8000 Fix default value in doc of SGD by ankishb · Pull Request #15967 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

Fix default value in doc of SGD #15967

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Dec 26, 2019
Merged

Fix default value in doc of SGD #15967

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
7e1db03
Fix default value in doc of SGD
ankishb Dec 24, 2019
459ee56
Fix default value in sgd in master branch
ankishb Dec 24, 2019
217832b
Fix array-like keyword, and penality string
ankishb Dec 24, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
115 changes: 56 additions & 59 deletions sklearn/linear_model/_stochastic_gradient.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -244,12 +244,12 @@ def _make_validation_split(self, y):

Parameters
----------
y : array, shape (n_samples, )
y : ndarray of shape (n_samples, )
Target values.

Returns
-------
validation_mask : array, shape (n_samples, )
validation_mask : ndarray of shape (n_samples, )
Equal to 1 on the validation set, 0 on the training set.
"""
n_samples = y.shape[0]
Expand Down Expand Up @@ -362,11 +362,11 @@ def fit_binary(est, i, X, y, alpha, C, learning_rate, max_iter,
sample_weight : numpy array of shape [n_samples, ]
The weight of each sample

validation_mask : numpy array of shape [n_samples, ] or None
validation_mask : numpy array of shape [n_samples, ], default=None
Precomputed validation mask in case _fit_binary is called in the
context of a one-vs-rest reduction.

random_state : int, RandomState instance or None, optional (default=None)
random_state : int, RandomState instance, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
Expand Down Expand Up @@ -641,18 +641,18 @@ def partial_fit(self, X, y, classes=None, sample_weight=None):
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data.

y : numpy array, shape (n_samples,)
y : ndarray of shape (n_samples,)
Subset of the target values.

classes : array, shape (n_classes,)
classes : ndarray of shape (n_classes,), default=None
Classes across all calls to partial_fit.
Can be obtained by via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn't need to contain all labels in `classes`.

sample_weight : array-like, shape (n_samples,), optional
sample_weight : array-like, shape (n_samples,), default=None
Weights applied to individual samples.
If not provided, uniform weights are assumed.

Expand Down Expand Up @@ -685,16 +685,16 @@ def fit(self, X, y, coef_init=None, intercept_init=None,
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.

y : numpy array, shape (n_samples,)
y : ndarray of shape (n_samples,)
Target values.

coef_init : array, shape (n_classes, n_features)
coef_init : ndarray of shape (n_classes, n_features), default=None
The initial coefficients to warm-start the optimization.

intercept_init : array, shape (n_classes,)
intercept_init : ndarray of shape (n_classes,), default=None
The initial intercept to warm-start the optimization.

sample_weight : array-like, shape (n_samples,), optional
sample_weight : array-like, shape (n_samples,), default=None
Weights applied to individual samples.
If not provided, uniform weights are assumed. These weights will
be multiplied with class_weight (passed through the
Expand Down Expand Up @@ -738,7 +738,7 @@ class SGDClassifier(BaseSGDClassifier):

Parameters
----------
loss : str, default: 'hinge'
loss : str, default='hinge'
The loss function to be used. Defaults to 'hinge', which gives a
linear SVM.

Expand All @@ -754,42 +754,41 @@ class SGDClassifier(BaseSGDClassifier):
The other losses are designed for regression but can be useful in
classification as well; see SGDRegressor for a description.

penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
penalty : {'l2', 'l1', 'elasticnet'}, default='l2'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.

alpha : float
alpha : float, default=0.0001
Constant that multiplies the regularization term. Defaults to 0.0001.
Also used to compute learning_rate when set to 'optimal'.

l1_ratio : float
l1_ratio : float, default=0.15
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.

fit_intercept : bool
fit_intercept : bool, default=True
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.

max_iter : int, optional (default=1000)
max_iter : int, default=1000
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.

.. versionadded:: 0.19

tol : float or None, optional (default=1e-3)
tol : float, default=1e-3
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs.

.. versionadded:: 0.19

shuffle : bool, optional
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.
Defaults to True.

verbose : int, default=0
The verbosity level.
Expand All @@ -802,21 +801,21 @@ class SGDClassifier(BaseSGDClassifier):
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.

n_jobs : int or None, optional (default=None)
n_jobs : int, default=None
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

random_state : int, RandomState instance or None, optional (default=None)
random_state : int, RandomState instance, default=None
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.

learning_rate : str, optional
learning_rate : str, default='optimal'
The learning rate schedule:

'constant':
Expand All @@ -832,12 +831,12 @@ class SGDClassifier(BaseSGDClassifier):
training loss by tol or fail to increase validation score by tol if
early_stopping is True, the current learning rate is divided by 5.

eta0 : double
eta0 : double, default=0.0
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.0 as eta0 is not used by
the default schedule 'optimal'.

power_t : double
power_t : double, default=0.5
The exponent for inverse scaling learning rate [default 0.5].

early_stopping : bool, default=False
Expand All @@ -861,7 +860,7 @@ class SGDClassifier(BaseSGDClassifier):

.. versionadded:: 0.20

class_weight : dict, {class_label: weight} or "balanced" or None, optional
class_weight : dict, {class_label: weight} or "balanced", default=None
Preset for the class_weight fit parameter.

