def test_knn_graph_sparse(): k = 3 n_pca = 20 pca = TruncatedSVD(n_pca, random_state=42).fit(data) data_nu = pca.transform(data) pdx = squareform(pdist(data_nu, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) K = np.empty_like(pdx) for i in range(len(pdx)): K[i, pdx[i, :] <= epsilon[i]] = 1 K[i, pdx[i, :] > epsilon[i]] = 0 K = K + K.T W = np.divide(K, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( sp.coo_matrix(data), n_pca=n_pca, decay=None, knn=k - 1, random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_allclose(G2.W.toarray(), G.W.toarray()) assert isinstance(G2, graphtools.graphs.kNNGraph)
def test_sparse_alpha_knn_graph(): data = datasets.make_swiss_roll()[0] k = 5 a = 0.45 thresh = 0.01 bandwidth_scale = 1.3 pdx = squareform(pdist(data, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) * bandwidth_scale pdx = (pdx.T / epsilon).T K = np.exp(-1 * pdx**a) K = K + K.T W = np.divide(K, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( data, n_pca=None, # n_pca, decay=a, knn=k - 1, thresh=thresh, bandwidth_scale=bandwidth_scale, random_state=42, use_pygsp=True, ) assert np.abs(G.W - G2.W).max() < thresh assert G.N == G2.N assert isinstance(G2, graphtools.graphs.kNNGraph)
def test_knn_graph_anisotropy(): k = 3 a = 13 n_pca = 20 anisotropy = 0.9 thresh = 1e-4 data_small = data[np.random.choice(len(data), len(data) // 2, replace=False)] pca = PCA(n_pca, svd_solver="randomized", random_state=42).fit(data_small) data_small_nu = pca.transform(data_small) pdx = squareform(pdist(data_small_nu, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) weighted_pdx = (pdx.T / epsilon).T K = np.exp(-1 * weighted_pdx ** a) K[K < thresh] = 0 K = K + K.T K = np.divide(K, 2) d = K.sum(1) W = K / (np.outer(d, d) ** anisotropy) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( data_small, n_pca=n_pca, thresh=thresh, decay=a, knn=k - 1, random_state=42, use_pygsp=True, anisotropy=anisotropy, ) assert isinstance(G2, graphtools.graphs.kNNGraph) assert G.N == G2.N np.testing.assert_allclose(G.dw, G2.dw, atol=1e-14, rtol=1e-14) np.testing.assert_allclose((G2.W - G.W).data, 0, atol=1e-14, rtol=1e-14)
def test_knn_graph_multiplication_symm(): k = 3 n_pca = 20 pca = PCA(n_pca, svd_solver="randomized", random_state=42).fit(data) data_nu = pca.transform(data) pdx = squareform(pdist(data_nu, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) K = np.empty_like(pdx) for i in range(len(pdx)): K[i, pdx[i, :] <= epsilon[i]] = 1 K[i, pdx[i, :] > epsilon[i]] = 0 W = K * K.T np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( data, n_pca=n_pca, decay=None, knn=k - 1, random_state=42, use_pygsp=True, kernel_symm="*", ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W - G2.W).nnz == 0 assert (G2.W - G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.kNNGraph)
def test_knn_graph(): k = 3 n_pca = 20 pca = PCA(n_pca, svd_solver='randomized', random_state=42).fit(data) data_nu = pca.transform(data) pdx = squareform(pdist(data_nu, metric='euclidean')) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) K = np.empty_like(pdx) for i in range(len(pdx)): K[i, pdx[i, :] <= epsilon[i]] = 1 K[i, pdx[i, :] > epsilon[i]] = 0 K = K + K.T W = np.divide(K, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph(data, n_pca=n_pca, decay=None, knn=k, random_state=42, use_pygsp=True) assert(G.N == G2.N) assert(np.all(G.d == G2.d)) assert((G.W != G2.W).nnz == 0) assert((G2.W != G.W).sum() == 0) assert(isinstance(G2, graphtools.graphs.kNNGraph))
def test_knnmax(): data = datasets.make_swiss_roll()[0] k = 5 k_max = 10 a = 0.45 thresh = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", "K should be symmetric", RuntimeWarning) G = build_graph( data, n_pca=None, # n_pca, decay=a, knn=k - 1, knn_max=k_max - 1, thresh=0, random_state=42, kernel_symm=None, ) assert np.all((G.K > 0).sum(axis=1) == k_max) pdx = squareform(pdist(data, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] knn_max_dist = np.max(np.partition(pdx, k_max, axis=1)[:, :k_max], axis=1) epsilon = np.max(knn_dist, axis=1) pdx_scale = (pdx.T / epsilon).T K = np.where(pdx <= knn_max_dist[:, None], np.exp(-1 * pdx_scale**a), 0) K = K + K.T W = np.divide(K, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( data, n_pca=None, # n_pca, decay=a, knn=k - 1, knn_max=k_max - 1, thresh=0, random_state=42, use_pygsp=True, ) assert isinstance(G2, graphtools.graphs.kNNGraph) assert G.N == G2.N assert np.all(G.dw == G2.dw) assert (G.W - G2.W).nnz == 0
