8000 DOC_Standardized formatting for default values in manifold tsne by amy12xx · Pull Request #17511 · scikit-learn/scikit-learn · GitHub
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Jun 6, 2020
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DOC_Standardized formatting for default values in manifold tsne #17511

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Standardize formatting for default values
amy12xx Jun 6, 2020
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DOC_Standardize formatting for default values
amy12xx Jun 6, 2020
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48 changes: 24 additions & 24 deletions sklearn/manifold/_t_sne.py
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Expand Up @@ -293,39 +293,39 @@ def _gradient_descent(objective, p0, it, n_iter,
n_iter : int
Maximum number of gradient descent iterations.

n_iter_check : int
n_iter_check : int, default=1
Number of iterations before evaluating the global error. If the error
is sufficiently low, we abort the optimization.

n_iter_without_progress : int, optional (default: 300)
n_iter_without_progress : int, default=300
Maximum number of iterations without progress before we abort the
optimization.

momentum : float, within (0.0, 1.0), optional (default: 0.8)
momentum : float, within (0.0, 1.0), default=0.8
The momentum generates a weight for previous gradients that decays
exponentially.

learning_rate : float, optional (default: 200.0)
learning_rate : float, default=200.0
The learning rate for t-SNE is usually in the range [10.0, 1000.0]. If
the learning rate is too high, the data may look like a 'ball' with any
point approximately equidistant from its nearest neighbours. If the
learning rate is too low, most points may look compressed in a dense
cloud with few outliers.

min_gain : float, optional (default: 0.01)
min_gain : float, default=0.01
Minimum individual gain for each parameter.

min_grad_norm : float, optional (default: 1e-7)
min_grad_norm : float, default=1e-7
If the gradient norm is below this threshold, the optimization will
be aborted.

verbose : int, optional (default: 0)
verbose : int, default=0
Verbosity level.

args : sequence
args : sequence, default=None
Arguments to pass to objective function.

kwargs : dict
kwargs : dict, default=None
Keyword arguments to pass to objective function.

Returns
Expand Down Expand Up @@ -430,10 +430,10 @@ def trustworthiness(X, X_embedded, *, n_neighbors=5, metric='euclidean'):
X_embedded : array, shape (n_samples, n_components)
Embedding of the training data in low-dimensional space.

n_neighbors : int, optional (default: 5)
n_neighbors : int, default=5
Number of neighbors k that will be considered.

metric : string, or callable, optional, default 'euclidean'
metric : string, or callable, default='euclidean'
Which metric to use for computing pairwise distances between samples
from the original input space. If metric is 'precomputed', X must be a
matrix of pairwise distances or squared distances. Otherwise, see the
Expand Down Expand Up @@ -495,17 +495,17 @@ class TSNE(BaseEstimator):

Parameters
----------
n_components : int, optional (default: 2)
n_components : int, default=2
Dimension of the embedded space.

perplexity : float, optional (default: 30)
perplexity : float, default=30
The perplexity is related to the number of nearest neighbors that
is used in other manifold learning algorithms. Larger datasets
usually require a larger perplexity. Consider selecting a value
between 5 and 50. Different values can result in significanlty
different results.

early_exaggeration : float, optional (default: 12.0)
early_exaggeration : float, default=12.0
Controls how tight natural clusters in the original space are in
the embedded space and how much space will be between them. For
larger values, the space between natural clusters will be larger
Expand All @@ -514,19 +514,19 @@ class TSNE(BaseEstimator):
optimization, the early exaggeration factor or the learning rate
might be too high.

learning_rate : float, optional (default: 200.0)
learning_rate : float, default=200.0
The learning rate for t-SNE is usually in the range [10.0, 1000.0]. If
the learning rate is too high, the data may look like a 'ball' with any
point approximately equidistant from its nearest neighbours. If the
learning rate is too low, most points may look compressed in a dense
cloud with few outliers. If the cost function gets stuck in a bad local
minimum increasing the learning rate may help.

n_iter : int, optional (default: 1000)
n_iter : int, default=1000
Maximum number of iterations for the optimization. Should be at
least 250.

n_iter_without_progress : int, optional (default: 300)
n_iter_without_progress : int, default=300
Maximum number of iterations without progress before we abort the
optimization, used after 250 initial iterations with early
exaggeration. Note that progress is only checked every 50 iterations so
Expand All @@ -535,11 +535,11 @@ class TSNE(BaseEstimator):
10000 .. versionadded:: 0.17
parameter *n_iter_without_progress* to control stopping criteria.

min_grad_norm : float, optional (default: 1e-7)
min_grad_norm : float, default=1e-7
If the gradient norm is below this threshold, the optimization will
be stopped.

metric : string or callable, optional
metric : string or callable, default='euclidean'
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by scipy.spatial.distance.pdist for its metric parameter, or
Expand All @@ -551,13 +551,13 @@ class TSNE(BaseEstimator):
the distance between them. The default is "euclidean" which is
interpreted as squared euclidean distance.

init : string or numpy array, optional (default: "random")
init : string or numpy array, default="random"
Initialization of embedding. Possible options are 'random', 'pca',
and a numpy array of shape (n_samples, n_components).
PCA initialization cannot be used with precomputed distances and is
usually more globally stable than random initialization.

verbose : int, optional (default: 0)
verbose : int, default=0
Verbosity level.

random_state : int, RandomState instance, default=None
Expand All @@ -566,7 +566,7 @@ class TSNE(BaseEstimator):
initializations might result in different local minima of the cost
function. See :term: `Glossary <random_state>`.

