(range(offspring.shape[1])) + + offspring[chromosome_idx, random_gene_idx] += numpy.random.random() + + return offspring + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=Test().fitness_func, + parent_selection_type=Test().parent_selection_func, + crossover_type=Test().crossover_func, + mutation_type=Test().mutation_func) + + ga_instance.run() + ga_instance.plot_fitness() + +.. _more-about-the-genespace-parameter: + +More about the ``gene_space`` Parameter +======================================= + +The ``gene_space`` parameter customizes the space of values of each +gene. + +Assuming that all genes have the same global space which include the +values 0.3, 5.2, -4, and 8, then those values can be assigned to the +``gene_space`` parameter as a list, tuple, or range. Here is a list +assigned to this parameter. By doing that, then the gene values are +restricted to those assigned to the ``gene_space`` parameter. + +.. code:: python + + gene_space = [0.3, 5.2, -4, 8] + +If some genes have different spaces, then ``gene_space`` should accept a +nested list or tuple. In this case, the elements could be: + +1. Number (of ``int``, ``float``, or ``NumPy`` data types): A single + value to be assigned to the gene. This means this gene will have the + same value across all generations. + +2. ``list``, ``tuple``, ``numpy.ndarray``, or any range like ``range``, + ``numpy.arange()``, or ``numpy.linspace``: It holds the space for + each individual gene. But this space is usually discrete. That is + there is a set of finite values to select from. + +3. ``dict``: To sample a value for a gene from a continuous range. The + dictionary must have 2 mandatory keys which are ``"low"`` and + ``"high"`` in addition to an optional key which is ``"step"``. A + random value is returned between the values assigned to the items + with ``"low"`` and ``"high"`` keys. If the ``"step"`` exists, then + this works as the previous options (i.e. discrete set of values). + +4. ``None``: A gene with its space set to ``None`` is initialized + randomly from the range specified by the 2 parameters + ``init_range_low`` and ``init_range_high``. For mutation, its value + is mutated based on a random value from the range specified by the 2 + parameters ``random_mutation_min_val`` and + ``random_mutation_max_val``. If all elements in the ``gene_space`` + parameter are ``None``, the parameter will not have any effect. + +Assuming that a chromosome has 2 genes and each gene has a different +value space. Then the ``gene_space`` could be assigned a nested +list/tuple where each element determines the space of a gene. + +According to the next code, the space of the first gene is ``[0.4, -5]`` +which has 2 values and the space for the second gene is +``[0.5, -3.2, 8.8, -9]`` which has 4 values. + +.. code:: python + + gene_space = [[0.4, -5], [0.5, -3.2, 8.2, -9]] + +For a 2 gene chromosome, if the first gene space is restricted to the +discrete values from 0 to 4 and the second gene is restricted to the +values from 10 to 19, then it could be specified according to the next +code. + +.. code:: python + + gene_space = [range(5), range(10, 20)] + +The ``gene_space`` can also be assigned to a single range, as given +below, where the values of all genes are sampled from the same range. + +.. code:: python + + gene_space = numpy.arange(15) + +The ``gene_space`` can be assigned a dictionary to sample a value from a +continuous range. + +.. code:: python + + gene_space = {"low": 4, "high": 30} + +A step also can be assigned to the dictionary. This works as if a range +is used. + +.. code:: python + + gene_space = {"low": 4, "high": 30, "step": 2.5} + +If a ``None`` is assigned to only a single gene, then its value will be +randomly generated initially using the ``init_range_low`` and +``init_range_high`` parameters in the ``pygad.GA`` class's constructor. +During mutation, the value are sampled from the range defined by the 2 +parameters ``random_mutation_min_val`` and ``random_mutation_max_val``. +This is an example where the second gene is given a ``None`` value. + +.. code:: python + + gene_space = [range(5), None, numpy.linspace(10, 20, 300)] + +If the user did not assign the initial population to the +``initial_population`` parameter, the initial population is created +randomly based on the ``gene_space`` parameter. Moreover, the mutation +is applied based on this parameter. + +.. _more-about-the-genetype-parameter: + +More about the ``gene_type`` Parameter +====================================== + +The ``gene_type`` parameter allows the user to control the data type for +all genes at once or each individual gene. In `PyGAD +2.15.0 `__, +the ``gene_type`` parameter also supports customizing the precision for +``float`` data types. As a result, the ``gene_type`` parameter helps to: + +1. Select a data type for all genes with or without precision. + +2. Select a data type for each individual gene with or without + precision. + +Let's discuss things by examples. + +Data Type for All Genes without Precision +----------------------------------------- + +The data type for all genes can be specified by assigning the numeric +data type directly to the ``gene_type`` parameter. This is an example to +make all genes of ``int`` data types. + +.. code:: python + + gene_type=int + +Given that the supported numeric data types of PyGAD include Python's +``int`` and ``float`` in addition to all numeric types of ``NumPy``, +then any of these types can be assigned to the ``gene_type`` parameter. + +If no precision is specified for a ``float`` data type, then the +complete floating-point number is kept. + +The next code uses an ``int`` data type for all genes where the genes in +the initial and final population are only integers. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4, -2, 3.5, 8, -2] + desired_output = 2671.1234 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + gene_type=int) + + print("Initial Population") + print(ga_instance.initial_population) + + ga_instance.run() + + print("Final Population") + print(ga_instance.population) + +.. code:: python + + Initial Population + [[ 1 -1 2 0 -3] + [ 0 -2 0 -3 -1] + [ 0 -1 -1 2 0] + [-2 3 -2 3 3] + [ 0 0 2 -2 -2]] + + Final Population + [[ 1 -1 2 2 0] + [ 1 -1 2 2 0] + [ 1 -1 2 2 0] + [ 1 -1 2 2 0] + [ 1 -1 2 2 0]] + +Data Type for All Genes with Precision +-------------------------------------- + +A precision can only be specified for a ``float`` data type and cannot +be specified for integers. Here is an example to use a precision of 3 +for the ``float`` data type. In this case, all genes are of type +``float`` and their maximum precision is 3. + +.. code:: python + + gene_type=[float, 3] + +The next code uses prints the initial and final population where the +genes are of type ``float`` with precision 3. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4, -2, 3.5, 8, -2] + desired_output = 2671.1234 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + + return fitness + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + gene_type=[float, 3]) + + print("Initial Population") + print(ga_instance.initial_population) + + ga_instance.run() + + print("Final Population") + print(ga_instance.population) + +.. code:: python + + Initial Population + [[-2.417 -0.487 3.623 2.457 -2.362] + [-1.231 0.079 -1.63 1.629 -2.637] + [ 0.692 -2.098 0.705 0.914 -3.633] + [ 2.637 -1.339 -1.107 -0.781 -3.896] + [-1.495 1.378 -1.026 3.522 2.379]] + + Final Population + [[ 1.714 -1.024 3.623 3.185 -2.362] + [ 0.692 -1.024 3.623 3.185 -2.362] + [ 0.692 -1.024 3.623 3.375 -2.362] + [ 0.692 -1.024 4.041 3.185 -2.362] + [ 1.714 -0.644 3.623 3.185 -2.362]] + +Data Type for each Individual Gene without Precision +---------------------------------------------------- + +In `PyGAD +2.14.0 `__, +the ``gene_type`` parameter allows customizing the gene type for each +individual gene. This is by using a ``list``/``tuple``/``numpy.ndarray`` +with number of elements equal to the number of genes. For each element, +a type is specified for the corresponding gene. + +This is an example for a 5-gene problem where different types are +assigned to the genes. + +.. code:: python + + gene_type=[int, float, numpy.float16, numpy.int8, float] + +This is a complete code that prints the initial and final population for +a custom-gene data type. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4, -2, 3.5, 8, -2] + desired_output = 2671.1234 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + gene_type=[int, float, numpy.float16, numpy.int8, float]) + + print("Initial Population") + print(ga_instance.initial_population) + + ga_instance.run() + + print("Final Population") + print(ga_instance.population) + +.. code:: python + + Initial Population + [[0 0.8615522360026828 0.7021484375 -2 3.5301821368185866] + [-3 2.648189378595294 -3.830078125 1 -0.9586271572917742] + [3 3.7729827570110714 1.2529296875 -3 1.395741994211889] + [0 1.0490687178053282 1.51953125 -2 0.7243617940450235] + [0 -0.6550158436937226 -2.861328125 -2 1.8212734549263097]] + + Final Population + [[3 3.7729827570110714 2.055 0 0.7243617940450235] + [3 3.7729827570110714 1.458 0 -0.14638754050305036] + [3 3.7729827570110714 1.458 0 0.0869406120516778] + [3 3.7729827570110714 1.458 0 0.7243617940450235] + [3 3.7729827570110714 1.458 0 -0.14638754050305036]] + +Data Type for each Individual Gene with Precision +------------------------------------------------- + +The precision can also be specified for the ``float`` data types as in +the next line where the second gene precision is 2 and last gene +precision is 1. + +.. code:: python + + gene_type=[int, [float, 2], numpy.float16, numpy.int8, [float, 1]] + +This is a complete example where the initial and final populations are +printed where the genes comply with the data types and precisions +specified. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4, -2, 3.5, 8, -2] + desired_output = 2671.1234 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + gene_type=[int, [float, 2], numpy.float16, numpy.int8, [float, 1]]) + + print("Initial Population") + print(ga_instance.initial_population) + + ga_instance.run() + + print("Final Population") + print(ga_instance.population) + +.. code:: python + + Initial Population + [[-2 -1.22 1.716796875 -1 0.2] + [-1 -1.58 -3.091796875 0 -1.3] + [3 3.35 -0.107421875 1 -3.3] + [-2 -3.58 -1.779296875 0 0.6] + [2 -3.73 2.65234375 3 -0.5]] + + Final Population + [[2 -4.22 3.47 3 -1.3] + [2 -3.73 3.47 3 -1.3] + [2 -4.22 3.47 2 -1.3] + [2 -4.58 3.47 3 -1.3] + [2 -3.73 3.47 3 -1.3]] + +Visualization in PyGAD +====================== + +This section discusses the different options to visualize the results in +PyGAD through these methods: + +1. ``plot_fitness()`` + +2. ``plot_genes()`` + +3. ``plot_new_solution_rate()`` + +In the following code, the ``save_solutions`` flag is set to ``True`` +which means all solutions are saved in the ``solutions`` attribute. The +code runs for only 10 generations. + +.. code:: python + + import pygad + import numpy + + equation_inputs = [4, -2, 3.5, 8, -2, 3.5, 8] + desired_output = 2671.1234 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=10, + num_parents_mating=5, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + gene_space=[range(1, 10), range(10, 20), range(15, 30), range(20, 40), range(25, 50), range(10, 30), range(20, 50)], + gene_type=int, + save_solutions=True) + + ga_instance.run() + +Let's explore how to visualize the results by the above mentioned +methods. + +.. _plotfitness-2: + +``plot_fitness()`` +------------------ + +The ``plot_fitness()`` method shows the fitness value for each +generation. + +.. _plottypeplot: + +``plot_type="plot"`` +~~~~~~~~~~~~~~~~~~~~ + +The simplest way to call this method is as follows leaving the +``plot_type`` with its default value ``"plot"`` to create a continuous +line connecting the fitness values across all generations: + +.. code:: python + + ga_instance.plot_fitness() + # ga_instance.plot_fitness(plot_type="plot") + +.. figure:: https://user-images.githubusercontent.com/16560492/122472609-d02f5280-cf8e-11eb-88a7-f9366ff6e7c6.png + :alt: + +.. _plottypescatter: + +``plot_type="scatter"`` +~~~~~~~~~~~~~~~~~~~~~~~ + +The ``plot_type`` can also be set to ``"scatter"`` to create a scatter +graph with each individual fitness represented as a dot. The size of +these dots can be changed using the ``linewidth`` parameter. + +.. code:: python + + ga_instance.plot_fitness(plot_type="scatter") + +.. figure:: https://user-images.githubusercontent.com/16560492/122473159-75e2c180-cf8f-11eb-942d-31279b286dbd.png + :alt: + +.. _plottypebar: + +``plot_type="bar"`` +~~~~~~~~~~~~~~~~~~~ + +The third value for the ``plot_type`` parameter is ``"bar"`` to create a +bar graph with each individual fitness represented as a bar. + +.. code:: python + + ga_instance.plot_fitness(plot_type="bar") + +.. figure:: https://user-images.githubusercontent.com/16560492/122473340-b7736c80-cf8f-11eb-89c5-4f7db3b653cc.png + :alt: + +.. _plotnewsolutionrate-2: + +``plot_new_solution_rate()`` +---------------------------- + +The ``plot_new_solution_rate()`` method presents the number of new +solutions explored in each generation. This helps to figure out if the +genetic algorithm is able to find new solutions as an indication of more +possible evolution. If no new solutions are explored, this is an +indication that no further evolution is possible. + +The ``plot_new_solution_rate()`` method accepts the same parameters as +in the ``plot_fitness()`` method with 3 possible values for +``plot_type`` parameter. + +.. _plottypeplot-2: + +``plot_type="plot"`` +~~~~~~~~~~~~~~~~~~~~ + +The default value for the ``plot_type`` parameter is ``"plot"``. + +.. code:: python + + ga_instance.plot_new_solution_rate() + # ga_instance.plot_new_solution_rate(plot_type="plot") + +The next figure shows that, for example, generation 6 has the least +number of new solutions which is 4. The number of new solutions in the +first generation is always equal to the number of solutions in the +population (i.e. the value assigned to the ``sol_per_pop`` parameter in +the constructor of the ``pygad.GA`` class) which is 10 in this example. + +.. figure:: https://user-images.githubusercontent.com/16560492/122475815-3322e880-cf93-11eb-9648-bf66f823234b.png + :alt: + +.. _plottypescatter-2: + +``plot_type="scatter"`` +~~~~~~~~~~~~~~~~~~~~~~~ + +The previous graph can be represented as scattered points by setting +``plot_type="scatter"``. + +.. code:: python + + ga_instance.plot_new_solution_rate(plot_type="scatter") + +.. figure:: https://user-images.githubusercontent.com/16560492/122476108-adec0380-cf93-11eb-80ac-7588bf90492f.png + :alt: + +.. _plottypebar-2: + +``plot_type="bar"`` +~~~~~~~~~~~~~~~~~~~ + +By setting ``plot_type="scatter"``, each value is represented as a +vertical bar. + +.. code:: python + + ga_instance.plot_new_solution_rate(plot_type="bar") + +.. figure:: https://user-images.githubusercontent.com/16560492/122476173-c2c89700-cf93-11eb-9e77-d39737cd3a96.png + :alt: + +.. _plotgenes-2: + +``plot_genes()`` +---------------- + +The ``plot_genes()`` method is the third option to visualize the PyGAD +results. This method has 3 control variables: + +1. ``graph_type="plot"``: Can be ``"plot"`` (default), ``"boxplot"``, or + ``"histogram"``. + +2. ``plot_type="plot"``: Identical to the ``plot_type`` parameter + explored in the ``plot_fitness()`` and ``plot_new_solution_rate()`` + methods. + +3. ``solutions="all"``: Can be ``"all"`` (default) or ``"best"``. + +These 3 parameters controls the style of the output figure. + +The ``graph_type`` parameter selects the type of the graph which helps +to explore the gene values as: + +1. A normal plot. + +2. A histogram. + +3. A box and whisker plot. + +The ``plot_type`` parameter works only when the type of the graph is set +to ``"plot"``. + +The ``solutions`` parameter selects whether the genes come from all +solutions in the population or from just the best solutions. + +.. _graphtypeplot: + +``graph_type="plot"`` +~~~~~~~~~~~~~~~~~~~~~ + +When ``graph_type="plot"``, then the figure creates a normal graph where +the relationship between the gene values and the generation numbers is +represented as a continuous plot, scattered points, or bars. + +.. _plottypeplot-3: + +``plot_type="plot"`` +^^^^^^^^^^^^^^^^^^^^ + +Because the default value for both ``graph_type`` and ``plot_type`` is +``"plot"``, then all of the lines below creates the same figure. This +figure is helpful to know whether a gene value lasts for more +generations as an indication of the best value for this gene. For +example, the value 16 for the gene with index 5 (at column 2 and row 2 +of the next graph) lasted for 83 generations. + +.. code:: python + + ga_instance.plot_genes() + + ga_instance.plot_genes(graph_type="plot") + + ga_instance.plot_genes(plot_type="plot") + + ga_instance.plot_genes(graph_type="plot", + plot_type="plot") + +.. figure:: https://user-images.githubusercontent.com/16560492/122477158-4a62d580-cf95-11eb-8c93-9b6e74cb814c.png + :alt: + +As the default value for the ``solutions`` parameter is ``"all"``, then +the following method calls generate the same plot. + +.. code:: python + + ga_instance.plot_genes(solutions="all") + + ga_instance.plot_genes(graph_type="plot", + solutions="all") + + ga_instance.plot_genes(plot_type="plot", + solutions="all") + + ga_instance.plot_genes(graph_type="plot", + plot_type="plot", + solutions="all") + +.. _plottypescatter-3: + +``plot_type="scatter"`` +^^^^^^^^^^^^^^^^^^^^^^^ + +The following calls of the ``plot_genes()`` method create the same +scatter plot. + +.. code:: python + + ga_instance.plot_genes(plot_type="scatter") + + ga_instance.plot_genes(graph_type="plot", + plot_type="scatter", + solutions='all') + +.. figure:: https://user-images.githubusercontent.com/16560492/122477273-73836600-cf95-11eb-828f-f357c7b0f815.png + :alt: + +.. _plottypebar-3: + +``plot_type="bar"`` +^^^^^^^^^^^^^^^^^^^ + +.. code:: python + + ga_instance.plot_genes(plot_type="bar") + + ga_instance.plot_genes(graph_type="plot", + plot_type="bar", + solutions='all') + +.. figure:: https://user-images.githubusercontent.com/16560492/122477370-99106f80-cf95-11eb-8643-865b55e6b844.png + :alt: + +.. _graphtypeboxplot: + +``graph_type="boxplot"`` +~~~~~~~~~~~~~~~~~~~~~~~~ + +By setting ``graph_type`` to ``"boxplot"``, then a box and whisker graph +is created. Now, the ``plot_type`` parameter has no effect. + +The following 2 calls of the ``plot_genes()`` method create the same +figure as the default value for the ``solutions`` parameter is +``"all"``. + +.. code:: python + + ga_instance.plot_genes(graph_type="boxplot") + + ga_instance.plot_genes(graph_type="boxplot", + solutions='all') + +.. figure:: https://user-images.githubusercontent.com/16560492/122479260-beeb4380-cf98-11eb-8f08-23707929b12c.png + :alt: + +.. _graphtypehistogram: + +``graph_type="histogram"`` +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For ``graph_type="boxplot"``, then a histogram is created for each gene. +Similar to ``graph_type="boxplot"``, the ``plot_type`` parameter has no +effect. + +The following 2 calls of the ``plot_genes()`` method create the same +figure as the default value for the ``solutions`` parameter is +``"all"``. + +.. code:: python + + ga_instance.plot_genes(graph_type="histogram") + + ga_instance.plot_genes(graph_type="histogram", + solutions='all') + +.. figure:: https://user-images.githubusercontent.com/16560492/122477314-8007be80-cf95-11eb-9c95-da3f49204151.png + :alt: + +All the previous figures can be created for only the best solutions by +setting ``solutions="best"``. + +Parallel Processing in PyGAD +============================ + +Starting from `PyGAD +2.17.0 `__, +parallel processing becomes supported. This section explains how to use +parallel processing in PyGAD. + +According to the `PyGAD +lifecycle `__, +parallel processing can be parallelized in only 2 operations: + +1. Population fitness calculation. + +2. Mutation. + +The reason is that the calculations in these 2 operations are +independent (i.e. each solution/chromosome is handled independently from +the others) and can be distributed across different processes or +threads. + +For the mutation operation, it does not do intensive calculations on the +CPU. Its calculations are simple like flipping the values of some genes +from 0 to 1 or adding a random value to some genes. So, it does not take +much CPU processing time. Experiments proved that parallelizing the +mutation operation across the solutions increases the time instead of +reducing it. This is because running multiple processes or threads adds +overhead to manage them. Thus, parallel processing cannot be applied on +the mutation operation. + +For the population fitness calculation, parallel processing can help +make a difference and reduce the processing time. But this is +conditional on the type of calculations done in the fitness function. If +the fitness function makes intensive calculations and takes much +processing time from the CPU, then it is probably that parallel +processing will help to cut down the overall time. + +This section explains how parallel processing works in PyGAD and how to +use parallel processing in PyGAD + +How to Use Parallel Processing in PyGAD +--------------------------------------- + +Starting from `PyGAD +2.17.0 `__, +a new parameter called ``parallel_processing`` added to the constructor +of the ``pygad.GA`` class. + +.. code:: python + + import pygad + ... + ga_instance = pygad.GA(..., + parallel_processing=...) + ... + +This parameter allows the user to do the following: + +1. Enable parallel processing. + +2. Select whether processes or threads are used. + +3. Specify the number of processes or threads to be used. + +These are 3 possible values for the ``parallel_processing`` parameter: + +1. ``None``: (Default) It means no parallel processing is used. + +2. A positive integer referring to the number of threads to be used + (i.e. threads, not processes, are used. + +3. ``list``/``tuple``: If a list or a tuple of exactly 2 elements is + assigned, then: + + 1. The first element can be either ``'process'`` or ``'thread'`` to + specify whether processes or threads are used, respectively. + + 2. The second element can be: + + 1. A positive integer to select the maximum number of processes or + threads to be used + + 2. ``0`` to indicate that 0 processes or threads are used. It + means no parallel processing. This is identical to setting + ``parallel_processing=None``. + + 3. ``None`` to use the default value as calculated by the + ``concurrent.futures module``. + +These are examples of the values assigned to the ``parallel_processing`` +parameter: + +- ``parallel_processing=4``: Because the parameter is assigned a + positive integer, this means parallel processing is activated where 4 + threads are used. + +- ``parallel_processing=["thread", 5]``: Use parallel processing with 5 + threads. This is identical to ``parallel_processing=5``. + +- ``parallel_processing=["process", 8]``: Use parallel processing with + 8 processes. + +- ``parallel_processing=["process", 0]``: As the second element is + given the value 0, this means do not use parallel processing. This is + identical to ``parallel_processing=None``. + +Examples +-------- + +The examples will help you know the difference between using processes +and threads. Moreover, it will give an idea when parallel processing +would make a difference and reduce the time. These are dummy examples +where the fitness function is made to always return 0. + +The first example uses 10 genes, 5 solutions in the population where +only 3 solutions mate, and 9999 generations. The fitness function uses a +``for`` loop with 100 iterations just to have some calculations. In the +constructor of the ``pygad.GA`` class, ``parallel_processing=None`` +means no parallel processing is used. + +.. code:: python + + import pygad + import time + + def fitness_func(ga_instance, solution, solution_idx): + for _ in range(99): + pass + return 0 + + ga_instance = pygad.GA(num_generations=9999, + num_parents_mating=3, + sol_per_pop=5, + num_genes=10, + fitness_func=fitness_func, + suppress_warnings=True, + parallel_processing=None) + + if __name__ == '__main__': + t1 = time.time() + + ga_instance.run() + + t2 = time.time() + print("Time is", t2-t1) + +When parallel processing is not used, the time it takes to run the +genetic algorithm is ``1.5`` seconds. + +In the comparison, let's do a second experiment where parallel +processing is used with 5 threads. In this case, it take ``5`` seconds. + +.. code:: python + + ... + ga_instance = pygad.GA(..., + parallel_processing=5) + ... + +For the third experiment, processes instead of threads are used. Also, +only 99 generations are used instead of 9999. The time it takes is +``99`` seconds. + +.. code:: python + + ... + ga_instance = pygad.GA(num_generations=99, + ..., + parallel_processing=["process", 5]) + ... + +This is the summary of the 3 experiments: + +1. No parallel processing & 9999 generations: 1.5 seconds. + +2. Parallel processing with 5 threads & 9999 generations: 5 seconds + +3. Parallel processing with 5 processes & 99 generations: 99 seconds + +Because the fitness function does not need much CPU time, the normal +processing takes the least time. Running processes for this simple +problem takes 99 compared to only 5 seconds for threads because managing +processes is much heavier than managing threads. Thus, most of the CPU +time is for swapping the processes instead of executing the code. + +In the second example, the loop makes 99999999 iterations and only 5 +generations are used. With no parallelization, it takes 22 seconds. + +.. code:: python + + import pygad + import time + + def fitness_func(ga_instance, solution, solution_idx): + for _ in range(99999999): + pass + return 0 + + ga_instance = pygad.GA(num_generations=5, + num_parents_mating=3, + sol_per_pop=5, + num_genes=10, + fitness_func=fitness_func, + suppress_warnings=True, + parallel_processing=None) + + if __name__ == '__main__': + t1 = time.time() + ga_instance.run() + t2 = time.time() + print("Time is", t2-t1) + +It takes 15 seconds when 10 processes are used. + +.. code:: python + + ... + ga_instance = pygad.GA(..., + parallel_processing=["process", 10]) + ... + +This is compared to 20 seconds when 10 threads are used. + +.. code:: python + + ... + ga_instance = pygad.GA(..., + parallel_processing=["thread", 10]) + ... + +Based on the second example, using parallel processing with 10 processes +takes the least time because there is much CPU work done. Generally, +processes are preferred over threads when most of the work in on the +CPU. Threads are preferred over processes in some situations like doing +input/output operations. + +*Before releasing* `PyGAD +2.17.0 `__\ *,* +`László +Fazekas `__ +*wrote an article to parallelize the fitness function with PyGAD. Check +it:* `How Genetic Algorithms Can Compete with Gradient Descent and +Backprop `__. + +Print Lifecycle Summary +======================= + +In `PyGAD +2.19.0 `__, +a new method called ``summary()`` is supported. It prints a Keras-like +summary of the PyGAD lifecycle showing the steps, callback functions, +parameters, etc. + +This method accepts the following parameters: + +- ``line_length=70``: An integer representing the length of the single + line in characters. + +- ``fill_character=" "``: A character to fill the lines. + +- ``line_character="-"``: A character for creating a line separator. + +- ``line_character2="="``: A secondary character to create a line + separator. + +- ``columns_equal_len=False``: The table rows are split into + equal-sized columns or split subjective to the width needed. + +- ``print_step_parameters=True``: Whether to print extra parameters + about each step inside the step. If ``print_step_parameters=False`` + and ``print_parameters_summary=True``, then the parameters of each + step are printed at the end of the table. + +- ``print_parameters_summary=True``: Whether to print parameters + summary at the end of the table. If ``print_step_parameters=False``, + then the parameters of each step are printed at the end of the table + too. + +This is a quick example to create a PyGAD example. + +.. code:: python + + import pygad + import numpy + + function_inputs = [4,-2,3.5,5,-11,-4.7] + desired_output = 44 + + def genetic_fitness(solution, solution_idx): + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + def on_gen(ga): + pass + + def on_crossover_callback(a, b): + pass + + ga_instance = pygad.GA(num_generations=100, + num_parents_mating=10, + sol_per_pop=20, + num_genes=len(function_inputs), + on_crossover=on_crossover_callback, + on_generation=on_gen, + parallel_processing=2, + stop_criteria="reach_10", + fitness_batch_size=4, + crossover_probability=0.4, + fitness_func=genetic_fitness) + +Then call the ``summary()`` method to print the summary with the default +parameters. Note that entries for the crossover and generation callback +function are created because their callback functions are implemented +through the ``on_crossover_callback()`` and ``on_gen()``, respectively. + +.. code:: python + + ga_instance.summary() + +.. code:: bash + + ---------------------------------------------------------------------- + PyGAD Lifecycle + ====================================================================== + Step Handler Output Shape + ====================================================================== + Fitness Function genetic_fitness() (1) + Fitness batch size: 4 + ---------------------------------------------------------------------- + Parent Selection steady_state_selection() (10, 6) + Number of Parents: 10 + ---------------------------------------------------------------------- + Crossover single_point_crossover() (10, 6) + Crossover probability: 0.4 + ---------------------------------------------------------------------- + On Crossover on_crossover_callback() None + ---------------------------------------------------------------------- + Mutation random_mutation() (10, 6) + Mutation Genes: 1 + Random Mutation Range: (-1.0, 1.0) + Mutation by Replacement: False + Allow Duplicated Genes: True + ---------------------------------------------------------------------- + On Generation on_gen() None + Stop Criteria: [['reach', 10.0]] + ---------------------------------------------------------------------- + ====================================================================== + Population Size: (20, 6) + Number of Generations: 100 + Initial Population Range: (-4, 4) + Keep Elitism: 1 + Gene DType: [, None] + Parallel Processing: ['thread', 2] + Save Best Solutions: False + Save Solutions: False + ====================================================================== + +We can set the ``print_step_parameters`` and +``print_parameters_summary`` parameters to ``False`` to not print the +parameters. + +.. code:: python + + ga_instance.summary(print_step_parameters=False, + print_parameters_summary=False) + +.. code:: bash + + ---------------------------------------------------------------------- + PyGAD Lifecycle + ====================================================================== + Step Handler Output Shape + ====================================================================== + Fitness Function genetic_fitness() (1) + ---------------------------------------------------------------------- + Parent Selection steady_state_selection() (10, 6) + ---------------------------------------------------------------------- + Crossover single_point_crossover() (10, 6) + ---------------------------------------------------------------------- + On Crossover on_crossover_callback() None + ---------------------------------------------------------------------- + Mutation random_mutation() (10, 6) + ---------------------------------------------------------------------- + On Generation on_gen() None + ---------------------------------------------------------------------- + ====================================================================== + +Logging Outputs +=============== + +In `PyGAD +3.0.0 `__, +the ``print()`` statement is no longer used and the outputs are printed +using the `logging `__ +module. A a new parameter called ``logger`` is supported to accept the +user-defined logger. + +.. code:: python + + import logging + + logger = ... + + ga_instance = pygad.GA(..., + logger=logger, + ...) + +The default value for this parameter is ``None``. If there is no logger +passed (i.e. ``logger=None``), then a default logger is created to log +the messages to the console exactly like how the ``print()`` statement +works. + +Some advantages of using the the +`logging `__ module +instead of the ``print()`` statement are: + +1. The user has more control over the printed messages specially if + there is a project that uses multiple modules where each module + prints its messages. A logger can organize the outputs. + +2. Using the proper ``Handler``, the user can log the output messages to + files and not only restricted to printing it to the console. So, it + is much easier to record the outputs. + +3. The format of the printed messages can be changed by customizing the + ``Formatter`` assigned to the Logger. + +This section gives some quick examples to use the ``logging`` module and +then gives an example to use the logger with PyGAD. + +Logging to the Console +---------------------- + +This is an example to create a logger to log the messages to the +console. + +.. code:: python + + import logging + + # Create a logger + logger = logging.getLogger(__name__) + + # Set the logger level to debug so that all the messages are printed. + logger.setLevel(logging.DEBUG) + + # Create a stream handler to log the messages to the console. + stream_handler = logging.StreamHandler() + + # Set the handler level to debug. + stream_handler.setLevel(logging.DEBUG) + + # Create a formatter + formatter = logging.Formatter('%(message)s') + + # Add the formatter to handler. + stream_handler.setFormatter(formatter) + + # Add the stream handler to the logger + logger.addHandler(stream_handler) + +Now, we can log messages to the console with the format specified in the +``Formatter``. + +.. code:: python + + logger.debug('Debug message.') + logger.info('Info message.') + logger.warning('Warn message.') + logger.error('Error message.') + logger.critical('Critical message.') + +The outputs are identical to those returned using the ``print()`` +statement. + +.. code:: + + Debug message. + Info message. + Warn message. + Error message. + Critical message. + +By changing the format of the output messages, we can have more +information about each message. + +.. code:: python + + formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') + +This is a sample output. + +.. code:: python + + 2023-04-03 18:46:27 DEBUG: Debug message. + 2023-04-03 18:46:27 INFO: Info message. + 2023-04-03 18:46:27 WARNING: Warn message. + 2023-04-03 18:46:27 ERROR: Error message. + 2023-04-03 18:46:27 CRITICAL: Critical message. + +Note that you may need to clear the handlers after finishing the +execution. This is to make sure no cached handlers are used in the next +run. If the cached handlers are not cleared, then the single output +message may be repeated. + +.. code:: python + + logger.handlers.clear() + +Logging to a File +----------------- + +This is another example to log the messages to a file named +``logfile.txt``. The formatter prints the following about each message: + +1. The date and time at which the message is logged. + +2. The log level. + +3. The message. + +4. The path of the file. + +5. The lone number of the log message. + +.. code:: python + + import logging + + level = logging.DEBUG + name = 'logfile.txt' + + logger = logging.getLogger(name) + logger.setLevel(level) + + file_handler = logging.FileHandler(name, 'a+', 'utf-8') + file_handler.setLevel(logging.DEBUG) + file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s - %(pathname)s:%(lineno)d', datefmt='%Y-%m-%d %H:%M:%S') + file_handler.setFormatter(file_format) + logger.addHandler(file_handler) + +This is how the outputs look like. + +.. code:: python + + 2023-04-03 18:54:03 DEBUG: Debug message. - c:\users\agad069\desktop\logger\example2.py:46 + 2023-04-03 18:54:03 INFO: Info message. - c:\users\agad069\desktop\logger\example2.py:47 + 2023-04-03 18:54:03 WARNING: Warn message. - c:\users\agad069\desktop\logger\example2.py:48 + 2023-04-03 18:54:03 ERROR: Error message. - c:\users\agad069\desktop\logger\example2.py:49 + 2023-04-03 18:54:03 CRITICAL: Critical message. - c:\users\agad069\desktop\logger\example2.py:50 + +Consider clearing the handlers if necessary. + +.. code:: python + + logger.handlers.clear() + +Log to Both the Console and a File +---------------------------------- + +This is an example to create a single Logger associated with 2 handlers: + +1. A file handler. + +2. A stream handler. + +.. code:: python + + import logging + + level = logging.DEBUG + name = 'logfile.txt' + + logger = logging.getLogger(name) + logger.setLevel(level) + + file_handler = logging.FileHandler(name,'a+','utf-8') + file_handler.setLevel(logging.DEBUG) + file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s - %(pathname)s:%(lineno)d', datefmt='%Y-%m-%d %H:%M:%S') + file_handler.setFormatter(file_format) + logger.addHandler(file_handler) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + console_format = logging.Formatter('%(message)s') + console_handler.setFormatter(console_format) + logger.addHandler(console_handler) + +When a log message is executed, then it is both printed to the console +and saved in the ``logfile.txt``. + +Consider clearing the handlers if necessary. + +.. code:: python + + logger.handlers.clear() + +PyGAD Example +------------- + +To use the logger in PyGAD, just create your custom logger and pass it +to the ``logger`` parameter. + +.. code:: python + + import logging + import pygad + import numpy + + level = logging.DEBUG + name = 'logfile.txt' + + logger = logging.getLogger(name) + logger.setLevel(level) + + file_handler = logging.FileHandler(name,'a+','utf-8') + file_handler.setLevel(logging.DEBUG) + file_format = logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') + file_handler.setFormatter(file_format) + logger.addHandler(file_handler) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + console_format = logging.Formatter('%(message)s') + console_handler.setFormatter(console_format) + logger.addHandler(console_handler) + + equation_inputs = [4, -2, 8] + desired_output = 2671.1234 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + def on_generation(ga_instance): + ga_instance.logger.info("Generation = {generation}".format(generation=ga_instance.generations_completed)) + ga_instance.logger.info("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1])) + + ga_instance = pygad.GA(num_generations=10, + sol_per_pop=40, + num_parents_mating=2, + keep_parents=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + on_generation=on_generation, + logger=logger) + ga_instance.run() + + logger.handlers.clear() + +By executing this code, the logged messages are printed to the console +and also saved in the text file. + +.. code:: python + + 2023-04-03 19:04:27 INFO: Generation = 1 + 2023-04-03 19:04:27 INFO: Fitness = 0.00038086960368076276 + 2023-04-03 19:04:27 INFO: Generation = 2 + 2023-04-03 19:04:27 INFO: Fitness = 0.00038214871408010853 + 2023-04-03 19:04:27 INFO: Generation = 3 + 2023-04-03 19:04:27 INFO: Fitness = 0.0003832795907974678 + 2023-04-03 19:04:27 INFO: Generation = 4 + 2023-04-03 19:04:27 INFO: Fitness = 