"""
The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the
parameters of an estimator.
"""

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
#         Gael Varoquaux <gael.varoquaux@normalesup.org>
#         Andreas Mueller <amueller@ais.uni-bonn.de>
#         Olivier Grisel <olivier.grisel@ensta.org>
#         Raghav RV <rvraghav93@gmail.com>
# License: BSD 3 clause

import numbers
import operator
import time
import warnings
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from collections.abc import Iterable, Mapping, Sequence
from functools import partial, reduce
from itertools import product

import numpy as np
from numpy.ma import MaskedArray
from scipy.stats import rankdata

from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
from ..exceptions import NotFittedError
from ..metrics import check_scoring
from ..metrics._scorer import (
    _check_multimetric_scoring,
    _MultimetricScorer,
    get_scorer_names,
)
from ..utils import Bunch, check_random_state
from ..utils._param_validation import HasMethods, Interval, StrOptions
from ..utils._tags import _safe_tags
from ..utils.metadata_routing import (
    MetadataRouter,
    MethodMapping,
    _raise_for_params,
    _routing_enabled,
    process_routing,
)
from ..utils.metaestimators import available_if
from ..utils.parallel import Parallel, delayed
from ..utils.random import sample_without_replacement
from ..utils.validation import _check_method_params, check_is_fitted, indexable
from ._split import check_cv
from ._validation import (
    _aggregate_score_dicts,
    _fit_and_score,
    _insert_error_scores,
    _normalize_score_results,
    _warn_or_raise_about_fit_failures,
)

__all__ = ["GridSearchCV", "ParameterGrid", "ParameterSampler", "RandomizedSearchCV"]

class ParameterGrid:
    """Grid of parameters with a discrete number of values for each.

    Can be used to iterate over parameter value combinations with the
    Python built-in function iter.
    The order of the generated parameter combinations is deterministic.

    Read more in the :ref:`User Guide <grid_search>`.

    Parameters
    ----------
    param_grid : dict of str to sequence, or sequence of such
        The parameter grid to explore, as a dictionary mapping estimator
        parameters to sequences of allowed values.

        An empty dict signifies default parameters.

        A sequence of dicts signifies a sequence of grids to search, and is
        useful to avoid exploring parameter combinations that make no sense
        or have no effect. See the examples below.

    Examples
    --------
    >>> from sklearn.model_selection import ParameterGrid
    >>> param_grid = {'a': [1, 2], 'b': [True, False]}
    >>> list(ParameterGrid(param_grid)) == (
    ...    [{'a': 1, 'b': True}, {'a': 1, 'b': False},
    ...     {'a': 2, 'b': True}, {'a': 2, 'b': False}])
    True

    >>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
    >>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
    ...                               {'kernel': 'rbf', 'gamma': 1},
    ...                               {'kernel': 'rbf', 'gamma': 10}]
    True
    >>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
    True

    See Also
    --------
    GridSearchCV : Uses :class:`ParameterGrid` to perform a full parallelized
        parameter search.
    """

    def __init__(self, param_grid):
        if not isinstance(param_grid, (Mapping, Iterable)):
            raise TypeError(
                f"Parameter grid should be a dict or a list, got: {param_grid!r} of"
                f" type {type(param_grid).__name__}"
            )

        if isinstance(param_grid, Mapping):
            # wrap dictionary in a singleton list to support either dict
            # or list of dicts
            param_grid = [param_grid]

        # check if all entries are dictionaries of lists
        for grid in param_grid:
            if not isinstance(grid, dict):
                raise TypeError(f"Parameter grid is not a dict ({grid!r})")
            for key, value in grid.items():
                if isinstance(value, np.ndarray) and value.ndim > 1:
                    raise ValueError(
                        f"Parameter array for {key!r} should be one-dimensional, got:"
                        f" {value!r} with shape {value.shape}"
                    )
                if isinstance(value, str) or not isinstance(
                    value, (np.ndarray, Sequence)
                ):
                    raise TypeError(
                        f"Parameter grid for parameter {key!r} needs to be a list or a"
                        f" numpy array, but got {value!r} (of type "
                        f"{type(value).__name__}) instead. Single values "
                        "need to be wrapped in a list with one element."
                    )
                if len(value) == 0:
                    raise ValueError(
                        f"Parameter grid for parameter {key!r} need "
                        f"to be a non-empty sequence, got: {value!r}"
                    )

        self.param_grid = param_grid

    def __iter__(self):
        """Iterate over the points in the grid.

        Returns
        -------
        params : iterator over dict of str to any
            Yields dictionaries mapping each estimator parameter to one of its
            allowed values.
        """
        for p in self.param_grid:
            # Always sort the keys of a dictionary, for reproducibility
            items = sorted(p.items())
            if not items:
                yield {}
            else:
                keys, values = zip(*items)
                for v in product(*values):
                    params = dict(zip(keys, v))
                    yield params

    def __len__(self):
        """Number of points on the grid."""
        # Product function that can handle iterables (np.prod can't).
        product = partial(reduce, operator.mul)
        return sum(
            product(len(v) for v in p.values()) if p else 1 for p in self.param_grid
        )

    def __getitem__(self, ind):
        """Get the parameters that would be ``ind``th in iteration

        Parameters
        ----------
        ind : int
            The iteration index

        Returns
        -------
        params : dict of str to any
            Equal to list(self)[ind]
        """
        # This is used to make discrete sampling without replacement memory
        # efficient.
        for sub_grid in self.param_grid:
            # XXX: could memoize information used here
            if not sub_grid:
                if ind == 0:
                    return {}
                else:
                    ind -= 1
                    continue

            # Reverse so most frequent cycling parameter comes first
            keys, values_lists = zip(*sorted(sub_grid.items())[::-1])
            sizes = [len(v_list) for v_list in values_lists]
            total = np.prod(sizes)

            if ind >= total:
                # Try the next grid
                ind -= total
            else:
                out = {}
                for key, v_list, n in zip(keys, values_lists, sizes):
                    ind, offset = divmod(ind, n)
                    out[key] = v_list[offset]
                return out

        raise IndexError("ParameterGrid index out of range")

class ParameterSampler:
    """Generator on parameters sampled from given distributions.

    Non-deterministic iterable over random candidate combinations for hyper-
    parameter search. If all parameters are presented as a list,
    sampling without replacement is performed. If at least one parameter
    is given as a distribution, sampling with replacement is used.
    It is highly recommended to use continuous distributions for continuous
    parameters.

    Read more in the :ref:`User Guide <grid_search>`.

    Parameters
    ----------
    param_distributions : dict
        Dictionary with parameters names (`str`) as keys and distributions
        or lists of parameters to try. Distributions must provide a ``rvs``
        method for sampling (such as those from scipy.stats.distributions).
        If a list is given, it is sampled uniformly.
        If a list of dicts is given, first a dict is sampled uniformly, and
        then a parameter is sampled using that dict as above.

    n_iter : int
        Number of parameter settings that are produced.

    random_state : int, RandomState instance or None, default=None
        Pseudo random number generator state used for random uniform sampling
        from lists of possible values instead of scipy.stats distributions.
        Pass an int for reproducible output across multiple
        function calls.
        See :term:`Glossary <random_state>`.

    Returns
    -------
    params : dict of str to any
        **Yields** dictionaries mapping each estimator parameter to
        as sampled value.

    Examples
    --------
    >>> from sklearn.model_selection import ParameterSampler
    >>> from scipy.stats.distributions import expon
    >>> import numpy as np
    >>> rng = np.random.RandomState(0)
    >>> param_grid = {'a':[1, 2], 'b': expon()}
    >>> param_list = list(ParameterSampler(param_grid, n_iter=4,
    ...                                    random_state=rng))
    >>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())
    ...                 for d in param_list]
    >>> rounded_list == [{'b': 0.89856, 'a': 1},
    ...                  {'b': 0.923223, 'a': 1},
    ...                  {'b': 1.878964, 'a': 2},
    ...                  {'b': 1.038159, 'a': 2}]
    True
    """

    def __init__(self, param_distributions, n_iter, *, random_state=None):
        if not isinstance(param_distributions, (Mapping, Iterable)):
            raise TypeError(
                "Parameter distribution is not a dict or a list,"
                f" got: {param_distributions!r} of type "
                f"{type(param_distributions).__name__}"
            )

        if isinstance(param_distributions, Mapping):
            # wrap dictionary in a singleton list to support either dict
            # or list of dicts
            param_distributions = [param_distributions]

        for dist in param_distributions:
            if not isinstance(dist, dict):
                raise TypeError(
                    "Parameter distribution is not a dict ({!r})".format(dist)
                )
            for key in dist:
                if not isinstance(dist[key], Iterable) and not hasattr(
                    dist[key], "rvs"
                ):
                    raise TypeError(
                        f"Parameter grid for parameter {key!r} is not iterable "
                        f"or a distribution (value={dist[key]})"
                    )
        self.n_iter = n_iter
        self.random_state = random_state
        self.param_distributions = param_distributions

    def _is_all_lists(self):
        return all(
            all(not hasattr(v, "rvs") for v in dist.values())
            for dist in self.param_distributions
        )

    def __iter__(self):
        rng = check_random_state(self.random_state)

