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