"""
This module implements the SMOTE_IPF method.
"""

import numpy as np

from sklearn.model_selection import StratifiedKFold

from ..base import coalesce, coalesce_dict
from ..base import OverSampling
from ._smote import SMOTE
from ..base import instantiate_obj

from .._logger import logger

_logger = logger

__all__ = ["SMOTE_IPF"]


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

            @article{smote_ipf,
                        title = "SMOTE–IPF: Addressing the noisy and borderline
                                    examples problem in imbalanced
                                    classification by a re-sampling method
                                    with filtering",
                        journal = "Information Sciences",
                        volume = "291",
                        pages = "184 - 203",
                        year = "2015",
                        issn = "0020-0255",
                        doi = "https://doi.org/10.1016/j.ins.2014.08.051",
                        author = "José A. Sáez and Julián Luengo and Jerzy
                                    Stefanowski and Francisco Herrera",
                        keywords = "Imbalanced classification,
                                        Borderline examples,
                                        Noisy data,
                                        Noise filters,
                                        SMOTE"
                        }
    """

    categories = [
        OverSampling.cat_changes_majority,
        OverSampling.cat_uses_classifier,
        OverSampling.cat_metric_learning,
    ]

    def __init__(
        self,
        proportion=1.0,
        n_neighbors=5,
        *,
        nn_params=None,
        ss_params=None,
        n_folds=9,
        k=3,
        p=0.01,
        voting="majority",
        classifier=("sklearn.tree", "DecisionTreeClassifier", {"random_state": 2}),
        n_jobs=1,
        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
            n_neighbors (int): number of neighbors in SMOTE sampling
            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
            n_folds (int): the number of partitions
            k (int): used in stopping condition
            p (float): percentage value ([0,1]) used in stopping condition
            voting (str): 'majority'/'consensus'
            classifier (obj): classifier object
            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_greater_or_equal(n_folds, "n_folds", 2)
        self.check_greater_or_equal(k, "k", 1)
        self.check_greater_or_equal(p, "p", 0)
        self.check_isin(voting, "voting", ["majority", "consensus"])
        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.params = {
            "n_folds": n_folds,
            "k": k,
            "p": p,
            "voting": voting,
            "classifier": classifier,
        }
        self.classifier_obj = instantiate_obj(classifier)
        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
        """
        classifiers = [("sklearn.tree", "DecisionTreeClassifier", {"random_state": 2})]
        parameter_combinations = {
            "proportion": [0.1, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0],
            "n_neighbors": [3, 5, 7, 11, 17],
            "n_folds": [9],
            "k": [3],
            "p": [0.01],
            "voting": ["majority", "consensus"],
            "classifier": classifiers,
        }
        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)

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

        # do SMOTE sampling
        X_samp, y_samp = SMOTE(
            proportion=self.proportion,
            n_neighbors=self.n_neighbors,
            nn_params=self.nn_params,
            ss_params=self.ss_params,
            n_jobs=self.n_jobs,
            random_state=self._random_state_init,
        ).sample(X, y)

        n_folds = min([self.params["n_folds"], np.sum(y == self.min_label)])

        condition = 0
        while condition < self.params["k"]:
            # validating the sampled dataset
            validator = StratifiedKFold(n_folds)
            predictions = []
            for train_index, _ in validator.split(X_samp, y_samp):
                self.classifier_obj.fit(X_samp[train_index], y_samp[train_index])
                predictions.append(self.classifier_obj.predict(X_samp))

            # do decision based on one of the voting schemes
            if self.params["voting"] == "majority":
                pred_votes = (np.mean(predictions, axis=0) > 0.5).astype(int)
                to_remove = np.where(np.not_equal(pred_votes, y_samp))[0]
            elif self.params["voting"] == "consensus":
                pred_votes = (np.mean(predictions, axis=0) > 0.5).astype(int)
                sum_votes = np.sum(predictions, axis=0)
                to_remove = np.where(
                    np.logical_and(
                        np.not_equal(pred_votes, y_samp),
                        np.equal(sum_votes, self.params["n_folds"]),
                    )
                )[0]

            # delete samples incorrectly classified
            _logger.info(
                "%s: Removing %d elements", self.__class__.__name__, len(to_remove)
            )
            X_samp = np.delete(X_samp, to_remove, axis=0)
            y_samp = np.delete(y_samp, to_remove)

            # if the number of samples removed becomes small or k iterations
            # were done quit
            check = int(len(to_remove) < len(X_samp) * self.params["p"])
            condition = (condition + 1) * check

        return X_samp, y_samp

    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_folds": self.params["n_folds"],
            "k": self.params["k"],
            "p": self.params["p"],
            "voting": self.params["voting"],
            "n_jobs": self.n_jobs,
            "classifier": self.params["classifier"],
            **OverSampling.get_params(self),
        }
