def test_sequential():
graph = next(pg.load_datasets_graph(["graph5"]))
for _ in supported_backends():
prior = pg.to_signal(graph, {"A": 2})
posterior1 = pg.Normalize(pg.PageRank(), "range").rank(prior)
posterior2 = pg.Normalize("range")(pg.PageRank()(prior))
posterior3 = pg.Sequential(pg.PageRank(),
pg.Normalize("range")).rank(prior)
assert pg.sum(pg.abs(posterior1 - posterior2)) < pg.epsilon(
) # TODO: investigate when not exactly zero
assert pg.sum(pg.abs(posterior1 - posterior3)) < pg.epsilon(
) # TODO: investigate when not exactly zero
def test_norm_maintain():
# TODO: investigate that 2.5*epsilon is truly something to be expected
graph = next(pg.load_datasets_graph(["graph5"]))
for _ in supported_backends():
prior = pg.to_signal(graph, {"A": 2})
posterior = pg.MabsMaintain(pg.Normalize(pg.PageRank(),
"range")).rank(prior)
assert abs(pg.sum(pg.abs(posterior.np)) - 2) < 2.5 * pg.epsilon()
def test_separate_and_combine():
for _ in supported_backends():
table = pg.to_primitive([[1, 2, 3], [4, 5, 6]])
cols = pg.separate_cols(table)
assert len(cols) == 3
for col in cols:
assert pg.length(col) == 2
new_table = pg.combine_cols(cols)
assert pg.sum(pg.abs(table - new_table)) == 0
def test_preprocessor_types():
def test_graph():
return next(pg.load_datasets_graph(["graph5"]))
for _ in supported_backends():
from random import random
graph = test_graph()
signal = pg.to_signal(graph, {v: random() for v in graph})
laplacian = pg.preprocessor(normalization="laplacian")(graph)
symmetric = pg.preprocessor(normalization="symmetric")(graph)
assert pg.abs(pg.sum(pg.conv(signal, laplacian) + pg.conv(signal, symmetric) - signal)) <= pg.epsilon()