        # if all distributions are given as lists, we want to sample without
        # replacement
        if self._is_all_lists():
            # look up sampled parameter settings in parameter grid
            param_grid = ParameterGrid(self.param_distributions)
            grid_size = len(param_grid)
            n_iter = self.n_iter

            if grid_size < n_iter:
                warnings.warn(
                    "The total space of parameters %d is smaller "
                    "than n_iter=%d. Running %d iterations. For exhaustive "
                    "searches, use GridSearchCV." % (grid_size, self.n_iter, grid_size),
                    UserWarning,
                )
                n_iter = grid_size
            for i in sample_without_replacement(grid_size, n_iter, random_state=rng):
                yield param_grid[i]

        else:
            for _ in range(self.n_iter):
                dist = rng.choice(self.param_distributions)
                # Always sort the keys of a dictionary, for reproducibility
                items = sorted(dist.items())
                params = dict()
                for k, v in items:
                    if hasattr(v, "rvs"):
                        params[k] = v.rvs(random_state=rng)
                    else:
                        params[k] = v[rng.randint(len(v))]
                yield params

    def __len__(self):
        """Number of points that will be sampled."""
        if self._is_all_lists():
            grid_size = len(ParameterGrid(self.param_distributions))
            return min(self.n_iter, grid_size)
        else:
            return self.n_iter

def _check_refit(search_cv, attr):
    if not search_cv.refit:
        raise AttributeError(
            f"This {type(search_cv).__name__} instance was initialized with "
            f"`refit=False`. {attr} is available only after refitting on the best "
            "parameters. You can refit an estimator manually using the "
            "`best_params_` attribute"
        )

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

    Calling a prediction method will only be available if `refit=True`. In
    such case, we check first the fitted best estimator. If it is not
    fitted, we check the unfitted estimator.

    Checking the unfitted estimator allows to use `hasattr` on the `SearchCV`
    instance even before calling `fit`.
    """

    def check(self):
        _check_refit(self, attr)
        if hasattr(self, "best_estimator_"):
            # raise an AttributeError if `attr` does not exist
            getattr(self.best_estimator_, attr)
            return True
        # raise an AttributeError if `attr` does not exist
        getattr(self.estimator, attr)
        return True

    return check

class BaseSearchCV(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
    """Abstract base class for hyper parameter search with cross-validation."""

    _parameter_constraints: dict = {
        "estimator": [HasMethods(["fit"])],
        "scoring": [
            StrOptions(set(get_scorer_names())),
            callable,
            list,
            tuple,
            dict,
            None,
        ],
        "n_jobs": [numbers.Integral, None],
        "refit": ["boolean", str, callable],
        "cv": ["cv_object"],
        "verbose": ["verbose"],
        "pre_dispatch": [numbers.Integral, str],
        "error_score": [StrOptions({"raise"}), numbers.Real],
        "return_train_score": ["boolean"],
    }

    @abstractmethod
    def __init__(
        self,
        estimator,
        *,
        scoring=None,
        n_jobs=None,
        refit=True,
        cv=None,
        verbose=0,
        pre_dispatch="2*n_jobs",
        error_score=np.nan,
        return_train_score=True,
    ):
        self.scoring = scoring
        self.estimator = estimator
        self.n_jobs = n_jobs
        self.refit = refit
        self.cv = cv
        self.verbose = verbose
        self.pre_dispatch = pre_dispatch
        self.error_score = error_score
        self.return_train_score = return_train_score

    @property
    def _estimator_type(self):
        return self.estimator._estimator_type

    def _more_tags(self):
        # allows cross-validation to see 'precomputed' metrics
        return {
            "pairwise": _safe_tags(self.estimator, "pairwise"),
            "_xfail_checks": {
                "check_supervised_y_2d": "DataConversionWarning not caught"
            },
        }

    def score(self, X, y=None, **params):
        """Return the score on the given data, if the estimator has been refit.

        This uses the score defined by ``scoring`` where provided, and the
        ``best_estimator_.score`` method otherwise.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Input data, where `n_samples` is the number of samples and
            `n_features` is the number of features.

        y : array-like of shape (n_samples, n_output) \
            or (n_samples,), default=None
            Target relative to X for classification or regression;
            None for unsupervised learning.

        **params : dict
            Parameters to be passed to the underlying scorer(s).

            ..versionadded:: 1.4
                Only available if `enable_metadata_routing=True`. See
                :ref:`Metadata Routing User Guide <metadata_routing>` for more
                details.

        Returns
        -------
        score : float
            The score defined by ``scoring`` if provided, and the
            ``best_estimator_.score`` method otherwise.
        """
        _check_refit(self, "score")
        check_is_fitted(self)

        _raise_for_params(params, self, "score")

        if _routing_enabled():
            score_params = process_routing(self, "score", **params).scorer["score"]
        else:
            score_params = dict()

        if self.scorer_ is None:
            raise ValueError(
                "No score function explicitly defined, "
                "and the estimator doesn't provide one %s"
                % self.best_estimator_
            )
        if isinstance(self.scorer_, dict):
            if self.multimetric_:
                scorer = self.scorer_[self.refit]
            else:
                scorer = self.scorer_
            return scorer(self.best_estimator_, X, y, **score_params)

        # callable
        score = self.scorer_(self.best_estimator_, X, y, **score_params)
        if self.multimetric_:
            score = score[self.refit]
        return score

    @available_if(_estimator_has("score_samples"))
    def score_samples(self, X):
        """Call score_samples on the estimator with the best found parameters.

        Only available if ``refit=True`` and the underlying estimator supports
        ``score_samples``.

        .. versionadded:: 0.24

        Parameters
        ----------
        X : iterable
            Data to predict on. Must fulfill input requirements
            of the underlying estimator.

        Returns
        -------
        y_score : ndarray of shape (n_samples,)
            The ``best_estimator_.score_samples`` method.
        """
        check_is_fitted(self)
        return self.best_estimator_.score_samples(X)

    @available_if(_estimator_has("predict"))
    def predict(self, X):
        """Call predict on the estimator with the best found parameters.

        Only available if ``refit=True`` and the underlying estimator supports
        ``predict``.

        Parameters
        ----------
        X : indexable, length n_samples
            Must fulfill the input assumptions of the
            underlying estimator.

        Returns
        -------
        y_pred : ndarray of shape (n_samples,)
            The predicted labels or values for `X` based on the estimator with
            the best found parameters.
        """
        check_is_fitted(self)
        return self.best_estimator_.predict(X)

    @available_if(_estimator_has("predict_proba"))
    def predict_proba(self, X):
        """Call predict_proba on the estimator with the best found parameters.

        Only available if ``refit=True`` and the underlying estimator supports
        ``predict_proba``.

        Parameters
        ----------
        X : indexable, length n_samples
            Must fulfill the input assumptions of the
            underlying estimator.

        Returns
        -------
        y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes)
            Predicted class probabilities for `X` based on the estimator with
            the best found parameters. The order of the classes corresponds
            to that in the fitted attribute :term:`classes_`.
        """
        check_is_fitted(self)
        return self.best_estimator_.predict_proba(X)

    @available_if(_estimator_has("predict_log_proba"))
    def predict_log_proba(self, X):
        """Call predict_log_proba on the estimator with the best found parameters.

        Only available if ``refit=True`` and the underlying estimator supports
        ``predict_log_proba``.

        Parameters
        ----------
        X : indexable, length n_samples
            Must fulfill the input assumptions of the
            underlying estimator.

        Returns
        -------
        y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes)
            Predicted class log-probabilities for `X` based on the estimator
            with the best found parameters. The order of the classes
            corresponds to that in the fitted attribute :term:`classes_`.
        """
        check_is_fitted(self)
        return self.best_estimator_.predict_log_proba(X)

    @available_if(_estimator_has("decision_function"))
    def decision_function(self, X):
        """Call decision_function on the estimator with the best found parameters.

        Only available if ``refit=True`` and the underlying estimator supports
        ``decision_function``.

        Parameters
        ----------
        X : indexable, length n_samples
            Must fulfill the input assumptions of the
            underlying estimator.

        Returns
        -------
        y_score : ndarray of shape (n_samples,) or (n_samples, n_classes) \
                or (n_samples, n_classes * (n_classes-1) / 2)
            Result of the decision function for `X` based on the estimator with
            the best found parameters.
        """
        check_is_fitted(self)
        return self.best_estimator_.decision_function(X)

    @available_if(_estimator_has("transform"))
    def transform(self, X):
        """Call transform on the estimator with the best found parameters.

        Only available if the underlying estimator supports ``transform`` and
        ``refit=True``.

        Parameters
        ----------
        X : indexable, length n_samples
            Must fulfill the input assumptions of the
            underlying estimator.

        Returns
        -------
        Xt : {ndarray, sparse matrix} of shape (n_samples, n_features)
            `X` transformed in the new space based on the estimator with
            the best found parameters.
        """
        check_is_fitted(self)
        return self.best_estimator_.transform(X)

    @available_if(_estimator_has("inverse_transform"))
    def inverse_transform(self, Xt):
        """Call inverse_transform on the estimator with the best found params.

        Only available if the underlying estimator implements
        ``inverse_transform`` and ``refit=True``.

        Parameters
        ----------
        Xt : indexable, length n_samples
            Must fulfill the input assumptions of the
            underlying estimator.

        Returns
        -------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Result of the `inverse_transform` function for `Xt` based on the
            estimator with the best found parameters.
        """
        check_is_fitted(self)
        return self.best_estimator_.inverse_transform(Xt)

    @property
    def n_features_in_(self):
        """Number of features seen during :term:`fit`.

