"""Recursive feature elimination for feature ranking"""
import math
import numbers
import numpy as np
from joblib import Parallel, effective_n_jobs
from sklearn.base import BaseEstimator, MetaEstimatorMixin, clone, is_classifier
from sklearn.feature_selection._base import SelectorMixin, _get_feature_importances
from sklearn.metrics import check_scoring
from sklearn.model_selection import check_cv
from sklearn.model_selection._validation import _score
from sklearn.utils._tags import _safe_tags
from sklearn.utils.deprecation import deprecated
from sklearn.utils.fixes import delayed
from sklearn.utils.metaestimators import _safe_split, if_delegate_has_method
from sklearn.utils.validation import check_is_fitted
from numpy import ndarray
from sklearn.svm._classes import SVC
from typing import Dict, List, Optional
# def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer):
# """
# Return the score for a fit across one fold.
# """
# X_train, y_train = _safe_split(estimator, X, y, train)
# X_test, y_test = _safe_split(estimator, X, y, test, train)
# return rfe._fit(
# X_train,
# y_train,
# lambda estimator, features: _score(
# estimator, X_test[:, features], y_test, scorer
# ),
# ).scores_
def _rfe_single_fit(
rfe, estimator, X, y, train_idx, val_idx, scorer, sample_weight=None
):
"""
Return the score for a fit across one fold.
"""
X_train, y_train = X[train_idx], y[train_idx]
X_test, y_test = X[val_idx], y[val_idx]
if sample_weight is not None:
return (
rfe._fit(
X_train,
y_train,
lambda estimator, features: _score(
estimator, X_test[:, features], y_test, scorer
),
sample_weight=sample_weight,
).scores_,
rfe._fit(
X_train,
y_train,
lambda estimator, features: _score(
estimator, X_test[:, features], y_test, scorer
),
sample_weight=sample_weight,
).ranking_,
)
else:
return (
rfe._fit(
X_train,
y_train,
lambda estimator, features: _score(
estimator, X_test[:, features], y_test, scorer
),
).scores_,
rfe._fit(
X_train,
y_train,
lambda estimator, features: _score(
estimator, X_test[:, features], y_test, scorer
),
).ranking_,
)
class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
"""Feature ranking with recursive feature elimination.
Given an external estimator that assigns weights to features (e.g., the
coefficients of a linear model), the goal of recursive feature elimination
(RFE) is to select features by recursively considering smaller and smaller
sets of features. First, the estimator is trained on the initial set of
features and the importance of each feature is obtained either through
any specific attribute or callable.
Then, the least important features are pruned from current set of features.
That procedure is recursively repeated on the pruned set until the desired
number of features to select is eventually reached.
Read more in the :ref:`User Guide <rfe>`.
Parameters
----------
estimator : ``Estimator`` instance
A supervised learning estimator with a ``fit`` method that provides
information about feature importance
(e.g. `coef_`, `feature_importances_`).
n_features_to_select : int or float, default=None
The number of features to select. If `None`, half of the features are
selected. If integer, the parameter is the absolute number of features
to select. If float between 0 and 1, it is the fraction of features to
select.
.. versionchanged:: 0.24
Added float values for fractions.
step : int or float, default=1
If greater than or equal to 1, then ``step`` corresponds to the
(integer) number of features to remove at each iteration.
If within (0.0, 1.0), then ``step`` corresponds to the percentage
(rounded down) of features to remove at each iteration.
verbose : int, default=0
Controls verbosity of output.
importance_getter : str or callable, default='auto'
If 'auto', uses the feature importance either through a `coef_`
or `feature_importances_` attributes of estimator.
Also accepts a string that specifies an attribute name/path
for extracting feature importance (implemented with `attrgetter`).
For example, give `regressor_.coef_` in case of
:class:`~sklearn.compose.TransformedTargetRegressor` or
`named_steps.clf.feature_importances_` in case of
class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
If `callable`, overrides the default feature importance getter.
The callable is passed with the fitted estimator and it should
return importance for each feature.
