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,
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()