def test_des_clustering():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
des_clustering = DESClustering(pool_classifiers, rng=rng)
des_clustering.fit(X_dsel, y_dsel)
assert np.isclose(des_clustering.score(X_test, y_test), 0.97872340425531912)
def test_diversity_metric_ratio():
test = DESClustering(create_pool_classifiers() * 10, metric='ratio')
# Mocking this method to avoid preprocessing the cluster information that
# is not required in this test.
test._preprocess_clusters = MagicMock(return_value=1)
test.fit(X_dsel_ex1, y_dsel_ex1)
assert test.diversity_func_ == ratio_errors
def test_estimate_competence(create_pool_classifiers,
example_estimate_competence):
query = np.atleast_2d([1, 1])
clustering_test = DESClustering(create_pool_classifiers * 2,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33)
X, y = example_estimate_competence[0:2]
# Keep the original predict method to change after
clustering_test.clustering.predict = MagicMock(
return_value=return_cluster_index_ex2)
clustering_test.fit(X, y)
clustering_test.clustering_.predict = MagicMock(return_value=0)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences,
clustering_test.performance_cluster_[0, :])
clustering_test.clustering_.predict = MagicMock(return_value=1)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences,
clustering_test.performance_cluster_[1, :])
def test_fit_heterogeneous_clusters(example_estimate_competence,
create_pool_classifiers):
clustering_test = DESClustering(create_pool_classifiers,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33)
X, y = example_estimate_competence[0:2]
clustering_test.clustering.predict = MagicMock(
return_value=return_cluster_index_ex2)
clustering_test.fit(X, y)
# Index selected should be of any classifier that predicts the label 0
assert np.isclose(clustering_test.performance_cluster_[:, 1],
[0.428, 0.375],
atol=0.01).all()
assert np.isclose(clustering_test.performance_cluster_[:, 0],
[0.572, 0.625],
atol=0.01).all()
assert clustering_test.indices_[0,
0] == 0 or clustering_test.indices_[0,
0] == 2
assert clustering_test.indices_[1,
0] == 0 or clustering_test.indices_[1,
0] == 2
def test_diversity_metric_ratio(create_X_y):
X, y = create_X_y
test = DESClustering(metric_diversity='ratio')
# Mocking this method to avoid preprocessing the cluster
# information that is not required in this test.
test._preprocess_clusters = MagicMock(return_value=1)
test.fit(X, y)
assert test.diversity_func_ == ratio_errors
def test_des_clustering():
from sklearn.cluster import KMeans
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
cluster = KMeans(n_clusters=5, random_state=rng)
des_clustering = DESClustering(pool_classifiers, clustering=cluster)
des_clustering.fit(X_dsel, y_dsel)
assert np.isclose(des_clustering.score(X_test, y_test),
0.97872340425531912)
def test_diversity_metric_DF(create_X_y):
X, y = create_X_y
test = DESClustering(metric='DF')
