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()