318 lines
11 KiB
Python
318 lines
11 KiB
Python
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"""Permutation importance for estimators."""
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import numbers
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import numpy as np
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from ..ensemble._bagging import _generate_indices
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from ..metrics import check_scoring, get_scorer_names
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from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer
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from ..model_selection._validation import _aggregate_score_dicts
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from ..utils import Bunch, _safe_indexing, check_array, check_random_state
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from ..utils._param_validation import (
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HasMethods,
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Integral,
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Interval,
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RealNotInt,
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StrOptions,
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validate_params,
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)
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from ..utils.parallel import Parallel, delayed
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def _weights_scorer(scorer, estimator, X, y, sample_weight):
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if sample_weight is not None:
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return scorer(estimator, X, y, sample_weight=sample_weight)
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return scorer(estimator, X, y)
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def _calculate_permutation_scores(
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estimator,
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X,
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y,
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sample_weight,
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col_idx,
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random_state,
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n_repeats,
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scorer,
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max_samples,
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):
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"""Calculate score when `col_idx` is permuted."""
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random_state = check_random_state(random_state)
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# Work on a copy of X to ensure thread-safety in case of threading based
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# parallelism. Furthermore, making a copy is also useful when the joblib
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# backend is 'loky' (default) or the old 'multiprocessing': in those cases,
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# if X is large it will be automatically be backed by a readonly memory map
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# (memmap). X.copy() on the other hand is always guaranteed to return a
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# writable data-structure whose columns can be shuffled inplace.
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if max_samples < X.shape[0]:
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row_indices = _generate_indices(
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random_state=random_state,
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bootstrap=False,
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n_population=X.shape[0],
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n_samples=max_samples,
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)
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X_permuted = _safe_indexing(X, row_indices, axis=0)
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y = _safe_indexing(y, row_indices, axis=0)
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if sample_weight is not None:
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sample_weight = _safe_indexing(sample_weight, row_indices, axis=0)
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else:
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X_permuted = X.copy()
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scores = []
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shuffling_idx = np.arange(X_permuted.shape[0])
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for _ in range(n_repeats):
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random_state.shuffle(shuffling_idx)
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if hasattr(X_permuted, "iloc"):
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col = X_permuted.iloc[shuffling_idx, col_idx]
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col.index = X_permuted.index
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X_permuted[X_permuted.columns[col_idx]] = col
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else:
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X_permuted[:, col_idx] = X_permuted[shuffling_idx, col_idx]
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scores.append(_weights_scorer(scorer, estimator, X_permuted, y, sample_weight))
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if isinstance(scores[0], dict):
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scores = _aggregate_score_dicts(scores)
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else:
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scores = np.array(scores)
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return scores
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def _create_importances_bunch(baseline_score, permuted_score):
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"""Compute the importances as the decrease in score.
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Parameters
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----------
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baseline_score : ndarray of shape (n_features,)
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The baseline score without permutation.
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permuted_score : ndarray of shape (n_features, n_repeats)
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The permuted scores for the `n` repetitions.
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Returns
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-------
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importances : :class:`~sklearn.utils.Bunch`
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Dictionary-like object, with the following attributes.
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importances_mean : ndarray, shape (n_features, )
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Mean of feature importance over `n_repeats`.
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importances_std : ndarray, shape (n_features, )
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Standard deviation over `n_repeats`.
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importances : ndarray, shape (n_features, n_repeats)
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Raw permutation importance scores.
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"""
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importances = baseline_score - permuted_score
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return Bunch(
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importances_mean=np.mean(importances, axis=1),
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importances_std=np.std(importances, axis=1),
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importances=importances,
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)
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@validate_params(
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{
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"estimator": [HasMethods(["fit"])],
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"X": ["array-like"],
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"y": ["array-like", None],
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"scoring": [
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StrOptions(set(get_scorer_names())),
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callable,
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list,
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tuple,
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dict,
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None,
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],
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"n_repeats": [Interval(Integral, 1, None, closed="left")],
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"n_jobs": [Integral, None],
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"random_state": ["random_state"],
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"sample_weight": ["array-like", None],
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"max_samples": [
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Interval(Integral, 1, None, closed="left"),
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Interval(RealNotInt, 0, 1, closed="right"),
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],
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},
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prefer_skip_nested_validation=True,
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)
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def permutation_importance(
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estimator,
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X,
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y,
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*,
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scoring=None,
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n_repeats=5,
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n_jobs=None,
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random_state=None,
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sample_weight=None,
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max_samples=1.0,
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):
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"""Permutation importance for feature evaluation [BRE]_.
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The :term:`estimator` is required to be a fitted estimator. `X` can be the
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data set used to train the estimator or a hold-out set. The permutation
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importance of a feature is calculated as follows. First, a baseline metric,
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defined by :term:`scoring`, is evaluated on a (potentially different)
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dataset defined by the `X`. Next, a feature column from the validation set
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is permuted and the metric is evaluated again. The permutation importance
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is defined to be the difference between the baseline metric and metric from
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permutating the feature column.
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Read more in the :ref:`User Guide <permutation_importance>`.
