def optimize(**kwargs):
min = kwargs['min']
max = kwargs['max']
optimizer = kwargs['optimizer']
del kwargs['min']
del kwargs['max']
del kwargs['optimizer']
return hve(optimizer(**kwargs), min, max, 100000)
def optimize(**kwargs):
min=kwargs['min']
max=kwargs['max']
optimizer=kwargs['optimizer']
del kwargs['min']
del kwargs['max']
del kwargs['optimizer']
return hve(optimizer(**kwargs),min,max,100000)
max=max,
Model=model,
decnum=decnum,
objnum=objnum,
the_seed=-1,
candidates=[100, 10, 1000],
generations=1000,
mutation_rate=[0.05, 0.01, 0.5],
lifes=[5, 1, 10])
del kw['optimizer']
del kw['min']
del kw['max']
for the_seed in xrange(20):
pf1 = GeneticAlgorithm(Model=model,
decnum=decnum,
objnum=objnum,
the_seed=the_seed,
candidates=candidates,
generations=generations,
mutation_rate=mutation_rate,
lifes=lifes)
pf2 = GeneticAlgorithm(**kw)
#min,max=upminmax(min,max,pf)
hv[model.__name__][objnum][decnum]['untuned'].append(
hve(pf1, min, max, 100000))
hv[model.__name__][objnum][decnum]['tuned'].append(
hve(pf2, min, max, 100000))
with open('./data/hypervolumn.pickle', 'wb') as handle:
pickle.dump(hv, handle)
objs=[2,4,6,8]
decs=[10,20,40]
hv={}
for model in models:
hv[model.__name__]={}
for objnum in objs:
hv[model.__name__][objnum]={}
for decnum in decs:
hv[model.__name__][objnum][decnum]={'untuned':[],'tuned':[]}
min,max=init(model,decnum=decnum,objnum=objnum,num=100000)
kw=differential_evolution(Tunee,optimizer=GeneticAlgorithm,min=min,max=max,Model=model,decnum=decnum,
objnum=objnum,the_seed=-1,candidates=[100,10,1000],generations=1000,
mutation_rate=[0.05,0.01,0.5],lifes=[5,1,10])
del kw['optimizer']
del kw['min']
del kw['max']
for the_seed in xrange(20):
pf1=GeneticAlgorithm(Model=model,decnum=decnum,objnum=objnum,the_seed=the_seed,candidates=candidates,
generations=generations,mutation_rate=mutation_rate,lifes=lifes)
pf2=GeneticAlgorithm(**kw)
#min,max=upminmax(min,max,pf)
hv[model.__name__][objnum][decnum]['untuned'].append(hve(pf1,min,max,100000))
hv[model.__name__][objnum][decnum]['tuned'].append(hve(pf2,min,max,100000))
with open('./data/hypervolumn.pickle', 'wb') as handle:
pickle.dump(hv, handle)
models = [DTLZ1, DTLZ3, DTLZ5, DTLZ7]
objs = [2, 4, 6, 8]
decs = [10, 20, 40]
hv = {}
for model in models:
hv[model.__name__] = {}
for objnum in objs:
hv[model.__name__][objnum] = {}
for decnum in decs:
hv[model.__name__][objnum][decnum] = []
min, max = init(model, decnum=decnum, objnum=objnum, num=100000)
for the_seed in xrange(20):
pf = GeneticAlgorithm(
model,
decnum=decnum,
objnum=objnum,
the_seed=the_seed,
candidates=candidates,
generations=generations,
mutation_rate=mutation_rate,
lifes=lifes,
)
# min,max=upminmax(min,max,pf)
hv[model.__name__][objnum][decnum].append(hve(pf, min, max, 100000))
with open("hypervolumn.pickle", "wb") as handle:
pickle.dump(hv, handle)
set_trace()
with open("./data/hypervolumn.pickle", "wb") as handle:
pickle.dump(hv, handle)
decs = [10, 20, 40]
hv = {}
for model in models:
hv[model.__name__] = {}
for objnum in objs:
hv[model.__name__][objnum] = {}
for decnum in decs:
hv[model.__name__][objnum][decnum] = []
min, max = init(model,
decnum=decnum,
objnum=objnum,
num=100000)
for the_seed in xrange(20):
pf = GeneticAlgorithm(model,
decnum=decnum,
objnum=objnum,
the_seed=the_seed,
candidates=candidates,
generations=generations,
mutation_rate=mutation_rate,
lifes=lifes)
#min,max=upminmax(min,max,pf)
hv[model.__name__][objnum][decnum].append(
hve(pf, min, max, 100000))
with open('hypervolumn.pickle', 'wb') as handle:
pickle.dump(hv, handle)
set_trace()
with open('./data/hypervolumn.pickle', 'wb') as handle:
pickle.dump(hv, handle)
