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_load_dataset_load():
assert len(list(pg.load_datasets_all_communities(["graph9"]))) > 1
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="\\\\")