.. versionadded:: 0.24
Attributes
----------
classes_ : ndarray of shape (n_classes,)
The classes labels. Only available when `estimator` is a classifier.
estimator_ : ``Estimator`` instance
The fitted estimator used to select features.
n_features_ : int
The number of selected features.
n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying estimator exposes such an attribute when fit.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
ranking_ : ndarray of shape (n_features,)
The feature ranking, such that ``ranking_[i]`` corresponds to the
ranking position of the i-th feature. Selected (i.e., estimated
best) features are assigned rank 1.
support_ : ndarray of shape (n_features,)
The mask of selected features.
See Also
--------
RFECV : Recursive feature elimination with built-in cross-validated
selection of the best number of features.
SelectFromModel : Feature selection based on thresholds of importance
weights.
SequentialFeatureSelector : Sequential cross-validation based feature
selection. Does not rely on importance weights.
Notes
-----
Allows NaN/Inf in the input if the underlying estimator does as well.
References
----------
.. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
for cancer classification using support vector machines",
Mach. Learn., 46(1-3), 389--422, 2002.
Examples
--------
The following example shows how to retrieve the 5 most informative
features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.feature_selection import RFE
>>> from sklearn.svm import SVR
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
>>> estimator = SVR(kernel="linear")
>>> selector = RFE(estimator, n_features_to_select=5, step=1)
>>> selector = selector.fit(X, y)
>>> selector.support_
array([ True, True, True, True, True, False, False, False, False,
False])
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
"""
def __init__(
self,
estimator: SVC,
*,
n_features_to_select=None,
step=1,
verbose=0,
importance_getter="auto",
) -> None:
self.estimator = estimator
self.n_features_to_select = n_features_to_select
self.step = step
self.importance_getter = importance_getter
self.verbose = verbose
@property
def _estimator_type(self):
return self.estimator._estimator_type
@property
def classes_(self):
"""Classes labels available when `estimator` is a classifier.
Returns
-------
ndarray of shape (n_classes,)
"""
return self.estimator_.classes_
def fit(self, X: ndarray, y: ndarray, **fit_params) -> "RFE":
"""Fit the RFE model and then the underlying estimator on the selected features.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
y : array-like of shape (n_samples,)
The target values.
**fit_params : dict
Additional parameters passed to the `fit` method of the underlying
estimator.
Returns
-------
self : object
Fitted estimator.
"""
return self._fit(X, y, **fit_params)
def _fit(self, X: ndarray, y: ndarray, step_score: None=None, **fit_params) -> "RFE":
# Parameter step_score controls the calculation of self.scores_
# step_score is not exposed to users
# and is used when implementing RFECV
# self.scores_ will not be calculated when calling _fit through fit
tags = self._get_tags()
X, y = self._validate_data(
X,
y,
accept_sparse="csc",
ensure_min_features=2,
force_all_finite=not tags.get("allow_nan", True),
multi_output=True,
)
error_msg = (
"n_features_to_select must be either None, a "
"positive integer representing the absolute "
"number of features or a float in (0.0, 1.0] "
"representing a percentage of features to "
f"select. Got {self.n_features_to_select}"
)
# Initialization
n_features = X.shape[1]
if self.n_features_to_select is None:
n_features_to_select = n_features // 2
elif self.n_features_to_select < 0:
raise ValueError(error_msg)
elif isinstance(self.n_features_to_select, numbers.Integral): # int
n_features_to_select = self.n_features_to_select
elif self.n_features_to_select > 1.0: # float > 1
raise ValueError(error_msg)
else: # float
n_features_to_select = int(n_features * self.n_features_to_select)
# if 0.0 < self.step < 1.0:
# step = int(max(1, self.step * n_features))
# else:
# step = int(self.step)
# if step <= 0:
# raise ValueError("Step must be >0")
support_ = np.ones(n_features, dtype=bool)
ranking_ = np.ones(n_features, dtype=int)
if step_score:
self.scores_ = []
# collect feature importance score in each round of elimation
self.importances_ = []
# Elimination
while np.sum(support_) > n_features_to_select:
# Remaining features
features = np.arange(n_features)[support_]
# Rank the remaining features
estimator = clone(self.estimator)
if self.verbose > 0:
print("Fitting estimator with %d features." % np.sum(support_))
estimator.fit(X[:, features], y, **fit_params)
