def test_spectral_embedding_two_components(seed=36): """Test spectral embedding with two components""" random_state = np.random.RandomState(seed) n_sample = 10 affinity = np.zeros(shape=[n_sample * 2, n_sample * 2]) # first component affinity[0:n_sample, 0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2 # second component affinity[n_sample::, n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2 # connection affinity[0, n_sample + 1] = 1 affinity[n_sample + 1, 0] = 1 affinity.flat[::2 * n_sample + 1] = 0 affinity = 0.5 * (affinity + affinity.T) true_label = np.zeros(shape=2 * n_sample) true_label[0:n_sample] = 1 se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed", random_state=np.random.RandomState(seed)) embedded_corrdinate = np.squeeze(se_precomp.fit_transform(affinity)) # thresholding on the first components using 0. label_ = np.array(embedded_corrdinate < 0, dtype="float") assert_equal(normalized_mutual_info_score(true_label, label_), 1.0)
def test_spectral_embedding_precomputed_affinity(seed=36): """Test spectral embedding with precomputed kernel""" gamma = 1.0 se_precomp = SpectralEmbedding(n_components=2, affinity="precomputed", random_state=np.random.RandomState(seed)) se_rbf = SpectralEmbedding(n_components=2, affinity="rbf", gamma=gamma, random_state=np.random.RandomState(seed)) embed_precomp = se_precomp.fit_transform(rbf_kernel(S, gamma=gamma)) embed_rbf = se_rbf.fit_transform(S) assert_array_almost_equal( se_precomp.affinity_matrix_, se_rbf.affinity_matrix_) assert_true(_check_with_col_sign_flipping(embed_precomp, embed_rbf, 0.05))
def test_pipline_spectral_clustering(seed=36): """Test using pipline to do spectral clustering""" random_state = np.random.RandomState(seed) se_rbf = SpectralEmbedding(n_components=n_clusters, affinity="rbf", random_state=random_state) se_knn = SpectralEmbedding(n_components=n_clusters, affinity="nearest_neighbors", n_neighbors=5, random_state=random_state) for se in [se_rbf, se_knn]: km = KMeans(n_clusters=n_clusters, random_state=random_state) km.fit(se.fit_transform(S)) assert_array_almost_equal( normalized_mutual_info_score(km.labels_, true_labels), 1.0, 2)
def test_spectral_embedding_amg_solver(seed=36): """Test spectral embedding with amg solver""" try: from pyamg import smoothed_aggregation_solver except ImportError: raise SkipTest se_amg = SpectralEmbedding(n_components=2, affinity="nearest_neighbors", eigen_solver="amg", n_neighbors=5, random_state=np.random.RandomState(seed)) se_arpack = SpectralEmbedding(n_components=2, affinity="nearest_neighbors", eigen_solver="arpack", n_neighbors=5, random_state=np.random.RandomState(seed)) embed_amg = se_amg.fit_transform(S) embed_arpack = se_arpack.fit_transform(S) assert_true(_check_with_col_sign_flipping(embed_amg, embed_arpack, 0.05))
def test_spectral_embedding_unknown_eigensolver(seed=36): """Test that SpectralClustering fails with an unknown eigensolver""" se = SpectralEmbedding(n_components=1, affinity="precomputed", random_state=np.random.RandomState(seed), eigen_solver="<unknown>") assert_raises(ValueError, se.fit, S)
def test_spectral_embedding_amg_solver(seed=36): """Test spectral embedding with amg solver""" try: from pyamg import smoothed_aggregation_solver except ImportError: raise SkipTest gamma = 0.9 se_amg = SpectralEmbedding(n_components=3, affinity="rbf", gamma=gamma, eigen_solver="amg", random_state=np.random.RandomState(seed)) se_arpack = SpectralEmbedding(n_components=3, affinity="rbf", gamma=gamma, eigen_solver="arpack", random_state=np.random.RandomState(seed)) embed_amg = se_amg.fit_transform(S) embed_arpack = se_arpack.fit_transform(S) assert_array_almost_equal( se_amg.affinity_matrix_, se_arpack.affinity_matrix_) assert_true(_check_with_col_sign_flipping(embed_amg, embed_arpack, 0.01))
def test_spectral_embedding_two_components(seed=36): """Test spectral embedding with two components""" random_state = np.random.RandomState(seed) n_sample = 100 affinity = np.zeros(shape=[n_sample * 2, n_sample * 2]) # first component affinity[0:n_sample, 0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2 # second component affinity[n_sample::, n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2 # connection affinity[0, n_sample + 1] = 1 affinity[n_sample + 1, 0] = 1 affinity.flat[::2 * n_sample + 1] = 0 affinity = 0.5 * (affinity + affinity.T) true_label = np.zeros(shape=2 * n_sample) true_label[0:n_sample] = 1 se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed", random_state=np.random.RandomState(seed)) embedded_coordinate = se_precomp.fit_transform(affinity) # thresholding on the first components using 0. label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float") assert_equal(normalized_mutual_info_score(true_label, label_), 1.0) # test that we can still import spectral embedding from sklearn.cluster import spectral_embedding as se_deprecated with warnings.catch_warnings(record=True) as warning_list: embedded_depr = se_deprecated(affinity, n_components=1, random_state=np.random.RandomState(seed)) assert_equal(len(warning_list), 1) assert_true( _check_with_col_sign_flipping(embedded_coordinate, embedded_depr, 0.05))