        Only available when `refit=True`.
        """
        # For consistency with other estimators we raise a AttributeError so
        # that hasattr() fails if the search estimator isn't fitted.
        try:
            check_is_fitted(self)
        except NotFittedError as nfe:
            raise AttributeError(
                "{} object has no n_features_in_ attribute.".format(
                    self.__class__.__name__
                )
            ) from nfe

        return self.best_estimator_.n_features_in_

    @property
    def classes_(self):
        """Class labels.

        Only available when `refit=True` and the estimator is a classifier.
        """
        _estimator_has("classes_")(self)
        return self.best_estimator_.classes_

    def _run_search(self, evaluate_candidates):
        """Repeatedly calls `evaluate_candidates` to conduct a search.

        This method, implemented in sub-classes, makes it possible to
        customize the scheduling of evaluations: GridSearchCV and
        RandomizedSearchCV schedule evaluations for their whole parameter
        search space at once but other more sequential approaches are also
        possible: for instance is possible to iteratively schedule evaluations
        for new regions of the parameter search space based on previously
        collected evaluation results. This makes it possible to implement
        Bayesian optimization or more generally sequential model-based
        optimization by deriving from the BaseSearchCV abstract base class.
        For example, Successive Halving is implemented by calling
        `evaluate_candidates` multiples times (once per iteration of the SH
        process), each time passing a different set of candidates with `X`
        and `y` of increasing sizes.

        Parameters
        ----------
        evaluate_candidates : callable
            This callback accepts:
                - a list of candidates, where each candidate is a dict of
                  parameter settings.
                - an optional `cv` parameter which can be used to e.g.
                  evaluate candidates on different dataset splits, or
                  evaluate candidates on subsampled data (as done in the
                  SucessiveHaling estimators). By default, the original `cv`
                  parameter is used, and it is available as a private
                  `_checked_cv_orig` attribute.
                - an optional `more_results` dict. Each key will be added to
                  the `cv_results_` attribute. Values should be lists of
                  length `n_candidates`

            It returns a dict of all results so far, formatted like
            ``cv_results_``.

            Important note (relevant whether the default cv is used or not):
            in randomized splitters, and unless the random_state parameter of
            cv was set to an int, calling cv.split() multiple times will
            yield different splits. Since cv.split() is called in
            evaluate_candidates, this means that candidates will be evaluated
            on different splits each time evaluate_candidates is called. This
            might be a methodological issue depending on the search strategy
            that you're implementing. To prevent randomized splitters from
            being used, you may use _split._yields_constant_splits()

        Examples
        --------

        ::

            def _run_search(self, evaluate_candidates):
                'Try C=0.1 only if C=1 is better than C=10'
                all_results = evaluate_candidates([{'C': 1}, {'C': 10}])
                score = all_results['mean_test_score']
                if score[0] < score[1]:
                    evaluate_candidates([{'C': 0.1}])
        """
        raise NotImplementedError("_run_search not implemented.")

    def _check_refit_for_multimetric(self, scores):
        """Check `refit` is compatible with `scores` is valid"""
        multimetric_refit_msg = (
            "For multi-metric scoring, the parameter refit must be set to a "
            "scorer key or a callable to refit an estimator with the best "
            "parameter setting on the whole data and make the best_* "
            "attributes available for that metric. If this is not needed, "
            f"refit should be set to False explicitly. {self.refit!r} was "
            "passed."
        )

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

        if (
            self.refit is not False
            and not valid_refit_dict
            and not callable(self.refit)
        ):
            raise ValueError(multimetric_refit_msg)

    @staticmethod
    def _select_best_index(refit, refit_metric, results):
        """Select index of the best combination of hyperparemeters."""
        if callable(refit):
            # If callable, refit is expected to return the index of the best
            # parameter set.
            best_index = refit(results)
            if not isinstance(best_index, numbers.Integral):
                raise TypeError("best_index_ returned is not an integer")
            if best_index < 0 or best_index >= len(results["params"]):
                raise IndexError("best_index_ index out of range")
        else:
            best_index = results[f"rank_test_{refit_metric}"].argmin()
        return best_index

    def _get_scorers(self, convert_multimetric):
        """Get the scorer(s) to be used.

        This is used in ``fit`` and ``get_metadata_routing``.

        Parameters
        ----------
        convert_multimetric : bool
            Whether to convert a dict of scorers to a _MultimetricScorer. This
            is used in ``get_metadata_routing`` to include the routing info for
            multiple scorers.

        Returns
        -------
        scorers, refit_metric
        """
        refit_metric = "score"

        if callable(self.scoring):
            scorers = self.scoring
        elif self.scoring is None or isinstance(self.scoring, str):
            scorers = check_scoring(self.estimator, self.scoring)
        else:
            scorers = _check_multimetric_scoring(self.estimator, self.scoring)
            self._check_refit_for_multimetric(scorers)
            refit_metric = self.refit
            if convert_multimetric and isinstance(scorers, dict):
                scorers = _MultimetricScorer(
                    scorers=scorers, raise_exc=(self.error_score == "raise")
                )

        return scorers, refit_metric

    def _get_routed_params_for_fit(self, params):
        """Get the parameters to be used for routing.

        This is a method instead of a snippet in ``fit`` since it's used twice,
        here in ``fit``, and in ``HalvingRandomSearchCV.fit``.
        """
        if _routing_enabled():
            routed_params = process_routing(self, "fit", **params)
        else:
            params = params.copy()
            groups = params.pop("groups", None)
            routed_params = Bunch(
                estimator=Bunch(fit=params),
                splitter=Bunch(split={"groups": groups}),
                scorer=Bunch(score={}),
            )
        return routed_params

    @_fit_context(
        # *SearchCV.estimator is not validated yet
        prefer_skip_nested_validation=False
    )
    def fit(self, X, y=None, **params):
        """Run fit with all sets of parameters.

        Parameters
        ----------

        X : array-like of shape (n_samples, n_features)
            Training vector, where `n_samples` is the number of samples and
            `n_features` is the number of features.

        y : array-like of shape (n_samples, n_output) \
            or (n_samples,), default=None
            Target relative to X for classification or regression;
            None for unsupervised learning.

        **params : dict of str -> object
            Parameters passed to the ``fit`` method of the estimator, the scorer,
            and the CV splitter.

            If a fit parameter is an array-like whose length is equal to
            `num_samples` then it will be split across CV groups along with `X`
            and `y`. For example, the :term:`sample_weight` parameter is split
            because `len(sample_weights) = len(X)`.

        Returns
        -------
        self : object
            Instance of fitted estimator.
        """
        estimator = self.estimator
        # Here we keep a dict of scorers as is, and only convert to a
        # _MultimetricScorer at a later stage. Issue:
        # https://github.com/scikit-learn/scikit-learn/issues/27001
        scorers, refit_metric = self._get_scorers(convert_multimetric=False)

        X, y = indexable(X, y)
        params = _check_method_params(X, params=params)

        routed_params = self._get_routed_params_for_fit(params)

        cv_orig = check_cv(self.cv, y, classifier=is_classifier(estimator))
        n_splits = cv_orig.get_n_splits(X, y, **routed_params.splitter.split)

        base_estimator = clone(self.estimator)

        parallel = Parallel(n_jobs=self.n_jobs, pre_dispatch=self.pre_dispatch)

        fit_and_score_kwargs = dict(
            scorer=scorers,
            fit_params=routed_params.estimator.fit,
            score_params=routed_params.scorer.score,
            return_train_score=self.return_train_score,
            return_n_test_samples=True,
            return_times=True,
            return_parameters=False,
            error_score=self.error_score,
            verbose=self.verbose,
        )
        results = {}
        with parallel:
            all_candidate_params = []
            all_out = []
            all_more_results = defaultdict(list)

            def evaluate_candidates(candidate_params, cv=None, more_results=None):
                cv = cv or cv_orig
                candidate_params = list(candidate_params)
                n_candidates = len(candidate_params)

                if self.verbose > 0:
                    print(
                        "Fitting {0} folds for each of {1} candidates,"
                        " totalling {2} fits".format(
                            n_splits, n_candidates, n_candidates * n_splits
                        )
                    )

                out = parallel(
                    delayed(_fit_and_score)(
                        clone(base_estimator),
                        X,
                        y,
                        train=train,
                        test=test,
                        parameters=parameters,
                        split_progress=(split_idx, n_splits),
                        candidate_progress=(cand_idx, n_candidates),
                        **fit_and_score_kwargs,
                    )
                    for (cand_idx, parameters), (split_idx, (train, test)) in product(
                        enumerate(candidate_params),
                        enumerate(cv.split(X, y, **routed_params.splitter.split)),
                    )
                )

                if len(out) < 1:
                    raise ValueError(
                        "No fits were performed. "
                        "Was the CV iterator empty? "
                        "Were there no candidates?"
                    )
                elif len(out) != n_candidates * n_splits:
                    raise ValueError(
                        "cv.split and cv.get_n_splits returned "
                        "inconsistent results. Expected {} "
                        "splits, got {}".format(n_splits, len(out) // n_candidates)
                    )

                _warn_or_raise_about_fit_failures(out, self.error_score)