# Get importance and rank them
importances = _get_feature_importances(
estimator,
self.importance_getter,
transform_func="square",
)
ranks = np.argsort(importances)
# for sparse case ranks is matrix
ranks = np.ravel(ranks)
nstep = self.step * np.sum(support_)
# Eliminate the worse features
threshold = min(math.ceil(nstep), np.sum(support_) - n_features_to_select)
# Compute step score on the previous selection iteration
# because 'estimator' must use features
# that have not been eliminated yet
if step_score:
self.scores_.append(step_score(estimator, features))
self.importances_.append(importances)
support_[features[ranks][:threshold]] = False
ranking_[np.logical_not(support_)] += 1
# Set final attributes
features = np.arange(n_features)[support_]
self.estimator_ = clone(self.estimator)
self.estimator_.fit(X[:, features], y, **fit_params)
# Compute step score when only n_features_to_select features left
if step_score:
self.scores_.append(step_score(self.estimator_, features))
self.n_features_ = support_.sum()
self.support_ = support_
self.ranking_ = ranking_
return self
@if_delegate_has_method(delegate="estimator")
def predict(self, X):
"""Reduce X to the selected features and then predict using the underlying estimator.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
Returns
-------
y : array of shape [n_samples]
The predicted target values.
"""
check_is_fitted(self)
return self.estimator_.predict(self.transform(X))
@if_delegate_has_method(delegate="estimator")
def score(self, X, y, **fit_params):
"""Reduce X to the selected features and return the score of the underlying estimator.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
y : array of shape [n_samples]
The target values.
**fit_params : dict
Parameters to pass to the `score` method of the underlying
estimator.
.. versionadded:: 1.0
Returns
-------
score : float
Score of the underlying base estimator computed with the selected
features returned by `rfe.transform(X)` and `y`.
"""
check_is_fitted(self)
return self.estimator_.score(self.transform(X), y, **fit_params)
def _get_support_mask(self):
check_is_fitted(self)
return self.support_
@if_delegate_has_method(delegate="estimator")
def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like or sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
score : array, shape = [n_samples, n_classes] or [n_samples]
The decision function of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
Regression and binary classification produce an array of shape
[n_samples].
"""
check_is_fitted(self)
return self.estimator_.decision_function(self.transform(X))
@if_delegate_has_method(delegate="estimator")
def predict_proba(self, X):
"""Predict class probabilities for X.
Parameters
----------
X : {array-like or sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
p : array of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
check_is_fitted(self)
return self.estimator_.predict_proba(self.transform(X))
@if_delegate_has_method(delegate="estimator")
def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
Returns
-------
p : array of shape (n_samples, n_classes)
The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
check_is_fitted(self)
return self.estimator_.predict_log_proba(self.transform(X))
def _more_tags(self) -> Dict[str, bool]:
return {
"poor_score": True,
"allow_nan": _safe_tags(self.estimator, key="allow_nan"),
"requires_y": True,
}
class RFECV(RFE):
"""Recursive feature elimination with cross-validation to select the number of features.
See glossary entry for :term:`cross-validation estimator`.
Read more in the :ref:`User Guide <rfe>`.
Parameters
----------
estimator : ``Estimator`` instance
A supervised learning estimator with a ``fit`` method that provides
information about feature importance either through a ``coef_``
attribute or through a ``feature_importances_`` attribute.
step : int or float, default=1
If greater than or equal to 1, then ``step`` corresponds to the
(integer) number of features to remove at each iteration.
If within (0.0, 1.0), then ``step`` corresponds to the percentage
(rounded down) of features to remove at each iteration.
Note that the last iteration may remove fewer than ``step`` features in
order to reach ``min_features_to_select``.
min_features_to_select : int, default=1
The minimum number of features to be selected. This number of features
will always be scored, even if the difference between the original
feature count and ``min_features_to_select`` isn't divisible by
``step``.
.. versionadded:: 0.20
cv : int, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- integer, to specify the number of folds.