0.00038398612055017196 + 2023-04-03 19:04:27 INFO: Generation = 5 + 2023-04-03 19:04:27 INFO: Fitness = 0.00038442348890867516 + 2023-04-03 19:04:27 INFO: Generation = 6 + 2023-04-03 19:04:27 INFO: Fitness = 0.0003854406039137763 + 2023-04-03 19:04:27 INFO: Generation = 7 + 2023-04-03 19:04:27 INFO: Fitness = 0.00038646083174063284 + 2023-04-03 19:04:27 INFO: Generation = 8 + 2023-04-03 19:04:27 INFO: Fitness = 0.0003875169193024936 + 2023-04-03 19:04:27 INFO: Generation = 9 + 2023-04-03 19:04:27 INFO: Fitness = 0.0003888816727311021 + 2023-04-03 19:04:27 INFO: Generation = 10 + 2023-04-03 19:04:27 INFO: Fitness = 0.000389832593101348 + +Batch Fitness Calculation +========================= + +In `PyGAD +2.19.0 `__, +a new optional parameter called ``fitness_batch_size`` is supported. A +new optional parameter called ``fitness_batch_size`` is supported to +calculate the fitness function in batches. Thanks to `Linan +Qiu `__ for opening the `GitHub issue +#136 `__. + +Its values can be: + +- ``1`` or ``None``: If the ``fitness_batch_size`` parameter is + assigned the value ``1`` or ``None`` (default), then the normal flow + is used where the fitness function is called for each individual + solution. That is if there are 15 solutions, then the fitness + function is called 15 times. + +- ``1 < fitness_batch_size <= sol_per_pop``: If the + ``fitness_batch_size`` parameter is assigned a value satisfying this + condition ``1 < fitness_batch_size <= sol_per_pop``, then the + solutions are grouped into batches of size ``fitness_batch_size`` and + the fitness function is called once for each batch. In this case, the + fitness function must return a list/tuple/numpy.ndarray with a length + equal to the number of solutions passed. + +.. _example-without-fitnessbatchsize-parameter: + +Example without ``fitness_batch_size`` Parameter +------------------------------------------------ + +This is an example where the ``fitness_batch_size`` parameter is given +the value ``None`` (which is the default value). This is equivalent to +using the value ``1``. In this case, the fitness function will be called +for each solution. This means the fitness function ``fitness_func`` will +receive only a single solution. This is an example of the passed +arguments to the fitness function: + +.. code:: + + solution: [ 2.52860734, -0.94178795, 2.97545704, 0.84131987, -3.78447118, 2.41008358] + solution_idx: 3 + +The fitness function also must return a single numeric value as the +fitness for the passed solution. + +As we have a population of ``20`` solutions, then the fitness function +is called 20 times per generation. For 5 generations, then the fitness +function is called ``20*5 = 100`` times. In PyGAD, the fitness function +is called after the last generation too and this adds additional 20 +times. So, the total number of calls to the fitness function is +``20*5 + 20 = 120``. + +Note that the ``keep_elitism`` and ``keep_parents`` parameters are set +to ``0`` to make sure no fitness values are reused and to force calling +the fitness function for each individual solution. + +.. code:: python + + import pygad + import numpy + + function_inputs = [4,-2,3.5,5,-11,-4.7] + desired_output = 44 + + number_of_calls = 0 + + def fitness_func(ga_instance, solution, solution_idx): + global number_of_calls + number_of_calls = number_of_calls + 1 + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + ga_instance = pygad.GA(num_generations=5, + num_parents_mating=10, + sol_per_pop=20, + fitness_func=fitness_func, + fitness_batch_size=None, + # fitness_batch_size=1, + num_genes=len(function_inputs), + keep_elitism=0, + keep_parents=0) + + ga_instance.run() + print(number_of_calls) + +.. code:: + + 120 + +.. _example-with-fitnessbatchsize-parameter: + +Example with ``fitness_batch_size`` Parameter +--------------------------------------------- + +This is an example where the ``fitness_batch_size`` parameter is used +and assigned the value ``4``. This means the solutions will be grouped +into batches of ``4`` solutions. The fitness function will be called +once for each patch (i.e. called once for each 4 solutions). + +This is an example of the arguments passed to it: + +.. code:: python + + solutions: + [[ 3.1129432 -0.69123589 1.93792414 2.23772968 -1.54616001 -0.53930799] + [ 3.38508121 0.19890812 1.93792414 2.23095014 -3.08955597 3.10194128] + [ 2.37079504 -0.88819803 2.97545704 1.41742256 -3.95594055 2.45028256] + [ 2.52860734 -0.94178795 2.97545704 0.84131987 -3.78447118 2.41008358]] + solutions_indices: + [16, 17, 18, 19] + +As we have 20 solutions, then there are ``20/4 = 5`` patches. As a +result, the fitness function is called only 5 times per generation +instead of 20. For each call to the fitness function, it receives a +batch of 4 solutions. + +As we have 5 generations, then the function will be called ``5*5 = 25`` +times. Given the call to the fitness function after the last generation, +then the total number of calls is ``5*5 + 5 = 30``. + +.. code:: python + + import pygad + import numpy + + function_inputs = [4,-2,3.5,5,-11,-4.7] + desired_output = 44 + + number_of_calls = 0 + + def fitness_func_batch(ga_instance, solutions, solutions_indices): + global number_of_calls + number_of_calls = number_of_calls + 1 + batch_fitness = [] + for solution in solutions: + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + batch_fitness.append(fitness) + return batch_fitness + + ga_instance = pygad.GA(num_generations=5, + num_parents_mating=10, + sol_per_pop=20, + fitness_func=fitness_func_batch, + fitness_batch_size=4, + num_genes=len(function_inputs), + keep_elitism=0, + keep_parents=0) + + ga_instance.run() + print(number_of_calls) + +.. code:: + + 30 + +When batch fitness calculation is used, then we saved ``120 - 30 = 90`` +calls to the fitness function. + +Use Functions and Methods to Build Fitness and Callbacks +======================================================== + +In PyGAD 2.19.0, it is possible to pass user-defined functions or +methods to the following parameters: + +1. ``fitness_func`` + +2. ``on_start`` + +3. ``on_fitness`` + +4. ``on_parents`` + +5. ``on_crossover`` + +6. ``on_mutation`` + +7. ``on_generation`` + +8. ``on_stop`` + +This section gives 2 examples to assign these parameters user-defined: + +1. Functions. + +2. Methods. + +Assign Functions +---------------- + +This is a dummy example where the fitness function returns a random +value. Note that the instance of the ``pygad.GA`` class is passed as the +last parameter of all functions. + +.. code:: python + + import pygad + import numpy + + def fitness_func(ga_instanse, solution, solution_idx): + return numpy.random.rand() + + def on_start(ga_instanse): + print("on_start") + + def on_fitness(ga_instanse, last_gen_fitness): + print("on_fitness") + + def on_parents(ga_instanse, last_gen_parents): + print("on_parents") + + def on_crossover(ga_instanse, last_gen_offspring): + print("on_crossover") + + def on_mutation(ga_instanse, last_gen_offspring): + print("on_mutation") + + def on_generation(ga_instanse): + print("on_generation\n") + + def on_stop(ga_instanse, last_gen_fitness): + print("on_stop") + + ga_instance = pygad.GA(num_generations=5, + num_parents_mating=4, + sol_per_pop=10, + num_genes=2, + on_start=on_start, + on_fitness=on_fitness, + on_parents=on_parents, + on_crossover=on_crossover, + on_mutation=on_mutation, + on_generation=on_generation, + on_stop=on_stop, + fitness_func=fitness_func) + + ga_instance.run() + +Assign Methods +-------------- + +The next example has all the method defined inside the class ``Test``. +All of the methods accept an additional parameter representing the +method's object of the class ``Test``. + +All methods accept ``self`` as the first parameter and the instance of +the ``pygad.GA`` class as the last parameter. + +.. code:: python + + import pygad + import numpy + + class Test: + def fitness_func(self, ga_instanse, solution, solution_idx): + return numpy.random.rand() + + def on_start(self, ga_instanse): + print("on_start") + + def on_fitness(self, ga_instanse, last_gen_fitness): + print("on_fitness") + + def on_parents(self, ga_instanse, last_gen_parents): + print("on_parents") + + def on_crossover(self, ga_instanse, last_gen_offspring): + print("on_crossover") + + def on_mutation(self, ga_instanse, last_gen_offspring): + print("on_mutation") + + def on_generation(self, ga_instanse): + print("on_generation\n") + + def on_stop(self, ga_instanse, last_gen_fitness): + print("on_stop") + + ga_instance = pygad.GA(num_generations=5, + num_parents_mating=4, + sol_per_pop=10, + num_genes=2, + on_start=Test().on_start, + on_fitness=Test().on_fitness, + on_parents=Test().on_parents, + on_crossover=Test().on_crossover, + on_mutation=Test().on_mutation, + on_generation=Test().on_generation, + on_stop=Test().on_stop, + fitness_func=Test().fitness_func) + + ga_instance.run() + +.. _examples-2: + +Examples +======== + +This section gives the complete code of some examples that use +``pygad``. Each subsection builds a different example. + +Linear Model Optimization +------------------------- + +This example is discussed in the `Steps to Use +PyGAD `__ +section which optimizes a linear model. Its complete code is listed +below. + +.. code:: python + + import pygad + import numpy + + """ + Given the following function: + y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6 + where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=44 + What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function. + """ + + function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs. + desired_output = 44 # Function output. + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution*function_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + num_generations = 100 # Number of generations. + num_parents_mating = 10 # Number of solutions to be selected as parents in the mating pool. + + sol_per_pop = 20 # Number of solutions in the population. + num_genes = len(function_inputs) + + last_fitness = 0 + def on_generation(ga_instance): + global last_fitness + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1])) + print("Change = {change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness)) + last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + sol_per_pop=sol_per_pop, + num_genes=num_genes, + fitness_func=fitness_func, + on_generation=on_generation) + + # Running the GA to optimize the parameters of the function. + ga_instance.run() + + ga_instance.plot_fitness() + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness) + print("Parameters of the best solution : {solution}".format(solution=solution)) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + prediction = numpy.sum(numpy.array(function_inputs)*solution) + print("Predicted output based on the best solution : {prediction}".format(prediction=prediction)) + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + + # Saving the GA instance. + filename = 'genetic' # The filename to which the instance is saved. The name is without extension. + ga_instance.save(filename=filename) + + # Loading the saved GA instance. + loaded_ga_instance = pygad.load(filename=filename) + loaded_ga_instance.plot_fitness() + +Reproducing Images +------------------ + +This project reproduces a single image using PyGAD by evolving pixel +values. This project works with both color and gray images. Check this +project at `GitHub `__: +https://github.com/ahmedfgad/GARI. + +For more information about this project, read this tutorial titled +`Reproducing Images using a Genetic Algorithm with +Python `__ +available at these links: + +- `Heartbeat `__: + https://heartbeat.fritz.ai/reproducing-images-using-a-genetic-algorithm-with-python-91fc701ff84 + +- `LinkedIn `__: + https://www.linkedin.com/pulse/reproducing-images-using-genetic-algorithm-python-ahmed-gad + +Project Steps +~~~~~~~~~~~~~ + +The steps to follow in order to reproduce an image are as follows: + +- Read an image + +- Prepare the fitness function + +- Create an instance of the pygad.GA class with the appropriate + parameters + +- Run PyGAD + +- Plot results + +- Calculate some statistics + +The next sections discusses the code of each of these steps. + +Read an Image +~~~~~~~~~~~~~ + +There is an image named ``fruit.jpg`` in the `GARI +project `__ which is read according +to the next code. + +.. code:: python + + import imageio + import numpy + + target_im = imageio.imread('fruit.jpg') + target_im = numpy.asarray(target_im/255, dtype=float) + +Here is the read image. + +.. figure:: https://user-images.githubusercontent.com/16560492/36948808-f0ac882e-1fe8-11e8-8d07-1307e3477fd0.jpg + :alt: + +Based on the chromosome representation used in the example, the pixel +values can be either in the 0-255, 0-1, or any other ranges. + +Note that the range of pixel values affect other parameters like the +range from which the random values are selected during mutation and also +the range of the values used in the initial population. So, be +consistent. + +Prepare the Fitness Function +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The next code creates a function that will be used as a fitness function +for calculating the fitness value for each solution in the population. +This function must be a maximization function that accepts 3 parameters +representing the instance of the ``pygad.GA`` class, a solution, and its +index. It returns a value representing the fitness value. + +.. code:: python + + import gari + + target_chromosome = gari.img2chromosome(target_im) + + def fitness_fun(ga_instance, solution, solution_idx): + fitness = numpy.sum(numpy.abs(target_chromosome-solution)) + + # Negating the fitness value to make it increasing rather than decreasing. + fitness = numpy.sum(target_chromosome) - fitness + return fitness + +The fitness value is calculated using the sum of absolute difference +between genes values in the original and reproduced chromosomes. The +``gari.img2chromosome()`` function is called before the fitness function +to represent the image as a vector because the genetic algorithm can +work with 1D chromosomes. + +The implementation of the ``gari`` module is available at the `GARI +GitHub +project `__ and +its code is listed below. + +.. code:: python + + import numpy + import functools + import operator + + def img2chromosome(img_arr): + return numpy.reshape(a=img_arr, newshape=(functools.reduce(operator.mul, img_arr.shape))) + + def chromosome2img(vector, shape): + if len(vector) != functools.reduce(operator.mul, shape): + raise ValueError("A vector of length {vector_length} into an array of shape {shape}.".format(vector_length=len(vector), shape=shape)) + + return numpy.reshape(a=vector, newshape=shape) + +.. _create-an-instance-of-the-pygadga-class-2: + +Create an Instance of the ``pygad.GA`` Class +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +It is very important to use random mutation and set the +``mutation_by_replacement`` to ``True``. Based on the range of pixel +values, the values assigned to the ``init_range_low``, +``init_range_high``, ``random_mutation_min_val``, and +``random_mutation_max_val`` parameters should be changed. + +If the image pixel values range from 0 to 255, then set +``init_range_low`` and ``random_mutation_min_val`` to 0 as they are but +change ``init_range_high`` and ``random_mutation_max_val`` to 255. + +Feel free to change the other parameters or add other parameters. Please +check the `PyGAD's documentation `__ for +the full list of parameters. + +.. code:: python + + import pygad + + ga_instance = pygad.GA(num_generations=20000, + num_parents_mating=10, + fitness_func=fitness_fun, + sol_per_pop=20, + num_genes=target_im.size, + init_range_low=0.0, + init_range_high=1.0, + mutation_percent_genes=0.01, + mutation_type="random", + mutation_by_replacement=True, + random_mutation_min_val=0.0, + random_mutation_max_val=1.0) + +Run PyGAD +~~~~~~~~~ + +Simply, call the ``run()`` method to run PyGAD. + +.. code:: python + + ga_instance.run() + +Plot Results +~~~~~~~~~~~~ + +After the ``run()`` method completes, the fitness values of all +generations can be viewed in a plot using the ``plot_fitness()`` method. + +.. code:: python + + ga_instance.plot_fitness() + +Here is the plot after 20,000 generations. + +.. figure:: https://user-images.githubusercontent.com/16560492/82232124-77762c00-992e-11ea-9fc6-14a1cd7a04ff.png + :alt: + +Calculate Some Statistics +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Here is some information about the best solution. + +.. code:: python + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + if ga_instance.best_solution_generation != -1: + print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + + result = gari.chromosome2img(solution, target_im.shape) + matplotlib.pyplot.imshow(result) + matplotlib.pyplot.title("PyGAD & GARI for Reproducing Images") + matplotlib.pyplot.show() + +Evolution by Generation +~~~~~~~~~~~~~~~~~~~~~~~ + +The solution reached after the 20,000 generations is shown below. + +.. figure:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png + :alt: + +After more generations, the result can be enhanced like what shown +below. + +.. figure:: https://user-images.githubusercontent.com/16560492/82232345-cf149780-992e-11ea-8390-bf1a57a19de7.png + :alt: + +The results can also be enhanced by changing the parameters passed to +the constructor of the ``pygad.GA`` class. + +Here is how the image is evolved from generation 0 to generation +20,000s. + +Generation 0 + +.. figure:: https://user-images.githubusercontent.com/16560492/36948589-b47276f0-1fe5-11e8-8efe-0cd1a225ea3a.png + :alt: + +Generation 1,000 + +.. figure:: https://user-images.githubusercontent.com/16560492/36948823-16f490ee-1fe9-11e8-97db-3e8905ad5440.png + :alt: + +Generation 2,500 + +.. figure:: https://user-images.githubusercontent.com/16560492/36948832-3f314b60-1fe9-11e8-8f4a-4d9a53b99f3d.png + :alt: + +Generation 4,500 + +.. figure:: https://user-images.githubusercontent.com/16560492/36948837-53d1849a-1fe9-11e8-9b36-e9e9291e347b.png + :alt: + +Generation 7,000 + +.. figure:: https://user-images.githubusercontent.com/16560492/36948852-66f1b176-1fe9-11e8-9f9b-460804e94004.png + :alt: + +Generation 8,000 + +.. figure:: https://user-images.githubusercontent.com/16560492/36948865-7fbb5158-1fe9-11e8-8c04-8ac3c1f7b1b1.png + :alt: + +Generation 20,000 + +.. figure:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png + :alt: + +Clustering +---------- + +For a 2-cluster problem, the code is available +`here `__. +For a 3-cluster problem, the code is +`here `__. +The 2 examples are using artificial samples. + +Soon a tutorial will be published at +`Paperspace `__ to explain how +clustering works using the genetic algorithm with examples in PyGAD. + +CoinTex Game Playing using PyGAD +-------------------------------- + +The code is available the `CoinTex GitHub +project `__. +CoinTex is an Android game written in Python using the Kivy framework. +Find CoinTex at `Google +Play `__: +https://play.google.com/store/apps/details?id=coin.tex.cointexreactfast + +Check this `Paperspace +tutorial `__ +for how the genetic algorithm plays CoinTex: +https://blog.paperspace.com/building-agent-for-cointex-using-genetic-algorithm. +Check also this `YouTube video `__ showing +the genetic algorithm while playing CoinTex. diff --git a/docs/source/Footer.rst b/docs/source/releases.rst similarity index 97% rename from docs/source/Footer.rst rename to docs/source/releases.rst index 54814ba..3458aaf 100644 --- a/docs/source/Footer.rst +++ b/docs/source/releases.rst @@ -1,2046 +1,2046 @@ -Release History -=============== - -.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png - :alt: - -.. _pygad-1017: - -PyGAD 1.0.17 ------------- - -Release Date: 15 April 2020 - -1. The **pygad.GA** class accepts a new argument named ``fitness_func`` - which accepts a function to be used for calculating the fitness - values for the solutions. This allows the project to be customized to - any problem by building the right fitness function. - -.. _pygad-1020: - -PyGAD 1.0.20 -------------- - -Release Date: 4 May 2020 - -1. The **pygad.GA** attributes are moved from the class scope to the - instance scope. - -2. Raising an exception for incorrect values of the passed parameters. - -3. Two new parameters are added to the **pygad.GA** class constructor - (``init_range_low`` and ``init_range_high``) allowing the user to - customize the range from which the genes values in the initial - population are selected. - -4. The code object ``__code__`` of the passed fitness function is - checked to ensure it has the right number of parameters. - -.. _pygad-200: - -PyGAD 2.0.0 ------------- - -Release Date: 13 May 2020 - -1. The fitness function accepts a new argument named ``sol_idx`` - representing the index of the solution within the population. - -2. A new parameter to the **pygad.GA** class constructor named - ``initial_population`` is supported to allow the user to use a custom - initial population to be used by the genetic algorithm. If not None, - then the passed population will be used. If ``None``, then the - genetic algorithm will create the initial population using the - ``sol_per_pop`` and ``num_genes`` parameters. - -3. The parameters ``sol_per_pop`` and ``num_genes`` are optional and set - to ``None`` by default. - -4. A new parameter named ``callback_generation`` is introduced in the - **pygad.GA** class constructor. It accepts a function with a single - parameter representing the **pygad.GA** class instance. This function - is called after each generation. This helps the user to do - post-processing or debugging operations after each generation. - -.. _pygad-210: - -PyGAD 2.1.0 ------------ - -Release Date: 14 May 2020 - -1. The ``best_solution()`` method in the **pygad.GA** class returns a - new output representing the index of the best solution within the - population. Now, it returns a total of 3 outputs and their order is: - best solution, best solution fitness, and best solution index. Here - is an example: - -.. code:: python - - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Parameters of the best solution :", solution) - print("Fitness value of the best solution :", solution_fitness, "\n") - print("Index of the best solution :", solution_idx, "\n") - -1. | A new attribute named ``best_solution_generation`` is added to the - instances of the **pygad.GA** class. it holds the generation number - at which the best solution is reached. It is only assigned the - generation number after the ``run()`` method completes. Otherwise, - its value is -1. - | Example: - -.. code:: python - - print("Best solution reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) - -1. The ``best_solution_fitness`` attribute is renamed to - ``best_solutions_fitness`` (plural solution). - -2. Mutation is applied independently for the genes. - -.. _pygad-221: - -PyGAD 2.2.1 ------------ - -Release Date: 17 May 2020 - -1. Adding 2 extra modules (pygad.nn and pygad.gann) for building and - training neural networks with the genetic algorithm. - -.. _pygad-222: - -PyGAD 2.2.2 ------------ - -Release Date: 18 May 2020 - -1. The initial value of the ``generations_completed`` attribute of - instances from the pygad.GA class is ``0`` rather than ``None``. - -2. An optional bool parameter named ``mutation_by_replacement`` is added - to the constructor of the pygad.GA class. It works only when the - selected type of mutation is random (``mutation_type="random"``). In - this case, setting ``mutation_by_replacement=True`` means replace the - gene by the randomly generated value. If ``False``, then it has no - effect and random mutation works by adding the random value to the - gene. This parameter should be used when the gene falls within a - fixed range and its value must not go out of this range. Here are - some examples: - -Assume there is a gene with the value 0.5. - -If ``mutation_type="random"`` and ``mutation_by_replacement=False``, -then the generated random value (e.g. 0.1) will be added to the gene -value. The new gene value is **0.5+0.1=0.6**. - -If ``mutation_type="random"`` and ``mutation_by_replacement=True``, then -the generated random value (e.g. 0.1) will replace the gene value. The -new gene value is **0.1**. - -1. ``None`` value could be assigned to the ``mutation_type`` and - ``crossover_type`` parameters of the pygad.GA class constructor. When - ``None``, this means the step is bypassed and has no action. - -.. _pygad-230: - -PyGAD 2.3.0 ------------ - -Release date: 1 June 2020 - -1. A new module named ``pygad.cnn`` is supported for building - convolutional neural networks. - -2. A new module named ``pygad.gacnn`` is supported for training - convolutional neural networks using the genetic algorithm. - -3. The ``pygad.plot_result()`` method has 3 optional parameters named - ``title``, ``xlabel``, and ``ylabel`` to customize the plot title, - x-axis label, and y-axis label, respectively. - -4. The ``pygad.nn`` module supports the softmax activation function. - -5. The name of the ``pygad.nn.predict_outputs()`` function is changed to - ``pygad.nn.predict()``. - -6. The name of the ``pygad.nn.train_network()`` function is changed to - ``pygad.nn.train()``. - -.. _pygad-240: - -PyGAD 2.4.0 ------------ - -Release date: 5 July 2020 - -1. A new parameter named ``delay_after_gen`` is added which accepts a - non-negative number specifying the time in seconds to wait after a - generation completes and before going to the next generation. It - defaults to ``0.0`` which means no delay after the generation. - -2. The passed function to the ``callback_generation`` parameter of the - pygad.GA class constructor can terminate the execution of the genetic - algorithm if it returns the string ``stop``. This causes the - ``run()`` method to stop. - -One important use case for that feature is to stop the genetic algorithm -when a condition is met before passing though all the generations. The -user may assigned a value of 100 to the ``num_generations`` parameter of -the pygad.GA class constructor. Assuming that at generation 50, for -example, a condition is met and the user wants to stop the execution -before waiting the remaining 50 generations. To do that, just make the -function passed to the ``callback_generation`` parameter to return the -string ``stop``. - -Here is an example of a function to be passed to the -``callback_generation`` parameter which stops the execution if the -fitness value 70 is reached. The value 70 might be the best possible -fitness value. After being reached, then there is no need to pass -through more generations because no further improvement is possible. - -.. code:: python - - def func_generation(ga_instance): - if ga_instance.best_solution()[1] >= 70: - return "stop" - -.. _pygad-250: - -PyGAD 2.5.0 ------------ - -Release date: 19 July 2020 - -1. | 2 new optional parameters added to the constructor of the - ``pygad.GA`` class which are ``crossover_probability`` and - ``mutation_probability``. - | While applying the crossover operation, each parent has a random - value generated between 0.0 and 1.0. If this random value is less - than or equal to the value assigned to the - ``crossover_probability`` parameter, then the parent is selected - for the crossover operation. - | For the mutation operation, a random value between 0.0 and 1.0 is - generated for each gene in the solution. If this value is less than - or equal to the value assigned to the ``mutation_probability``, - then this gene is selected for mutation. - -2. A new optional parameter named ``linewidth`` is added to the - ``plot_result()`` method to specify the width of the curve in the - plot. It defaults to 3.0. - -3. Previously, the indices of the genes selected for mutation was - randomly generated once for all solutions within the generation. - Currently, the genes' indices are randomly generated for each - solution in the population. If the population has 4 solutions, the - indices are randomly generated 4 times inside the single generation, - 1 time for each solution. - -4. Previously, the position of the point(s) for the single-point and - two-points crossover was(were) randomly selected once for all - solutions within the generation. Currently, the position(s) is(are) - randomly selected for each solution in the population. If the - population has 4 solutions, the position(s) is(are) randomly - generated 4 times inside the single generation, 1 time for each - solution. - -5. A new optional parameter named ``gene_space`` as added to the - ``pygad.GA`` class constructor. It is used to specify the possible - values for each gene in case the user wants to restrict the gene - values. It is useful if the gene space is restricted to a certain - range or to discrete values. For more information, check the `More - about the ``gene_space`` - Parameter `__ - section. Thanks to `Prof. Tamer A. - Farrag `__ for requesting this useful - feature. - -.. _pygad-260: - -PyGAD 2.6.0 ------------- - -Release Date: 6 August 2020 - -1. A bug fix in assigning the value to the ``initial_population`` - parameter. - -2. A new parameter named ``gene_type`` is added to control the gene - type. It can be either ``int`` or ``float``. It has an effect only - when the parameter ``gene_space`` is ``None``. - -3. 7 new parameters that accept callback functions: ``on_start``, - ``on_fitness``, ``on_parents``, ``on_crossover``, ``on_mutation``, - ``on_generation``, and ``on_stop``. - -.. _pygad-270: - -PyGAD 2.7.0 ------------ - -Release Date: 11 September 2020 - -1. The ``learning_rate`` parameter in the ``pygad.nn.train()`` function - defaults to **0.01**. - -2. Added support of building neural networks for regression using the - new parameter named ``problem_type``. It is added as a parameter to - both ``pygad.nn.train()`` and ``pygad.nn.predict()`` functions. The - value of this parameter can be either **classification** or - **regression** to define the problem type. It defaults to - **classification**. - -3. The activation function for a layer can be set to the string - ``"None"`` to refer that there is no activation function at this - layer. As a result, the supported values for the activation function - are ``"sigmoid"``, ``"relu"``, ``"softmax"``, and ``"None"``. - -To build a regression network using the ``pygad.nn`` module, just do the -following: - -1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and - ``pygad.nn.predict()`` functions to the string ``"regression"``. - -2. Set the activation function for the output layer to the string - ``"None"``. This sets no limits on the range of the outputs as it - will be from ``-infinity`` to ``+infinity``. If you are sure that all - outputs will be nonnegative values, then use the ReLU function. - -Check the documentation of the ``pygad.nn`` module for an example that -builds a neural network for regression. The regression example is also -available at `this GitHub -project `__: -https://github.com/ahmedfgad/NumPyANN - -To build and train a regression network using the ``pygad.gann`` module, -do the following: - -1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and - ``pygad.nn.predict()`` functions to the string ``"regression"``. - -2. Set the ``output_activation`` parameter in the constructor of the - ``pygad.gann.GANN`` class to ``"None"``. - -Check the documentation of the ``pygad.gann`` module for an example that -builds and trains a neural network for regression. The regression -example is also available at `this GitHub -project `__: -https://github.com/ahmedfgad/NeuralGenetic - -To build a classification network, either ignore the ``problem_type`` -parameter or set it to ``"classification"`` (default value). In this -case, the activation function of the last layer can be set to any type -(e.g. softmax). - -.. _pygad-271: - -PyGAD 2.7.1 ------------ - -Release Date: 11 September 2020 - -1. A bug fix when the ``problem_type`` argument is set to - ``regression``. - -.. _pygad-272: - -PyGAD 2.7.2 ------------ - -Release Date: 14 September 2020 - -1. Bug fix to support building and training regression neural networks - with multiple outputs. - -.. _pygad-280: - -PyGAD 2.8.0 ------------ - -Release Date: 20 September 2020 - -1. Support of a new module named ``kerasga`` so that the Keras models - can be trained by the genetic algorithm using PyGAD. - -.. _pygad-281: - -PyGAD 2.8.1 ------------ - -Release Date: 3 October 2020 - -1. Bug fix in applying the crossover operation when the - ``crossover_probability`` parameter is used. Thanks to `Eng. Hamada - Kassem, Research and Teaching Assistant, Construction Engineering and - Management, Faculty of Engineering, Alexandria University, - Egypt `__. - -.. _pygad-290: - -PyGAD 2.9.0 ------------- - -Release Date: 06 December 2020 - -1. The fitness values of the initial population are considered in the - ``best_solutions_fitness`` attribute. - -2. An optional parameter named ``save_best_solutions`` is added. It - defaults to ``False``. When it is ``True``, then the best solution - after each generation is saved into an attribute named - ``best_solutions``. If ``False``, then no solutions are saved and the - ``best_solutions`` attribute will be empty. - -3. Scattered crossover is supported. To use it, assign the - ``crossover_type`` parameter the value ``"scattered"``. - -4. NumPy arrays are now supported by the ``gene_space`` parameter. - -5. The following parameters (``gene_type``, ``crossover_probability``, - ``mutation_probability``, ``delay_after_gen``) can be assigned to a - numeric value of any of these data types: ``int``, ``float``, - ``numpy.int``, ``numpy.int8``, ``numpy.int16``, ``numpy.int32``, - ``numpy.int64``, ``numpy.float``, ``numpy.float16``, - ``numpy.float32``, or ``numpy.float64``. - -.. _pygad-2100: - -PyGAD 2.10.0 ------------- - -Release Date: 03 January 2021 - -1. Support of a new module ``pygad.torchga`` to train PyTorch models - using PyGAD. Check `its - documentation `__. - -2. Support of adaptive mutation where the mutation rate is determined - by the fitness value of each solution. Read the `Adaptive - Mutation `__ - section for more details. Also, read this paper: `Libelli, S. - Marsili, and P. Alba. "Adaptive mutation in genetic algorithms." - Soft computing 4.2 (2000): - 76-80. `__ - -3. Before the ``run()`` method completes or exits, the fitness value of - the best solution in the current population is appended to the - ``best_solution_fitness`` list attribute. Note that the fitness - value of the best solution in the initial population is already - saved at the beginning of the list. So, the fitness value of the - best solution is saved before the genetic algorithm starts and after - it ends. - -4. When the parameter ``parent_selection_type`` is set to ``sss`` - (steady-state selection), then a warning message is printed if the - value of the ``keep_parents`` parameter is set to 0. - -5. More validations to the user input parameters. - -6. The default value of the ``mutation_percent_genes`` is set to the - string ``"default"`` rather than the integer 10. This change helps - to know whether the user explicitly passed a value to the - ``mutation_percent_genes`` parameter or it is left to its default - one. The ``"default"`` value is later translated into the integer - 10. - -7. The ``mutation_percent_genes`` parameter is no longer accepting the - value 0. It must be ``>0`` and ``<=100``. - -8. The built-in ``warnings`` module is used to show warning messages - rather than just using the ``print()`` function. - -9. A new ``bool`` parameter called ``suppress_warnings`` is added to - the constructor of the ``pygad.GA`` class. It allows the user to - control whether the warning messages are printed or not. It defaults - to ``False`` which means the messages are printed. - -10. A helper method called ``adaptive_mutation_population_fitness()`` is - created to calculate the average fitness value used in adaptive - mutation to filter the solutions. - -11. The ``best_solution()`` method accepts a new optional parameter - called ``pop_fitness``. It accepts a list of the fitness values of - the solutions in the population. If ``None``, then the - ``cal_pop_fitness()`` method is called to calculate the fitness - values of the population. - -.. _pygad-2101: - -PyGAD 2.10.1 ------------- - -Release Date: 10 January 2021 - -1. In the ``gene_space`` parameter, any ``None`` value (regardless of - its index or axis), is replaced by a randomly generated number based - on the 3 parameters ``init_range_low``, ``init_range_high``, and - ``gene_type``. So, the ``None`` value in ``[..., None, ...]`` or - ``[..., [..., None, ...], ...]`` are replaced with random values. - This gives more freedom in building the space of values for the - genes. - -2. All the numbers passed to the ``gene_space`` parameter are casted to - the type specified in the ``gene_type`` parameter. - -3. The ``numpy.uint`` data type is supported for the parameters that - accept integer values. - -4. In the ``pygad.kerasga`` module, the ``model_weights_as_vector()`` - function uses the ``trainable`` attribute of the model's layers to - only return the trainable weights in the network. So, only the - trainable layers with their ``trainable`` attribute set to ``True`` - (``trainable=True``), which is the default value, have their weights - evolved. All non-trainable layers with the ``trainable`` attribute - set to ``False`` (``trainable=False``) will not be evolved. Thanks to - `Prof. Tamer A. Farrag `__ for - pointing about that at - `GitHub `__. - -.. _pygad-2102: - -PyGAD 2.10.2 ------------- - -Release Date: 15 January 2021 - -1. A bug fix when ``save_best_solutions=True``. Refer to this issue for - more information: - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/25 - -.. _pygad-2110: - -PyGAD 2.11.0 ------------- - -Release Date: 16 February 2021 - -1. In the ``gene_space`` argument, the user can use a dictionary to - specify the lower and upper limits of the gene. This dictionary must - have only 2 items with keys ``low`` and ``high`` to specify the low - and high limits of the gene, respectively. This way, PyGAD takes care - of not exceeding the value limits of the gene. For a problem with - only 2 genes, then using - ``gene_space=[{'low': 1, 'high': 5}, {'low': 0.2, 'high': 0.81}]`` - means the accepted values in the first gene start from 1 (inclusive) - to 5 (exclusive) while the second one has values between 0.2 - (inclusive) and 0.85 (exclusive). For more information, please check - the `Limit the Gene Value - Range `__ - section of the documentation. - -2. The ``plot_result()`` method returns the figure so that the user can - save it. - -3. Bug fixes in copying elements from the gene space. - -4. For a gene with a set of discrete values (more than 1 value) in the - ``gene_space`` parameter like ``[0, 1]``, it was possible that the - gene value may not change after mutation. That is if the current - value is 0, then the randomly selected value could also be 0. Now, it - is verified that the new value is changed. So, if the current value - is 0, then the new value after mutation will not be 0 but 1. - -.. _pygad-2120: - -PyGAD 2.12.0 ------------- - -Release Date: 20 February 2021 - -1. 