def test_truncated_exact_graph_no_pca(): k = 3 a = 13 n_pca = None thresh = 1e-4 data_small = data[np.random.choice(len(data), len(data) // 10, replace=False)] pdx = squareform(pdist(data_small, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) weighted_pdx = (pdx.T / epsilon).T K = np.exp(-1 * weighted_pdx**a) K[K < thresh] = 0 W = K + K.T W = np.divide(W, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( data_small, thresh=thresh, graphtype="exact", n_pca=n_pca, decay=a, knn=k - 1, random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph( sp.csr_matrix(data_small), thresh=thresh, graphtype="exact", n_pca=n_pca, decay=a, knn=k - 1, random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph)
def test_knn_graph(): k = 3 n_pca = 20 pca = PCA(n_pca, svd_solver="randomized", random_state=42).fit(data) data_nu = pca.transform(data) pdx = squareform(pdist(data_nu, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) K = np.empty_like(pdx) for i in range(len(pdx)): K[i, pdx[i, :] <= epsilon[i]] = 1 K[i, pdx[i, :] > epsilon[i]] = 0 K = K + K.T W = np.divide(K, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph(data, n_pca=n_pca, decay=None, knn=k - 1, random_state=42, use_pygsp=True) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W - G2.W).nnz == 0 assert (G2.W - G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.kNNGraph) K2 = G2.build_kernel_to_data(G2.data_nu, knn=k) K2 = (K2 + K2.T) / 2 assert (G2.K - K2).nnz == 0 assert (G2.build_kernel_to_data( G2.data_nu, knn=data.shape[0]).nnz == data.shape[0] * data.shape[0]) with assert_warns_message( UserWarning, "Cannot set knn ({}) to be greater than " "n_samples ({}). Setting knn={}".format(data.shape[0] + 1, data.shape[0], data.shape[0]), ): G2.build_kernel_to_data( Y=G2.data_nu, knn=data.shape[0] + 1, )
def test_exact_graph_anisotropy(): k = 3 a = 13 n_pca = 20 anisotropy = 0.9 data_small = data[np.random.choice(len(data), len(data) // 2, replace=False)] pca = PCA(n_pca, svd_solver="randomized", random_state=42).fit(data_small) data_small_nu = pca.transform(data_small) pdx = squareform(pdist(data_small_nu, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) weighted_pdx = (pdx.T / epsilon).T K = np.exp(-1 * weighted_pdx**a) K = K + K.T K = np.divide(K, 2) d = K.sum(1) W = K / (np.outer(d, d)**anisotropy) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( data_small, thresh=0, n_pca=n_pca, decay=a, knn=k - 1, random_state=42, use_pygsp=True, anisotropy=anisotropy, ) assert isinstance(G2, graphtools.graphs.TraditionalGraph) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G2.W != G.W).sum() == 0 assert (G.W != G2.W).nnz == 0 with assert_raises_message(ValueError, "Expected 0 <= anisotropy <= 1. Got -1"): build_graph( data_small, thresh=0, n_pca=n_pca, decay=a, knn=k - 1, random_state=42, use_pygsp=True, anisotropy=-1, ) with assert_raises_message(ValueError, "Expected 0 <= anisotropy <= 1. Got 2"): build_graph( data_small, thresh=0, n_pca=n_pca, decay=a, knn=k - 1, random_state=42, use_pygsp=True, anisotropy=2, ) with assert_raises_message(ValueError, "Expected 0 <= anisotropy <= 1. Got invalid"): build_graph( data_small, thresh=0, n_pca=n_pca, decay=a, knn=k - 1, random_state=42, use_pygsp=True, anisotropy="invalid", )
