Source code for imblearn.ensemble.easy_ensemble

"""Class to perform under-sampling using easy ensemble."""

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
#          Christos Aridas
# License: MIT

import numpy as np

from sklearn.utils import check_random_state

from .base import BaseEnsembleSampler
from ..under_sampling import RandomUnderSampler

MAX_INT = np.iinfo(np.int32).max


[docs]class EasyEnsemble(BaseEnsembleSampler): """Create an ensemble sets by iteratively applying random under-sampling. This method iteratively select a random subset and make an ensemble of the different sets. Parameters ---------- ratio : str, dict, or callable, optional (default='auto') Ratio to use for resampling the data set. - If ``str``, has to be one of: (i) ``'minority'``: resample the minority class; (ii) ``'majority'``: resample the majority class, (iii) ``'not minority'``: resample all classes apart of the minority class, (iv) ``'all'``: resample all classes, and (v) ``'auto'``: correspond to ``'all'`` with for over-sampling methods and ``'not minority'`` for under-sampling methods. The classes targeted will be over-sampled or under-sampled to achieve an equal number of sample with the majority or minority class. - If ``dict``, the keys correspond to the targeted classes. The values correspond to the desired number of samples. - If callable, function taking ``y`` and returns a ``dict``. The keys correspond to the targeted classes. The values correspond to the desired number of samples. return_indices : bool, optional (default=False) Whether or not to return the indices of the samples randomly selected from the majority class. random_state : int, RandomState instance or None, optional (default=None) If int, ``random_state`` is the seed used by the random number generator; If ``RandomState`` instance, random_state is the random number generator; If ``None``, the random number generator is the ``RandomState`` instance used by ``np.random``. replacement : bool, optional (default=False) Whether or not to sample randomly with replacement or not. n_subsets : int, optional (default=10) Number of subsets to generate. Notes ----- The method is described in [1]_. Supports mutli-class resampling. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.ensemble import \ EasyEnsemble # doctest: +NORMALIZE_WHITESPACE >>> X, y = make_classification(n_classes=2, class_sep=2, ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) >>> print('Original dataset shape {}'.format(Counter(y))) Original dataset shape Counter({1: 900, 0: 100}) >>> ee = EasyEnsemble(random_state=42) >>> X_res, y_res = ee.fit_sample(X, y) >>> print('Resampled dataset shape {}'.format(Counter(y_res[0]))) Resampled dataset shape Counter({0: 100, 1: 100}) References ---------- .. [1] X. Y. Liu, J. Wu and Z. H. Zhou, "Exploratory Undersampling for Class-Imbalance Learning," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539-550, April 2009. """
[docs] def __init__(self, ratio='auto', return_indices=False, random_state=None, replacement=False, n_subsets=10): super(EasyEnsemble, self).__init__(ratio=ratio, random_state=random_state) self.return_indices = return_indices self.replacement = replacement self.n_subsets = n_subsets
def _sample(self, X, y): """Resample the dataset. Parameters ---------- X : ndarray, shape (n_samples, n_features) Matrix containing the data which have to be sampled. y : ndarray, shape (n_samples, ) Corresponding label for each sample in X. Returns ------- X_resampled : ndarray, shape (n_subset, n_samples_new, n_features) The array containing the resampled data. y_resampled : ndarray, shape (n_subset, n_samples_new) The corresponding label of `X_resampled` idx_under : ndarray, shape (n_subset, n_samples, ) If `return_indices` is `True`, a boolean array will be returned containing the which samples have been selected. """ random_state = check_random_state(self.random_state) X_resampled = [] y_resampled = [] if self.return_indices: idx_under = [] for _ in range(self.n_subsets): rus = RandomUnderSampler( ratio=self.ratio_, return_indices=True, random_state=random_state.randint(MAX_INT), replacement=self.replacement) sel_x, sel_y, sel_idx = rus.fit_sample(X, y) X_resampled.append(sel_x) y_resampled.append(sel_y) if self.return_indices: idx_under.append(sel_idx) if self.return_indices: return (np.array(X_resampled), np.array(y_resampled), np.array(idx_under)) else: return np.array(X_resampled), np.array(y_resampled)