4 new instance attributes are added to hold temporary results after - each generation: ``last_generation_fitness`` holds the fitness values - of the solutions in the last generation, ``last_generation_parents`` - holds the parents selected from the last generation, - ``last_generation_offspring_crossover`` holds the offspring generated - after applying the crossover in the last generation, and - ``last_generation_offspring_mutation`` holds the offspring generated - after applying the mutation in the last generation. You can access - these attributes inside the ``on_generation()`` method for example. - -2. A bug fixed when the ``initial_population`` parameter is used. The - bug occurred due to a mismatch between the data type of the array - assigned to ``initial_population`` and the gene type in the - ``gene_type`` attribute. Assuming that the array assigned to the - ``initial_population`` parameter is - ``((1, 1), (3, 3), (5, 5), (7, 7))`` which has type ``int``. When - ``gene_type`` is set to ``float``, then the genes will not be float - but casted to ``int`` because the defined array has ``int`` type. The - bug is fixed by forcing the array assigned to ``initial_population`` - to have the data type in the ``gene_type`` attribute. Check the - `issue at - GitHub `__: - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/27 - -Thanks to Andrei Rozanski [PhD Bioinformatics Specialist, Department of -Tissue Dynamics and Regeneration, Max Planck Institute for Biophysical -Chemistry, Germany] for opening my eye to the first change. - -Thanks to `Marios -Giouvanakis `__, -a PhD candidate in Electrical & Computer Engineer, `Aristotle University -of Thessaloniki (Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης), -Greece `__, for emailing me about the second -issue. - -.. _pygad-2130: - -PyGAD 2.13.0 -------------- - -Release Date: 12 March 2021 - -1. A new ``bool`` parameter called ``allow_duplicate_genes`` is - supported. If ``True``, which is the default, then a - solution/chromosome may have duplicate gene values. If ``False``, - then each gene will have a unique value in its solution. Check the - `Prevent Duplicates in Gene - Values `__ - section for more details. - -2. The ``last_generation_fitness`` is updated at the end of each - generation not at the beginning. This keeps the fitness values of the - most up-to-date population assigned to the - ``last_generation_fitness`` parameter. - -.. _pygad-2140: - -PyGAD 2.14.0 ------------- - -PyGAD 2.14.0 has an issue that is solved in PyGAD 2.14.1. Please -consider using 2.14.1 not 2.14.0. - -Release Date: 19 May 2021 - -1. `Issue - #40 `__ - is solved. Now, the ``None`` value works with the ``crossover_type`` - and ``mutation_type`` parameters: - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/40 - -2. The ``gene_type`` parameter supports accepting a - ``list/tuple/numpy.ndarray`` of numeric data types for the genes. - This helps to control the data type of each individual gene. - Previously, the ``gene_type`` can be assigned only to a single data - type that is applied for all genes. For more information, check the - `More about the ``gene_type`` - Parameter `__ - section. Thanks to `Rainer - Engel `__ - for asking about this feature in `this - discussion `__: - https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43 - -3. A new ``bool`` attribute named ``gene_type_single`` is added to the - ``pygad.GA`` class. It is ``True`` when there is a single data type - assigned to the ``gene_type`` parameter. When the ``gene_type`` - parameter is assigned a ``list/tuple/numpy.ndarray``, then - ``gene_type_single`` is set to ``False``. - -4. The ``mutation_by_replacement`` flag now has no effect if - ``gene_space`` exists except for the genes with ``None`` values. For - example, for ``gene_space=[None, [5, 6]]`` the - ``mutation_by_replacement`` flag affects only the first gene which - has ``None`` for its value space. - -5. When an element has a value of ``None`` in the ``gene_space`` - parameter (e.g. ``gene_space=[None, [5, 6]]``), then its value will - be randomly generated for each solution rather than being generate - once for all solutions. Previously, the gene with ``None`` value in - ``gene_space`` is the same across all solutions - -6. Some changes in the documentation according to `issue - #32 `__: - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/32 - -.. _pygad-2142: - -PyGAD 2.14.2 ------------- - -Release Date: 27 May 2021 - -1. Some bug fixes when the ``gene_type`` parameter is nested. Thanks to - `Rainer - Engel `__ - for opening `a - discussion `__ - to report this bug: - https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43#discussioncomment-763342 - -`Rainer -Engel `__ -helped a lot in suggesting new features and suggesting enhancements in -2.14.0 to 2.14.2 releases. - -.. _pygad-2143: - -PyGAD 2.14.3 ------------- - -Release Date: 6 June 2021 - -1. Some bug fixes when setting the ``save_best_solutions`` parameter to - ``True``. Previously, the best solution for generation ``i`` was - added into the ``best_solutions`` attribute at generation ``i+1``. - Now, the ``best_solutions`` attribute is updated by each best - solution at its exact generation. - -.. _pygad-2150: - -PyGAD 2.15.0 ------------- - -Release Date: 17 June 2021 - -1. Control the precision of all genes/individual genes. Thanks to - `Rainer `__ for asking about this - feature: - https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43#discussioncomment-763452 - -2. A new attribute named ``last_generation_parents_indices`` holds the - indices of the selected parents in the last generation. - -3. In adaptive mutation, no need to recalculate the fitness values of - the parents selected in the last generation as these values can be - returned based on the ``last_generation_fitness`` and - ``last_generation_parents_indices`` attributes. This speeds-up the - adaptive mutation. - -4. When a sublist has a value of ``None`` in the ``gene_space`` - parameter (e.g. ``gene_space=[[1, 2, 3], [5, 6, None]]``), then its - value will be randomly generated for each solution rather than being - generated once for all solutions. Previously, a value of ``None`` in - a sublist of the ``gene_space`` parameter was identical across all - solutions. - -5. The dictionary assigned to the ``gene_space`` parameter itself or - one of its elements has a new key called ``"step"`` to specify the - step of moving from the start to the end of the range specified by - the 2 existing keys ``"low"`` and ``"high"``. An example is - ``{"low": 0, "high": 30, "step": 2}`` to have only even values for - the gene(s) starting from 0 to 30. For more information, check the - `More about the ``gene_space`` - Parameter `__ - section. - https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/48 - -6. A new function called ``predict()`` is added in both the - ``pygad.kerasga`` and ``pygad.torchga`` modules to make predictions. - This makes it easier than using custom code each time a prediction - is to be made. - -7. A new parameter called ``stop_criteria`` allows the user to specify - one or more stop criteria to stop the evolution based on some - conditions. Each criterion is passed as ``str`` which has a stop - word. The current 2 supported words are ``reach`` and ``saturate``. - ``reach`` stops the ``run()`` method if the fitness value is equal - to or greater than a given fitness value. An example for ``reach`` - is ``"reach_40"`` which stops the evolution if the fitness is >= 40. - ``saturate`` means stop the evolution if the fitness saturates for a - given number of consecutive generations. An example for ``saturate`` - is ``"saturate_7"`` which means stop the ``run()`` method if the - fitness does not change for 7 consecutive generations. Thanks to - `Rainer `__ for asking about this - feature: - https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/44 - -8. A new bool parameter, defaults to ``False``, named - ``save_solutions`` is added to the constructor of the ``pygad.GA`` - class. If ``True``, then all solutions in each generation are - appended into an attribute called ``solutions`` which is NumPy - array. - -9. The ``plot_result()`` method is renamed to ``plot_fitness()``. The - users should migrate to the new name as the old name will be removed - in the future. - -10. Four new optional parameters are added to the ``plot_fitness()`` - function in the ``pygad.GA`` class which are ``font_size=14``, - ``save_dir=None``, ``color="#3870FF"``, and ``plot_type="plot"``. - Use ``font_size`` to change the font of the plot title and labels. - ``save_dir`` accepts the directory to which the figure is saved. It - defaults to ``None`` which means do not save the figure. ``color`` - changes the color of the plot. ``plot_type`` changes the plot type - which can be either ``"plot"`` (default), ``"scatter"``, or - ``"bar"``. - https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/47 - -11. The default value of the ``title`` parameter in the - ``plot_fitness()`` method is ``"PyGAD - Generation vs. Fitness"`` - rather than ``"PyGAD - Iteration vs. Fitness"``. - -12. A new method named ``plot_new_solution_rate()`` creates, shows, and - returns a figure showing the rate of new/unique solutions explored - in each generation. It accepts the same parameters as in the - ``plot_fitness()`` method. This method only works when - ``save_solutions=True`` in the ``pygad.GA`` class's constructor. - -13. A new method named ``plot_genes()`` creates, shows, and returns a - figure to show how each gene changes per each generation. It accepts - similar parameters like the ``plot_fitness()`` method in addition to - the ``graph_type``, ``fill_color``, and ``solutions`` parameters. - The ``graph_type`` parameter can be either ``"plot"`` (default), - ``"boxplot"``, or ``"histogram"``. ``fill_color`` accepts the fill - color which works when ``graph_type`` is either ``"boxplot"`` or - ``"histogram"``. ``solutions`` can be either ``"all"`` or ``"best"`` - to decide whether all solutions or only best solutions are used. - -14. The ``gene_type`` parameter now supports controlling the precision - of ``float`` data types. For a gene, rather than assigning just the - data type like ``float``, assign a - ``list``/``tuple``/``numpy.ndarray`` with 2 elements where the first - one is the type and the second one is the precision. For example, - ``[float, 2]`` forces a gene with a value like ``0.1234`` to be - ``0.12``. For more information, check the `More about the - ``gene_type`` - Parameter `__ - section. - -.. _pygad-2151: - -PyGAD 2.15.1 ------------- - -Release Date: 18 June 2021 - -1. Fix a bug when ``keep_parents`` is set to a positive integer. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/49 - -.. _pygad-2152: - -PyGAD 2.15.2 ------------- - -Release Date: 18 June 2021 - -1. Fix a bug when using the ``kerasga`` or ``torchga`` modules. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/51 - -.. _pygad-2160: - -PyGAD 2.16.0 ------------- - -Release Date: 19 June 2021 - -1. A user-defined function can be passed to the ``mutation_type``, - ``crossover_type``, and ``parent_selection_type`` parameters in the - ``pygad.GA`` class to create a custom mutation, crossover, and parent - selection operators. Check the `User-Defined Crossover, Mutation, and - Parent Selection - Operators `__ - section for more details. - https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50 - -.. _pygad-2161: - -PyGAD 2.16.1 ------------- - -Release Date: 28 September 2021 - -1. The user can use the ``tqdm`` library to show a progress bar. - https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50. - -.. code:: python - - import pygad - import numpy - import tqdm - - equation_inputs = [4,-2,3.5] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - num_generations = 10000 - with tqdm.tqdm(total=num_generations) as pbar: - ga_instance = pygad.GA(num_generations=num_generations, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - on_generation=lambda _: pbar.update(1)) - - ga_instance.run() - - ga_instance.plot_result() - -But this work does not work if the ``ga_instance`` will be pickled (i.e. -the ``save()`` method will be called. - -.. code:: python - - ga_instance.save("test") - -To solve this issue, define a function and pass it to the -``on_generation`` parameter. In the next code, the -``on_generation_progress()`` function is defined which updates the -progress bar. - -.. code:: python - - import pygad - import numpy - import tqdm - - equation_inputs = [4,-2,3.5] - desired_output = 44 - - def fitness_func(ga_instance, solution, solution_idx): - output = numpy.sum(solution * equation_inputs) - fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) - return fitness - - def on_generation_progress(ga): - pbar.update(1) - - num_generations = 100 - with tqdm.tqdm(total=num_generations) as pbar: - ga_instance = pygad.GA(num_generations=num_generations, - sol_per_pop=5, - num_parents_mating=2, - num_genes=len(equation_inputs), - fitness_func=fitness_func, - on_generation=on_generation_progress) - - ga_instance.run() - - ga_instance.plot_result() - - ga_instance.save("test") - -1. Solved the issue of unequal length between the ``solutions`` and - ``solutions_fitness`` when the ``save_solutions`` parameter is set to - ``True``. Now, the fitness of the last population is appended to the - ``solutions_fitness`` array. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/64 - -2. There was an issue of getting the length of these 4 variables - (``solutions``, ``solutions_fitness``, ``best_solutions``, and - ``best_solutions_fitness``) doubled after each call of the ``run()`` - method. This is solved by resetting these variables at the beginning - of the ``run()`` method. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/62 - -3. Bug fixes when adaptive mutation is used - (``mutation_type="adaptive"``). - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/65 - -.. _pygad-2162: - -PyGAD 2.16.2 ------------- - -Release Date: 2 February 2022 - -1. A new instance attribute called ``previous_generation_fitness`` added - in the ``pygad.GA`` class. It holds the fitness values of one - generation before the fitness values saved in the - ``last_generation_fitness``. - -2. Issue in the ``cal_pop_fitness()`` method in getting the correct - indices of the previous parents. This is solved by using the previous - generation's fitness saved in the new attribute - ``previous_generation_fitness`` to return the parents' fitness - values. Thanks to Tobias Tischhauser (M.Sc. - `Mitarbeiter Institut - EMS, Departement Technik, OST – Ostschweizer Fachhochschule, - Switzerland `__) - for detecting this bug. - -.. _pygad-2163: - -PyGAD 2.16.3 ------------- - -Release Date: 2 February 2022 - -1. Validate the fitness value returned from the fitness function. An - exception is raised if something is wrong. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/67 - -.. _pygad-2170: - -PyGAD 2.17.0 ------------- - -Release Date: 8 July 2022 - -1. An issue is solved when the ``gene_space`` parameter is given a fixed - value. e.g. gene_space=[range(5), 4]. The second gene's value is - static (4) which causes an exception. - -2. Fixed the issue where the ``allow_duplicate_genes`` parameter did not - work when mutation is disabled (i.e. ``mutation_type=None``). This is - by checking for duplicates after crossover directly. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/39 - -3. Solve an issue in the ``tournament_selection()`` method as the - indices of the selected parents were incorrect. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/89 - -4. Reuse the fitness values of the previously explored solutions rather - than recalculating them. This feature only works if - ``save_solutions=True``. - -5. Parallel processing is supported. This is by the introduction of a - new parameter named ``parallel_processing`` in the constructor of the - ``pygad.GA`` class. Thanks to - `@windowshopr `__ for opening the - issue - `#78 `__ - at GitHub. Check the `Parallel Processing in - PyGAD `__ - section for more information and examples. - -.. _pygad-2180: - -PyGAD 2.18.0 ------------- - -Release Date: 9 September 2022 - -1. Raise an exception if the sum of fitness values is zero while either - roulette wheel or stochastic universal parent selection is used. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/129 - -2. Initialize the value of the ``run_completed`` property to ``False``. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/122 - -3. The values of these properties are no longer reset with each call to - the ``run()`` method - ``self.best_solutions, self.best_solutions_fitness, self.solutions, self.solutions_fitness``: - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/123. Now, - the user can have the flexibility of calling the ``run()`` method - more than once while extending the data collected after each - generation. Another advantage happens when the instance is loaded and - the ``run()`` method is called, as the old fitness value are shown on - the graph alongside with the new fitness values. Read more in this - section: `Continue without Loosing - Progress `__ - -4. Thanks `Prof. Fernando Jiménez - Barrionuevo `__ (Dept. of Information and - Communications Engineering, University of Murcia, Murcia, Spain) for - editing this - `comment `__ - in the code. - https://github.com/ahmedfgad/GeneticAlgorithmPython/commit/5315bbec02777df96ce1ec665c94dece81c440f4 - -5. A bug fixed when ``crossover_type=None``. - -6. Support of elitism selection through a new parameter named - ``keep_elitism``. It defaults to 1 which means for each generation - keep only the best solution in the next generation. If assigned 0, - then it has no effect. Read more in this section: `Elitism - Selection `__. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/74 - -7. A new instance attribute named ``last_generation_elitism`` added to - hold the elitism in the last generation. - -8. A new parameter called ``random_seed`` added to accept a seed for the - random function generators. Credit to this issue - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/70 and - `Prof. Fernando Jiménez Barrionuevo `__. - Read more in this section: `Random - Seed `__. - -9. Editing the ``pygad.TorchGA`` module to make sure the tensor data is - moved from GPU to CPU. Thanks to Rasmus Johansson for opening this - pull request: https://github.com/ahmedfgad/TorchGA/pull/2 - -.. _pygad-2181: - -PyGAD 2.18.1 ------------- - -Release Date: 19 September 2022 - -1. A big fix when ``keep_elitism`` is used. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/132 - -.. _pygad-2182: - -PyGAD 2.18.2 ------------- - -Release Date: 14 February 2023 - -1. Remove ``numpy.int`` and ``numpy.float`` from the list of supported - data types. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/151 - https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/152 - -2. Call the ``on_crossover()`` callback function even if - ``crossover_type`` is ``None``. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138 - -3. Call the ``on_mutation()`` callback function even if - ``mutation_type`` is ``None``. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138 - -.. _pygad-2183: - -PyGAD 2.18.3 ------------- - -Release Date: 14 February 2023 - -1. Bug fixes. - -.. _pygad-2190: - -PyGAD 2.19.0 ------------- - -Release Date: 22 February 2023 - -1. A new ``summary()`` method is supported to return a Keras-like - summary of the PyGAD lifecycle. - -2. A new optional parameter called ``fitness_batch_size`` is supported - to calculate the fitness in batches. If it is assigned the value - ``1`` or ``None`` (default), then the normal flow is used where the - fitness function is called for each individual solution. If the - ``fitness_batch_size`` parameter is assigned a value satisfying this - condition ``1 < fitness_batch_size <= sol_per_pop``, then the - solutions are grouped into batches of size ``fitness_batch_size`` - and the fitness function is called once for each batch. In this - case, the fitness function must return a list/tuple/numpy.ndarray - with a length equal to the number of solutions passed. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/136. - -3. The ``cloudpickle`` library - (https://github.com/cloudpipe/cloudpickle) is used instead of the - ``pickle`` library to pickle the ``pygad.GA`` objects. This solves - the issue of having to redefine the functions (e.g. fitness - function). The ``cloudpickle`` library is added as a dependency in - the ``requirements.txt`` file. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/159 - -4. Support of assigning methods to these parameters: ``fitness_func``, - ``crossover_type``, ``mutation_type``, ``parent_selection_type``, - ``on_start``, ``on_fitness``, ``on_parents``, ``on_crossover``, - ``on_mutation``, ``on_generation``, and ``on_stop``. - https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92 - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138 - -5. Validating the output of the parent selection, crossover, and - mutation functions. - -6. The built-in parent selection operators return the parent's indices - as a NumPy array. - -7. The outputs of the parent selection, crossover, and mutation - operators must be NumPy arrays. - -8. Fix an issue when ``allow_duplicate_genes=True``. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/39 - -9. Fix an issue creating scatter plots of the solutions' fitness. - -10. Sampling from a ``set()`` is no longer supported in Python 3.11. - Instead, sampling happens from a ``list()``. Thanks ``Marco Brenna`` - for pointing to this issue. - -11. The lifecycle is updated to reflect that the new population's - fitness is calculated at the end of the lifecycle not at the - beginning. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/154#issuecomment-1438739483 - -12. There was an issue when ``save_solutions=True`` that causes the - fitness function to be called for solutions already explored and - have their fitness pre-calculated. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/160 - -13. A new instance attribute named ``last_generation_elitism_indices`` - added to hold the indices of the selected elitism. This attribute - helps to re-use the fitness of the elitism instead of calling the - fitness function. - -14. Fewer calls to the ``best_solution()`` method which in turns saves - some calls to the fitness function. - -15. Some updates in the documentation to give more details about the - ``cal_pop_fitness()`` method. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/79#issuecomment-1439605442 - -.. _pygad-2191: - -PyGAD 2.19.1 ------------- - -Release Date: 22 February 2023 - -1. Add the `cloudpickle `__ - library as a dependency. - -.. _pygad-2192: - -PyGAD 2.19.2 ------------- - -Release Date 23 February 2023 - -1. Fix an issue when parallel processing was used where the elitism - solutions' fitness values are not re-used. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/160#issuecomment-1441718184 - -.. _pygad-300: - -PyGAD 3.0.0 ------------ - -Release Date 8 April 2023 - -1. The structure of the library is changed and some methods defined in - the ``pygad.py`` module are moved to the ``pygad.utils``, - ``pygad.helper``, and ``pygad.visualize`` submodules. - -2. The ``pygad.utils.parent_selection`` module has a class named - ``ParentSelection`` where all the parent selection operators exist. - The ``pygad.GA`` class extends this class. - -3. The ``pygad.utils.crossover`` module has a class named ``Crossover`` - where all the crossover operators exist. The ``pygad.GA`` class - extends this class. - -4. The ``pygad.utils.mutation`` module has a class named ``Mutation`` - where all the mutation operators exist. The ``pygad.GA`` class - extends this class. - -5. The ``pygad.helper.unique`` module has a class named ``Unique`` some - helper methods exist to solve duplicate genes and make sure every - gene is unique. The ``pygad.GA`` class extends this class. - -6. The ``pygad.visualize.plot`` module has a class named ``Plot`` where - all the methods that create plots exist. The ``pygad.GA`` class - extends this class. - -7. Support of using the ``logging`` module to log the outputs to both - the console and text file instead of using the ``print()`` function. - This is by assigning the ``logging.Logger`` to the new ``logger`` - parameter. Check the `Logging - Outputs `__ - for more information. - -8. A new instance attribute called ``logger`` to save the logger. - -9. The function/method passed to the ``fitness_func`` parameter accepts - a new parameter that refers to the instance of the ``pygad.GA`` - class. Check this for an example: `Use Functions and Methods to - Build Fitness Function and - Callbacks `__. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/163 - -10. Update the documentation to include an example of using functions - and methods to calculate the fitness and build callbacks. Check this - for more details: `Use Functions and Methods to Build Fitness - Function and - Callbacks `__. - https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92#issuecomment-1443635003 - -11. Validate the value passed to the ``initial_population`` parameter. - -12. Validate the type and length of the ``pop_fitness`` parameter of the - ``best_solution()`` method. - -13. Some edits in the documentation. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/106 - -14. Fix an issue when building the initial population as (some) genes - have their value taken from the mutation range (defined by the - parameters ``random_mutation_min_val`` and - ``random_mutation_max_val``) instead of using the parameters - ``init_range_low`` and ``init_range_high``. - -15. The ``summary()`` method returns the summary as a single-line - string. Just log/print the returned string it to see it properly. - -16. The ``callback_generation`` parameter is removed. Use the - ``on_generation`` parameter instead. - -17. There was an issue when using the ``parallel_processing`` parameter - with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the - ``predict()`` method gives incorrect and weird results when more - than 1 thread is used. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/145 - https://github.com/ahmedfgad/TorchGA/issues/5 - https://github.com/ahmedfgad/KerasGA/issues/6. Thanks to this - `StackOverflow - answer `__. - -18. Replace ``numpy.float`` by ``float`` in the 2 parent selection - operators roulette wheel and stochastic universal. - https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/168 - -.. _pygad-301: - -PyGAD 3.0.1 ------------ - -Release Date 20 April 2023 - -1. Fix an issue with passing user-defined function/method for parent - selection. - https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/179 - -PyGAD Projects at GitHub -======================== - -The PyGAD library is available at PyPI at this page -https://pypi.org/project/pygad. PyGAD is built out of a number of -open-source GitHub projects. A brief note about these projects is given -in the next subsections. - -`GeneticAlgorithmPython `__ --------------------------------------------------------------------------------- - -GitHub Link: https://github.com/ahmedfgad/GeneticAlgorithmPython - -`GeneticAlgorithmPython `__ -is the first project which is an open-source Python 3 project for -implementing the genetic algorithm based on NumPy. - -`NumPyANN `__ ----------------------------------------------------- - -GitHub Link: https://github.com/ahmedfgad/NumPyANN - -`NumPyANN `__ builds artificial -neural networks in **Python 3** using **NumPy** from scratch. The -purpose of this project is to only implement the **forward pass** of a -neural network without using a training algorithm. Currently, it only -supports classification and later regression will be also supported. -Moreover, only one class is supported per sample. - -`NeuralGenetic `__ --------------------------------------------------------------- - -GitHub Link: https://github.com/ahmedfgad/NeuralGenetic - -`NeuralGenetic `__ trains -neural networks using the genetic algorithm based on the previous 2 -projects -`GeneticAlgorithmPython `__ -and `NumPyANN `__. - -`NumPyCNN `__ ----------------------------------------------------- - -GitHub Link: https://github.com/ahmedfgad/NumPyCNN - -`NumPyCNN `__ builds -convolutional neural networks using NumPy. The purpose of this project -is to only implement the **forward pass** of a convolutional neural -network without using a training algorithm. - -`CNNGenetic `__ --------------------------------------------------------- - -GitHub Link: https://github.com/ahmedfgad/CNNGenetic - -`CNNGenetic `__ trains -convolutional neural networks using the genetic algorithm. It uses the -`GeneticAlgorithmPython `__ -project for building the genetic algorithm. - -`KerasGA `__ --------------------------------------------------- - -GitHub Link: https://github.com/ahmedfgad/KerasGA - -`KerasGA `__ trains -`Keras `__ models using the genetic algorithm. It uses -the -`GeneticAlgorithmPython `__ -project for building the genetic algorithm. - -`TorchGA `__ --------------------------------------------------- - -GitHub Link: https://github.com/ahmedfgad/TorchGA - -`TorchGA `__ trains -`PyTorch `__ models using the genetic algorithm. It -uses the -`GeneticAlgorithmPython `__ -project for building the genetic algorithm. - -`pygad.torchga `__: -https://github.com/ahmedfgad/TorchGA - -Stackoverflow Questions about PyGAD -=================================== - -.. _how-do-i-proceed-to-load-a-gainstance-as-pkl-format-in-pygad: - -`How do I proceed to load a ga_instance as “.pkl” format in PyGad? `__ ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -`Binary Classification NN Model Weights not being Trained in PyGAD `__ --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -`How to solve TSP problem using pyGAD package? `__ ---------------------------------------------------------------------------------------------------------------------------------------------- - -`How can I save a matplotlib plot that is the output of a function in jupyter? `__ -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -`How do I query the best solution of a pyGAD GA instance? `__ -------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -`Multi-Input Multi-Output in Genetic algorithm (python) `__ --------------------------------------------------------------------------------------------------------------------------------------------------------------- - -https://www.linkedin.com/pulse/validation-short-term-parametric-trading-model-genetic-landolfi - -https://itchef.ru/articles/397758 - -https://audhiaprilliant.medium.com/genetic-algorithm-based-clustering-algorithm-in-searching-robust-initial-centroids-for-k-means-e3b4d892a4be - -https://python.plainenglish.io/validation-of-a-short-term-parametric-trading-model-with-genetic-optimization-and-walk-forward-89708b789af6 - -https://ichi.pro/ko/pygadwa-hamkke-yujeon-algolijeum-eul-sayonghayeo-keras-model-eul-hunlyeonsikineun-bangbeob-173299286377169 - -https://ichi.pro/tr/pygad-ile-genetik-algoritmayi-kullanarak-keras-modelleri-nasil-egitilir-173299286377169 - -https://ichi.pro/ru/kak-obucit-modeli-keras-s-pomos-u-geneticeskogo-algoritma-s-pygad-173299286377169 - -https://blog.csdn.net/sinat_38079265/article/details/108449614 - -Submitting Issues -================= - -If there is an issue using PyGAD, then use any of your preferred option -to discuss that issue. - -One way is `submitting an -issue `__ -into this GitHub project -(`github.com/ahmedfgad/GeneticAlgorithmPython `__) -in case something is not working properly or to ask for questions. - -If this is not a proper option for you, then check the `Contact -Us `__ -section for more contact details. - -Ask for Feature -=============== - -PyGAD is actively developed with the goal of building a dynamic library -for suporting a wide-range of problems to be optimized using the genetic -algorithm. - -To ask for a new feature, either `submit an -issue `__ -into this GitHub project -(`github.com/ahmedfgad/GeneticAlgorithmPython `__) -or send an e-mail to ahmed.f.gad@gmail.com. - -Also check the `Contact -Us `__ -section for more contact details. - -Projects Built using PyGAD -========================== - -If you created a project that uses PyGAD, then we can support you by -mentioning this project here in PyGAD's documentation. - -To do that, please send a message at ahmed.f.gad@gmail.com or check the -`Contact -Us `__ -section for more contact details. - -Within your message, please send the following details: - -- Project title - -- Brief description - -- Preferably, a link that directs the readers to your project - -Tutorials about PyGAD -===================== - -`Adaptive Mutation in Genetic Algorithm with Python Examples `__ ------------------------------------------------------------------------------------------------------------------------------------------------------ - -In this tutorial, we’ll see why mutation with a fixed number of genes is -bad, and how to replace it with adaptive mutation. Using the `PyGAD -Python 3 library `__, we’ll discuss a few -examples that use both random and adaptive mutation. - -`Clustering Using the Genetic Algorithm in Python `__ -------------------------------------------------------------------------------------------------------------------------- - -This tutorial discusses how the genetic algorithm is used to cluster -data, starting from random clusters and running until the optimal -clusters are found. We'll start by briefly revising the K-means -clustering algorithm to point out its weak points, which are later -solved by the genetic algorithm. The code examples in this tutorial are -implemented in Python using the `PyGAD -library `__. - -`Working with Different Genetic Algorithm Representations in Python `__ --------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -Depending on the nature of the problem being optimized, the genetic -algorithm (GA) supports two different gene representations: binary, and -decimal. The binary GA has only two values for its genes, which are 0 -and 1. This is easier to manage as its gene values are limited compared -to the decimal GA, for which we can use different formats like float or -integer, and limited or unlimited ranges. - -This tutorial discusses how the -`PyGAD `__ library supports the two GA -representations, binary and decimal. - -.. _5-genetic-algorithm-applications-using-pygad: - -`5 Genetic Algorithm Applications Using PyGAD `__ -------------------------------------------------------------------------------------------------------------------------- - -This tutorial introduces PyGAD, an open-source Python library for -implementing the genetic algorithm and training machine learning -algorithms. PyGAD supports 19 parameters for customizing the genetic -algorithm for various applications. - -Within this tutorial we'll discuss 5 different applications of the -genetic algorithm and build them using PyGAD. - -`Train Neural Networks Using a Genetic Algorithm in Python with PyGAD `__ -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The genetic algorithm (GA) is a biologically-inspired optimization -algorithm. It has in recent years gained importance, as it’s simple -while also solving complex problems like travel route optimization, -training machine learning algorithms, working with single and -multi-objective problems, game playing, and more. - -Deep neural networks are inspired by the idea of how the biological -brain works. It’s a universal function approximator, which is capable of -simulating any function, and is now used to solve the most complex -problems in machine learning. What’s more, they’re able to work with all -types of data (images, audio, video, and text). - -Both genetic algorithms (GAs) and neural networks (NNs) are similar, as -both are biologically-inspired techniques. This similarity motivates us -to create a hybrid of both to see whether a GA can train NNs with high -accuracy. - -This tutorial uses `PyGAD `__, a Python -library that supports building and training NNs using a GA. -`PyGAD `__ offers both classification and -regression NNs. - -`Building a Game-Playing Agent for CoinTex Using the Genetic Algorithm `__ ----------------------------------------------------------------------------------------------------------------------------------------------------------- - -In this tutorial we'll see how to build a game-playing agent using only -the genetic algorithm to play a game called -`CoinTex `__, -which is developed in the Kivy Python framework. The objective of -CoinTex is to collect the randomly distributed coins while avoiding -collision with fire and monsters (that move randomly). The source code -of CoinTex can be found `on -GitHub `__. - -The genetic algorithm is the only AI used here; there is no other -machine/deep learning model used with it. We'll implement the genetic -algorithm using -`PyGad `__. -This tutorial starts with a quick overview of CoinTex followed by a -brief explanation of the genetic algorithm, and how it can be used to -create the playing agent. Finally, we'll see how to implement these -ideas in Python. - -The source code of the genetic algorithm agent is available -`here `__, -and you can download the code used in this tutorial from -`here `__. - -`How To Train Keras Models Using the Genetic Algorithm with PyGAD `__ --------------------------------------------------------------------------------------------------------------------------------------------------------- - -PyGAD is an open-source Python library for building the genetic -algorithm and training machine learning algorithms. It offers a wide -range of parameters to customize the genetic algorithm to work with -different types of problems. - -PyGAD has its own modules that support building and training neural -networks (NNs) and convolutional neural networks (CNNs). Despite these -modules working well, they are implemented in Python without any -additional optimization measures. This leads to comparatively high -computational times for even simple problems. - -The latest PyGAD version, 2.8.0 (released on 20 September 2020), -supports a new module to train Keras models. Even though Keras is built -in Python, it's fast. The reason is that Keras uses TensorFlow as a -backend, and TensorFlow is highly optimized. - -This tutorial discusses how to train Keras models using PyGAD. The -discussion includes building Keras models using either the Sequential -Model or the Functional API, building an initial population of Keras -model parameters, creating an appropriate fitness function, and more. - -|image1| - -`Train PyTorch Models Using Genetic Algorithm with PyGAD `__ ---------------------------------------------------------------------------------------------------------------------------------------------- - -`PyGAD `__ is a genetic algorithm Python -3 library for solving optimization problems. One of these problems is -training machine learning algorithms. - -PyGAD has a module called -`pygad.kerasga `__. It trains -Keras models using the genetic algorithm. On January 3rd, 2021, a new -release of `PyGAD 2.10.0 `__ brought a -new module called -`pygad.torchga `__ to train -PyTorch models. It’s very easy to use, but there are a few tricky steps. - -So, in this tutorial, we’ll explore how to use PyGAD to train PyTorch -models. - -|image2| - -`A Guide to Genetic ‘Learning’ Algorithms for Optimization `__ -------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -PyGAD in Other Languages -======================== - -French ------- - -`Cómo los algoritmos genéticos pueden competir con el descenso de -gradiente y el -backprop `__ - -Bien que la manière standard d'entraîner les réseaux de neurones soit la -descente de gradient et la rétropropagation, il y a d'autres joueurs -dans le jeu. L'un d'eux est les algorithmes évolutionnaires, tels que -les algorithmes génétiques. - -Utiliser un