                # For callable self.scoring, the return type is only know after
                # calling. If the return type is a dictionary, the error scores
                # can now be inserted with the correct key. The type checking
                # of out will be done in `_insert_error_scores`.
                if callable(self.scoring):
                    _insert_error_scores(out, self.error_score)

                all_candidate_params.extend(candidate_params)
                all_out.extend(out)

                if more_results is not None:
                    for key, value in more_results.items():
                        all_more_results[key].extend(value)

                nonlocal results
                results = self._format_results(
                    all_candidate_params, n_splits, all_out, all_more_results
                )

                return results

            self._run_search(evaluate_candidates)

            # multimetric is determined here because in the case of a callable
            # self.scoring the return type is only known after calling
            first_test_score = all_out[0]["test_scores"]
            self.multimetric_ = isinstance(first_test_score, dict)

            # check refit_metric now for a callabe scorer that is multimetric
            if callable(self.scoring) and self.multimetric_:
                self._check_refit_for_multimetric(first_test_score)
                refit_metric = self.refit

        # For multi-metric evaluation, store the best_index_, best_params_ and
        # best_score_ iff refit is one of the scorer names
        # In single metric evaluation, refit_metric is "score"
        if self.refit or not self.multimetric_:
            self.best_index_ = self._select_best_index(
                self.refit, refit_metric, results
            )
            if not callable(self.refit):
                # With a non-custom callable, we can select the best score
                # based on the best index
                self.best_score_ = results[f"mean_test_{refit_metric}"][
                    self.best_index_
                ]
            self.best_params_ = results["params"][self.best_index_]

        if self.refit:
            # here we clone the estimator as well as the parameters, since
            # sometimes the parameters themselves might be estimators, e.g.
            # when we search over different estimators in a pipeline.
            # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
            self.best_estimator_ = clone(base_estimator).set_params(
                **clone(self.best_params_, safe=False)
            )

            refit_start_time = time.time()
            if y is not None:
                self.best_estimator_.fit(X, y, **routed_params.estimator.fit)
            else:
                self.best_estimator_.fit(X, **routed_params.estimator.fit)
            refit_end_time = time.time()
            self.refit_time_ = refit_end_time - refit_start_time

            if hasattr(self.best_estimator_, "feature_names_in_"):
                self.feature_names_in_ = self.best_estimator_.feature_names_in_

        # Store the only scorer not as a dict for single metric evaluation
        self.scorer_ = scorers

        self.cv_results_ = results
        self.n_splits_ = n_splits

        return self

    def _format_results(self, candidate_params, n_splits, out, more_results=None):
        n_candidates = len(candidate_params)
        out = _aggregate_score_dicts(out)

        results = dict(more_results or {})
        for key, val in results.items():
            # each value is a list (as per evaluate_candidate's convention)
            # we convert it to an array for consistency with the other keys
            results[key] = np.asarray(val)

        def _store(key_name, array, weights=None, splits=False, rank=False):
            """A small helper to store the scores/times to the cv_results_"""
            # When iterated first by splits, then by parameters
            # We want `array` to have `n_candidates` rows and `n_splits` cols.
            array = np.array(array, dtype=np.float64).reshape(n_candidates, n_splits)
            if splits:
                for split_idx in range(n_splits):
                    # Uses closure to alter the results
                    results["split%d_%s" % (split_idx, key_name)] = array[:, split_idx]

            array_means = np.average(array, axis=1, weights=weights)
            results["mean_%s" % key_name] = array_means

            if key_name.startswith(("train_", "test_")) and np.any(
                ~np.isfinite(array_means)
            ):
                warnings.warn(
                    (
                        f"One or more of the {key_name.split('_')[0]} scores "
                        f"are non-finite: {array_means}"
                    ),
                    category=UserWarning,
                )

            # Weighted std is not directly available in numpy
            array_stds = np.sqrt(
                np.average(
                    (array - array_means[:, np.newaxis]) ** 2, axis=1, weights=weights
                )
            )
            results["std_%s" % key_name] = array_stds

            if rank:
                # When the fit/scoring fails `array_means` contains NaNs, we
                # will exclude them from the ranking process and consider them
                # as tied with the worst performers.
                if np.isnan(array_means).all():
                    # All fit/scoring routines failed.
                    rank_result = np.ones_like(array_means, dtype=np.int32)
                else:
                    min_array_means = np.nanmin(array_means) - 1
                    array_means = np.nan_to_num(array_means, nan=min_array_means)
                    rank_result = rankdata(-array_means, method="min").astype(
                        np.int32, copy=False
                    )
                results["rank_%s" % key_name] = rank_result

        _store("fit_time", out["fit_time"])
        _store("score_time", out["score_time"])
        # Use one MaskedArray and mask all the places where the param is not
        # applicable for that candidate. Use defaultdict as each candidate may
        # not contain all the params
        param_results = defaultdict(
            partial(
                MaskedArray,
                np.empty(
                    n_candidates,
                ),
                mask=True,
                dtype=object,
            )
        )
        for cand_idx, params in enumerate(candidate_params):
            for name, value in params.items():
                # An all masked empty array gets created for the key
                # `"param_%s" % name` at the first occurrence of `name`.
                # Setting the value at an index also unmasks that index
                param_results["param_%s" % name][cand_idx] = value

        results.update(param_results)
        # Store a list of param dicts at the key 'params'
        results["params"] = candidate_params

        test_scores_dict = _normalize_score_results(out["test_scores"])
        if self.return_train_score:
            train_scores_dict = _normalize_score_results(out["train_scores"])

        for scorer_name in test_scores_dict:
            # Computed the (weighted) mean and std for test scores alone
            _store(
                "test_%s" % scorer_name,
                test_scores_dict[scorer_name],
                splits=True,
                rank=True,
                weights=None,
            )
            if self.return_train_score:
                _store(
                    "train_%s" % scorer_name,
                    train_scores_dict[scorer_name],
                    splits=True,
                )

        return results

    def get_metadata_routing(self):
        """Get metadata routing of this object.

        Please check :ref:`User Guide <metadata_routing>` on how the routing
        mechanism works.

        .. versionadded:: 1.4

        Returns
        -------
        routing : MetadataRouter
            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
            routing information.
        """
        router = MetadataRouter(owner=self.__class__.__name__)
        router.add(
            estimator=self.estimator,
            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
        )

        scorer, _ = self._get_scorers(convert_multimetric=True)
        router.add(
            scorer=scorer,
            method_mapping=MethodMapping()
            .add(caller="score", callee="score")
            .add(caller="fit", callee="score"),
        )
        router.add(
            splitter=self.cv,
            method_mapping=MethodMapping().add(caller="fit", callee="split"),
        )
        return router

class GridSearchCV(BaseSearchCV):
    """Exhaustive search over specified parameter values for an estimator.

    Important members are fit, predict.

    GridSearchCV implements a "fit" and a "score" method.
    It also implements "score_samples", "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 grid-search over a parameter grid.

    Read more in the :ref:`User Guide <grid_search>`.

    Parameters
    ----------
    estimator : estimator object
        This is assumed to implement the scikit-learn estimator interface.
        Either estimator needs to provide a ``score`` function,
        or ``scoring`` must be passed.

    param_grid : dict or list of dictionaries
        Dictionary with parameters names (`str`) as keys and lists of
        parameter settings to try as values, or a list of such
        dictionaries, in which case the grids spanned by each dictionary
        in the list are explored. This enables searching over any sequence
        of parameter settings.

    scoring : str, callable, list, tuple or dict, default=None
        Strategy to evaluate the performance of the cross-validated model on
        the test set.

        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.

        See :ref:`multimetric_grid_search` for an example.

    n_jobs : int, default=None
        Number of jobs to run in parallel.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

        .. versionchanged:: v0.20
           `n_jobs` default changed from 1 to None

    refit : bool, str, or callable, default=True
        Refit an estimator using the best found parameters on the whole
        dataset.

        For multiple metric evaluation, this needs to be a `str` denoting the
        scorer that would be used to find the best parameters for refitting
        the estimator at the end.

        Where there are considerations other than maximum score in
        choosing a best estimator, ``refit`` can be set to a function which
        returns the selected ``best_index_`` given ``cv_results_``. In that
        case, the ``best_estimator_`` and ``best_params_`` will be set
        according to the returned ``best_index_`` while the ``best_score_``
        attribute will not be available.

        The refitted estimator is made available at the ``best_estimator_``
        attribute and permits using ``predict`` directly on this
        ``GridSearchCV`` instance.

        Also for multiple metric evaluation, the attributes ``best_index_``,
        ``best_score_`` and ``best_params_`` will only be available if
        ``refit`` is set and all of them will be determined w.r.t this specific
        scorer.

        See ``scoring`` parameter to know more about multiple metric
        evaluation.

        See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`
        to see how to design a custom selection strategy using a callable
        via `refit`.

        .. versionchanged:: 0.20
            Support for callable added.

    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 in a `(Stratified)KFold`,
        - :term:`CV splitter`,
        - An iterable yielding (train, test) splits as arrays of indices.

        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. These splitters are instantiated
        with `shuffle=False` so the splits will be the same across calls.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

        .. versionchanged:: 0.22
            ``cv`` default value if None changed from 3-fold to 5-fold.

