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# Source code for skopt.searchcv

    import warnings

    try:
        pass
    except ImportError:
        pass

    import numpy as np
    from scipy.stats import rankdata
    from sklearn.model_selection._search import BaseSearchCV
    from sklearn.utils import check_random_state
    from sklearn.utils.validation import check_is_fitted

    try:
        pass
    except ImportError:
        pass

    from . import Optimizer
    from .callbacks import check_callback
    from .space import check_dimension
    from .utils import dimensions_aslist, eval_callbacks, point_asdict

    def _get_score_names(cv_results, *, kind="test"):
        prefix = f"mean_{kind}_"
        return {key[len(prefix) :] for key in cv_results.keys() if key.startswith(prefix)}

    class BayesSearchCV(BaseSearchCV):
        """Bayesian optimization over hyper parameters.

        BayesSearchCV implements a "fit" and a "score" method.
        It also implements "predict", "predict_proba", "decision_function",
        "transform" and "inverse_transform" if they are implemented in the
        estimator used.

        The parameters of the estimator used to apply these methods are optimized
        by cross-validated search over parameter settings.

        In contrast to GridSearchCV, not all parameter values are tried out, but
        rather a fixed number of parameter settings is sampled from the specified
        distributions. The number of parameter settings that are tried is
        given by n_iter.

        Parameters are presented as a list of skopt.space.Dimension objects.

        Parameters
        ----------
        estimator : estimator object.
            A object of that type is instantiated for each search point.
            This object is assumed to implement the scikit-learn estimator api.
            Either estimator needs to provide a ``score`` function,
            or ``scoring`` must be passed.

        search_spaces : dict, list of dict or list of tuple containing (dict, int).
            One of these cases:
            1. dictionary, where keys are parameter names (strings)
            and values are skopt.space.Dimension instances (Real, Integer
            or Categorical) or any other valid value that defines skopt
            dimension (see skopt.Optimizer docs). Represents search space
            over parameters of the provided estimator.
            2. list of dictionaries: a list of dictionaries, where every
            dictionary fits the description given in case 1 above.
            If a list of dictionary objects is given, then the search is
            performed sequentially for every parameter space with maximum
            number of evaluations set to self.n_iter.
            3. list of (dict, int > 0): an extension of case 2 above,
            where first element of every tuple is a dictionary representing
            some search subspace, similarly as in case 2, and second element
            is a number of iterations that will be spent optimizing over
            this subspace.

        n_iter : int, default=50
            Number of parameter settings that are sampled. n_iter trades
            off runtime vs quality of the solution. Consider increasing
            ``n_points`` if you want to try more parameter settings in
            parallel.

        optimizer_kwargs : dict, optional
            Dict of arguments passed to :class:`Optimizer`.  For example,
            ``{'base_estimator': 'RF'}`` would use a Random Forest surrogate
            instead of the default Gaussian Process.

        scoring : str, callable, list, tuple or dict, default=None
            Strategy to evaluate the performance of the cross-validated model on
            the test set. If ``None``, the ``score`` method of the estimator is
            used.
            If `scoring` represents a single score, one can use:

            - a single string (see :ref:`scoring_parameter`);
            - a callable (see :ref:`scoring`) that returns a single value.

            If `scoring` represents multiple scores, one can use:

            - a list or tuple of unique strings;
            - a callable returning a dictionary where the keys are the metric
              names and the values are the metric scores;
            - a dictionary with metric names as keys and callables a values.

            Callables must have the signature ``scorer(estimator, X, y=None)``

        fit_params : dict, optional
            Parameters to pass to the fit method.

        n_jobs : int, default=1
            Number of jobs to run in parallel. At maximum there are
            ``n_points`` times ``cv`` jobs available during each iteration.

        n_points : int, default=1
            Number of parameter settings to sample in parallel. If this does
            not align with ``n_iter``, the last iteration will sample less
            points. See also :func:`~Optimizer.ask`

        pre_dispatch : int, or string, optional
            Controls the number of jobs that get dispatched during parallel
            execution. Reducing this number can be useful to avoid an
            explosion of memory consumption when more jobs get dispatched
            than CPUs can process. This parameter can be:

