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[WIP] DOC Ensures that FeatureAgglomeration passes numpydoc validation #20851

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[WIP] DOC Ensures that FeatureAgglomeration passes numpydoc validation #20851

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2ed7728
`FeatureAgglomeration` removed from `DOCSTRING_IGNORE_LIST` in test_d…
baam25simo Aug 26, 2021
1a0596c
In `FeatureAgglomeration` "See Also" section added.
baam25simo Aug 26, 2021
aa16608
In `FeatureAgglomeration.fit` `**params` parameter added, `self` desc…
baam25simo Aug 26, 2021
47df2c7
In `AgglomerationTransform.inverse_transform` : summary modified.
baam25simo Aug 26, 2021
b11dfc6
In `AgglomerationTransform.transform` : summary modified.
baam25simo Aug 26, 2021
26449cc
In `AgglomerativeClustering.fit_predict` : summary modified.
baam25simo Aug 26, 2021
050a543
Merge branch 'scikit-learn:main' into FeatureAgglomeration-numpydoc-v…
baam25simo Aug 28, 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 @@
"DictionaryLearning",
"ElasticNetCV",
"FactorAnalysis",
"FeatureAgglomeration",
"FeatureHasher",
"FeatureUnion",
"FunctionTransformer",
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19 changes: 14 additions & 5 deletions sklearn/cluster/_agglomerative.py
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Expand Up @@ -983,8 +983,7 @@ def fit(self, X, y=None):
return self

def fit_predict(self, X, y=None):
"""Fit the hierarchical clustering from features or distance matrix,
and return cluster labels.
"""Fit the hierarchical clustering from features or distance matrix.

Parameters
----------
Expand Down Expand Up @@ -1123,6 +1122,11 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
Only computed if `distance_threshold` is used or `compute_distances`
is set to `True`.

See Also
--------
AgglomerativeClustering : It performs a hierarchical clustering using
a bottom up approach.

Examples
--------
>>> import numpy as np
Expand Down Expand Up @@ -1164,18 +1168,23 @@ def __init__(
self.pooling_func = pooling_func

def fit(self, X, y=None, **params):
"""Fit the hierarchical clustering on the data
"""Fit the hierarchical clustering on the data.

Parameters
----------
X : array-like of shape (n_samples, n_features)
The data
The data.

y : Ignored
Ignored parameter.

**params : dict
Additional fit parameters.

Returns
-------
self
self : object
FeatureAgglomeration class instance.
"""
X = self._validate_data(
X,
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10 changes: 5 additions & 5 deletions sklearn/cluster/_feature_agglomeration.py
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Expand Up @@ -22,7 +22,7 @@ class AgglomerationTransform(TransformerMixin):

def transform(self, X):
"""
Transform a new matrix using the built clustering
Transform a new matrix using the built clustering.

Parameters
----------
Expand Down Expand Up @@ -54,15 +54,15 @@ def transform(self, X):
return nX

def inverse_transform(self, Xred):
"""
Inverse the transformation.
"""Inverse the transformation.

Return a vector of size nb_features with the values of Xred assigned
to each group of features
to each group of features.

Parameters
----------
Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,)
The values to be assigned to each cluster of samples
The values to be assigned to each cluster of samples.

Returns
-------
Expand Down