def test_estimate_competence_ones():
query = np.atleast_2d([1, 1])
probabilistic_test = BaseProbabilistic(create_pool_classifiers())
probabilistic_test.k_ = 7
distances = distances_ex1[0, 0:3].reshape(1, -1)
neighbors = np.array([[0, 2, 1]])
probabilistic_test.C_src_ = np.ones((3, 3))
competence = probabilistic_test.estimate_competence(
query, neighbors, distances)
assert (competence == 1.0).all()
def test_estimate_competence_ones(example_estimate_competence):
distances = example_estimate_competence[3]
query = np.atleast_2d([1, 1])
probabilistic_test = BaseProbabilistic()
probabilistic_test.k_ = 7
distances = distances[0, 0:3].reshape(1, -1)
neighbors = np.array([[0, 2, 1]])
probabilistic_test.C_src_ = np.ones((3, 3))
competence = probabilistic_test.estimate_competence(neighbors,
distances)
assert (competence == 1.0).all()
def test_estimate_competence_zeros(example_estimate_competence):
distances = example_estimate_competence[3]
query = np.atleast_2d([1, 1])
probabilistic_test = BaseProbabilistic()
probabilistic_test.k_ = 7
distances = distances[0, 0:3].reshape(1, -1)
neighbors = np.array([[0, 2, 1]])
probabilistic_test.C_src_ = np.zeros((3, 3))
competence = probabilistic_test.estimate_competence(
competence_region=neighbors,
distances=distances)
assert np.sum(competence) == 0.0
def test_estimate_competence_batch():
n_samples = 10
query = np.ones((n_samples, 2))
probabilistic_test = BaseProbabilistic()
probabilistic_test.k_ = 7
distances = np.tile([0.5, 1.0, 2.0], (n_samples, 1))
neighbors = np.tile([0, 1, 2], (n_samples, 1))
probabilistic_test.C_src_ = np.array(
[[0.5, 0.2, 0.8], [1.0, 1.0, 1.0], [1.0, 0.6, 0.3]])
expected = np.tile([0.665, 0.458, 0.855], (n_samples, 1))
competence = probabilistic_test.estimate_competence(
competence_region=neighbors,
distances=distances)
assert np.allclose(competence, expected, atol=0.01)