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DOC - Ensure that SGDOneClassSVM pass numpydoc validation #21105

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Sep 23, 2021
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DOC - Ensure that SGDOneClassSVM pass numpydoc validation #21105

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aba8a6c
(#20308) Changed order of sections for SGDOneClassSVM Docstring
frellwan Sep 22, 2021
0b6fa2e
(#20308) Changed parameter 'verbose' to int and added period in SGDOn…
frellwan Sep 22, 2021
667667c
(#20308) Changed parameter 'learning rate' to str in SGDOneClassSVM D…
frellwan Sep 22, 2021
4e6c49f
(#20308) Add description for 'Returns' section of SGDOneClassSVM.fit …
frellwan Sep 22, 2021
dfc3fb7
(#20308) Add parametr 'y' and description for SGDOneClassSVM.fit Docs…
frellwan Sep 22, 2021
ab99a6f
(#20308) Add description for 'Returns' section SGDOneClassSVM.fit Doc…
frellwan Sep 22, 2021
e390454
(#20308) Add parameter 'y' and description for SGDOneClassSVM.partial…
frellwan Sep 22, 2021
76d4108
(#20308) Add description for 'Return' section for SGDOneClassSVM.part…
frellwan Sep 22, 2021
455d2ec
(#20308) Remove SGDOneClassSVM
frellwan Sep 22, 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 @@ -34,7 +34,6 @@
"QuadraticDiscriminantAnalysis",
"RandomizedSearchCV",
"RobustScaler",
"SGDOneClassSVM",
"SGDRegressor",
"SelfTrainingClassifier",
"SparseRandomProjection",
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38 changes: 22 additions & 16 deletions sklearn/linear_model/_stochastic_gradient.py
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Expand Up @@ -1975,8 +1975,8 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):
Whether or not the training data should be shuffled after each epoch.
Defaults to True.

verbose : integer, optional
The verbosity level
verbose : int, optional
The verbosity level.

random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
Expand All @@ -1985,7 +1985,7 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):
generator; If None, the random number generator is the RandomState
instance used by `np.random`.

learning_rate : string, optional
learning_rate : str, optional
The learning rate schedule to use with `fit`. (If using `partial_fit`,
learning rate must be controlled directly).

Expand Down Expand Up @@ -2059,6 +2059,17 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):

.. versionadded:: 1.0

See Also
--------
sklearn.svm.OneClassSVM : Unsupervised Outlier Detection.

Notes
-----
This estimator has a linear complexity in the number of training samples
and is thus better suited than the `sklearn.svm.OneClassSVM`
implementation for datasets with a large number of training samples (say
> 10,000).

Examples
--------
>>> import numpy as np
Expand All @@ -2070,17 +2081,6 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):

>>> print(clf.predict([[4, 4]]))
[1]

See also
--------
sklearn.svm.OneClassSVM

Notes
-----
This estimator has a linear complexity in the number of training samples
and is thus better suited than the `sklearn.svm.OneClassSVM`
implementation for datasets with a large number of training samples (say
> 10,000).
"""

loss_functions = {"hinge": (Hinge, 1.0)}
Expand Down Expand Up @@ -2295,14 +2295,17 @@ def partial_fit(self, X, y=None, sample_weight=None):
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data.
y : Ignored
Not used, present for API consistency by convention.

sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.

Returns
-------
self : returns an instance of self.
self : object
Returns a fitted instance of self.
"""

alpha = self.nu / 2
Expand Down Expand Up @@ -2383,6 +2386,8 @@ def fit(self, X, y=None, coef_init=None, offset_init=None, sample_weight=None):
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
y : Ignored
Not used, present for API consistency by convention.

coef_init : array, shape (n_classes, n_features)
The initial coefficients to warm-start the optimization.
Expand All @@ -2398,7 +2403,8 @@ def fit(self, X, y=None, coef_init=None, offset_init=None, sample_weight=None):

Returns
-------
self : returns an instance of self.
self : object
Returns a fitted instance of self.
"""

alpha = self.nu / 2
Expand Down