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_best_direction():
assert pg.Conductance().best_direction() == -1
assert pg.Density().best_direction() == 1
assert pg.Modularity().best_direction() == 1
assert pg.AUC([1, 2, 3]).best_direction() == 1
assert pg.Cos([1, 2, 3]).best_direction() == 1
assert pg.Dot([1, 2, 3]).best_direction() == 1
assert pg.TPR([1, 2, 3]).best_direction() == 1
assert pg.TNR([1, 2, 3]).best_direction() == 1
def test_computations():
for _ in supported_backends():
assert pg.Accuracy([1, 2, 3])([1, 2, 3]) == 1
assert pg.Mabs([3, 1, 1])([2, 0, 2]) == 1
assert pg.CrossEntropy([1, 1, 1])([1, 1, 1]) < 1.E-12
assert float(pg.Cos([2, 0, 1])([2, 0, 1])) == 1
assert float(pg.Cos([2, 0, 1])([-2, 0, -1])) == -1
assert float(pg.Cos([0, 0, 0])([0, 0, 0])) == 0
assert float(pg.Dot([1, 1, 1])([1, 1, 1])) == 3
assert float(pg.TPR([1, 0, 0, 0])([1, 1, 0, 0])) == 0.5
assert float(pg.TNR([0, 0, 0, 1])([1, 1, 0, 0])) == 0.5
def test_fair_personalizer_mistreatment():
H = pg.PageRank(assume_immutability=True, normalization="symmetric")
algorithms = {
"Base": lambda G, p, s: H.rank(G, p),
"FairPersMistreat": pg.Normalize(pg.FairPersonalizer(H, parity_type="mistreatment", pRule_weight=10)),
"FairPersTPR": pg.Normalize(pg.FairPersonalizer(H, parity_type="TPR", pRule_weight=10)),
"FairPersTNR": pg.Normalize(pg.FairPersonalizer(H, parity_type="TNR", pRule_weight=-1)) # TNR optimization increases mistreatment for this example
}
mistreatment = lambda known_scores, sensitive_signal, exclude: \
pg.AM([pg.Disparity([pg.TPR(known_scores, exclude=1 - (1 - exclude) * sensitive_signal),
pg.TPR(known_scores, exclude=1 - (1 - exclude) * (1 - sensitive_signal))]),
pg.Disparity([pg.TNR(known_scores, exclude=1 - (1 - exclude) * sensitive_signal),
pg.TNR(known_scores, exclude=1 - (1 - exclude) * (1 - sensitive_signal))])])
_, graph, groups = next(pg.load_datasets_multiple_communities(["synthfeats"]))
labels = pg.to_signal(graph, groups[0])
sensitive = pg.to_signal(graph, groups[1])
train, test = pg.split(labels)
# TODO: maybe try to check for greater improvement
base_mistreatment = mistreatment(test, sensitive, train)(algorithms["Base"](graph, train, sensitive))
for algorithm in algorithms.values():
if algorithm != algorithms["Base"]:
print(algorithm.cite())
assert base_mistreatment >= mistreatment(test, sensitive, train)(algorithm(graph, train, sensitive))
.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')
