def test_predict_no_centroids1(): with pytest.raises(AttributeError): kmeans = MultiviewSphericalKMeans() kmeans.centroids_ = [None, None] view1 = np.random.random((10,11)) view2 = np.random.random((10,10)) kmeans.predict([view1, view2])
def perform_clustering(seed, m_data, labels, n_clusters): # Singleview spherical kmeans clustering # Cluster each view separately s_kmeans = SphericalKMeans(n_clusters=n_clusters, random_state=seed, n_init=100) s_clusters_v1 = s_kmeans.fit_predict(m_data[0]) s_clusters_v2 = s_kmeans.fit_predict(m_data[1]) # Concatenate the multiple views into a single view s_data = np.hstack(m_data) s_clusters = s_kmeans.fit_predict(s_data) # Compute nmi between true class labels and singleview cluster labels s_nmi_v1 = nmi_score(labels, s_clusters_v1) s_nmi_v2 = nmi_score(labels, s_clusters_v2) s_nmi = nmi_score(labels, s_clusters) print('Singleview View 1 NMI Score: {0:.3f}\n'.format(s_nmi_v1)) print('Singleview View 2 NMI Score: {0:.3f}\n'.format(s_nmi_v2)) print('Singleview Concatenated NMI Score: {0:.3f}\n'.format(s_nmi)) # Multiview spherical kmeans clustering # Use the MultiviewKMeans instance to cluster the data m_kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, n_init=100, random_state=seed) m_clusters = m_kmeans.fit_predict(m_data) # Compute nmi between true class labels and multiview cluster labels m_nmi = nmi_score(labels, m_clusters) print('Multiview NMI Score: {0:.3f}\n'.format(m_nmi)) return m_clusters
def test_not_2_views(): with pytest.raises(ValueError): view1 = np.random.random((10,)) view2 = np.random.random((10,)) view3 = np.random.random((10,)) kmeans = MultiviewSphericalKMeans() kmeans.fit([view1, view2, view3])
def test_init_not_2_views(data_small): with pytest.raises(ValueError): view1 = np.random.random((2,8)) view2 = np.random.random((2,9)) view3 = np.random.random((2,9)) kmeans = MultiviewSphericalKMeans(init=[view1, view2, view3]) kmeans.fit(data_small)
def test_final_centroids_less_than_n_clusters(): with pytest.warns(ConvergenceWarning): kmeans = MultiviewSphericalKMeans(n_clusters=3, random_state=RANDOM_SEED) view1 = np.random.random((2, 11)) view2 = np.random.random((2, 10)) kmeans.fit([view1, view2])
def test_preprocess_data(data_random): n_clusters = data_random['n_clusters'] kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters) processed = kmeans._preprocess_data(data_random['test_data']) for mat in processed: mat = np.linalg.norm(mat, axis=1) ones = np.ones(mat.shape) assert(np.allclose(mat, ones))
def test_final_centroids_less_than_n_clusters(): with pytest.raises(ConvergenceWarning): kmeans = MultiviewSphericalKMeans(n_clusters=3, random_state=RANDOM_SEED) view1 = np.random.random((2,5)) view2 = np.random.random((2,6)) v1_centroids = np.random.random((3, 5)) v2_centroids = np.random.random((3, 6)) centroids = [v1_centroids, v2_centroids] kmeans._final_centroids([view1, view2], centroids)
def test_final_centroids_no_consensus(): with pytest.raises(ConvergenceWarning): kmeans = MultiviewSphericalKMeans(random_state=RANDOM_SEED) view1 = np.array([[0, 1], [1, 0]]) view2 = np.array([[1, 0], [0, 1]]) v1_centroids = np.array([[0, 1],[1, 0]]) v2_centroids = np.array([[0, 1],[1, 0]]) centroids = [v1_centroids, v2_centroids] kmeans._final_centroids([view1, view2], centroids)
def test_fit_predict_n_jobs_all(data_random): n_clusters = data_random['n_clusters'] kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, n_jobs=-1) cluster_pred = kmeans.fit_predict(data_random['test_data']) assert(data_random['n_test'] == cluster_pred.shape[0]) for cl in cluster_pred: assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_patience(data_random): n_clusters = data_random['n_clusters'] patience=10 kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, patience=patience) cluster_pred = kmeans.fit_predict(data_random['test_data']) assert(data_random['n_test'] == cluster_pred.shape[0]) for cl in cluster_pred: assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_max_iter(data_random): n_clusters = data_random['n_clusters'] max_iter = 5 kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, max_iter=max_iter) cluster_pred = kmeans.fit_predict(data_random['test_data']) assert (data_random['n_test'] == cluster_pred.shape[0]) for cl in cluster_pred: assert (cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_init_random(data_random): n_clusters = data_random['n_clusters'] init = 'random' kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, init='random') cluster_pred = kmeans.fit_predict(data_random['test_data']) assert(data_random['n_test'] == cluster_pred.shape[0]) for cl in cluster_pred: assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_init_predefined(): n_clusters = 2 v1_centroid = np.array([[-1, -1],[1, 1]]) v2_centroid = np.array([[-1, -1],[1, 1]]) centroids = [v1_centroid, v2_centroid] v1_data = np.array([[-1, -1],[-2, -2],[0.5, 0.5],[0.7, 0.7],[1, 1]]) v2_data = np.array([[-1, -1],[-2, -2],[0.5, 0.5],[0.4, 0.7],[1, 1]]) data = [v1_data, v2_data] kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, init=centroids) cluster_pred = kmeans.fit_predict(data)
