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DOC Ensures that ColumnTransformer passes numpydoc validation #20836

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DOC Ensures that ColumnTransformer passes numpydoc validation #20836

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1e4b641
"ColumnTransformer" removed from DOCSTRING_IGNORE_LIST in test_docstr…
baam25simo Aug 25, 2021
b3ea273
In "ColumnTransformer.__init__" : "See Also" section moved in the rig…
baam25simo Aug 25, 2021
91cc0e3
In "ColumnTransformer.fit" : "self" description modified.
baam25simo Aug 25, 2021
c379c6c
In "ColumnTransformer.fit_transform" : "X_t" description modified.
baam25simo Aug 25, 2021
10ab20a
In "ColumnTransformer.transform" : "X_t" description modified.
baam25simo Aug 25, 2021
5ea9615
In "ColumnTransformer.named_transformers_" : blank line removed
baam25simo Aug 25, 2021
bfb0f16
In "ColumnTransformer.set_params" : "**kwargs" and "self" description…
baam25simo Aug 25, 2021
19f8120
Merge branch 'scikit-learn:main' into ColumnTransformer-numpydoc-vali…
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 @@ -14,7 +14,6 @@
"Birch",
"CalibratedClassifierCV",
"ClassifierChain",
"ColumnTransformer",
"DecisionTreeRegressor",
"DictVectorizer",
"DictionaryLearning",
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47 changes: 22 additions & 25 deletions sklearn/compose/_column_transformer.py
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Expand Up @@ -147,6 +147,14 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):

.. versionadded:: 0.24

See Also
--------
make_column_transformer : Convenience function for
combining the outputs of multiple transformer objects applied to
column subsets of the original feature space.
make_column_selector : Convenience function for selecting
columns based on datatype or the columns name with a regex pattern.

Notes
-----
The order of the columns in the transformed feature matrix follows the
Expand All @@ -156,14 +164,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
in the `passthrough` keyword. Those columns specified with `passthrough`
are added at the right to the output of the transformers.

See Also
--------
make_column_transformer : Convenience function for
combining the outputs of multiple transformer objects applied to
column subsets of the original feature space.
make_column_selector : Convenience function for selecting
columns based on datatype or the columns name with a regex pattern.

Examples
--------
>>> import numpy as np
Expand All @@ -180,7 +180,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
>>> ct.fit_transform(X)
array([[0. , 1. , 0.5, 0.5],
[0.5, 0.5, 0. , 1. ]])

"""

_required_parameters = ["transformers"]
Expand Down Expand Up @@ -242,13 +241,17 @@ def get_params(self, deep=True):
def set_params(self, **kwargs):
"""Set the parameters of this estimator.

Valid parameter keys can be listed with ``get_params()``. Note that you
can directly set the parameters of the estimators contained in
`transformers` of `ColumnTransformer`.
Parameters
----------
**kwargs : dict
Valid parameter keys can be listed with ``get_params()``. Note that you
can directly set the parameters of the estimators contained in
`transformers` of `ColumnTransformer`.

Returns
-------
self
self : object
ColumnTransformer class instance.
"""
self._set_params("_transformers", **kwargs)
return self
Expand Down Expand Up @@ -364,7 +367,6 @@ def named_transformers_(self):
Read-only attribute to access any transformer by given name.
Keys are transformer names and values are the fitted transformer
objects.

"""
# Use Bunch object to improve autocomplete
return Bunch(**{name: trans for name, trans, _ in self.transformers_})
Expand Down Expand Up @@ -512,9 +514,8 @@ def fit(self, X, y=None):

Returns
-------
self : ColumnTransformer
This estimator

self : object
ColumnTransformer class instance.
"""
# we use fit_transform to make sure to set sparse_output_ (for which we
# need the transformed data) to have consistent output type in predict
Expand All @@ -535,13 +536,11 @@ def fit_transform(self, X, y=None):

Returns
-------
X_t : {array-like, sparse matrix} of \
shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the
X_t : {array-like, sparse matrix} of shape (n_samples, sum_n_components)
The "_hstack" of results of transformers. The "sum_n_components" is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.

"""
self._check_feature_names(X, reset=True)

Expand Down Expand Up @@ -588,13 +587,11 @@ def transform(self, X):

Returns
-------
X_t : {array-like, sparse matrix} of \
shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the
X_t : {array-like, sparse matrix} of shape (n_samples, sum_n_components)
The _"hstack" of results of transformers. The "sum_n_components" is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.

"""
check_is_fitted(self)
X = _check_X(X)
Expand Down