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_knn_graph_sparse_no_pca():
build_graph(
sp.coo_matrix(data),
n_pca=None, # n_pca,
decay=10,
knn=3,
thresh=1e-4,
random_state=42,
use_pygsp=True,
)
def test_knn_graph_sparse_no_pca():
with assert_warns_message(
UserWarning,
"cannot use tree with sparse input: using brute force"):
build_graph(
sp.coo_matrix(data),
n_pca=None, # n_pca,
decay=10,
knn=3,
thresh=1e-4,
random_state=42,
use_pygsp=True,
)
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))