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if ``y`` is binary or multiclass,
:class:`~sklearn.model_selection.StratifiedKFold` is used. If the
estimator is a classifier or if ``y`` is neither binary nor multiclass,
:class:`~sklearn.model_selection.KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
.. versionchanged:: 0.22
``cv`` default value of None changed from 3-fold to 5-fold.
scoring : str, callable or None, default=None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
verbose : int, default=0
Controls verbosity of output.
n_jobs : int or None, default=None
Number of cores to run in parallel while fitting across folds.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
.. versionadded:: 0.18
importance_getter : str or callable, default='auto'
If 'auto', uses the feature importance either through a `coef_`
or `feature_importances_` attributes of estimator.
Also accepts a string that specifies an attribute name/path
for extracting feature importance.
For example, give `regressor_.coef_` in case of
:class:`~sklearn.compose.TransformedTargetRegressor` or
`named_steps.clf.feature_importances_` in case of
:class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
If `callable`, overrides the default feature importance getter.
The callable is passed with the fitted estimator and it should
return importance for each feature.
.. versionadded:: 0.24
Attributes
----------
classes_ : ndarray of shape (n_classes,)
The classes labels. Only available when `estimator` is a classifier.
estimator_ : ``Estimator`` instance
The fitted estimator used to select features.
grid_scores_ : ndarray of shape (n_subsets_of_features,)
The cross-validation scores such that
``grid_scores_[i]`` corresponds to
the CV score of the i-th subset of features.
.. deprecated:: 1.0
The `grid_scores_` attribute is deprecated in version 1.0 in favor
of `cv_results_` and will be removed in version 1.2.
cv_results_ : dict of ndarrays
A dict with keys:
split(k)_test_score : ndarray of shape (n_features,)
The cross-validation scores across (k)th fold.
mean_test_score : ndarray of shape (n_features,)
Mean of scores over the folds.
std_test_score : ndarray of shape (n_features,)
Standard deviation of scores over the folds.
.. versionadded:: 1.0
n_features_ : int
The number of selected features with cross-validation.
n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying estimator exposes such an attribute when fit.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
ranking_ : narray of shape (n_features,)
The feature ranking, such that `ranking_[i]`
corresponds to the ranking
position of the i-th feature.
Selected (i.e., estimated best)
features are assigned rank 1.
support_ : ndarray of shape (n_features,)
The mask of selected features.
See Also
--------
RFE : Recursive feature elimination.
Notes
-----
The size of ``grid_scores_`` is equal to
``ceil((n_features - min_features_to_select) / step) + 1``,
where step is the number of features removed at each iteration.
Allows NaN/Inf in the input if the underlying estimator does as well.
References
----------
.. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
for cancer classification using support vector machines",
Mach. Learn., 46(1-3), 389--422, 2002.
Examples
--------
The following example shows how to retrieve the a-priori not known 5
informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.feature_selection import RFECV
>>> from sklearn.svm import SVR
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
>>> estimator = SVR(kernel="linear")
>>> selector = RFECV(estimator, step=1, cv=5)
>>> selector = selector.fit(X, y)
>>> selector.support_
array([ True, True, True, True, True, False, False, False, False,
False])
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
"""
def __init__(
self,
estimator: SVC,
*,
step=1,
min_features_to_select=1,
cv=None,
scoring=None,
verbose=0,
n_jobs=None,
importance_getter="auto",
) -> None:
self.estimator = estimator
self.step = step
self.importance_getter = importance_getter
self.cv = cv
self.scoring = scoring
self.verbose = verbose
self.n_jobs = n_jobs
self.min_features_to_select = min_features_to_select
def fit(
self, X: ndarray, y: ndarray, train_idx_list: List[List[int]], val_idx_list: List[List[int]], groups: None=None, sample_weight_list: Optional[List[List[float]]]=None
) -> "RFECV":
"""Fit the RFE model and automatically tune the number of selected features.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the total number of features.
y : array-like of shape (n_samples,)
Target values (integers for classification, real numbers for
regression).
groups : array-like of shape (n_samples,) or None, default=None
Group labels for the samples used while splitting the dataset into
train/test set. Only used in conjunction with a "Group" :term:`cv`
instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).
.. versionadded:: 0.20
Returns
-------
self : object
Fitted estimator.