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

        - >1 : the computation time for each fold and parameter candidate is
          displayed;
        - >2 : the score is also displayed;
        - >3 : the fold and candidate parameter indexes are also displayed
          together with the starting time of the computation.

    pre_dispatch : int, or str, default='2*n_jobs'
        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 str, giving an expression as a function of n_jobs,
              as in '2*n_jobs'

    error_score : 'raise' or numeric, default=np.nan
        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 : bool, default=False
        If ``False``, the ``cv_results_`` attribute will not include training
        scores.
        Computing training scores is used to get insights on how different
        parameter settings impact the overfitting/underfitting trade-off.
        However computing the scores on the training set can be computationally
        expensive and is not strictly required to select the parameters that
        yield the best generalization performance.

        .. versionadded:: 0.19

        .. versionchanged:: 0.21
            Default value was changed from ``True`` to ``False``

    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|param_degree|split0_test_score|...|rank_t...|
        +============+===========+============+=================+===+=========+
        |  'poly'    |     --    |      2     |       0.80      |...|    2    |
        +------------+-----------+------------+-----------------+---+---------+
        |  'poly'    |     --    |      3     |       0.70      |...|    4    |
        +------------+-----------+------------+-----------------+---+---------+
        |  'rbf'     |     0.1   |     --     |       0.80      |...|    3    |
        +------------+-----------+------------+-----------------+---+---------+
        |  'rbf'     |     0.2   |     --     |       0.93      |...|    1    |
        +------------+-----------+------------+-----------------+---+---------+

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

            {
            'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
                                         mask = [False False False False]...)
            'param_gamma': masked_array(data = [-- -- 0.1 0.2],
                                        mask = [ True  True False False]...),
            'param_degree': masked_array(data = [2.0 3.0 -- --],
                                         mask = [False False  True  True]...),
            'split0_test_score'  : [0.80, 0.70, 0.80, 0.93],
            'split1_test_score'  : [0.82, 0.50, 0.70, 0.78],
            'mean_test_score'    : [0.81, 0.60, 0.75, 0.85],
            'std_test_score'     : [0.01, 0.10, 0.05, 0.08],
            'rank_test_score'    : [2, 4, 3, 1],
            'split0_train_score' : [0.80, 0.92, 0.70, 0.93],
            'split1_train_score' : [0.82, 0.55, 0.70, 0.87],
            'mean_train_score'   : [0.81, 0.74, 0.70, 0.90],
            'std_train_score'    : [0.01, 0.19, 0.00, 0.03],
            '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'    : [0.01, 0.06, 0.04, 0.04],
            'std_score_time'     : [0.00, 0.00, 0.00, 0.01],
            'params'             : [{'kernel': 'poly', 'degree': 2}, ...],
            }

        NOTE

        The key ``'params'`` is used to store a list of parameter
        settings dicts for all the parameter candidates.

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

        For multi-metric evaluation, the scores for all the scorers are
        available in the ``cv_results_`` dict at the keys ending with that
        scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
        above. ('split0_test_precision', 'mean_train_precision' etc.)

    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``.

        See ``refit`` parameter for more information on allowed values.

    best_score_ : float
        Mean cross-validated score of the best_estimator

        For multi-metric evaluation, this is present only if ``refit`` is
        specified.

        This attribute is not available if ``refit`` is a function.

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

        For multi-metric evaluation, this is present only if ``refit`` is
        specified.

    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_``).

        For multi-metric evaluation, this is present only if ``refit`` is
        specified.

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

        For multi-metric evaluation, this attribute holds the validated
        ``scoring`` dict which maps the scorer key to the scorer callable.

    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.

        .. versionadded:: 0.20

    multimetric_ : bool
        Whether or not the scorers compute several metrics.

    classes_ : ndarray of shape (n_classes,)
        The classes labels. This is present only if ``refit`` is specified and
        the underlying estimator is a classifier.

    n_features_in_ : int
        Number of features seen during :term:`fit`. Only defined if
        `best_estimator_` is defined (see the documentation for the `refit`
        parameter for more details) and that `best_estimator_` exposes
        `n_features_in_` when fit.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Only defined if
        `best_estimator_` is defined (see the documentation for the `refit`
        parameter for more details) and that `best_estimator_` exposes
        `feature_names_in_` when fit.

        .. versionadded:: 1.0

    See Also
    --------
    ParameterGrid : Generates all the combinations of a hyperparameter grid.
    train_test_split : Utility function to split the data into a development
        set usable for fitting a GridSearchCV instance and an evaluation set
        for its final evaluation.
    sklearn.metrics.make_scorer : Make a scorer from a performance metric or
        loss function.

    Notes
    -----
    The parameters selected are those that maximize the score of the left out
    data, unless an explicit score is passed in which case it is used instead.

    If `n_jobs` was set to a value higher than one, the data is copied for each
    point in the grid (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`.

    Examples
    --------
    >>> from sklearn import svm, datasets
    >>> from sklearn.model_selection import GridSearchCV
    >>> iris = datasets.load_iris()
    >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
    >>> svc = svm.SVC()
    >>> clf = GridSearchCV(svc, parameters)
    >>> clf.fit(iris.data, iris.target)
    GridSearchCV(estimator=SVC(),
                 param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})
    >>> sorted(clf.cv_results_.keys())
    ['mean_fit_time', 'mean_score_time', 'mean_test_score',...
     'param_C', 'param_kernel', 'params',...
     'rank_test_score', 'split0_test_score',...
     'split2_test_score', ...
     'std_fit_time', 'std_score_time', 'std_test_score']
    """

    _required_parameters = ["estimator", "param_grid"]

    _parameter_constraints: dict = {
        **BaseSearchCV._parameter_constraints,
        "param_grid": [dict, list],
    }

    def __init__(
        self,
        estimator,
        param_grid,
        *,
        scoring=None,
        n_jobs=None,
        refit=True,
        cv=None,
        verbose=0,
        pre_dispatch="2*n_jobs",
        error_score=np.nan,
        return_train_score=False,
    ):
        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,
        )
        self.param_grid = param_grid

    def _run_search(self, evaluate_candidates):
        """Search all candidates in param_grid"""
        evaluate_candidates(ParameterGrid(self.param_grid))

class RandomizedSearchCV(BaseSearchCV):
    """Randomized search on hyper parameters.

    RandomizedSearchCV implements a "fit" and a "score" method.
    It also implements "score_samples", "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.

    If all parameters are presented as a list,
    sampling without replacement is performed. If at least one parameter
    is given as a distribution, sampling with replacement is used.
    It is highly recommended to use continuous distributions for continuous
    parameters.

    Read more in the :ref:`User Guide <randomized_parameter_search>`.

    .. versionadded:: 0.14

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

    param_distributions : dict or list of dicts
        Dictionary with parameters names (`str`) as keys and distributions
        or lists of parameters to try. Distributions must provide a ``rvs``
        method for sampling (such as those from scipy.stats.distributions).
        If a list is given, it is sampled uniformly.
        If a list of dicts is given, first a dict is sampled uniformly, and
        then a parameter is sampled using that dict as above.

    n_iter : int, default=10
        Number of parameter settings that are sampled. n_iter trades
        off runtime vs quality of the solution.

    scoring : str, callable, list, tuple or dict, default=None
        Strategy to evaluate the performance of the cross-validated model on
        the test set.

        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.

        See :ref:`multimetric_grid_search` for an example.

        If None, the estimator's score method is used.

    n_jobs : int, default=None
        Number of jobs to run in parallel.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

        .. versionchanged:: v0.20
           `n_jobs` default changed from 1 to None

    refit : bool, str, or callable, default=True
        Refit an estimator using the best found parameters on the whole
        dataset.

        For multiple metric evaluation, this needs to be a `str` denoting the
        scorer that would be used to find the best parameters for refitting
        the estimator at the end.

        Where there are considerations other than maximum score in
        choosing a best estimator, ``refit`` can be set to a function which
        returns the selected ``best_index_`` given the ``cv_results``. In that
        case, the ``best_estimator_`` and ``best_params_`` will be set
        according to the returned ``best_index_`` while the ``best_score_``
        attribute will not be available.

        The refitted estimator is made available at the ``best_estimator_``
        attribute and permits using ``predict`` directly on this
        ``RandomizedSearchCV`` instance.

        Also for multiple metric evaluation, the attributes ``best_index_``,
        ``best_score_`` and ``best_params_`` will only be available if
        ``refit`` is set and all of them will be determined w.r.t this specific
        scorer.

        See ``scoring`` parameter to know more about multiple metric
        evaluation.

        .. versionchanged:: 0.20
            Support for callable added.

    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 in a `(Stratified)KFold`,
        - :term:`CV splitter`,
        - An iterable yielding (train, test) splits as arrays of indices.

        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. These splitters are instantiated
        with `shuffle=False` so the splits will be the same across calls.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

        .. versionchanged:: 0.22
            ``cv`` default value if None changed from 3-fold to 5-fold.

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

        - >1 : the computation time for each fold and parameter candidate is
          displayed;
        - >2 : the score is also displayed;
        - >3 : the fold and candidate parameter indexes are also displayed
          together with the starting time of the computation.

    pre_dispatch : int, or str, default='2*n_jobs'
        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 str, giving an expression as a function of n_jobs,
              as in '2*n_jobs'

    random_state : int, RandomState instance or None, default=None
        Pseudo random number generator state used for random uniform sampling
        from lists of possible values instead of scipy.stats distributions.
        Pass an int for reproducible output across multiple
        function calls.
        See :term:`Glossary <random_state>`.

    error_score : 'raise' or numeric, default=np.nan
        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 : bool, default=False
        If ``False``, the ``cv_results_`` attribute will not include training
        scores.
        Computing training scores is used to get insights on how different
        parameter settings impact the overfitting/underfitting trade-off.
        However computing the scores on the training set can be computationally
        expensive and is not strictly required to select the parameters that
        yield the best generalization performance.

