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test_instance_hardness_threshold.py
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test_instance_hardness_threshold.py
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"""Test the module ."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
from __future__ import print_function
import numpy as np
from pytest import raises
from sklearn.utils.testing import assert_array_equal
from sklearn.ensemble import GradientBoostingClassifier
from imblearn.under_sampling import InstanceHardnessThreshold
RND_SEED = 0
X = np.array([[-0.3879569, 0.6894251], [-0.09322739, 1.28177189],
[-0.77740357, 0.74097941], [0.91542919, -0.65453327],
[-0.03852113, 0.40910479], [-0.43877303, 1.07366684],
[-0.85795321, 0.82980738], [-0.18430329, 0.52328473],
[-0.30126957, -0.66268378], [-0.65571327, 0.42412021],
[-0.28305528, 0.30284991], [0.20246714, -0.34727125],
[1.06446472, -1.09279772], [0.30543283, -0.02589502],
[-0.00717161, 0.00318087]])
Y = np.array([0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0])
ESTIMATOR = 'gradient-boosting'
def test_iht_wrong_estimator():
ratio = 0.7
est = 'rnd'
iht = InstanceHardnessThreshold(
estimator=est, ratio=ratio, random_state=RND_SEED)
with raises(NotImplementedError):
iht.fit_sample(X, Y)
def test_iht_init():
ratio = 'auto'
iht = InstanceHardnessThreshold(
ESTIMATOR, ratio=ratio, random_state=RND_SEED)
assert iht.ratio == ratio
assert iht.random_state == RND_SEED
def test_iht_fit_sample():
iht = InstanceHardnessThreshold(ESTIMATOR, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_with_indices():
iht = InstanceHardnessThreshold(
ESTIMATOR, return_indices=True, random_state=RND_SEED)
X_resampled, y_resampled, idx_under = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
idx_gt = np.array([0, 3, 9, 12, 13, 14, 1, 2, 5, 6, 7, 10])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
assert_array_equal(idx_under, idx_gt)
def test_iht_fit_sample_half():
ratio = 0.7
iht = InstanceHardnessThreshold(
ESTIMATOR, ratio=ratio, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.03852113, 0.40910479],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.30126957, -0.66268378],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_knn():
est = 'knn'
iht = InstanceHardnessThreshold(est, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.30126957, -0.66268378],
[0.20246714, -0.34727125]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_decision_tree():
est = 'decision-tree'
iht = InstanceHardnessThreshold(est, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_random_forest():
est = 'random-forest'
iht = InstanceHardnessThreshold(est, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.03852113, 0.40910479],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_adaboost():
est = 'adaboost'
iht = InstanceHardnessThreshold(est, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_gradient_boosting():
est = 'gradient-boosting'
iht = InstanceHardnessThreshold(est, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_linear_svm():
est = 'linear-svm'
iht = InstanceHardnessThreshold(est, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.03852113, 0.40910479],
[-0.43877303, 1.07366684],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_class_obj():
est = GradientBoostingClassifier(random_state=RND_SEED)
iht = InstanceHardnessThreshold(estimator=est, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_sample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251],
[0.91542919, -0.65453327],
[-0.65571327, 0.42412021],
[1.06446472, -1.09279772],
[0.30543283, -0.02589502],
[-0.00717161, 0.00318087],
[-0.09322739, 1.28177189],
[-0.77740357, 0.74097941],
[-0.43877303, 1.07366684],
[-0.85795321, 0.82980738],
[-0.18430329, 0.52328473],
[-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_iht_fit_sample_wrong_class_obj():
from sklearn.cluster import KMeans
est = KMeans()
iht = InstanceHardnessThreshold(estimator=est, random_state=RND_SEED)
with raises(ValueError, match="Invalid parameter `estimator`"):
iht.fit_sample(X, Y)