def test_one_community_benchmarks():
pg.load_backend("numpy")
datasets = ["graph9", "bigraph"]
pre = pg.preprocessor(assume_immutability=True, normalization="symmetric")
algorithms = {
"ppr0.85":
pg.PageRank(alpha=0.85, preprocessor=pre, max_iters=10000, tol=1.E-9),
"ppr0.99":
pg.PageRank(alpha=0.99, preprocessor=pre, max_iters=10000, tol=1.E-9),
"hk3":
pg.HeatKernel(t=3, preprocessor=pre, max_iters=10000, tol=1.E-9),
"hk5":
pg.HeatKernel(t=5, preprocessor=pre, max_iters=10000, tol=1.E-9),
"tuned":
pg.ParameterTuner(preprocessor=pre, max_iters=10000, tol=1.E-9),
}
# algorithms = benchmark.create_variations(algorithms, {"": pg.Tautology, "+SO": pg.SeedOversampling})
# loader = pg.load_datasets_one_community(datasets)
# pg.benchmark(algorithms, loader, "time", verbose=True)
loader = pg.load_datasets_one_community(datasets)
pg.benchmark_print(
pg.benchmark_average(
pg.benchmark_ranks(
pg.benchmark(algorithms,
loader,
pg.AUC,
fraction_of_training=.8))))
def test_all_communities_benchmarks():
datasets = ["bigraph"]
pre = pg.preprocessor(assume_immutability=True, normalization="symmetric")
tol = 1.E-9
optimization = pg.SelfClearDict()
algorithms = {
"ppr0.85": pg.PageRank(alpha=0.85, preprocessor=pre, max_iters=10000, tol=tol),
"ppr0.9": pg.PageRank(alpha=0.9, preprocessor=pre, max_iters=10000, tol=tol),
"ppr0.99": pg.PageRank(alpha=0.99, preprocessor=pre, max_iters=10000, tol=tol),
"hk3": pg.HeatKernel(t=3, preprocessor=pre, max_iters=10000, tol=tol, optimization_dict=optimization),
"hk5": pg.HeatKernel(t=5, preprocessor=pre, max_iters=10000, tol=tol, optimization_dict=optimization),
"hk7": pg.HeatKernel(t=7, preprocessor=pre, max_iters=10000, tol=tol, optimization_dict=optimization),
}
tuned = {"selected": pg.AlgorithmSelection(algorithms.values(), fraction_of_training=0.8)}
loader = pg.load_datasets_all_communities(datasets, min_group_size=50)
pg.benchmark_print(pg.benchmark(algorithms | tuned, loader, pg.AUC, fraction_of_training=.8, seed=list(range(1))),
decimals=3, delimiter=" & ", end_line="\\\\")
loader = pg.load_datasets_all_communities(datasets, min_group_size=50)
pg.benchmark_print(pg.benchmark(algorithms | tuned, loader, pg.Modularity, sensitive=pg.pRule, fraction_of_training=.8, seed=list(range(1))),
decimals=3, delimiter=" & ", end_line="\\\\")
mistreatment = lambda known_scores, sensitive_signal, exclude: \
pg.AM([pg.Disparity([pg.TPR(known_scores, exclude=1 - (1 - exclude.np) * sensitive_signal.np),
pg.TPR(known_scores, exclude=1 - (1 - exclude.np) * (1 - sensitive_signal.np))]),
pg.Disparity([pg.TNR(known_scores, exclude=1 - (1 - exclude.np) * sensitive_signal.np),
pg.TNR(known_scores, exclude=1 - (1 - exclude.np) * (1 - sensitive_signal.np))])])
loader = pg.load_datasets_all_communities(datasets, min_group_size=50)
pg.benchmark_print(pg.benchmark(algorithms | tuned, loader, pg.Modularity, sensitive=mistreatment, fraction_of_training=.8, seed=list(range(1))),
decimals=3, delimiter=" & ", end_line="\\\\")
def test_autorefs():
"""
Tests that different (base) algorithms yield different citations, that all citations have at least one
reference to a publication and that wrapping the same base algorithms yields the same citations.
