"""
This module implements the ROSE method.
"""

import numpy as np

from ..base import OverSampling

from .._logger import logger

_logger = logger

__all__ = ["ROSE"]


class ROSE(OverSampling):
    """
    References:
        * BibTex::

            @Article{rose,
                    author="Menardi, Giovanna
                    and Torelli, Nicola",
                    title="Training and assessing classification rules with
                            imbalanced data",
                    journal="Data Mining and Knowledge Discovery",
                    year="2014",
                    month="Jan",
                    day="01",
                    volume="28",
                    number="1",
                    pages="92--122",
                    issn="1573-756X",
                    doi="10.1007/s10618-012-0295-5",
                    url="https://doi.org/10.1007/s10618-012-0295-5"
                    }

    Notes:
        * It is not entirely clear if the authors propose kernel density
            estimation or the fitting of simple multivariate Gaussians
            on the minority samples. The latter seems to be more likely,
            I implement that approach.
    """

    categories = [OverSampling.cat_extensive, OverSampling.cat_sample_componentwise]

    def __init__(self, proportion=1.0, *, random_state=None, **_kwargs):
        """
        Constructor of the sampling object

        Args:
            proportion (float): proportion of the difference of n_maj and n_min
                                to sample e.g. 1.0 means that after sampling
                                the number of minority samples will be equal to
                                the number of majority samples
            random_state (int/RandomState/None): initializer of random_state,
                                                    like in sklearn
        """
        super().__init__(random_state=random_state)
        self.check_greater_or_equal(proportion, "proportion", 0.0)

        self.proportion = proportion

    @classmethod
    def parameter_combinations(cls, raw=False):
        """
        Generates reasonable parameter combinations.

        Returns:
            list(dict): a list of meaningful parameter combinations
        """
        parameter_combinations = {"proportion": [0.1, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0]}
        return cls.generate_parameter_combinations(parameter_combinations, raw)

    def sampling_algorithm(self, X, y):
        """
        Does the sample generation according to the class parameters.

        Args:
            X (np.ndarray): training set
            y (np.array): target labels

        Returns:
            (np.ndarray, np.array): the extended training set and target labels
        """
        n_to_sample = self.det_n_to_sample(
            self.proportion,
            self.class_stats[self.maj_label],
            self.class_stats[self.min_label],
        )

        if n_to_sample == 0:
            return self.return_copies(X, y, "Sampling is not needed")

        X_min = X[y == self.min_label]

        # Estimating the H matrix
        std = np.std(X_min, axis=0)
        n, d = X.shape  # pylint: disable=invalid-name
        H = std * (4.0 / ((d + 1) * n)) ** (  # pylint: disable=invalid-name
            1.0 / (d + 4)
        )

        base_indices = self.random_state.choice(np.arange(X_min.shape[0]), n_to_sample)
        base_vectors = X_min[base_indices]
        random = self.random_state.normal(size=base_vectors.shape)

        samples = base_vectors + random * H

        return (
            np.vstack([X, samples]),
            np.hstack([y, np.repeat(self.min_label, len(samples))]),
        )

    def get_params(self, deep=False):
        """
        Returns:
            dict: the parameters of the current sampling object
        """
        return {"proportion": self.proportion, **OverSampling.get_params(self)}