Weights associated with classes. If not given, all classes
Expand Down Expand Up @@ -893,11 +892,11 @@ class SGDClassifier(BaseSGDClassifier):

Attributes
----------
coef_ : array, shape (1, n_features) if n_classes == 2 else (n_classes,\
n_features)
coef_ : ndarray of shape (1, n_features) if n_classes == 2 else \
(n_classes, n_features)
Weights assigned to the features.

intercept_ : array, shape (1,) if n_classes == 2 else (n_classes,)
intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,)
Constants in decision function.

n_iter_ : int
Expand Down Expand Up @@ -979,7 +978,7 @@ def predict_proba(self):

Returns
-------
array, shape (n_samples, n_classes)
ndarray of shape (n_samples, n_classes)
Returns the probability of the sample for each class in the model,
where classes are ordered as they are in `self.classes_`.

Expand Down Expand Up @@ -1140,7 +1139,7 @@ def partial_fit(self, X, y, sample_weight=None):
y : numpy array of shape (n_samples,)
Subset of target values

sample_weight : array-like, shape (n_samples,), optional
sample_weight : array-like, shape (n_samples,), default=None
Weights applied to individual samples.
If not provided, uniform weights are assumed.

Expand Down Expand Up @@ -1198,16 +1197,16 @@ def fit(self, X, y, coef_init=None, intercept_init=None,
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data

y : numpy array, shape (n_samples,)
y : ndarray of shape (n_samples,)
Target values

coef_init : array, shape (n_features,)
coef_init : ndarray of shape (n_features,), default=None
The initial coefficients to warm-start the optimization.

intercept_init : array, shape (1,)
intercept_init : ndarray of shape (1,), default=None
The initial intercept to warm-start the optimization.

sample_weight : array-like, shape (n_samples,), optional
sample_weight : array-like, shape (n_samples,), default=None
Weights applied to individual samples (1. for unweighted).

Returns
Expand All @@ -1229,7 +1228,7 @@ def _decision_function(self, X):

Returns
-------
array, shape (n_samples,)
ndarray of shape (n_samples,)
Predicted target values per element in X.
"""
check_is_fitted(self)
Expand All @@ -1249,7 +1248,7 @@ def predict(self, X):

Returns
-------
array, shape (n_samples,)
ndarray of shape (n_samples,)
Predicted target values per element in X.
"""
return self._decision_function(X)
Expand Down Expand Up @@ -1359,7 +1358,7 @@ class SGDRegressor(BaseSGDRegressor):

Parameters
----------
loss : str, default: 'squared_loss'
loss : str, default='squared_loss'
The loss function to be used. The possible values are 'squared_loss',
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'

Expand All @@ -1371,44 +1370,42 @@ class SGDRegressor(BaseSGDRegressor):
'squared_epsilon_insensitive' is the same but becomes squared loss past
a tolerance of epsilon.

penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
penalty : {'l2', 'l1', 'elasticnet'}, default='l2'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.

alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001
alpha : float, default=0.0001
Constant that multiplies the regularization term.
Also used to compute learning_rate when set to 'optimal'.

l1_ratio : float
l1_ratio : float, default=0.15
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.

fit_intercept : bool
fit_intercept : bool, default=True
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
data is assumed to be already centered.

max_iter : int, optional (default=1000)
max_iter : int, default=1000
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.

.. versionadded:: 0.19

tol : float or None, optional (default=1e-3)
tol : float, default=1e-3
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs.

.. versionadded:: 0.19

shuffle : bool, optional
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.
Defaults to True.

verbose : integer, default=0
verbose : int, default=0
The verbosity level.

epsilon : float, default=0.1
Expand All @@ -1419,14 +1416,14 @@ class SGDRegressor(BaseSGDRegressor):
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.

random_state : int, RandomState instance or None, optional (default=None)
random_state : int, RandomState instance, default=None
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.

learning_rate : string, optional
learning_rate : string, default='invscaling'
The learning rate schedule:

'constant':
Expand All @@ -1442,12 +1439,12 @@ class SGDRegressor(BaseSGDRegressor):
training loss by tol or fail to increase validation score by tol if
early_stopping is True, the current learning rate is divided by 5.

eta0 : double
eta0 : double, default=0.01
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.01.

power_t : double
The exponent for inverse scaling learning rate [default 0.25].
power_t : double, default=0.25
The exponent for inverse scaling learning rate.

early_stopping : bool, default=False
Whether to use early stopping to terminate training when validation
Expand Down Expand Up @@ -1492,16 +1489,16 @@ class SGDRegressor(BaseSGDRegressor):

Attributes
----------
coef_ : array, shape (n_features,)
coef_ : ndarray of shape (n_features,)
Weights assigned to the features.

intercept_ : array, shape (1,)
intercept_ : ndarray of shape (1,)
The intercept term.

average_coef_ : array, shape (n_features,)
average_coef_ : ndarray of shape (n_features,)
Averaged weights assigned to the features.

average_intercept_ : array, shape (1,)
average_intercept_ : ndarray of shape (1,)
The averaged intercept term.

n_iter_ : int
Expand Down
0