"""Class to perform under-sampling using easy ensemble."""

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
#          Christos Aridas
# License: MIT

import copy
import numbers
import warnings

import numpy as np
import sklearn
from sklearn.base import clone
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
from sklearn.ensemble._bagging import _parallel_decision_function
from sklearn.ensemble._base import _partition_estimators
from sklearn.exceptions import NotFittedError
from sklearn.utils import parse_version
from sklearn.utils._tags import _safe_tags
from sklearn.utils.validation import check_is_fitted

try:
    # scikit-learn >= 1.2
    from sklearn.utils.parallel import Parallel, delayed
except (ImportError, ModuleNotFoundError):
    from joblib import Parallel
    from sklearn.utils.fixes import delayed

from ..base import _ParamsValidationMixin
from ..pipeline import Pipeline
from ..under_sampling import RandomUnderSampler
from ..under_sampling.base import BaseUnderSampler
from ..utils import Substitution, check_sampling_strategy, check_target_type
from ..utils._available_if import available_if
from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
from ..utils._param_validation import Interval, StrOptions
from ..utils.fixes import _fit_context
from ._common import _bagging_parameter_constraints, _estimator_has

MAX_INT = np.iinfo(np.int32).max
sklearn_version = parse_version(sklearn.__version__)


@Substitution(
    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class EasyEnsembleClassifier(_ParamsValidationMixin, BaggingClassifier):
    """Bag of balanced boosted learners also known as EasyEnsemble.

    This algorithm is known as EasyEnsemble [1]_. The classifier is an
    ensemble of AdaBoost learners trained on different balanced bootstrap
    samples. The balancing is achieved by random under-sampling.

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

    .. versionadded:: 0.4

    Parameters
    ----------
    n_estimators : int, default=10
        Number of AdaBoost learners in the ensemble.

    estimator : estimator object, default=AdaBoostClassifier()
        The base AdaBoost classifier used in the inner ensemble. Note that you
        can set the number of inner learner by passing your own instance.

        .. versionadded:: 0.10

    warm_start : bool, default=False
        When set to True, reuse the solution of the previous call to fit
        and add more estimators to the ensemble, otherwise, just fit
        a whole new ensemble.

    {sampling_strategy}

    replacement : bool, default=False
        Whether or not to sample randomly with replacement or not.

    {n_jobs}

    {random_state}

    verbose : int, default=0
        Controls the verbosity of the building process.

    Attributes
    ----------
    estimator_ : estimator
        The base estimator from which the ensemble is grown.

        .. versionadded:: 0.10

    estimators_ : list of estimators
        The collection of fitted base estimators.

    estimators_samples_ : list of arrays
        The subset of drawn samples for each base estimator.

    estimators_features_ : list of arrays
        The subset of drawn features for each base estimator.

    classes_ : array, shape (n_classes,)
        The classes labels.

    n_classes_ : int or list
        The number of classes.

    n_features_ : int
        The number of features when `fit` is performed.

        .. deprecated:: 1.0
           `n_features_` is deprecated in `scikit-learn` 1.0 and will be removed
           in version 1.2. When the minimum version of `scikit-learn` supported
           by `imbalanced-learn` will reach 1.2, this attribute will be removed.

    n_features_in_ : int
        Number of features in the input dataset.

        .. versionadded:: 0.9

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during `fit`. Defined only when `X` has feature
        names that are all strings.

        .. versionadded:: 0.9

    See Also
    --------
    BalancedBaggingClassifier : Bagging classifier for which each base
        estimator is trained on a balanced bootstrap.

    BalancedRandomForestClassifier : Random forest applying random-under
        sampling to balance the different bootstraps.

    RUSBoostClassifier : AdaBoost classifier were each bootstrap is balanced
        using random-under sampling at each round of boosting.

    Notes
    -----
    The method is described in [1]_.

    Supports multi-class resampling by sampling each class independently.

    References
    ----------
    .. [1] X. Y. Liu, J. Wu and Z. H. Zhou, "Exploratory Undersampling for
       Class-Imbalance Learning," in IEEE Transactions on Systems, Man, and
       Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539-550,
       April 2009.

    Examples
    --------
    >>> from collections import Counter
    >>> from sklearn.datasets import make_classification
    >>> from sklearn.model_selection import train_test_split
    >>> from sklearn.metrics import confusion_matrix
    >>> from imblearn.ensemble import EasyEnsembleClassifier
    >>> X, y = make_classification(n_classes=2, class_sep=2,
    ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
    ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
    >>> print('Original dataset shape %s' % Counter(y))
    Original dataset shape Counter({{1: 900, 0: 100}})
    >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
    ...                                                     random_state=0)
    >>> eec = EasyEnsembleClassifier(random_state=42)
    >>> eec.fit(X_train, y_train)
    EasyEnsembleClassifier(...)
    >>> y_pred = eec.predict(X_test)
    >>> print(confusion_matrix(y_test, y_pred))
    [[ 23   0]
     [  2 225]]
    """

    # make a deepcopy to not modify the original dictionary
    if sklearn_version >= parse_version("1.4"):
        _parameter_constraints = copy.deepcopy(BaggingClassifier._parameter_constraints)
    else:
        _parameter_constraints = copy.deepcopy(_bagging_parameter_constraints)

    excluded_params = {
        "bootstrap",
        "bootstrap_features",
        "max_features",
        "oob_score",
        "max_samples",
    }
    for param in excluded_params:
        _parameter_constraints.pop(param, None)

