"""
This module implements the SMOTE_TomekLinks method.
"""

from ..base import coalesce, coalesce_dict
from ..base import OverSampling
from ..noise_removal import TomekLinkRemoval
from ._smote import SMOTE

from .._logger import logger

_logger = logger

__all__ = ["SMOTE_TomekLinks"]


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

            @article{smote_tomeklinks_enn,
                     author = {Batista, Gustavo E. A. P. A. and Prati,
                                Ronaldo C. and Monard, Maria Carolina},
                     title = {A Study of the Behavior of Several Methods for
                                Balancing Machine Learning Training Data},
                     journal = {SIGKDD Explor. Newsl.},
                     issue_date = {June 2004},
                     volume = {6},
                     number = {1},
                     month = jun,
                     year = {2004},
                     issn = {1931-0145},
                     pages = {20--29},
                     numpages = {10},
                     url = {http://doi.acm.org/10.1145/1007730.1007735},
                     doi = {10.1145/1007730.1007735},
                     acmid = {1007735},
                     publisher = {ACM},
                     address = {New York, NY, USA},
                    }
    """

    categories = [
        OverSampling.cat_sample_ordinary,
        OverSampling.cat_noise_removal,
        OverSampling.cat_changes_majority,
        OverSampling.cat_metric_learning,
    ]

    def __init__(
        self,
        proportion=1.0,
        n_neighbors=5,
        *,
        nn_params=None,
        ss_params=None,
        n_jobs=1,
        random_state=None,
        **_kwargs
    ):
        """
        Constructor of the SMOTE 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
            n_neighbors (int): control parameter of the nearest neighbor
                                technique
            nn_params (dict): additional parameters for nearest neighbor calculations, any
                                parameter NearestNeighbors accepts, and additionally use
                                {'metric': 'precomputed', 'metric_learning': '<method>', ...}
                                with <method> in 'ITML', 'LSML' to enable the learning of
                                the metric to be used for neighborhood calculations
            ss_params (dict): simplex sampling parameters
            preferential (bool): whether to use preferential connectivity in the
                                    neighborhoods
            n_jobs (int): number of parallel jobs
            random_state (int/RandomState/None): initializer of random_state,
                                                    like in sklearn
        """
        ss_params_default = {
            "n_dim": 2,
            "simplex_sampling": "random",
            "within_simplex_sampling": "random",
            "gaussian_component": None,
        }

        super().__init__(random_state=random_state)

        self.check_greater_or_equal(proportion, "proportion", 0)
        self.check_greater_or_equal(n_neighbors, "n_neighbors", 1)
        self.check_n_jobs(n_jobs, "n_jobs")

        self.proportion = proportion
        self.n_neighbors = n_neighbors
        self.nn_params = coalesce(nn_params, {})
        self.ss_params = coalesce_dict(ss_params, ss_params_default)
        self.n_jobs = n_jobs

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

        Returns:
            list(dict): a list of meaningful parameter combinations
        """
        return SMOTE.parameter_combinations(raw)

    def return_results(self, X_samp, y_samp, X, y):
        """
        Prepare the final dataset.

        Args:
            X_samp (np.array): the sampled vectors
            y_samp (np.array): the labels of the sampled vectors
            X (np.array): the original training vectors
            y (np.array): the original target labels

        Returns:
            np.array, np.array: the oversampled dataset and the labels
        """
        if len(X_samp) == 0:
            return self.return_copies(
                X,
                y,
                "All samples have been removed, returning the original dataset.",
            )
        return X_samp, y_samp

    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
        """
        nn_params = {**self.nn_params}
        nn_params["metric_tensor"] = self.metric_tensor_from_nn_params(nn_params, X, y)

        smote = SMOTE(
            self.proportion,
            self.n_neighbors,
            nn_params=nn_params,
            ss_params=self.ss_params,
            n_jobs=self.n_jobs,
            random_state=OverSampling.get_params(self)["random_state"],
        )
        X_new, y_new = smote.sample(X, y)

        tomek = TomekLinkRemoval(
            strategy="remove_both", n_jobs=self.n_jobs, nn_params=nn_params
        )

        X_samp, y_samp = tomek.remove_noise(X_new, y_new)

        return self.return_results(X_samp, y_samp, X, y)

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