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_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="\\\\")
algorithms = {
"ppr0.5": pg.PageRank(alpha=0.5, preprocessor=pre, **convergence),
"ppr0.85": pg.PageRank(alpha=0.85, preprocessor=pre, **convergence),
"ppr0.9": pg.PageRank(alpha=0.9, preprocessor=pre, **convergence),
"ppr0.99": pg.PageRank(alpha=0.99, preprocessor=pre, **convergence),
"hk2": pg.HeatKernel(t=2, preprocessor=pre, **convergence),
"hk3": pg.HeatKernel(t=3, preprocessor=pre, **convergence),
"hk5": pg.HeatKernel(t=5, preprocessor=pre, **convergence),
"hk7": pg.HeatKernel(t=7, preprocessor=pre, **convergence),
}
postprocessor = pg.Tautology
algorithms = pg.benchmarks.create_variations(algorithms, postprocessor)
measure = pg.AUC
optimization = pg.SelfClearDict()
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,