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
|
from collections import Counter
import numpy as np
import pytest
from sklearn.cluster import MiniBatchKMeans
from imblearn.over_sampling import (
ADASYN,
SMOTE,
SMOTEN,
SMOTENC,
SVMSMOTE,
BorderlineSMOTE,
KMeansSMOTE,
)
from imblearn.utils.testing import _CustomNearestNeighbors
@pytest.fixture
def numerical_data():
rng = np.random.RandomState(0)
X = rng.randn(100, 2)
y = np.repeat([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0], 5)
return X, y
@pytest.fixture
def categorical_data():
rng = np.random.RandomState(0)
feature_1 = ["A"] * 10 + ["B"] * 20 + ["C"] * 30
feature_2 = ["A"] * 40 + ["B"] * 20
feature_3 = ["A"] * 20 + ["B"] * 20 + ["C"] * 10 + ["D"] * 10
X = np.array([feature_1, feature_2, feature_3], dtype=object).T
rng.shuffle(X)
y = np.array([0] * 20 + [1] * 40, dtype=np.int32)
y_labels = np.array(["not apple", "apple"], dtype=object)
y = y_labels[y]
return X, y
@pytest.fixture
def heterogeneous_data():
rng = np.random.RandomState(42)
X = np.empty((30, 4), dtype=object)
X[:, :2] = rng.randn(30, 2)
X[:, 2] = rng.choice(["a", "b", "c"], size=30).astype(object)
X[:, 3] = rng.randint(3, size=30)
y = np.array([0] * 10 + [1] * 20)
return X, y, [2, 3]
@pytest.mark.parametrize(
"smote", [BorderlineSMOTE(), SVMSMOTE()], ids=["borderline", "svm"]
)
def test_smote_m_neighbors(numerical_data, smote):
# check that m_neighbors is properly set. Regression test for:
# https://github.com/scikit-learn-contrib/imbalanced-learn/issues/568
X, y = numerical_data
_ = smote.fit_resample(X, y)
assert smote.nn_k_.n_neighbors == 6
assert smote.nn_m_.n_neighbors == 11
@pytest.mark.parametrize(
"smote, neighbor_estimator_name",
[
(ADASYN(random_state=0), "n_neighbors"),
(BorderlineSMOTE(random_state=0), "k_neighbors"),
(
KMeansSMOTE(
kmeans_estimator=MiniBatchKMeans(n_init=1, random_state=0),
random_state=1,
),
"k_neighbors",
),
(SMOTE(random_state=0), "k_neighbors"),
(SVMSMOTE(random_state=0), "k_neighbors"),
],
ids=["adasyn", "borderline", "kmeans", "smote", "svm"],
)
def test_numerical_smote_custom_nn(numerical_data, smote, neighbor_estimator_name):
X, y = numerical_data
params = {
neighbor_estimator_name: _CustomNearestNeighbors(n_neighbors=5),
}
smote.set_params(**params)
X_res, _ = smote.fit_resample(X, y)
assert X_res.shape[0] >= 120
def test_categorical_smote_k_custom_nn(categorical_data):
X, y = categorical_data
smote = SMOTEN(k_neighbors=_CustomNearestNeighbors(n_neighbors=5))
X_res, y_res = smote.fit_resample(X, y)
assert X_res.shape == (80, 3)
assert Counter(y_res) == {"apple": 40, "not apple": 40}
def test_heterogeneous_smote_k_custom_nn(heterogeneous_data):
X, y, categorical_features = heterogeneous_data
smote = SMOTENC(
categorical_features, k_neighbors=_CustomNearestNeighbors(n_neighbors=5)
)
X_res, y_res = smote.fit_resample(X, y)
assert X_res.shape == (40, 4)
assert Counter(y_res) == {0: 20, 1: 20}
@pytest.mark.parametrize(
"smote",
[BorderlineSMOTE(random_state=0), SVMSMOTE(random_state=0)],
ids=["borderline", "svm"],
)
def test_numerical_smote_extra_custom_nn(numerical_data, smote):
X, y = numerical_data
smote.set_params(m_neighbors=_CustomNearestNeighbors(n_neighbors=5))
X_res, y_res = smote.fit_resample(X, y)
assert X_res.shape == (120, 2)
assert Counter(y_res) == {0: 60, 1: 60}
# FIXME: to be removed in 0.12
@pytest.mark.parametrize(
"sampler",
[
ADASYN(random_state=0),
BorderlineSMOTE(random_state=0),
SMOTE(random_state=0),
SMOTEN(random_state=0),
SMOTENC([0], random_state=0),
SVMSMOTE(random_state=0),
],
)
def test_n_jobs_deprecation_warning(numerical_data, sampler):
X, y = numerical_data
sampler.set_params(n_jobs=2)
warning_msg = "The parameter `n_jobs` has been deprecated"
with pytest.warns(FutureWarning, match=warning_msg):
sampler.fit_resample(X, y)
|
