def test_weighted_instance_failure_aware():
"""Checks that the CF and PICEF formulations agree on the optimal
result for an instance with weighted edges, using failure-aware solving.
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
fns = [k_ip.optimise_picef, k_ip.optimise_ccf]
for max_cycle in [0, 1, 2, 3, 4]:
for max_chain in [0, 1, 2, 3]:
cfg = k_ip.OptConfig(d,
ndds,
max_cycle,
max_chain,
edge_success_prob=0.7)
opt_result_cf = k_ip.optimise_ccf(cfg)
opt_result_picef = k_ip.optimise_picef(cfg)
opt_result_cf_relabelled = k_ip.optimise_relabelled(
k_ip.optimise_ccf, cfg)
opt_result_picef_relabelled = k_ip.optimise_relabelled(
k_ip.optimise_picef, cfg)
assert abs(opt_result_cf.total_score -
opt_result_picef.total_score) < EPS
assert abs(opt_result_cf.total_score -
opt_result_cf_relabelled.total_score) < EPS
assert abs(opt_result_cf.total_score -
opt_result_picef_relabelled.total_score) < EPS
def solve_kep(cfg, formulation, use_relabelled=True):
formulations = {
"uef": ("Uncapped edge formulation", kidney_ip.optimise_uuef),
"eef": ("EEF", kidney_ip.optimise_eef),
"eef_full_red": ("EEF with full reduction by cycle generation",
kidney_ip.optimise_eef_full_red),
"hpief_prime": ("HPIEF'", kidney_ip.optimise_hpief_prime),
"hpief_prime_full_red":
("HPIEF' with full reduction by cycle generation",
kidney_ip.optimise_hpief_prime_full_red),
"hpief_2prime": ("HPIEF''", kidney_ip.optimise_hpief_2prime),
"hpief_2prime_full_red":
("HPIEF'' with full reduction by cycle generation",
kidney_ip.optimise_hpief_2prime_full_red),
"picef": ("PICEF", kidney_ip.optimise_picef),
"nhs": ("PICEF-NHS", kidney_ip.optimise_picef_nhs),
#"nhs_chains": ("PICEF-NHS-CHAINS", kidney_ip.optimise_picef_nhs_chains),
"cf": ("Cycle formulation", kidney_ip.optimise_ccf)
}
if formulation in formulations:
formulation_name, formulation_fun = formulations[formulation]
if use_relabelled:
opt_result = kidney_ip.optimise_relabelled(formulation_fun, cfg)
else:
opt_result = formulation_fun(cfg)
if opt_result:
kidney_utils.check_validity(opt_result, cfg.digraph, cfg.ndds,
cfg.max_cycle, cfg.max_chain)
opt_result.formulation_name = formulation_name
return opt_result
else:
raise ValueError("Unrecognised IP formulation name")
def test_weighted_instance_failure_aware():
"""Checks that the CF and PICEF formulations agree on the optimal
result for an instance with weighted edges, using failure-aware solving.
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
fns = [k_ip.optimise_picef, k_ip.optimise_ccf]
for max_cycle in [0, 1, 2, 3, 4]:
for max_chain in [0, 1, 2, 3]:
cfg = k_ip.OptConfig(d, ndds, max_cycle, max_chain, edge_success_prob=0.7)
opt_result_cf = k_ip.optimise_ccf(cfg)
opt_result_picef = k_ip.optimise_picef(cfg)
opt_result_cf_relabelled = k_ip.optimise_relabelled(k_ip.optimise_ccf, cfg)
opt_result_picef_relabelled = k_ip.optimise_relabelled(k_ip.optimise_picef, cfg)
assert abs(opt_result_cf.total_score - opt_result_picef.total_score) < EPS
assert abs(opt_result_cf.total_score - opt_result_cf_relabelled.total_score) < EPS
assert abs(opt_result_cf.total_score - opt_result_picef_relabelled.total_score) < EPS
def test_relabelled_solve():
"""Checks whether the vertex-ordering heuristic affects the
result for a weighted instance
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
for max_cycle in [0, 3]:
for max_chain in [0, 5]:
opt_result_0 = k_ip.optimise_picef(k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print opt_result_0.total_score
opt_result = k_ip.optimise_relabelled(
k_ip.optimise_picef, k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print " ", opt_result.total_score
assert abs(opt_result.total_score - opt_result_0.total_score) < EPS
def test_relabelled_solve():
"""Checks whether the vertex-ordering heuristic affects the
result for a weighted instance
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
for max_cycle in [0, 3]:
for max_chain in [0, 5]:
opt_result_0 = k_ip.optimise_picef(
k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print(opt_result_0.total_score)
opt_result = k_ip.optimise_relabelled(
k_ip.optimise_picef,
k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print(" ", opt_result.total_score)
assert abs(opt_result.total_score - opt_result_0.total_score) < EPS