# Mocking this method to avoid preprocessing the cluster
# information that is not required in this test.
test._preprocess_clusters = MagicMock(return_value=1)
test.fit(X, y)
assert test.diversity_func_ == negative_double_fault
def test_des_clustering_proba():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
des_clustering = DESClustering(pool_classifiers, rng=rng)
des_clustering.fit(X_dsel, y_dsel)
probas = des_clustering.predict_proba(X_test)
expected = np.load('deslib/tests/expected_values/des_clustering_proba_integration.npy')
assert np.allclose(probas, expected)
def test_des_clustering_proba():
from sklearn.cluster import KMeans
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
cluster = KMeans(n_clusters=5, random_state=rng)
des_clustering = DESClustering(pool_classifiers, clustering=cluster)
des_clustering.fit(X_dsel, y_dsel)
probas = des_clustering.predict_proba(X_test)
expected = np.load(
'deslib/tests/expected_values/des_clustering_proba_integration.npy')
assert np.allclose(probas, expected)
def test_fit_homogeneous_clusters():
clustering_test = DESClustering(create_pool_classifiers()*2, k=2, pct_accuracy=0.5, pct_diversity=0.33)
clustering_test.roc_algorithm.fit_predict = MagicMock(return_value=return_cluster_index_ex1)
clustering_test.DFP_mask = np.ones(clustering_test.n_classifiers)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
assert clustering_test.accuracy_cluster[0, 1] == 0.0 and clustering_test.accuracy_cluster[0, [0, 2]].all() == 1.0
assert clustering_test.accuracy_cluster[1, 1] == 1.0 and clustering_test.accuracy_cluster[1, [0, 2]].all() == 0.0
for idx in clustering_test.indices[0, :]:
assert idx in (0, 2, 3, 5)
def test_fit_heterogeneous_clusters():
clustering_test = DESClustering(create_pool_classifiers(), k=2, pct_accuracy=0.5, pct_diversity=0.33)
clustering_test.roc_algorithm.fit_predict = MagicMock(return_value=return_cluster_index_ex2)
clustering_test.DFP_mask = np.ones(clustering_test.n_classifiers)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
# Index selected should be of any classifier that predicts the class label 0
assert np.isclose(clustering_test.accuracy_cluster[:, 1], [0.428, 0.375], atol=0.01).all()
assert np.isclose(clustering_test.accuracy_cluster[:, 0], [0.572, 0.625], atol=0.01).all()
assert clustering_test.indices[0, 0] == 0 or clustering_test.indices[0, 0] == 2
assert clustering_test.indices[1, 0] == 0 or clustering_test.indices[1, 0] == 2
def test_estimate_competence():
query = np.atleast_2d([1, 1])
clustering_test = DESClustering(create_pool_classifiers(), k=2, pct_accuracy=0.5, pct_diversity=0.33)
clustering_test.roc_algorithm.fit_predict = MagicMock(return_value=return_cluster_index_ex2)
clustering_test.DFP_mask = np.ones(clustering_test.n_classifiers)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
clustering_test.roc_algorithm.predict = MagicMock(return_value=0)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences, clustering_test.accuracy_cluster[0, :])
clustering_test.roc_algorithm.predict = MagicMock(return_value=1)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences, clustering_test.accuracy_cluster[1, :])
def test_fit_clusters_less_diverse():
clustering_test = DESClustering(create_pool_classifiers() * 2,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33,
more_diverse=False)
clustering_test.clustering.predict = MagicMock(
return_value=return_cluster_index_ex1)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
assert (clustering_test.accuracy_cluster_[0, 1] == 0.0
and clustering_test.accuracy_cluster_[0, [0, 2]].all() == 1.0)
assert (clustering_test.accuracy_cluster_[1, 1] == 1.0
and clustering_test.accuracy_cluster_[1, [0, 2]].all() == 0.0)
for idx in clustering_test.indices_[0, :]:
assert idx in (1, 3, 4, 5)
def test_fit_clusters_less_diverse(example_estimate_competence,
create_pool_classifiers):
clustering_test = DESClustering(create_pool_classifiers * 2,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5, pct_diversity=0.33,
more_diverse=False)
X, y = example_estimate_competence[0:2]
clustering_test.clustering.predict = MagicMock(return_value=y)
clustering_test.fit(X, y)
assert (clustering_test.performance_cluster_[0, 1] == 0.0 and