                - None, in which case all the jobs are immediately
                  created and spawned. Use this for lightweight and
                  fast-running jobs, to avoid delays due to on-demand
                  spawning of the jobs
                - An int, giving the exact number of total jobs that are
                  spawned
                - A string, giving an expression as a function of n_jobs,
                  as in '2*n_jobs'

        cv : int, cross-validation generator or an iterable, optional
            Determines the cross-validation splitting strategy.
            Possible inputs for cv are:

              - None, to use the default 3-fold cross validation,
              - integer, to specify the number of folds in a `(Stratified)KFold`,
              - An object to be used as a cross-validation generator.
              - An iterable yielding train, test splits.

            For integer/None inputs, if the estimator is a classifier and ``y`` is
            either binary or multiclass, :class:`StratifiedKFold` is used. In all
            other cases, :class:`KFold` is used.

        refit : bool, str, default=True
            Refit the best estimator with the entire dataset.
            If "False", it is impossible to make predictions using
            this BayesSearchCV instance after fitting.
            For multiple metric evaluation, this needs to be a `str` denoting the
            scorer that would be used to direct the optimization process, and find
            the best parameters for refitting the estimator at the end.

        verbose : integer
            Controls the verbosity: the higher, the more messages.

        random_state : int or RandomState
            Pseudo random number generator state used for random uniform sampling
            from lists of possible values instead of scipy.stats distributions.

        error_score : 'raise' (default) or numeric
            Value to assign to the score if an error occurs in estimator fitting.
            If set to 'raise', the error is raised. If a numeric value is given,
            FitFailedWarning is raised. This parameter does not affect the refit
            step, which will always raise the error.

        return_train_score : boolean, default=False
            If ``'True'``, the ``cv_results_`` attribute will include training
            scores.

        Examples
        --------

        >>> from skopt import BayesSearchCV
        >>> # parameter ranges are specified by one of below
        >>> from skopt.space import Real, Categorical, Integer
        >>>
        >>> from sklearn.datasets import load_iris
        >>> from sklearn.svm import SVC
        >>> from sklearn.model_selection import train_test_split
        >>>
        >>> X, y = load_iris(return_X_y=True)
        >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
        ...                                                     train_size=0.75,
        ...                                                     random_state=0)
        >>>
        >>> # log-uniform: understand as search over p = exp(x) by varying x
        >>> opt = BayesSearchCV(
        ...     SVC(),
        ...     {
        ...         'C': Real(1e-6, 1e+6, prior='log-uniform'),
        ...         'gamma': Real(1e-6, 1e+1, prior='log-uniform'),
        ...         'degree': Integer(1,8),
        ...         'kernel': Categorical(['linear', 'poly', 'rbf']),
        ...     },
        ...     n_iter=10, n_jobs=-1,
        ...     random_state=0
        ... )
        >>>
        >>> # executes bayesian optimization
        >>> _ = opt.fit(X_train, y_train)
        >>>
        >>> # model can be saved, used for predictions or scoring
        >>> print(opt.score(X_test, y_test))
        0.973...

        Attributes
        ----------
        cv_results_ : dict of numpy (masked) ndarrays
            A dict with keys as column headers and values as columns, that can be
            imported into a pandas ``DataFrame``.

            For instance the below given table

            +--------------+-------------+-------------------+---+---------------+
            | param_kernel | param_gamma | split0_test_score |...|rank_test_score|
            +==============+=============+===================+===+===============+
            |    'rbf'     |     0.1     |        0.8        |...|       2       |
            +--------------+-------------+-------------------+---+---------------+
            |    'rbf'     |     0.2     |        0.9        |...|       1       |
            +--------------+-------------+-------------------+---+---------------+
            |    'rbf'     |     0.3     |        0.7        |...|       1       |
            +--------------+-------------+-------------------+---+---------------+

            will be represented by a ``cv_results_`` dict of::

