import gpbo
import scipy as sp
#dimensionality
D=2
#noise variance
s=0.
#number of step to take
n=100
#define a simple 2d objective in x which also varies with respect to the environmental variable
def f(x,**ev):
y=-sp.cos(x[0])-sp.cos(x[1])+2
#fixed cost
c=1.
#noise
n = sp.random.normal()*s
#we want to check the noiseless value when evaluating performance
if 'cheattrue' in ev.keys():
if ev['cheattrue']:
n=0
print('f inputs x:{} ev:{} outputs y:{} (n:{}) c:{}'.format(x,ev,y+n,n,c))
return y+n,c,dict()
#arguments to generate default config are objective function, dimensionality,number of initialization points, number of steps, noise variance, result directory and result filename
C=gpbo.core.config.switchdefault(f,D,10,n,s,'results','stopping.csv')
#set the target global regret
C.choosepara['regretswitch']=1e-2
out = gpbo.search(C)
print out
def runexp(f, lb, ub, path, nreps, confs, indexoffset=0):
for i_ in xrange(nreps):
ii = i_ + indexoffset
for C in confs:
if C[0][:2] == 'ei':
C[1].path = path
C[1].fname = '{}_{}.csv'.format(C[0], ii)
C[1].aqpara['lb'] = lb
C[1].aqpara['ub'] = ub
C[1].reccpara['lb'] = lb
C[1].reccpara['ub'] = ub
def wrap(x, **ev):
tmp = copy.deepcopy(ev)
tmp['xa'] = 0
return f(x, **tmp)
C[1].ojf = wrap
try:
out = gpbo.search(C[1])
except:
pass
elif C[0][:5] == 'pesfs':
C[1].path = path
C[1].fname = '{}_{}.csv'.format(C[0], ii)
C[1].aqpara['lb'] = lb
C[1].aqpara['ub'] = ub
C[1].reccpara['lb'] = lb
C[1].reccpara['ub'] = ub
def wrap(x, **ev):
tmp = copy.deepcopy(ev)
tmp['xa'] = 0
return f(x, **tmp)
C[1].ojf = wrap
try:
out = gpbo.search(C[1])
except:
pass
elif C[0][:5] == 'pesbs':
C[1].path = path
C[1].fname = '{}_{}.csv'.format(C[0], ii)
C[1].aqpara['lb'] = [i for i in lb]
C[1].aqpara['ub'] = [i for i in ub]
C[1].reccpara['lb'] = [i for i in lb]
C[1].reccpara['ub'] = [i for i in ub]
C[1].ojf = f
try:
out = gpbo.search(C[1])
except:
pass
elif C[0][:9] == 'switching':
C[1].path = path
C[1].fname = '{}_{}.csv'.format(C[0], ii)
C[1].ojf = f
try:
out = gpbo.search(C[1])
except:
pass
elif C[0][:5] == 'pesvs':
C[1].path = path
C[1].fname = '{}_{}.csv'.format(C[0], ii)
C[1].aqpara['lb'] = [i for i in lb]
C[1].aqpara['ub'] = [i for i in ub]
C[1].reccpara['lb'] = [i for i in lb]
C[1].reccpara['ub'] = [i for i in ub]
C[1].ojf = f
try:
out = gpbo.search(C[1])
except:
pass
elif C[0][:4] == 'mtbo':
try:
optmtbo(f,
lb,
ub,
1. - (1. / C[1]['lowtask']),
C[1]['nsteps'],
ninit=C[1]['ninit'],
fpath=path,
fname='{}_{}.csv'.format(C[0], ii),
mod=C[1]['switchestimator'])
except:
pass
elif C[0][:7] == 'fabolas':
try:
optfabolas(f,
lb,
ub,
C[1]['nsteps'],
C[1]['ninit'],
fname='{}_{}.csv'.format(C[0], ii),
fpath=path)
except:
raise
elif C[0][:6] == 'fabmod':
try:
optfabolas_mod(f,
lb,
ub,
C[1]['nsteps'],
C[1]['ninit'],
fname='{}_{}.csv'.format(C[0], ii),
fpath=path,
switchestimator=C[1]['switchestimator'],
switchkernel=C[1]['switchkernel'],
timelimit=C[1]['timelimit'])
except:
raise
else:
print("not an optimization method")
