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DOC Ensures that PassiveAggressiveRegressor passes numpydoc validation #21413

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Oct 23, 2021
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DOC Ensures that PassiveAggressiveRegressor passes numpydoc validation #21413

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DOC Ensures that PassiveAggressiveRegressor passes numpydoc validation
g4brielvs Oct 22, 2021
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Merge branch 'main' into doc-passiveaggressiveregressor
glemaitre Oct 23, 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 @@ -15,7 +15,6 @@
DOCSTRING_IGNORE_LIST = [
"LabelSpreading",
"MultiTaskElasticNetCV",
"PassiveAggressiveRegressor",
"SpectralCoclustering",
"SpectralEmbedding",
"StackingRegressor",
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27 changes: 13 additions & 14 deletions sklearn/linear_model/_passive_aggressive.py
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Expand Up @@ -301,7 +301,7 @@ def fit(self, X, y, coef_init=None, intercept_init=None):


class PassiveAggressiveRegressor(BaseSGDRegressor):
"""Passive Aggressive Regressor
"""Passive Aggressive Regressor.

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

Expand Down Expand Up @@ -352,7 +352,7 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.

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

loss : str, default="epsilon_insensitive"
Expand Down Expand Up @@ -416,6 +416,17 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
Number of weight updates performed during training.
Same as ``(n_iter_ * n_samples)``.

See Also
--------
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).

Examples
--------
>>> from sklearn.linear_model import PassiveAggressiveRegressor
Expand All @@ -432,18 +443,6 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
[-0.02306214]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-0.02306214]

See Also
--------
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).

"""

def __init__(
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