                {
                'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
                                              mask = False),
                'param_gamma'  : masked_array(data = [0.1 0.2 0.3], mask = False),
                'split0_test_score'  : [0.8, 0.9, 0.7],
                'split1_test_score'  : [0.82, 0.5, 0.7],
                'mean_test_score'    : [0.81, 0.7, 0.7],
                'std_test_score'     : [0.02, 0.2, 0.],
                'rank_test_score'    : [3, 1, 1],
                'split0_train_score' : [0.8, 0.9, 0.7],
                'split1_train_score' : [0.82, 0.5, 0.7],
                'mean_train_score'   : [0.81, 0.7, 0.7],
                'std_train_score'    : [0.03, 0.03, 0.04],
                'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
                'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
                'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
                'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
                'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
                }

            NOTE that the key ``'params'`` is used to store a list of parameter
            settings dict for all the parameter candidates.

            The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
            ``std_score_time`` are all in seconds.

        best_estimator_ : estimator
            Estimator that was chosen by the search, i.e. estimator
            which gave highest score (or smallest loss if specified)
            on the left out data. Not available if refit=False.

        optimizer_results_ : list of `OptimizeResult`
            Contains a `OptimizeResult` for each search space. The search space
            parameter are sorted by its name.

        best_score_ : float
            Score of best_estimator on the left out data.

        best_params_ : dict
            Parameter setting that gave the best results on the hold out data.

        best_index_ : int
            The index (of the ``cv_results_`` arrays) which corresponds to the best
            candidate parameter setting.

            The dict at ``search.cv_results_['params'][search.best_index_]`` gives
            the parameter setting for the best model, that gives the highest
            mean score (``search.best_score_``).

        scorer_ : function
            Scorer function used on the held out data to choose the best
            parameters for the model.

        n_splits_ : int
            The number of cross-validation splits (folds/iterations).

        refit_time_ : float
            Seconds used for refitting the best model on the whole dataset.
            This is present only if ``refit`` is not False.
        multimetric_ : bool
            Whether or not the scorers compute several metrics.

        Notes
        -----
        The parameters selected are those that maximize the score of the held-out
        data, according to the scoring parameter.

        If `n_jobs` was set to a value higher than one, the data is copied for each
        parameter setting (and not `n_jobs` times). This is done for efficiency
        reasons if individual jobs take very little time, but may raise errors if
        the dataset is large and not enough memory is available.  A workaround in
        this case is to set `pre_dispatch`. Then, the memory is copied only
        `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
        n_jobs`.

        See Also
        --------
        :class:`GridSearchCV`:
            Does exhaustive search over a grid of parameters.
        """

        def __init__(
            self,
            estimator,
            search_spaces,
            optimizer_kwargs=None,
            n_iter=50,
            scoring=None,
            fit_params=None,
            n_jobs=1,
            n_points=1,
            iid='deprecated',
            refit=True,
            cv=None,
            verbose=0,
            pre_dispatch='2*n_jobs',
            random_state=None,
            error_score='raise',
            return_train_score=False,
        ):

            self.search_spaces = search_spaces
            self.n_iter = n_iter
            self.n_points = n_points
            self.random_state = random_state
            self.optimizer_kwargs = optimizer_kwargs
            self._check_search_space(self.search_spaces)
            # Temporary fix for compatibility with sklearn 0.20 and 0.21
            # See scikit-optimize#762
            # To be consistent with sklearn 0.21+, fit_params should be deprecated
            # in the constructor and be passed in ``fit``.
            self.fit_params = fit_params

            if iid != "deprecated":
                warnings.warn(
                    "The `iid` parameter has been deprecated " "and will be ignored."
                )
            self.iid = iid  # For sklearn repr pprint

            super().__init__(
                estimator=estimator,
                scoring=scoring,
                n_jobs=n_jobs,
                refit=refit,
                cv=cv,
                verbose=verbose,
                pre_dispatch=pre_dispatch,
                error_score=error_score,
                return_train_score=return_train_score,
            )

        def _check_search_space(self, search_space):
            """Checks whether the search space argument is correct."""

            if len(search_space) == 0:
                raise ValueError(
                    "The search_spaces parameter should contain at least one"
                    "non-empty search space, got %s" % search_space
                )

