def test_estimate_competence_woods(index, expected):
lca_test = LCA(create_pool_classifiers())
lca_test.processed_dsel = dsel_processed_ex1
lca_test.neighbors = neighbors_ex1[index, :]
lca_test.distances = distances_ex1[index, :]
lca_test.DFP_mask = [1, 1, 1]
lca_test.DSEL_target = y_dsel_ex1
query = np.array([1, 1])
competences = lca_test.estimate_competence(query.reshape(1, -1))
assert np.isclose(competences, expected).all()
def test_estimate_competence_diff_target(index):
query = np.array([1, 1])
lca = LCA(create_pool_classifiers())
lca.processed_dsel = dsel_processed_ex1
lca.DSEL_target = np.ones(15, dtype=int) * 3
lca.neighbors = neighbors_ex1[index, :]
lca.distances = distances_ex1[index, :]
lca.DFP_mask = [1, 1, 1]
expected = [0.0, 0.0, 0.0]
competences = lca.estimate_competence(query.reshape(1, -1))
assert np.isclose(competences, expected).all()
def test_estimate_competence_woods(index, expected):
lca_test = LCA(create_pool_classifiers())
lca_test.processed_dsel = dsel_processed_ex1
lca_test.neighbors = neighbors_ex1[index, :]
lca_test.distances = distances_ex1[index, :]
lca_test.DFP_mask = [1, 1, 1]
lca_test.DSEL_target = y_dsel_ex1
query = np.atleast_2d([1, 1])
predictions = []
for clf in lca_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = lca_test.estimate_competence(query, predictions=np.array(predictions))
assert np.allclose(competences, expected)
def test_estimate_competence_diff_target(index):
lca_test = LCA(create_pool_classifiers())
lca_test.processed_dsel = dsel_processed_ex1
lca_test.DSEL_target = np.ones(15, dtype=int) * 3
lca_test.neighbors = neighbors_ex1[index, :]
lca_test.distances = distances_ex1[index, :]
lca_test.DFP_mask = [1, 1, 1]
query = np.atleast_2d([1, 1])
expected = [0.0, 0.0, 0.0]
predictions = []
for clf in lca_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = lca_test.estimate_competence(query, predictions=np.array(predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_batch(example_estimate_competence):
_, y, neighbors, distances, dsel_processed, _ = example_estimate_competence
expected = np.array([[0.75000000, 0.66666667, 0.75000000],
[0.80000000, 1.00000000, 0.80000000],
[1.00000000, 0.60000000, 0.50000000]])
lca_test = LCA()
lca_test.DSEL_processed_ = dsel_processed
lca_test.DSEL_target_ = y
query = np.ones((3, 2))
predictions = np.array([[0, 1, 0]])
competences = lca_test.estimate_competence(neighbors,
distances=distances,
predictions=np.array(
predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_batch():
expected = np.array([[0.75000000, 0.66666667, 0.75000000],
[0.80000000, 1.00000000, 0.80000000],
[1.00000000, 0.60000000, 0.50000000]])
lca_test = LCA(create_pool_classifiers())
lca_test.processed_dsel = dsel_processed_ex1
lca_test.neighbors = neighbors_ex1
lca_test.distances = distances_ex1
lca_test.DFP_mask = np.ones((3, 3))
lca_test.DSEL_target = y_dsel_ex1
query = np.ones((3, 2))
predictions = []
for clf in lca_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = lca_test.estimate_competence(query, predictions=np.array(predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_diff_target(index, example_estimate_competence,
create_pool_classifiers):
_, y, neighbors, distances, dsel_processed, _ = example_estimate_competence
lca_test = LCA(create_pool_classifiers)
lca_test.DSEL_processed_ = dsel_processed
lca_test.DSEL_target_ = np.ones(15, dtype=int) * 3
neighbors = neighbors[index, :].reshape(1, -1)
distances = distances[index, :].reshape(1, -1)
query = np.atleast_2d([1, 1])
expected = [0.0, 0.0, 0.0]
predictions = np.array([[0, 1, 0]])
competences = lca_test.estimate_competence(
query,
neighbors,
distances=distances,
predictions=np.array(predictions))
assert np.isclose(competences, expected).all()