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"""Test the module SMOTE."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils._testing import assert_allclose, assert_array_equal
from imblearn.over_sampling import SMOTE
RND_SEED = 0
X = np.array(
[
[0.11622591, -0.0317206],
[0.77481731, 0.60935141],
[1.25192108, -0.22367336],
[0.53366841, -0.30312976],
[1.52091956, -0.49283504],
[-0.28162401, -2.10400981],
[0.83680821, 1.72827342],
[0.3084254, 0.33299982],
[0.70472253, -0.73309052],
[0.28893132, -0.38761769],
[1.15514042, 0.0129463],
[0.88407872, 0.35454207],
[1.31301027, -0.92648734],
[-1.11515198, -0.93689695],
[-0.18410027, -0.45194484],
[0.9281014, 0.53085498],
[-0.14374509, 0.27370049],
[-0.41635887, -0.38299653],
[0.08711622, 0.93259929],
[1.70580611, -0.11219234],
]
)
Y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
R_TOL = 1e-4
def test_sample_regular():
smote = SMOTE(random_state=RND_SEED)
X_resampled, y_resampled = smote.fit_resample(X, Y)
X_gt = np.array(
[
[0.11622591, -0.0317206],
[0.77481731, 0.60935141],
[1.25192108, -0.22367336],
[0.53366841, -0.30312976],
[1.52091956, -0.49283504],
[-0.28162401, -2.10400981],
[0.83680821, 1.72827342],
[0.3084254, 0.33299982],
[0.70472253, -0.73309052],
[0.28893132, -0.38761769],
[1.15514042, 0.0129463],
[0.88407872, 0.35454207],
[1.31301027, -0.92648734],
[-1.11515198, -0.93689695],
[-0.18410027, -0.45194484],
[0.9281014, 0.53085498],
[-0.14374509, 0.27370049],
[-0.41635887, -0.38299653],
[0.08711622, 0.93259929],
[1.70580611, -0.11219234],
[0.29307743, -0.14670439],
[0.84976473, -0.15570176],
[0.61319159, -0.11571668],
[0.66052536, -0.28246517],
]
)
y_gt = np.array(
[0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0]
)
assert_allclose(X_resampled, X_gt, rtol=R_TOL)
assert_array_equal(y_resampled, y_gt)
def test_sample_regular_half():
sampling_strategy = {0: 9, 1: 12}
smote = SMOTE(sampling_strategy=sampling_strategy, random_state=RND_SEED)
X_resampled, y_resampled = smote.fit_resample(X, Y)
X_gt = np.array(
[
[0.11622591, -0.0317206],
[0.77481731, 0.60935141],
[1.25192108, -0.22367336],
[0.53366841, -0.30312976],
[1.52091956, -0.49283504],
[-0.28162401, -2.10400981],
[0.83680821, 1.72827342],
[0.3084254, 0.33299982],
[0.70472253, -0.73309052],
[0.28893132, -0.38761769],
[1.15514042, 0.0129463],
[0.88407872, 0.35454207],
[1.31301027, -0.92648734],
[-1.11515198, -0.93689695],
[-0.18410027, -0.45194484],
[0.9281014, 0.53085498],
[-0.14374509, 0.27370049],
[-0.41635887, -0.38299653],
[0.08711622, 0.93259929],
[1.70580611, -0.11219234],
[0.36784496, -0.1953161],
]
)
y_gt = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0])
assert_allclose(X_resampled, X_gt, rtol=R_TOL)
assert_array_equal(y_resampled, y_gt)
def test_sample_regular_with_nn():
nn_k = NearestNeighbors(n_neighbors=6)
smote = SMOTE(random_state=RND_SEED, k_neighbors=nn_k)
X_resampled, y_resampled = smote.fit_resample(X, Y)
X_gt = np.array(
[
[0.11622591, -0.0317206],
[0.77481731, 0.60935141],
[1.25192108, -0.22367336],
[0.53366841, -0.30312976],
[1.52091956, -0.49283504],
[-0.28162401, -2.10400981],
[0.83680821, 1.72827342],
[0.3084254, 0.33299982],
[0.70472253, -0.73309052],
[0.28893132, -0.38761769],
[1.15514042, 0.0129463],
[0.88407872, 0.35454207],
[1.31301027, -0.92648734],
[-1.11515198, -0.93689695],
[-0.18410027, -0.45194484],
[0.9281014, 0.53085498],
[-0.14374509, 0.27370049],
[-0.41635887, -0.38299653],
[0.08711622, 0.93259929],
[1.70580611, -0.11219234],
[0.29307743, -0.14670439],
[0.84976473, -0.15570176],
[0.61319159, -0.11571668],
[0.66052536, -0.28246517],
]
)
y_gt = np.array(
[0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0]
)
assert_allclose(X_resampled, X_gt, rtol=R_TOL)
assert_array_equal(y_resampled, y_gt)
|