            # check if space is a single dict, convert to list if so
            if isinstance(search_space, dict):
                search_space = [search_space]

            # check if the structure of the space is proper
            if isinstance(search_space, list):
                # convert to just a list of dicts
                dicts_only = []

                # 1. check the case when a tuple of space, n_iter is provided
                for elem in search_space:
                    if isinstance(elem, tuple):
                        if len(elem) != 2:
                            raise ValueError(
                                "All tuples in list of search spaces should have"
                                "length 2, and contain (dict, int), got %s" % elem
                            )
                        subspace, n_iter = elem

                        if (not isinstance(n_iter, int)) or n_iter < 0:
                            raise ValueError(
                                "Number of iterations in search space should be"
                                "positive integer, got %s in tuple %s " % (n_iter, elem)
                            )

                        # save subspaces here for further checking
                        dicts_only.append(subspace)
                    elif isinstance(elem, dict):
                        dicts_only.append(elem)
                    else:
                        raise TypeError(
                            "A search space should be provided as a dict or"
                            "tuple (dict, int), got %s" % elem
                        )

                # 2. check all the dicts for correctness of contents
                for subspace in dicts_only:
                    for _, v in subspace.items():
                        check_dimension(v)
            else:
                raise TypeError(
                    "Search space should be provided as a dict or list of dict,"
                    "got %s" % search_space
                )

        @property
        def optimizer_results_(self):
            check_is_fitted(self, '_optim_results')
            return self._optim_results

        def _make_optimizer(self, params_space):
            """Instantiate skopt Optimizer class.

            Parameters
            ----------
            params_space : dict
                Represents parameter search space. The keys are parameter
                names (strings) and values are skopt.space.Dimension instances,
                one of Real, Integer or Categorical.

            Returns
            -------
            optimizer: Instance of the `Optimizer` class used for for search
                in some parameter space.
            """

            kwargs = self.optimizer_kwargs_.copy()
            kwargs['dimensions'] = dimensions_aslist(params_space)
            optimizer = Optimizer(**kwargs)
            for i in range(len(optimizer.space.dimensions)):
                if optimizer.space.dimensions[i].name is not None:
                    continue
                optimizer.space.dimensions[i].name = list(sorted(params_space.keys()))[i]

            return optimizer

        def _step(
            self, search_space, optimizer, score_name, evaluate_candidates, n_points=1
        ):
            """Generate n_jobs parameters and evaluate them in parallel."""
            # get parameter values to evaluate
            params = optimizer.ask(n_points=n_points)

            # convert parameters to python native types
            params = [[np.array(v).item() for v in p] for p in params]

            # make lists into dictionaries
            params_dict = [point_asdict(search_space, p) for p in params]

            all_results = evaluate_candidates(params_dict)

            # if self.scoring is a callable, we have to wait until here
            # to get the score name
            if score_name is None:
                score_names = _get_score_names(all_results)
                if len(score_names) > 1:
                    # multimetric case
                    # early check to fail before lengthy computations, as
                    # BaseSearchCV only performs this check *after* _run_search
                    self._check_refit_for_multimetric(score_names)
                    score_name = f"mean_test_{self.refit}"
                elif len(score_names) == 1:
                    # single metric, or a callable self.scoring returning a dict
                    # with a single value
                    # In both case, we just use the score that is available
                    score_name = f"mean_test_{score_names.pop()}"
                else:
                    # failsafe, shouldn't happen
                    raise ValueError(
                        "No score was detected after fitting. This is probably "
                        "due to a callable 'scoring' returning an empty dict."
                    )

            # Feed the point and objective value back into optimizer
            # Optimizer minimizes objective, hence provide negative score
            local_results = all_results[score_name][-len(params) :]
            # return the score_name to cache it if callable refit
            # this avoids checking self.refit all the time
            return (optimizer.tell(params, [-score for score in local_results]), score_name)

        @property
        def total_iterations(self):
            """Count total iterations that will be taken to explore all subspaces with `fit`
            method.