"""
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),
"hk5'": pg.HeatKernel(5, preprocessor=pre, tol=1.E-9, max_iters=1000),
}
algs = algs | pg.create_variations(
algs, {
"+Sweep": pg.Sweep,
"+SO": pg.SeedOversampling,
"+BSO": pg.BoostedSeedOversampling
})
citations = set()
for alg in algs.values():
citation = alg.cite()
assert "\\cite{" in citation
citations.add(citation)
assert len(citations) == len(algs) - 4
def test_sweep_streaming():
_, graph, group = next(pg.load_datasets_one_community(["bigraph"]))
for _ in supported_backends():
training, evaluation = pg.split(list(group), training_samples=0.1)
auc1 = pg.AUC({v: 1
for v in evaluation}, exclude=training).evaluate(
(pg.PageRank() >> pg.Sweep()).rank(
graph, {v: 1
for v in training}))
auc2 = pg.AUC({v: 1
for v in evaluation},
exclude=training).evaluate(pg.PageRank().rank(
graph, {v: 1
for v in training}))
auc3 = pg.AUC(
{v: 1
for v in evaluation}, exclude=training).evaluate(
pg.PageRank() >> pg.Transformer(pg.log) >> pg.LinearSweep()
| pg.to_signal(graph, {v: 1
for v in training}))
assert auc1 > auc2
assert abs(auc1 - auc3) < pg.epsilon()
with pytest.raises(Exception):
pg.Sweep() << "a"
def test_autotune_manual():
_, 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.PageRank().rank(training))
alg2 = pg.ParameterTuner(lambda params: pg.PageRank(params[0]), max_vals=[0.99], min_vals=[0.5]).tune(training)
auc2 = pg.AUC(evaluation, exclude=training)(alg2.rank(training))
assert auc1 <= auc2
def test_filter_citations():
assert pg.PageRank().cite() != pg.GraphFilter().cite()
assert pg.HeatKernel().cite() != pg.GraphFilter().cite()
assert pg.AbsorbingWalks().cite() != pg.GraphFilter().cite()
assert pg.HeatKernel().cite() != pg.GraphFilter().cite()
assert pg.PageRank(alpha=0.85).cite() != pg.PageRank(alpha=0.99).cite()
assert pg.HeatKernel(krylov_dims=0).cite() != pg.HeatKernel(krylov_dims=5).cite()
assert pg.HeatKernel(coefficient_type="taylor").cite() != pg.HeatKernel(coefficient_type="chebyshev").cite()
assert pg.HeatKernel(optimization_dict=dict()).cite() != pg.HeatKernel(optimization_dict=None).cite()
def test_multigroup_benchmarks():
datasets = ["bigraph"]
pre = pg.preprocessor(assume_immutability=True, normalization="symmetric")
tol = 1.E-9
optimization = pg.SelfClearDict()
algorithms = {
"ppr0.85":
pg.PageRank(alpha=0.85, preprocessor=pre, max_iters=10000, tol=tol),
"ppr0.9":
pg.PageRank(alpha=0.9, preprocessor=pre, max_iters=10000, tol=tol),
"ppr0.99":
pg.PageRank(alpha=0.99, preprocessor=pre, max_iters=10000, tol=tol),
"hk3":
pg.HeatKernel(t=3,
preprocessor=pre,
max_iters=10000,
tol=tol,
optimization_dict=optimization),
"hk5":
pg.HeatKernel(t=5,
preprocessor=pre,
max_iters=10000,
tol=tol,
optimization_dict=optimization),
"hk7":
pg.HeatKernel(t=7,
preprocessor=pre,
max_iters=10000,
tol=tol,
optimization_dict=optimization),
}
tuned = {
"selected":
pg.AlgorithmSelection(algorithms.values(), fraction_of_training=0.8)
}
loader = pg.load_datasets_multiple_communities(datasets, min_group_size=50)
pg.benchmark_print(pg.benchmark(
algorithms | tuned,
loader,
lambda ground_truth, exclude: pg.MultiSupervised(
pg.AUC, ground_truth, exclude),
fraction_of_training=.8,
seed=list(range(1))),
decimals=3,
delimiter=" & ",
end_line="\\\\")
loader = pg.load_datasets_multiple_communities(datasets, min_group_size=50)
