示例#1
0
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)
示例#2
0
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",
        )