            Returns
            -------
            max_iter: int, total number of iterations to explore
            """
            total_iter = 0

            for elem in self.search_spaces:

                if isinstance(elem, tuple):
                    space, n_iter = elem
                else:
                    n_iter = self.n_iter

                total_iter += n_iter

            return total_iter

        # TODO: Accept callbacks via the constructor?

        def fit(self, X, y=None, *, groups=None, callback=None, **fit_params):
            """Run fit on the estimator with randomly drawn parameters.

            Parameters
            ----------
            X : array-like or sparse matrix, shape = [n_samples, n_features]
                The training input samples.

            y : array-like, shape = [n_samples] or [n_samples, n_output]
                Target relative to X for classification or regression (class
                labels should be integers or strings).

            groups : array-like, with shape (n_samples,), optional
                Group labels for the samples used while splitting the dataset into
                train/test set.

            callback: [callable, list of callables, optional]
                If callable then `callback(res)` is called after each parameter
                combination tested. If list of callables, then each callable in
                the list is called.
            """
            self._callbacks = check_callback(callback)

            if self.optimizer_kwargs is None:
                self.optimizer_kwargs_ = {}
            else:
                self.optimizer_kwargs_ = dict(self.optimizer_kwargs)

            if callable(self.refit):
                raise ValueError(
                    "BayesSearchCV doesn't support a callable refit, "
                    "as it doesn't define an implicit score to "
                    "optimize"
                )

            super().fit(X=X, y=y, groups=groups, **fit_params)

            # BaseSearchCV never ranked train scores,
            # but apparently we used to ship this (back-compat)
            if self.return_train_score:
                for score in _get_score_names(self.cv_results_, kind="train"):
                    self.cv_results_[f"rank_train_{score}"] = rankdata(
                        -np.array(self.cv_results_[f"mean_train_{score}"]), method='min'
                    ).astype(int)
            return self

        def _run_search(self, evaluate_candidates):
            # check if space is a single dict, convert to list if so
            search_spaces = self.search_spaces
            if isinstance(search_spaces, dict):
                search_spaces = [search_spaces]

            callbacks = self._callbacks

            random_state = check_random_state(self.random_state)
            self.optimizer_kwargs_['random_state'] = random_state

            # Adapted from BaseSearchCV fit() method
            if callable(self.scoring):
                # will be determined later
                score_name = None
            elif self.scoring is None or isinstance(self.scoring, str):
                score_name = "mean_test_score"
            else:
                # proper checking took place before in BaseSearchCV.fit()
                score_name = f"mean_test_{self.refit}"

            # Instantiate optimizers for all the search spaces.
            optimizers = []
            for search_space in search_spaces:
                if isinstance(search_space, tuple):
                    search_space = search_space[0]
                optimizers.append(self._make_optimizer(search_space))
            self.optimizers_ = optimizers  # will save the states of the optimizers

            self._optim_results = []

            n_points = self.n_points

            for search_space, optimizer in zip(search_spaces, optimizers):
                # if not provided with search subspace, n_iter is taken as
                # self.n_iter
                if isinstance(search_space, tuple):
                    search_space, n_iter = search_space
                else:
                    n_iter = self.n_iter

                # do the optimization for particular search space
                while n_iter > 0:
                    # when n_iter < n_points points left for evaluation
                    n_points_adjusted = min(n_iter, n_points)

                    optim_result, score_name = self._step(
                        search_space,
                        optimizer,
                        score_name,
                        evaluate_candidates,
                        n_points=n_points_adjusted,
                    )
                    n_iter -= n_points

                    if eval_callbacks(callbacks, optim_result):
                        break
                self._optim_results.append(optim_result)

        def _check_refit_for_multimetric(self, scores):
            """Check `refit` is compatible with `scores` and valid."""
            # override parent method to exclude False and callables
            multimetric_refit_msg = (
                "For multi-metric scoring, the 'refit' parameter must be set to a "
                "scorer key, used to guide the bayesian optimization process "
                "and refit an estimator with the best parameter settings on the "
                "whole dataset (making the best_* attributes available for that "
                f" metric). {self.refit!r} was passed."
            )

            is_refit_valid = isinstance(self.refit, str) and self.refit in scores

            if not is_refit_valid:
                raise ValueError(multimetric_refit_msg)