        .. versionadded:: 0.19

        .. versionchanged:: 0.21
            Default value was changed from ``True`` to ``False``

    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     |       0.80        |...|       1       |
        +--------------+-------------+-------------------+---+---------------+
        |    'rbf'     |     0.2     |       0.84        |...|       3       |
        +--------------+-------------+-------------------+---+---------------+
        |    'rbf'     |     0.3     |       0.70        |...|       2       |
        +--------------+-------------+-------------------+---+---------------+

        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'  : [0.80, 0.84, 0.70],
            'split1_test_score'  : [0.82, 0.50, 0.70],
            'mean_test_score'    : [0.81, 0.67, 0.70],
            'std_test_score'     : [0.01, 0.24, 0.00],
            'rank_test_score'    : [1, 3, 2],
            'split0_train_score' : [0.80, 0.92, 0.70],
            'split1_train_score' : [0.82, 0.55, 0.70],
            'mean_train_score'   : [0.81, 0.74, 0.70],
            'std_train_score'    : [0.01, 0.19, 0.00],
            'mean_fit_time'      : [0.73, 0.63, 0.43],
            'std_fit_time'       : [0.01, 0.02, 0.01],
            'mean_score_time'    : [0.01, 0.06, 0.04],
            'std_score_time'     : [0.00, 0.00, 0.00],
            'params'             : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
            }

        NOTE

        The key ``'params'`` is used to store a list of parameter
        settings dicts for all the parameter candidates.

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

        For multi-metric evaluation, the scores for all the scorers are
        available in the ``cv_results_`` dict at the keys ending with that
        scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
        above. ('split0_test_precision', 'mean_train_precision' etc.)

    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``.

        For multi-metric evaluation, this attribute is present only if
        ``refit`` is specified.

        See ``refit`` parameter for more information on allowed values.

    best_score_ : float
        Mean cross-validated score of the best_estimator.

        For multi-metric evaluation, this is not available if ``refit`` is
        ``False``. See ``refit`` parameter for more information.

        This attribute is not available if ``refit`` is a function.

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

        For multi-metric evaluation, this is not available if ``refit`` is
        ``False``. See ``refit`` parameter for more information.

    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_``).

        For multi-metric evaluation, this is not available if ``refit`` is
        ``False``. See ``refit`` parameter for more information.

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

        For multi-metric evaluation, this attribute holds the validated
        ``scoring`` dict which maps the scorer key to the scorer callable.

    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.

        .. versionadded:: 0.20

    multimetric_ : bool
        Whether or not the scorers compute several metrics.

    classes_ : ndarray of shape (n_classes,)
        The classes labels. This is present only if ``refit`` is specified and
        the underlying estimator is a classifier.

    n_features_in_ : int
        Number of features seen during :term:`fit`. Only defined if
        `best_estimator_` is defined (see the documentation for the `refit`
        parameter for more details) and that `best_estimator_` exposes
        `n_features_in_` when fit.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Only defined if
        `best_estimator_` is defined (see the documentation for the `refit`
        parameter for more details) and that `best_estimator_` exposes
        `feature_names_in_` when fit.

        .. versionadded:: 1.0

    See Also
    --------
    GridSearchCV : Does exhaustive search over a grid of parameters.
    ParameterSampler : A generator over parameter settings, constructed from
        param_distributions.

    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`.

    Examples
    --------
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.linear_model import LogisticRegression
    >>> from sklearn.model_selection import RandomizedSearchCV
    >>> from scipy.stats import uniform
    >>> iris = load_iris()
    >>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200,
    ...                               random_state=0)
    >>> distributions = dict(C=uniform(loc=0, scale=4),
    ...                      penalty=['l2', 'l1'])
    >>> clf = RandomizedSearchCV(logistic, distributions, random_state=0)
    >>> search = clf.fit(iris.data, iris.target)
    >>> search.best_params_
    {'C': 2..., 'penalty': 'l1'}
    """

    _required_parameters = ["estimator", "param_distributions"]

    _parameter_constraints: dict = {
        **BaseSearchCV._parameter_constraints,
        "param_distributions": [dict, list],
        "n_iter": [Interval(numbers.Integral, 1, None, closed="left")],
        "random_state": ["random_state"],
    }

    def __init__(
        self,
        estimator,
        param_distributions,
        *,
        n_iter=10,
        scoring=None,
        n_jobs=None,
        refit=True,
        cv=None,
        verbose=0,
        pre_dispatch="2*n_jobs",
        random_state=None,
        error_score=np.nan,
        return_train_score=False,
    ):
        self.param_distributions = param_distributions
        self.n_iter = n_iter
        self.random_state = random_state
        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 _run_search(self, evaluate_candidates):
        """Search n_iter candidates from param_distributions"""
        evaluate_candidates(
            ParameterSampler(
                self.param_distributions, self.n_iter, random_state=self.random_state
            )
        )


import logging
import sys
import re

class Color:

    @staticmethod
    def hex_valid(hex_code):

        # Remove prefix # for simpler code
        hex_code = hex_code.lstrip('#')
        hex_s_re = r'^([A-Fa-f0-9]{3})[A-Fa-f0-9]?$'
        hex_l_re = r'^([A-Fa-f0-9]{6})([A-Fa-f0-9]{2})?$'

        if re.match(hex_s_re, hex_code):
            r = hex_code[0]
            g = hex_code[1]
            b = hex_code[2]
            if len(hex_code) == 4:
                a = hex_code[3]
            else:
                a = "f"
            hex_code = f"{r}{r}{g}{g}{b}{b}{a}{a}"

        elif not re.match(hex_l_re, hex_code):
            raise ValueError("Invalid HEX color code! "
                             "Proper format is #xxx[xxx] or XXX[XXX].")
            return None

        if len(hex_code) != 8:
            hex_code = f"{hex_code}ff"

        return hex_code

    @staticmethod
    def hex_rgba(hex_code):
        # logging.debug(f"hex_code: {hex_code}")
        try:
            hex = Color.hex_valid(hex_code)
        except ValueError as e:
            logging.error(f"ValueError: {e}")
            sys.exit(1)
        # logging.debug(f"hex_code: {hex_code}")
        # logging.debug(f"hex: {hex}")
        r = int(hex[0:2], 16)
        g = int(hex[2:4], 16)
        b = int(hex[4:6], 16)
        a = int(hex[6:8], 16)
        return (r, g, b, a)

    @staticmethod
    def rgba_hex(rgba):
        r, g, b, a = rgba
        return f"{r:02x}{g:02x}{b:02x}{a:02x}"

    @staticmethod
    def rgb_rgba(rgb):
        rgb_l = len(rgb)
        if rgb_l == 3:
            r = rgb[0]
            g = rgb[1]
            b = rgb[2]
            return (r, g, b, 255)
        elif rgb_l == 4:
            return rgb
        else:
            logging.error("rgb_rgba: invalid rgb value of neither 3 or 4 length")
            sys.exit(1)


"""Optimisation Routines for Variational Problems"""

import casadi
import numpy as np

from collections import namedtuple
from operator import mul
from functools import reduce

from sysopt.backends.casadi.expression_graph import substitute
from sysopt.problems.problem_data import MinimumPathProblem, CollocationSolverOptions
from sysopt.backends.casadi.path import InterpolatedPath

Problem = namedtuple(
    'Problem',
    ['state', 'control', 'parameters', 'running_cost', 'terminal_cost',
     'vector_field', 'initial_state', 'constraints']
)

default_options = CollocationSolverOptions()

def get_collocation_matrices(degree):
    collocation_times = np.append(
        0, casadi.collocation_points(degree, 'legendre')
    )
    collocation_coeeff = np.zeros((degree + 1, degree + 1))
    continuity_coeff = np.zeros(degree + 1)
    quad_coeff = np.zeros_like(continuity_coeff)

    for i in range(degree + 1):
        tau_i = collocation_times[i]
        factors = [
            np.poly1d([1, -tau_j]) / (tau_i - tau_j)
            for tau_j in collocation_times if tau_i != tau_j
        ]
        basis_i = reduce(mul, factors)
        dbasis_i = np.polyder(basis_i)
        continuity_coeff[i] = basis_i(1)
        collocation_coeeff[i, :] = [
            dbasis_i(tau_j) for tau_j in collocation_times
        ]
        quad_coeff[i] = np.polyint(basis_i)(1.0)

    return collocation_times, collocation_coeeff, continuity_coeff, quad_coeff

def _get_solver(t_final: float,
                problem: Problem,
                options: CollocationSolverOptions):

