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_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_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_benchmark_print():
assert pg.benchmarks.utils._fraction2str(0.1) == ".10"
assert pg.benchmarks.utils._fraction2str(0.00001) == "0"
assert pg.benchmarks.utils._fraction2str(1) == "1.00"
loader = pg.load_datasets_one_community(["graph9", "bigraph"])
console = pg.benchmark_print(pg.benchmark(pg.create_demo_filters(), loader),
out=io.StringIO(""), err=None).getvalue()
loader = pg.load_datasets_one_community(["graph9", "bigraph"])
ret = pg.benchmark_dict(pg.benchmark(pg.create_demo_filters(), loader, sensitive=pg.MannWhitneyParity))
assert isinstance(ret, dict)
assert len(ret) == 3
assert isinstance(ret["graph9"], dict)
assert (len(str(ret)) - len(console)) < (len(str(ret)) + len(console))/2
.8,
pRule_weight=10,
max_residual=1,
error_type=pg.Mabs,
error_skewing=False,
parameter_buckets=1,
parity_type="impact")
#"FFfix-C": pg.FairTradeoff(filter, .8, pRule_weight=10, error_type=pg.Mabs)
#"FairTf": pg.FairnessTf(filter)
}
algorithms = pg.create_variations(algorithms, {"": pg.Normalize})
#import cProfile as profile
#pr = profile.Profile()
#pr.enable()
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))])])
pg.benchmark_print(pg.benchmark(algorithms,
pg.load_datasets_multiple_communities(
datasets, max_group_number=2),
metric=pg.AUC,
sensitive=pg.pRule,
fraction_of_training=seed_fractions),
delimiter=" & ",
end_line="\\\\")
#pr.disable()
#pr.dump_stats('profile.pstat')
personalization: pg.GraphSignalData = None,
**kwargs):
personalization = pg.to_signal(graph, personalization)
graph = personalization.graph
ranks = self.ranker(personalization)
ret = 0
total_sum = pg.sum(ranks)
accum_sum = 0
for threshold in sorted(ranks.values()):
accum_sum += threshold
if accum_sum > total_sum * 0.1:
break
for i, v in enumerate(ranks):
pg.utils.log(f"{i}/{len(ranks)}")
if ranks[v] >= threshold:
partial = ranks >> pg.Threshold(ranks[v],
inclusive=True) >> self.ranker
ret = partial * ranks[v] + ret
return ret
algs = {
"ppr": pg.PageRank(0.9),
"ppr+so": pg.PageRank(0.9) >> pg.SeedOversampling(),
"ppr+bso": pg.PageRank(0.9) >> pg.BoostedSeedOversampling(),
"ppr+sso": pg.PageRank(0.9) >> StochasticSeedOversampling(),
}
loader = pg.load_datasets_one_community(["citeseer"])
pg.benchmark_print(pg.benchmark(algs, loader, pg.AUC, 3))
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="\\\\")
import pygrank as pg
loader = list(pg.load_datasets_multiple_communities(["bigraph", "cora", "citeseer"]))
algorithms = pg.create_variations(pg.create_demo_filters(), pg.create_many_variation_types())
algorithms = pg.create_variations(algorithms, pg.Normalize) # add normalization to all algorithms
print("Algorithms", len(algorithms))
measures = {"AUC": lambda ground_truth, exlude: pg.MultiSupervised(pg.AUC, ground_truth, exlude),
"NDCG": lambda ground_truth, exlude: pg.MultiSupervised(pg.NDCG, ground_truth, exlude),
"Density": lambda graph: pg.MultiUnsupervised(pg.Density, graph),
"Modularity": lambda graph: pg.MultiUnsupervised(pg.Modularity, graph),
"LinkCC": lambda graph: pg.ClusteringCoefficient(graph, similarity="dot"),
"LinkAUCcos": lambda graph: pg.LinkAssessment(graph, similarity="cos"),
"HopAUCdot": lambda graph: pg.LinkAssessment(graph, similarity="dot", hops=2),
}
scores = {measure: pg.benchmark_scores(pg.benchmark(algorithms, loader, measures[measure])) for measure in measures}
evaluations_vs_auc = dict()
evaluations_vs_ndcg = dict()
for measure in measures:
evaluations_vs_auc[measure] = abs(pg.SpearmanCorrelation(scores["AUC"])(scores[measure]))
evaluations_vs_ndcg[measure] = abs(pg.SpearmanCorrelation(scores["NDCG"])(scores[measure]))
pg.benchmark_print([("Measure", "AUC corr", "NDCG corr")]
+ [(measure, evaluations_vs_auc[measure], evaluations_vs_ndcg[measure]) for measure in measures])
import pygrank as pg
datasets = ["friendster"]
pre = pg.preprocessor(assume_immutability=True,
normalization="symmetric") # common preprocessor
algs = {
"ppr.85": pg.PageRank(.85, preprocessor=pre),
"ppr.99": pg.PageRank(.99, preprocessor=pre, max_iters=1000),
"hk3": pg.HeatKernel(3, preprocessor=pre),
"hk5": pg.HeatKernel(5, preprocessor=pre),
"tuned": pg.ParameterTuner(preprocessor=pre)
}
loader = pg.load_datasets_one_community(datasets)
pg.benchmark_print(pg.benchmark(algs, loader, pg.AUC, fraction_of_training=.5))
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)
tuned = {
"select": pg.AlgorithmSelection(algorithms.values(), fraction_of_training=0.9, measure=measure),
"tune": create_param_tuner(),
"tuneLBFGSB": create_param_tuner(pg.lbfgsb)
}
for name, graph, group in pg.load_datasets_all_communities(datasets, min_group_size=community_size, max_group_number=3):
print(" & ".join([str(val) for val in [name, len(graph), graph.number_of_edges(), len(group)]])+" \\\\")
loader = pg.load_datasets_all_communities(datasets, min_group_size=community_size, max_group_number=3)
pg.benchmark_print(
pg.benchmark_average((pg.benchmark(algorithms | tuned, loader, measure,
fraction_of_training=[0.1, 0.2, 0.3], seed=list(range(1)))), posthocs=True),
decimals=3, delimiter=" & ", end_line="\\\\")
tuned = {
"selected":
pg.AlgorithmSelection(algorithms.values(), fraction_of_training=0.8),
#"tuned": pg.ParameterTuner(preprocessor=pre, fraction_of_training=0.8, tol=tol, optimization_dict=optimization, measure=pg.AUC),
"arnoldi":
pg.HopTuner(preprocessor=pre,
basis="arnoldi",
measure=pg.Cos,
tol=tol,
optimization_dict=optimization),
#"arnoldi2": pg.ParameterTuner(lambda params: pg.HopTuner(preprocessor=pre, basis="arnoldi", num_parameters=int(params[0]),
# measure=pg.Cos,
# tol=tol, optimization_dict=optimization, tunable_offset=None),
# max_vals=[40], min_vals=[5], divide_range=2, fraction_of_training=0.1),
}
#algorithms = pg.create_variations(algorithms, {"": pg.Tautology, "+Sweep": pg.Sweep})
#print(algorithms.keys())
#for name, graph, group in pg.load_datasets_all_communities(datasets, min_group_size=50):
# print(" & ".join([str(val) for val in [name, len(graph), graph.number_of_edges(), len(group)]])+" \\\\")
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="\\\\")