algorithme génétique pour former un réseau de neurones -simple pour résoudre le OpenAI CartPole Jeu. Dans cet article, nous -allons former un simple réseau de neurones pour résoudre le OpenAI -CartPole . J'utiliserai PyTorch et PyGAD . - -|image3| - -Spanish -------- - -`Cómo los algoritmos genéticos pueden competir con el descenso de -gradiente y el -backprop `__ - -Aunque la forma estandar de entrenar redes neuronales es el descenso de -gradiente y la retropropagacion, hay otros jugadores en el juego, uno de -ellos son los algoritmos evolutivos, como los algoritmos geneticos. - -Usa un algoritmo genetico para entrenar una red neuronal simple para -resolver el Juego OpenAI CartPole. En este articulo, entrenaremos una -red neuronal simple para resolver el OpenAI CartPole . Usare PyTorch y -PyGAD . - -|image4| - -Korean ------- - -`[PyGAD] Python 에서 Genetic Algorithm 을 사용해보기 `__ -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -|image5| - -파이썬에서 genetic algorithm을 사용하는 패키지들을 다 사용해보진 -않았지만, 확장성이 있어보이고, 시도할 일이 있어서 살펴봤다. - -이 패키지에서 가장 인상 깊었던 것은 neural network에서 hyper parameter -탐색을 gradient descent 방식이 아닌 GA로도 할 수 있다는 것이다. - -개인적으로 이 부분이 어느정도 초기치를 잘 잡아줄 수 있는 역할로도 쓸 수 -있고, Loss가 gradient descent 하기 어려운 구조에서 대안으로 쓸 수 있을 -것으로도 생각된다. - -일단 큰 흐름은 다음과 같이 된다. - -사실 완전히 흐름이나 각 parameter에 대한 이해는 부족한 상황 - -Turkish -------- - -`PyGAD ile Genetik Algoritmayı Kullanarak Keras Modelleri Nasıl Eğitilir `__ -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -This is a translation of an original English tutorial published at -Paperspace: `How To Train Keras Models Using the Genetic Algorithm with -PyGAD `__ - -PyGAD, genetik algoritma oluşturmak ve makine öğrenimi algoritmalarını -eğitmek için kullanılan açık kaynaklı bir Python kitaplığıdır. Genetik -algoritmayı farklı problem türleri ile çalışacak şekilde özelleştirmek -için çok çeşitli parametreler sunar. - -PyGAD, sinir ağları (NN’ler) ve evrişimli sinir ağları (CNN’ler) -oluşturmayı ve eğitmeyi destekleyen kendi modüllerine sahiptir. Bu -modüllerin iyi çalışmasına rağmen, herhangi bir ek optimizasyon önlemi -olmaksızın Python’da uygulanırlar. Bu, basit problemler için bile -nispeten yüksek hesaplama sürelerine yol açar. - -En son PyGAD sürümü 2.8.0 (20 Eylül 2020'de piyasaya sürüldü), Keras -modellerini eğitmek için yeni bir modülü destekliyor. Keras Python’da -oluşturulmuş olsa da hızlıdır. Bunun nedeni, Keras’ın arka uç olarak -TensorFlow kullanması ve TensorFlow’un oldukça optimize edilmiş -olmasıdır. - -Bu öğreticide, PyGAD kullanılarak Keras modellerinin nasıl eğitileceği -anlatılmaktadır. Tartışma, Sıralı Modeli veya İşlevsel API’yi kullanarak -Keras modellerini oluşturmayı, Keras model parametrelerinin ilk -popülasyonunu oluşturmayı, uygun bir uygunluk işlevi oluşturmayı ve daha -fazlasını içerir. - -|image6| - -Hungarian ---------- - -.. _tensorflow-alapozó-10-neurális-hálózatok-tenyésztése-genetikus-algoritmussal-pygad-és-openai-gym-használatával: - -`Tensorflow alapozó 10. Neurális hálózatok tenyésztése genetikus algoritmussal PyGAD és OpenAI Gym használatával `__ -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Hogy kontextusba helyezzem a genetikus algoritmusokat, ismételjük kicsit -át, hogy hogyan működik a gradient descent és a backpropagation, ami a -neurális hálók tanításának általános módszere. Az erről írt cikkemet itt -tudjátok elolvasni. - -A hálózatok tenyésztéséhez a -`PyGAD `__ nevű -programkönyvtárat használjuk, így mindenek előtt ezt kell telepítenünk, -valamint a Tensorflow-t és a Gym-et, amit Colabban már eleve telepítve -kapunk. - -Maga a PyGAD egy teljesen általános genetikus algoritmusok futtatására -képes rendszer. Ennek a kiterjesztése a KerasGA, ami az általános motor -Tensorflow (Keras) neurális hálókon történő futtatását segíti. A 47. -sorban létrehozott KerasGA objektum ennek a kiterjesztésnek a része és -arra szolgál, hogy a paraméterként átadott modellből a második -paraméterben megadott számosságú populációt hozzon létre. Mivel a -hálózatunk 386 állítható paraméterrel rendelkezik, ezért a DNS-ünk itt -386 elemből fog állni. A populáció mérete 10 egyed, így a kezdő -populációnk egy 10x386 elemű mátrix lesz. Ezt adjuk át az 51. sorban az -initial_population paraméterben. - -|image7| - -Russian -------- - -`PyGAD: библиотека для имплементации генетического алгоритма `__ -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -PyGAD — это библиотека для имплементации генетического алгоритма. Кроме -того, библиотека предоставляет доступ к оптимизированным реализациям -алгоритмов машинного обучения. PyGAD разрабатывали на Python 3. - -Библиотека PyGAD поддерживает разные типы скрещивания, мутации и -селекции родителя. PyGAD позволяет оптимизировать проблемы с помощью -генетического алгоритма через кастомизацию целевой функции. - -Кроме генетического алгоритма, библиотека содержит оптимизированные -имплементации алгоритмов машинного обучения. На текущий момент PyGAD -поддерживает создание и обучение нейросетей для задач классификации. - -Библиотека находится в стадии активной разработки. Создатели планируют -добавление функционала для решения бинарных задач и имплементации новых -алгоритмов. - -PyGAD разрабатывали на Python 3.7.3. Зависимости включают в себя NumPy -для создания и манипуляции массивами и Matplotlib для визуализации. Один -из изкейсов использования инструмента — оптимизация весов, которые -удовлетворяют заданной функции. - -|image8| - -Research Papers using PyGAD -=========================== - -A number of research papers used PyGAD and here are some of them: - -- Jaros, Marta, and Jiri Jaros. "Performance-Cost Optimization of - Moldable Scientific Workflows." - -- Thorat, Divya. "Enhanced genetic algorithm to reduce makespan of - multiple jobs in map-reduce application on serverless platform". - Diss. Dublin, National College of Ireland, 2020. - -- Koch, Chris, and Edgar Dobriban. "AttenGen: Generating Live - Attenuated Vaccine Candidates using Machine Learning." (2021). - -- Bhardwaj, Bhavya, et al. "Windfarm optimization using Nelder-Mead and - Particle Swarm optimization." *2021 7th International Conference on - Electrical Energy Systems (ICEES)*. IEEE, 2021. - -- Bernardo, Reginald Christian S. and J. Said. “Towards a - model-independent reconstruction approach for late-time Hubble data.” - (2021). - -- Duong, Tri Dung, Qian Li, and Guandong Xu. "Prototype-based - Counterfactual Explanation for Causal Classification." *arXiv - preprint arXiv:2105.00703* (2021). - -- Farrag, Tamer Ahmed, and Ehab E. Elattar. "Optimized Deep Stacked - Long Short-Term Memory Network for Long-Term Load Forecasting." *IEEE - Access* 9 (2021): 68511-68522. - -- Antunes, E. D. O., Caetano, M. F., Marotta, M. A., Araujo, A., - Bondan, L., Meneguette, R. I., & Rocha Filho, G. P. (2021, August). - Soluções Otimizadas para o Problema de Localização de Máxima - Cobertura em Redes Militarizadas 4G/LTE. In *Anais do XXVI Workshop - de Gerência e Operação de Redes e Serviços* (pp. 152-165). SBC. - -- M. Yani, F. Ardilla, A. A. Saputra and N. Kubota, "Gradient-Free Deep - Q-Networks Reinforcement learning: Benchmark and Evaluation," *2021 - IEEE Symposium Series on Computational Intelligence (SSCI)*, 2021, - pp. 1-5, doi: 10.1109/SSCI50451.2021.9659941. - -- Yani, Mohamad, and Naoyuki Kubota. "Deep Convolutional Networks with - Genetic Algorithm for Reinforcement Learning Problem." - -- Mahendra, Muhammad Ihza, and Isman Kurniawan. "Optimizing - Convolutional Neural Network by Using Genetic Algorithm for COVID-19 - Detection in Chest X-Ray Image." *2021 International Conference on - Data Science and Its Applications (ICoDSA)*. IEEE, 2021. - -- Glibota, Vjeko. *Umjeravanje mikroskopskog prometnog modela primjenom - genetskog algoritma*. Diss. University of Zagreb. Faculty of - Transport and Traffic Sciences. Division of Intelligent Transport - Systems and Logistics. Department of Intelligent Transport Systems, - 2021. - -- Zhu, Mingda. *Genetic Algorithm-based Parameter Identification for - Ship Manoeuvring Model under Wind Disturbance*. MS thesis. NTNU, - 2021. - -- Abdalrahman, Ahmed, and Weihua Zhuang. "Dynamic pricing for - differentiated pev charging services using deep reinforcement - learning." *IEEE Transactions on Intelligent Transportation Systems* - (2020). - -More Links -========== - -https://rodriguezanton.com/identifying-contact-states-for-2d-objects-using-pygad-and/ - -https://torvaney.github.io/projects/t9-optimised - -For More Information -==================== - -There are different resources that can be used to get started with the -genetic algorithm and building it in Python. - -Tutorial: Implementing Genetic Algorithm in Python --------------------------------------------------- - -To start with coding the genetic algorithm, you can check the tutorial -titled `Genetic Algorithm Implementation in -Python `__ -available at these links: - -- `LinkedIn `__ - -- `Towards Data - Science `__ - -- `KDnuggets `__ - -`This -tutorial `__ -is prepared based on a previous version of the project but it still a -good resource to start with coding the genetic algorithm. - -|image9| - -Tutorial: Introduction to Genetic Algorithm -------------------------------------------- - -Get started with the genetic algorithm by reading the tutorial titled -`Introduction to Optimization with Genetic -Algorithm `__ -which is available at these links: - -- `LinkedIn `__ - -- `Towards Data - Science `__ - -- `KDnuggets `__ - -|image10| - -Tutorial: Build Neural Networks in Python ------------------------------------------ - -Read about building neural networks in Python through the tutorial -titled `Artificial Neural Network Implementation using NumPy and -Classification of the Fruits360 Image -Dataset `__ -available at these links: - -- `LinkedIn `__ - -- `Towards Data - Science `__ - -- `KDnuggets `__ - -|image11| - -Tutorial: Optimize Neural Networks with Genetic Algorithm ---------------------------------------------------------- - -Read about training neural networks using the genetic algorithm through -the tutorial titled `Artificial Neural Networks Optimization using -Genetic Algorithm with -Python `__ -available at these links: - -- `LinkedIn `__ - -- `Towards Data - Science `__ - -- `KDnuggets `__ - -|image12| - -Tutorial: Building CNN in Python --------------------------------- - -To start with coding the genetic algorithm, you can check the tutorial -titled `Building Convolutional Neural Network using NumPy from -Scratch `__ -available at these links: - -- `LinkedIn `__ - -- `Towards Data - Science `__ - -- `KDnuggets `__ - -- `Chinese Translation `__ - -`This -tutorial `__) -is prepared based on a previous version of the project but it still a -good resource to start with coding CNNs. - -|image13| - -Tutorial: Derivation of CNN from FCNN -------------------------------------- - -Get started with the genetic algorithm by reading the tutorial titled -`Derivation of Convolutional Neural Network from Fully Connected Network -Step-By-Step `__ -which is available at these links: - -- `LinkedIn `__ - -- `Towards Data - Science `__ - -- `KDnuggets `__ - -|image14| - -Book: Practical Computer Vision Applications Using Deep Learning with CNNs --------------------------------------------------------------------------- - -You can also check my book cited as `Ahmed Fawzy Gad 'Practical Computer -Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, -978-1-4842-4167-7 `__ -which discusses neural networks, convolutional neural networks, deep -learning, genetic algorithm, and more. - -Find the book at these links: - -- `Amazon `__ - -- `Springer `__ - -- `Apress `__ - -- `O'Reilly `__ - -- `Google Books `__ - -.. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg - :alt: - -Contact Us -========== - -- E-mail: ahmed.f.gad@gmail.com - -- `LinkedIn `__ - -- `Amazon Author Page `__ - -- `Heartbeat `__ - -- `Paperspace `__ - -- `KDnuggets `__ - -- `TowardsDataScience `__ - -- `GitHub `__ - -.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png - :alt: - -Thank you for using -`PyGAD `__ :) - -.. |image1| image:: https://user-images.githubusercontent.com/16560492/111009628-2b372500-8362-11eb-90cf-01b47d831624.png - :target: https://blog.paperspace.com/train-keras-models-using-genetic-algorithm-with-pygad -.. |image2| image:: https://user-images.githubusercontent.com/16560492/111009678-5457b580-8362-11eb-899a-39e2f96984df.png - :target: https://neptune.ai/blog/train-pytorch-models-using-genetic-algorithm-with-pygad -.. |image3| image:: https://user-images.githubusercontent.com/16560492/111009275-3178d180-8361-11eb-9e86-7fb1519acde7.png - :target: https://www.hebergementwebs.com/nouvelles/comment-les-algorithmes-genetiques-peuvent-rivaliser-avec-la-descente-de-gradient-et-le-backprop -.. |image4| image:: https://user-images.githubusercontent.com/16560492/111009257-232ab580-8361-11eb-99a5-7226efbc3065.png - :target: https://www.hebergementwebs.com/noticias/como-los-algoritmos-geneticos-pueden-competir-con-el-descenso-de-gradiente-y-el-backprop -.. |image5| image:: https://user-images.githubusercontent.com/16560492/108586306-85bd0280-731b-11eb-874c-7ac4ce1326cd.jpg - :target: https://data-newbie.tistory.com/m/685 -.. |image6| image:: https://user-images.githubusercontent.com/16560492/108586601-85be0200-731d-11eb-98a4-161c75a1f099.jpg - :target: https://erencan34.medium.com/pygad-ile-genetik-algoritmay%C4%B1-kullanarak-keras-modelleri-nas%C4%B1l-e%C4%9Fitilir-cf92639a478c -.. |image7| image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png - :target: https://thebojda.medium.com/tensorflow-alapoz%C3%B3-10-24f7767d4a2c -.. |image8| image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png - :target: https://neurohive.io/ru/frameworki/pygad-biblioteka-dlya-implementacii-geneticheskogo-algoritma -.. |image9| image:: https://user-images.githubusercontent.com/16560492/78830052-a3c19300-79e7-11ea-8b9b-4b343ea4049c.png - :target: https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad -.. |image10| image:: https://user-images.githubusercontent.com/16560492/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg - :target: https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad -.. |image11| image:: https://user-images.githubusercontent.com/16560492/82078281-30472b80-96e1-11ea-8017-6a1f4383d602.jpg - :target: https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad -.. |image12| image:: https://user-images.githubusercontent.com/16560492/82078300-376e3980-96e1-11ea-821c-aa6b8ceb44d4.jpg - :target: https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad -.. |image13| image:: https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png - :target: https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad -.. |image14| image:: https://user-images.githubusercontent.com/16560492/82431369-db176b00-9a8e-11ea-99bd-e845192873fc.png - :target: https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad +Release History +=============== + +.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png + :alt: + +.. _pygad-1017: + +PyGAD 1.0.17 +------------ + +Release Date: 15 April 2020 + +1. The **pygad.GA** class accepts a new argument named ``fitness_func`` + which accepts a function to be used for calculating the fitness + values for the solutions. This allows the project to be customized to + any problem by building the right fitness function. + +.. _pygad-1020: + +PyGAD 1.0.20 +------------- + +Release Date: 4 May 2020 + +1. The **pygad.GA** attributes are moved from the class scope to the + instance scope. + +2. Raising an exception for incorrect values of the passed parameters. + +3. Two new parameters are added to the **pygad.GA** class constructor + (``init_range_low`` and ``init_range_high``) allowing the user to + customize the range from which the genes values in the initial + population are selected. + +4. The code object ``__code__`` of the passed fitness function is + checked to ensure it has the right number of parameters. + +.. _pygad-200: + +PyGAD 2.0.0 +------------ + +Release Date: 13 May 2020 + +1. The fitness function accepts a new argument named ``sol_idx`` + representing the index of the solution within the population. + +2. A new parameter to the **pygad.GA** class constructor named + ``initial_population`` is supported to allow the user to use a custom + initial population to be used by the genetic algorithm. If not None, + then the passed population will be used. If ``None``, then the + genetic algorithm will create the initial population using the + ``sol_per_pop`` and ``num_genes`` parameters. + +3. The parameters ``sol_per_pop`` and ``num_genes`` are optional and set + to ``None`` by default. + +4. A new parameter named ``callback_generation`` is introduced in the + **pygad.GA** class constructor. It accepts a function with a single + parameter representing the **pygad.GA** class instance. This function + is called after each generation. This helps the user to do + post-processing or debugging operations after each generation. + +.. _pygad-210: + +PyGAD 2.1.0 +----------- + +Release Date: 14 May 2020 + +1. The ``best_solution()`` method in the **pygad.GA** class returns a + new output representing the index of the best solution within the + population. Now, it returns a total of 3 outputs and their order is: + best solution, best solution fitness, and best solution index. Here + is an example: + +.. code:: python + + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Parameters of the best solution :", solution) + print("Fitness value of the best solution :", solution_fitness, "\n") + print("Index of the best solution :", solution_idx, "\n") + +1. | A new attribute named ``best_solution_generation`` is added to the + instances of the **pygad.GA** class. it holds the generation number + at which the best solution is reached. It is only assigned the + generation number after the ``run()`` method completes. Otherwise, + its value is -1. + | Example: + +.. code:: python + + print("Best solution reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation)) + +1. The ``best_solution_fitness`` attribute is renamed to + ``best_solutions_fitness`` (plural solution). + +2. Mutation is applied independently for the genes. + +.. _pygad-221: + +PyGAD 2.2.1 +----------- + +Release Date: 17 May 2020 + +1. Adding 2 extra modules (pygad.nn and pygad.gann) for building and + training neural networks with the genetic algorithm. + +.. _pygad-222: + +PyGAD 2.2.2 +----------- + +Release Date: 18 May 2020 + +1. The initial value of the ``generations_completed`` attribute of + instances from the pygad.GA class is ``0`` rather than ``None``. + +2. An optional bool parameter named ``mutation_by_replacement`` is added + to the constructor of the pygad.GA class. It works only when the + selected type of mutation is random (``mutation_type="random"``). In + this case, setting ``mutation_by_replacement=True`` means replace the + gene by the randomly generated value. If ``False``, then it has no + effect and random mutation works by adding the random value to the + gene. This parameter should be used when the gene falls within a + fixed range and its value must not go out of this range. Here are + some examples: + +Assume there is a gene with the value 0.5. + +If ``mutation_type="random"`` and ``mutation_by_replacement=False``, +then the generated random value (e.g. 0.1) will be added to the gene +value. The new gene value is **0.5+0.1=0.6**. + +If ``mutation_type="random"`` and ``mutation_by_replacement=True``, then +the generated random value (e.g. 0.1) will replace the gene value. The +new gene value is **0.1**. + +1. ``None`` value could be assigned to the ``mutation_type`` and + ``crossover_type`` parameters of the pygad.GA class constructor. When + ``None``, this means the step is bypassed and has no action. + +.. _pygad-230: + +PyGAD 2.3.0 +----------- + +Release date: 1 June 2020 + +1. A new module named ``pygad.cnn`` is supported for building + convolutional neural networks. + +2. A new module named ``pygad.gacnn`` is supported for training + convolutional neural networks using the genetic algorithm. + +3. The ``pygad.plot_result()`` method has 3 optional parameters named + ``title``, ``xlabel``, and ``ylabel`` to customize the plot title, + x-axis label, and y-axis label, respectively. + +4. The ``pygad.nn`` module supports the softmax activation function. + +5. The name of the ``pygad.nn.predict_outputs()`` function is changed to + ``pygad.nn.predict()``. + +6. The name of the ``pygad.nn.train_network()`` function is changed to + ``pygad.nn.train()``. + +.. _pygad-240: + +PyGAD 2.4.0 +----------- + +Release date: 5 July 2020 + +1. A new parameter named ``delay_after_gen`` is added which accepts a + non-negative number specifying the time in seconds to wait after a + generation completes and before going to the next generation. It + defaults to ``0.0`` which means no delay after the generation. + +2. The passed function to the ``callback_generation`` parameter of the + pygad.GA class constructor can terminate the execution of the genetic + algorithm if it returns the string ``stop``. This causes the + ``run()`` method to stop. + +One important use case for that feature is to stop the genetic algorithm +when a condition is met before passing though all the generations. The +user may assigned a value of 100 to the ``num_generations`` parameter of +the pygad.GA class constructor. Assuming that at generation 50, for +example, a condition is met and the user wants to stop the execution +before waiting the remaining 50 generations. To do that, just make the +function passed to the ``callback_generation`` parameter to return the +string ``stop``. + +Here is an example of a function to be passed to the +``callback_generation`` parameter which stops the execution if the +fitness value 70 is reached. The value 70 might be the best possible +fitness value. After being reached, then there is no need to pass +through more generations because no further improvement is possible. + +.. code:: python + + def func_generation(ga_instance): + if ga_instance.best_solution()[1] >= 70: + return "stop" + +.. _pygad-250: + +PyGAD 2.5.0 +----------- + +Release date: 19 July 2020 + +1. | 2 new optional parameters added to the constructor of the + ``pygad.GA`` class which are ``crossover_probability`` and + ``mutation_probability``. + | While applying the crossover operation, each parent has a random + value generated between 0.0 and 1.0. If this random value is less + than or equal to the value assigned to the + ``crossover_probability`` parameter, then the parent is selected + for the crossover operation. + | For the mutation operation, a random value between 0.0 and 1.0 is + generated for each gene in the solution. If this value is less than + or equal to the value assigned to the ``mutation_probability``, + then this gene is selected for mutation. + +2. A new optional parameter named ``linewidth`` is added to the + ``plot_result()`` method to specify the width of the curve in the + plot. It defaults to 3.0. + +3. Previously, the indices of the genes selected for mutation was + randomly generated once for all solutions within the generation. + Currently, the genes' indices are randomly generated for each + solution in the population. If the population has 4 solutions, the + indices are randomly generated 4 times inside the single generation, + 1 time for each solution. + +4. Previously, the position of the point(s) for the single-point and + two-points crossover was(were) randomly selected once for all + solutions within the generation. Currently, the position(s) is(are) + randomly selected for each solution in the population. If the + population has 4 solutions, the position(s) is(are) randomly + generated 4 times inside the single generation, 1 time for each + solution. + +5. A new optional parameter named ``gene_space`` as added to the + ``pygad.GA`` class constructor. It is used to specify the possible + values for each gene in case the user wants to restrict the gene + values. It is useful if the gene space is restricted to a certain + range or to discrete values. For more information, check the `More + about the ``gene_space`` + Parameter `__ + section. Thanks to `Prof. Tamer A. + Farrag `__ for requesting this useful + feature. + +.. _pygad-260: + +PyGAD 2.6.0 +------------ + +Release Date: 6 August 2020 + +1. A bug fix in assigning the value to the ``initial_population`` + parameter. + +2. A new parameter named ``gene_type`` is added to control the gene + type. It can be either ``int`` or ``float``. It has an effect only + when the parameter ``gene_space`` is ``None``. + +3. 7 new parameters that accept callback functions: ``on_start``, + ``on_fitness``, ``on_parents``, ``on_crossover``, ``on_mutation``, + ``on_generation``, and ``on_stop``. + +.. _pygad-270: + +PyGAD 2.7.0 +----------- + +Release Date: 11 September 2020 + +1. The ``learning_rate`` parameter in the ``pygad.nn.train()`` function + defaults to **0.01**. + +2. Added support of building neural networks for regression using the + new parameter named ``problem_type``. It is added as a parameter to + both ``pygad.nn.train()`` and ``pygad.nn.predict()`` functions. The + value of this parameter can be either **classification** or + **regression** to define the problem type. It defaults to + **classification**. + +3. The activation function for a layer can be set to the string + ``"None"`` to refer that there is no activation function at this + layer. As a result, the supported values for the activation function + are ``"sigmoid"``, ``"relu"``, ``"softmax"``, and ``"None"``. + +To build a regression network using the ``pygad.nn`` module, just do the +following: + +1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and + ``pygad.nn.predict()`` functions to the string ``"regression"``. + +2. Set the activation function for the output layer to the string + ``"None"``. This sets no limits on the range of the outputs as it + will be from ``-infinity`` to ``+infinity``. If you are sure that all + outputs will be nonnegative values, then use the ReLU function. + +Check the documentation of the ``pygad.nn`` module for an example that +builds a neural network for regression. The regression example is also +available at `this GitHub +project `__: +https://github.com/ahmedfgad/NumPyANN + +To build and train a regression network using the ``pygad.gann`` module, +do the following: + +1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and + ``pygad.nn.predict()`` functions to the string ``"regression"``. + +2. Set the ``output_activation`` parameter in the constructor of the + ``pygad.gann.GANN`` class to ``"None"``. + +Check the documentation of the ``pygad.gann`` module for an example that +builds and trains a neural network for regression. The regression +example is also available at `this GitHub +project `__: +https://github.com/ahmedfgad/NeuralGenetic + +To build a classification network, either ignore the ``problem_type`` +parameter or set it to ``"classification"`` (default value). In this +case, the activation function of the last layer can be set to any type +(e.g. softmax). + +.. _pygad-271: + +PyGAD 2.7.1 +----------- + +Release Date: 11 September 2020 + +1. A bug fix when the ``problem_type`` argument is set to + ``regression``. + +.. _pygad-272: + +PyGAD 2.7.2 +----------- + +Release Date: 14 September 2020 + +1. Bug fix to support building and training regression neural networks + with multiple outputs. + +.. _pygad-280: + +PyGAD 2.8.0 +----------- + +Release Date: 20 September 2020 + +1. Support of a new module named ``kerasga`` so that the Keras models + can be trained by the genetic algorithm using PyGAD. + +.. _pygad-281: + +PyGAD 2.8.1 +----------- + +Release Date: 3 October 2020 + +1. Bug fix in applying the crossover operation when the + ``crossover_probability`` parameter is used. Thanks to `Eng. Hamada + Kassem, Research and Teaching Assistant, Construction Engineering and + Management, Faculty of Engineering, Alexandria University, + Egypt `__. + +.. _pygad-290: + +PyGAD 2.9.0 +------------ + +Release Date: 06 December 2020 + +1. The fitness values of the initial population are considered in the + ``best_solutions_fitness`` attribute. + +2. An optional parameter named ``save_best_solutions`` is added. It + defaults to ``False``. When it is ``True``, then the best solution + after each generation is saved into an attribute named + ``best_solutions``. If ``False``, then no solutions are saved and the + ``best_solutions`` attribute will be empty. + +3. Scattered crossover is supported. To use it, assign the + ``crossover_type`` parameter the value ``"scattered"``. + +4. NumPy arrays are now supported by the ``gene_space`` parameter. + +5. The following parameters (``gene_type``, ``crossover_probability``, + ``mutation_probability``, ``delay_after_gen``) can be assigned to a + numeric value of any of these data types: ``int``, ``float``, + ``numpy.int``, ``numpy.int8``, ``numpy.int16``, ``numpy.int32``, + ``numpy.int64``, ``numpy.float``, ``numpy.float16``, + ``numpy.float32``, or ``numpy.float64``. + +.. _pygad-2100: + +PyGAD 2.10.0 +------------ + +Release Date: 03 January 2021 + +1. Support of a new module ``pygad.torchga`` to train PyTorch models + using PyGAD. Check `its + documentation `__. + +2. Support of adaptive mutation where the mutation rate is determined + by the fitness value of each solution. Read the `Adaptive + Mutation `__ + section for more details. Also, read this paper: `Libelli, S. + Marsili, and P. Alba. "Adaptive mutation in genetic algorithms." + Soft computing 4.2 (2000): + 76-80. `__ + +3. Before the ``run()`` method completes or exits, the fitness value of + the best solution in the current population is appended to the + ``best_solution_fitness`` list attribute. Note that the fitness + value of the best solution in the initial population is already + saved at the beginning of the list. So, the fitness value of the + best solution is saved before the genetic algorithm starts and after + it ends. + +4. When the parameter ``parent_selection_type`` is set to ``sss`` + (steady-state selection), then a warning message is printed if the + value of the ``keep_parents`` parameter is set to 0. + +5. More validations to the user input parameters. + +6. The default value of the ``mutation_percent_genes`` is set to the + string ``"default"`` rather than the integer 10. This change helps + to know whether the user explicitly passed a value to the + ``mutation_percent_genes`` parameter or it is left to its default + one. The ``"default"`` value is later translated into the integer + 10. + +7. The ``mutation_percent_genes`` parameter is no longer accepting the + value 0. It must be ``>0`` and ``<=100``. + +8. The built-in ``warnings`` module is used to show warning messages + rather than just using the ``print()`` function. + +9. A new ``bool`` parameter called ``suppress_warnings`` is added to + the constructor of the ``pygad.GA`` class. It allows the user to + control whether the warning messages are printed or not. It defaults + to ``False`` which means the messages are printed. + +10. A helper method called ``adaptive_mutation_population_fitness()`` is + created to calculate the average fitness value used in adaptive + mutation to filter the solutions. + +11. The ``best_solution()`` method accepts a new optional parameter + called ``pop_fitness``. It accepts a list of the fitness values of + the solutions in the population. If ``None``, then the + ``cal_pop_fitness()`` method is called to calculate the fitness + values of the population. + +.. _pygad-2101: + +PyGAD 2.10.1 +------------ + +Release Date: 10 January 2021 + +1. In the ``gene_space`` parameter, any ``None`` value (regardless of + its index or axis), is replaced by a randomly generated number based + on the 3 parameters ``init_range_low``, ``init_range_high``, and + ``gene_type``. So, the ``None`` value in ``[..., None, ...]`` or + ``[..., [..., None, ...], ...]`` are replaced with random values. + This gives more freedom in building the space of values for the + genes. + +2. All the numbers passed to the ``gene_space`` parameter are casted to + the type specified in the ``gene_type`` parameter. + +3. The ``numpy.uint`` data type is supported for the parameters that + accept integer values. + +4. In the ``pygad.kerasga`` module, the ``model_weights_as_vector()`` + function uses the ``trainable`` attribute of the model's layers to + only return the trainable weights in the network. So, only the + trainable layers with their ``trainable`` attribute set to ``True`` + (``trainable=True``), which is the default value, have their weights + evolved. All non-trainable layers with the ``trainable`` attribute + set to ``False`` (``trainable=False``) will not be evolved. Thanks to + `Prof. Tamer A. Farrag `__ for + pointing about that at + `GitHub `__. + +.. _pygad-2102: + +PyGAD 2.10.2 +------------ + +Release Date: 15 January 2021 + +1. A bug fix when ``save_best_solutions=True``. Refer to this issue for + more information: + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/25 + +.. _pygad-2110: + +PyGAD 2.11.0 +------------ + +Release Date: 16 February 2021 + +1. In the ``gene_space`` argument, the user can use a dictionary to + specify the lower and upper limits of the gene. This dictionary must + have only 2 items with keys ``low`` and ``high`` to specify the low + and high limits of the gene, respectively. This way, PyGAD takes care + of not exceeding the value limits of the gene. For a problem with + only 2 genes, then using + ``gene_space=[{'low': 1, 'high': 5}, {'low': 0.2, 'high': 0.81}]`` + means the accepted values in the first gene start from 1 (inclusive) + to 5 (exclusive) while the second one has values between 0.2 + (inclusive) and 0.85 (exclusive). For more information, please check + the `Limit the Gene Value + Range `__ + section of the documentation. + +2. The ``plot_result()`` method returns the figure so that the user can + save it. + +3. Bug fixes in copying elements from the gene space. + +4. For a gene with a set of discrete values (more than 1 value) in the + ``gene_space`` parameter like ``[0, 1]``, it was possible that the + gene value may not change after mutation. That is if the current + value is 0, then the randomly selected value could also be 0. Now, it + is verified that the new value is changed. So, if the current value + is 0, then the new value after mutation will not be 0 but 1. + +.. _pygad-2120: + +PyGAD 2.12.0 +------------ + +Release Date: 20 February 2021 + +1. 