207 lines
7.0 KiB
Python
207 lines
7.0 KiB
Python
# -*- coding: utf-8 -*-
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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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from abc import ABCMeta, abstractmethod
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from warnings import warn
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from operator import attrgetter
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import numpy as np
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from scipy.sparse import issparse, csc_matrix
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from ..base import TransformerMixin
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from ..utils import (
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check_array,
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safe_mask,
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safe_sqr,
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)
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from ..utils._tags import _safe_tags
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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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"""
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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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# note: we use _safe_tags instead of _get_tags because this is a
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# public Mixin.
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X = check_array(
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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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)
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mask = self.get_support()
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if not mask.any():
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warn("No features were selected: either the data is"
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" too noisy or the selection test too strict.",
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UserWarning)
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return np.empty(0).reshape((X.shape[0], 0))
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if len(mask) != X.shape[1]:
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raise ValueError("X has a different shape than during fitting.")
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return X[:, safe_mask(X, mask)]
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def inverse_transform(self, X):
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"""
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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((X.data, X.indices, indptr),
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shape=(X.shape[0], len(indptr) - 1), dtype=X.dtype)
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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_importances(estimator, getter, transform_func=None,
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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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f"when `importance_getter=='auto'`, the underlying "
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f"estimator {estimator.__class__.__name__} should have "
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f"`coef_` or `feature_importances_` attribute. Either "
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f"pass a fitted estimator to feature selector or call fit "
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f"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(
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'`importance_getter` has to be a string or `callable`'
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)
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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,
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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("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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return importances
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