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_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, :])