def test_optimize_20(self):
self.experiment.bandit_algo.build_helpers()
HL = self.experiment.bandit_algo.helper_locals
assert len(HL['Gsamples']) == 1
Gpseudocounts = HL['Gsamples'][0].vals.owner.inputs[1]
Bpseudocounts = HL['Bsamples'][0].vals.owner.inputs[1]
f = self.experiment.bandit_algo._helper
debug = theano.function(
[HL['n_to_draw'], HL['n_to_keep'], HL['y_thresh'], HL['yvals']]
+ HL['s_obs'].flatten(),
(HL['Gobs'].flatten()
+ [Gpseudocounts]
+ [Bpseudocounts]
+ [HL['yvals'][where(HL['yvals'] < HL['y_thresh'])]]
+ [HL['yvals'][where(HL['yvals'] >= HL['y_thresh'])]]
),
allow_input_downcast=True,
)
debug_rval = [None]
def _helper(*args):
rval = f(*args)
debug_rval[0] = debug(*args)
return rval
self.experiment.bandit_algo._helper = _helper
self.experiment.run(200)
gobs_idxs, gobs_vals, Gpseudo, Bpseudo, Gyvals, Byvals = debug_rval[0]
print gobs_idxs
print 'Gpseudo', Gpseudo
print 'Bpseudo', Bpseudo
import matplotlib.pyplot as plt
plt.subplot(1,4,1)
Xs = [t['x'] for t in self.experiment.trials]
Ys = self.experiment.losses()
plt.plot(Ys)
plt.xlabel('time')
plt.ylabel('loss')
plt.subplot(1,4,2)
plt.scatter(Xs,Ys )
plt.xlabel('X')
plt.ylabel('loss')
plt.subplot(1,4,3)
plt.hist(Xs )
plt.xlabel('X')
plt.ylabel('freq')
plt.subplot(1,4,4)
plt.hist(Gyvals, bins=20)
plt.hist(Byvals, bins=20)
print self.experiment.losses()
print 'MIN', min(self.experiment.losses())
assert min(self.experiment.losses()) < -3.00
if 0:
plt.show()
def build_helpers(self):
s_prior = IdxsValsList.fromlists(self.s_idxs, self.s_vals)
s_obs = s_prior.new_like_self()
# y_thresh is the boundary between 'good' and 'bad' regions of the
# search space.
y_thresh = tensor.scalar()
yvals = tensor.vector()
n_to_draw = self.s_N
n_to_keep = tensor.iscalar()
s_rng = montetheano.RandomStreams(self.seed + 9)
GE = self.good_estimator
BE = self.bad_estimator
Gobs = s_obs.symbolic_take(where(yvals < y_thresh))
Bobs = s_obs.symbolic_take(where(yvals >= y_thresh))
# To "optimize" EI we just draw a pile of samples from the density
# of good points and then just take the best of those.
Gsamples = GE.posterior(s_prior, Gobs, s_rng)
Bsamples = BE.posterior(s_prior, Bobs, s_rng)
G_ll = GE.log_likelihood(Gsamples,
Gsamples,
llik=tensor.zeros((n_to_draw, )))
B_ll = BE.log_likelihood(Bsamples,
Gsamples,
llik=tensor.zeros((n_to_draw, )))
# subtract B_ll from G_ll
log_EI = G_ll - B_ll
keep_idxs = argsort(log_EI)[-n_to_keep:]
# store all these vars for the unittests
self.helper_locals = locals()
del self.helper_locals['self']
def build_helpers(self):
s_prior = IdxsValsList.fromlists(self.s_idxs, self.s_vals)
s_obs = s_prior.new_like_self()
# y_thresh is the boundary between 'good' and 'bad' regions of the
# search space.
y_thresh = tensor.scalar()
yvals = tensor.vector()
n_to_draw = self.s_N
n_to_keep = tensor.iscalar()
s_rng = montetheano.RandomStreams(self.seed + 9)
GE = self.good_estimator
BE = self.bad_estimator
Gobs = s_obs.symbolic_take(where(yvals < y_thresh))
Bobs = s_obs.symbolic_take(where(yvals >= y_thresh))
# To "optimize" EI we just draw a pile of samples from the density
# of good points and then just take the best of those.
Gsamples = GE.posterior(s_prior, Gobs, s_rng)
Bsamples = BE.posterior(s_prior, Bobs, s_rng)
G_ll = GE.log_likelihood(Gsamples, Gsamples,
llik = tensor.zeros((n_to_draw,)))
B_ll = BE.log_likelihood(Bsamples, Gsamples,
llik = tensor.zeros((n_to_draw,)))
# subtract B_ll from G_ll
log_EI = G_ll - B_ll
keep_idxs = argsort(log_EI)[-n_to_keep:]
# store all these vars for the unittests
self.helper_locals = locals()
del self.helper_locals['self']