def test_truncated_exact_graph_sparse(): k = 3 a = 13 n_pca = 20 thresh = 1e-4 data_small = data[np.random.choice(len(data), len(data) // 2, replace=False)] pca = TruncatedSVD(n_pca, random_state=42).fit(data_small) data_small_nu = pca.transform(data_small) pdx = squareform(pdist(data_small_nu, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) weighted_pdx = (pdx.T / epsilon).T K = np.exp(-1 * weighted_pdx**a) K[K < thresh] = 0 W = K + K.T W = np.divide(W, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( sp.coo_matrix(data_small), thresh=thresh, graphtype="exact", n_pca=n_pca, decay=a, knn=k - 1, random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_allclose(G2.W.toarray(), G.W.toarray()) assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph( sp.bsr_matrix(pdx), n_pca=None, precomputed="distance", thresh=thresh, decay=a, knn=k - 1, random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph( sp.lil_matrix(K), n_pca=None, precomputed="affinity", thresh=thresh, random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph( sp.dok_matrix(W), n_pca=None, precomputed="adjacency", random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph)
def test_exact_graph(): k = 3 a = 13 n_pca = 20 bandwidth_scale = 1.3 data_small = data[np.random.choice(len(data), len(data) // 2, replace=False)] pca = PCA(n_pca, svd_solver="randomized", random_state=42).fit(data_small) data_small_nu = pca.transform(data_small) pdx = squareform(pdist(data_small_nu, metric="euclidean")) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) * bandwidth_scale weighted_pdx = (pdx.T / epsilon).T K = np.exp(-1 * weighted_pdx**a) W = K + K.T W = np.divide(W, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph( data_small, thresh=0, n_pca=n_pca, decay=a, knn=k - 1, random_state=42, bandwidth_scale=bandwidth_scale, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph( pdx, n_pca=None, precomputed="distance", bandwidth_scale=bandwidth_scale, decay=a, knn=k - 1, random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph( sp.coo_matrix(K), n_pca=None, precomputed="affinity", random_state=42, use_pygsp=True, ) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph(K, n_pca=None, precomputed="affinity", random_state=42, use_pygsp=True) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph) G2 = build_graph(W, n_pca=None, precomputed="adjacency", random_state=42, use_pygsp=True) assert G.N == G2.N np.testing.assert_equal(G.dw, G2.dw) assert (G.W != G2.W).nnz == 0 assert (G2.W != G.W).sum() == 0 assert isinstance(G2, graphtools.graphs.TraditionalGraph)
def test_exact_graph(): k = 3 a = 13 n_pca = 20 data_small = data[np.random.choice(len(data), len(data) // 2, replace=False)] pca = PCA(n_pca, svd_solver='randomized', random_state=42).fit(data_small) data_small_nu = pca.transform(data_small) pdx = squareform(pdist(data_small_nu, metric='euclidean')) knn_dist = np.partition(pdx, k, axis=1)[:, :k] epsilon = np.max(knn_dist, axis=1) weighted_pdx = (pdx.T / epsilon).T K = np.exp(-1 * weighted_pdx**a) W = K + K.T W = np.divide(W, 2) np.fill_diagonal(W, 0) G = pygsp.graphs.Graph(W) G2 = build_graph(data_small, thresh=0, n_pca=n_pca, decay=a, knn=k, random_state=42, use_pygsp=True) assert (G.N == G2.N) assert (np.all(G.d == G2.d)) assert ((G.W != G2.W).nnz == 0) assert ((G2.W != G.W).sum() == 0) assert (isinstance(G2, graphtools.graphs.TraditionalGraph)) G2 = build_graph(pdx, n_pca=None, precomputed='distance', decay=a, knn=k, random_state=42, use_pygsp=True) assert (G.N == G2.N) assert (np.all(G.d == G2.d)) assert ((G.W != G2.W).nnz == 0) assert ((G2.W != G.W).sum() == 0) assert (isinstance(G2, graphtools.graphs.TraditionalGraph)) G2 = build_graph(sp.coo_matrix(K), n_pca=None, precomputed='affinity', random_state=42, use_pygsp=True) assert (G.N == G2.N) assert (np.all(G.d == G2.d)) assert ((G.W != G2.W).nnz == 0) assert ((G2.W != G.W).sum() == 0) assert (isinstance(G2, graphtools.graphs.TraditionalGraph)) G2 = build_graph(K, n_pca=None, precomputed='affinity', random_state=42, use_pygsp=True) assert (G.N == G2.N) assert (np.all(G.d == G2.d)) assert ((G.W != G2.W).nnz == 0) assert ((G2.W != G.W).sum() == 0) assert (isinstance(G2, graphtools.graphs.TraditionalGraph)) G2 = build_graph(W, n_pca=None, precomputed='adjacency', random_state=42, use_pygsp=True) assert (G.N == G2.N) assert (np.all(G.d == G2.d)) assert ((G.W != G2.W).nnz == 0) assert ((G2.W != G.W).sum() == 0) assert (isinstance(G2, graphtools.graphs.TraditionalGraph))