def show_bandit_algo(self, trials, results, xlim_low=-5, xlim_high=5,
n_candidates=5):
ivls = self.idxs_vals_by_status(trials, results)
X_IVLs = ivls['x_IVLs']
Ys = ivls['losses']
Ys_var = ivls['losses_variance']
prepared_data = self.prepare_GP_training_data(ivls)
x_all, y_all, y_mean, y_var, y_std = prepared_data
self.fit_GP(*prepared_data)
candidates = self._prior_sampler(n_candidates)
EI = self.GP_EI(IdxsValsList.fromflattened(candidates))
print ''
print 'Candidates'
print 'idxs', candidates[0]
print 'vals', candidates[1]
print 'EI', EI
#print 'optimizing candidates'
candidates_opt = self.GP_EI_optimize(
IdxsValsList.fromflattened(candidates))
self.post_refinement(candidates_opt)
EI_opt = self.GP_EI(candidates_opt)
print ''
print 'Optimized candidates'
print 'idxs', candidates_opt[0].idxs
print 'vals', candidates_opt[0].vals
print "EI", EI_opt
num = len(candidates_opt)
plt.scatter(x_all[0].vals,
y_all * self._GP_y_std + self._GP_y_mean)
plt.scatter(candidates[1], numpy.zeros_like(candidates[1]),
c='y')
plt.scatter(candidates_opt[0].vals,
numpy.zeros_like(candidates[1]) - .1,
c='k')
plt.figure()
plt.xlim([xlim_low, xlim_high])
xmesh = numpy.linspace(xlim_low, xlim_high)
N = len(xmesh)
XmeshN = [numpy.arange(N) for _ind in range(num)]
Xmesh = [numpy.linspace(xlim_low, xlim_high)
for _ind in range(num)]
print Xmesh
IVL = IdxsValsList.fromlists(XmeshN, Xmesh)
gp_mean, gp_var = self.GP_mean_variance(IVL)
gp_EI = self.GP_EI(IVL)
print "GP_VAR", gp_var
plt.plot(xmesh, gp_mean)
plt.plot(xmesh, gp_mean + numpy.sqrt(gp_var), c='g')
plt.plot(xmesh, gp_mean - numpy.sqrt(gp_var), c='g')
plt.plot(xmesh, gp_EI, c='r')
plt.show()
def setUp(self):
self.TE = IndependentNullEstimator()
self.bandit = NestedUniform()
self.experiment = SerialExperiment(
GM_BanditAlgo(self.bandit,
good_estimator=IndependentNullEstimator(),
bad_estimator=IndependentNullEstimator()))
self.s_rng = montetheano.RandomStreams(123)
prior_idxs, prior_vals, s_N = self.bandit.template.theano_sampler(self.s_rng)
#print prior_idxs
#print prior_vals
self.prior = IdxsValsList.fromlists(
[i for i in prior_idxs if i is not None],
[v for v in prior_vals if v is not None])
self.s_N = s_N
self.observations = self.prior.new_like_self()
for i, o in enumerate(self.observations):
o.idxs.name = 'Obs_idxs{%i}' % i
o.vals.name = 'Obs_vals{%i}' % i
def ivl_fl(ilist, vlist):
return IdxsValsList.fromlists(ilist, vlist)