Source code for imblearn.under_sampling._prototype_selection._one_sided_selection

"""Class to perform under-sampling based on one-sided selection method."""

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

from __future__ import division

from collections import Counter

import numpy as np

from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import check_random_state, safe_indexing

from ..base import BaseCleaningSampler
from ._tomek_links import TomekLinks
from ...utils import Substitution
from ...utils.deprecation import deprecate_parameter
from ...utils._docstring import _random_state_docstring


[docs]@Substitution( sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring, random_state=_random_state_docstring) class OneSidedSelection(BaseCleaningSampler): """Class to perform under-sampling based on one-sided selection method. Read more in the :ref:`User Guide <condensed_nearest_neighbors>`. Parameters ---------- {sampling_strategy} return_indices : bool, optional (default=False) Whether or not to return the indices of the samples randomly selected. .. deprecated:: 0.4 ``return_indices`` is deprecated. Use the attribute ``sample_indices_`` instead. {random_state} n_neighbors : int or object, optional (default=\ KNeighborsClassifier(n_neighbors=1)) If ``int``, size of the neighbourhood to consider to compute the nearest neighbors. If object, an estimator that inherits from :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to find the nearest-neighbors. n_seeds_S : int, optional (default=1) Number of samples to extract in order to build the set S. 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. Attributes ---------- sample_indices_ : ndarray, shape (n_new_samples) Indices of the samples selected. .. versionadded:: 0.4 ``sample_indices_`` used instead of ``return_indices=True``. Notes ----- The method is based on [1]_. Supports multi-class resampling. A one-vs.-one scheme is used when sampling a class as proposed in [1]_. For each class to be sampled, all samples of this class and the minority class are used during the sampling procedure. References ---------- .. [1] M. Kubat, S. Matwin, "Addressing the curse of imbalanced training sets: one-sided selection," In ICML, vol. 97, pp. 179-186, 1997. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.under_sampling import \ OneSidedSelection # 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}}) >>> oss = OneSidedSelection(random_state=42) >>> X_res, y_res = oss.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({{1: 496, 0: 100}}) """
[docs] def __init__(self, sampling_strategy='auto', return_indices=False, random_state=None, n_neighbors=None, n_seeds_S=1, n_jobs=1, ratio=None): super(OneSidedSelection, self).__init__( sampling_strategy=sampling_strategy, ratio=ratio) self.random_state = random_state self.return_indices = return_indices self.n_neighbors = n_neighbors self.n_seeds_S = n_seeds_S self.n_jobs = n_jobs
def _validate_estimator(self): """Private function to create the NN estimator""" if self.n_neighbors is None: self.estimator_ = KNeighborsClassifier( n_neighbors=1, n_jobs=self.n_jobs) elif isinstance(self.n_neighbors, int): self.estimator_ = KNeighborsClassifier( n_neighbors=self.n_neighbors, n_jobs=self.n_jobs) elif isinstance(self.n_neighbors, KNeighborsClassifier): self.estimator_ = clone(self.n_neighbors) else: raise ValueError('`n_neighbors` has to be a int or an object' ' inherited from KNeighborsClassifier.' ' Got {} instead.'.format(type(self.n_neighbors))) def _fit_resample(self, X, y): if self.return_indices: deprecate_parameter(self, '0.4', 'return_indices', 'sample_indices_') self._validate_estimator() random_state = check_random_state(self.random_state) target_stats = Counter(y) class_minority = min(target_stats, key=target_stats.get) idx_under = np.empty((0, ), dtype=int) for target_class in np.unique(y): if target_class in self.sampling_strategy_.keys(): # select a sample from the current class idx_maj = np.flatnonzero(y == target_class) sel_idx_maj = random_state.randint( low=0, high=target_stats[target_class], size=self.n_seeds_S) idx_maj_sample = idx_maj[sel_idx_maj] minority_class_indices = np.flatnonzero(y == class_minority) C_indices = np.append(minority_class_indices, idx_maj_sample) # create the set composed of all minority samples and one # sample from the current class. C_x = safe_indexing(X, C_indices) C_y = safe_indexing(y, C_indices) # create the set S with removing the seed from S # since that it will be added anyway idx_maj_extracted = np.delete(idx_maj, sel_idx_maj, axis=0) S_x = safe_indexing(X, idx_maj_extracted) S_y = safe_indexing(y, idx_maj_extracted) self.estimator_.fit(C_x, C_y) pred_S_y = self.estimator_.predict(S_x) S_misclassified_indices = np.flatnonzero(pred_S_y != S_y) idx_tmp = idx_maj_extracted[S_misclassified_indices] idx_under = np.concatenate( (idx_under, idx_maj_sample, idx_tmp), axis=0) else: idx_under = np.concatenate( (idx_under, np.flatnonzero(y == target_class)), axis=0) X_resampled = safe_indexing(X, idx_under) y_resampled = safe_indexing(y, idx_under) # apply Tomek cleaning tl = TomekLinks( sampling_strategy=list(self.sampling_strategy_.keys())) X_cleaned, y_cleaned = tl.fit_resample(X_resampled, y_resampled) self.sample_indices_ = safe_indexing(idx_under, tl.sample_indices_) if self.return_indices: return (X_cleaned, y_cleaned, self.sample_indices_) return X_cleaned, y_cleaned