/
_one_sided_selection.py
227 lines (180 loc) · 8.07 KB
/
_one_sided_selection.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
"""Class to perform under-sampling based on one-sided selection method."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numbers
import warnings
from collections import Counter
import numpy as np
from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import _safe_indexing, check_random_state
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring, _random_state_docstring
from ...utils._param_validation import HasMethods, Interval
from ..base import BaseCleaningSampler
from ._tomek_links import TomekLinks
@Substitution(
sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
n_jobs=_n_jobs_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}
{random_state}
n_neighbors : int or estimator object, default=None
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. If `None`, a
:class:`~sklearn.neighbors.KNeighborsClassifier` with a 1-NN rules will
be used.
n_seeds_S : int, default=1
Number of samples to extract in order to build the set S.
{n_jobs}
Attributes
----------
sampling_strategy_ : dict
Dictionary containing the information to sample the dataset. The keys
corresponds to the class labels from which to sample and the values
are the number of samples to sample.
estimator_ : estimator object
Validated K-nearest neighbors estimator created from parameter `n_neighbors`.
.. deprecated:: 0.12
`estimator_` is deprecated in 0.12 and will be removed in 0.14. Use
`estimators_` instead that contains the list of all K-nearest
neighbors estimator used for each pair of class.
estimators_ : list of estimator objects of shape (n_resampled_classes - 1,)
Contains the K-nearest neighbor estimator used for per of classes.
.. versionadded:: 0.12
sample_indices_ : ndarray of shape (n_new_samples,)
Indices of the samples selected.
.. versionadded:: 0.4
n_features_in_ : int
Number of features in the input dataset.
.. versionadded:: 0.9
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during `fit`. Defined only when `X` has feature
names that are all strings.
.. versionadded:: 0.10
See Also
--------
EditedNearestNeighbours : Undersample by editing noisy samples.
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
>>> 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}})
"""
_parameter_constraints: dict = {
**BaseCleaningSampler._parameter_constraints,
"n_neighbors": [
Interval(numbers.Integral, 1, None, closed="left"),
HasMethods(["kneighbors", "kneighbors_graph"]),
None,
],
"n_seeds_S": [Interval(numbers.Integral, 1, None, closed="left")],
"n_jobs": [numbers.Integral, None],
"random_state": ["random_state"],
}
def __init__(
self,
*,
sampling_strategy="auto",
random_state=None,
n_neighbors=None,
n_seeds_S=1,
n_jobs=None,
):
super().__init__(sampling_strategy=sampling_strategy)
self.random_state = random_state
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:
estimator = KNeighborsClassifier(n_neighbors=1, n_jobs=self.n_jobs)
elif isinstance(self.n_neighbors, int):
estimator = KNeighborsClassifier(
n_neighbors=self.n_neighbors, n_jobs=self.n_jobs
)
elif isinstance(self.n_neighbors, KNeighborsClassifier):
estimator = clone(self.n_neighbors)
return estimator
def _fit_resample(self, X, y):
estimator = 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)
self.estimators_ = []
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.estimators_.append(clone(estimator).fit(C_x, C_y))
pred_S_y = self.estimators_[-1].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_)
return X_cleaned, y_cleaned
@property
def estimator_(self):
"""Last fitted k-NN estimator."""
warnings.warn(
"`estimator_` attribute has been deprecated in 0.12 and will be "
"removed in 0.14. Use `estimators_` instead.",
FutureWarning,
)
return self.estimators_[-1]
def _more_tags(self):
return {"sample_indices": True}