def test_stream_diff():
graph = next(pg.load_datasets_graph(["graph9"]))
for _ in supported_backends():
ranks1 = pg.GenericGraphFilter(
[0, 0, 1], max_iters=4, error_type="iters") | pg.to_signal(
graph, {"A": 1})
ranks2 = pg.GenericGraphFilter(
[1, 1, 1], tol=None) & ~pg.GenericGraphFilter(
[1, 1], tol=None) | pg.to_signal(graph, {"A": 1})
assert pg.Mabs(ranks1)(ranks2) < pg.epsilon()
def test_explicit_citations():
assert "unknown node ranking algorithm" == pg.NodeRanking().cite()
assert "with parameters tuned \cite{krasanakis2021pygrank}" in pg.ParameterTuner(
lambda params: pg.PageRank(params[0])).cite()
assert "Postprocessor" in pg.Postprocessor().cite()
assert pg.PageRank().cite() in pg.AlgorithmSelection().cite()
assert "krasanakis2021pygrank" in pg.ParameterTuner().cite()
assert "ortega2018graph" in pg.ParameterTuner().cite()
assert pg.HeatKernel().cite() in pg.SeedOversampling(pg.HeatKernel()).cite()
assert pg.AbsorbingWalks().cite() in pg.BoostedSeedOversampling(pg.AbsorbingWalks()).cite()
assert "krasanakis2018venuerank" in pg.BiasedKernel(converge_to_eigenvectors=True).cite()
assert "yu2021chebyshev" in pg.HeatKernel(coefficient_type="chebyshev").cite()
assert "susnjara2015accelerated" in pg.HeatKernel(krylov_dims=5).cite()
assert "krasanakis2021pygrank" in pg.GenericGraphFilter(optimization_dict=dict()).cite()
assert "tautology" in pg.Tautology().cite()
assert pg.PageRank().cite() == pg.Tautology(pg.PageRank()).cite()
assert "mabs" in pg.MabsMaintain(pg.PageRank()).cite()
assert "max normalization" in pg.Normalize(pg.PageRank()).cite()
assert "[0,1] range" in pg.Normalize(pg.PageRank(), "range").cite()
assert "ordinal" in pg.Ordinals(pg.PageRank()).cite()
assert "exp" in pg.Transformer(pg.PageRank()).cite()
assert "0.5" in pg.Threshold(pg.PageRank(), 0.5).cite()
assert "andersen2007local" in pg.Sweep(pg.PageRank()).cite()
assert pg.HeatKernel().cite() in pg.Sweep(pg.PageRank(), pg.HeatKernel()).cite()
assert "LFPRO" in pg.AdHocFairness("O").cite()
assert "LFPRO" in pg.AdHocFairness(pg.PageRank(), "LFPRO").cite()
assert "multiplicative" in pg.AdHocFairness(pg.PageRank(), "B").cite()
assert "multiplicative" in pg.AdHocFairness(pg.PageRank(), "mult").cite()
assert "tsioutsiouliklis2020fairness" in pg.AdHocFairness().cite()
assert "rahman2019fairwalk" in pg.FairWalk(pg.PageRank()).cite()
assert "krasanakis2020prioredit" in pg.FairPersonalizer(pg.PageRank()).cite()
def test_lowpass_tuning():
_, G, groups = next(pg.load_datasets_multiple_communities(["bigraph"]))
group = groups[0]
training, evaluation = pg.split(pg.to_signal(G, {v: 1 for v in group}), training_samples=0.1)
auc1 = pg.AUC(evaluation, exclude=training)(pg.ParameterTuner(lambda params: pg.GenericGraphFilter(params)).rank(training))
auc2 = pg.AUC(evaluation, exclude=training)(pg.ParameterTuner(lambda params: pg.LowPassRecursiveGraphFilter(params)).rank(training))
assert auc2 > auc1*0.8
def test_hoptuner_explicit_algorithm():
_, G, groups = next(pg.load_datasets_multiple_communities(["bigraph"]))
group = groups[0]
training, evaluation = pg.split(pg.to_signal(G, {v: 1 for v in group}), training_samples=0.5)
auc1 = pg.AUC(evaluation, exclude=training)(pg.HopTuner(lambda params: pg.GenericGraphFilter(params, krylov_dims=10), basis="arnoldi", measure=pg.AUC).rank(training))
auc2 = pg.AUC(evaluation, exclude=training)(pg.HopTuner(basis="arnoldi", krylov_dims=10, measure=pg.AUC).rank(training))
assert abs(auc1-auc2) < 0.005
def __init__(self, num_inputs, num_outputs, hidden=64):
super().__init__([
Dropout(0.5, input_shape=(num_inputs,)),
Dense(hidden, activation="relu", kernel_regularizer=L2(1.E-5)),