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Parameters
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----------
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estimator : object
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An estimator that has already been :term:`fitted` and is compatible
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with :term:`scorer`.
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X : ndarray or DataFrame, shape (n_samples, n_features)
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Data on which permutation importance will be computed.
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y : array-like or None, shape (n_samples, ) or (n_samples, n_classes)
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Targets for supervised or `None` for unsupervised.
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scoring : str, callable, list, tuple, or dict, default=None
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Scorer to use.
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If `scoring` represents a single score, one can use:
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- a single string (see :ref:`scoring_parameter`);
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- a callable (see :ref:`scoring`) that returns a single value.
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If `scoring` represents multiple scores, one can use:
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- a list or tuple of unique strings;
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- a callable returning a dictionary where the keys are the metric
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names and the values are the metric scores;
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- a dictionary with metric names as keys and callables a values.
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Passing multiple scores to `scoring` is more efficient than calling
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`permutation_importance` for each of the scores as it reuses
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predictions to avoid redundant computation.
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If None, the estimator's default scorer is used.
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n_repeats : int, default=5
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Number of times to permute a feature.
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n_jobs : int or None, default=None
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Number of jobs to run in parallel. The computation is done by computing
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permutation score for each columns and parallelized over the columns.
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`None` means 1 unless in a :obj:`joblib.parallel_backend` context.
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`-1` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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random_state : int, RandomState instance, default=None
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Pseudo-random number generator to control the permutations of each
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feature.
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Pass an int to get reproducible results across function calls.
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See :term:`Glossary <random_state>`.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights used in scoring.
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.. versionadded:: 0.24
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max_samples : int or float, default=1.0
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The number of samples to draw from X to compute feature importance
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in each repeat (without replacement).
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples.
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- If `max_samples` is equal to `1.0` or `X.shape[0]`, all samples
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will be used.
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While using this option may provide less accurate importance estimates,
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it keeps the method tractable when evaluating feature importance on
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large datasets. In combination with `n_repeats`, this allows to control
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the computational speed vs statistical accuracy trade-off of this method.
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.. versionadded:: 1.0
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Returns
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-------
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result : :class:`~sklearn.utils.Bunch` or dict of such instances
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Dictionary-like object, with the following attributes.
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importances_mean : ndarray of shape (n_features, )
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Mean of feature importance over `n_repeats`.
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importances_std : ndarray of shape (n_features, )
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Standard deviation over `n_repeats`.
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importances : ndarray of shape (n_features, n_repeats)
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Raw permutation importance scores.
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If there are multiple scoring metrics in the scoring parameter
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`result` is a dict with scorer names as keys (e.g. 'roc_auc') and
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`Bunch` objects like above as values.
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References
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----------
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.. [BRE] :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32,
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2001. <10.1023/A:1010933404324>`
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Examples
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--------
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>>> from sklearn.linear_model import LogisticRegression
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>>> from sklearn.inspection import permutation_importance
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>>> X = [[1, 9, 9],[1, 9, 9],[1, 9, 9],
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... [0, 9, 9],[0, 9, 9],[0, 9, 9]]
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>>> y = [1, 1, 1, 0, 0, 0]
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>>> clf = LogisticRegression().fit(X, y)
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>>> result = permutation_importance(clf, X, y, n_repeats=10,
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... random_state=0)
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>>> result.importances_mean
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array([0.4666..., 0. , 0. ])
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>>> result.importances_std
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array([0.2211..., 0. , 0. ])
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"""
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if not hasattr(X, "iloc"):
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X = check_array(X, force_all_finite="allow-nan", dtype=None)
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# Precompute random seed from the random state to be used
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# to get a fresh independent RandomState instance for each
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# parallel call to _calculate_permutation_scores, irrespective of
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# the fact that variables are shared or not depending on the active
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# joblib backend (sequential, thread-based or process-based).
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random_state = check_random_state(random_state)
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random_seed = random_state.randint(np.iinfo(np.int32).max + 1)
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if not isinstance(max_samples, numbers.Integral):
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max_samples = int(max_samples * X.shape[0])
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elif max_samples > X.shape[0]:
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raise ValueError("max_samples must be <= n_samples")
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if callable(scoring):
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scorer = scoring
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elif scoring is None or isinstance(scoring, str):
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scorer = check_scoring(estimator, scoring=scoring)
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else:
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scorers_dict = _check_multimetric_scoring(estimator, scoring)
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scorer = _MultimetricScorer(scorers=scorers_dict)
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baseline_score = _weights_scorer(scorer, estimator, X, y, sample_weight)
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scores = Parallel(n_jobs=n_jobs)(
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delayed(_calculate_permutation_scores)(
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estimator,
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X,
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y,
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sample_weight,
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col_idx,
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random_seed,
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n_repeats,
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scorer,
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max_samples,
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)
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for col_idx in range(X.shape[1])
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)
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if isinstance(baseline_score, dict):
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return {
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name: _create_importances_bunch(
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baseline_score[name],
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# unpack the permuted scores
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np.array([scores[col_idx][name] for col_idx in range(X.shape[1])]),
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)
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for name in baseline_score
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}
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else:
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return _create_importances_bunch(baseline_score, np.array(scores))
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