pg.benchmark_print(pg.benchmark(algorithms | tuned,
loader,
pg.Modularity,
sensitive=pg.pRule,
fraction_of_training=.8,
seed=list(range(1))),
decimals=3,
delimiter=" & ",
end_line="\\\\")
def test_correlation_compliance():
graph = next(pg.load_datasets_graph(["graph5"]))
# TODO: Make spearman and pearson correlation support tensorflow
alg1 = pg.PageRank(alpha=0.5)
alg2 = pg.PageRank(alpha=0.99)
pearson_ordinals = pg.PearsonCorrelation(pg.Ordinals(alg1)(graph))(
pg.Ordinals(alg2)(graph))
spearman = pg.SpearmanCorrelation(alg1(graph))(alg2(graph))
assert pearson_ordinals == spearman
def test_completion():
graph = next(pg.load_datasets_graph(["graph9"]))
for _ in supported_backends():
pg.PageRank().rank(graph)
pg.PageRank(normalization="both").rank(graph)
pg.HeatKernel().rank(graph)
pg.AbsorbingWalks().rank(graph)
pg.SymmetricAbsorbingRandomWalks().rank(graph)
pg.HeatKernel().rank(graph)
assert True
def test_quotient():
graph = next(pg.load_datasets_graph(["graph9"]))
for _ in supported_backends():
test_result = pg.PageRank(normalization='symmetric',
tol=max(1.E-9, pg.epsilon()),
use_quotient=True).rank(graph)
norm_result = pg.PageRank(normalization='symmetric',
tol=max(1.E-9, pg.epsilon()),
use_quotient=pg.Normalize("sum")).rank(graph)
assert pg.Mabs(test_result)(norm_result) < pg.epsilon()
def test_tautology():
graph = next(pg.load_datasets_graph(["bigraph"]))
r = pg.PageRank().rank(graph)
tr = pg.Tautology(pg.PageRank()).rank(graph)
rt = pg.Tautology().transform(r)
for u in graph:
assert r[u] == rt[u]
assert r[u] == tr[u]
u = pg.Tautology().rank(graph)
assert float(sum(u.np)) == len(graph)
def test_transform():
import math
graph = next(pg.load_datasets_graph(["graph5"]))
for _ in supported_backends():
r1 = pg.Normalize(pg.PageRank(), "sum").rank(graph)
r2 = pg.Transformer(pg.PageRank(), lambda x: x / pg.sum(x)).rank(graph)
assert pg.Mabs(r1)(r2) < pg.epsilon()
r1 = pg.Transformer(math.exp).transform(pg.PageRank()(graph))
r2 = pg.Transformer(pg.PageRank(), pg.exp).rank(graph)
assert pg.Mabs(r1)(r2) < pg.epsilon()
def test_automatic_graph_casting():
graph = next(pg.load_datasets_graph(["graph9"]))
for _ in supported_backends():
signal = pg.to_signal(graph, {"A": 1})
test_result1 = pg.PageRank(normalization='col').rank(signal, signal)
test_result2 = pg.PageRank(normalization='col').rank(personalization=signal)
assert pg.Mabs(test_result1)(test_result2) < pg.epsilon()
with pytest.raises(Exception):
pg.PageRank(normalization='col').rank(personalization={"A": 1})
with pytest.raises(Exception):
pg.PageRank(normalization='col').rank(graph.copy(), signal)
def test_stream():
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.to_signal(graph, {"A": 1}) >> pg.PageRank(
0.85, tol=pg.epsilon(),
max_iters=1000) + pg.Tautology() >> pg.Normalize()
assert pg.Mabs(ranks1)(ranks2) < pg.epsilon()
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_threshold():
_, graph, group = next(pg.load_datasets_one_community(["bigraph"]))
for _ in supported_backends():
training, evaluation = pg.split(list(group), training_samples=0.5)
cond1 = pg.Conductance().evaluate(
pg.Threshold(pg.Sweep(pg.PageRank())).rank(
graph, {v: 1
for v in training}))
cond2 = pg.Conductance().evaluate(
pg.Threshold("gap").transform(pg.PageRank().rank(
graph, {v: 1
for v in training}))) # try all api types
