# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#          Vincent Michel <vincent.michel@inria.fr>
#          Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause

"""Recursive feature elimination for feature ranking"""

from numbers import Integral

import numpy as np
from joblib import effective_n_jobs

from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
from ..metrics import check_scoring
from ..model_selection import check_cv
from ..model_selection._validation import _score
from ..utils._param_validation import HasMethods, Interval, RealNotInt
from ..utils._tags import _safe_tags
from ..utils.metaestimators import _safe_split, available_if
from ..utils.parallel import Parallel, delayed
from ..utils.validation import check_is_fitted
from ._base import SelectorMixin, _get_feature_importances


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 _estimator_has(attr):
    """Check if we can delegate a method to the underlying estimator.

    First, we check the first fitted estimator if available, otherwise we
    check the unfitted estimator.
    """
    return lambda self: (
        hasattr(self.estimator_, attr)
        if hasattr(self, "estimator_")
        else hasattr(self.estimator, attr)
    )


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])
    """

    _parameter_constraints: dict = {
        "estimator": [HasMethods(["fit"])],
        "n_features_to_select": [
            None,
            Interval(RealNotInt, 0, 1, closed="right"),
            Interval(Integral, 0, None, closed="neither"),
        ],
        "step": [
            Interval(Integral, 0, None, closed="neither"),
            Interval(RealNotInt, 0, 1, closed="neither"),
        ],
        "verbose": ["verbose"],
        "importance_getter": [str, callable],
    }

    def __init__(
        self,
        estimator,
        *,
        n_features_to_select=None,
        step=1,
        verbose=0,
        importance_getter="auto",
    ):
        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_

    @_fit_context(
        # RFE.estimator is not validated yet
        prefer_skip_nested_validation=False
    )
    def fit(self, X, y, **fit_params):
        """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, y, step_score=None, **fit_params):
        # 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,
        )

        # Initialization
        n_features = X.shape[1]
        if self.n_features_to_select is None:
            n_features_to_select = n_features // 2
        elif isinstance(self.n_features_to_select, Integral):  # int
            n_features_to_select = self.n_features_to_select
        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)

        support_ = np.ones(n_features, dtype=bool)
        ranking_ = np.ones(n_features, dtype=int)

        if step_score:
            self.scores_ = []

        # 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)

            # Eliminate the worse features
            threshold = min(step, 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))
            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

    @available_if(_estimator_has("predict"))
    def predict(self, X):
        """Reduce X to the selected features and predict using the 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))

    @available_if(_estimator_has("score"))
    def score(self, X, y, **fit_params):
        """Reduce X to the selected features and return the score of the 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_

    @available_if(_estimator_has("decision_function"))
    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))

    @available_if(_estimator_has("predict_proba"))
    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))

    @available_if(_estimator_has("predict_log_proba"))
    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):
        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 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.

    cv_results_ : dict of ndarrays
        A dict with keys:

        split(k)_test_score : ndarray of shape (n_subsets_of_features,)
            The cross-validation scores across (k)th fold.

        mean_test_score : ndarray of shape (n_subsets_of_features,)
            Mean of scores over the folds.

        std_test_score : ndarray of shape (n_subsets_of_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 all values in ``cv_results_`` 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])
    """

    _parameter_constraints: dict = {
        **RFE._parameter_constraints,
        "min_features_to_select": [Interval(Integral, 0, None, closed="neither")],
        "cv": ["cv_object"],
        "scoring": [None, str, callable],
        "n_jobs": [None, Integral],
    }
    _parameter_constraints.pop("n_features_to_select")

    def __init__(
        self,
        estimator,
        *,
        step=1,
        min_features_to_select=1,
        cv=None,
        scoring=None,
        verbose=0,
        n_jobs=None,
        importance_getter="auto",
    ):
        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

    @_fit_context(
        # RFECV.estimator is not validated yet
        prefer_skip_nested_validation=False
    )
    def fit(self, X, y, groups=None):
        """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)

        # 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)

        scores = parallel(
            func(rfe, self.estimator, X, y, train, test, scorer)
            for train, test in cv.split(X, y, groups)
        )

        scores = np.array(scores)
        scores_sum = np.sum(scores, axis=0)
        scores_sum_rev = scores_sum[::-1]
        argmax_idx = len(scores_sum) - np.argmax(scores_sum_rev) - 1
        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_results_["mean_test_score"] = np.mean(scores_rev, axis=0)
        self.cv_results_["std_test_score"] = np.std(scores_rev, axis=0)

        for i in range(scores.shape[0]):
            self.cv_results_[f"split{i}_test_score"] = scores_rev[i]

        return self