"""
tags = self._get_tags()
X, y = self._validate_data(
X,
y,
accept_sparse="csr",
ensure_min_features=2,
force_all_finite=not tags.get("allow_nan", True),
multi_output=True,
)
# Initialization
# cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))
scorer = check_scoring(self.estimator, scoring=self.scoring)
n_features = X.shape[1]
# if 0.0 < self.step < 1.0:
# step = int(max(1, self.step * n_features))
# else:
# step = int(self.step)
# if step <= 0:
# raise ValueError("Step must be >0")
# Build an RFE object, which will evaluate and score each possible
# feature count, down to self.min_features_to_select
rfe = RFE(
estimator=self.estimator,
n_features_to_select=self.min_features_to_select,
importance_getter=self.importance_getter,
step=self.step,
verbose=self.verbose,
)
# Determine the number of subsets of features by fitting across
# the train folds and choosing the "features_to_select" parameter
# that gives the least averaged error across all folds.
# Note that joblib raises a non-picklable error for bound methods
# even if n_jobs is set to 1 with the default multiprocessing
# backend.
# This branching is done so that to
# make sure that user code that sets n_jobs to 1
# and provides bound methods as scorers is not broken with the
# addition of n_jobs parameter in version 0.18.
if effective_n_jobs(self.n_jobs) == 1:
parallel, func = list, _rfe_single_fit
else:
parallel = Parallel(n_jobs=self.n_jobs)
func = delayed(_rfe_single_fit)
res = parallel(
func(rfe, self.estimator, X, y, train_idx, val_idx, scorer, sample_weight)
for train_idx, val_idx, sample_weight in zip(
train_idx_list, val_idx_list, sample_weight_list
)
)
# scores = _rfe_single_fit(rfe, self.estimator, X, y, X_ts, y_ts, scorer)
scores = []
ranking_ = []
for i in res:
scores.append(i[0])
ranking_.append(i[1])
scores = np.array(scores)
ranking_ = np.array(ranking_)
# scores_sum = np.sum(scores, axis=0)
# scores_sum_rev = scores_sum[::-1]
# argmax_idx = len(scores_sum) - np.argmax(scores_sum_rev) - 1 #indices of the maximum value
# n_features_to_select = max(
# n_features - (argmax_idx * step), self.min_features_to_select
# )
# # Re-execute an elimination with best_k over the whole set
# rfe = RFE(
# estimator=self.estimator,
# n_features_to_select=n_features_to_select,
# step=self.step,
# importance_getter=self.importance_getter,
# verbose=self.verbose,
# )
# rfe.fit(X, y)
# # Set final attributes
# self.support_ = rfe.support_
# self.n_features_ = rfe.n_features_
# self.ranking_ = rfe.ranking_
# self.estimator_ = clone(self.estimator)
# self.estimator_.fit(self.transform(X), y)
# reverse to stay consistent with before
scores_rev = scores[:, ::-1]
self.cv_results_ = {}
self.cv_ranking_ = {}
self.cv_results_["mean_test_score"] = np.mean(scores_rev, axis=0)
self.cv_results_["std_test_score"] = np.std(scores_rev, axis=0)
self.cv_ranking_["mean_feature_ranking"] = np.mean(ranking_, axis=0)
self.top_features = {}
self.top_features["mean_feature_ranking"] = np.mean(ranking_, axis=0)
for i in range(scores.shape[0]):
self.cv_results_[f"split{i}_test_score"] = scores_rev[i]
self.cv_ranking_[f"split{i}_test_score"] = ranking_[i]
self.ranking_ = ranking_
self.scores = scores
return self
# TODO: Remove in v1.2 when grid_scores_ is removed
# mypy error: Decorated property not supported
@deprecated( # type: ignore
"The `grid_scores_` attribute is deprecated in version 1.0 in favor "
"of `cv_results_` and will be removed in version 1.2."
)
@property
def grid_scores_(self):
# remove 2 for mean_test_score, std_test_score
grid_size = len(self.cv_results_) - 2
return np.asarray(
[self.cv_results_[f"split{i}_test_score"] for i in range(grid_size)]
).T
Datasets
Models
