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DOC Ensures that PassiveAggressiveClassifier passes numpydoc validation #21226

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Oct 12, 2021
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DOC Ensures that PassiveAggressiveClassifier passes numpydoc validation #21226

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e75b16b
Address all but one numpydoc validation test
ovynnej Oct 2, 2021
a268b33
solve remaining issues
glemaitre Oct 12, 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 @@ -20,7 +20,6 @@
"MultiTaskLassoCV",
"OrthogonalMatchingPursuit",
"OrthogonalMatchingPursuitCV",
"PassiveAggressiveClassifier",
"PassiveAggressiveRegressor",
"PatchExtractor",
"PolynomialFeatures",
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94 changes: 48 additions & 46 deletions sklearn/linear_model/_passive_aggressive.py
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Expand Up @@ -7,13 +7,12 @@


class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
"""Passive Aggressive Classifier.

Read more in the :ref:`User Guide <passive_aggressive>`.

Parameters
----------

C : float, default=1.0
Maximum step size (regularization). Defaults to 1.0.

Expand Down Expand Up @@ -58,10 +57,10 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.

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

loss : string, default="hinge"
loss : str, default="hinge"
The loss function to be used:
hinge: equivalent to PA-I in the reference paper.
squared_hinge: equivalent to PA-II in the reference paper.
Expand Down Expand Up @@ -97,7 +96,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):

The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
as ``n_samples / (n_classes * np.bincount(y))``.

.. versionadded:: 0.17
parameter *class_weight* to automatically weight samples.
Expand All @@ -109,15 +108,15 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
average. So average=10 will begin averaging after seeing 10 samples.

.. versionadded:: 0.19
parameter *average* to use weights averaging in SGD
parameter *average* to use weights averaging in SGD.

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_features_in_ : int
Expand All @@ -135,7 +134,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
The actual number of iterations to reach the stopping criterion.
For multiclass fits, it is the maximum over every binary fit.

classes_ : array of shape (n_classes,)
classes_ : ndarray of shape (n_classes,)
The unique classes labels.

t_ : int
Expand All @@ -145,11 +144,21 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
loss_function_ : callable
Loss function used by the algorithm.

See Also
--------
SGDClassifier : Incrementally trained logistic regression.
Perceptron : Linear perceptron classifier.

References
----------
Online Passive-Aggressive Algorithms
<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)

Examples
--------
>>> from sklearn.linear_model import PassiveAggressiveClassifier
>>> from sklearn.datasets import make_classification

>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = PassiveAggressiveClassifier(max_iter=1000, random_state=0,
... tol=1e-3)
Expand All @@ -161,18 +170,6 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
[1.84127814]
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]

See Also
--------
SGDClassifier
Perceptron

References
----------
Online Passive-Aggressive Algorithms
<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)

"""

def __init__(
Expand Down Expand Up @@ -221,12 +218,12 @@ def partial_fit(self, X, y, classes=None):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Subset of the training data
Subset of the training data.

y : numpy array of shape [n_samples]
Subset of the target values
y : array-like of shape (n_samples,)
Subset of the target values.

classes : array, shape = [n_classes]
classes : ndarray of shape (n_classes,)
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.
Expand All @@ -236,7 +233,8 @@ def partial_fit(self, X, y, classes=None):

Returns
-------
self : returns an instance of self.
self : object
Fitted estimator.
"""
self._validate_params(for_partial_fit=True)
if self.class_weight == "balanced":
Expand Down Expand Up @@ -272,20 +270,21 @@ def fit(self, X, y, coef_init=None, intercept_init=None):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data
Training data.

y : numpy array of shape [n_samples]
Target values
y : array-like of shape (n_samples,)
Target values.

coef_init : array, shape = [n_classes,n_features]
coef_init : ndarray of shape (n_classes, n_features)
The initial coefficients to warm-start the optimization.

intercept_init : array, shape = [n_classes]
intercept_init : ndarray of shape (n_classes,)
The initial intercept to warm-start the optimization.

Returns
-------
self : returns an instance of self.
self : object
Fitted estimator.
"""
self._validate_params()
lr = "pa1" if self.loss == "hinge" else "pa2"
Expand Down Expand Up @@ -354,9 +353,9 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
Whether or not the training data should be shuffled after each epoch.

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

loss : string, default="epsilon_insensitive"
loss : str, default="epsilon_insensitive"
The loss function to be used:
epsilon_insensitive: equivalent to PA-I in the reference paper.
squared_epsilon_insensitive: equivalent to PA-II in the reference
Expand Down Expand Up @@ -388,7 +387,7 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
average. So average=10 will begin averaging after seeing 10 samples.

.. versionadded:: 0.19
parameter *average* to use weights averaging in SGD
parameter *average* to use weights averaging in SGD.

Attributes
----------
Expand Down Expand Up @@ -436,13 +435,14 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):

See Also
--------
SGDRegressor
SGDRegressor : Linear model fitted by minimizing a regularized
empirical loss with SGD.

References
----------
Online Passive-Aggressive Algorithms
<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006).

"""

Expand Down Expand Up @@ -490,14 +490,15 @@ def partial_fit(self, X, y):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Subset of training data
Subset of training data.

y : numpy array of shape [n_samples]
Subset of target values
Subset of target values.

Returns
-------
self : returns an instance of self.
self : object
Fitted estimator.
"""
self._validate_params(for_partial_fit=True)
lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2"
Expand All @@ -520,10 +521,10 @@ def fit(self, X, y, coef_init=None, intercept_init=None):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data
Training data.

y : numpy array of shape [n_samples]
Target values
Target values.

coef_init : array, shape = [n_features]
The initial coefficients to warm-start the optimization.
Expand All @@ -533,7 +534,8 @@ def fit(self, X, y, coef_init=None, intercept_init=None):

Returns
-------
self : returns an instance of self.
self : object
Fitted estimator.
"""
self._validate_params()
lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2"
Expand Down