clustering_test.performance_cluster_[0, [0, 2]].all() == 1.0)
assert (clustering_test.performance_cluster_[1, 1] == 1.0 and
clustering_test.performance_cluster_[1, [0, 2]].all() == 0.0)
for idx in clustering_test.indices_[0, :]:
assert idx in (1, 3, 4, 5)
def test_fit_homogeneous_clusters(create_pool_classifiers,
example_estimate_competence):
clustering_test = DESClustering(create_pool_classifiers * 2,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33)
X, y = example_estimate_competence[0:2]
clustering_test.clustering.predict = MagicMock(return_value=y)
clustering_test.fit(X, y)
assert (clustering_test.accuracy_cluster_[0, 1] == 0.0
and clustering_test.accuracy_cluster_[0, [0, 2]].all() == 1.0)
assert (clustering_test.accuracy_cluster_[1, 1] == 1.0
and clustering_test.accuracy_cluster_[1, [0, 2]].all() == 0.0)
for idx in clustering_test.indices_[0, :]:
assert idx in (0, 2, 3, 5)
def test_fit_homogeneous_clusters():
clustering_test = DESClustering(create_pool_classifiers() * 2,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33)
clustering_test.clustering.predict = MagicMock(
return_value=return_cluster_index_ex1)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
assert clustering_test.accuracy_cluster_[
0, 1] == 0.0 and clustering_test.accuracy_cluster_[0,
[0, 2]].all() == 1.0
assert clustering_test.accuracy_cluster_[
1, 1] == 1.0 and clustering_test.accuracy_cluster_[1,
[0, 2]].all() == 0.0
for idx in clustering_test.indices_[0, :]:
assert idx in (0, 2, 3, 5)
def test_fit_heterogeneous_clusters():
clustering_test = DESClustering(create_pool_classifiers(),
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33)
clustering_test.clustering.predict = MagicMock(
return_value=return_cluster_index_ex2)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
# Index selected should be of any classifier that predicts the label 0
assert np.isclose(clustering_test.accuracy_cluster_[:, 1], [0.428, 0.375],
atol=0.01).all()
assert np.isclose(clustering_test.accuracy_cluster_[:, 0], [0.572, 0.625],
atol=0.01).all()
assert clustering_test.indices_[0,
0] == 0 or clustering_test.indices_[0,
0] == 2
assert clustering_test.indices_[1,
0] == 0 or clustering_test.indices_[1,
0] == 2
def test_fit_clusters_less_diverse():
clustering_test = DESClustering(create_pool_classifiers() * 2,
k=2,
pct_accuracy=1.0,
pct_diversity=0.60,
more_diverse=False)
clustering_test.roc_algorithm.fit_predict = MagicMock(
return_value=return_cluster_index_ex1)
clustering_test.DFP_mask = np.ones(clustering_test.n_classifiers)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
assert clustering_test.accuracy_cluster[
0, 1] == 0.0 and clustering_test.accuracy_cluster[0,
[0, 2]].all() == 1.0
assert clustering_test.accuracy_cluster[
1, 1] == 1.0 and clustering_test.accuracy_cluster[1,
[0, 2]].all() == 0.0
assert np.isin(clustering_test.indices[0, :], np.array([1, 3, 5, 4])).all()
def test_not_clustering_algorithm(create_X_y):
X, y = create_X_y
des_clustering = DESClustering(clustering=Perceptron())
with pytest.raises(ValueError):
des_clustering.fit(X, y)
def test_J_higher_than_N(create_X_y):
X, y = create_X_y
with pytest.raises(ValueError):
des_clustering = DESClustering(pct_accuracy=0.3, pct_diversity=0.5)
des_clustering.fit(X, y)
def test_J_N_values(create_X_y):
X, y = create_X_y
with pytest.raises(ValueError):
des_clustering = DESClustering(pct_accuracy=0.5, pct_diversity=0)
des_clustering.fit(X, y)
def test_input_diversity_parameter(create_X_y):
X, y = create_X_y
with pytest.raises(ValueError):
des_clustering = DESClustering(metric_diversity='abc')
des_clustering.fit(X, y)
def test_not_clustering_algorithm():
des_clustering = DESClustering(create_pool_classifiers(),
clustering=Perceptron())
with pytest.raises(ValueError):
des_clustering.fit(X_dsel_ex1, y_dsel_ex1)
def test_input_diversity_parameter():
with pytest.raises(ValueError):
des_clustering = DESClustering(create_pool_classifiers() * 10,
metric='abc')
des_clustering.fit(X_dsel_ex1, y_dsel_ex1)
def test_J_higher_than_N():
with pytest.raises(ValueError):
des_clustering = DESClustering(create_pool_classifiers() * 100,
pct_accuracy=0.3,
pct_diversity=0.5)
des_clustering.fit(X_dsel_ex1, y_dsel_ex1)