4 new instance attributes are added to hold temporary results after + each generation: ``last_generation_fitness`` holds the fitness values + of the solutions in the last generation, ``last_generation_parents`` + holds the parents selected from the last generation, + ``last_generation_offspring_crossover`` holds the offspring generated + after applying the crossover in the last generation, and + ``last_generation_offspring_mutation`` holds the offspring generated + after applying the mutation in the last generation. You can access + these attributes inside the ``on_generation()`` method for example. + +2. A bug fixed when the ``initial_population`` parameter is used. The + bug occurred due to a mismatch between the data type of the array + assigned to ``initial_population`` and the gene type in the + ``gene_type`` attribute. Assuming that the array assigned to the + ``initial_population`` parameter is + ``((1, 1), (3, 3), (5, 5), (7, 7))`` which has type ``int``. When + ``gene_type`` is set to ``float``, then the genes will not be float + but casted to ``int`` because the defined array has ``int`` type. The + bug is fixed by forcing the array assigned to ``initial_population`` + to have the data type in the ``gene_type`` attribute. Check the + `issue at + GitHub `__: + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/27 + +Thanks to Andrei Rozanski [PhD Bioinformatics Specialist, Department of +Tissue Dynamics and Regeneration, Max Planck Institute for Biophysical +Chemistry, Germany] for opening my eye to the first change. + +Thanks to `Marios +Giouvanakis `__, +a PhD candidate in Electrical & Computer Engineer, `Aristotle University +of Thessaloniki (Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης), +Greece `__, for emailing me about the second +issue. + +.. _pygad-2130: + +PyGAD 2.13.0 +------------- + +Release Date: 12 March 2021 + +1. A new ``bool`` parameter called ``allow_duplicate_genes`` is + supported. If ``True``, which is the default, then a + solution/chromosome may have duplicate gene values. If ``False``, + then each gene will have a unique value in its solution. Check the + `Prevent Duplicates in Gene + Values `__ + section for more details. + +2. The ``last_generation_fitness`` is updated at the end of each + generation not at the beginning. This keeps the fitness values of the + most up-to-date population assigned to the + ``last_generation_fitness`` parameter. + +.. _pygad-2140: + +PyGAD 2.14.0 +------------ + +PyGAD 2.14.0 has an issue that is solved in PyGAD 2.14.1. Please +consider using 2.14.1 not 2.14.0. + +Release Date: 19 May 2021 + +1. `Issue + #40 `__ + is solved. Now, the ``None`` value works with the ``crossover_type`` + and ``mutation_type`` parameters: + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/40 + +2. The ``gene_type`` parameter supports accepting a + ``list/tuple/numpy.ndarray`` of numeric data types for the genes. + This helps to control the data type of each individual gene. + Previously, the ``gene_type`` can be assigned only to a single data + type that is applied for all genes. For more information, check the + `More about the ``gene_type`` + Parameter `__ + section. Thanks to `Rainer + Engel `__ + for asking about this feature in `this + discussion `__: + https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43 + +3. A new ``bool`` attribute named ``gene_type_single`` is added to the + ``pygad.GA`` class. It is ``True`` when there is a single data type + assigned to the ``gene_type`` parameter. When the ``gene_type`` + parameter is assigned a ``list/tuple/numpy.ndarray``, then + ``gene_type_single`` is set to ``False``. + +4. The ``mutation_by_replacement`` flag now has no effect if + ``gene_space`` exists except for the genes with ``None`` values. For + example, for ``gene_space=[None, [5, 6]]`` the + ``mutation_by_replacement`` flag affects only the first gene which + has ``None`` for its value space. + +5. When an element has a value of ``None`` in the ``gene_space`` + parameter (e.g. ``gene_space=[None, [5, 6]]``), then its value will + be randomly generated for each solution rather than being generate + once for all solutions. Previously, the gene with ``None`` value in + ``gene_space`` is the same across all solutions + +6. Some changes in the documentation according to `issue + #32 `__: + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/32 + +.. _pygad-2142: + +PyGAD 2.14.2 +------------ + +Release Date: 27 May 2021 + +1. Some bug fixes when the ``gene_type`` parameter is nested. Thanks to + `Rainer + Engel `__ + for opening `a + discussion `__ + to report this bug: + https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43#discussioncomment-763342 + +`Rainer +Engel `__ +helped a lot in suggesting new features and suggesting enhancements in +2.14.0 to 2.14.2 releases. + +.. _pygad-2143: + +PyGAD 2.14.3 +------------ + +Release Date: 6 June 2021 + +1. Some bug fixes when setting the ``save_best_solutions`` parameter to + ``True``. Previously, the best solution for generation ``i`` was + added into the ``best_solutions`` attribute at generation ``i+1``. + Now, the ``best_solutions`` attribute is updated by each best + solution at its exact generation. + +.. _pygad-2150: + +PyGAD 2.15.0 +------------ + +Release Date: 17 June 2021 + +1. Control the precision of all genes/individual genes. Thanks to + `Rainer `__ for asking about this + feature: + https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43#discussioncomment-763452 + +2. A new attribute named ``last_generation_parents_indices`` holds the + indices of the selected parents in the last generation. + +3. In adaptive mutation, no need to recalculate the fitness values of + the parents selected in the last generation as these values can be + returned based on the ``last_generation_fitness`` and + ``last_generation_parents_indices`` attributes. This speeds-up the + adaptive mutation. + +4. When a sublist has a value of ``None`` in the ``gene_space`` + parameter (e.g. ``gene_space=[[1, 2, 3], [5, 6, None]]``), then its + value will be randomly generated for each solution rather than being + generated once for all solutions. Previously, a value of ``None`` in + a sublist of the ``gene_space`` parameter was identical across all + solutions. + +5. The dictionary assigned to the ``gene_space`` parameter itself or + one of its elements has a new key called ``"step"`` to specify the + step of moving from the start to the end of the range specified by + the 2 existing keys ``"low"`` and ``"high"``. An example is + ``{"low": 0, "high": 30, "step": 2}`` to have only even values for + the gene(s) starting from 0 to 30. For more information, check the + `More about the ``gene_space`` + Parameter `__ + section. + https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/48 + +6. A new function called ``predict()`` is added in both the + ``pygad.kerasga`` and ``pygad.torchga`` modules to make predictions. + This makes it easier than using custom code each time a prediction + is to be made. + +7. A new parameter called ``stop_criteria`` allows the user to specify + one or more stop criteria to stop the evolution based on some + conditions. Each criterion is passed as ``str`` which has a stop + word. The current 2 supported words are ``reach`` and ``saturate``. + ``reach`` stops the ``run()`` method if the fitness value is equal + to or greater than a given fitness value. An example for ``reach`` + is ``"reach_40"`` which stops the evolution if the fitness is >= 40. + ``saturate`` means stop the evolution if the fitness saturates for a + given number of consecutive generations. An example for ``saturate`` + is ``"saturate_7"`` which means stop the ``run()`` method if the + fitness does not change for 7 consecutive generations. Thanks to + `Rainer `__ for asking about this + feature: + https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/44 + +8. A new bool parameter, defaults to ``False``, named + ``save_solutions`` is added to the constructor of the ``pygad.GA`` + class. If ``True``, then all solutions in each generation are + appended into an attribute called ``solutions`` which is NumPy + array. + +9. The ``plot_result()`` method is renamed to ``plot_fitness()``. The + users should migrate to the new name as the old name will be removed + in the future. + +10. Four new optional parameters are added to the ``plot_fitness()`` + function in the ``pygad.GA`` class which are ``font_size=14``, + ``save_dir=None``, ``color="#3870FF"``, and ``plot_type="plot"``. + Use ``font_size`` to change the font of the plot title and labels. + ``save_dir`` accepts the directory to which the figure is saved. It + defaults to ``None`` which means do not save the figure. ``color`` + changes the color of the plot. ``plot_type`` changes the plot type + which can be either ``"plot"`` (default), ``"scatter"``, or + ``"bar"``. + https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/47 + +11. The default value of the ``title`` parameter in the + ``plot_fitness()`` method is ``"PyGAD - Generation vs. Fitness"`` + rather than ``"PyGAD - Iteration vs. Fitness"``. + +12. A new method named ``plot_new_solution_rate()`` creates, shows, and + returns a figure showing the rate of new/unique solutions explored + in each generation. It accepts the same parameters as in the + ``plot_fitness()`` method. This method only works when + ``save_solutions=True`` in the ``pygad.GA`` class's constructor. + +13. A new method named ``plot_genes()`` creates, shows, and returns a + figure to show how each gene changes per each generation. It accepts + similar parameters like the ``plot_fitness()`` method in addition to + the ``graph_type``, ``fill_color``, and ``solutions`` parameters. + The ``graph_type`` parameter can be either ``"plot"`` (default), + ``"boxplot"``, or ``"histogram"``. ``fill_color`` accepts the fill + color which works when ``graph_type`` is either ``"boxplot"`` or + ``"histogram"``. ``solutions`` can be either ``"all"`` or ``"best"`` + to decide whether all solutions or only best solutions are used. + +14. The ``gene_type`` parameter now supports controlling the precision + of ``float`` data types. For a gene, rather than assigning just the + data type like ``float``, assign a + ``list``/``tuple``/``numpy.ndarray`` with 2 elements where the first + one is the type and the second one is the precision. For example, + ``[float, 2]`` forces a gene with a value like ``0.1234`` to be + ``0.12``. For more information, check the `More about the + ``gene_type`` + Parameter `__ + section. + +.. _pygad-2151: + +PyGAD 2.15.1 +------------ + +Release Date: 18 June 2021 + +1. Fix a bug when ``keep_parents`` is set to a positive integer. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/49 + +.. _pygad-2152: + +PyGAD 2.15.2 +------------ + +Release Date: 18 June 2021 + +1. Fix a bug when using the ``kerasga`` or ``torchga`` modules. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/51 + +.. _pygad-2160: + +PyGAD 2.16.0 +------------ + +Release Date: 19 June 2021 + +1. A user-defined function can be passed to the ``mutation_type``, + ``crossover_type``, and ``parent_selection_type`` parameters in the + ``pygad.GA`` class to create a custom mutation, crossover, and parent + selection operators. Check the `User-Defined Crossover, Mutation, and + Parent Selection + Operators `__ + section for more details. + https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50 + +.. _pygad-2161: + +PyGAD 2.16.1 +------------ + +Release Date: 28 September 2021 + +1. The user can use the ``tqdm`` library to show a progress bar. + https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50. + +.. code:: python + + import pygad + import numpy + import tqdm + + equation_inputs = [4,-2,3.5] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + num_generations = 10000 + with tqdm.tqdm(total=num_generations) as pbar: + ga_instance = pygad.GA(num_generations=num_generations, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + on_generation=lambda _: pbar.update(1)) + + ga_instance.run() + + ga_instance.plot_result() + +But this work does not work if the ``ga_instance`` will be pickled (i.e. +the ``save()`` method will be called. + +.. code:: python + + ga_instance.save("test") + +To solve this issue, define a function and pass it to the +``on_generation`` parameter. In the next code, the +``on_generation_progress()`` function is defined which updates the +progress bar. + +.. code:: python + + import pygad + import numpy + import tqdm + + equation_inputs = [4,-2,3.5] + desired_output = 44 + + def fitness_func(ga_instance, solution, solution_idx): + output = numpy.sum(solution * equation_inputs) + fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) + return fitness + + def on_generation_progress(ga): + pbar.update(1) + + num_generations = 100 + with tqdm.tqdm(total=num_generations) as pbar: + ga_instance = pygad.GA(num_generations=num_generations, + sol_per_pop=5, + num_parents_mating=2, + num_genes=len(equation_inputs), + fitness_func=fitness_func, + on_generation=on_generation_progress) + + ga_instance.run() + + ga_instance.plot_result() + + ga_instance.save("test") + +1. Solved the issue of unequal length between the ``solutions`` and + ``solutions_fitness`` when the ``save_solutions`` parameter is set to + ``True``. Now, the fitness of the last population is appended to the + ``solutions_fitness`` array. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/64 + +2. There was an issue of getting the length of these 4 variables + (``solutions``, ``solutions_fitness``, ``best_solutions``, and + ``best_solutions_fitness``) doubled after each call of the ``run()`` + method. This is solved by resetting these variables at the beginning + of the ``run()`` method. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/62 + +3. Bug fixes when adaptive mutation is used + (``mutation_type="adaptive"``). + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/65 + +.. _pygad-2162: + +PyGAD 2.16.2 +------------ + +Release Date: 2 February 2022 + +1. A new instance attribute called ``previous_generation_fitness`` added + in the ``pygad.GA`` class. It holds the fitness values of one + generation before the fitness values saved in the + ``last_generation_fitness``. + +2. Issue in the ``cal_pop_fitness()`` method in getting the correct + indices of the previous parents. This is solved by using the previous + generation's fitness saved in the new attribute + ``previous_generation_fitness`` to return the parents' fitness + values. Thanks to Tobias Tischhauser (M.Sc. - `Mitarbeiter Institut + EMS, Departement Technik, OST – Ostschweizer Fachhochschule, + Switzerland `__) + for detecting this bug. + +.. _pygad-2163: + +PyGAD 2.16.3 +------------ + +Release Date: 2 February 2022 + +1. Validate the fitness value returned from the fitness function. An + exception is raised if something is wrong. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/67 + +.. _pygad-2170: + +PyGAD 2.17.0 +------------ + +Release Date: 8 July 2022 + +1. An issue is solved when the ``gene_space`` parameter is given a fixed + value. e.g. gene_space=[range(5), 4]. The second gene's value is + static (4) which causes an exception. + +2. Fixed the issue where the ``allow_duplicate_genes`` parameter did not + work when mutation is disabled (i.e. ``mutation_type=None``). This is + by checking for duplicates after crossover directly. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/39 + +3. Solve an issue in the ``tournament_selection()`` method as the + indices of the selected parents were incorrect. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/89 + +4. Reuse the fitness values of the previously explored solutions rather + than recalculating them. This feature only works if + ``save_solutions=True``. + +5. Parallel processing is supported. This is by the introduction of a + new parameter named ``parallel_processing`` in the constructor of the + ``pygad.GA`` class. Thanks to + `@windowshopr `__ for opening the + issue + `#78 `__ + at GitHub. Check the `Parallel Processing in + PyGAD `__ + section for more information and examples. + +.. _pygad-2180: + +PyGAD 2.18.0 +------------ + +Release Date: 9 September 2022 + +1. Raise an exception if the sum of fitness values is zero while either + roulette wheel or stochastic universal parent selection is used. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/129 + +2. Initialize the value of the ``run_completed`` property to ``False``. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/122 + +3. The values of these properties are no longer reset with each call to + the ``run()`` method + ``self.best_solutions, self.best_solutions_fitness, self.solutions, self.solutions_fitness``: + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/123. Now, + the user can have the flexibility of calling the ``run()`` method + more than once while extending the data collected after each + generation. Another advantage happens when the instance is loaded and + the ``run()`` method is called, as the old fitness value are shown on + the graph alongside with the new fitness values. Read more in this + section: `Continue without Loosing + Progress `__ + +4. Thanks `Prof. Fernando Jiménez + Barrionuevo `__ (Dept. of Information and + Communications Engineering, University of Murcia, Murcia, Spain) for + editing this + `comment `__ + in the code. + https://github.com/ahmedfgad/GeneticAlgorithmPython/commit/5315bbec02777df96ce1ec665c94dece81c440f4 + +5. A bug fixed when ``crossover_type=None``. + +6. Support of elitism selection through a new parameter named + ``keep_elitism``. It defaults to 1 which means for each generation + keep only the best solution in the next generation. If assigned 0, + then it has no effect. Read more in this section: `Elitism + Selection `__. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/74 + +7. A new instance attribute named ``last_generation_elitism`` added to + hold the elitism in the last generation. + +8. A new parameter called ``random_seed`` added to accept a seed for the + random function generators. Credit to this issue + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/70 and + `Prof. Fernando Jiménez Barrionuevo `__. + Read more in this section: `Random + Seed `__. + +9. Editing the ``pygad.TorchGA`` module to make sure the tensor data is + moved from GPU to CPU. Thanks to Rasmus Johansson for opening this + pull request: https://github.com/ahmedfgad/TorchGA/pull/2 + +.. _pygad-2181: + +PyGAD 2.18.1 +------------ + +Release Date: 19 September 2022 + +1. A big fix when ``keep_elitism`` is used. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/132 + +.. _pygad-2182: + +PyGAD 2.18.2 +------------ + +Release Date: 14 February 2023 + +1. Remove ``numpy.int`` and ``numpy.float`` from the list of supported + data types. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/151 + https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/152 + +2. Call the ``on_crossover()`` callback function even if + ``crossover_type`` is ``None``. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138 + +3. Call the ``on_mutation()`` callback function even if + ``mutation_type`` is ``None``. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138 + +.. _pygad-2183: + +PyGAD 2.18.3 +------------ + +Release Date: 14 February 2023 + +1. Bug fixes. + +.. _pygad-2190: + +PyGAD 2.19.0 +------------ + +Release Date: 22 February 2023 + +1. A new ``summary()`` method is supported to return a Keras-like + summary of the PyGAD lifecycle. + +2. A new optional parameter called ``fitness_batch_size`` is supported + to calculate the fitness in batches. If it is assigned the value + ``1`` or ``None`` (default), then the normal flow is used where the + fitness function is called for each individual solution. If the + ``fitness_batch_size`` parameter is assigned a value satisfying this + condition ``1 < fitness_batch_size <= sol_per_pop``, then the + solutions are grouped into batches of size ``fitness_batch_size`` + and the fitness function is called once for each batch. In this + case, the fitness function must return a list/tuple/numpy.ndarray + with a length equal to the number of solutions passed. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/136. + +3. The ``cloudpickle`` library + (https://github.com/cloudpipe/cloudpickle) is used instead of the + ``pickle`` library to pickle the ``pygad.GA`` objects. This solves + the issue of having to redefine the functions (e.g. fitness + function). The ``cloudpickle`` library is added as a dependency in + the ``requirements.txt`` file. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/159 + +4. Support of assigning methods to these parameters: ``fitness_func``, + ``crossover_type``, ``mutation_type``, ``parent_selection_type``, + ``on_start``, ``on_fitness``, ``on_parents``, ``on_crossover``, + ``on_mutation``, ``on_generation``, and ``on_stop``. + https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92 + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138 + +5. Validating the output of the parent selection, crossover, and + mutation functions. + +6. The built-in parent selection operators return the parent's indices + as a NumPy array. + +7. The outputs of the parent selection, crossover, and mutation + operators must be NumPy arrays. + +8. Fix an issue when ``allow_duplicate_genes=True``. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/39 + +9. Fix an issue creating scatter plots of the solutions' fitness. + +10. Sampling from a ``set()`` is no longer supported in Python 3.11. + Instead, sampling happens from a ``list()``. Thanks ``Marco Brenna`` + for pointing to this issue. + +11. The lifecycle is updated to reflect that the new population's + fitness is calculated at the end of the lifecycle not at the + beginning. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/154#issuecomment-1438739483 + +12. There was an issue when ``save_solutions=True`` that causes the + fitness function to be called for solutions already explored and + have their fitness pre-calculated. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/160 + +13. A new instance attribute named ``last_generation_elitism_indices`` + added to hold the indices of the selected elitism. This attribute + helps to re-use the fitness of the elitism instead of calling the + fitness function. + +14. Fewer calls to the ``best_solution()`` method which in turns saves + some calls to the fitness function. + +15. Some updates in the documentation to give more details about the + ``cal_pop_fitness()`` method. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/79#issuecomment-1439605442 + +.. _pygad-2191: + +PyGAD 2.19.1 +------------ + +Release Date: 22 February 2023 + +1. Add the `cloudpickle `__ + library as a dependency. + +.. _pygad-2192: + +PyGAD 2.19.2 +------------ + +Release Date 23 February 2023 + +1. Fix an issue when parallel processing was used where the elitism + solutions' fitness values are not re-used. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/160#issuecomment-1441718184 + +.. _pygad-300: + +PyGAD 3.0.0 +----------- + +Release Date 8 April 2023 + +1. The structure of the library is changed and some methods defined in + the ``pygad.py`` module are moved to the ``pygad.utils``, + ``pygad.helper``, and ``pygad.visualize`` submodules. + +2. The ``pygad.utils.parent_selection`` module has a class named + ``ParentSelection`` where all the parent selection operators exist. + The ``pygad.GA`` class extends this class. + +3. The ``pygad.utils.crossover`` module has a class named ``Crossover`` + where all the crossover operators exist. The ``pygad.GA`` class + extends this class. + +4. The ``pygad.utils.mutation`` module has a class named ``Mutation`` + where all the mutation operators exist. The ``pygad.GA`` class + extends this class. + +5. The ``pygad.helper.unique`` module has a class named ``Unique`` some + helper methods exist to solve duplicate genes and make sure every + gene is unique. The ``pygad.GA`` class extends this class. + +6. The ``pygad.visualize.plot`` module has a class named ``Plot`` where + all the methods that create plots exist. The ``pygad.GA`` class + extends this class. + +7. Support of using the ``logging`` module to log the outputs to both + the console and text file instead of using the ``print()`` function. + This is by assigning the ``logging.Logger`` to the new ``logger`` + parameter. Check the `Logging + Outputs `__ + for more information. + +8. A new instance attribute called ``logger`` to save the logger. + +9. The function/method passed to the ``fitness_func`` parameter accepts + a new parameter that refers to the instance of the ``pygad.GA`` + class. Check this for an example: `Use Functions and Methods to + Build Fitness Function and + Callbacks `__. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/163 + +10. Update the documentation to include an example of using functions + and methods to calculate the fitness and build callbacks. Check this + for more details: `Use Functions and Methods to Build Fitness + Function and + Callbacks `__. + https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92#issuecomment-1443635003 + +11. Validate the value passed to the ``initial_population`` parameter. + +12. Validate the type and length of the ``pop_fitness`` parameter of the + ``best_solution()`` method. + +13. Some edits in the documentation. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/106 + +14. Fix an issue when building the initial population as (some) genes + have their value taken from the mutation range (defined by the + parameters ``random_mutation_min_val`` and + ``random_mutation_max_val``) instead of using the parameters + ``init_range_low`` and ``init_range_high``. + +15. The ``summary()`` method returns the summary as a single-line + string. Just log/print the returned string it to see it properly. + +16. The ``callback_generation`` parameter is removed. Use the + ``on_generation`` parameter instead. + +17. There was an issue when using the ``parallel_processing`` parameter + with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the + ``predict()`` method gives incorrect and weird results when more + than 1 thread is used. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/145 + https://github.com/ahmedfgad/TorchGA/issues/5 + https://github.com/ahmedfgad/KerasGA/issues/6. Thanks to this + `StackOverflow + answer `__. + +18. Replace ``numpy.float`` by ``float`` in the 2 parent selection + operators roulette wheel and stochastic universal. + https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/168 + +.. _pygad-301: + +PyGAD 3.0.1 +----------- + +Release Date 20 April 2023 + +1. Fix an issue with passing user-defined function/method for parent + selection. + https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/179 + +PyGAD Projects at GitHub +======================== + +The PyGAD library is available at PyPI at this page +https://pypi.org/project/pygad. PyGAD is built out of a number of +open-source GitHub projects. A brief note about these projects is given +in the next subsections. + +`GeneticAlgorithmPython `__ +-------------------------------------------------------------------------------- + +GitHub Link: https://github.com/ahmedfgad/GeneticAlgorithmPython + +`GeneticAlgorithmPython `__ +is the first project which is an open-source Python 3 project for +implementing the genetic algorithm based on NumPy. + +`NumPyANN `__ +---------------------------------------------------- + +GitHub Link: https://github.com/ahmedfgad/NumPyANN + +`NumPyANN `__ builds artificial +neural networks in **Python 3** using **NumPy** from scratch. The +purpose of this project is to only implement the **forward pass** of a +neural network without using a training algorithm. Currently, it only +supports classification and later regression will be also supported. +Moreover, only one class is supported per sample. + +`NeuralGenetic `__ +-------------------------------------------------------------- + +GitHub Link: https://github.com/ahmedfgad/NeuralGenetic + +`NeuralGenetic `__ trains +neural networks using the genetic algorithm based on the previous 2 +projects +`GeneticAlgorithmPython `__ +and `NumPyANN `__. + +`NumPyCNN `__ +---------------------------------------------------- + +GitHub Link: https://github.com/ahmedfgad/NumPyCNN + +`NumPyCNN `__ builds +convolutional neural networks using NumPy. The purpose of this project +is to only implement the **forward pass** of a convolutional neural +network without using a training algorithm. + +`CNNGenetic `__ +-------------------------------------------------------- + +GitHub Link: https://github.com/ahmedfgad/CNNGenetic + +`CNNGenetic `__ trains +convolutional neural networks using the genetic algorithm. It uses the +`GeneticAlgorithmPython `__ +project for building the genetic algorithm. + +`KerasGA `__ +-------------------------------------------------- + +GitHub Link: https://github.com/ahmedfgad/KerasGA + +`KerasGA `__ trains +`Keras `__ models using the genetic algorithm. It uses +the +`GeneticAlgorithmPython `__ +project for building the genetic algorithm. + +`TorchGA `__ +-------------------------------------------------- + +GitHub Link: https://github.com/ahmedfgad/TorchGA + +`TorchGA `__ trains +`PyTorch `__ models using the genetic algorithm. It +uses the +`GeneticAlgorithmPython `__ +project for building the genetic algorithm. + +`pygad.torchga `__: +https://github.com/ahmedfgad/TorchGA + +Stackoverflow Questions about PyGAD +=================================== + +.. _how-do-i-proceed-to-load-a-gainstance-as-pkl-format-in-pygad: + +`How do I proceed to load a ga_instance as “.pkl” format in PyGad? `__ +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +`Binary Classification NN Model Weights not being Trained in PyGAD `__ +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +`How to solve TSP problem using pyGAD package? `__ +--------------------------------------------------------------------------------------------------------------------------------------------- + +`How can I save a matplotlib plot that is the output of a function in jupyter? `__ +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +`How do I query the best solution of a pyGAD GA instance? `__ +------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +`Multi-Input Multi-Output in Genetic algorithm (python) `__ +-------------------------------------------------------------------------------------------------------------------------------------------------------------- + +https://www.linkedin.com/pulse/validation-short-term-parametric-trading-model-genetic-landolfi + +https://itchef.ru/articles/397758 + +https://audhiaprilliant.medium.com/genetic-algorithm-based-clustering-algorithm-in-searching-robust-initial-centroids-for-k-means-e3b4d892a4be + +https://python.plainenglish.io/validation-of-a-short-term-parametric-trading-model-with-genetic-optimization-and-walk-forward-89708b789af6 + +https://ichi.pro/ko/pygadwa-hamkke-yujeon-algolijeum-eul-sayonghayeo-keras-model-eul-hunlyeonsikineun-bangbeob-173299286377169 + +https://ichi.pro/tr/pygad-ile-genetik-algoritmayi-kullanarak-keras-modelleri-nasil-egitilir-173299286377169 + +https://ichi.pro/ru/kak-obucit-modeli-keras-s-pomos-u-geneticeskogo-algoritma-s-pygad-173299286377169 + +https://blog.csdn.net/sinat_38079265/article/details/108449614 + +Submitting Issues +================= + +If there is an issue using PyGAD, then use any of your preferred option +to discuss that issue. + +One way is `submitting an +issue `__ +into this GitHub project +(`github.com/ahmedfgad/GeneticAlgorithmPython `__) +in case something is not working properly or to ask for questions. + +If this is not a proper option for you, then check the `Contact +Us `__ +section for more contact details. + +Ask for Feature +=============== + +PyGAD is actively developed with the goal of building a dynamic library +for suporting a wide-range of problems to be optimized using the genetic +algorithm. + +To ask for a new feature, either `submit an +issue `__ +into this GitHub project +(`github.com/ahmedfgad/GeneticAlgorithmPython `__) +or send an e-mail to ahmed.f.gad@gmail.com. + +Also check the `Contact +Us `__ +section for more contact details. + +Projects Built using PyGAD +========================== + +If you created a project that uses PyGAD, then we can support you by +mentioning this project here in PyGAD's documentation. + +To do that, please send a message at ahmed.f.gad@gmail.com or check the +`Contact +Us `__ +section for more contact details. + +Within your message, please send the following details: + +- Project title + +- Brief description + +- Preferably, a link that directs the readers to your project + +Tutorials about PyGAD +===================== + +`Adaptive Mutation in Genetic Algorithm with Python Examples `__ +----------------------------------------------------------------------------------------------------------------------------------------------------- + +In this tutorial, we’ll see why mutation with a fixed number of genes is +bad, and how to replace it with adaptive mutation. Using the `PyGAD +Python 3 library `__, we’ll discuss a few +examples that use both random and adaptive mutation. + +`Clustering Using the Genetic Algorithm in Python `__ +------------------------------------------------------------------------------------------------------------------------- + +This tutorial discusses how the genetic algorithm is used to cluster +data, starting from random clusters and running until the optimal +clusters are found. We'll start by briefly revising the K-means +clustering algorithm to point out its weak points, which are later +solved by the genetic algorithm. The code examples in this tutorial are +implemented in Python using the `PyGAD +library `__. + +`Working with Different Genetic Algorithm Representations in Python `__ +-------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Depending on the nature of the problem being optimized, the genetic +algorithm (GA) supports two different gene representations: binary, and +decimal. The binary GA has only two values for its genes, which are 0 +and 1. This is easier to manage as its gene values are limited compared +to the decimal GA, for which we can use different formats like float or +integer, and limited or unlimited ranges. + +This tutorial discusses how the +`PyGAD `__ library supports the two GA +representations, binary and decimal. + +.. _5-genetic-algorithm-applications-using-pygad: + +`5 Genetic Algorithm Applications Using PyGAD `__ +------------------------------------------------------------------------------------------------------------------------- + +This tutorial introduces PyGAD, an open-source Python library for +implementing the genetic algorithm and training machine learning +algorithms. PyGAD supports 19 parameters for customizing the genetic +algorithm for various applications. + +Within this tutorial we'll discuss 5 different applications of the +genetic algorithm and build them using PyGAD. + +`Train Neural Networks Using a Genetic Algorithm in Python with PyGAD `__ +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The genetic algorithm (GA) is a biologically-inspired optimization +algorithm. It has in recent years gained importance, as it’s simple +while also solving complex problems like travel route optimization, +training machine learning algorithms, working with single and +multi-objective problems, game playing, and more. + +Deep neural networks are inspired by the idea of how the biological +brain works. It’s a universal function approximator, which is capable of +simulating any function, and is now used to solve the most complex +problems in machine learning. What’s more, they’re able to work with all +types of data (images, audio, video, and text). + +Both genetic algorithms (GAs) and neural networks (NNs) are similar, as +both are biologically-inspired techniques. This similarity motivates us +to create a hybrid of both to see whether a GA can train NNs with high +accuracy. + +This tutorial uses `PyGAD `__, a Python +library that supports building and training NNs using a GA. +`PyGAD `__ offers both classification and +regression NNs. + +`Building a Game-Playing Agent for CoinTex Using the Genetic Algorithm `__ +---------------------------------------------------------------------------------------------------------------------------------------------------------- + +In this tutorial we'll see how to build a game-playing agent using only +the genetic algorithm to play a game called +`CoinTex `__, +which is developed in the Kivy Python framework. The objective of +CoinTex is to collect the randomly distributed coins while avoiding +collision with fire and monsters (that move randomly). The source code +of CoinTex can be found `on +GitHub `__. + +The genetic algorithm is the only AI used here; there is no other +machine/deep learning model used with it. We'll implement the genetic +algorithm using +`PyGad `__. +This tutorial starts with a quick overview of CoinTex followed by a +brief explanation of the genetic algorithm, and how it can be used to +create the playing agent. Finally, we'll see how to implement these +ideas in Python. + +The source code of the genetic algorithm agent is available +`here `__, +and you can download the code used in this tutorial from +`here `__. + +`How To Train Keras Models Using the Genetic Algorithm with PyGAD `__ +-------------------------------------------------------------------------------------------------------------------------------------------------------- + +PyGAD is an open-source Python library for building the genetic +algorithm and training machine learning algorithms. It offers a wide +range of parameters to customize the genetic algorithm to work with +different types of problems. + +PyGAD has its own modules that support building and training neural +networks (NNs) and convolutional neural networks (CNNs). Despite these +modules working well, they are implemented in Python without any +additional optimization measures. This leads to comparatively high +computational times for even simple problems. + +The latest PyGAD version, 2.8.0 (released on 20 September 2020), +supports a new module to train Keras models. Even though Keras is built +in Python, it's fast. The reason is that Keras uses TensorFlow as a +backend, and TensorFlow is highly optimized. + +This tutorial discusses how to train Keras models using PyGAD. The +discussion includes building Keras models using either the Sequential +Model or the Functional API, building an initial population of Keras +model parameters, creating an appropriate fitness function, and more. + +|image1| + +`Train PyTorch Models Using Genetic Algorithm with PyGAD `__ +--------------------------------------------------------------------------------------------------------------------------------------------- + +`PyGAD `__ is a genetic algorithm Python +3 library for solving optimization problems. One of these problems is +training machine learning algorithms. + +PyGAD has a module called +`pygad.kerasga `__. It trains +Keras models using the genetic algorithm. On January 3rd, 2021, a new +release of `PyGAD 2.10.0 `__ brought a +new module called +`pygad.torchga `__ to train +PyTorch models. It’s very easy to use, but there are a few tricky steps. + +So, in this tutorial, we’ll explore how to use PyGAD to train PyTorch +models. + +|image2| + +`A Guide to Genetic ‘Learning’ Algorithms for Optimization `__ +------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +PyGAD in Other Languages +======================== + +French +------ + +`Cómo los algoritmos genéticos pueden competir con el descenso de +gradiente y el +backprop `__ + +Bien que la manière standard d'entraîner les réseaux de neurones soit la +descente de gradient et la rétropropagation, il y a d'autres joueurs +dans le jeu. L'un d'eux est les algorithmes évolutionnaires, tels que +les algorithmes génétiques. + +Utiliser un algorithme génétique pour former un réseau de neurones +simple pour résoudre le OpenAI CartPole Jeu. Dans cet article, nous +allons former un simple réseau de neurones pour résoudre le OpenAI +CartPole . J'utiliserai PyTorch et PyGAD . + +|image3| + +Spanish +------- + +`Cómo los algoritmos genéticos pueden competir con el descenso de +gradiente y el +backprop `__ + +Aunque la forma estandar de entrenar redes neuronales es el descenso de +gradiente y la retropropagacion, hay otros jugadores en el juego, uno de +ellos son los algoritmos evolutivos, como los algoritmos geneticos. + +Usa un algoritmo genetico para entrenar una red neuronal simple para +resolver el Juego OpenAI CartPole. En este articulo, entrenaremos una +red neuronal simple para resolver el OpenAI CartPole . Usare PyTorch y +PyGAD . + +|image4| + +Korean +------ + +`[PyGAD] Python 에서 Genetic Algorithm 을 사용해보기 `__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +|image5| + +파이썬에서 genetic algorithm을 사용하는 패키지들을 다 사용해보진 +않았지만, 확장성이 있어보이고, 시도할 일이 있어서 살펴봤다. + +이 패키지에서 가장 인상 깊었던 것은 neural network에서 hyper parameter +탐색을 gradient descent 방식이 아닌 GA로도 할 수 있다는 것이다. + +개인적으로 이 부분이 어느정도 초기치를 잘 잡아줄 수 있는 역할로도 쓸 수 +있고, Loss가 gradient descent 하기 어려운 구조에서 대안으로 쓸 수 있을 +것으로도 생각된다. + +일단 큰 흐름은 다음과 같이 된다. + +사실 완전히 흐름이나 각 parameter에 대한 이해는 부족한 상황 + +Turkish +------- + +`PyGAD ile Genetik Algoritmayı Kullanarak Keras Modelleri Nasıl Eğitilir `__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This is a translation of an original English tutorial published at +Paperspace: `How To Train Keras Models Using the Genetic Algorithm with +PyGAD `__ + +PyGAD, genetik algoritma oluşturmak ve makine öğrenimi algoritmalarını +eğitmek için kullanılan açık kaynaklı bir Python kitaplığıdır. Genetik +algoritmayı farklı problem türleri ile çalışacak şekilde özelleştirmek +için çok çeşitli parametreler sunar. + +PyGAD, sinir ağları (NN’ler) ve evrişimli sinir ağları (CNN’ler) +oluşturmayı ve eğitmeyi destekleyen kendi modüllerine sahiptir. Bu +modüllerin iyi çalışmasına rağmen, herhangi bir ek optimizasyon önlemi +olmaksızın Python’da uygulanırlar. Bu, basit problemler için bile +nispeten yüksek hesaplama sürelerine yol açar. + +En son PyGAD sürümü 2.8.0 (20 Eylül 2020'de piyasaya sürüldü), Keras +modellerini eğitmek için yeni bir modülü destekliyor. Keras Python’da +oluşturulmuş olsa da hızlıdır. Bunun nedeni, Keras’ın arka uç olarak +TensorFlow kullanması ve TensorFlow’un oldukça optimize edilmiş +olmasıdır. + +Bu öğreticide, PyGAD kullanılarak Keras modellerinin nasıl eğitileceği +anlatılmaktadır. Tartışma, Sıralı Modeli veya İşlevsel API’yi kullanarak +Keras modellerini oluşturmayı, Keras model parametrelerinin ilk +popülasyonunu oluşturmayı, uygun bir uygunluk işlevi oluşturmayı ve daha +fazlasını içerir. + +|image6| + +Hungarian +--------- + +.. _tensorflow-alapozó-10-neurális-hálózatok-tenyésztése-genetikus-algoritmussal-pygad-és-openai-gym-használatával: + +`Tensorflow alapozó 10. Neurális hálózatok tenyésztése genetikus algoritmussal PyGAD és OpenAI Gym használatával `__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Hogy kontextusba helyezzem a genetikus algoritmusokat, ismételjük kicsit +át, hogy hogyan működik a gradient descent és a backpropagation, ami a +neurális hálók tanításának általános módszere. Az erről írt cikkemet itt +tudjátok elolvasni. + +A hálózatok tenyésztéséhez a +`PyGAD `__ nevű +programkönyvtárat használjuk, így mindenek előtt ezt kell telepítenünk, +valamint a Tensorflow-t és a Gym-et, amit Colabban már eleve telepítve +kapunk. + +Maga a PyGAD egy teljesen általános genetikus algoritmusok futtatására +képes rendszer. Ennek a kiterjesztése a KerasGA, ami az általános motor +Tensorflow (Keras) neurális hálókon történő futtatását segíti. A 47. +sorban létrehozott KerasGA objektum ennek a kiterjesztésnek a része és +arra szolgál, hogy a paraméterként átadott modellből a második +paraméterben megadott számosságú populációt hozzon létre. Mivel a +hálózatunk 386 állítható paraméterrel rendelkezik, ezért a DNS-ünk itt +386 elemből fog állni. A populáció mérete 10 egyed, így a kezdő +populációnk egy 10x386 elemű mátrix lesz. Ezt adjuk át az 51. sorban az +initial_population paraméterben. + +|image7| + +Russian +------- + +`PyGAD: библиотека для имплементации генетического алгоритма `__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +PyGAD — это библиотека для имплементации генетического алгоритма. Кроме +того, библиотека предоставляет доступ к оптимизированным реализациям +алгоритмов машинного обучения. PyGAD разрабатывали на Python 3. + +Библиотека PyGAD поддерживает разные типы скрещивания, мутации и +селекции родителя. PyGAD позволяет оптимизировать проблемы