from collections import Counter

import numpy as np
from imblearn.over_sampling import SMOTE
from imblearn.base import BaseSampler


class StaticSMOTE(BaseSampler):
    """
    Static SMOTE implementation:

    Reference:
    <NAME>., <NAME>., <NAME>.: A dynamic over-sampling
    procedure based on sensitivity for multi-class problems. Pattern Recognit. 44, 1821–1833
    (2011)
    """
    def __init__(self):
        super().__init__()
        self._sampling_type = 'over-sampling'

    def _fit_resample(self, X, y):
        """
        Performs resampling

        :param X:
            two dimensional numpy array (number of samples x number of features) with float numbers
        :param y:
            one dimensional numpy array with labels for rows in X
        :return:
            Resampled X and y as numpy arrays
        """
        cnt = Counter(y)
        min_class = min(cnt, key=cnt.get)
        X_original, y_original = X.copy(), y.copy()
        X_resampled, y_resampled = X.copy(), y.copy()

        M = len(list(cnt.keys()))
        for _ in range(M):
            sm = SMOTE(sampling_strategy={min_class: cnt[min_class] * 2})
            X_smote, y_smote = sm.fit_resample(X_original, y_original)
            X_added_examples = X_smote[y_smote == min_class][cnt[min_class]:, :]
            X_resampled = np.vstack([X_resampled, X_added_examples])
            y_resampled = np.hstack([y_resampled, y_smote[y_smote == min_class][cnt[min_class]:]])
            cnt = Counter(y_resampled)
            min_class = min(cnt, key=cnt.get)

        return X_resampled, y_resampled

from collections import Counter

import numpy as np
from imblearn.over_sampling import SMOTE
from imblearn.base import BaseSampler


class StaticSMOTE(BaseSampler):
    """
    Static SMOTE implementation:

    Reference:
    <NAME>., <NAME>., <NAME>.: A dynamic over-sampling
    procedure based on sensitivity for multi-class problems. Pattern Recognit. 44, 1821–1833
    (2011)
    """
    def __init__(self):
        super().__init__()
        self._sampling_type = 'over-sampling'

    def _fit_resample(self, X, y):
        """
        Performs resampling

        :param X:
            two dimensional numpy array (number of samples x number of features) with float numbers
        :param y:
            one dimensional numpy array with labels for rows in X
        :return:
            Resampled X and y as numpy arrays
        """
        cnt = Counter(y)
        min_class = min(cnt, key=cnt.get)
        X_original, y_original = X.copy(), y.copy()
        X_resampled, y_resampled = X.copy(), y.copy()

        M = len(list(cnt.keys()))
        for _ in range(M):
            sm = SMOTE(sampling_strategy={min_class: cnt[min_class] * 2})
            X_smote, y_smote = sm.fit_resample(X_original, y_original)
            X_added_examples = X_smote[y_smote == min_class][cnt[min_class]:, :]
            X_resampled = np.vstack([X_resampled, X_added_examples])
            y_resampled = np.hstack([y_resampled, y_smote[y_smote == min_class][cnt[min_class]:]])
            cnt = Counter(y_resampled)
            min_class = min(cnt, key=cnt.get)

        return X_resampled, y_resampled

Static SMOTE implementation:      Reference:     <NAME>., <NAME>., <NAME>.: A dynamic over-sampling     procedure based on sensitivity for multi-class problems. Pattern Recognit. 44, 1821–1833     (2011) Performs resampling          :param X:             two dimensional numpy array (number of samples x number of features) with float numbers         :param y:             one dimensional numpy array with labels for rows in X         :return:             Resampled X and y as numpy arrays