"""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