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DOC Ensures that PolynomialFeatures passes numpydoc validation #21239

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Oct 5, 2021
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DOC Ensures that PolynomialFeatures passes numpydoc validation #21239

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f2abe52
Remove PolynomialFeatures from DOCSTRING_IGNORE_LIST
jmloyola Oct 5, 2021
57fccf6
Fix numpydocs from PolynomialFeatures
jmloyola Oct 5, 2021
c6121dc
Change docstrings to maintain consistency
jmloyola Oct 5, 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 @@ -22,7 +22,6 @@
"OrthogonalMatchingPursuitCV",
"PassiveAggressiveClassifier",
"PassiveAggressiveRegressor",
"PolynomialFeatures",
"QuadraticDiscriminantAnalysis",
"SelfTrainingClassifier",
"SparseRandomProjection",
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44 changes: 22 additions & 22 deletions sklearn/preprocessing/_polynomial.py
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Expand Up @@ -42,35 +42,35 @@ class PolynomialFeatures(TransformerMixin, BaseEstimator):
----------
degree : int or tuple (min_degree, max_degree), default=2
If a single int is given, it specifies the maximal degree of the
polynomial features. If a tuple ``(min_degree, max_degree)`` is
passed, then ``min_degree`` is the minimum and ``max_degree`` is the
maximum polynomial degree of the generated features. Note that
min_degree=0 and 1 are equivalent as outputting the degree zero term
is determined by ``include_bias``.
polynomial features. If a tuple `(min_degree, max_degree)` is passed,
then `min_degree` is the minimum and `max_degree` is the maximum
polynomial degree of the generated features. Note that `min_degree=0`
and `min_degree=1` are equivalent as outputting the degree zero term is
determined by `include_bias`.

interaction_only : bool, default=False
If true, only interaction features are produced: features that are
products of at most ``degree`` *distinct* input features, i.e. terms
with power of 2 or higher of the same input feature are excluded:
If `True`, only interaction features are produced: features that are
products of at most `degree` *distinct* input features, i.e. terms with
power of 2 or higher of the same input feature are excluded:

- included: ``x[0]``, `x[1]`, ``x[0] * x[1]``, etc.
- excluded: ``x[0] ** 2``, ``x[0] ** 2 * x[1]``, etc.
- included: `x[0]`, `x[1]`, `x[0] * x[1]`, etc.
- excluded: `x[0] ** 2`, `x[0] ** 2 * x[1]`, etc.

include_bias : bool, default=True
If True (default), then include a bias column, the feature in which
If `True` (default), then include a bias column, the feature in which
all polynomial powers are zero (i.e. a column of ones - acts as an
intercept term in a linear model).

order : {'C', 'F'}, default='C'
Order of output array in the dense case. 'F' order is faster to
Order of output array in the dense case. `'F'` order is faster to
compute, but may slow down subsequent estimators.

.. versionadded:: 0.21

Attributes
----------
powers_ : ndarray of shape (`n_output_features_`, `n_features_in_`)
powers_[i, j] is the exponent of the jth input in the ith output.
`powers_[i, j]` is the exponent of the jth input in the ith output.

n_input_features_ : int
The total number of input features.
Expand Down Expand Up @@ -98,7 +98,7 @@ class PolynomialFeatures(TransformerMixin, BaseEstimator):
See Also
--------
SplineTransformer : Transformer that generates univariate B-spline bases
for features
for features.

Notes
-----
Expand Down Expand Up @@ -181,6 +181,7 @@ def _num_combinations(

@property
def powers_(self):
"""Exponent for each of the inputs in the output."""
check_is_fitted(self)

combinations = self._combinations(
Expand All @@ -199,8 +200,7 @@ def powers_(self):
"in 1.2. Please use get_feature_names_out instead."
)
def get_feature_names(self, input_features=None):
"""
Return feature names for output features
"""Return feature names for output features.

Parameters
----------
Expand All @@ -211,6 +211,7 @@ def get_feature_names(self, input_features=None):
Returns
-------
output_feature_names : list of str of shape (n_output_features,)
Transformed feature names.
"""
powers = self.powers_
if input_features is None:
Expand Down Expand Up @@ -238,7 +239,7 @@ def get_feature_names_out(self, input_features=None):
input_features : array-like of str or None, default=None
Input features.

- If `input_features` is `None`, then `feature_names_in_` is
- If `input_features is None`, then `feature_names_in_` is
used as feature names in. If `feature_names_in_` is not defined,
then names are generated: `[x0, x1, ..., x(n_features_in_)]`.
- If `input_features` is an array-like, then `input_features` must
Expand Down Expand Up @@ -270,14 +271,13 @@ def fit(self, X, y=None):
"""
Compute number of output features.


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

y : None
Ignored.
y : Ignored
Not used, present here for API consistency by convention.

Returns
-------
Expand Down Expand Up @@ -359,10 +359,10 @@ def transform(self, X):
Returns
-------
XP : {ndarray, sparse matrix} of shape (n_samples, NP)
The matrix of features, where NP is the number of polynomial
The matrix of features, where `NP` is the number of polynomial
features generated from the combination of inputs. If a sparse
matrix is provided, it will be converted into a sparse
``csr_matrix``.
`csr_matrix`.
"""
check_is_fitted(self)

Expand Down