def test_sample_selection_working():
pool_classifiers = create_pool_all_agree(0, 10) + create_pool_all_agree(
1, 5)
meta_test = METADES(pool_classifiers=pool_classifiers)
meta_test.n_classifiers_ = len(pool_classifiers)
meta_test.DSEL_processed_ = np.ones((5, 15))
meta_test.DSEL_processed_[(1, 3, 4), 5:] = 0
expected = np.asarray([1, 1 / 3, 1, 1 / 3, 1 / 3])
value = meta_test._sample_selection_agreement()
assert np.array_equal(value, expected)
def test_compute_meta_features(example_estimate_competence,
create_pool_classifiers):
X, y, nn, _, dsel_processed, dsel_scores = example_estimate_competence
query = np.ones((1, 2))
pool = create_pool_classifiers
meta_test = METADES(pool_classifiers=[pool[0]])
meta_test.n_classifiers_ = 1
meta_test.k_ = 7
meta_test.Kp_ = 5
# Considering only one classifier in the pool (index = 0)
meta_test.DSEL_processed_ = dsel_processed[:, 0].reshape(-1, 1)
meta_test.dsel_scores_ = dsel_scores[:, 0, :].reshape(15, 1, 2)
meta_test.DSEL_target_ = y
meta_test.n_classes_ = 2
neighbors_op = nn[2, 0:meta_test.Kp]
# Expected values for each meta feature based on the data of ex1.
expected_f1 = [1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0]
expected_f2 = [1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0]
expected_f3 = [4.0 / 7.0]
expected_f4 = [0.0, 1.0, 1.0, 1.0, 0.0]
expected_f5 = [0.5]
scores = np.empty(
(query.shape[0], meta_test.n_classifiers_, meta_test.n_classes_))
for index, clf in enumerate(meta_test.pool_classifiers):
scores[:, index, :] = clf.predict_proba(query)
meta_features = meta_test.compute_meta_features(scores, nn[0, :],
neighbors_op)
expected = np.asarray(expected_f1 + expected_f2 + expected_f3 +
expected_f4 + expected_f5)
assert np.array_equal(meta_features, expected.reshape(1, -1))
def test_select_no_competent_classifiers_batch():
meta_test = METADES()
meta_test.n_classifiers_ = 3
competences = np.zeros((10, meta_test.n_classifiers_))
selected_matrix = meta_test.select(competences)
assert np.all(selected_matrix)
