"""Class to perform under-sampling based on the instance hardness
threshold."""
# Adapted from imbalanced-learn

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
#          Dayvid Oliveira
#          Christos Aridas
#          Zhining Liu <zhining.liu@outlook.com>
# License: MIT

# %%

from collections import Counter

import numpy as np

from sklearn.base import ClassifierMixin, clone
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble._base import _set_random_states
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_predict
from sklearn.utils import check_random_state
from sklearn.utils import _safe_indexing


from ..base import BaseUnderSampler
from ....utils._docstring import _n_jobs_docstring, Substitution
from ....utils._docstring import _random_state_docstring
from ....utils._validation import _deprecate_positional_args

# # For local test
# import sys
# sys.path.append("../../..")
# from sampler.under_sampling.base import BaseUnderSampler
# from utils._docstring import _n_jobs_docstring, Substitution
# from utils._docstring import _random_state_docstring
# from utils._validation import _deprecate_positional_args


@Substitution(
    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class InstanceHardnessThreshold(BaseUnderSampler):
    """Undersample based on the instance hardness threshold.

    Read more in the `User Guide <https://imbalanced-learn.org/stable/under_sampling.html#instance-hardness-threshold>`_.

    Parameters
    ----------
    estimator : estimator object, default=None
        Classifier to be used to estimate instance hardness of the samples.  By
        default a :class:`~sklearn.ensemble.RandomForestClassifier` will be
        used. If ``str``, the choices using a string are the following:
        ``'knn'``, ``'decision-tree'``, ``'random-forest'``, ``'adaboost'``,
        ``'gradient-boosting'`` and ``'linear-svm'``.  If object, an estimator
        inherited from :class:`~sklearn.base.ClassifierMixin` and having an
        attribute :func:`predict_proba`.

    {sampling_strategy}

    {random_state}

    cv : int, default=5
        Number of folds to be used when estimating samples' instance hardness.

    {n_jobs}

    Attributes
    ----------
    sample_indices_ : ndarray of shape (n_new_samples,)
        Indices of the samples selected.

    See Also
    --------
    NearMiss : Undersample based on near-miss search.

    RandomUnderSampler : Random under-sampling.

    Notes
    -----
    The method is based on [1]_.

    Supports multi-class resampling. A one-vs.-rest scheme is used when
    sampling a class as proposed in [1]_.

    References
    ----------
    .. [1] D. Smith, Michael R., Tony Martinez, and Christophe Giraud-Carrier.
       "An instance level analysis of data complexity." Machine learning
       95.2 (2014): 225-256.

    Examples
    --------
    >>> from collections import Counter
    >>> from sklearn.datasets import make_classification
    >>> from imbalanced_ensemble.sampler.under_sampling import InstanceHardnessThreshold
    >>> 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}})
    >>> iht = InstanceHardnessThreshold(random_state=42)
    >>> X_res, y_res = iht.fit_resample(X, y)
    >>> print('Resampled dataset shape %s' % Counter(y_res))  # doctest: +ELLIPSIS
    Resampled dataset shape Counter({{1: 5..., 0: 100}})
    """

    @_deprecate_positional_args
    def __init__(
        self,
        *,
        estimator=None,
        sampling_strategy="auto",
        random_state=None,
        cv=5,
        n_jobs=None,
    ):
        super().__init__(sampling_strategy=sampling_strategy)
        self.random_state = random_state
        self.estimator = estimator
        self.cv = cv
        self.n_jobs = n_jobs


    def _validate_estimator(self, random_state):
        """Private function to create the classifier"""

        if (
            self.estimator is not None
            and isinstance(self.estimator, ClassifierMixin)
            and hasattr(self.estimator, "predict_proba")
        ):
            self.estimator_ = clone(self.estimator)
            _set_random_states(self.estimator_, random_state)

        elif self.estimator is None:
            self.estimator_ = RandomForestClassifier(
                n_estimators=100,
                random_state=self.random_state,
                n_jobs=self.n_jobs,
            )
        else:
            raise ValueError(
                f"Invalid parameter `estimator`. Got {type(self.estimator)}."
            )


    def _fit_resample(self, X, y, sample_weight=None):
        random_state = check_random_state(self.random_state)
        self._validate_estimator(random_state)

        target_stats = Counter(y)
        skf = StratifiedKFold(
            n_splits=self.cv,
            shuffle=True,
            random_state=random_state,
        )
        probabilities = cross_val_predict(
            self.estimator_,
            X,
            y,
            cv=skf,
            n_jobs=self.n_jobs,
            method="predict_proba",
        )
        probabilities = probabilities[range(len(y)), y]

        idx_under = np.empty((0,), dtype=int)

        for target_class in np.unique(y):
            if target_class in self.sampling_strategy_.keys():
                n_samples = self.sampling_strategy_[target_class]
                threshold = np.percentile(
                    probabilities[y == target_class],
                    (1.0 - (n_samples / target_stats[target_class])) * 100.0,
                )
                index_target_class = np.flatnonzero(
                    probabilities[y == target_class] >= threshold
                )
            else:
                index_target_class = slice(None)

            idx_under = np.concatenate(
                (
                    idx_under,
                    np.flatnonzero(y == target_class)[index_target_class],
                ),
                axis=0,
            )

        self.sample_indices_ = idx_under

        if sample_weight is not None:
            # sample_weight is already validated in self.fit_resample()
            sample_weight_under = _safe_indexing(sample_weight, idx_under)
            return _safe_indexing(X, idx_under), _safe_indexing(y, idx_under), sample_weight_under
        else: return _safe_indexing(X, idx_under), _safe_indexing(y, idx_under)


    def _more_tags(self):
        return {"sample_indices": True}

# %%

if __name__ == "__main__":
    from collections import Counter
    from sklearn.datasets import make_classification
    X, y = make_classification(n_classes=3, class_sep=2,
        weights=[0.1, 0.3, 0.6], 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))

    origin_distr = Counter(y)
    target_distr = {2: 200, 1: 100, 0: 100}

    undersampler = InstanceHardnessThreshold(random_state=42, sampling_strategy=target_distr)
    X_res, y_res, weight_res = undersampler.fit_resample(X, y, sample_weight=y)

    print('Resampled dataset shape %s' % Counter(y_res))
    print('Test resampled weight shape %s' % Counter(weight_res))

# %%
