"""Class to perform over-sampling using ADASYN."""

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

import numpy as np
from scipy import sparse

from sklearn.utils import check_random_state
from sklearn.utils import _safe_indexing

from .base import BaseOverSampler
from ..utils import check_neighbors_object
from ..utils import Substitution
from ..utils._docstring import _n_jobs_docstring
from ..utils._docstring import _random_state_docstring
from ..utils._validation import _deprecate_positional_args


@Substitution(
    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class ADASYN(BaseOverSampler):
    """Oversample using Adaptive Synthetic (ADASYN) algorithm.

    This method is similar to SMOTE but it generates different number of
    samples depending on an estimate of the local distribution of the class
    to be oversampled.

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

    Parameters
    ----------
    {sampling_strategy}

    {random_state}

    n_neighbors : int or estimator object, default=5
        If ``int``, number of nearest neighbours to used to construct synthetic
        samples.  If object, an estimator that inherits from
        :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the k_neighbors.

    {n_jobs}

    Attributes
    ----------
    sampling_strategy_ : dict
        Dictionary containing the information to sample the dataset. The keys
        corresponds to the class labels from which to sample and the values
        are the number of samples to sample.

    nn_ : estimator object
        Validated K-nearest Neighbours estimator linked to the parameter `n_neighbors`.

    n_features_in_ : int
        Number of features in the input dataset.

        .. versionadded:: 0.9

    See Also
    --------
    SMOTE : Over-sample using SMOTE.

    SMOTENC : Over-sample using SMOTE for continuous and categorical features.

    SMOTEN : Over-sample using the SMOTE variant specifically for categorical
        features only.

    SVMSMOTE : Over-sample using SVM-SMOTE variant.

    BorderlineSMOTE : Over-sample using Borderline-SMOTE variant.

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

    Supports multi-class resampling. A one-vs.-rest scheme is used.

    References
    ----------
    .. [1] He, Haibo, Yang Bai, Edwardo A. Garcia, and Shutao Li. "ADASYN:
       Adaptive synthetic sampling approach for imbalanced learning," In IEEE
       International Joint Conference on Neural Networks (IEEE World Congress
       on Computational Intelligence), pp. 1322-1328, 2008.

    Examples
    --------
    >>> from collections import Counter
    >>> from sklearn.datasets import make_classification
    >>> from imblearn.over_sampling import \
ADASYN # doctest: +NORMALIZE_WHITESPACE
    >>> 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}})
    >>> ada = ADASYN(random_state=42)
    >>> X_res, y_res = ada.fit_resample(X, y)
    >>> print('Resampled dataset shape %s' % Counter(y_res))
    Resampled dataset shape Counter({{0: 904, 1: 900}})
    """

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

    def _validate_estimator(self):
        """Create the necessary objects for ADASYN"""
        self.nn_ = check_neighbors_object(
            "n_neighbors", self.n_neighbors, additional_neighbor=1
        )
        self.nn_.set_params(**{"n_jobs": self.n_jobs})

    def _fit_resample(self, X, y):
        self._validate_estimator()
        random_state = check_random_state(self.random_state)

        X_resampled = [X.copy()]
        y_resampled = [y.copy()]

        for class_sample, n_samples in self.sampling_strategy_.items():
            if n_samples == 0:
                continue
            target_class_indices = np.flatnonzero(y == class_sample)
            X_class = _safe_indexing(X, target_class_indices)

            self.nn_.fit(X)
            nns = self.nn_.kneighbors(X_class, return_distance=False)[:, 1:]
            # The ratio is computed using a one-vs-rest manner. Using majority
            # in multi-class would lead to slightly different results at the
            # cost of introducing a new parameter.
            n_neighbors = self.nn_.n_neighbors - 1
            ratio_nn = np.sum(y[nns] != class_sample, axis=1) / n_neighbors
            if not np.sum(ratio_nn):
                raise RuntimeError(
                    "Not any neigbours belong to the majority"
                    " class. This case will induce a NaN case"
                    " with a division by zero. ADASYN is not"
                    " suited for this specific dataset."
                    " Use SMOTE instead."
                )
            ratio_nn /= np.sum(ratio_nn)
            n_samples_generate = np.rint(ratio_nn * n_samples).astype(int)
            # rounding may cause new amount for n_samples
            n_samples = np.sum(n_samples_generate)
            if not n_samples:
                raise ValueError(
                    "No samples will be generated with the provided ratio settings."
                )

            # the nearest neighbors need to be fitted only on the current class
            # to find the class NN to generate new samples
            self.nn_.fit(X_class)
            nns = self.nn_.kneighbors(X_class, return_distance=False)[:, 1:]

            enumerated_class_indices = np.arange(len(target_class_indices))
            rows = np.repeat(enumerated_class_indices, n_samples_generate)
            cols = random_state.choice(n_neighbors, size=n_samples)
            diffs = X_class[nns[rows, cols]] - X_class[rows]
            steps = random_state.uniform(size=(n_samples, 1))

            if sparse.issparse(X):
                sparse_func = type(X).__name__
                steps = getattr(sparse, sparse_func)(steps)
                X_new = X_class[rows] + steps.multiply(diffs)
            else:
                X_new = X_class[rows] + steps * diffs

            X_new = X_new.astype(X.dtype)
            y_new = np.full(n_samples, fill_value=class_sample, dtype=y.dtype)
            X_resampled.append(X_new)
            y_resampled.append(y_new)

        if sparse.issparse(X):
            X_resampled = sparse.vstack(X_resampled, format=X.format)
        else:
            X_resampled = np.vstack(X_resampled)
        y_resampled = np.hstack(y_resampled)

        return X_resampled, y_resampled

    def _more_tags(self):
        return {
            "X_types": ["2darray"],
        }