Dropout(0.5),
Dense(num_outputs, activation="relu")])
self.ranker = pg.ParameterTuner(
lambda par: pg.GenericGraphFilter([par[0] ** i for i in range(int(10))],
error_type="iters", max_iters=int(10)),
max_vals=[0.95], min_vals=[0.5], verbose=False,
measure=pg.Mabs, deviation_tol=0.1, tuning_backend="numpy")
def __init__(self, num_inputs, num_outputs, hidden=64):
super().__init__()
self.layer1 = torch.nn.Linear(num_inputs, hidden)
self.layer2 = torch.nn.Linear(hidden, num_outputs)
self.activation = torch.nn.ReLU()
self.dropout = torch.nn.Dropout(0.5)
self.num_outputs = num_outputs
self.ranker = pg.ParameterTuner(
lambda par: pg.GenericGraphFilter([par[0] ** i for i in range(int(10))],
error_type="iters", max_iters=int(10)),
max_vals=[0.95], min_vals=[0.5], verbose=False,
measure=pg.Mabs, deviation_tol=0.1, tuning_backend="numpy")
def create_param_tuner(optimizer=pg.optimize):
return pg.ParameterTuner(lambda params:
pg.Normalize(
postprocessor(
pg.GenericGraphFilter([1]+params,
preprocessor=pre,
error_type="iters",
max_iters=41,
optimization_dict=optimization,
preserve_norm=False))),
deviation_tol=1.E-6,
measure=measure,
optimizer=optimizer,
max_vals=[1]*40,
min_vals=[0]*40)
def test_custom_runs():
graph = next(pg.load_datasets_graph(["graph9"]))
for _ in supported_backends():
ranks1 = pg.Normalize(
pg.PageRank(0.85,
tol=pg.epsilon(),
max_iters=1000,
use_quotient=False)).rank(graph, {"A": 1})
ranks2 = pg.Normalize(
pg.GenericGraphFilter([0.85**i * len(graph) for i in range(80)],
tol=pg.epsilon())).rank(graph, {"A": 1})
ranks3 = pg.Normalize(
pg.LowPassRecursiveGraphFilter([0.85 for _ in range(80)],
tol=pg.epsilon())).rank(
graph, {"A": 1})
assert pg.Mabs(ranks1)(ranks2) < 1.E-6
assert pg.Mabs(ranks1)(ranks3) < 1.E-6
def __init__(self, num_inputs, num_outputs, hidden=64):
super().__init__([
Dropout(0.5, input_shape=(num_inputs, )),
Dense(hidden, activation="relu", kernel_regularizer=L2(0.005)),
Dropout(0.5),
Dense(num_outputs)
])
pre = pg.preprocessor(renormalize=True, assume_immutability=True)
self.ranker = pg.ParameterTuner(lambda par: pg.GenericGraphFilter(
[par[0]**i for i in range(int(10))],
preprocessor=pre,
error_type="iters",
max_iters=10),
max_vals=[1],
min_vals=[0.5],
verbose=False,
measure=pg.Mabs,
deviation_tol=0.01,
tuning_backend="numpy")
def test_autotune_citations():
assert pg.ParameterTuner().cite() != pg.GenericGraphFilter().cite()
assert pg.HopTuner().cite() != pg.GenericGraphFilter().cite()
assert pg.AlgorithmSelection().cite() != pg.GenericGraphFilter().cite()
import pygrank as pg
datasets = ["EUCore", "Amazon"]
pre = pg.preprocessor(assume_immutability=True, normalization="symmetric")
algs = {
"ppr.85": pg.PageRank(.85, preprocessor=pre, tol=1.E-9, max_iters=1000),
"ppr.99": pg.PageRank(.99, preprocessor=pre, tol=1.E-9, max_iters=1000),
"hk3": pg.HeatKernel(3, preprocessor=pre, tol=1.E-9, max_iters=1000),
"hk5": pg.HeatKernel(5, preprocessor=pre, tol=1.E-9, max_iters=1000),
}
algs = algs | pg.create_variations(algs, {"+Sweep": pg.Sweep})
loader = pg.load_datasets_one_community(datasets)
algs["tuned"] = pg.ParameterTuner(preprocessor=pre, tol=1.E-9, max_iters=1000)
algs["selected"] = pg.AlgorithmSelection(
pg.create_demo_filters(preprocessor=pre, tol=1.E-9,
max_iters=1000).values())
algs["tuned+Sweep"] = pg.ParameterTuner(
ranker_generator=lambda params: pg.Sweep(
pg.GenericGraphFilter(
params, preprocessor=pre, tol=1.E-9, max_iters=1000)))
for alg in algs.values():
print(alg.cite()) # prints a list of algorithm citations
pg.benchmark_print(pg.benchmark(algs, loader, pg.AUC, fraction_of_training=.5),
delimiter=" & ",
end_line="\\\\")