assert cond1 <= cond2
def test_filter_stream():
graph = next(pg.load_datasets_graph(["graph9"]))
for _ in supported_backends():
test_result = pg.Normalize(
pg.PageRank(normalization='symmetric',
tol=max(1.E-9, pg.epsilon()),
use_quotient=True)).rank(graph)
norm_result = pg.PageRank(tol=max(1.E-9, pg.epsilon())) \
+ pg.preprocessor(normalization='symmetric') \
+ pg.Normalize("sum") \
>> pg.Normalize() \
| pg.to_signal(graph, {v: 1 for v in graph})
assert pg.Mabs(test_result)(norm_result) < pg.epsilon()
def test_venuerank():
graph = next(pg.load_datasets_graph(["bigraph"]))
for _ in supported_backends():
venuerank = pg.PageRank(alpha=0.85,
max_iters=10000,
converge_to_eigenvectors=True,
tol=1.E-12)
venuerank_result = venuerank.rank(graph)
small_restart = pg.PageRank(alpha=0.99, max_iters=10000, tol=1.E-12)
small_restart_result = small_restart.rank(graph)
#assert venuerank.convergence.iteration < small_restart.convergence.iteration / 2
corr = pg.SpearmanCorrelation(pg.Ordinals()(venuerank_result))(
pg.Ordinals()(small_restart_result))
assert corr > 0.99
def test_seed_oversampling_arguments():
_, graph, group = next(pg.load_datasets_one_community(["graph9"]))
with pytest.raises(Exception):
pg.SeedOversampling(pg.PageRank(), 'unknown').rank(graph, {"A": 1})
with pytest.raises(Exception):
pg.SeedOversampling(pg.PageRank()).rank(graph, {"A": 0.1, "B": 1})
with pytest.raises(Exception):
pg.BoostedSeedOversampling(pg.PageRank(),
'unknown').rank(graph, {"A": 1})
with pytest.raises(Exception):
pg.BoostedSeedOversampling(pg.PageRank(),
'naive',
oversample_from_iteration='unknown').rank(
graph, {"B": 1})
def test_normalize():
import networkx as nx
graph = next(pg.load_datasets_graph(["graph5"]))
for _ in supported_backends():
assert float(
pg.sum(
pg.Normalize("range").transform(
pg.to_signal(nx.Graph([("A", "B")]), [2, 2])).np)) == 4
r = pg.Normalize(pg.PageRank(), "range").rank(graph)
assert pg.min(r.np) == 0
assert pg.max(r.np) == 1
r = pg.Normalize(pg.PageRank(), "sum").rank(graph)
assert abs(pg.sum(r.np) - 1) < pg.epsilon()
with pytest.raises(Exception):
pg.Normalize(pg.PageRank(), "unknown").rank(graph)
def test_sweep():
_, graph, group = next(pg.load_datasets_one_community(["bigraph"]))
for _ in supported_backends():
training, evaluation = pg.split(list(group), training_samples=0.1)
auc1 = pg.AUC({v: 1
for v in evaluation}, exclude=training).evaluate(
pg.Sweep(pg.PageRank()).rank(
graph, {v: 1
for v in training}))
auc2 = pg.AUC({v: 1
for v in evaluation},
exclude=training).evaluate(pg.PageRank().rank(
graph, {v: 1
for v in training}))
assert auc1 > auc2
def test_fair_heuristics():
H = pg.PageRank(assume_immutability=True, normalization="symmetric")
algorithms = {
"FairO":
lambda G, p, s: pg.Normalize(pg.AdHocFairness(H, method="O")).rank(
G, sensitive=s),
"FairB":
lambda G, p, s: pg.Normalize()
(pg.AdHocFairness("B").transform(H.rank(G, p), sensitive=s)),
"FairWalk":
lambda G, p, s: pg.FairWalk(H).rank(G, p, sensitive=s)
}
_, graph, groups = next(pg.load_datasets_multiple_communities(["bigraph"]))
labels = pg.to_signal(graph, groups[0])
sensitive = pg.to_signal(graph, groups[1])
for algorithm in algorithms.values():
ranks = algorithm(graph, labels, sensitive)
assert pg.pRule(sensitive)(
ranks
) > 0.6 # TODO: Check why fairwalk fails by that much and increase the limit.