def test_spectral_embedding_two_components(seed=36): """Test spectral embedding with two components""" random_state = np.random.RandomState(seed) n_sample = 10 affinity = np.zeros(shape=[n_sample * 2, n_sample * 2]) # first component affinity[0:n_sample, 0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2 # second component affinity[n_sample::, n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2 # connection affinity[0, n_sample + 1] = 1 affinity[n_sample + 1, 0] = 1 affinity.flat[::2 * n_sample + 1] = 0 affinity = 0.5 * (affinity + affinity.T) true_label = np.zeros(shape=2 * n_sample) true_label[0:n_sample] = 1 se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed", random_state=np.random.RandomState(seed)) embedded_coordinate = se_precomp.fit_transform(affinity) # thresholding on the first components using 0. label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float") assert_equal(normalized_mutual_info_score(true_label, label_), 1.0) # test that we can still import spectral embedding from sklearn.cluster import spectral_embedding as se_deprecated with warnings.catch_warnings(record=True) as warning_list: embedded_depr = se_deprecated(affinity, n_components=1, random_state=np.random.RandomState(seed)) assert_equal(len(warning_list), 1) assert_true(_check_with_col_sign_flipping(embedded_coordinate, embedded_depr, 0.01))
def test_spectral_embedding_unknown_affinity(seed=36): """Test that SpectralClustering fails with an unknown affinity type""" centers = np.array([ [0., 0., 0.], [10., 10., 10.], [20., 20., 20.], ]) X, true_labels = make_blobs(n_samples=100, centers=centers, cluster_std=1., random_state=42) se_precomp = SpectralEmbedding(n_components=1, affinity="<unknown>", random_state=np.random.RandomState(seed)) assert_raises(ValueError, se_precomp.fit, S)
def test_spectral_embedding_callable_affinity(seed=36): """Test spectral embedding with callable affinity""" gamma = 0.9 kern = rbf_kernel(S, gamma=gamma) se_callable = SpectralEmbedding( n_components=3, affinity=(lambda x: rbf_kernel(x, gamma=gamma)), gamma=gamma, random_state=np.random.RandomState(seed)) se_rbf = SpectralEmbedding(n_components=3, affinity="rbf", gamma=gamma, random_state=np.random.RandomState(seed)) embed_rbf = se_rbf.fit_transform(S) embed_callable = se_callable.fit_transform(S) embed_rbf = se_rbf.fit_transform(S) embed_callable = se_callable.fit_transform(S) assert_array_almost_equal(se_callable.affinity_matrix_, se_rbf.affinity_matrix_) assert_true(_check_with_col_sign_flipping(embed_rbf, embed_callable, 0.01))
def test_spectral_embedding_precomputed_affinity(seed=36): """Test spectral embedding with precomputed kernel""" gamma = 1.0 se_precomp = SpectralEmbedding(n_components=2, affinity="precomputed", random_state=np.random.RandomState(seed)) se_rbf = SpectralEmbedding(n_components=2, affinity="rbf", gamma=gamma, random_state=np.random.RandomState(seed)) embed_precomp = se_precomp.fit_transform(rbf_kernel(S, gamma=gamma)) embed_rbf = se_rbf.fit_transform(S) assert_array_almost_equal(se_precomp.affinity_matrix_, se_rbf.affinity_matrix_) assert_true(_check_with_col_sign_flipping(embed_precomp, embed_rbf, 0.05))
def test_spectral_embedding_callable_affinity(seed=36): """Test spectral embedding with callable affinity""" gamma = 0.9 kern = rbf_kernel(S, gamma=gamma) se_callable = SpectralEmbedding(n_components=3, affinity=( lambda x: rbf_kernel(x, gamma=gamma)), gamma=gamma, random_state=np.random.RandomState(seed)) se_rbf = SpectralEmbedding(n_components=3, affinity="rbf", gamma=gamma, random_state=np.random.RandomState(seed)) embed_rbf = se_rbf.fit_transform(S) embed_callable = se_callable.fit_transform(S) embed_rbf = se_rbf.fit_transform(S) embed_callable = se_callable.fit_transform(S) assert_array_almost_equal( se_callable.affinity_matrix_, se_rbf.affinity_matrix_) assert_true( _check_with_col_sign_flipping(embed_rbf, embed_callable, 0.01))
def test_spectral_embedding_amg_solver(seed=36): """Test spectral embedding with amg solver""" try: from pyamg import smoothed_aggregation_solver except ImportError: raise SkipTest gamma = 0.9 se_amg = SpectralEmbedding(n_components=3, affinity="rbf", gamma=gamma, eigen_solver="amg", random_state=np.random.RandomState(seed)) se_arpack = SpectralEmbedding(n_components=3, affinity="rbf", gamma=gamma, eigen_solver="arpack", random_state=np.random.RandomState(seed)) embed_amg = se_amg.fit_transform(S) embed_arpack = se_arpack.fit_transform(S) assert_array_almost_equal(se_amg.affinity_matrix_, se_arpack.affinity_matrix_) assert_true(_check_with_col_sign_flipping(embed_amg, embed_arpack, 0.01))
def test_spectral_embedding_unknown_affinity(seed=36): """Test that SpectralClustering fails with an unknown affinity type""" se = SpectralEmbedding(n_components=1, affinity="<unknown>", random_state=np.random.RandomState(seed)) assert_raises(ValueError, se.fit, S)