    # Direct Collocation method
    times, colloc_coeff, diff_coeff, quad_coeff = get_collocation_matrices(
        options.polynomial_degree
    )

    params = [casadi.MX.sym(str(p), p.shape) for p in problem.parameters]
    f = casadi.Function(
        'f',
        [problem.state[0], problem.control[0]] + problem.parameters,
        [problem.vector_field, problem.running_cost]
    )

    g = casadi.Function(
        'g',
        [problem.state[0], problem.control[0]] + problem.parameters,
        [casadi.vertcat(*problem.constraints)]
    )

    phi = casadi.Function(
        'phi',
        [problem.state[0], problem.control[0]] + problem.parameters,
        [problem.terminal_cost]
    )

    g_lower = [0]*len(problem.constraints)
    g_upper = [np.inf]*len(problem.constraints)
    steps = int(options.grid_size - 1)
    dt = 1 / steps

    decision_vars = []      # tuple (lb < x < ub)
    constraints = []    # tuple (lb < g(x,u) < ub)

    cost = 0

    dim_x, _ = problem.state[0].shape
    dim_u, _ = problem.control[0].shape

    x_lower, x_upper = problem.state[1]
    u_lower, u_upper = problem.control[1]

    du_upper = [np.inf] * dim_u
    du_lower = [-np.inf] * dim_u

    u_0 = [0] * dim_u

    x = casadi.MX.sym('X_0', dim_x)
    u = casadi.MX.sym('U_0', dim_u)
    x_out = [casadi.vertcat(x, u)]
    x0 = casadi.Function('x0', problem.parameters, [problem.initial_state])
    x_0 = x0(*params)
    decision_vars.append((x_0, x, x_0))
    decision_vars.append((u_lower, u, u_upper))
    decision_vars_guess = [x_0, u_0]

    constraints.append((g_lower, g(x, u, *params), g_upper))

    for k in range(steps):
        collocation_points = []
        du = casadi.MX.sym(f'dU_{k}', dim_u)
        decision_vars_guess.append([0] * dim_u)
        decision_vars.append((du_lower, du, du_upper))
        for j in range(options.polynomial_degree):
            x_jk = casadi.MX.sym(f'X_{j},{k}', dim_x)
            decision_vars.append((x_lower, x_jk, x_upper))
            decision_vars_guess.append(x_0)
            collocation_points.append(x_jk)

        x_next = diff_coeff[0] * x
        for j in range(1, options.polynomial_degree + 1):
            # Todo: Check if this indexing is right as C[:, 0] is never used
            dx = colloc_coeff[0, j] * x
            dx += sum(
                c_ij * x_ij
                for c_ij, x_ij in zip(colloc_coeff[1:, j], collocation_points)
            )
            u_k = u + du * times[j - 1] * dt
            f_inter, q_inter = f(collocation_points[j - 1], u_k, *params)
            constraints.append(([0] * dim_x, dt * f_inter - dx, [0] * dim_x))

            x_next = x_next + diff_coeff[j] * collocation_points[j - 1]
            cost += quad_coeff[j] * q_inter * dt

        u_next = u + dt * du

        x = casadi.MX.sym(f'X_{k + 1}', dim_x)
        u = casadi.MX.sym(f'U_{k + 1}', dim_u)
        decision_vars.append((x_lower, x, x_upper))
        decision_vars.append((u_lower, u, u_upper))
        decision_vars_guess.append(x_0)
        decision_vars_guess.append(u_0)
        constraints.append(
            ([0] * dim_x, x - x_next, [0] * dim_x)
        )
        constraints.append((
            [0]*dim_u, u - u_next, [0] * dim_u
        ))

        x_out.append(casadi.vertcat(x, u))
        constraints.append((g_lower, g(x, u, *params), g_upper))

    cost += phi(x, u, *params)

    x_min, x_array, x_max = zip(*decision_vars)
    c_min, c_array, c_max = zip(*constraints)

    x_min = casadi.vertcat(*x_min)
    x_max = casadi.vertcat(*x_max)
    c_min = casadi.vertcat(*c_min)
    c_max = casadi.vertcat(*c_max)

    x_array = casadi.vertcat(*x_array)
    c_array = casadi.vertcat(*c_array)
    x_path = casadi.horzcat(*x_out)

    nlp_spec = {
        'f': cost,
        'x': x_array,
        'g': c_array,
        'p': casadi.vertcat(*params)
    }
    nlp_options = {}
    npl_solver = casadi.nlpsol('problems', 'ipopt', nlp_spec, nlp_options)
    x_initial = casadi.vertcat(*decision_vars_guess)
    param_to_args = casadi.Function(
        'nlp_args',
        params,
        [x_initial, casadi.vertcat(*params),  x_min, x_max, c_min, c_max,
         casadi.MX.zeros(x_initial.shape), casadi.MX.zeros(c_max.shape)],
        [str(p) for p in params],
        ['x0', 'p', 'lbx', 'ubx', 'lbg', 'ubg', 'lam_x0', 'lam_g']
    )
    t_path = casadi.MX([i * dt for i in range(steps + 1)])

    soln_to_path = casadi.Function(
        'trajectories',
        [x_array], [t_path, x_path],
        ['X'], ['t', 'x']
    )
    return param_to_args, npl_solver, soln_to_path

class VariationalIntegrator:
    """Function wrapper for an optimisation problem sovler."""
    def __init__(self, t_final, problem, options):
        prob = casadify_problem(problem)
        self._param_map, self._solver, self._post_processing = _get_solver(
            t_final, prob, options
        )

    def __call__(self,  parameters):

        solver_args = self._param_map(parameters)
        solution, *_ = self._solver(*solver_args)
        t, x = self._post_processing(solution)

        return InterpolatedPath('x', t.T, x)

def get_variational_solver(problem: MinimumPathProblem,
                               options=default_options):

    # choose initial number of co-location points per control update

    def func(t_final):
        return VariationalIntegrator(t_final, problem, options)
    return func

def casadify_problem(problem: MinimumPathProblem):

    if isinstance(problem.parameters, list):
        parameters = problem.parameters
    else:
        parameters = [problem.parameters]
    all_symbols = set([problem.state[0], problem.control[0]] + parameters)
    substitutions = {
        v: casadi.MX.sym(f'{v.name}', len(v))
        for v in all_symbols
    }
    non_negative_constraints = [
        substitute(c.to_graph(), substitutions)
        for c in problem.constraints
    ] if problem.constraints else []

    try:
        x_bounds = problem.state[1]
    except IndexError:
        x_bounds = (
            [-np.inf] * len(problem.state[0]),
            [np.inf] * len(problem.state[0])
        )

    try:
        u_bounds = problem.control[1]
    except IndexError:
        u_bounds = (
            [-np.inf] * len(problem.control[0]),
            [np.inf] * len(problem.control[0])
        )

    return Problem(
        state=(substitutions[problem.state[0]], x_bounds),
        control=(substitutions[problem.control[0]], u_bounds),
        parameters=[substitutions[p] for p in parameters],
        initial_state=substitute(problem.initial_state, substitutions),
        vector_field=substitute(problem.vector_field, substitutions),
        constraints=non_negative_constraints,
        running_cost=substitute(problem.running_cost, substitutions),
        terminal_cost=substitute(problem.terminal_cost, substitutions),
    )


import json
from datetime import datetime

import boto3
import cfn_flip
from awacs.aws import Statement, AWSPrincipal, Policy, Allow
from awacs.kms import Action as KmsAction
from botocore.exceptions import ClientError
from troposphere import GetAtt, Output, Name, Ref, Template, \
    Sub, Export
from troposphere.kms import Key, Alias
from troposphere.sqs import Queue, RedrivePolicy

USER = 'arn:aws:iam::${AWS::AccountId}:user'

All = '*'
AllResources = ['*']

# noinspection PyPep8Naming
def generate_queues_template(QueueNamePrefix, Environment):
    QueueName = f'{QueueNamePrefix}-{Environment}'
    DLQQueueName = f'{QueueNamePrefix}DLQ-{Environment}'

    t = Template(
        Description='A template for a messaging queue')
    t.version = '2010-09-09'

    KMSKey = t.add_resource(
        Key(
            'KMSKey',
            Description=f'KMS Key for encrypting {QueueName}',
            Enabled=True,
            EnableKeyRotation=True,
            KeyPolicy=Policy(
                Version='2012-10-17',
                Statement=[
                    Statement(
                        Sid='Enable IAM User Permissions',
                        Effect=Allow,
                        Principal=AWSPrincipal(
                            Sub('arn:aws:iam::${AWS::AccountId}:root')),
                        Action=[KmsAction(All)],
                        Resource=AllResources
                    ),
                    Statement(
                        Sid='Allow access for Key Administrators',
                        Effect=Allow,
                        Principal=AWSPrincipal(
                            [
                                Sub(f'{USER}/frank'),
                                Sub(f'{USER}/moonunit')
                            ]
                        ),
                        Action=[
                            KmsAction('Create*'),
                            KmsAction('Describe*'),
                            KmsAction('Enable*'),
                            KmsAction('List*'),
                            KmsAction('Put*'),
                            KmsAction('Update*'),
                            KmsAction('Revoke*'),
                            KmsAction('Disable*'),
                            KmsAction('Get*'),
                            KmsAction('Delete*'),
                            KmsAction('ScheduleKeyDeletion'),
                            KmsAction('CancelKeyDeletion')
                        ],
                        Resource=AllResources
                    )
                ]
            )
        )
    )

    t.add_resource(
        Alias(
            'KMSKeyAlias',
            AliasName=f'alias/{QueueName}',
            TargetKeyId=Ref(KMSKey)
        )
    )

