def test_source_competence_minimum_difference():
md_test = MinimumDifference(
[create_base_classifier(return_value=1, return_prob=1.0)])
md_test.dsel_scores = np.array([[0.3, 0.6,
0.1], [1.0 / 3, 1.0 / 3, 1.0 / 3],
[0.5, 0.2, 0.3], [0.5, 0.2, 0.3]])
md_test.DSEL_target = [1, 0, 0, 1]
md_test.n_classes = 3
md_test.n_samples = 4
C_src = md_test.source_competence()
expected = np.array([[0.3], [0.0], [0.2], [-0.3]])
assert np.allclose(C_src, expected, atol=0.01)
def test_source_competence_minimum_difference():
md_test = MinimumDifference()
md_test.n_classifiers_ = 1
md_test.dsel_scores_ = np.array([[[0.3, 0.6, 0.1],
[1.0 / 3, 1.0 / 3, 1.0 / 3],
[0.5, 0.2, 0.3],
[0.5, 0.2, 0.3]]]).reshape(4, 1, 3)
md_test.DSEL_target_ = [1, 0, 0, 1]
md_test.n_classes_ = 3
md_test.n_samples_ = 4
C_src = md_test.source_competence()
expected = np.array([[0.3], [0.0], [0.2], [-0.3]])
assert np.allclose(C_src, expected, atol=0.01)
def test_source_competence_minimum_difference():
pool_classifiers = [
create_base_classifier(return_value=1, return_prob=1.0)
]
md_test = MinimumDifference(pool_classifiers=pool_classifiers)
md_test.n_classifiers_ = len(pool_classifiers)
md_test.dsel_scores_ = np.array(
[[[0.3, 0.6, 0.1], [1.0 / 3, 1.0 / 3, 1.0 / 3], [0.5, 0.2, 0.3],
[0.5, 0.2, 0.3]]]).reshape(4, 1,
3) # 4 samples, 1 classifier, 3 classes
md_test.DSEL_target_ = [1, 0, 0, 1]
md_test.n_classes_ = 3
md_test.n_samples_ = 4
C_src = md_test.source_competence()
expected = np.array([[0.3], [0.0], [0.2], [-0.3]])
assert np.allclose(C_src, expected, atol=0.01)