с помощью +генетического алгоритма через кастомизацию целевой функции. + +Кроме генетического алгоритма, библиотека содержит оптимизированные +имплементации алгоритмов машинного обучения. На текущий момент PyGAD +поддерживает создание и обучение нейросетей для задач классификации. + +Библиотека находится в стадии активной разработки. Создатели планируют +добавление функционала для решения бинарных задач и имплементации новых +алгоритмов. + +PyGAD разрабатывали на Python 3.7.3. Зависимости включают в себя NumPy +для создания и манипуляции массивами и Matplotlib для визуализации. Один +из изкейсов использования инструмента — оптимизация весов, которые +удовлетворяют заданной функции. + +|image8| + +Research Papers using PyGAD +=========================== + +A number of research papers used PyGAD and here are some of them: + +- Jaros, Marta, and Jiri Jaros. "Performance-Cost Optimization of + Moldable Scientific Workflows." + +- Thorat, Divya. "Enhanced genetic algorithm to reduce makespan of + multiple jobs in map-reduce application on serverless platform". + Diss. Dublin, National College of Ireland, 2020. + +- Koch, Chris, and Edgar Dobriban. "AttenGen: Generating Live + Attenuated Vaccine Candidates using Machine Learning." (2021). + +- Bhardwaj, Bhavya, et al. "Windfarm optimization using Nelder-Mead and + Particle Swarm optimization." *2021 7th International Conference on + Electrical Energy Systems (ICEES)*. IEEE, 2021. + +- Bernardo, Reginald Christian S. and J. Said. “Towards a + model-independent reconstruction approach for late-time Hubble data.” + (2021). + +- Duong, Tri Dung, Qian Li, and Guandong Xu. "Prototype-based + Counterfactual Explanation for Causal Classification." *arXiv + preprint arXiv:2105.00703* (2021). + +- Farrag, Tamer Ahmed, and Ehab E. Elattar. "Optimized Deep Stacked + Long Short-Term Memory Network for Long-Term Load Forecasting." *IEEE + Access* 9 (2021): 68511-68522. + +- Antunes, E. D. O., Caetano, M. F., Marotta, M. A., Araujo, A., + Bondan, L., Meneguette, R. I., & Rocha Filho, G. P. (2021, August). + Soluções Otimizadas para o Problema de Localização de Máxima + Cobertura em Redes Militarizadas 4G/LTE. In *Anais do XXVI Workshop + de Gerência e Operação de Redes e Serviços* (pp. 152-165). SBC. + +- M. Yani, F. Ardilla, A. A. Saputra and N. Kubota, "Gradient-Free Deep + Q-Networks Reinforcement learning: Benchmark and Evaluation," *2021 + IEEE Symposium Series on Computational Intelligence (SSCI)*, 2021, + pp. 1-5, doi: 10.1109/SSCI50451.2021.9659941. + +- Yani, Mohamad, and Naoyuki Kubota. "Deep Convolutional Networks with + Genetic Algorithm for Reinforcement Learning Problem." + +- Mahendra, Muhammad Ihza, and Isman Kurniawan. "Optimizing + Convolutional Neural Network by Using Genetic Algorithm for COVID-19 + Detection in Chest X-Ray Image." *2021 International Conference on + Data Science and Its Applications (ICoDSA)*. IEEE, 2021. + +- Glibota, Vjeko. *Umjeravanje mikroskopskog prometnog modela primjenom + genetskog algoritma*. Diss. University of Zagreb. Faculty of + Transport and Traffic Sciences. Division of Intelligent Transport + Systems and Logistics. Department of Intelligent Transport Systems, + 2021. + +- Zhu, Mingda. *Genetic Algorithm-based Parameter Identification for + Ship Manoeuvring Model under Wind Disturbance*. MS thesis. NTNU, + 2021. + +- Abdalrahman, Ahmed, and Weihua Zhuang. "Dynamic pricing for + differentiated pev charging services using deep reinforcement + learning." *IEEE Transactions on Intelligent Transportation Systems* + (2020). + +More Links +========== + +https://rodriguezanton.com/identifying-contact-states-for-2d-objects-using-pygad-and/ + +https://torvaney.github.io/projects/t9-optimised + +For More Information +==================== + +There are different resources that can be used to get started with the +genetic algorithm and building it in Python. + +Tutorial: Implementing Genetic Algorithm in Python +-------------------------------------------------- + +To start with coding the genetic algorithm, you can check the tutorial +titled `Genetic Algorithm Implementation in +Python `__ +available at these links: + +- `LinkedIn `__ + +- `Towards Data + Science `__ + +- `KDnuggets `__ + +`This +tutorial `__ +is prepared based on a previous version of the project but it still a +good resource to start with coding the genetic algorithm. + +|image9| + +Tutorial: Introduction to Genetic Algorithm +------------------------------------------- + +Get started with the genetic algorithm by reading the tutorial titled +`Introduction to Optimization with Genetic +Algorithm `__ +which is available at these links: + +- `LinkedIn `__ + +- `Towards Data + Science `__ + +- `KDnuggets `__ + +|image10| + +Tutorial: Build Neural Networks in Python +----------------------------------------- + +Read about building neural networks in Python through the tutorial +titled `Artificial Neural Network Implementation using NumPy and +Classification of the Fruits360 Image +Dataset `__ +available at these links: + +- `LinkedIn `__ + +- `Towards Data + Science `__ + +- `KDnuggets `__ + +|image11| + +Tutorial: Optimize Neural Networks with Genetic Algorithm +--------------------------------------------------------- + +Read about training neural networks using the genetic algorithm through +the tutorial titled `Artificial Neural Networks Optimization using +Genetic Algorithm with +Python `__ +available at these links: + +- `LinkedIn `__ + +- `Towards Data + Science `__ + +- `KDnuggets `__ + +|image12| + +Tutorial: Building CNN in Python +-------------------------------- + +To start with coding the genetic algorithm, you can check the tutorial +titled `Building Convolutional Neural Network using NumPy from +Scratch `__ +available at these links: + +- `LinkedIn `__ + +- `Towards Data + Science `__ + +- `KDnuggets `__ + +- `Chinese Translation `__ + +`This +tutorial `__) +is prepared based on a previous version of the project but it still a +good resource to start with coding CNNs. + +|image13| + +Tutorial: Derivation of CNN from FCNN +------------------------------------- + +Get started with the genetic algorithm by reading the tutorial titled +`Derivation of Convolutional Neural Network from Fully Connected Network +Step-By-Step `__ +which is available at these links: + +- `LinkedIn `__ + +- `Towards Data + Science `__ + +- `KDnuggets `__ + +|image14| + +Book: Practical Computer Vision Applications Using Deep Learning with CNNs +-------------------------------------------------------------------------- + +You can also check my book cited as `Ahmed Fawzy Gad 'Practical Computer +Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, +978-1-4842-4167-7 `__ +which discusses neural networks, convolutional neural networks, deep +learning, genetic algorithm, and more. + +Find the book at these links: + +- `Amazon `__ + +- `Springer `__ + +- `Apress `__ + +- `O'Reilly `__ + +- `Google Books `__ + +.. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg + :alt: + +Contact Us +========== + +- E-mail: ahmed.f.gad@gmail.com + +- `LinkedIn `__ + +- `Amazon Author Page `__ + +- `Heartbeat `__ + +- `Paperspace `__ + +- `KDnuggets `__ + +- `TowardsDataScience `__ + +- `GitHub `__ + +.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png + :alt: + +Thank you for using +`PyGAD `__ :) + +.. |image1| image:: https://user-images.githubusercontent.com/16560492/111009628-2b372500-8362-11eb-90cf-01b47d831624.png + :target: https://blog.paperspace.com/train-keras-models-using-genetic-algorithm-with-pygad +.. |image2| image:: https://user-images.githubusercontent.com/16560492/111009678-5457b580-8362-11eb-899a-39e2f96984df.png + :target: https://neptune.ai/blog/train-pytorch-models-using-genetic-algorithm-with-pygad +.. |image3| image:: https://user-images.githubusercontent.com/16560492/111009275-3178d180-8361-11eb-9e86-7fb1519acde7.png + :target: https://www.hebergementwebs.com/nouvelles/comment-les-algorithmes-genetiques-peuvent-rivaliser-avec-la-descente-de-gradient-et-le-backprop +.. |image4| image:: https://user-images.githubusercontent.com/16560492/111009257-232ab580-8361-11eb-99a5-7226efbc3065.png + :target: https://www.hebergementwebs.com/noticias/como-los-algoritmos-geneticos-pueden-competir-con-el-descenso-de-gradiente-y-el-backprop +.. |image5| image:: https://user-images.githubusercontent.com/16560492/108586306-85bd0280-731b-11eb-874c-7ac4ce1326cd.jpg + :target: https://data-newbie.tistory.com/m/685 +.. |image6| image:: https://user-images.githubusercontent.com/16560492/108586601-85be0200-731d-11eb-98a4-161c75a1f099.jpg + :target: https://erencan34.medium.com/pygad-ile-genetik-algoritmay%C4%B1-kullanarak-keras-modelleri-nas%C4%B1l-e%C4%9Fitilir-cf92639a478c +.. |image7| image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png + :target: https://thebojda.medium.com/tensorflow-alapoz%C3%B3-10-24f7767d4a2c +.. |image8| image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png + :target: https://neurohive.io/ru/frameworki/pygad-biblioteka-dlya-implementacii-geneticheskogo-algoritma +.. |image9| image:: https://user-images.githubusercontent.com/16560492/78830052-a3c19300-79e7-11ea-8b9b-4b343ea4049c.png + :target: https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad +.. |image10| image:: https://user-images.githubusercontent.com/16560492/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg + :target: https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad +.. |image11| image:: https://user-images.githubusercontent.com/16560492/82078281-30472b80-96e1-11ea-8017-6a1f4383d602.jpg + :target: https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad +.. |image12| image:: https://user-images.githubusercontent.com/16560492/82078300-376e3980-96e1-11ea-821c-aa6b8ceb44d4.jpg + :target: https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad +.. |image13| image:: https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png + :target: https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad +.. |image14| image:: https://user-images.githubusercontent.com/16560492/82431369-db176b00-9a8e-11ea-99bd-e845192873fc.png + :target: https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad diff --git a/docs/source/README_pygad_torchga_ReadTheDocs.rst b/docs/source/torchga.rst similarity index 97% rename from docs/source/README_pygad_torchga_ReadTheDocs.rst rename to docs/source/torchga.rst index 66a554e..e49a2a2 100644 --- a/docs/source/README_pygad_torchga_ReadTheDocs.rst +++ b/docs/source/torchga.rst @@ -1,946 +1,946 @@ -.. _pygadtorchga-module: - -``pygad.torchga`` Module -======================== - -This section of the PyGAD's library documentation discusses the -**pygad.torchga** module. - -The ``pygad.torchga`` module has a helper class and 2 functions to train -PyTorch models using the genetic algorithm (PyGAD). - -The contents of this module are: - -1. ``TorchGA``: A class for creating an initial population of all - parameters in the PyTorch model. - -2. ``model_weights_as_vector()``: A function to reshape the PyTorch - model weights to a single vector. - -3. ``model_weights_as_dict()``: A function to restore the PyTorch model - weights from a vector. - -4. ``predict()``: A function to make predictions based on the PyTorch - model and a solution. - -More details are given in the next sections. - -Steps Summary -============= - -The summary of the steps used to train a PyTorch model using PyGAD is as -follows: - -1. Create a PyTorch model. - -2. Create an instance of the ``pygad.torchga.TorchGA`` class. - -3. Prepare the training data. - -4. Build the fitness function. - -5. Create an instance of the ``pygad.GA`` class. - -6. Run the genetic algorithm. - -Create PyTorch Model -==================== - -Before discussing training a PyTorch model using PyGAD, the first thing -to do is to create the PyTorch model. To get started, please check the -`PyTorch library -documentation `__. - -Here is an example of a PyTorch model. - -.. code:: python - - import torch - - input_layer = torch.nn.Linear(3, 5) - relu_layer = torch.nn.ReLU() - output_layer = torch.nn.Linear(5, 1) - - model = torch.nn.Sequential(input_layer, - relu_layer, - output_layer) - -Feel free to add the layers of your choice. - -.. _pygadtorchgatorchga-class: - -``pygad.torchga.TorchGA`` Class -=============================== - -The ``pygad.torchga`` module has a class named ``TorchGA`` for creating -an initial population for the genetic algorithm based on a PyTorch -model. The constructor, methods, and attributes within the class are -discussed in this section. - -.. _init: - -``__init__()`` --------------- - -The ``pygad.torchga.TorchGA`` class constructor accepts the following -parameters: - -- ``model``: An instance of the PyTorch model. - -- ``num_solutions``: Number of solutions in the population. Each - solution has different parameters of the model. - -Instance Attributes -------------------- - -All parameters in the ``pygad.torchga.TorchGA`` class constructor are -used as instance attributes in addition to adding a new attribute called -``population_weights``. - -Here is a list of all instance attributes: - -- ``model`` - -- ``num_solutions`` - -- ``population_weights``: A nested list holding the weights of all - solutions in the population. - -Methods in the ``TorchGA`` Class --------------------------------- - -This section discusses the methods available for instances of the -``pygad.torchga.TorchGA`` class. - -.. _createpopulation: - -``create_population()`` -~~~~~~~~~~~~~~~~~~~~~~~ - -The ``create_population()`` method creates the initial population of the -genetic algorithm as a list of solutions where each solution represents -different model parameters. The list of networks is assigned to the -``population_weights`` attribute of the instance. - -.. _functions-in-the-pygadtorchga-module: - -Functions in the ``pygad.torchga`` Module -========================================= - -This section discusses the functions in the ``pygad.torchga`` module. - -.. _pygadtorchgamodelweightsasvector: - -``pygad.torchga.model_weights_as_vector()`` --------------------------------------------- - -The ``model_weights_as_vector()`` function accepts a single parameter -named ``model`` representing the PyTorch model. It returns a vector -holding all model weights. The reason for representing the model weights -as a vector is that the genetic algorithm expects all parameters of any -solution to be in a 1D vector form. - -The function accepts the following parameters: - -- ``model``: The PyTorch model. - -It returns a 1D vector holding the model weights. - -.. _pygadtorchmodelweightsasdict: - -``pygad.torch.model_weights_as_dict()`` ---------------------------------------- - -The ``model_weights_as_dict()`` function accepts the following -parameters: - -1. ``model``: The PyTorch model. - -2. ``weights_vector``: The model parameters as a vector. - -It returns the restored model weights in the same form used by the -``state_dict()`` method. The returned dictionary is ready to be passed -to the ``load_state_dict()`` method for setting the PyTorch model's -parameters. - -.. _pygadtorchgapredict: - -``pygad.torchga.predict()`` ---------------------------- - -The ``predict()`` function makes a prediction based on a solution. It -accepts the following parameters: - -1. ``model``: The PyTorch model. - -2. ``solution``: The solution evolved. - -3. ``data``: The test data inputs. - -It returns the predictions for the data samples. - -Examples -======== - -This section gives the complete code of some examples that build and -train a PyTorch model using PyGAD. Each subsection builds a different -network. - -Example 1: Regression Example ------------------------------ - -The next code builds a simple PyTorch model for regression. The next -subsections discuss each part in the code. - -.. code:: python - - import torch - import torchga - import pygad - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, torch_ga, model, loss_function - - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - - abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001 - - solution_fitness = 1.0 / abs_error - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - # Create the PyTorch model. - input_layer = torch.nn.Linear(3, 5) - relu_layer = torch.nn.ReLU() - output_layer = torch.nn.Linear(5, 1) - - model = torch.nn.Sequential(input_layer, - relu_layer, - output_layer) - # print(model) - - # Create an instance of the pygad.torchga.TorchGA class to build the initial population. - torch_ga = torchga.TorchGA(model=model, - num_solutions=10) - - loss_function = torch.nn.L1Loss() - - # Data inputs - data_inputs = torch.tensor([[0.02, 0.1, 0.15], - [0.7, 0.6, 0.8], - [1.5, 1.2, 1.7], - [3.2, 2.9, 3.1]]) - - # Data outputs - data_outputs = torch.tensor([[0.1], - [0.6], - [1.3], - [2.5]]) - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 250 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = torch_ga.population_weights # Initial population of network weights - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Make predictions based on the best solution. - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - print("Predictions : \n", predictions.detach().numpy()) - - abs_error = loss_function(predictions, data_outputs) - print("Absolute Error : ", abs_error.detach().numpy()) - -Create a PyTorch model -~~~~~~~~~~~~~~~~~~~~~~ - -According to the steps mentioned previously, the first step is to create -a PyTorch model. Here is the code that builds the model using the -Functional API. - -.. code:: python - - import torch - - input_layer = torch.nn.Linear(3, 5) - relu_layer = torch.nn.ReLU() - output_layer = torch.nn.Linear(5, 1) - - model = torch.nn.Sequential(input_layer, - relu_layer, - output_layer) - -.. _create-an-instance-of-the-pygadtorchgatorchga-class: - -Create an Instance of the ``pygad.torchga.TorchGA`` Class -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The second step is to create an instance of the -``pygad.torchga.TorchGA`` class. There are 10 solutions per population. -Change this number according to your needs. - -.. code:: python - - import pygad.torchga - - torch_ga = torchga.TorchGA(model=model, - num_solutions=10) - -.. _prepare-the-training-data-1: - -Prepare the Training Data -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The third step is to prepare the training data inputs and outputs. Here -is an example where there are 4 samples. Each sample has 3 inputs and 1 -output. - -.. code:: python - - import numpy - - # Data inputs - data_inputs = numpy.array([[0.02, 0.1, 0.15], - [0.7, 0.6, 0.8], - [1.5, 1.2, 1.7], - [3.2, 2.9, 3.1]]) - - # Data outputs - data_outputs = numpy.array([[0.1], - [0.6], - [1.3], - [2.5]]) - -Build the Fitness Function -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The fourth step is to build the fitness function. This function must -accept 2 parameters representing the solution and its index within the -population. - -The next fitness function calculates the mean absolute error (MAE) of -the PyTorch model based on the parameters in the solution. The -reciprocal of the MAE is used as the fitness value. Feel free to use any -other loss function to calculate the fitness value. - -.. code:: python - - loss_function = torch.nn.L1Loss() - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, torch_ga, model, loss_function - - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - - abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001 - - solution_fitness = 1.0 / abs_error - - return solution_fitness - -.. _create-an-instance-of-the-pygadga-class: - -Create an Instance of the ``pygad.GA`` Class -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The fifth step is to instantiate the ``pygad.GA`` class. Note how the -``initial_population`` parameter is assigned to the initial weights of -the PyTorch models. - -For more information, please check the `parameters this class -accepts `__. - -.. code:: python - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 250 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = torch_ga.population_weights # Initial population of network weights - - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - -Run the Genetic Algorithm -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The sixth and last step is to run the genetic algorithm by calling the -``run()`` method. - -.. code:: python - - ga_instance.run() - -After the PyGAD completes its execution, then there is a figure that -shows how the fitness value changes by generation. Call the -``plot_fitness()`` method to show the figure. - -.. code:: python - - ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) - -Here is the figure. - -.. figure:: https://user-images.githubusercontent.com/16560492/103469779-22f5b480-4d37-11eb-80dc-95503065ebb1.png - :alt: - -To get information about the best solution found by PyGAD, use the -``best_solution()`` method. - -.. code:: python - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - -.. code:: python - - Fitness value of the best solution = 145.42425295191546 - Index of the best solution : 0 - -The next code restores the trained model weights using the -``model_weights_as_dict()`` function. The restored weights are used to -calculate the predicted values. - -.. code:: python - - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - print("Predictions : \n", predictions.detach().numpy()) - -.. code:: python - - Predictions : - [[0.08401088] - [0.60939324] - [1.3010881 ] - [2.5010352 ]] - -The next code measures the trained model error. - -.. code:: python - - abs_error = loss_function(predictions, data_outputs) - print("Absolute Error : ", abs_error.detach().numpy()) - -.. code:: - - Absolute Error : 0.006876422 - -Example 2: XOR Binary Classification ------------------------------------- - -The next code creates a PyTorch model to build the XOR binary -classification problem. Let's highlight the changes compared to the -previous example. - -.. code:: python - - import torch - import torchga - import pygad - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, torch_ga, model, loss_function - - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - - solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - # Create the PyTorch model. - input_layer = torch.nn.Linear(2, 4) - relu_layer = torch.nn.ReLU() - dense_layer = torch.nn.Linear(4, 2) - output_layer = torch.nn.Softmax(1) - - model = torch.nn.Sequential(input_layer, - relu_layer, - dense_layer, - output_layer) - # print(model) - - # Create an instance of the pygad.torchga.TorchGA class to build the initial population. - torch_ga = torchga.TorchGA(model=model, - num_solutions=10) - - loss_function = torch.nn.BCELoss() - - # XOR problem inputs - data_inputs = torch.tensor([[0.0, 0.0], - [0.0, 1.0], - [1.0, 0.0], - [1.0, 1.0]]) - - # XOR problem outputs - data_outputs = torch.tensor([[1.0, 0.0], - [0.0, 1.0], - [0.0, 1.0], - [1.0, 0.0]]) - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 250 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = torch_ga.population_weights # Initial population of network weights. - - # Create an instance of the pygad.GA class - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - # Start the genetic algorithm evolution. - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Make predictions based on the best solution. - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - print("Predictions : \n", predictions.detach().numpy()) - - # Calculate the binary crossentropy for the trained model. - print("Binary Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) - - # Calculate the classification accuracy of the trained model. - a = torch.max(predictions, axis=1) - b = torch.max(data_outputs, axis=1) - accuracy = torch.sum(a.indices == b.indices) / len(data_outputs) - print("Accuracy : ", accuracy.detach().numpy()) - -Compared to the previous regression example, here are the changes: - -- The PyTorch model is changed according to the nature of the problem. - Now, it has 2 inputs and 2 outputs with an in-between hidden layer of - 4 neurons. - -.. code:: python - - input_layer = torch.nn.Linear(2, 4) - relu_layer = torch.nn.ReLU() - dense_layer = torch.nn.Linear(4, 2) - output_layer = torch.nn.Softmax(1) - - model = torch.nn.Sequential(input_layer, - relu_layer, - dense_layer, - output_layer) - -- The train data is changed. Note that the output of each sample is a - 1D vector of 2 values, 1 for each class. - -.. code:: python - - # XOR problem inputs - data_inputs = torch.tensor([[0.0, 0.0], - [0.0, 1.0], - [1.0, 0.0], - [1.0, 1.0]]) - - # XOR problem outputs - data_outputs = torch.tensor([[1.0, 0.0], - [0.0, 1.0], - [0.0, 1.0], - [1.0, 0.0]]) - -- The fitness value is calculated based on the binary cross entropy. - -.. code:: python - - loss_function = torch.nn.BCELoss() - -After the previous code completes, the next figure shows how the fitness -value change by generation. - -.. figure:: https://user-images.githubusercontent.com/16560492/103469818-c646c980-4d37-11eb-98c3-d9d591acd5e2.png - :alt: - -Here is some information about the trained model. Its fitness value is -``100000000.0``, loss is ``0.0`` and accuracy is 100%. - -.. code:: python - - Fitness value of the best solution = 100000000.0 - - Index of the best solution : 0 - - Predictions : - [[1.0000000e+00 1.3627675e-10] - [3.8521746e-09 1.0000000e+00] - [4.2789325e-10 1.0000000e+00] - [1.0000000e+00 3.3668417e-09]] - - Binary Crossentropy : 0.0 - - Accuracy : 1.0 - -Example 3: Image Multi-Class Classification (Dense Layers) ----------------------------------------------------------- - -Here is the code. - -.. code:: python - - import torch - import torchga - import pygad - import numpy - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, torch_ga, model, loss_function - - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - - solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - # Build the PyTorch model using the functional API. - input_layer = torch.nn.Linear(360, 50) - relu_layer = torch.nn.ReLU() - dense_layer = torch.nn.Linear(50, 4) - output_layer = torch.nn.Softmax(1) - - model = torch.nn.Sequential(input_layer, - relu_layer, - dense_layer, - output_layer) - - # Create an instance of the pygad.torchga.TorchGA class to build the initial population. - torch_ga = torchga.TorchGA(model=model, - num_solutions=10) - - loss_function = torch.nn.CrossEntropyLoss() - - # Data inputs - data_inputs = torch.from_numpy(numpy.load("dataset_features.npy")).float() - - # Data outputs - data_outputs = torch.from_numpy(numpy.load("outputs.npy")).long() - # The next 2 lines are equivelant to this Keras function to perform 1-hot encoding: tensorflow.keras.utils.to_categorical(data_outputs) - # temp_outs = numpy.zeros((data_outputs.shape[0], numpy.unique(data_outputs).size), dtype=numpy.uint8) - # temp_outs[numpy.arange(data_outputs.shape[0]), numpy.uint8(data_outputs)] = 1 - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 200 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = torch_ga.population_weights # Initial population of network weights. - - # Create an instance of the pygad.GA class - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - # Start the genetic algorithm evolution. - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Fetch the parameters of the best solution. - best_solution_weights = torchga.model_weights_as_dict(model=model, - weights_vector=solution) - model.load_state_dict(best_solution_weights) - predictions = model(data_inputs) - # print("Predictions : \n", predictions) - - # Calculate the crossentropy loss of the trained model. - print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) - - # Calculate the classification accuracy for the trained model. - accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs) - print("Accuracy : ", accuracy.detach().numpy()) - -Compared to the previous binary classification example, this example has -multiple classes (4) and thus the loss is measured using cross entropy. - -.. code:: python - - loss_function = torch.nn.CrossEntropyLoss() - -.. _prepare-the-training-data-2: - -Prepare the Training Data -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Before building and training neural networks, the training data (input -and output) needs to be prepared. The inputs and the outputs of the -training data are NumPy arrays. - -The data used in this example is available as 2 files: - -1. `dataset_features.npy `__: - Data inputs. - https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy - -2. `outputs.npy `__: - Class labels. - https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy - -The data consists of 4 classes of images. The image shape is -``(100, 100, 3)``. The number of training samples is 1962. The feature -vector extracted from each image has a length 360. - -.. code:: python - - import numpy - - data_inputs = numpy.load("dataset_features.npy") - - data_outputs = numpy.load("outputs.npy") - -The next figure shows how the fitness value changes. - -.. figure:: https://user-images.githubusercontent.com/16560492/103469855-5d138600-4d38-11eb-84b1-b5eff8faa7bc.png - :alt: - -Here are some statistics about the trained model. - -.. code:: - - Fitness value of the best solution = 1.3446997034434534 - Index of the best solution : 0 - Crossentropy : 0.74366045 - Accuracy : 1.0 - -Example 4: Image Multi-Class Classification (Conv Layers) ---------------------------------------------------------- - -Compared to the previous example that uses only dense layers, this -example uses convolutional layers to classify the same dataset. - -Here is the complete code. - -.. code:: python - - import torch - import torchga - import pygad - import numpy - - def fitness_func(ga_instance, solution, sol_idx): - global data_inputs, data_outputs, torch_ga, model, loss_function - - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - - solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) - - return solution_fitness - - def callback_generation(ga_instance): - print("Generation = {generation}".format(generation=ga_instance.generations_completed)) - print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) - - # Build the PyTorch model. - input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7) - relu_layer1 = torch.nn.ReLU() - max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5) - - conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3) - relu_layer2 = torch.nn.ReLU() - - flatten_layer1 = torch.nn.Flatten() - # The value 768 is pre-computed by tracing the sizes of the layers' outputs. - dense_layer1 = torch.nn.Linear(in_features=768, out_features=15) - relu_layer3 = torch.nn.ReLU() - - dense_layer2 = torch.nn.Linear(in_features=15, out_features=4) - output_layer = torch.nn.Softmax(1) - - model = torch.nn.Sequential(input_layer, - relu_layer1, - max_pool1, - conv_layer2, - relu_layer2, - flatten_layer1, - dense_layer1, - relu_layer3, - dense_layer2, - output_layer) - - # Create an instance of the pygad.torchga.TorchGA class to build the initial population. - torch_ga = torchga.TorchGA(model=model, - num_solutions=10) - - loss_function = torch.nn.CrossEntropyLoss() - - # Data inputs - data_inputs = torch.from_numpy(numpy.load("dataset_inputs.npy")).float() - data_inputs = data_inputs.reshape((data_inputs.shape[0], data_inputs.shape[3], data_inputs.shape[1], data_inputs.shape[2])) - - # Data outputs - data_outputs = torch.from_numpy(numpy.load("dataset_outputs.npy")).long() - - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class - num_generations = 200 # Number of generations. - num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. - initial_population = torch_ga.population_weights # Initial population of network weights. - - # Create an instance of the pygad.GA class - ga_instance = pygad.GA(num_generations=num_generations, - num_parents_mating=num_parents_mating, - initial_population=initial_population, - fitness_func=fitness_func, - on_generation=callback_generation) - - # Start the genetic algorithm evolution. - ga_instance.run() - - # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. - ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) - - # Returning the details of the best solution. - solution, solution_fitness, solution_idx = ga_instance.best_solution() - print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) - print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) - - # Make predictions based on the best solution. - predictions = pygad.torchga.predict(model=model, - solution=solution, - data=data_inputs) - # print("Predictions : \n", predictions) - - # Calculate the crossentropy for the trained model. - print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) - - # Calculate the classification accuracy for the trained model. - accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs) - print("Accuracy : ", accuracy.detach().numpy()) - -Compared to the previous example, the only change is that the -architecture uses convolutional and max-pooling layers. The shape of -each input sample is 100x100x3. - -.. code:: python - - input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7) - relu_layer1 = torch.nn.ReLU() - max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5) - - conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3) - relu_layer2 = torch.nn.ReLU() - - flatten_layer1 = torch.nn.Flatten() - # The value 768 is pre-computed by tracing the sizes of the layers' outputs. - dense_layer1 = torch.nn.Linear(in_features=768, out_features=15) - relu_layer3 = torch.nn.ReLU() - - dense_layer2 = torch.nn.Linear(in_features=15, out_features=4) - output_layer = torch.nn.Softmax(1) - - model = torch.nn.Sequential(input_layer, - relu_layer1, - max_pool1, - conv_layer2, - relu_layer2, - flatten_layer1, - dense_layer1, - relu_layer3, - dense_layer2, - output_layer) - -.. _prepare-the-training-data-3: - -Prepare the Training Data -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The data used in this example is available as 2 files: - -1. `dataset_inputs.npy `__: - Data inputs. - https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy - -2. `dataset_outputs.npy `__: - Class labels. - https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy - -The data consists of 4 classes of images. The image shape is -``(100, 100, 3)`` and there are 20 images per class for a total of 80 -training samples. For more information about the dataset, check the -`Reading the -Data `__ -section of the ``pygad.cnn`` module. - -Simply download these 2 files and read them according to the next code. - -.. code:: python - - import numpy - - data_inputs = numpy.load("dataset_inputs.npy") - - data_outputs = numpy.load("dataset_outputs.npy") - -The next figure shows how the fitness value changes. - -.. figure:: https://user-images.githubusercontent.com/16560492/103469887-c7c4c180-4d38-11eb-98a7-1c5e73e918d0.png - :alt: - -Here are some statistics about the trained model. The model accuracy is -97.5% after the 200 generations. Note that just running the code again -may give different results. - -.. code:: - - Fitness value of the best solution = 1.3009520689219258 - Index of the best solution : 0 - Crossentropy : 0.7686678 - Accuracy : 0.975 +.. _pygadtorchga-module: + +``pygad.torchga`` Module +======================== + +This section of the PyGAD's library documentation discusses the +**pygad.torchga** module. + +The ``pygad.torchga`` module has a helper class and 2 functions to train +PyTorch models using the genetic algorithm (PyGAD). + +The contents of this module are: + +1. ``TorchGA``: A class for creating an initial population of all + parameters in the PyTorch model. + +2. ``model_weights_as_vector()``: A function to reshape the PyTorch + model weights to a single vector. + +3. ``model_weights_as_dict()``: A function to restore the PyTorch model + weights from a vector. + +4. ``predict()``: A function to make predictions based on the PyTorch + model and a solution. + +More details are given in the next sections. + +Steps Summary +============= + +The summary of the steps used to train a PyTorch model using PyGAD is as +follows: + +1. Create a PyTorch model. + +2. Create an instance of the ``pygad.torchga.TorchGA`` class. + +3. Prepare the training data. + +4. Build the fitness function. + +5. Create an instance of the ``pygad.GA`` class. + +6. Run the genetic algorithm. + +Create PyTorch Model +==================== + +Before discussing training a PyTorch model using PyGAD, the first thing +to do is to create the PyTorch model. To get started, please check the +`PyTorch library +documentation `__. + +Here is an example of a PyTorch model. + +.. code:: python + + import torch + + input_layer = torch.nn.Linear(3, 5) + relu_layer = torch.nn.ReLU() + output_layer = torch.nn.Linear(5, 1) + + model = torch.nn.Sequential(input_layer, + relu_layer, + output_layer) + +Feel free to add the layers of your choice. + +.. _pygadtorchgatorchga-class: + +``pygad.torchga.TorchGA`` Class +=============================== + +The ``pygad.torchga`` module has a class named ``TorchGA`` for creating +an initial population for the genetic algorithm based on a PyTorch +model. The constructor, methods, and attributes within the class are +discussed in this section. + +.. _init: + +``__init__()`` +-------------- + +The ``pygad.torchga.TorchGA`` class constructor accepts the following +parameters: + +- ``model``: An instance of the PyTorch model. + +- ``num_solutions``: Number of solutions in the population. Each + solution has different parameters of the model. + +Instance Attributes +------------------- + +All parameters in the ``pygad.torchga.TorchGA`` class constructor are +used as instance attributes in addition to adding a new attribute called +``population_weights``. + +Here is a list of all instance attributes: + +- ``model`` + +- ``num_solutions`` + +- ``population_weights``: A nested list holding the weights of all + solutions in the population. + +Methods in the ``TorchGA`` Class +-------------------------------- + +This section discusses the methods available for instances of the +``pygad.torchga.TorchGA`` class. + +.. _createpopulation: + +``create_population()`` +~~~~~~~~~~~~~~~~~~~~~~~ + +The ``create_population()`` method creates the initial population of the +genetic algorithm as a list of solutions where each solution represents +different model parameters. The list of networks is assigned to the +``population_weights`` attribute of the instance. + +.. _functions-in-the-pygadtorchga-module: + +Functions in the ``pygad.torchga`` Module +========================================= + +This section discusses the functions in the ``pygad.torchga`` module. + +.. _pygadtorchgamodelweightsasvector: + +``pygad.torchga.model_weights_as_vector()`` +-------------------------------------------- + +The ``model_weights_as_vector()`` function accepts a single parameter +named ``model`` representing the PyTorch model. It returns a vector +holding all model weights. The reason for representing the model weights +as a vector is that the genetic algorithm expects all parameters of any +solution to be in a 1D vector form. + +The function accepts the following parameters: + +- ``model``: The PyTorch model. + +It returns a 1D vector holding the model weights. + +.. _pygadtorchmodelweightsasdict: + +``pygad.torch.model_weights_as_dict()`` +--------------------------------------- + +The ``model_weights_as_dict()`` function accepts the following +parameters: + +1. ``model``: The PyTorch model. + +2. ``weights_vector``: The model parameters as a vector. + +It returns the restored model weights in the same form used by the +``state_dict()`` method. The returned dictionary is ready to be passed +to the ``load_state_dict()`` method for setting the PyTorch model's +parameters. + +.. _pygadtorchgapredict: + +``pygad.torchga.predict()`` +--------------------------- + +The ``predict()`` function makes a prediction based on a solution. It +accepts the following parameters: + +1. ``model``: The PyTorch model. + +2. ``solution``: The solution evolved. + +3. ``data``: The test data inputs. + +It returns the predictions for the data samples. + +Examples +======== + +This section gives the complete code of some examples that build and +train a PyTorch model using PyGAD. Each subsection builds a different +network. + +Example 1: Regression Example +----------------------------- + +The next code builds a simple PyTorch model for regression. The next +subsections discuss each part in the code. + +.. code:: python + + import torch + import torchga + import pygad + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001 + + solution_fitness = 1.0 / abs_error + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + # Create the PyTorch model. + input_layer = torch.nn.Linear(3, 5) + relu_layer = torch.nn.ReLU() + output_layer = torch.nn.Linear(5, 1) + + model = torch.nn.Sequential(input_layer, + relu_layer, + output_layer) + # print(model) + + # Create an instance of the pygad.torchga.TorchGA class to build the initial population. + torch_ga = torchga.TorchGA(model=model, + num_solutions=10) + + loss_function = torch.nn.L1Loss() + + # Data inputs + data_inputs = torch.tensor([[0.02, 0.1, 0.15], + [0.7, 0.6, 0.8], + [1.5, 1.2, 1.7], + [3.2, 2.9, 3.1]]) + + # Data outputs + data_outputs = torch.tensor([[0.1], + [0.6], + [1.3], + [2.5]]) + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 250 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = torch_ga.population_weights # Initial population of network weights + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Make predictions based on the best solution. + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + print("Predictions : \n", predictions.detach().numpy()) + + abs_error = loss_function(predictions, data_outputs) + print("Absolute Error : ", abs_error.detach().numpy()) + +Create a PyTorch model +~~~~~~~~~~~~~~~~~~~~~~ + +According to the steps mentioned previously, the first step is to create +a PyTorch model. Here is the code that builds the model using the +Functional API. + +.. code:: python + + import torch + + input_layer = torch.nn.Linear(3, 5) + relu_layer = torch.nn.ReLU() + output_layer = torch.nn.Linear(5, 1) + + model = torch.nn.Sequential(input_layer, + relu_layer, + output_layer) + +.. _create-an-instance-of-the-pygadtorchgatorchga-class: + +Create an Instance of the ``pygad.torchga.TorchGA`` Class +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The second step is to create an instance of the +``pygad.torchga.TorchGA`` class. There are 10 solutions per population. +Change this number according to your needs. + +.. code:: python + + import pygad.torchga + + torch_ga = torchga.TorchGA(model=model, + num_solutions=10) + +.. _prepare-the-training-data-1: + +Prepare the Training Data +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The third step is to prepare the training data inputs and outputs. Here +is an example where there are 4 samples. Each sample has 3 inputs and 1 +output. + +.. code:: python + + import numpy + + # Data inputs + data_inputs = numpy.array([[0.02, 0.1, 0.15], + [0.7, 0.6, 0.8], + [1.5, 1.2, 1.7], + [3.2, 2.9, 3.1]]) + + # Data outputs + data_outputs = numpy.array([[0.1], + [0.6], + [1.3], + [2.5]]) + +Build the Fitness Function +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The fourth step is to build the fitness function. This function must +accept 2 parameters representing the solution and its index within the +population. + +The next fitness function calculates the mean absolute error (MAE) of +the PyTorch model based on the parameters in the solution. The +reciprocal of the MAE is used as the fitness value. Feel free to use any +other loss function to calculate the fitness value. + +.. code:: python + + loss_function = torch.nn.L1Loss() + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001 + + solution_fitness = 1.0 / abs_error + + return solution_fitness + +.. _create-an-instance-of-the-pygadga-class: + +Create an Instance of the ``pygad.GA`` Class +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The fifth step is to instantiate the ``pygad.GA`` class. Note how the +``initial_population`` parameter is assigned to the initial weights of +the PyTorch models. + +For more information, please check the `parameters this class +accepts `__. + +.. code:: python + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 250 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = torch_ga.population_weights # Initial population of network weights + + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + +Run the Genetic Algorithm +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The sixth and last step is to run the genetic algorithm by calling the +``run()`` method. + +.. code:: python + + ga_instance.run() + +After the PyGAD completes its execution, then there is a figure that +shows how the fitness value changes by generation. Call the +``plot_fitness()`` method to show the figure. + +.. code:: python + + ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + +Here is the figure. + +.. figure:: https://user-images.githubusercontent.com/16560492/103469779-22f5b480-4d37-11eb-80dc-95503065ebb1.png + :alt: + +To get information about the best solution found by PyGAD, use the +``best_solution()`` method. + +.. code:: python + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + +.. code:: python + + Fitness value of the best solution = 145.42425295191546 + Index of the best solution : 0 + +The next code restores the trained model weights using the +``model_weights_as_dict()`` function. The restored weights are used to +calculate the predicted values. + +.. code:: python + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + print("Predictions : \n", predictions.detach().numpy()) + +.. code:: python + + Predictions : + [[0.08401088] + [0.60939324] + [1.3010881 ] + [2.5010352 ]] + +The next code measures the trained model error. + +.. code:: python + + abs_error = loss_function(predictions, data_outputs) + print("Absolute Error : ", abs_error.detach().numpy()) + +.. code:: + + Absolute Error : 0.006876422 + +Example 2: XOR Binary Classification +------------------------------------ + +The next code creates a PyTorch model to build the XOR binary +classification problem. Let's highlight the changes compared to the +previous example. + +.. code:: python + + import torch + import torchga + import pygad + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + # Create the PyTorch model. + input_layer = torch.nn.Linear(2, 4) + relu_layer = torch.nn.ReLU() + dense_layer = torch.nn.Linear(4, 2) + output_layer = torch.nn.Softmax(1) + + model = torch.nn.Sequential(input_layer, + relu_layer, + dense_layer, + output_layer) + # print(model) + + # Create an instance of the pygad.torchga.TorchGA class to build the initial population. + torch_ga = torchga.TorchGA(model=model, + num_solutions=10) + + loss_function = torch.nn.BCELoss() + + # XOR problem inputs + data_inputs = torch.tensor([[0.0, 0.0], + [0.0, 1.0], + [1.0, 0.0], + [1.0, 1.0]]) + + # XOR problem outputs + data_outputs = torch.tensor([[1.0, 0.0], + [0.0, 1.0], + [0.0, 1.0], + [1.0, 0.0]]) + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 250 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = torch_ga.population_weights # Initial population of network weights. + + # Create an instance of the pygad.GA class + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + # Start the genetic algorithm evolution. + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Make predictions based on the best solution. + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + print("Predictions : \n", predictions.detach().numpy()) + + # Calculate the binary crossentropy for the trained model. + print("Binary Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) + + # Calculate the classification accuracy of the trained model. + a = torch.max(predictions, axis=1) + b = torch.max(data_outputs, axis=1) + accuracy = torch.sum(a.indices == b.indices) / len(data_outputs) + print("Accuracy : ", accuracy.detach().numpy()) + +Compared to the previous regression example, here are the changes: + +- The PyTorch model is changed according to the nature of the problem. + Now, it has 2 inputs and 2 outputs with an in-between hidden layer of + 4 neurons. + +.. code:: python + + input_layer = torch.nn.Linear(2, 4) + relu_layer = torch.nn.ReLU() + dense_layer = torch.nn.Linear(4, 2) + output_layer = torch.nn.Softmax(1) + + model = torch.nn.Sequential(input_layer, + relu_layer, + dense_layer, + output_layer) + +- The train data is changed. Note that the output of each sample is a + 1D vector of 2 values, 1 for each class. + +.. code:: python + + # XOR problem inputs + data_inputs = torch.tensor([[0.0, 0.0], + [0.0, 1.0], + [1.0, 0.0], + [1.0, 1.0]]) + + # XOR problem outputs + data_outputs = torch.tensor([[1.0, 0.0], + [0.0, 1.0], + [0.0, 1.0], + [1.0, 0.0]]) + +- The fitness value is calculated based on the binary cross entropy. + +.. code:: python + + loss_function = torch.nn.BCELoss() + +After the previous code completes, the next figure shows how the fitness +value change by generation. + +.. figure:: https://user-images.githubusercontent.com/16560492/103469818-c646c980-4d37-11eb-98c3-d9d591acd5e2.png + :alt: + +Here is some information about the trained model. Its fitness value is +``100000000.0``, loss is ``0.0`` and accuracy is 100%. + +.. code:: python + + Fitness value of the best solution = 100000000.0 + + Index of the best solution : 0 + + Predictions : + [[1.0000000e+00 1.3627675e-10] + [3.8521746e-09 1.0000000e+00] + [4.2789325e-10 1.0000000e+00] + [1.0000000e+00 3.3668417e-09]] + + Binary Crossentropy : 0.0 + + Accuracy : 1.0 + +Example 3: Image Multi-Class Classification (Dense Layers) +---------------------------------------------------------- + +Here is the code. + +.. code:: python + + import torch + import torchga + import pygad + import numpy + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + # Build the PyTorch model using the functional API. + input_layer = torch.nn.Linear(360, 50) + relu_layer = torch.nn.ReLU() + dense_layer = torch.nn.Linear(50, 4) + output_layer = torch.nn.Softmax(1) + + model = torch.nn.Sequential(input_layer, + relu_layer, + dense_layer, + output_layer) + + # Create an instance of the pygad.torchga.TorchGA class to build the initial population. + torch_ga = torchga.TorchGA(model=model, + num_solutions=10) + + loss_function = torch.nn.CrossEntropyLoss() + + # Data inputs + data_inputs = torch.from_numpy(numpy.load("dataset_features.npy")).float() + + # Data outputs + data_outputs = torch.from_numpy(numpy.load("outputs.npy")).long() + # The next 2 lines are equivelant to this Keras function to perform 1-hot encoding: tensorflow.keras.utils.to_categorical(data_outputs) + # temp_outs = numpy.zeros((data_outputs.shape[0], numpy.unique(data_outputs).size), dtype=numpy.uint8) + # temp_outs[numpy.arange(data_outputs.shape[0]), numpy.uint8(data_outputs)] = 1 + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 200 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = torch_ga.population_weights # Initial population of network weights. + + # Create an instance of the pygad.GA class + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + # Start the genetic algorithm evolution. + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Fetch the parameters of the best solution. + best_solution_weights = torchga.model_weights_as_dict(model=model, + weights_vector=solution) + model.load_state_dict(best_solution_weights) + predictions = model(data_inputs) + # print("Predictions : \n", predictions) + + # Calculate the crossentropy loss of the trained model. + print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) + + # Calculate the classification accuracy for the trained model. + accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs) + print("Accuracy : ", accuracy.detach().numpy()) + +Compared to the previous binary classification example, this example has +multiple classes (4) and thus the loss is measured using cross entropy. + +.. code:: python + + loss_function = torch.nn.CrossEntropyLoss() + +.. _prepare-the-training-data-2: + +Prepare the Training Data +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Before building and training neural networks, the training data (input +and output) needs to be prepared. The inputs and the outputs of the +training data are NumPy arrays. + +The data used in this example is available as 2 files: + +1. `dataset_features.npy `__: + Data inputs. + https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy + +2. `outputs.npy `__: + Class labels. + https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy + +The data consists of 4 classes of images. The image shape is +``(100, 100, 3)``. The number of training samples is 1962. The feature +vector extracted from each image has a length 360. + +.. code:: python + + import numpy + + data_inputs = numpy.load("dataset_features.npy") + + data_outputs = numpy.load("outputs.npy") + +The next figure shows how the fitness value changes. + +.. figure:: https://user-images.githubusercontent.com/16560492/103469855-5d138600-4d38-11eb-84b1-b5eff8faa7bc.png + :alt: + +Here are some statistics about the trained model. + +.. code:: + + Fitness value of the best solution = 1.3446997034434534 + Index of the best solution : 0 + Crossentropy : 0.74366045 + Accuracy : 1.0 + +Example 4: Image Multi-Class Classification (Conv Layers) +--------------------------------------------------------- + +Compared to the previous example that uses only dense layers, this +example uses convolutional layers to classify the same dataset. + +Here is the complete code. + +.. code:: python + + import torch + import torchga + import pygad + import numpy + + def fitness_func(ga_instance, solution, sol_idx): + global data_inputs, data_outputs, torch_ga, model, loss_function + + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + + solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001) + + return solution_fitness + + def callback_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) + + # Build the PyTorch model. + input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7) + relu_layer1 = torch.nn.ReLU() + max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5) + + conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3) + relu_layer2 = torch.nn.ReLU() + + flatten_layer1 = torch.nn.Flatten() + # The value 768 is pre-computed by tracing the sizes of the layers' outputs. + dense_layer1 = torch.nn.Linear(in_features=768, out_features=15) + relu_layer3 = torch.nn.ReLU() + + dense_layer2 = torch.nn.Linear(in_features=15, out_features=4) + output_layer = torch.nn.Softmax(1) + + model = torch.nn.Sequential(input_layer, + relu_layer1, + max_pool1, + conv_layer2, + relu_layer2, + flatten_layer1, + dense_layer1, + relu_layer3, + dense_layer2, + output_layer) + + # Create an instance of the pygad.torchga.TorchGA class to build the initial population. + torch_ga = torchga.TorchGA(model=model, + num_solutions=10) + + loss_function = torch.nn.CrossEntropyLoss() + + # Data inputs + data_inputs = torch.from_numpy(numpy.load("dataset_inputs.npy")).float() + data_inputs = data_inputs.reshape((data_inputs.shape[0], data_inputs.shape[3], data_inputs.shape[1], data_inputs.shape[2])) + + # Data outputs + data_outputs = torch.from_numpy(numpy.load("dataset_outputs.npy")).long() + + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + num_generations = 200 # Number of generations. + num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. + initial_population = torch_ga.population_weights # Initial population of network weights. + + # Create an instance of the pygad.GA class + ga_instance = pygad.GA(num_generations=num_generations, + num_parents_mating=num_parents_mating, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=callback_generation) + + # Start the genetic algorithm evolution. + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution() + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + # Make predictions based on the best solution. + predictions = pygad.torchga.predict(model=model, + solution=solution, + data=data_inputs) + # print("Predictions : \n", predictions) + + # Calculate the crossentropy for the trained model. + print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) + + # Calculate the classification accuracy for the trained model. + accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs) + print("Accuracy : ", accuracy.detach().numpy()) + +Compared to the previous example, the only change is that the +architecture uses convolutional and max-pooling layers. The shape of +each input sample is 100x100x3. + +.. code:: python + + input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7) + relu_layer1 = torch.nn.ReLU() + max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5) + + conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3) + relu_layer2 = torch.nn.ReLU() + + flatten_layer1 = torch.nn.Flatten() + # The value 768 is pre-computed by tracing the sizes of the layers' outputs. + dense_layer1 = torch.nn.Linear(in_features=768, out_features=15) + relu_layer3 = torch.nn.ReLU() + + dense_layer2 = torch.nn.Linear(in_features=15, out_features=4) + output_layer = torch.nn.Softmax(1) + + model = torch.nn.Sequential(input_layer, + relu_layer1, + max_pool1, + conv_layer2, + relu_layer2, + flatten_layer1, + dense_layer1, + relu_layer3, + dense_layer2, + output_layer) + +.. _prepare-the-training-data-3: + +Prepare the Training Data +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The data used in this example is available as 2 files: + +1. `dataset_inputs.npy `__: + Data inputs. + https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy + +2. `dataset_outputs.npy `__: + Class labels. + https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy + +The data consists of 4 classes of images. The image shape is +``(100, 100, 3)`` and there are 20 images per class for a total of 80 +training samples. For more information about the dataset, check the +`Reading the +Data `__ +section of the ``pygad.cnn`` module. + +Simply download these 2 files and read them according to the next code. + +.. code:: python + + import numpy + + data_inputs = numpy.load("dataset_inputs.npy") + + data_outputs = numpy.load("dataset_outputs.npy") + +The next figure shows how the fitness value changes. + +.. figure:: https://user-images.githubusercontent.com/16560492/103469887-c7c4c180-4d38-11eb-98a7-1c5e73e918d0.png + :alt: + +Here are some statistics about the trained model. The model accuracy is +97.5% after the 200 generations. Note that just running the code again +may give different results. + +.. code:: + + Fitness value of the best solution = 1.3009520689219258 + Index of the best solution : 0 + Crossentropy : 0.7686678 + Accuracy : 0.975 diff --git a/examples/KerasGA/XOR_classification.py b/examples/KerasGA/XOR_classification.py index da3c752..3b0a1e1 100644 --- a/examples/KerasGA/XOR_classification.py +++ b/examples/KerasGA/XOR_classification.py @@ -15,7 +15,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -52,7 +52,7 @@ def callback_generation(ga_instance): num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() diff --git a/examples/KerasGA/image_classification_CNN.py b/examples/KerasGA/image_classification_CNN.py index a20ab03..9fb4563 100644 --- a/examples/KerasGA/image_classification_CNN.py +++ b/examples/KerasGA/image_classification_CNN.py @@ -15,7 +15,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -56,7 +56,7 @@ def callback_generation(ga_instance): num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() diff --git a/examples/KerasGA/image_classification_Dense.py b/examples/KerasGA/image_classification_Dense.py index aee14f5..002e36c 100644 --- a/examples/KerasGA/image_classification_Dense.py +++ b/examples/KerasGA/image_classification_Dense.py @@ -15,7 +15,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -47,7 +47,7 @@ def callback_generation(ga_instance): num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() diff --git a/examples/KerasGA/regression_example.py b/examples/KerasGA/regression_example.py index 36b78fb..2deec1f 100644 --- a/examples/KerasGA/regression_example.py +++ b/examples/KerasGA/regression_example.py @@ -16,7 +16,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -52,7 +52,7 @@ def callback_generation(ga_instance): num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) ga_instance.run() diff --git a/examples/TorchGA/XOR_classification.py b/examples/TorchGA/XOR_classification.py index 6cedb9c..f7a2f44 100644 --- a/examples/TorchGA/XOR_classification.py +++ b/examples/TorchGA/XOR_classification.py @@ -13,7 +13,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -58,7 +58,7 @@ def callback_generation(ga_instance): initial_population=initial_population, fitness_func=fitness_func, parallel_processing=3, - on_generation=callback_generation) + on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() diff --git a/examples/TorchGA/image_classification_CNN.py b/examples/TorchGA/image_classification_CNN.py index be26e00..baf1f1b 100644 --- a/examples/TorchGA/image_classification_CNN.py +++ b/examples/TorchGA/image_classification_CNN.py @@ -14,7 +14,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -68,7 +68,7 @@ def callback_generation(ga_instance): num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() diff --git a/examples/TorchGA/image_classification_Dense.py b/examples/TorchGA/image_classification_Dense.py index cedb169..91bb4c1 100644 --- a/examples/TorchGA/image_classification_Dense.py +++ b/examples/TorchGA/image_classification_Dense.py @@ -14,7 +14,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -54,7 +54,7 @@ def callback_generation(ga_instance): num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() diff --git a/examples/TorchGA/regression_example.py b/examples/TorchGA/regression_example.py index 4735e85..5bf2fc1 100644 --- a/examples/TorchGA/regression_example.py +++ b/examples/TorchGA/regression_example.py @@ -14,7 +14,7 @@ def fitness_func(ga_instanse, solution, sol_idx): return solution_fitness -def callback_generation(ga_instance): +def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -55,7 +55,7 @@ def callback_generation(ga_instance): num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) ga_instance.run() From 9c70c94c7985613e53936e82b6a22b37c5cbecb7 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Mon, 19 Jun 2023 16:45:32 -0400 Subject: [PATCH 070/220] Change copyright --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index eca9313..7fd05aa 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -18,7 +18,7 @@ # -- Project information ----------------------------------------------------- project = 'PyGAD' -copyright = '2020, Ahmed Fawzy Gad' +copyright = '2023, Ahmed Fawzy Gad' author = 'Ahmed Fawzy Gad' # The full version, including alpha/beta/rc tags From a05e52ef57706b4eb6326c98e9716e13391f7882 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Mon, 19 Jun 2023 17:05:26 -0400 Subject: [PATCH 071/220] Rename docs --- README.md | 6 +- docs/source/index.rst | 16 +- docs/source/kerasga.rst | 171 ++++++++++++--- docs/source/pygad.rst | 463 ++++++++++++++++++++++++++------------- docs/source/releases.rst | 38 ++-- docs/source/torchga.rst | 42 ++-- 6 files changed, 505 insertions(+), 231 deletions(-) diff --git a/README.md b/README.md index ef5a7ae..faf8f30 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ The library is under active development and more features are added regularly. I # Installation -To install [PyGAD](https://pypi.org/project/pygad), simply use pip to download and install the library from [PyPI](https://pypi.org/project/pygad) (Python Package Index). The library lives a PyPI at this page https://pypi.org/project/pygad. +To install [PyGAD](https://pypi.org/project/pygad), simply use pip to download and install the library from [PyPI](https://pypi.org/project/pygad) (Python Package Index). The library is at PyPI at this page https://pypi.org/project/pygad. Install PyGAD with the following command: @@ -29,7 +29,7 @@ Install PyGAD with the following command: pip install pygad ``` -To get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io) https://pygad.readthedocs.io. +To get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io. # PyGAD Source Code @@ -146,7 +146,7 @@ on_stop() # Example -Check the [PyGAD's documentation](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html) for information about the implementation of this example. +Check the [PyGAD's documentation](https://pygad.readthedocs.io/en/latest/pygad.html) for information about the implementation of this example. ```python import pygad diff --git a/docs/source/index.rst b/docs/source/index.rst index 3bd6ad6..b346229 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -247,7 +247,7 @@ pygad Module :maxdepth: 4 :caption: pygad Module TOC - pygad.rst + README_pygad_ReadTheDocs.rst @@ -262,7 +262,7 @@ pygad.nn Module :maxdepth: 4 :caption: pygad.nn Module TOC - nn.rst + README_pygad_nn_ReadTheDocs.rst @@ -278,7 +278,7 @@ pygad.gann Module :maxdepth: 4 :caption: pygad.gann Module TOC - gann.rst + README_pygad_gann_ReadTheDocs.rst @@ -298,7 +298,7 @@ pygad.cnn Module :maxdepth: 4 :caption: pygad.cnn Module TOC - cnn.rst + README_pygad_cnn_ReadTheDocs.rst @@ -318,7 +318,7 @@ pygad.gacnn Module :maxdepth: 4 :caption: pygad.gacnn Module TOC - gacnn.rst + README_pygad_gacnn_ReadTheDocs.rst @@ -333,7 +333,7 @@ pygad.kerasga Module :maxdepth: 4 :caption: pygad.kerasga Module TOC - kerasga.rst + README_pygad_kerasga_ReadTheDocs.rst @@ -348,7 +348,7 @@ pygad.torchga Module :maxdepth: 4 :caption: pygad.torchga Module TOC - torchga.rst + README_pygad_torchga_ReadTheDocs.rst @@ -363,7 +363,7 @@ More Information :maxdepth: 4 :caption: More Information - releases.rst + Footer.rst diff --git a/docs/source/kerasga.rst b/docs/source/kerasga.rst index a602468..e1c8316 100644 --- a/docs/source/kerasga.rst +++ b/docs/source/kerasga.rst @@ -4,7 +4,7 @@ ======================== This section of the PyGAD's library documentation discusses the -`pygad.kerasga `__ +`pygad.kerasga `__ module. The ``pygad.kerarsga`` module has helper a class and 2 functions to @@ -206,7 +206,19 @@ accepts the following parameters: 3. ``data``: The test data inputs. -It returns the predictions for the data samples. +4. ``batch_size=None``: The batch size (i.e. number of samples per step + or batch). + +5. ``verbose=None``: Verbosity mode. + +6. ``steps=None``: The total number of steps (batches of samples). + +Check documentation of the `Keras +Model.predict() `__ +method for more information about the ``batch_size``, ``verbose``, and +``steps`` parameters. + +It returns the predictions of the data samples. Examples ======== @@ -241,7 +253,7 @@ subsections discuss each part in the code. return solution_fitness - def callback_generation(ga_instance): + def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -266,7 +278,7 @@ subsections discuss each part in the code. [1.3], [2.5]]) - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 250 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = keras_ga.population_weights # Initial population of network weights @@ -275,7 +287,7 @@ subsections discuss each part in the code. num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) ga_instance.run() @@ -343,8 +355,6 @@ Change this number according to your needs. keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10) -.. _prepare-the-training-data-1: - Prepare the Training Data ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -406,11 +416,11 @@ The fifth step is to instantiate the ``pygad.GA`` class. Note how the the Keras models. For more information, please check the `parameters this class -accepts `__. +accepts `__. .. code:: python - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 250 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = keras_ga.population_weights # Initial population of network weights @@ -419,7 +429,7 @@ accepts `__ +Data `__ section of the ``pygad.cnn`` module. Simply download these 2 files and read them according to the next code. @@ -938,14 +948,14 @@ Note that the class labels are one-hot encoded using the import numpy - data_inputs = numpy.load("dataset_inputs.npy") + data_inputs = numpy.load("../data/dataset_inputs.npy") - data_outputs = numpy.load("dataset_outputs.npy") + data_outputs = numpy.load("../data/dataset_outputs.npy") data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) The next figure shows how the fitness value changes. -.. figure:: https://user-images.githubusercontent.com/16560492/93722654-cc55d780-fb98-11ea-8f95-7b65dc67f5c8.png +.. image:: https://user-images.githubusercontent.com/16560492/93722654-cc55d780-fb98-11ea-8f95-7b65dc67f5c8.png :alt: Here are some statistics about the trained model. The model accuracy is @@ -969,3 +979,100 @@ To improve the model performance, you can do the following: - Use different parameters for the genetic algorithm (e.g. number of solution, number of generations, etc) + +Example 5: Image Classification using Data Generator +---------------------------------------------------- + +This example uses the image data generator +``tensorflow.keras.preprocessing.image.ImageDataGenerator`` to feed data +to the model. Instead of reading all the data in the memory, the data +generator generates the data needed by the model and only save it in the +memory instead of saving all the data. This frees the memory but adds +more computational time. + +.. code:: python + + import tensorflow as tf + import tensorflow.keras + import pygad.kerasga + import pygad + + def fitness_func(ga_instanse, solution, sol_idx): + global train_generator, data_outputs, keras_ga, model + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=train_generator) + + cce = tensorflow.keras.losses.CategoricalCrossentropy() + solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) + + return solution_fitness + + def on_generation(ga_instance): + print("Generation = {generation}".format(generation=ga_instance.generations_completed)) + print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(ga_instance.last_generation_fitness)[1])) + + # The dataset path. + dataset_path = r'../data/Skin_Cancer_Dataset' + + num_classes = 2 + img_size = 224 + + # Create a simple CNN. This does not gurantee high classification accuracy. + model = tf.keras.models.Sequential() + model.add(tf.keras.layers.Input(shape=(img_size, img_size, 3))) + model.add(tf.keras.layers.Conv2D(32, (3,3), activation="relu", padding="same")) + model.add(tf.keras.layers.MaxPooling2D((2, 2))) + model.add(tf.keras.layers.Flatten()) + model.add(tf.keras.layers.Dropout(rate=0.2)) + model.add(tf.keras.layers.Dense(num_classes, activation="softmax")) + + # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. + keras_ga = pygad.kerasga.KerasGA(model=model, + num_solutions=10) + + data_generator = tf.keras.preprocessing.image.ImageDataGenerator() + train_generator = data_generator.flow_from_directory(dataset_path, + class_mode='categorical', + target_size=(224, 224), + batch_size=32, + shuffle=False) + # train_generator.class_indices + data_outputs = tf.keras.utils.to_categorical(train_generator.labels) + + # Check the documentation for more information about the parameters: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class + initial_population = keras_ga.population_weights # Initial population of network weights. + + # Create an instance of the pygad.GA class + ga_instance = pygad.GA(num_generations=10, + num_parents_mating=5, + initial_population=initial_population, + fitness_func=fitness_func, + on_generation=on_generation) + + # Start the genetic algorithm evolution. + ga_instance.run() + + # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. + ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) + + # Returning the details of the best solution. + solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness) + print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) + print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx)) + + predictions = pygad.kerasga.predict(model=model, + solution=solution, + data=train_generator) + # print("Predictions : \n", predictions) + + # Calculate the categorical crossentropy for the trained model. + cce = tensorflow.keras.losses.CategoricalCrossentropy() + print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy()) + + # Calculate the classification accuracy for the trained model. + ca = tensorflow.keras.metrics.CategoricalAccuracy() + ca.update_state(data_outputs, predictions) + accuracy = ca.result().numpy() + print("Accuracy : ", accuracy) diff --git a/docs/source/pygad.rst b/docs/source/pygad.rst index 4e09154..73d1ef6 100644 --- a/docs/source/pygad.rst +++ b/docs/source/pygad.rst @@ -39,7 +39,7 @@ The ``pygad.GA`` class constructor supports the following parameters: solution, and 3. its index in the population). If method, then it accepts a fourth parameter representing the method's class instance. Check the `Preparing the fitness_func - Parameter `__ + Parameter `__ section for information about creating such a function. - ``fitness_batch_size=None``: A new optional parameter called @@ -51,7 +51,7 @@ The ``pygad.GA`` class constructor supports the following parameters: ``1 < fitness_batch_size <= sol_per_pop``, then the solutions are grouped into batches of size ``fitness_batch_size`` and the fitness function is called once for each batch. Check the `Batch Fitness - Calculation `__ + Calculation `__ section for more details and examples. Added in from `PyGAD 2.19.0 `__. @@ -114,7 +114,7 @@ The ``pygad.GA`` class constructor supports the following parameters: function can be passed starting from `PyGAD 2.16.0 `__. Check the `User-Defined Crossover, Mutation, and Parent Selection - Operators `__ + Operators `__ section for more details about building a user-defined parent selection function. @@ -158,7 +158,7 @@ The ``pygad.GA`` class constructor supports the following parameters: function can be passed starting from `PyGAD 2.16.0 `__. Check the `User-Defined Crossover, Mutation, and Parent Selection - Operators `__ + Operators `__ section for more details about creating a user-defined crossover function. Starting from `PyGAD 2.2.2 `__ @@ -184,7 +184,7 @@ The ``pygad.GA`` class constructor supports the following parameters: starting from `PyGAD 2.16.0 `__. Check the `User-Defined Crossover, Mutation, and Parent Selection - Operators `__ + Operators `__ section for more details about creating a user-defined mutation function. Starting from `PyGAD 2.2.2 `__ @@ -195,10 +195,10 @@ The ``pygad.GA`` class constructor supports the following parameters: mutation is supported starting from `PyGAD 2.10.0 `__. For more information about adaptive mutation, go the the `Adaptive - Mutation `__ + Mutation `__ section. For example about using adaptive mutation, check the `Use Adaptive Mutation in - PyGAD `__ + PyGAD `__ section. - ``mutation_probability=None``: The probability of selecting a gene @@ -263,7 +263,7 @@ The ``pygad.GA`` class constructor supports the following parameters: - ``gene_space=None``: It is used to specify the possible values for each gene in case the user wants to restrict the gene values. It is useful if the gene space is restricted to a certain range or to - discrete values. It accepts a ``list``, ``tuple``, ``range``, or + discrete values. It accepts a ``list``, ``range``, or ``numpy.ndarray``. When all genes have the same global space, specify their values as a ``list``/``tuple``/``range``/``numpy.ndarray``. For example, ``gene_space = [0.3, 5.2, -4, 8]`` restricts the gene values @@ -399,7 +399,7 @@ The ``pygad.GA`` class constructor supports the following parameters: 5 threads which is equivalent to ``parallel_processing=["thread", 5]``. For more information, check the `Parallel Processing in - PyGAD `__ + PyGAD `__ section. - ``random_seed=None``: Added in `PyGAD @@ -417,7 +417,7 @@ The ``pygad.GA`` class constructor supports the following parameters: Added in `PyGAD 3.0.0 `__. Check the `Logging - Outputs `__ + Outputs `__ for more information. The user doesn't have to specify all of such parameters while creating @@ -553,7 +553,14 @@ Other Attributes module. Supported in `PyGAD 3.0.0 `__. -Note that the attributes with its name start with ``last_generation_`` +- ``gene_space_unpacked``: This is the unpacked version of the + ``gene_space`` parameter. For example, ``range(1, 5)`` is unpacked to + ``[1, 2, 3, 4]``. For an infinite range like + ``{'low': 2, 'high': 4}``, then it is unpacked to a limited number of + values (e.g. 100). Supported in `PyGAD + 3.1.0 `__. + +Note that the attributes with names starting with ``last_generation_`` are updated after each generation. Other Methods @@ -601,7 +608,7 @@ Other Methods `PyGAD 2.19.0 `__. Check the `Print Lifecycle - Summary `__ + Summary `__ section for more details and examples. The next sections discuss the methods available in the ``pygad.GA`` @@ -1286,7 +1293,7 @@ generations. ga_instance.plot_fitness() -.. figure:: https://user-images.githubusercontent.com/16560492/78830005-93111d00-79e7-11ea-9d8e-a8d8325a6101.png +.. image:: https://user-images.githubusercontent.com/16560492/78830005-93111d00-79e7-11ea-9d8e-a8d8325a6101.png :alt: Information about the Best Solution @@ -1411,7 +1418,7 @@ instance of the ``pygad.GA`` class. Note that PyGAD stops when either all generations are completed or when the function passed to the ``on_generation`` parameter returns the string ``stop``. -.. figure:: https://user-images.githubusercontent.com/16560492/220486073-c5b6089d-81e4-44d9-a53c-385f479a7273.jpg +.. image:: https://user-images.githubusercontent.com/16560492/220486073-c5b6089d-81e4-44d9-a53c-385f479a7273.jpg :alt: The next code implements all the callback functions to trace the @@ -1551,7 +1558,7 @@ In PyGAD, if ``f=f_avg``, then the solution is regarded of high quality. The next figure summarizes the previous steps. -.. figure:: https://user-images.githubusercontent.com/16560492/103468973-e3c26600-4d2c-11eb-8af3-b3bb39b50540.jpg +.. image:: https://user-images.githubusercontent.com/16560492/103468973-e3c26600-4d2c-11eb-8af3-b3bb39b50540.jpg :alt: This strategy is applied in PyGAD. @@ -1571,7 +1578,7 @@ the following 2 simple steps: ``mutation_probability``, ``mutation_num_genes``, and ``mutation_percent_genes``. Please check the `documentation of each of these - parameters `__ + parameters `__ for more information. When adaptive mutation is used, then the value assigned to any of the 3 @@ -1658,8 +1665,10 @@ Here is an example that uses adaptive mutation. ga_instance.plot_fitness(title="PyGAD with Adaptive Mutation", linewidth=5) -Limit the Gene Value Range -========================== +.. _limit-the-gene-value-range-using-the-genespace-parameter: + +Limit the Gene Value Range using the ``gene_space`` Parameter +============================================================= In `PyGAD 2.11.0 `__, @@ -1735,6 +1744,157 @@ take any floating-point value from the range that starts from 1 gene_space = [{'low': 1, 'high': 5}, {'low': 0.3, 'high': 1.4}, {'low': -0.2, 'high': 4.5}] +.. _more-about-the-genespace-parameter: + +More about the ``gene_space`` Parameter +======================================= + +The ``gene_space`` parameter customizes the space of values of each +gene. + +Assuming that all genes have the same global space which include the +values 0.3, 5.2, -4, and 8, then those values can be assigned to the +``gene_space`` parameter as a list, tuple, or range. Here is a list +assigned to this parameter. By doing that, then the gene values are +restricted to those assigned to the ``gene_space`` parameter. + +.. code:: python + + gene_space = [0.3, 5.2, -4, 8] + +If some genes have different spaces, then ``gene_space`` should accept a +nested list or tuple. In this case, the elements could be: + +1. Number (of ``int``, ``float``, or ``NumPy`` data types): A single + value to be assigned to the gene. This means this gene will have the + same value across all generations. + +2. ``list``, ``tuple``, ``numpy.ndarray``, or any range like ``range``, + ``numpy.arange()``, or ``numpy.linspace``: It holds the space for + each individual gene. But this space is usually discrete. That is + there is a set of finite values to select from. + +3. ``dict``: To sample a value for a gene from a continuous range. The + dictionary must have 2 mandatory keys which are ``"low"`` and + ``"high"`` in addition to an optional key which is ``"step"``. A + random value is returned between the values assigned to the items + with ``"low"`` and ``"high"`` keys. If the ``"step"`` exists, then + this works as the previous options (i.e. discrete set of values). + +4. ``None``: A gene with its space set to ``None`` is initialized + randomly from the range specified by the 2 parameters + ``init_range_low`` and ``init_range_high``. For mutation, its value + is mutated based on a random value from the range specified by the 2 + parameters ``random_mutation_min_val`` and + ``random_mutation_max_val``. If all elements in the ``gene_space`` + parameter are ``None``, the parameter will not have any effect. + +Assuming that a chromosome has 2 genes and each gene has a different +value space. Then the ``gene_space`` could be assigned a nested +list/tuple where each element determines the space of a gene. + +According to the next code, the space of the first gene is ``[0.4, -5]`` +which has 2 values and the space for the second gene is +``[0.5, -3.2, 8.8, -9]`` which has 4 values. + +.. code:: python + + gene_space = [[0.4, -5], [0.5, -3.2, 8.2, -9]] + +For a 2 gene chromosome, if the first gene space is restricted to the +discrete values from 0 to 4 and the second gene is restricted to the +values from 10 to 19, then it could be specified according to the next +code. + +.. code:: python + + gene_space = [range(5), range(10, 20)] + +The ``gene_space`` can also be assigned to a single range, as given +below, where the values of all genes are sampled from the same range. + +.. code:: python + + gene_space = numpy.arange(15) + +The ``gene_space`` can be assigned a dictionary to sample a value from a +continuous range. + +.. code:: python + + gene_space = {"low": 4, "high": 30} + +A step also can be assigned to the dictionary. This works as if a range +is used. + +.. code:: python + + gene_space = {"low": 4, "high": 30, "step": 2.5} + +.. + + Setting a ``dict`` like ``{"low": 0, "high": 10}`` in the + ``gene_space`` means that random values from the continuous range [0, + 10) are sampled. Note that ``0`` is included but ``10`` is not + included while sampling. Thus, the maximum value that could be + returned is less than ``10`` like ``9.9999``. But if the user decided + to round the genes using, for example, ``[float, 2]``, then this + value will become 10. So, the user should be careful to the inputs. + +If a ``None`` is assigned to only a single gene, then its value will be +randomly generated initially using the ``init_range_low`` and +``init_range_high`` parameters in the ``pygad.GA`` class's constructor. +During mutation, the value are sampled from the range defined by the 2 +parameters ``random_mutation_min_val`` and ``random_mutation_max_val``. +This is an example where the second gene is given a ``None`` value. + +.. code:: python + + gene_space = [range(5), None, numpy.linspace(10, 20, 300)] + +If the user did not assign the initial population to the +``initial_population`` parameter, the initial population is created +randomly based on the ``gene_space`` parameter. Moreover, the mutation +is applied based on this parameter. + +.. _how-mutation-works-with-the-genespace-parameter: + +How Mutation Works with the ``gene_space`` Parameter? +----------------------------------------------------- + +If a gene has its static space defined in the ``gene_space`` parameter, +then mutation works by replacing the gene value by a value randomly +selected from the gene space. This happens for both ``int`` and +``float`` data types. + +For example, the following ``gene_space`` has the static space +``[1, 2, 3]`` defined for the first gene. So, this gene can only have a +value out of these 3 values. + +.. code:: python + + Gene space: [[1, 2, 3], + None] + Solution: [1, 5] + +For a solution like ``[1, -0.5, 4]``, then mutation happens for the +first gene by simply replacing its current value by a randomly selected +value (other than its current value if possible). So, the value 1 will +be replaced by either 2 or 3. + +For the second gene, its space is set to ``None``. So, traditional +mutation happens for this gene by: + +1. Generating a random value from the range defined by the + ``random_mutation_min_val`` and ``random_mutation_max_val`` + parameters. + +2. Adding this random value to the current gene's value. + +If its current value is 5 and the random value is ``-0.5``, then the new +value is 4.5. If the gene type is integer, then the value will be +rounded. + Stop at Any Generation ====================== @@ -1882,7 +2042,7 @@ offspring will be created. ga_instance.run() -.. figure:: https://user-images.githubusercontent.com/16560492/189273225-67ffad41-97ab-45e1-9324-429705e17b20.png +.. image:: https://user-images.githubusercontent.com/16560492/189273225-67ffad41-97ab-45e1-9324-429705e17b20.png :alt: Note that if the ``keep_elitism`` parameter is effective (i.e. is @@ -2179,6 +2339,118 @@ non-duplicating genes that may make a room for a unique value in one the duplicates. The resultant gene is then ``[3 4 2 0]``. But this option is not yet supported in PyGAD. +Solve Duplicates using a Third Gene +----------------------------------- + +When ``allow_duplicate_genes=False`` and a user-defined ``gene_space`` +is used, it sometimes happen that there is no room to solve the +duplicates between the 2 genes by simply replacing the value of one gene +by another gene. In `PyGAD +3.1.0 `__, +the duplicates are solved by looking for a third gene that will help in +solving the duplicates. The following examples explain how it works. + +Example 1: + +Let's assume that this gene space is used and there is a solution with 2 +duplicate genes with the same value 4. + +.. code:: python + + Gene space: [[2, 3], + [3, 4], + [4, 5], + [5, 6]] + Solution: [3, 4, 4, 5] + +By checking the gene space, the second gene can have the values +``[3, 4]`` and the third gene can have the values ``[4, 5]``. To solve +the duplicates, we have the value of any of these 2 genes. + +If the value of the second gene changes from 4 to 3, then it will be +duplicate with the first gene. If we are to change the value of the +third gene from 4 to 5, then it will duplicate with the fourth gene. As +a conclusion, trying to just selecting a different gene value for either +the second or third genes will introduce new duplicating genes. + +When there are 2 duplicate genes but there is no way to solve their +duplicates, then the solution is to change a third gene that makes a +room to solve the duplicates between the 2 genes. + +In our example, duplicates between the second and third genes can be +solved by, for example,: + +- Changing the first gene from 3 to 2 then changing the second gene + from 4 to 3. + +- Or changing the fourth gene from 5 to 6 then changing the third gene + from 4 to 5. + +Generally, this is how to solve such duplicates: + +1. For any duplicate gene **GENE1**, select another value. + +2. Check which other gene **GENEX** has duplicate with this new value. + +3. Find if **GENEX** can have another value that will not cause any more + duplicates. If so, go to step 7. + +4. If all the other values of **GENEX** will cause duplicates, then try + another gene **GENEY**. + +5. Repeat steps 3 and 4 until exploring all the genes. + +6. If there is no possibility to solve the duplicates, then there is not + way to solve the duplicates and we have to keep the duplicate value. + +7. If a value for a gene **GENEM** is found that will not cause more + duplicates, then use this value for the gene **GENEM**. + +8. Replace the value of the gene **GENE1** by the old value of the gene + **GENEM**. This solves the duplicates. + +This is an example to solve the duplicate for the solution +``[3, 4, 4, 5]``: + +1. Let's use the second gene with value 4. Because the space of this + gene is ``[3, 4]``, then the only other value we can select is 3. + +2. The first gene also have the value 3. + +3. The first gene has another value 2 that will not cause more + duplicates in the solution. Then go to step 7. + +4. Skip. + +5. Skip. + +6. Skip. + +7. The value of the first gene 3 will be replaced by the new value 2. + The new solution is [2, 4, 4, 5]. + +8. Replace the value of the second gene 4 by the old value of the first + gene which is 3. The new solution is [2, 3, 4, 5]. The duplicate is + solved. + +Example 2: + +.. code:: python + + Gene space: [[0, 1], + [1, 2], + [2, 3], + [3, 4]] + Solution: [1, 2, 2, 3] + +The quick summary is: + +- Change the value of the first gene from 1 to 0. The solution becomes + [0, 2, 2, 3]. + +- Change the value of the second gene from 2 to 1. The solution becomes + [0, 1, 2, 3]. The duplicate is solved. + User-Defined Crossover, Mutation, and Parent Selection Operators ================================================================ @@ -2200,7 +2472,7 @@ Starting from `PyGAD the user can create a custom crossover, mutation, and parent selection operators and assign these functions to the above parameters. Thus, a new operator can be plugged easily into the `PyGAD -Lifecycle `__. +Lifecycle `__. This is a sample code that does not use any custom function. @@ -2586,109 +2858,6 @@ This is the same example but using methods instead of functions. ga_instance.run() ga_instance.plot_fitness() -.. _more-about-the-genespace-parameter: - -More about the ``gene_space`` Parameter -======================================= - -The ``gene_space`` parameter customizes the space of values of each -gene. - -Assuming that all genes have the same global space which include the -values 0.3, 5.2, -4, and 8, then those values can be assigned to the -``gene_space`` parameter as a list, tuple, or range. Here is a list -assigned to this parameter. By doing that, then the gene values are -restricted to those assigned to the ``gene_space`` parameter. - -.. code:: python - - gene_space = [0.3, 5.2, -4, 8] - -If some genes have different spaces, then ``gene_space`` should accept a -nested list or tuple. In this case, the elements could be: - -1. Number (of ``int``, ``float``, or ``NumPy`` data types): A single - value to be assigned to the gene. This means this gene will have the - same value across all generations. - -2. ``list``, ``tuple``, ``numpy.ndarray``, or any range like ``range``, - ``numpy.arange()``, or ``numpy.linspace``: It holds the space for - each individual gene. But this space is usually discrete. That is - there is a set of finite values to select from. - -3. ``dict``: To sample a value for a gene from a continuous range. The - dictionary must have 2 mandatory keys which are ``"low"`` and - ``"high"`` in addition to an optional key which is ``"step"``. A - random value is returned between the values assigned to the items - with ``"low"`` and ``"high"`` keys. If the ``"step"`` exists, then - this works as the previous options (i.e. discrete set of values). - -4. ``None``: A gene with its space set to ``None`` is initialized - randomly from the range specified by the 2 parameters - ``init_range_low`` and ``init_range_high``. For mutation, its value - is mutated based on a random value from the range specified by the 2 - parameters ``random_mutation_min_val`` and - ``random_mutation_max_val``. If all elements in the ``gene_space`` - parameter are ``None``, the parameter will not have any effect. - -Assuming that a chromosome has 2 genes and each gene has a different -value space. Then the ``gene_space`` could be assigned a nested -list/tuple where each element determines the space of a gene. - -According to the next code, the space of the first gene is ``[0.4, -5]`` -which has 2 values and the space for the second gene is -``[0.5, -3.2, 8.8, -9]`` which has 4 values. - -.. code:: python - - gene_space = [[0.4, -5], [0.5, -3.2, 8.2, -9]] - -For a 2 gene chromosome, if the first gene space is restricted to the -discrete values from 0 to 4 and the second gene is restricted to the -values from 10 to 19, then it could be specified according to the next -code. - -.. code:: python - - gene_space = [range(5), range(10, 20)] - -The ``gene_space`` can also be assigned to a single range, as given -below, where the values of all genes are sampled from the same range. - -.. code:: python - - gene_space = numpy.arange(15) - -The ``gene_space`` can be assigned a dictionary to sample a value from a -continuous range. - -.. code:: python - - gene_space = {"low": 4, "high": 30} - -A step also can be assigned to the dictionary. This works as if a range -is used. - -.. code:: python - - gene_space = {"low": 4, "high": 30, "step": 2.5} - -If a ``None`` is assigned to only a single gene, then its value will be -randomly generated initially using the ``init_range_low`` and -``init_range_high`` parameters in the ``pygad.GA`` class's constructor. -During mutation, the value are sampled from the range defined by the 2 -parameters ``random_mutation_min_val`` and ``random_mutation_max_val``. -This is an example where the second gene is given a ``None`` value. - -.. code:: python - - gene_space = [range(5), None, numpy.linspace(10, 20, 300)] - -If the user did not assign the initial population to the -``initial_population`` parameter, the initial population is created -randomly based on the ``gene_space`` parameter. Moreover, the mutation -is applied based on this parameter. - .. _more-about-the-genetype-parameter: More about the ``gene_type`` Parameter @@ -3020,7 +3189,7 @@ line connecting the fitness values across all generations: ga_instance.plot_fitness() # ga_instance.plot_fitness(plot_type="plot") -.. figure:: https://user-images.githubusercontent.com/16560492/122472609-d02f5280-cf8e-11eb-88a7-f9366ff6e7c6.png +.. image:: https://user-images.githubusercontent.com/16560492/122472609-d02f5280-cf8e-11eb-88a7-f9366ff6e7c6.png :alt: .. _plottypescatter: @@ -3036,7 +3205,7 @@ these dots can be changed using the ``linewidth`` parameter. ga_instance.plot_fitness(plot_type="scatter") -.. figure:: https://user-images.githubusercontent.com/16560492/122473159-75e2c180-cf8f-11eb-942d-31279b286dbd.png +.. image:: https://user-images.githubusercontent.com/16560492/122473159-75e2c180-cf8f-11eb-942d-31279b286dbd.png :alt: .. _plottypebar: @@ -3051,7 +3220,7 @@ bar graph with each individual fitness represented as a bar. ga_instance.plot_fitness(plot_type="bar") -.. figure:: https://user-images.githubusercontent.com/16560492/122473340-b7736c80-cf8f-11eb-89c5-4f7db3b653cc.png +.. image:: https://user-images.githubusercontent.com/16560492/122473340-b7736c80-cf8f-11eb-89c5-4f7db3b653cc.png :alt: .. _plotnewsolutionrate-2: @@ -3087,7 +3256,7 @@ first generation is always equal to the number of solutions in the population (i.e. the value assigned to the ``sol_per_pop`` parameter in the constructor of the ``pygad.GA`` class) which is 10 in this example. -.. figure:: https://user-images.githubusercontent.com/16560492/122475815-3322e880-cf93-11eb-9648-bf66f823234b.png +.. image:: https://user-images.githubusercontent.com/16560492/122475815-3322e880-cf93-11eb-9648-bf66f823234b.png :alt: .. _plottypescatter-2: @@ -3102,7 +3271,7 @@ The previous graph can be represented as scattered points by setting ga_instance.plot_new_solution_rate(plot_type="scatter") -.. figure:: https://user-images.githubusercontent.com/16560492/122476108-adec0380-cf93-11eb-80ac-7588bf90492f.png +.. image:: https://user-images.githubusercontent.com/16560492/122476108-adec0380-cf93-11eb-80ac-7588bf90492f.png :alt: .. _plottypebar-2: @@ -3117,7 +3286,7 @@ vertical bar. ga_instance.plot_new_solution_rate(plot_type="bar") -.. figure:: https://user-images.githubusercontent.com/16560492/122476173-c2c89700-cf93-11eb-9e77-d39737cd3a96.png +.. image:: https://user-images.githubusercontent.com/16560492/122476173-c2c89700-cf93-11eb-9e77-d39737cd3a96.png :alt: .. _plotgenes-2: @@ -3186,7 +3355,7 @@ of the next graph) lasted for 83 generations. ga_instance.plot_genes(graph_type="plot", plot_type="plot") -.. figure:: https://user-images.githubusercontent.com/16560492/122477158-4a62d580-cf95-11eb-8c93-9b6e74cb814c.png +.. image:: https://user-images.githubusercontent.com/16560492/122477158-4a62d580-cf95-11eb-8c93-9b6e74cb814c.png :alt: As the default value for the ``solutions`` parameter is ``"all"``, then @@ -3222,7 +3391,7 @@ scatter plot. plot_type="scatter", solutions='all') -.. figure:: https://user-images.githubusercontent.com/16560492/122477273-73836600-cf95-11eb-828f-f357c7b0f815.png +.. image:: https://user-images.githubusercontent.com/16560492/122477273-73836600-cf95-11eb-828f-f357c7b0f815.png :alt: .. _plottypebar-3: @@ -3238,7 +3407,7 @@ scatter plot. plot_type="bar", solutions='all') -.. figure:: https://user-images.githubusercontent.com/16560492/122477370-99106f80-cf95-11eb-8643-865b55e6b844.png +.. image:: https://user-images.githubusercontent.com/16560492/122477370-99106f80-cf95-11eb-8643-865b55e6b844.png :alt: .. _graphtypeboxplot: @@ -3260,7 +3429,7 @@ figure as the default value for the ``solutions`` parameter is ga_instance.plot_genes(graph_type="boxplot", solutions='all') -.. figure:: https://user-images.githubusercontent.com/16560492/122479260-beeb4380-cf98-11eb-8f08-23707929b12c.png +.. image:: https://user-images.githubusercontent.com/16560492/122479260-beeb4380-cf98-11eb-8f08-23707929b12c.png :alt: .. _graphtypehistogram: @@ -3283,7 +3452,7 @@ figure as the default value for the ``solutions`` parameter is ga_instance.plot_genes(graph_type="histogram", solutions='all') -.. figure:: https://user-images.githubusercontent.com/16560492/122477314-8007be80-cf95-11eb-9c95-da3f49204151.png +.. image:: https://user-images.githubusercontent.com/16560492/122477314-8007be80-cf95-11eb-9c95-da3f49204151.png :alt: All the previous figures can be created for only the best solutions by @@ -3298,7 +3467,7 @@ parallel processing becomes supported. This section explains how to use parallel processing in PyGAD. According to the `PyGAD -lifecycle `__, +lifecycle `__, parallel processing can be parallelized in only 2 operations: 1. Population fitness calculation. @@ -4275,7 +4444,7 @@ Linear Model Optimization ------------------------- This example is discussed in the `Steps to Use -PyGAD `__ +PyGAD `__ section which optimizes a linear model. Its complete code is listed below. @@ -4401,7 +4570,7 @@ to the next code. Here is the read image. -.. figure:: https://user-images.githubusercontent.com/16560492/36948808-f0ac882e-1fe8-11e8-8d07-1307e3477fd0.jpg +.. image:: https://user-images.githubusercontent.com/16560492/36948808-f0ac882e-1fe8-11e8-8d07-1307e3477fd0.jpg :alt: Based on the chromosome representation used in the example, the pixel @@ -4517,7 +4686,7 @@ generations can be viewed in a plot using the ``plot_fitness()`` method. Here is the plot after 20,000 generations. -.. figure:: https://user-images.githubusercontent.com/16560492/82232124-77762c00-992e-11ea-9fc6-14a1cd7a04ff.png +.. image:: https://user-images.githubusercontent.com/16560492/82232124-77762c00-992e-11ea-9fc6-14a1cd7a04ff.png :alt: Calculate Some Statistics @@ -4545,13 +4714,13 @@ Evolution by Generation The solution reached after the 20,000 generations is shown below. -.. figure:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png +.. image:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png :alt: After more generations, the result can be enhanced like what shown below. -.. figure:: https://user-images.githubusercontent.com/16560492/82232345-cf149780-992e-11ea-8390-bf1a57a19de7.png +.. image:: https://user-images.githubusercontent.com/16560492/82232345-cf149780-992e-11ea-8390-bf1a57a19de7.png :alt: The results can also be enhanced by changing the parameters passed to @@ -4562,37 +4731,37 @@ Here is how the image is evolved from generation 0 to generation Generation 0 -.. figure:: https://user-images.githubusercontent.com/16560492/36948589-b47276f0-1fe5-11e8-8efe-0cd1a225ea3a.png +.. image:: https://user-images.githubusercontent.com/16560492/36948589-b47276f0-1fe5-11e8-8efe-0cd1a225ea3a.png :alt: Generation 1,000 -.. figure:: https://user-images.githubusercontent.com/16560492/36948823-16f490ee-1fe9-11e8-97db-3e8905ad5440.png +.. image:: https://user-images.githubusercontent.com/16560492/36948823-16f490ee-1fe9-11e8-97db-3e8905ad5440.png :alt: Generation 2,500 -.. figure:: https://user-images.githubusercontent.com/16560492/36948832-3f314b60-1fe9-11e8-8f4a-4d9a53b99f3d.png +.. image:: https://user-images.githubusercontent.com/16560492/36948832-3f314b60-1fe9-11e8-8f4a-4d9a53b99f3d.png :alt: Generation 4,500 -.. figure:: https://user-images.githubusercontent.com/16560492/36948837-53d1849a-1fe9-11e8-9b36-e9e9291e347b.png +.. image:: https://user-images.githubusercontent.com/16560492/36948837-53d1849a-1fe9-11e8-9b36-e9e9291e347b.png :alt: Generation 7,000 -.. figure:: https://user-images.githubusercontent.com/16560492/36948852-66f1b176-1fe9-11e8-9f9b-460804e94004.png +.. image:: https://user-images.githubusercontent.com/16560492/36948852-66f1b176-1fe9-11e8-9f9b-460804e94004.png :alt: Generation 8,000 -.. figure:: https://user-images.githubusercontent.com/16560492/36948865-7fbb5158-1fe9-11e8-8c04-8ac3c1f7b1b1.png +.. image:: https://user-images.githubusercontent.com/16560492/36948865-7fbb5158-1fe9-11e8-8c04-8ac3c1f7b1b1.png :alt: Generation 20,000 -.. figure:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png +.. image:: https://user-images.githubusercontent.com/16560492/82232405-e0f63a80-992e-11ea-984f-b6ed76465bd1.png :alt: Clustering diff --git a/docs/source/releases.rst b/docs/source/releases.rst index 3458aaf..cd992cb 100644 --- a/docs/source/releases.rst +++ b/docs/source/releases.rst @@ -1,7 +1,7 @@ Release History =============== -.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png +.. image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png :alt: .. _pygad-1017: @@ -250,7 +250,7 @@ Release date: 19 July 2020 values. It is useful if the gene space is restricted to a certain range or to discrete values. For more information, check the `More about the ``gene_space`` - Parameter `__ + Parameter `__ section. Thanks to `Prof. Tamer A. Farrag `__ for requesting this useful feature. @@ -412,11 +412,11 @@ Release Date: 03 January 2021 1. Support of a new module ``pygad.torchga`` to train PyTorch models using PyGAD. Check `its - documentation `__. + documentation `__. 2. Support of adaptive mutation where the mutation rate is determined by the fitness value of each solution. Read the `Adaptive - Mutation `__ + Mutation `__ section for more details. Also, read this paper: `Libelli, S. Marsili, and P. Alba. "Adaptive mutation in genetic algorithms." Soft computing 4.2 (2000): @@ -525,7 +525,7 @@ Release Date: 16 February 2021 to 5 (exclusive) while the second one has values between 0.2 (inclusive) and 0.85 (exclusive). For more information, please check the `Limit the Gene Value - Range `__ + Range `__ section of the documentation. 2. The ``plot_result()`` method returns the figure so that the user can @@ -594,7 +594,7 @@ Release Date: 12 March 2021 solution/chromosome may have duplicate gene values. If ``False``, then each gene will have a unique value in its solution. Check the `Prevent Duplicates in Gene - Values `__ + Values `__ section for more details. 2. The ``last_generation_fitness`` is updated at the end of each @@ -624,7 +624,7 @@ Release Date: 19 May 2021 Previously, the ``gene_type`` can be assigned only to a single data type that is applied for all genes. For more information, check the `More about the ``gene_type`` - Parameter `__ + Parameter `__ section. Thanks to `Rainer Engel `__ for asking about this feature in `this @@ -721,7 +721,7 @@ Release Date: 17 June 2021 ``{"low": 0, "high": 30, "step": 2}`` to have only even values for the gene(s) starting from 0 to 30. For more information, check the `More about the ``gene_space`` - Parameter `__ + Parameter `__ section. https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/48 @@ -794,7 +794,7 @@ Release Date: 17 June 2021 ``[float, 2]`` forces a gene with a value like ``0.1234`` to be ``0.12``. For more information, check the `More about the ``gene_type`` - Parameter `__ + Parameter `__ section. .. _pygad-2151: @@ -829,7 +829,7 @@ Release Date: 19 June 2021 ``pygad.GA`` class to create a custom mutation, crossover, and parent selection operators. Check the `User-Defined Crossover, Mutation, and Parent Selection - Operators `__ + Operators `__ section for more details. https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50 @@ -994,7 +994,7 @@ Release Date: 8 July 2022 issue `#78 `__ at GitHub. Check the `Parallel Processing in - PyGAD `__ + PyGAD `__ section for more information and examples. .. _pygad-2180: @@ -1021,7 +1021,7 @@ Release Date: 9 September 2022 the ``run()`` method is called, as the old fitness value are shown on the graph alongside with the new fitness values. Read more in this section: `Continue without Loosing - Progress `__ + Progress `__ 4. Thanks `Prof. Fernando Jiménez Barrionuevo `__ (Dept. of Information and @@ -1037,7 +1037,7 @@ Release Date: 9 September 2022 ``keep_elitism``. It defaults to 1 which means for each generation keep only the best solution in the next generation. If assigned 0, then it has no effect. Read more in this section: `Elitism - Selection `__. + Selection `__. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/74 7. A new instance attribute named ``last_generation_elitism`` added to @@ -1048,7 +1048,7 @@ Release Date: 9 September 2022 https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/70 and `Prof. Fernando Jiménez Barrionuevo `__. Read more in this section: `Random - Seed `__. + Seed `__. 9. Editing the ``pygad.TorchGA`` module to make sure the tensor data is moved from GPU to CPU. Thanks to Rasmus Johansson for opening this @@ -1226,7 +1226,7 @@ Release Date 8 April 2023 the console and text file instead of using the ``print()`` function. This is by assigning the ``logging.Logger`` to the new ``logger`` parameter. Check the `Logging - Outputs `__ + Outputs `__ for more information. 8. A new instance attribute called ``logger`` to save the logger. @@ -1235,14 +1235,14 @@ Release Date 8 April 2023 a new parameter that refers to the instance of the ``pygad.GA`` class. Check this for an example: `Use Functions and Methods to Build Fitness Function and - Callbacks `__. + Callbacks `__. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/163 10. Update the documentation to include an example of using functions and methods to calculate the fitness and build callbacks. Check this for more details: `Use Functions and Methods to Build Fitness Function and - Callbacks `__. + Callbacks `__. https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92#issuecomment-1443635003 11. Validate the value passed to the ``initial_population`` parameter. @@ -1988,7 +1988,7 @@ Find the book at these links: - `Google Books `__ -.. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg +.. image:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg :alt: Contact Us @@ -2010,7 +2010,7 @@ Contact Us - `GitHub `__ -.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png +.. image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png :alt: Thank you for using diff --git a/docs/source/torchga.rst b/docs/source/torchga.rst index e49a2a2..6d35d4b 100644 --- a/docs/source/torchga.rst +++ b/docs/source/torchga.rst @@ -211,7 +211,7 @@ subsections discuss each part in the code. return solution_fitness - def callback_generation(ga_instance): + def on_generation(ga_instance): print("Generation = {generation}".format(generation=ga_instance.generations_completed)) print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1])) @@ -243,7 +243,7 @@ subsections discuss each part in the code. [1.3], [2.5]]) - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 250 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = torch_ga.population_weights # Initial population of network weights @@ -252,7 +252,7 @@ subsections discuss each part in the code. num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, - on_generation=callback_generation) + on_generation=on_generation) ga_instance.run() @@ -308,8 +308,6 @@ Change this number according to your needs. torch_ga = torchga.TorchGA(model=model, num_solutions=10) -.. _prepare-the-training-data-1: - Prepare the Training Data ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -372,11 +370,11 @@ The fifth step is to instantiate the ``pygad.GA`` class. Note how the the PyTorch models. For more information, please check the `parameters this class -accepts `__. +accepts `__. .. code:: python - # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class + # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 250 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = torch_ga.population_weights # Initial population of network weights @@ -385,7 +383,7 @@ accepts `__ +Data `__ section of the ``pygad.cnn`` module. Simply download these 2 files and read them according to the next code. @@ -931,7 +929,7 @@ Simply download these 2 files and read them according to the next code. The next figure shows how the fitness value changes. -.. figure:: https://user-images.githubusercontent.com/16560492/103469887-c7c4c180-4d38-11eb-98a7-1c5e73e918d0.png +.. image:: https://user-images.githubusercontent.com/16560492/103469887-c7c4c180-4d38-11eb-98a7-1c5e73e918d0.png :alt: Here are some statistics about the trained model. The model accuracy is From a21c6ab9d21ff2d0a94c0aaf263fe6bc43980849 Mon Sep 17 00:00:00 2001 From: Ahmed Gad Date: Tue, 20 Jun 2023 11:35:27 -0400 Subject: [PATCH 072/220] More examples --- Tutorial Project/README.md | 19 +-- docs/source/conf.py | 2 +- docs/source/index.rst | 16 +- examples/cnn/example_image_classification.py | 72 ++++++++ examples/data/Fish.csv | 160 ++++++++++++++++++ examples/data/Fruit360/apple/0_100.jpg | Bin 0 -> 4773 bytes examples/data/Fruit360/apple/100_100.jpg | Bin 0 -> 4748 bytes examples/data/Fruit360/apple/101_100.jpg | Bin 0 -> 4755 bytes examples/data/Fruit360/apple/102_100.jpg | Bin 0 -> 4750 bytes examples/data/Fruit360/apple/103_100.jpg | Bin 0 -> 4717 bytes examples/data/Fruit360/apple/104_100.jpg | Bin 0 -> 4756 bytes examples/data/Fruit360/apple/105_100.jpg | Bin 0 -> 4742 bytes examples/data/Fruit360/apple/106_100.jpg | Bin 0 -> 4757 bytes examples/data/Fruit360/apple/107_100.jpg | Bin 0 -> 4787 bytes examples/data/Fruit360/apple/108_100.jpg | 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pygad_lifecycle.py} (96%) diff --git a/Tutorial Project/README.md b/Tutorial Project/README.md index e9e32eb..3b2f2fb 100644 --- a/Tutorial Project/README.md +++ b/Tutorial Project/README.md @@ -1,24 +1,11 @@ # GeneticAlgorithmPython -Genetic algorithm implementation in Python - -This folder under the project has the code built in the tutorial titled [**Genetic Algorithm Implementation in Python**](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad) which is available in these links: +This folder has the code for the tutorial titled [**Genetic Algorithm Implementation in Python**](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad) which is available at these links: * https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad * https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6 * https://www.kdnuggets.com/2018/07/genetic-algorithm-implementation-python.html -The `ga.py` file holds the implementation of the GA operations such as mutation and crossover. The other file gives an example of using the GA.py file. - -It is important to note that this project does not implement everything in GA and there are a wide number of variations to be applied. For example, this project uses decimal representation for the chromosome and the binary representations might be preferred for other problems. - -## For Contacting the Author +The `ga.py` file has the implementation of the GA operations such as mutation and crossover. It is a primitive implementation of the genetic algorithm. The other file gives an example of using the ga.py file. -* E-mail: ahmed.f.gad@gmail.com -* [LinkedIn](https://www.linkedin.com/in/ahmedfgad) -* [Amazon Author Page](https://amazon.com/author/ahmedgad) -* [Paperspace](https://blog.paperspace.com/author/ahmed) -* [Hearbeat](https://heartbeat.fritz.ai/@ahmedfgad) -* [KDnuggets](https://kdnuggets.com/author/ahmed-gad) -* [TowardsDataScience](https://towardsdatascience.com/@ahmedfgad) -* [GitHub](https://github.com/ahmedfgad) +It is important to note that this project does not implement everything in GA and there are a wide number of variations to be applied. For example, this project uses decimal representation for the chromosome and the binary representations might be preferred for other problems. Check [PyGAD](https://pygad.readthedocs.io/en) for extensive features. diff --git a/docs/source/conf.py b/docs/source/conf.py index 7fd05aa..806bb02 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,7 @@ author = 'Ahmed Fawzy Gad' # The full version, including alpha/beta/rc tags -release = '3.0.1' +release = '3.1.0' master_doc = 'index' diff --git a/docs/source/index.rst b/docs/source/index.rst index b346229..3bd6ad6 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -247,7 +247,7 @@ pygad Module :maxdepth: 4 :caption: pygad Module TOC - README_pygad_ReadTheDocs.rst + pygad.rst @@ -262,7 +262,7 @@ pygad.nn Module :maxdepth: 4 :caption: pygad.nn Module TOC - README_pygad_nn_ReadTheDocs.rst + nn.rst @@ -278,7 +278,7 @@ pygad.gann Module :maxdepth: 4 :caption: pygad.gann Module TOC - README_pygad_gann_ReadTheDocs.rst + gann.rst @@ -298,7 +298,7 @@ pygad.cnn Module :maxdepth: 4 :caption: pygad.cnn Module TOC - README_pygad_cnn_ReadTheDocs.rst + cnn.rst @@ -318,7 +318,7 @@ pygad.gacnn Module :maxdepth: 4 :caption: pygad.gacnn Module TOC - README_pygad_gacnn_ReadTheDocs.rst + gacnn.rst @@ -333,7 +333,7 @@ pygad.kerasga Module :maxdepth: 4 :caption: pygad.kerasga Module TOC - README_pygad_kerasga_ReadTheDocs.rst + kerasga.rst @@ -348,7 +348,7 @@ pygad.torchga Module :maxdepth: 4 :caption: pygad.torchga Module TOC - README_pygad_torchga_ReadTheDocs.rst + torchga.rst @@ -363,7 +363,7 @@ More Information :maxdepth: 4 :caption: More Information - Footer.rst + releases.rst diff --git a/examples/cnn/example_image_classification.py b/examples/cnn/example_image_classification.py new file mode 100644 index 0000000..9b90a8b --- /dev/null +++ b/examples/cnn/example_image_classification.py @@ -0,0 +1,72 @@ +import numpy +import pygad.cnn + +""" +Convolutional neural network implementation using NumPy +A tutorial that helps to get started (Building Convolutional Neural Network using NumPy from Scratch) available in these links: + https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad + https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a + https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html +It is also translated into Chinese: http://m.aliyun.com/yunqi/articles/585741 +""" + +train_inputs = numpy.load("../data/dataset_inputs.npy") +train_outputs = numpy.load("../data/dataset_outputs.npy") + +sample_shape = train_inputs.shape[1:] +num_classes = 4 + +input_layer = pygad.cnn.Input2D(input_shape=sample_shape) +conv_layer1 = pygad.cnn.Conv2D(num_filters=2, + kernel_size=3, + previous_layer=input_layer, + activation_function=None) +relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1) +average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, + previous_layer=relu_layer1, + stride=2) + +conv_layer2 = pygad.cnn.Conv2D(num_filters=3, + kernel_size=3, + previous_layer=average_pooling_layer, + activation_function=None) +relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2) +max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2, + previous_layer=relu_layer2, + stride=2) + +conv_layer3 = pygad.cnn.Conv2D(num_filters=1, + kernel_size=3, + previous_layer=max_pooling_layer, + activation_function=None) +relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3) +pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, + previous_layer=relu_layer3, + stride=2) + +flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer) +dense_layer1 = pygad.cnn.Dense(num_neurons=100, + previous_layer=flatten_layer, + activation_function="relu") +dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes, + previous_layer=dense_layer1, + activation_function="softmax") + +model = pygad.cnn.Model(last_layer=dense_layer2, + epochs=1, + learning_rate=0.01) + +model.summary() + +model.train(train_inputs=train_inputs, + train_outputs=train_outputs) + +predictions = model.predict(data_inputs=train_inputs) +print(predictions) + +num_wrong = numpy.where(predictions != train_outputs)[0] +num_correct = train_outputs.size - num_wrong.size +accuracy = 100 * (num_correct/train_outputs.size) +print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct)) +print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size)) +print("Classification accuracy : {accuracy}.".format(accuracy=accuracy)) diff --git a/examples/data/Fish.csv b/examples/data/Fish.csv new file mode 100644 index 0000000..a0bbd32 --- /dev/null +++ b/examples/data/Fish.csv @@ -0,0 +1,160 @@ +Species,Weight,Length1,Length2,Length3,Height,Width +Bream,242,23.2,25.4,30,11.52,4.02 +Bream,290,24,26.3,31.2,12.48,4.3056 +Bream,340,23.9,26.5,31.1,12.3778,4.6961 +Bream,363,26.3,29,33.5,12.73,4.4555 +Bream,430,26.5,29,34,12.444,5.134 +Bream,450,26.8,29.7,34.7,13.6024,4.9274 +Bream,500,26.8,29.7,34.5,14.1795,5.2785 +Bream,390,27.6,30,35,12.67,4.69 +Bream,450,27.6,30,35.1,14.0049,4.8438 +Bream,500,28.5,30.7,36.2,14.2266,4.9594 +Bream,475,28.4,31,36.2,14.2628,5.1042 +Bream,500,28.7,31,36.2,14.3714,4.8146 +Bream,500,29.1,31.5,36.4,13.7592,4.368 +Bream,340,29.5,32,37.3,13.9129,5.0728 +Bream,600,29.4,32,37.2,14.9544,5.1708 +Bream,600,29.4,32,37.2,15.438,5.58 +Bream,700,30.4,33,38.3,14.8604,5.2854 +Bream,700,30.4,33,38.5,14.938,5.1975 +Bream,610,30.9,33.5,38.6,15.633,5.1338 +Bream,650,31,33.5,38.7,14.4738,5.7276 +Bream,575,31.3,34,39.5,15.1285,5.5695 +Bream,685,31.4,34,39.2,15.9936,5.3704 +Bream,620,31.5,34.5,39.7,15.5227,5.2801 +Bream,680,31.8,35,40.6,15.4686,6.1306 +Bream,700,31.9,35,40.5,16.2405,5.589 +Bream,725,31.8,35,40.9,16.36,6.0532 +Bream,720,32,35,40.6,16.3618,6.09 +Bream,714,32.7,36,41.5,16.517,5.8515 +Bream,850,32.8,36,41.6,16.8896,6.1984 +Bream,1000,33.5,37,42.6,18.957,6.603 +Bream,920,35,38.5,44.1,18.0369,6.3063 +Bream,955,35,38.5,44,18.084,6.292 +Bream,925,36.2,39.5,45.3,18.7542,6.7497 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