def test_predict_random_small(data_random): kmeans = MultiviewSphericalKMeans() input_data = [data_random['fit_data'][0][:2],data_random['fit_data'][1][:2]] kmeans.fit(input_data) cluster_pred = kmeans.predict(data_random['test_data']) assert(data_random['n_test'] == cluster_pred.shape[0]) for cl in cluster_pred: assert(cl >= 0 and cl < data_random['n_clusters'])
def test_n_init_not_positive_int(): with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(n_init=-1) kmeans.fit(data_small) with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(n_init=0) kmeans.fit(data_small)
def test_max_iter_not_positive_int(data_small): with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(max_iter=-1) kmeans.fit(data_small) with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(max_iter=0) kmeans.fit(data_small)
def data_random(): num_fit_samples = 200 num_test_samples = 5 n_feats1 = 20 n_feats2 = 18 n_clusters = 2 np.random.seed(RANDOM_SEED) fit_data = [] fit_data.append(np.random.rand(num_fit_samples, n_feats1)) fit_data.append(np.random.rand(num_fit_samples, n_feats2)) test_data = [] test_data.append(np.random.rand(num_test_samples, n_feats1)) test_data.append(np.random.rand(num_test_samples, n_feats2)) kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, random_state=RANDOM_SEED) return { 'n_test': num_test_samples, 'n_feats1': n_feats1, 'n_feats2': n_feats2, 'n_clusters': n_clusters, 'kmeans': kmeans, 'fit_data': fit_data, 'test_data': test_data }
def test_predict_no_centroids2(): kmeans = MultiviewSphericalKMeans() with pytest.raises(ConvergenceWarning): view1 = np.array([[0, 1], [1, 0]]) view2 = np.array([[1, 0], [0, 1]]) v1_centroids = np.array([[0, 1],[1, 0]]) v2_centroids = np.array([[0, 1],[1, 0]]) centroids = [v1_centroids, v2_centroids] kmeans._final_centroids([view1, view2], centroids) with pytest.raises(AttributeError): kmeans.predict([view1, view2])
def test_patience_not_nonnegative_int(data_small): with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(patience=-1) kmeans.fit(data_small)
def test_init_samples_not_2D_2(data_small): with pytest.raises(ValueError): view1 = np.random.random((2,)) view2 = np.random.random((2,)) kmeans = MultiviewSphericalKMeans(init=[view1, view2]) kmeans.fit(data_small)
def test_samples_not_2D_2(): with pytest.raises(ValueError): view1 = np.random.random((10,)) view2 = np.random.random((10,)) kmeans = MultiviewSphericalKMeans() kmeans.fit([view1, view2])
def test_samples_not_list(): with pytest.raises(ValueError): view1 = 1 view2 = 3 kmeans = MultiviewSphericalKMeans() kmeans.fit([view1, view2])
def test_predict_not_fit(): with pytest.raises(NotFittedError): kmeans = MultiviewSphericalKMeans() view1 = np.random.random((10,11)) view2 = np.random.random((10,10)) kmeans.predict([view1, view2])
def test_init_samples_not_list(data_small): with pytest.raises(ValueError): view1 = 1 view2 = 3 kmeans = MultiviewSphericalKMeans(init=[view1, view2]) kmeans.fit(data_small)
def test_init_not_n_clusters(data_small): with pytest.raises(ValueError): view1 = np.random.random((3, 8)) view2 = np.random.random((3, 9)) kmeans = MultiviewSphericalKMeans(init=[view1, view2]) kmeans.fit(data_small)
def test_init_not_feat_dimensions(data_small): with pytest.raises(ValueError): view1 = np.random.random((2, 9)) view2 = np.random.random((2, 9)) kmeans = MultiviewSphericalKMeans(init=[view1, view2]) kmeans.fit(data_small)
def test_tol_not_nonnegative_float(data_small): with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(tol=-1) kmeans.fit(data_small)
def test_random_state_not_convertible(data_small): with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(random_state='ab') kmeans.fit(data_small)
def test_not_init1(data_small): with pytest.raises(ValueError): kmeans = MultiviewSphericalKMeans(init='Not_Init') kmeans.fit(data_small)