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DOC Ensures that MissingIndicator passes numpydoc validation #21149

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Sep 27, 2021
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DOC Ensures that MissingIndicator passes numpydoc validation #21149

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ce3e5b6
Remove MissingIndicator from DOCSTRING_IGNORE_LIST
jmloyola Sep 25, 2021
032f404
Fix numpydocs from MissingIndicator
jmloyola Sep 25, 2021
06edb51
Change docstrings to maintain consistency
jmloyola Sep 25, 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 @@ -16,7 +16,6 @@
"LocalOutlierFactor",
"LocallyLinearEmbedding",
"MiniBatchKMeans",
"MissingIndicator",
"MultiLabelBinarizer",
"MultiTaskElasticNet",
"MultiTaskElasticNetCV",
Expand Down
92 changes: 49 additions & 43 deletions sklearn/impute/_base.py
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Expand Up @@ -610,7 +610,7 @@ class MissingIndicator(TransformerMixin, BaseEstimator):

Parameters
----------
missing_values : int, float, string, np.nan or None, default=np.nan
missing_values : int, float, str, np.nan or None, default=np.nan
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For pandas' dataframes with
nullable integer dtypes with missing values, `missing_values`
Expand All @@ -620,29 +620,29 @@ class MissingIndicator(TransformerMixin, BaseEstimator):
Whether the imputer mask should represent all or a subset of
features.

- If 'missing-only' (default), the imputer mask will only represent
- If `'missing-only'` (default), the imputer mask will only represent
features containing missing values during fit time.
- If 'all', the imputer mask will represent all features.
- If `'all'`, the imputer mask will represent all features.

sparse : bool or 'auto', default='auto'
Whether the imputer mask format should be sparse or dense.

- If 'auto' (default), the imputer mask will be of same type as
- If `'auto'` (default), the imputer mask will be of same type as
input.
- If True, the imputer mask will be a sparse matrix.
- If False, the imputer mask will be a numpy array.
- If `True`, the imputer mask will be a sparse matrix.
- If `False`, the imputer mask will be a numpy array.

error_on_new : bool, default=True
If True, transform will raise an error when there are features with
missing values in transform that have no missing values in fit. This is
applicable only when `features='missing-only'`.
If `True`, :meth:`transform` will raise an error when there are
features with missing values that have no missing values in
:meth:`fit`. This is applicable only when `features='missing-only'`.

Attributes
----------
features_ : ndarray, shape (n_missing_features,) or (n_features,)
The features indices which will be returned when calling ``transform``.
They are computed during ``fit``. For ``features='all'``, it is
to ``range(n_features)``.
features_ : ndarray of shape (n_missing_features,) or (n_features,)
The features indices which will be returned when calling
:meth:`transform`. They are computed during :meth:`fit`. If
`features='all'`, `features_` is equal to `range(n_features)`.

n_features_in_ : int
Number of features seen during :term:`fit`.
Expand All @@ -655,6 +655,11 @@ class MissingIndicator(TransformerMixin, BaseEstimator):

.. versionadded:: 1.0

See Also
--------
SimpleImputer : Univariate imputation of missing values.
IterativeImputer : Multivariate imputation of missing values.

Examples
--------
>>> import numpy as np
Expand All @@ -673,7 +678,6 @@ class MissingIndicator(TransformerMixin, BaseEstimator):
array([[False, True],
[ True, False],
[False, False]])

"""

def __init__(
Expand All @@ -695,19 +699,19 @@ def _get_missing_features_info(self, X):

Parameters
----------
X : {ndarray or sparse matrix}, shape (n_samples, n_features)
The input data with missing values. Note that ``X`` has been
checked in ``fit`` and ``transform`` before to call this function.
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input data with missing values. Note that `X` has been
checked in :meth:`fit` and :meth:`transform` before to call this
function.

Returns
-------
imputer_mask : {ndarray or sparse matrix}, shape \
imputer_mask : {ndarray, sparse matrix} of shape \
(n_samples, n_features)
The imputer mask of the original data.

features_with_missing : ndarray, shape (n_features_with_missing)
features_with_missing : ndarray of shape (n_features_with_missing)
The features containing missing values.

"""
if not self._precomputed:
imputer_mask = _get_mask(X, self.missing_values)
Expand Down Expand Up @@ -778,25 +782,23 @@ def _validate_input(self, X, in_fit):
return X

def _fit(self, X, y=None, precomputed=False):
"""Fit the transformer on X.
"""Fit the transformer on `X`.

Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where ``n_samples`` is the number of samples and
``n_features`` is the number of features.
If `precomputed` is True, then `X` is a mask of the
input data.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
If `precomputed=True`, then `X` is a mask of the input data.

precomputed : bool
Whether the input data is a mask.

Returns
-------
imputer_mask : {ndarray or sparse matrix}, shape (n_samples, \
imputer_mask : {ndarray, sparse matrix} of shape (n_samples, \
n_features)
The imputer mask of the original data.

"""
if precomputed:
if not (hasattr(X, "dtype") and X.dtype.kind == "b"):
Expand Down Expand Up @@ -834,38 +836,40 @@ def _fit(self, X, y=None, precomputed=False):
return missing_features_info[0]

def fit(self, X, y=None):
"""Fit the transformer on X.
"""Fit the transformer on `X`.

Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where ``n_samples`` is the number of samples and
``n_features`` is the number of features.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : Ignored
Not used, present for API consistency by convention.

Returns
-------
self : object
Returns self.
Fitted estimator.
"""
self._fit(X, y)

return self

def transform(self, X):
"""Generate missing values indicator for X.
"""Generate missing values indicator for `X`.

Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data to complete.

Returns
-------
Xt : {ndarray or sparse matrix}, shape (n_samples, n_features) \
Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) \
or (n_samples, n_features_with_missing)
The missing indicator for input data. The data type of ``Xt``
The missing indicator for input data. The data type of `Xt`
will be boolean.

"""
check_is_fitted(self)

Expand Down Expand Up @@ -894,20 +898,22 @@ def transform(self, X):
return imputer_mask

def fit_transform(self, X, y=None):
"""Generate missing values indicator for X.
"""Generate missing values indicator for `X`.

Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data to complete.

y : Ignored
Not used, present for API consistency by convention.

Returns
-------
Xt : {ndarray or sparse matrix}, shape (n_samples, n_features) \
Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) \
or (n_samples, n_features_with_missing)
The missing indicator for input data. The data type of ``Xt``
The missing indicator for input data. The data type of `Xt`
will be boolean.

"""
imputer_mask = self._fit(X, y)

Expand Down