def test_predict_no_centroids1(): with pytest.raises(AttributeError): kmeans = MultiviewKMeans() kmeans.centroids_ = [None, None] view1 = np.random.random((10,11)) view2 = np.random.random((10,10)) kmeans.predict([view1, view2])
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 = MultiviewKMeans(init=[view1, view2]) kmeans.fit(data_small)
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 = MultiviewKMeans() kmeans.fit([view1, view2, view3])
def test_fit_predict_n_jobs_all(data_random): n_clusters = data_random['n_clusters'] kmeans = MultiviewKMeans(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_final_centroids_no_consensus(): with pytest.raises(ConvergenceWarning): kmeans = MultiviewKMeans(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_final_centroids_less_than_n_clusters(): with pytest.raises(ConvergenceWarning): kmeans = MultiviewKMeans(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_fit_predict_patience(data_random): n_clusters = data_random['n_clusters'] patience=10 kmeans = MultiviewKMeans(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_init_random(data_random): n_clusters = data_random['n_clusters'] init = 'random' kmeans = MultiviewKMeans(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_n_clusters(): n_clusters = 3 v1_data = np.array([[0, 0],[1, 0],[0, 1]]) v2_data = np.array([[0, 0],[1, 0],[0, 1]]) data = [v1_data, v2_data] kmeans = MultiviewKMeans(n_clusters=n_clusters) cluster_pred = kmeans.fit_predict(data) cluster_pred = list(set(cluster_pred)) assert(len(cluster_pred) == n_clusters)
def test_fit_predict_max_iter(data_random): n_clusters = data_random['n_clusters'] max_iter = 5 kmeans = MultiviewKMeans(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_predict_random_small(data_random): kmeans = MultiviewKMeans() 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_fit_predict_init_predefined(): n_clusters = 2 v1_centroid = np.array([[0, 0],[1, 1]]) v2_centroid = np.array([[0, 0],[1, 1]]) centroids = [v1_centroid, v2_centroid] v1_data = np.array([[0, 0],[0.3, 0.2],[0.5, 0.5],[0.7, 0.7],[1, 1]]) v2_data = np.array([[0, 0],[0.2, 0.4],[0.5, 0.5],[0.4, 0.7],[1, 1]]) data = [v1_data, v2_data] kmeans = MultiviewKMeans(n_clusters=n_clusters, init=centroids) cluster_pred = kmeans.fit_predict(data)
def test_n_init_not_positive_int(): with pytest.raises(ValueError): kmeans = MultiviewKMeans(n_init=-1) kmeans.fit(data_small) with pytest.raises(ValueError): kmeans = MultiviewKMeans(n_init=0) kmeans.fit(data_small)
def test_max_iter_not_positive_int(data_small): with pytest.raises(ValueError): kmeans = MultiviewKMeans(max_iter=-1) kmeans.fit(data_small) with pytest.raises(ValueError): kmeans = MultiviewKMeans(max_iter=0) kmeans.fit(data_small)
def test_predict_deterministic(): n_clusters = 2 v1_centroid = np.array([[0, 0],[1, 1]]) v2_centroid = np.array([[0, 0],[1, 1]]) centroids = [v1_centroid, v2_centroid] v1_data = np.array([[0, 0],[0.3, 0.2],[0.5, 0.5],[0.7, 0.7],[1, 1]]) v2_data = np.array([[0, 0],[0.2, 0.4],[0.5, 0.5],[0.4, 0.7],[1, 1]]) data = [v1_data, v2_data] kmeans = MultiviewKMeans(n_clusters=n_clusters) kmeans.centroids_ = centroids cluster_pred = kmeans.predict(data) true_clusters = [0, 0, 0, 1, 1] for ind in range(len(true_clusters)): assert cluster_pred[ind] == true_clusters[ind]
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 = MultiviewKMeans(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 = MultiviewKMeans() 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_final_centroids_less_than_n_clusters(): with pytest.raises(ConvergenceWarning): kmeans = MultiviewKMeans(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_predict_not_fit(): with pytest.raises(NotFittedError): kmeans = MultiviewKMeans() view1 = np.random.random((10,11)) view2 = np.random.random((10,10)) kmeans.predict([view1, view2])
def test_not_init1(data_small): with pytest.raises(ValueError): kmeans = MultiviewKMeans(init='Not_Init') kmeans.fit(data_small)
def test_patience_not_nonnegative_int(data_small): with pytest.raises(ValueError): kmeans = MultiviewKMeans(patience=-1) kmeans.fit(data_small)
def test_samples_not_list(): with pytest.raises(ValueError): view1 = 1 view2 = 3 kmeans = MultiviewKMeans() kmeans.fit([view1, view2])
def test_samples_not_2D_2(): with pytest.raises(ValueError): view1 = np.random.random((10,)) view2 = np.random.random((10,)) kmeans = MultiviewKMeans() kmeans.fit([view1, view2])
def test_init_clusters_not_same(data_small): with pytest.raises(ValueError): view1 = np.random.random((2, 8)) view2 = np.random.random((3, 9)) kmeans = MultiviewKMeans(init=[view1, view2]) kmeans.fit(data_small)
def test_init_samples_not_list(data_small): with pytest.raises(ValueError): view1 = 1 view2 = 3 kmeans = MultiviewKMeans(init=[view1, view2]) 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 = MultiviewKMeans(init=[view1, view2]) kmeans.fit(data_small)
def test_tol_not_nonnegative_float(data_small): with pytest.raises(ValueError): kmeans = MultiviewKMeans(tol=-0.05) kmeans.fit(data_small)
def test_random_state_not_convertible(data_small): with pytest.raises(ValueError): kmeans = MultiviewKMeans(random_state='ab') kmeans.fit(data_small)