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_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_thresh_small():
data = datasets.make_swiss_roll()[0]
G = graphtools.Graph(data, thresh=1e-30)
assert G.thresh == np.finfo("float").eps