Source code for imblearn.under_sampling._prototype_generation._cluster_centroids

"""Class to perform under-sampling by generating centroids based on
clustering."""

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

from __future__ import division

import numpy as np
from scipy import sparse

from sklearn.base import clone
from sklearn.cluster import KMeans
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import safe_indexing

from ..base import BaseUnderSampler
from ...utils import Substitution
from ...utils._docstring import _random_state_docstring

VOTING_KIND = ('auto', 'hard', 'soft')


[docs]@Substitution( sampling_strategy=BaseUnderSampler._sampling_strategy_docstring, random_state=_random_state_docstring) class ClusterCentroids(BaseUnderSampler): """Perform under-sampling by generating centroids based on clustering methods. Method that under samples the majority class by replacing a cluster of majority samples by the cluster centroid of a KMeans algorithm. This algorithm keeps N majority samples by fitting the KMeans algorithm with N cluster to the majority class and using the coordinates of the N cluster centroids as the new majority samples. Read more in the :ref:`User Guide <cluster_centroids>`. Parameters ---------- {sampling_strategy} {random_state} estimator : object, optional(default=KMeans()) Pass a :class:`sklearn.cluster.KMeans` estimator. voting : str, optional (default='auto') Voting strategy to generate the new samples: - If ``'hard'``, the nearest-neighbors of the centroids found using the clustering algorithm will be used. - If ``'soft'``, the centroids found by the clustering algorithm will be used. - If ``'auto'``, if the input is sparse, it will default on ``'hard'`` otherwise, ``'soft'`` will be used. .. versionadded:: 0.3.0 n_jobs : int, optional (default=1) The number of threads to open if possible. ratio : str, dict, or callable .. deprecated:: 0.4 Use the parameter ``sampling_strategy`` instead. It will be removed in 0.6. Notes ----- Supports multi-class resampling by sampling each class independently. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.under_sampling import \ ClusterCentroids # 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 %s' % Counter(y)) Original dataset shape Counter({{1: 900, 0: 100}}) >>> cc = ClusterCentroids(random_state=42) >>> X_res, y_res = cc.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) ... # doctest: +ELLIPSIS Resampled dataset shape Counter({{...}}) """
[docs] def __init__(self, sampling_strategy='auto', random_state=None, estimator=None, voting='auto', n_jobs=1, ratio=None): super(ClusterCentroids, self).__init__( sampling_strategy=sampling_strategy, ratio=ratio) self.random_state = random_state self.estimator = estimator self.voting = voting self.n_jobs = n_jobs
def _validate_estimator(self): """Private function to create the KMeans estimator""" if self.estimator is None: self.estimator_ = KMeans( random_state=self.random_state, n_jobs=self.n_jobs) elif isinstance(self.estimator, KMeans): self.estimator_ = clone(self.estimator) else: raise ValueError('`estimator` has to be a KMeans clustering.' ' Got {} instead.'.format(type(self.estimator))) def _generate_sample(self, X, y, centroids, target_class): if self.voting_ == 'hard': nearest_neighbors = NearestNeighbors(n_neighbors=1) nearest_neighbors.fit(X, y) indices = nearest_neighbors.kneighbors( centroids, return_distance=False) X_new = safe_indexing(X, np.squeeze(indices)) else: if sparse.issparse(X): X_new = sparse.csr_matrix(centroids, dtype=X.dtype) else: X_new = centroids y_new = np.array([target_class] * centroids.shape[0], dtype=y.dtype) return X_new, y_new def _fit_resample(self, X, y): self._validate_estimator() if self.voting == 'auto': if sparse.issparse(X): self.voting_ = 'hard' else: self.voting_ = 'soft' else: if self.voting in VOTING_KIND: self.voting_ = self.voting else: raise ValueError("'voting' needs to be one of {}. Got {}" " instead.".format(VOTING_KIND, self.voting)) X_resampled, y_resampled = [], [] for target_class in np.unique(y): if target_class in self.sampling_strategy_.keys(): n_samples = self.sampling_strategy_[target_class] self.estimator_.set_params(**{'n_clusters': n_samples}) self.estimator_.fit(X[y == target_class]) X_new, y_new = self._generate_sample( X, y, self.estimator_.cluster_centers_, target_class) X_resampled.append(X_new) y_resampled.append(y_new) else: target_class_indices = np.flatnonzero(y == target_class) X_resampled.append(safe_indexing(X, target_class_indices)) y_resampled.append(safe_indexing(y, target_class_indices)) if sparse.issparse(X): X_resampled = sparse.vstack(X_resampled) else: X_resampled = np.vstack(X_resampled) y_resampled = np.hstack(y_resampled) return X_resampled, np.array(y_resampled, dtype=y.dtype)