sensitive = 1 - sensitive.np
for algorithm in algorithms.values():
ranks = algorithm(graph, labels, sensitive)
assert pg.pRule(sensitive)(ranks) > 0.6
def test_fair_heuristics():
H = pg.PageRank(assume_immutability=True, normalization="symmetric")
algorithms = {
"FairO": lambda G, p, s: pg.Normalize(pg.AdHocFairness(H, method="O")).rank(G, sensitive=s),
"FairB": lambda G, p, s: pg.Normalize()(pg.AdHocFairness("B").transform(H.rank(G, p), sensitive=s)),
"LFPRN": lambda G, p, s: pg.Normalize()(pg.LFPR().rank(G, p, sensitive=s)),
"LFPRP": lambda G, p, s: pg.Normalize()(pg.LFPR(redistributor="original").rank(G, p, sensitive=s)),
"FairWalk": lambda G, p, s: pg.FairWalk(H).rank(G, p, sensitive=s)
}
import networkx as nx
_, graph, groups = next(pg.load_datasets_multiple_communities(["bigraph"], graph_api=nx))
# TODO: networx needed due to edge weighting by some algorithms
labels = pg.to_signal(graph, groups[0])
sensitive = pg.to_signal(graph, groups[1])
for name, algorithm in algorithms.items():
ranks = algorithm(graph, labels, sensitive)
if name == "FairWalk":
assert pg.pRule(sensitive)(ranks) > 0.6 # TODO: Check why fairwalk fails by that much and increase the limit.
else:
assert pg.pRule(sensitive)(ranks) > 0.98
sensitive = 1 - sensitive.np
for name, algorithm in algorithms.items():
ranks = algorithm(graph, labels, sensitive)
if name == "FairWalk":
assert pg.pRule(sensitive)(ranks) > 0.6
else:
assert pg.pRule(sensitive)(ranks) > 0.98
def test_absorbing_vs_pagerank():
graph = next(pg.load_datasets_graph(["graph9"]))
personalization = {"A": 1, "B": 1}
for _ in supported_backends():
pagerank_result = pg.PageRank(normalization='col').rank(graph, personalization)
absorbing_result = pg.AbsorbingWalks(0.85, normalization='col', max_iters=1000).rank(graph, personalization)
assert pg.Mabs(pagerank_result)(absorbing_result) < pg.epsilon()
def test_fair_personalizer():
H = pg.PageRank(assume_immutability=True, normalization="symmetric")
algorithms = {
"FairPers":
lambda G, p, s: pg.Normalize(
pg.FairPersonalizer(H, error_type=pg.Mabs, max_residual=0)).rank(
G, p, sensitive=s),
"FairPers-C":
lambda G, p, s: pg.Normalize(
pg.FairPersonalizer(
H, .80, pRule_weight=10, error_type=pg.Mabs, max_residual=0)).
rank(G, p, sensitive=s),
"FairPersSkew":
lambda G, p, s: pg.Normalize(
pg.FairPersonalizer(H, error_skewing=True, max_residual=0)).rank(
G, p, sensitive=s),
"FairPersSkew-C":
lambda G, p, s: pg.Normalize(
pg.FairPersonalizer(
H, .80, error_skewing=True, pRule_weight=10, max_residual=0)
).rank(G, p, sensitive=s),
}
_, graph, groups = next(pg.load_datasets_multiple_communities(["bigraph"]))
labels = pg.to_signal(graph, groups[0])
sensitive = pg.to_signal(graph, groups[1])
for algorithm in algorithms.values():
ranks = algorithm(graph, labels, sensitive)
assert pg.pRule(sensitive)(
ranks
) > 0.79 # allow a leeway for generalization capabilities compared to 80%
def test_separate_normalization():
graph = next(pg.load_datasets_graph(["graph5"]))
for _ in supported_backends():
algorithm = pg.PageRank(
preserve_norm=False) + pg.SeparateNormalization(["A", "B"])
ranks = algorithm(graph, {"A": 2})
assert abs(ranks["A"] + ranks["B"] - 1) < pg.epsilon()
def test_convergence_string_conversion():
# TODO: make convergence trackable from wrapping objects
graph = next(pg.load_datasets_graph(["graph5"]))
ranker = pg.PageRank() >> pg.Normalize()
ranker(graph)
assert str(ranker.convergence.iteration) + " iterations" in str(
ranker.convergence)
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 __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.PageRank(0.9, renormalize=True, assume_immutability=True,
use_quotient=False, error_type="iters", max_iters=10) # 10 iterations
def test_ordinals():
graph = next(pg.load_datasets_graph(["graph5"]))
for _ in supported_backends():
test_result = pg.Ordinals(
pg.Ordinals(pg.Ordinals(pg.PageRank(normalization='col')))).rank(
graph,
{"A": 1}) # three ordinal transformations are the same as one
assert test_result["A"] == 1
