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DOC Ensures that BernoulliRBM passes numpydoc validation #20533

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Sep 1, 2021
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DOC Ensures that BernoulliRBM passes numpydoc validation #20533

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7ceed3f
fixed typo (in --> is)
reshamas Jul 15, 2021
5d42849
fixed typos; reduced length of description
reshamas Jul 15, 2021
d56f723
added description for y
reshamas Jul 15, 2021
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added description for y
reshamas Jul 15, 2021
f278d87
remove BernoulliRBM from test_docstrings.py; fix commenting error
reshamas Jul 15, 2021
2d9544c
Update sklearn/neural_network/_rbm.py
reshamas Jul 15, 2021
b6cf1d4
Update sklearn/neural_network/_rbm.py
reshamas Jul 15, 2021
ba8df84
Update sklearn/neural_network/_rbm.py
reshamas Jul 15, 2021
b665104
Update sklearn/neural_network/_rbm.py
reshamas Jul 15, 2021
c2feaa2
Update sklearn/neural_network/_rbm.py
reshamas Jul 15, 2021
4c17c1e
Update sklearn/neural_network/_rbm.py
reshamas Jul 15, 2021
6075422
added range of values for `verbose` parameter
reshamas Jul 15, 2021
d8914ac
commented out AdditiveChi2Sampler
reshamas Jul 15, 2021
2238ab3
fixing merge conflicts in test_docstrings.py
reshamas Aug 8, 2021
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Merge branch 'main' of github.com:scikit-learn/scikit-learn into Bern…
reshamas Aug 20, 2021
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Merge remote-tracking branch 'origin/main' into pr/reshamas/20533
glemaitre Sep 1, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,6 @@

# List of modules ignored when checking for numpydoc validation.
DOCSTRING_IGNORE_LIST = [
"BernoulliRBM",
"Birch",
"FeatureHasher",
"FeatureUnion",
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43 changes: 28 additions & 15 deletions sklearn/neural_network/_rbm.py
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Expand Up @@ -53,7 +53,8 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
during training.

verbose : int, default=0
The verbosity level. The default, zero, means silent mode.
The verbosity level. The default, zero, means silent mode. Range
of values is [0, inf].

random_state : int, RandomState instance or None, default=None
Determines random number generation for:
Expand All @@ -76,13 +77,13 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
Biases of the visible units.

components_ : array-like of shape (n_components, n_features)
Weight matrix, where n_features in the number of
visible units and n_components is the number of hidden units.
Weight matrix, where `n_features` is the number of
visible units and `n_components` is the number of hidden units.

h_samples_ : array-like of shape (batch_size, n_components)
Hidden Activation sampled from the model distribution,
where batch_size in the number of examples per minibatch and
n_components is the number of hidden units.
where `batch_size` is the number of examples per minibatch and
`n_components` is the number of hidden units.

n_features_in_ : int
Number of features seen during :term:`fit`.
Expand All @@ -95,15 +96,12 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):

.. versionadded:: 1.0

Examples
See Also
--------

>>> import numpy as np
>>> from sklearn.neural_network import BernoulliRBM
>>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
>>> model = BernoulliRBM(n_components=2)
>>> model.fit(X)
BernoulliRBM(n_components=2)
sklearn.neural_network.MLPRegressor : Multi-layer Perceptron regressor.
sklearn.neural_network.MLPClassifier : Multi-layer Perceptron classifier.
sklearn.decomposition.PCA : An unsupervised linear dimensionality
reduction model.

References
----------
Expand All @@ -115,6 +113,16 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
[2] Tieleman, T. Training Restricted Boltzmann Machines using
Approximations to the Likelihood Gradient. International Conference
on Machine Learning (ICML) 2008

Examples
--------

>>> import numpy as np
>>> from sklearn.neural_network import BernoulliRBM
>>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
>>> model = BernoulliRBM(n_components=2)
>>> model.fit(X)
BernoulliRBM(n_components=2)
"""

def __init__(
Expand Down Expand Up @@ -250,14 +258,16 @@ def gibbs(self, v):
return v_

def partial_fit(self, X, y=None):
"""Fit the model to the data X which should contain a partial
segment of the data.
"""Fit the model to the partial segment of the data X.

Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.

y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).

Returns
-------
self : BernoulliRBM
Expand Down Expand Up @@ -362,6 +372,9 @@ def fit(self, X, y=None):
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.

y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).

Returns
-------
self : BernoulliRBM
Expand Down