    _parameter_constraints.update(
        {
            "sampling_strategy": [
                Interval(numbers.Real, 0, 1, closed="right"),
                StrOptions({"auto", "majority", "not minority", "not majority", "all"}),
                dict,
                callable,
            ],
            "replacement": ["boolean"],
        }
    )
    # TODO: remove when minimum supported version of scikit-learn is 1.4
    if "base_estimator" in _parameter_constraints:
        del _parameter_constraints["base_estimator"]

    def __init__(
        self,
        n_estimators=10,
        estimator=None,
        *,
        warm_start=False,
        sampling_strategy="auto",
        replacement=False,
        n_jobs=None,
        random_state=None,
        verbose=0,
    ):
        super().__init__(
            n_estimators=n_estimators,
            max_samples=1.0,
            max_features=1.0,
            bootstrap=False,
            bootstrap_features=False,
            oob_score=False,
            warm_start=warm_start,
            n_jobs=n_jobs,
            random_state=random_state,
            verbose=verbose,
        )
        self.estimator = estimator
        self.sampling_strategy = sampling_strategy
        self.replacement = replacement

    def _validate_y(self, y):
        y_encoded = super()._validate_y(y)
        if isinstance(self.sampling_strategy, dict):
            self._sampling_strategy = {
                np.where(self.classes_ == key)[0][0]: value
                for key, value in check_sampling_strategy(
                    self.sampling_strategy,
                    y,
                    "under-sampling",
                ).items()
            }
        else:
            self._sampling_strategy = self.sampling_strategy
        return y_encoded

    def _validate_estimator(self, default=AdaBoostClassifier(algorithm="SAMME")):
        """Check the estimator and the n_estimator attribute, set the
        `estimator_` attribute."""
        if self.estimator is not None:
            estimator = clone(self.estimator)
        else:
            estimator = clone(default)

        sampler = RandomUnderSampler(
            sampling_strategy=self._sampling_strategy,
            replacement=self.replacement,
        )
        self.estimator_ = Pipeline([("sampler", sampler), ("classifier", estimator)])

    # TODO: remove when supporting scikit-learn>=1.2
    @property
    def n_features_(self):
        """Number of features when ``fit`` is performed."""
        warnings.warn(
            "`n_features_` was deprecated in scikit-learn 1.0. This attribute will "
            "not be accessible when the minimum supported version of scikit-learn "
            "is 1.2.",
            FutureWarning,
        )
        return self.n_features_in_

    @_fit_context(prefer_skip_nested_validation=False)
    def fit(self, X, y):
        """Build a Bagging ensemble of estimators from the training set (X, y).

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples. Sparse matrices are accepted only if
            they are supported by the base estimator.

        y : array-like of shape (n_samples,)
            The target values (class labels in classification, real numbers in
            regression).

        Returns
        -------
        self : object
            Fitted estimator.
        """
        self._validate_params()
        # overwrite the base class method by disallowing `sample_weight`
        return super().fit(X, y)

    def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
        check_target_type(y)
        # RandomUnderSampler is not supporting sample_weight. We need to pass
        # None.
        return super()._fit(X, y, self.max_samples, sample_weight=None)

    # TODO: remove when minimum supported version of scikit-learn is 1.1
    @available_if(_estimator_has("decision_function"))
    def decision_function(self, X):
        """Average of the decision functions of the base classifiers.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples. Sparse matrices are accepted only if
            they are supported by the base estimator.

        Returns
        -------
        score : ndarray of shape (n_samples, k)
            The decision function of the input samples. The columns correspond
            to the classes in sorted order, as they appear in the attribute
            ``classes_``. Regression and binary classification are special
            cases with ``k == 1``, otherwise ``k==n_classes``.
        """
        check_is_fitted(self)

        # Check data
        X = self._validate_data(
            X,
            accept_sparse=["csr", "csc"],
            dtype=None,
            force_all_finite=False,
            reset=False,
        )

        # Parallel loop
        n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs)

        all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
            delayed(_parallel_decision_function)(
                self.estimators_[starts[i] : starts[i + 1]],
                self.estimators_features_[starts[i] : starts[i + 1]],
                X,
            )
            for i in range(n_jobs)
        )

        # Reduce
        decisions = sum(all_decisions) / self.n_estimators

        return decisions

    @property
    def base_estimator_(self):
        """Attribute for older sklearn version compatibility."""
        error = AttributeError(
            f"{self.__class__.__name__} object has no attribute 'base_estimator_'."
        )
        if sklearn_version < parse_version("1.2"):
            # The base class require to have the attribute defined. For scikit-learn
            # > 1.2, we are going to raise an error.
            try:
                check_is_fitted(self)
                return self.estimator_
            except NotFittedError:
                raise error
        raise error

    def _more_tags(self):
        if self.estimator is None:
            estimator = AdaBoostClassifier(algorithm="SAMME")
        else:
            estimator = self.estimator
        return {"allow_nan": _safe_tags(estimator, "allow_nan")}
