def default_config(wf, rm, estimator):
selector = lambda env, pop: tools.selTournament(pop, len(pop), 4)
return {
"interact_individuals_count":
22,
"generations":
5,
"env":
Env(wf, rm, estimator),
"species": [
Specie(name=MAPPING_SPECIE,
pop_size=10,
cxb=0.8,
mb=0.5,
mate=lambda env, child1, child2: tools.cxOnePoint(
child1, child2),
mutate=mapping_default_mutate,
select=selector,
initialize=mapping_default_initialize),
Specie(
name=ORDERING_SPECIE,
pop_size=10,
cxb=0.8,
mb=0.5,
mate=ordering_default_crossover,
mutate=ordering_default_mutate,
select=selector,
initialize=ordering_default_initialize,
)
],
"operators": {
# "choose": default_choose,
"build_solutions": default_build_solutions,
"fitness": fitness_mapping_and_ordering,
"assign_credits": default_assign_credits
}
}
def test_fixed_specie(self):
wf_name = "Montage_25"
wf = WF(wf_name)
manager = ExperimentResourceManager(
ResourceGenerator.r([10, 15, 15, 25]))
estimator = ExperimentEstimator(None, 20, 1.0, transfer_time=100)
config = default_config(wf, manager, estimator)
ms_ideal_ind = build_ms_ideal_ind(wf, manager)
config["species"][0] = Specie(
name=MAPPING_SPECIE,
fixed=True,
representative_individual=ListBasedIndividual(ms_ideal_ind))
solution, pops, logbook, initial_pops = run_cooperative_ga(**config)
schedule = build_schedule(wf, estimator, manager, solution)
for k, items in schedule.mapping.items():
for item in items:
item.state = ScheduleItem.FINISHED
## TODO: refactor this
ExecutorRunner.extract_result(schedule, True, wf)
pass
Env(_wf, rm, estimator),
"species": [
Specie(
name=MAPPING_SPECIE,
pop_size=50,
cxb=0.9,
mb=0.9,
mate=lambda env, child1, child2: tools.cxOnePoint(child1, child2),
# mutate=mapping_all_mutate,
# mutate=mapping_all_mutate_variable,
mutate=mapping_mut_reg(mapping_all_mutate_configurable),
# mutate=mapping_all_mutate_variable2,
# mutate=mapping_improving_mutation,
# mutate=mapping_default_mutate,
# mutate=MappingArchiveMutate(),
select=mapping_selector,
# initialize=mapping_default_initialize,
initialize=lambda ctx, pop: mapping_heft_based_initialize(
ctx, pop, heft_mapping, 3),
stat=lambda pop: {
"hamming_distances":
hamming_distances([to_seq(p)
for p in pop], to_seq(ms_ideal_ind)),
"unique_inds_count":
unique_individuals(pop),
"pcm":
pcm(pop),
"gdm":
gdm(pop)
}),
Specie(
name=ORDERING_SPECIE,
def do_exp():
config = {
"interact_individuals_count":
100,
"generations":
300,
"env":
Env(_wf, rm, estimator),
"species": [
Specie(
name=MAPPING_SPECIE,
pop_size=50,
cxb=0.9,
mb=0.9,
mate=lambda env, child1, child2: tools.cxOnePoint(
child1, child2),
# mutate=mapping_default_mutate,
# mutate=lambda ctx, mutant: mapping_k_mutate(ctx, 3, mutant)
mutate=mapping_all_mutate,
# mutate=OnlyUniqueMutant()(mapping_all_mutate),
select=selector,
# initialize=mapping_default_initialize,
initialize=lambda ctx, pop: mapping_heft_based_initialize(
ctx, pop, heft_mapping, 3),
stat=lambda pop: {
"hamming_distances":
hamming_distances([to_seq(p)
for p in pop], to_seq(ms_ideal_ind)),
"unique_inds_count":
unique_individuals(pop),
"pcm":
pcm(pop),
"gdm":
gdm(pop)
}),
Specie(name=ORDERING_SPECIE,
fixed=True,
representative_individual=ListBasedIndividual(
os_representative))
],
"solstat":
lambda sols: {
"best_components":
hamming_for_best_components(sols, ms_ideal_ind, os_ideal_ind),
"best_components_itself":
best_components_itself(sols),
"best":
-1 * Utility.makespan(
build_schedule(_wf, estimator, rm,
max(sols, key=lambda x: x.fitness)))
},
"operators": {
# "choose": default_choose,
"build_solutions": default_build_solutions,
"fitness": fitness_mapping_and_ordering,
# "fitness": overhead_fitness_mapping_and_ordering,
# "assign_credits": default_assign_credits
# "assign_credits": max_assign_credits
"assign_credits": assign_from_transfer_overhead
}
}
return do_experiment(saver, config, _wf, rm, estimator)
heft_mapping = extract_mapping_from_ga_file(
"{0}/temp/heft_etalon_full_tr100_m50.json".format(__root_path__), rm)
os_ideal_ind = build_os_ideal_ind(_wf)
ms_ideal_ind = heft_mapping
config = {
"interact_individuals_count":
200,
"generations":
300,
"env":
Env(_wf, rm, estimator),
"species": [
Specie(
name=MAPPING_SPECIE,
fixed=True,
representative_individual=ListBasedIndividual(ms_representative)),
Specie(
name=ORDERING_SPECIE,
pop_size=50,
cxb=0.8,
mb=0.5,
mate=ordering_default_crossover,
mutate=ordering_default_mutate,
select=selector,
# initialize=ordering_default_initialize,
initialize=lambda ctx, pop: ordering_heft_based_initialize(
ctx, pop, heft_ordering, 3),
stat=lambda pop:
{"hamming_distances": hamming_distances(pop, os_ideal_ind)})
],