import numpy as np

from .._metric_tensor import NearestNeighborsWithMetricTensor, MetricTensor
from ._OverSampling import OverSampling

from ._SMOTE import SMOTE

from .._logger import logger
_logger= logger

__all__= ['NDO_sampling']

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

            @INPROCEEDINGS{ndo_sampling,
                            author={Zhang, L. and Wang, W.},
                            booktitle={2011 International Conference of
                                        Information Technology, Computer
                                        Engineering and Management Sciences},
                            title={A Re-sampling Method for Class Imbalance
                                    Learning with Credit Data},
                            year={2011},
                            volume={1},
                            number={},
                            pages={393-397},
                            keywords={data handling;sampling methods;
                                        resampling method;class imbalance
                                        learning;credit rating;imbalance
                                        problem;synthetic minority
                                        over-sampling technique;sample
                                        distribution;synthetic samples;
                                        credit data set;Training;
                                        Measurement;Support vector machines;
                                        Logistics;Testing;Noise;Classification
                                        algorithms;class imbalance;credit
                                        rating;SMOTE;sample distribution},
                            doi={10.1109/ICM.2011.34},
                            ISSN={},
                            month={Sept}}
    """

    categories = [OverSampling.cat_extensive,
                  OverSampling.cat_sample_ordinary,
                  OverSampling.cat_application,
                  OverSampling.cat_metric_learning]

    def __init__(self,
                 proportion=1.0,
                 n_neighbors=5,
                 *,
                 nn_params={},
                 T=0.5,
                 n_jobs=1,
                 random_state=None):
        """
        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
            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
            T (float): threshold parameter
            n_jobs (int): number of parallel jobs
            random_state (int/RandomState/None): initializer of random_state,
                                                    like in sklearn
        """
        super().__init__()
        self.check_greater_or_equal(proportion, "proportion", 0)
        self.check_greater_or_equal(n_neighbors, "n_neighbors", 1)
        self.check_greater_or_equal(T, "T", 0)
        self.check_n_jobs(n_jobs, 'n_jobs')

        self.proportion = proportion
        self.n_neighbors = n_neighbors
        self.nn_params = nn_params
        self.T = T
        self.n_jobs = n_jobs

        self.set_random_state(random_state)

    @ 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],
                                  'n_neighbors': [3, 5, 7],
                                  'T': [0.5]}
        return cls.generate_parameter_combinations(parameter_combinations, raw)

    def sample(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
        """
        _logger.info(self.__class__.__name__ + ": " +
                     "Running sampling via %s" % self.descriptor())

        self.class_label_statistics(X, y)

        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:
            _logger.warning(self.__class__.__name__ +
                            ": " + "Sampling is not needed")
            return X.copy(), y.copy()

        X_min = X[y == self.min_label]

        # fitting nearest neighbors model to find the neighbors of minority
        # samples among all elements
        nn_params= {**self.nn_params}
        nn_params['metric_tensor']= self.metric_tensor_from_nn_params(nn_params, X, y)
        
        n_neighbors = min([len(X), self.n_neighbors+1])
        nn = NearestNeighborsWithMetricTensor(n_neighbors=n_neighbors, 
                                                n_jobs=self.n_jobs, 
                                                **(nn_params))
        nn.fit(X)
        dist, ind = nn.kneighbors(X_min)

        # calculating the distances between samples in the same and different
        # classes
        d_intra = []
        d_exter = []
        for i in range(len(X_min)):
            min_mask = np.where(y[ind[i][1:]] == self.min_label)[0]
            maj_mask = np.where(y[ind[i][1:]] == self.maj_label)[0]
            if len(min_mask) > 0:
                d_intra.append(np.mean(dist[i][1:][min_mask]))
            if len(maj_mask) > 0:
                d_exter.append(np.mean(dist[i][1:][maj_mask]))
        d_intra_mean = np.mean(np.array(d_intra))
        d_exter_mean = np.mean(np.array(d_exter))

        # calculating the alpha value
        alpha = d_intra_mean/d_exter_mean

        # deciding if SMOTE is enough
        if alpha < self.T:
            smote = SMOTE(self.proportion, 
                          nn_params=nn_params,
                          random_state=self._random_state_init)
            return smote.sample(X, y)

        # do the sampling
        samples = []
        while len(samples) < n_to_sample:
            idx = self.random_state.randint(len(X_min))
            random_idx = self.random_state.choice(ind[idx][1:])
            # create sample close to the initial minority point
            samples.append(X_min[idx] + (X[random_idx] - X_min[idx])
                           * self.random_state.random_sample()/2.0)
            if y[random_idx] == self.min_label:
                # create another sample close to the neighboring minority point
                samples.append(X[random_idx] + (X_min[idx] - X[random_idx])
                               * self.random_state.random_sample()/2.0)

        return (np.vstack([X, np.vstack(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,
                'n_neighbors': self.n_neighbors,
                'nn_params': self.nn_params,
                'T': self.T,
                'n_jobs': self.n_jobs,
                'random_state': self._random_state_init}

