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DOC Ensures that MLPClassifier passes numpydoc validation #20444

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Jul 6, 2021
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DOC Ensures that MLPClassifier passes numpydoc validation #20444

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b93bc95
Remove MLPClassifier from DOCSTRING_IGNORE_LIST.
jmloyola Jul 2, 2021
8b78e07
Fix numpydocs from MLPClassifier.
jmloyola Jul 2, 2021
919501a
Update sklearn/neural_network/_multilayer_perceptron.py
glemaitre Jul 6, 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 @@ -88,7 +88,6 @@
"LocalOutlierFactor",
"LocallyLinearEmbedding",
"MDS",
"MLPClassifier",
"MLPRegressor",
"MaxAbsScaler",
"MeanShift",
Expand Down
54 changes: 30 additions & 24 deletions sklearn/neural_network/_multilayer_perceptron.py
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Expand Up @@ -742,7 +742,8 @@ def fit(self, X, y):

Returns
-------
self : returns a trained MLP model.
self : object
Returns a trained MLP model.
"""
return self._fit(X, y, incremental=False)

Expand Down Expand Up @@ -825,7 +826,7 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):
batch_size : int, default='auto'
Size of minibatches for stochastic optimizers.
If the solver is 'lbfgs', the classifier will not use minibatch.
When set to "auto", `batch_size=min(200, n_samples)`
When set to "auto", `batch_size=min(200, n_samples)`.

learning_rate : {'constant', 'invscaling', 'adaptive'}, default='constant'
Learning rate schedule for weight updates.
Expand Down Expand Up @@ -904,27 +905,27 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):
If early stopping is False, then the training stops when the training
loss does not improve by more than tol for n_iter_no_change consecutive
passes over the training set.
Only effective when solver='sgd' or 'adam'
Only effective when solver='sgd' or 'adam'.

validation_fraction : float, default=0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if early_stopping is True
Only used if early_stopping is True.

beta_1 : float, default=0.9
Exponential decay rate for estimates of first moment vector in adam,
should be in [0, 1). Only used when solver='adam'
should be in [0, 1). Only used when solver='adam'.

beta_2 : float, default=0.999
Exponential decay rate for estimates of second moment vector in adam,
should be in [0, 1). Only used when solver='adam'
should be in [0, 1). Only used when solver='adam'.

epsilon : float, default=1e-8
Value for numerical stability in adam. Only used when solver='adam'
Value for numerical stability in adam. Only used when solver='adam'.

n_iter_no_change : int, default=10
Maximum number of epochs to not meet ``tol`` improvement.
Only effective when solver='sgd' or 'adam'
Only effective when solver='sgd' or 'adam'.

.. versionadded:: 0.20

Expand Down Expand Up @@ -979,21 +980,10 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):
out_activation_ : str
Name of the output activation function.

Examples
See Also
--------
>>> from sklearn.neural_network import MLPClassifier
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_classification(n_samples=100, random_state=1)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,
... random_state=1)
>>> clf = MLPClassifier(random_state=1, max_iter=300).fit(X_train, y_train)
>>> clf.predict_proba(X_test[:1])
array([[0.038..., 0.961...]])
>>> clf.predict(X_test[:5, :])
array([1, 0, 1, 0, 1])
>>> clf.score(X_test, y_test)
0.8...
MLPRegressor : Multi-layer Perceptron regressor.
BernoulliRBM : Bernoulli Restricted Boltzmann Machine (RBM).

Notes
-----
Expand Down Expand Up @@ -1023,6 +1013,22 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):

Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
optimization." arXiv preprint arXiv:1412.6980 (2014).

Examples
--------
>>> from sklearn.neural_network import MLPClassifier
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_classification(n_samples=100, random_state=1)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,
... random_state=1)
>>> clf = MLPClassifier(random_state=1, max_iter=300).fit(X_train, y_train)
>>> clf.predict_proba(X_test[:1])
array([[0.038..., 0.961...]])
>>> clf.predict(X_test[:5, :])
array([1, 0, 1, 0, 1])
>>> clf.score(X_test, y_test)
0.8...
"""

def __init__(
Expand Down Expand Up @@ -1134,7 +1140,7 @@ def _validate_input(self, X, y, incremental, reset):
return X, y

def predict(self, X):
"""Predict using the multi-layer perceptron classifier
"""Predict using the multi-layer perceptron classifier.

Parameters
----------
Expand Down Expand Up @@ -1211,7 +1217,7 @@ def predict_log_proba(self, X):
log_y_prob : ndarray of shape (n_samples, n_classes)
The predicted log-probability of the sample for each class
in the model, where classes are ordered as they are in
`self.classes_`. Equivalent to log(predict_proba(X))
`self.classes_`. Equivalent to `log(predict_proba(X))`.
"""
y_prob = self.predict_proba(X)
return np.log(y_prob, out=y_prob)
Expand Down