ai-content-maker/.venv/Lib/site-packages/sklearn/feature_selection/_base.py

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2024-05-03 04:18:51 +03:00
"""Generic feature selection mixin"""
# Authors: G. Varoquaux, A. Gramfort, L. Buitinck, J. Nothman
# License: BSD 3 clause
import warnings
from abc import ABCMeta, abstractmethod
from operator import attrgetter
import numpy as np
from scipy.sparse import csc_matrix, issparse
from ..base import TransformerMixin
from ..utils import (
_is_pandas_df,
_safe_indexing,
check_array,
safe_sqr,
)
from ..utils._set_output import _get_output_config
from ..utils._tags import _safe_tags
from ..utils.validation import _check_feature_names_in, check_is_fitted
class SelectorMixin(TransformerMixin, metaclass=ABCMeta):
"""
Transformer mixin that performs feature selection given a support mask
This mixin provides a feature selector implementation with `transform` and
`inverse_transform` functionality given an implementation of
`_get_support_mask`.
Examples
--------
>>> import numpy as np
>>> from sklearn.datasets import load_iris
>>> from sklearn.base import BaseEstimator
>>> from sklearn.feature_selection import SelectorMixin
>>> class FeatureSelector(SelectorMixin, BaseEstimator):
... def fit(self, X, y=None):
... self.n_features_in_ = X.shape[1]
... return self
... def _get_support_mask(self):
... mask = np.zeros(self.n_features_in_, dtype=bool)
... mask[:2] = True # select the first two features
... return mask
>>> X, y = load_iris(return_X_y=True)
>>> FeatureSelector().fit_transform(X, y).shape
(150, 2)
"""
def get_support(self, indices=False):
"""
Get a mask, or integer index, of the features selected.
Parameters
----------
indices : bool, default=False
If True, the return value will be an array of integers, rather
than a boolean mask.
Returns
-------
support : array
An index that selects the retained features from a feature vector.
If `indices` is False, this is a boolean array of shape
[# input features], in which an element is True iff its
corresponding feature is selected for retention. If `indices` is
True, this is an integer array of shape [# output features] whose
values are indices into the input feature vector.
"""
mask = self._get_support_mask()
return mask if not indices else np.where(mask)[0]
@abstractmethod
def _get_support_mask(self):
"""
Get the boolean mask indicating which features are selected
Returns
-------
support : boolean array of shape [# input features]
An element is True iff its corresponding feature is selected for
retention.
"""
def transform(self, X):
"""Reduce X to the selected features.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
Returns
-------
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.
"""
# Preserve X when X is a dataframe and the output is configured to
# be pandas.
output_config_dense = _get_output_config("transform", estimator=self)["dense"]
preserve_X = output_config_dense != "default" and _is_pandas_df(X)
# note: we use _safe_tags instead of _get_tags because this is a
# public Mixin.
X = self._validate_data(
X,
dtype=None,
accept_sparse="csr",
force_all_finite=not _safe_tags(self, key="allow_nan"),
cast_to_ndarray=not preserve_X,
reset=False,
)
return self._transform(X)
def _transform(self, X):
"""Reduce X to the selected features."""
mask = self.get_support()
if not mask.any():
warnings.warn(
(
"No features were selected: either the data is"
" too noisy or the selection test too strict."
),
UserWarning,
)
if hasattr(X, "iloc"):
return X.iloc[:, :0]
return np.empty(0, dtype=X.dtype).reshape((X.shape[0], 0))
return _safe_indexing(X, mask, axis=1)
def inverse_transform(self, X):
"""Reverse the transformation operation.
Parameters
----------
X : array of shape [n_samples, n_selected_features]
The input samples.
Returns
-------
X_r : array of shape [n_samples, n_original_features]
`X` with columns of zeros inserted where features would have
been removed by :meth:`transform`.
"""
if issparse(X):
X = X.tocsc()
# insert additional entries in indptr:
# e.g. if transform changed indptr from [0 2 6 7] to [0 2 3]
# col_nonzeros here will be [2 0 1] so indptr becomes [0 2 2 3]
it = self.inverse_transform(np.diff(X.indptr).reshape(1, -1))
col_nonzeros = it.ravel()
indptr = np.concatenate([[0], np.cumsum(col_nonzeros)])
Xt = csc_matrix(
(X.data, X.indices, indptr),
shape=(X.shape[0], len(indptr) - 1),
dtype=X.dtype,
)
return Xt
support = self.get_support()
X = check_array(X, dtype=None)
if support.sum() != X.shape[1]:
raise ValueError("X has a different shape than during fitting.")
if X.ndim == 1:
X = X[None, :]
Xt = np.zeros((X.shape[0], support.size), dtype=X.dtype)
Xt[:, support] = X
return Xt
def get_feature_names_out(self, input_features=None):
"""Mask feature names according to selected features.
Parameters
----------
input_features : array-like of str or None, default=None
Input features.
- If `input_features` is `None`, then `feature_names_in_` is
used as feature names in. If `feature_names_in_` is not defined,
then the following input feature names are generated:
`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
- If `input_features` is an array-like, then `input_features` must
match `feature_names_in_` if `feature_names_in_` is defined.
Returns
-------
feature_names_out : ndarray of str objects
Transformed feature names.
"""
check_is_fitted(self)
input_features = _check_feature_names_in(self, input_features)
return input_features[self.get_support()]
def _get_feature_importances(estimator, getter, transform_func=None, norm_order=1):
"""
Retrieve and aggregate (ndim > 1) the feature importances
from an estimator. Also optionally applies transformation.
Parameters
----------
estimator : estimator
A scikit-learn estimator from which we want to get the feature
importances.
getter : "auto", str or callable
An attribute or a callable to get the feature importance. If `"auto"`,
`estimator` is expected to expose `coef_` or `feature_importances`.
transform_func : {"norm", "square"}, default=None
The transform to apply to the feature importances. By default (`None`)
no transformation is applied.
norm_order : int, default=1
The norm order to apply when `transform_func="norm"`. Only applied
when `importances.ndim > 1`.
Returns
-------
importances : ndarray of shape (n_features,)
The features importances, optionally transformed.
"""
if isinstance(getter, str):
if getter == "auto":
if hasattr(estimator, "coef_"):
getter = attrgetter("coef_")
elif hasattr(estimator, "feature_importances_"):
getter = attrgetter("feature_importances_")
else:
raise ValueError(
"when `importance_getter=='auto'`, the underlying "
f"estimator {estimator.__class__.__name__} should have "
"`coef_` or `feature_importances_` attribute. Either "
"pass a fitted estimator to feature selector or call fit "
"before calling transform."
)
else:
getter = attrgetter(getter)
elif not callable(getter):
raise ValueError("`importance_getter` has to be a string or `callable`")
importances = getter(estimator)
if transform_func is None:
return importances
elif transform_func == "norm":
if importances.ndim == 1:
importances = np.abs(importances)
else:
importances = np.linalg.norm(importances, axis=0, ord=norm_order)
elif transform_func == "square":
if importances.ndim == 1:
importances = safe_sqr(importances)
else:
importances = safe_sqr(importances).sum(axis=0)
else:
raise ValueError(
"Valid values for `transform_func` are "
+ "None, 'norm' and 'square'. Those two "
+ "transformation are only supported now"
)
return importances