method : string (default: 'barnes_hut')
method : string, default='barnes_hut'
By default the gradient calculation algorithm uses Barnes-Hut
approximation running in O(NlogN) time. method='exact'
will run on the slower, but exact, algorithm in O(N^2) time. The
Expand All @@ -577,7 +577,7 @@ class TSNE(BaseEstimator):
.. versionadded:: 0.17
Approximate optimization *method* via the Barnes-Hut.

angle : float (default: 0.5)
angle : float, default=0.5
Only used if method='barnes_hut'
This is the trade-off between speed and accuracy for Barnes-Hut T-SNE.
'angle' is the angular size (referred to as theta in [3]) of a distant
Expand All @@ -587,7 +587,7 @@ class TSNE(BaseEstimator):
in the range of 0.2 - 0.8. Angle less than 0.2 has quickly increasing
computation time and angle greater 0.8 has quickly increasing error.

n_jobs : int or None, optional (default=None)
n_jobs : int or None, default=None
The number of parallel jobs to run for neighbors search. This parameter
has no impact when ``metric="precomputed"`` or
(``metric="euclidean"`` and ``method="exact"``).
Expand Down
34 changes: 17 additions & 17 deletions sklearn/mixture/_bayesian_mixture.py
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Expand Up @@ -82,14 +82,14 @@ class BayesianGaussianMixture(BaseMixture):

Parameters
----------
n_components : int, defaults to 1.
n_components : int, defaults=1.
The number of mixture components. Depending on the data and the value
of the `weight_concentration_prior` the model can decide to not use
all the components by setting some component `weights_` to values very
close to zero. The number of effective components is therefore smaller
than n_components.

covariance_type : {'full', 'tied', 'diag', 'spherical'}, defaults to 'full'
covariance_type : {'full', 'tied', 'diag', 'spherical'}, default='full'
String describing the type of covariance parameters to use.
Must be one of::

Expand All @@ -98,38 +98,38 @@ class BayesianGaussianMixture(BaseMixture):
'diag' (each component has its own diagonal covariance matrix),
'spherical' (each component has its own single variance).

tol : float, defaults to 1e-3.
tol : float, defaults=1e-3.
The convergence threshold. EM iterations will stop when the
lower bound average gain on the likelihood (of the training data with
respect to the model) is below this threshold.

reg_covar : float, defaults to 1e-6.
reg_covar : float, defaults=1e-6.
Non-negative regularization added to the diagonal of covariance.
Allows to assure that the covariance matrices are all positive.

max_iter : int, defaults to 100.
max_iter : int, defaults=100.
The number of EM iterations to perform.

n_init : int, defaults to 1.
n_init : int, defaults=1.
The number of initializations to perform. The result with the highest
lower bound value on the likelihood is kept.

init_params : {'kmeans', 'random'}, defaults to 'kmeans'.
init_params : {'kmeans', 'random'}, defaults='kmeans'.
The method used to initialize the weights, the means and the
covariances.
Must be one of::

'kmeans' : responsibilities are initialized using kmeans.
'random' : responsibilities are initialized randomly.

weight_concentration_prior_type : str, defaults to 'dirichlet_process'.
weight_concentration_prior_type : str, defaults='dirichlet_process'.
String describing the type of the weight concentration prior.
Must be one of::

'dirichlet_process' (using the Stick-breaking representation),
'dirichlet_distribution' (can favor more uniform weights).

weight_concentration_prior : float | None, optional.
weight_concentration_prior : float | None, default=None.
The dirichlet concentration of each component on the weight
distribution (Dirichlet). This is commonly called gamma in the
literature. The higher concentration puts more mass in
Expand All @@ -138,22 +138,22 @@ class BayesianGaussianMixture(BaseMixture):
mixture weights simplex. The value of the parameter must be greater
than 0. If it is None, it's set to ``1. / n_components``.

mean_precision_prior : float | None, optional.
mean_precision_prior : float | None, default=None.
The precision prior on the mean distribution (Gaussian).
Controls the extent of where means can be placed. Larger
values concentrate the cluster means around `mean_prior`.
The value of the parameter must be greater than 0.
If it is None, it is set to 1.

mean_prior : array-like, shape (n_features,), optional
mean_prior : array-like, shape (n_features,), default=None.
The prior on the mean distribution (Gaussian).
If it is None, it is set to the mean of X.

degrees_of_freedom_prior : float | None, optional.
degrees_of_freedom_prior : float | None, default=None.
The prior of the number of degrees of freedom on the covariance
distributions (Wishart). If it is None, it's set to `n_features`.

covariance_prior : float or array-like, optional
covariance_prior : float or array-like, default=None.
The prior on the covariance distribution (Wishart).
If it is None, the emiprical covariance prior is initialized using the
covariance of X. The shape depends on `covariance_type`::
Expand All @@ -163,27 +163,27 @@ class BayesianGaussianMixture(BaseMixture):
(n_features) if 'diag',
float if 'spherical'

random_state : int, RandomState instance or None, optional (default=None)
random_state : int, RandomState instance or None, default=None.
Controls the random seed given to the method chosen to initialize the
parameters (see `init_params`).
In addition, it controls the generation of random samples from the
fitted distribution (see the method `sample`).
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.

warm_start : bool, default to False.
warm_start : bool, default=False.
If 'warm_start' is True, the solution of the last fitting is used as
initialization for the next call of fit(). This can speed up
convergence when fit is called several times on similar problems.
See :term:`the Glossary <warm_start>`.

verbose : int, default to 0.
verbose : int, default=0.
Enable verbose output. If 1 then it prints the current
initialization and each iteration step. If greater than 1 then
it prints also the log probability and the time needed
for each step.

verbose_interval : int, default to 10.
verbose_interval : int, default=10.
Number of iteration done before the next print.

Attributes
Expand Down
0