def mutate(ind):
os = ind.ordering
ordering_default_mutate({'env': Env(wf=_wf, rm=rm, estimator=estimator)},
os)
child = ParticleIndividual(ind.entity)
child.ordering = os
return child,
def run_dcga(wf, estimator, rm, heft_mapping, heft_ordering, **params):
cxpb = params["cxpb"] #0.9
mutpb = params["mutpb"] #0.9
ngen = params["ngen"] #100
pop_size = params["pop_size"] #100
ctx = {'env': Env(wf, rm, estimator)}
toolbox = Toolbox()
toolbox.register("select", tools.selTournament, tournsize=2)
toolbox.register("mate", _mate, ctx)
toolbox.register("mutate", _mutate, ctx)
toolbox.register(
"evaluate", lambda x: [
fitness_mapping_and_ordering(ctx, {
MAPPING_SPECIE: x[0],
ORDERING_SPECIE: x[1]
})
])
# heft_mapping = extract_mapping_from_ga_file("../../temp/heft_etalon_full_tr100_m100.json", rm)
pop_mapping = mapping_heft_based_initialize(ctx, pop_size, heft_mapping, 3)
pop_ordering = ordering_heft_based_initialize(ctx, pop_size, heft_ordering,
3)
pop = [ListBasedIndividual(el) for el in zip(pop_mapping, pop_ordering)]
for p in pop:
p.fitness = Fitness(0)
stat = tools.Statistics(key=lambda x: x.fitness)
stat.register("solsstat", lambda pop: [{"best": numpy.max(pop).values[0]}])
final_pop, logbook = deap.algorithms.eaSimple(pop, toolbox, cxpb, mutpb,
ngen, stat)
best = max(final_pop, key=lambda x: x.fitness)
return best.fitness.values[0], logbook
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 setUp(self):
wf_name = "Montage_25"
self.wf = WF(wf_name)
self.env = Env(self.wf, None, None)
ms_ideal_ind = heft_mapping
os_ideal_ind = heft_ordering
ms_str_repr = [{k: v} for k, v in ms_ideal_ind]
mapping_mut_reg = MutRegulator()
config = {
"hall_of_fame_size":
0,
"interact_individuals_count":
50,
"generations":
10,
"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_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,
from heft.core.environment import ResourceGenerator as rg
from heft.core.environment.Utility import wf
from heft.experiments.cga.mobjective.utility import fitness_makespan_and_cost_map_ord, SimpleTimeCostEstimator
from heft.experiments.cga.utilities.common import extract_ordering_from_ga_file, extract_mapping_from_ga_file
from heft.experiments.cga.utilities.double_chromosome_ga import _mate, _mutate
_wf = wf("Montage_100")
# rm = ExperimentResourceManager(VMResGen.r([10, 15, 25, 30], 4))
rm = ExperimentResourceManager(rg.r([10, 15, 25, 30]))
# estimator = ExperimentEstimator(None, ideal_flops=20, transfer_time=100)
estimator = SimpleTimeCostEstimator(comp_time_cost=10,
transf_time_cost=2,
transferMx=None,
ideal_flops=20,
transfer_time=100)
env = Env(_wf, rm, estimator)
pop_size = 200
NGEN = 300
MU = 40
CXPB = 0.9
heft_mapping = extract_mapping_from_ga_file(
"../../../temp/heft_etalon_full_tr100_m100.json", rm)
heft_ordering = extract_ordering_from_ga_file(
"../../../temp/heft_etalon_full_tr100_m100.json")
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
creator.create("Individual",
ListBasedIndividual,
typecode='d',
# heft_mapping = extract_mapping_from_ga_file("../../temp/heft_etalon_full_tr100_m25.json", rm)
# heft_ordering = extract_ordering_from_ga_file("../../temp/heft_etalon_full_tr100_m25.json")
ms_ideal_ind = heft_mapping
os_ideal_ind = heft_ordering
ms_str_repr = [{k: v} for k, v in ms_ideal_ind]
mapping_mut_reg = MutRegulator()
config = {
"hall_of_fame_size": 0,
"interact_individuals_count": 50,
"generations": 100,
"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_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),
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)