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DOC - Ensure TheilSenRegressor passes numpydoc validation #21087

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Sep 20, 2021
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DOC - Ensure TheilSenRegressor passes numpydoc validation #21087

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db46c68
(#20308) Reordered TheilSenRegressor Docstring sections
frellwan Sep 19, 2021
4864802
(#20308) Added 'See Also' section to TheilSenRegressor Docstring
frellwan Sep 19, 2021
d5e2991
(#20308) Added period to parameter random_state description in TheilS…
frellwan Sep 19, 2021
534ef21
(#20308) Added description to Returns section TheilSenRegressor.fit D…
frellwan Sep 19, 2021
c78d1f2
(#20308) Removed TheilSenRegressor for DOCSTRING_IGNORE_LIST
frellwan Sep 19, 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 @@ -47,7 +47,6 @@
"SplineTransformer",
"StackingClassifier",
"StackingRegressor",
"TheilSenRegressor",
"TransformedTargetRegressor",
"TweedieRegressor",
]
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21 changes: 14 additions & 7 deletions sklearn/linear_model/_theil_sen.py
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Expand Up @@ -255,7 +255,7 @@ class TheilSenRegressor(RegressorMixin, LinearModel):
A random number generator instance to define the state of the random
permutations generator. Pass an int for reproducible output across
multiple function calls.
See :term:`Glossary <random_state>`
See :term:`Glossary <random_state>`.

n_jobs : int, default=None
Number of CPUs to use during the cross validation.
Expand Down Expand Up @@ -295,6 +295,18 @@ class TheilSenRegressor(RegressorMixin, LinearModel):

.. versionadded:: 1.0

See Also
--------
HuberRegressor : Linear regression model that is robust to outliers.
RANSACRegressor : RANSAC (RANdom SAmple Consensus) algorithm.
SGDRegressor : Fitted by minimizing a regularized empirical loss with SGD.

References
----------
- Theil-Sen Estimators in a Multiple Linear Regression Model, 2009
Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang
http://home.olemiss.edu/~xdang/papers/MTSE.pdf

Examples
--------
>>> from sklearn.linear_model import TheilSenRegressor
Expand All @@ -306,12 +318,6 @@ class TheilSenRegressor(RegressorMixin, LinearModel):
0.9884...
>>> reg.predict(X[:1,])
array([-31.5871...])

References
----------
- Theil-Sen Estimators in a Multiple Linear Regression Model, 2009
Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang
http://home.olemiss.edu/~xdang/papers/MTSE.pdf
"""

def __init__(
Expand Down Expand Up @@ -394,6 +400,7 @@ def fit(self, X, y):
Returns
-------
self : returns an instance of self.
Fitted `TheilSenRegressor` estimator.
"""
random_state = check_random_state(self.random_state)
X, y = self._validate_data(X, y, y_numeric=True)
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