def test_clustering_AffinityPropagationNative_direct(self, ens1): method = encore.AffinityPropagationNative() distance_matrix = encore.get_distance_matrix(ens1) cluster_assignment, details = method(distance_matrix) expected_value = 7 assert len(set(cluster_assignment)) == expected_value, \ "Unexpected result: {0}".format(cluster_assignment)
def test_clustering_AffinityPropagationNative_direct(self, ens1): method = encore.AffinityPropagationNative() distance_matrix = encore.get_distance_matrix(ens1) cluster_assignment, details = method(distance_matrix) expected_value = 7 assert len(set(cluster_assignment)) == expected_value, \ "Unexpected result: {0}".format(cluster_assignment)
def test_clustering_DBSCAN_direct(self, ens1): pytest.importorskip('sklearn') method = encore.DBSCAN(eps=0.5, min_samples=2) distance_matrix = encore.get_distance_matrix(ens1) cluster_assignment, details = method(distance_matrix) expected_value = 2 assert len(set(cluster_assignment)) == expected_value, \ "Unexpected result: {0}".format(cluster_assignment)
def test_dimensionality_reduction_SPENative_direct(self, ens1): dimension = 2 method = encore.StochasticProximityEmbeddingNative(dimension=dimension) distance_matrix = encore.get_distance_matrix(ens1) coordinates, details = method(distance_matrix) assert_equal(coordinates.shape[0], dimension, err_msg="Unexpected result in dimensionality reduction: {0}".format( coordinates))
def test_clustering_DBSCAN_direct(self, ens1): pytest.importorskip('sklearn') method = encore.DBSCAN(eps=0.5, min_samples=2) distance_matrix = encore.get_distance_matrix(ens1) cluster_assignment, details = method(distance_matrix) expected_value = 2 assert len(set(cluster_assignment)) == expected_value, \ "Unexpected result: {0}".format(cluster_assignment)
def test_clustering_AffinityPropagation_direct(self, ens1): pytest.importorskip('sklearn') method = encore.AffinityPropagation() distance_matrix = encore.get_distance_matrix(ens1) cluster_assignment = method(distance_matrix) expected_value = 7 assert len(set(cluster_assignment)) == expected_value, \ "Unexpected result: {0}".format(cluster_assignment)
def test_clustering_DBSCAN_direct(self): method = encore.DBSCAN(eps=0.5, min_samples=2) distance_matrix = encore.get_distance_matrix(self.ens1) cluster_assignment, details = method(distance_matrix) expected_value = 2 assert_equal( len(set(cluster_assignment)), expected_value, err_msg="Unexpected result: {0}".format(cluster_assignment))
def test_clustering_AffinityPropagation_direct(self): method = encore.AffinityPropagation() distance_matrix = encore.get_distance_matrix(self.ens1) cluster_assignment, details = method(distance_matrix) expected_value = 7 assert_equal( len(set(cluster_assignment)), expected_value, err_msg="Unexpected result: {0}".format(cluster_assignment))
def test_dimensionality_reduction_SPENative_direct(self, ens1): dimension = 2 method = encore.StochasticProximityEmbeddingNative(dimension=dimension) distance_matrix = encore.get_distance_matrix(ens1) coordinates, details = method(distance_matrix) assert_equal( coordinates.shape[0], dimension, err_msg="Unexpected result in dimensionality reduction: {0}". format(coordinates))
def test_dres_without_superimposition(self, ens1, ens2): distance_matrix = encore.get_distance_matrix( encore.merge_universes([ens1, ens2]), superimpose=False) results, details = encore.dres([ens1, ens2], distance_matrix = distance_matrix) result_value = results[0,1] expected_value = 0.68 assert_almost_equal(result_value, expected_value, decimal=1, err_msg="Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}.".format(result_value, expected_value))
def test_dres_without_superimposition(self, ens1, ens2): distance_matrix = encore.get_distance_matrix(encore.merge_universes( [ens1, ens2]), superimpose=False) results, details = encore.dres([ens1, ens2], distance_matrix=distance_matrix) result_value = results[0, 1] expected_value = 0.68 assert_almost_equal( result_value, expected_value, decimal=1, err_msg= "Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}." .format(result_value, expected_value))