8000 DOC Ensures that NearestCentroid passes numpydoc validation by jmloyola · Pull Request #21084 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that NearestCentroid passes numpydoc validation #21084

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merged 4 commits into from
Sep 20, 2021
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DOC Ensures that NearestCentroid passes numpydoc validation #21084

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2759223
Remove NearestCentroid from DOCSTRING_IGNORE_LIST
jmloyola Sep 19, 2021
02c423f
Fix numpydocs from NearestCentroid
jmloyola Sep 19, 2021
6eac3f8
Change docstrings to maintain consistency
jmloyola Sep 19, 2021
86a1f62
Merge branch 'main' into fix_numpydoc_nearestcentroid
glemaitre Sep 20, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -22,7 +22,6 @@
"MultiTaskElasticNetCV",
"MultiTaskLasso",
"MultiTaskLassoCV",
"NearestCentroid",
"OrthogonalMatchingPursuit",
"OrthogonalMatchingPursuitCV",
"PLSCanonical",
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60 changes: 33 additions & 27 deletions sklearn/neighbors/_nearest_centroid.py
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Expand Up @@ -33,16 +33,16 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
metric : str or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by metrics.pairwise.pairwise_distances for its
metric parameter.
The centroids for the samples corresponding to each class is the point
from which the sum of the distances (according to the metric) of all
samples that belong to that particular class are minimized.
If the "manhattan" metric is provided, this centroid is the median and
for all other metrics, the centroid is now set to be the mean.
the options allowed by
:func:`~sklearn.metrics.pairwise_distances` for its metric
parameter. The centroids for the samples corresponding to each class is
the point from which the sum of the distances (according to the metric)
of all samples that belong to that particular class are minimized.
If the `"manhattan"` metric is provided, this centroid is the median
and for all other metrics, the centroid is now set to be the mean.

.. versionchanged:: 0.19
``metric='precomputed'`` was deprecated and now raises an error
`metric='precomputed'` was deprecated and now raises an error

shrink_threshold : float, default=None
Threshold for shrinking centroids to remove features.
Expand All @@ -66,18 +66,6 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):

.. versionadded:: 1.0

Examples
--------
>>> from sklearn.neighbors import NearestCentroid
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid()
>>> print(clf.predict([[-0.8, -1]]))
[1]

See Also
--------
KNeighborsClassifier : Nearest neighbors classifier.
Expand All @@ -94,6 +82,17 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
of the National Academy of Sciences of the United States of America,
99(10), 6567-6572. The National Academy of Sciences.

Examples
--------
>>> from sklearn.neighbors import NearestCentroid
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid()
>>> print(clf.predict([[-0.8, -1]]))
[1]
"""

def __init__(self, metric="euclidean", *, shrink_threshold=None):
Expand All @@ -111,7 +110,12 @@ def fit(self, X, y):
`n_features` is the number of features.
Note that centroid shrinking cannot be used with sparse matrices.
y : array-like of shape (n_samples,)
Target values (integers)
Target values.

Returns
-------
self : object
Fitted estimator.
"""
if self.metric == "precomputed":
raise ValueError("Precomputed is not supported.")
Expand Down Expand Up @@ -191,23 +195,25 @@ def fit(self, X, y):
return self

def predict(self, X):
"""Perform classification on an array of test vectors X.
"""Perform classification on an array of test vectors `X`.

The predicted class C for each sample in X is returned.
The predicted class `C` for each sample in `X` is returned.

Parameters
----------
X : array-like of shape (n_samples, n_features)
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Test samples.

Returns
-------
C : ndarray of shape (n_samples,)
The predicted classes.

Notes
-----
If the metric constructor parameter is "precomputed", X is assumed to
be the distance matrix between the data to be predicted and
``self.centroids_``.
If the metric constructor parameter is `"precomputed"`, `X` is assumed
to be the distance matrix between the data to be predicted and
`self.centroids_`.
"""
check_is_fitted(self)

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0