def test_estimate_competence2(index, expected, example_estimate_competence):
_, _, neighbors, distances, dsel_processed, _ = example_estimate_competence
mcb_test = MCB()
mcb_test.n_classifiers_ = 3
mcb_test.DSEL_processed_ = dsel_processed
neighbors = neighbors[index, :].reshape(1, -1)
distances = distances[index, :].reshape(1, -1)
# Only changing the pre-processed BKS to see if the filter works.
mcb_test.BKS_DSEL_ = bks_dsel_ex2
predictions = np.array([[0, 1, 0]])
competences = mcb_test.estimate_competence(
neighbors, distances=distances, predictions=np.atleast_2d(predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence(index, expected):
query = np.atleast_2d([1, 1])
mcb_test = MCB(create_pool_classifiers())
mcb_test.n_classifiers_ = 3
mcb_test.DSEL_processed_ = dsel_processed_ex1
neighbors = neighbors_ex1[index, :].reshape(1, -1)
distances = distances_ex1[index, :].reshape(1, -1)
mcb_test.BKS_DSEL_ = bks_dsel_ex1
predictions = []
for clf in mcb_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = mcb_test.estimate_competence(
query,
neighbors,
distances=distances,
predictions=np.atleast_2d(predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_batch(example_estimate_competence):
_, _, neighbors, distances, dsel_processed, _ = example_estimate_competence
expected = np.array([[0.57142857, 0.71428571, 0.71428571],
[0.71428571, 0.85714286, 0.71428571],
[0.57142857, 0.71428571, 0.57142857]])
mcb_test = MCB()
mcb_test.n_classifiers_ = 3
mcb_test.DSEL_processed_ = dsel_processed
# Only changing the pre-processed BKS to see if the filter works.
mcb_test.BKS_DSEL_ = bks_dsel_ex3
predictions = np.array([0, 1, 0])
competences = mcb_test.estimate_competence(neighbors,
distances=distances,
predictions=np.tile(
predictions, (3, 1)))
assert np.isclose(competences, expected).all()
def test_estimate_competence_batch():
query = np.ones((3, 2))
expected = np.array([[0.57142857, 0.71428571, 0.71428571],
[0.71428571, 0.85714286, 0.71428571],
[0.57142857, 0.71428571, 0.57142857]])
mcb_test = MCB(create_pool_classifiers())
mcb_test.n_classifiers_ = 3
mcb_test.DSEL_processed_ = dsel_processed_ex1
neighbors = neighbors_ex1
distances = distances_ex1
# Only changing the pre-processed BKS to see if the filter works.
mcb_test.BKS_DSEL_ = bks_dsel_ex3
predictions = []
for clf in mcb_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = mcb_test.estimate_competence(query,
neighbors,
distances=distances,
predictions=np.tile(
predictions, (3, 1)))
assert np.isclose(competences, expected).all()