    dlq = t.add_resource(
        Queue(
            'DeadLetterQueue',
            QueueName=DLQQueueName,
            MaximumMessageSize=262144,  # 256KiB
            MessageRetentionPeriod=1209600,  # 14 days
            VisibilityTimeout=30
        )
    )

    t.add_resource(
        Queue(
            'PrimaryQueue',
            QueueName=QueueName,
            MaximumMessageSize=262144,  # 256KiB
            MessageRetentionPeriod=1209600,  # 14 days
            VisibilityTimeout=30,
            RedrivePolicy=RedrivePolicy(
                deadLetterTargetArn=GetAtt(
                    dlq.title,
                    'Arn'),
                maxReceiveCount=10
            ),
            KmsMasterKeyId=Ref(KMSKey),
            KmsDataKeyReusePeriodSeconds=300
        )
    )

    t.add_output([
        Output(
            'QueueArn',
            Description=f'ARN of {QueueName} Queue',
            Value=GetAtt('PrimaryQueue', 'Arn'),
            Export=Export(Name(Sub('${AWS::StackName}:PrimaryQueueArn')))
        ),
        Output(
            'KmsKeyArn',
            Description=f'KMS Key ARN for {QueueName} Queue',
            Value=GetAtt('KMSKey', 'Arn'),
            Export=Export(Name(Sub('${AWS::StackName}:KmsKeyArn')))
        )
    ])

    return t

def stack_exists(client, stack_name):
    stacks = client.list_stacks()['StackSummaries']
    for stack in stacks:
        if stack['StackStatus'] == 'DELETE_COMPLETE':
            continue
        if stack_name == stack['StackName']:
            return True
    return False

def json_serial(obj):
    """JSON serializer for objects not serializable by default json code"""
    if isinstance(obj, datetime):
        serial = obj.isoformat()
        return serial
    raise TypeError("Type not serializable")

def create_or_update_queues(*, region, resource_suffix, queue_name_prefix):
    template_yaml = cfn_flip.to_yaml(
        generate_queues_template(queue_name_prefix, resource_suffix).to_json()
    )

    client = boto3.client('cloudformation', region_name=region)
    stack_name = f'dweezil-queues-{resource_suffix}'
    params = {
        'StackName': stack_name,
        'TemplateBody': template_yaml
    }

    try:
        if stack_exists(client, stack_name):
            print(f'Updating stack {stack_name}...', end='', flush=True)
            stack_result = client.update_stack(**params)
            waiter = client.get_waiter('stack_update_complete')
        else:
            print(f'Requesting "{stack_name}" stack creation... ',
                  end='', flush=True)
            stack_result = client.create_stack(**params)
            waiter = client.get_waiter('stack_create_complete')
        print(f'waiting for complete... ', end='', flush=True)
        waiter.config.delay = 15
        waiter.config.max_attempts = 20
        waiter.wait(StackName=stack_name)
    except ClientError as ex:
        error_message = ex.response['Error']['Message']
        if error_message == 'No updates are to be performed.':
            print(' no changes.')
        else:
            raise
    else:
        print(' finished.')
        print('Stack output:')
        print(json.dumps(
            client.describe_stacks(StackName=stack_result['StackId']),
            indent=2,
            default=json_serial
        ))


from ib_tws_server.ib_imports import *
import inspect
from typing import Type

def is_builtin_type(t: Type):
    return t in [ str, int, float, bool ]

def is_builtin_type_str(t: str):
    return t in [ 'str', 'int', 'float', 'bool' ]

def is_builtin_container_type(t: Type):
    return t in [ dict, set, list ]

def is_custom_type(t: Type):
    return not is_builtin_type(t) and not is_builtin_container_type(t)

def find_sym_from_full_name(s: str):
    syms = s.split(".")
    cur = globals()
    if len(syms) > 0:
        for s in syms:
            if isinstance(cur, dict):
                if s in cur:
                    cur = cur[s]
                else:
                    return None
            elif inspect.ismodule(cur):
                if hasattr(cur,s):
                    cur = getattr(cur,s)
                else:
                    return None
        return cur
    return None

def find_sym_in_module(s: str, mod: any):
    for m in dir(mod):
        m = getattr(mod, m)
        if inspect.ismodule(m):
            if hasattr(m,s):
                return getattr(m,s)
    return None

def find_sym_from_full_name_or_module(s: str, mod: any):
    sym = find_sym_from_full_name(s)
    if sym is None:
        sym = find_sym_in_module(s, ibapi)
    return sym

# From https://stackoverflow.com/questions/2020014/get-fully-qualified-class-name-of-an-object-in-python
def full_class_name(o) -> str:
    if not inspect.isclass(o):
        klass = o.__class__
    else:
        klass = o
    module = klass.__module__
    if module == 'builtins':
        return klass.__qualname__ # avoid outputs like 'builtins.str'
    return module + '.' + klass.__qualname__

def full_type_name_for_annotation(annotation: str, module: any):
    ret = find_sym_in_module(annotation, module)
    if ret is not None:
        return full_class_name(ret)
    return annotation


#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar  7 12:00:12 2019

@author: vladgriguta
"""

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 28 13:59:10 2019

@author: vladgriguta
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime

import os
import itertools

#ML libraries
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score

import dataPrep
import gc
gc.collect()

def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',
                          cmap=plt.cm.Blues,directory=''):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Showing normalized confusion matrix")
    else:
        print('Showing confusion matrix, without normalization')
    #print(cm)
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),fontsize = 6,
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.savefig(directory+title+'.png')
    plt.gcf().clear()

def NeuralNet(trim_columns,input_table, n_jobs=-1):

    # Get data from database
    x, dummy_y,encoder,class_weights,_ = dataPrep.getDataNN(input_table, trim_columns)

    # Split data 60:20:20
    x_train, x_test, dummy_y_train, dummy_y_test = train_test_split(x, dummy_y, test_size=0.2, random_state=0)
    x_train, x_val, dummy_y_train, dummy_y_val = train_test_split(x_train, dummy_y_train, test_size=0.2, random_state=0)

    input_dim = len(x[0])
    output_dim = len(dummy_y[0])
    # Importing the Keras libraries and packages
    from keras.models import Sequential
    from keras.utils import Sequence
    from keras.layers import Dense
    from keras.layers import Dropout

    class DataSequenceGenerator(Sequence):

        def __init__(self, x_train, y_train, batch_size):
            self.x, self.y = x_train, y_train
            self.batch_size = batch_size

        def __len__(self):
            return int(np.ceil(len(self.x) / float(self.batch_size)))

        def __getitem__(self, idx):
            batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]
            batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]

            return np.array(batch_x), np.array(batch_y)

    params = {'batch_size': 128}
    training_generator = DataSequenceGenerator(x_train, dummy_y_train, **params)
    steps_train = len(x_train)/float(params['batch_size'])
    validation_generator = DataSequenceGenerator(x_val, dummy_y_val, **params)
    steps_val = len(x_val)/float(params['batch_size'])

    # define baseline model
    def baseline_model():
       	# create model
        model = Sequential()
        model.add(Dense(units = 5, kernel_initializer = 'uniform', activation = 'sigmoid', input_dim=input_dim))
        #model.add(Dropout(0.2))
        model.add(Dense(units=output_dim, activation='softmax'))
    	# Compile model
        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

        return model

    classifier = baseline_model()

    """
    classifier.fit_generator(generator=training_generator,steps_per_epoch=steps_train,
                             validation_data=validation_generator,validation_steps=steps_val,
                             epochs=5,use_multiprocessing=True,workers=n_jobs)
    """
    # train the model
    classifier.fit_generator(generator=training_generator,epochs=5,use_multiprocessing=True,workers=n_jobs)
    # First round of evaluation
    evaluation = classifier.evaluate(x=x_test, y=dummy_y_test, batch_size=params['batch_size'])
    print('The accuracy on test data is: a= ' + str(evaluation[1]))

    """
    # Compute the F1 Score
    f1 = f1_score(encoded_y_test,prediction_classes,average='macro')
    # Compute and plot the confusion matrix
    #cnf_matrix = confusion_matrix(encoded_y_test, prediction_classes,labels=classes_y_test)
    acc = accuracy_score(encoded_y_test,prediction_classes)
    print('The f1 score of NN is:   '+str(f1))
    print('The accuracy of NN is:   '+str(acc))
    """

    # Prepare the data required for confusion matrix plot
    dummy_y_pred = classifier.predict(x_test)
    classes_y_pred,encoded_y_pred = dataPrep.decode(dummy_y_pred,encoder)
    classes_y_test,encoded_y_test = dataPrep.decode(dummy_y_test,encoder)

    return classes_y_test, classes_y_pred

if __name__ == "__main__":

    input_table = '../moreData/test_query_table_1M'
    trim_columns=['#ra', 'dec', 'z', 'peak','integr','rms','subclass','class']

    classes_y_test, classes_y_pred = NeuralNet(trim_columns,input_table,n_jobs=3)

    labels = ['GALAXY','QSO','STAR']

    cnf_matrix = confusion_matrix(classes_y_test, classes_y_pred,labels=labels)

    directory = 'ANN_classification_7Mar/'
    if not os.path.exists(directory):
        os.makedirs(directory)

    plot_confusion_matrix(cm=cnf_matrix,classes=labels,directory=directory,title='ANN_Confusion_Matrix')