def work(self):
bandit = self.bandit
assert bandit.name is not None
print 'Bandit', bandit.name
algo = TreeParzenEstimator(
bandit,
gamma=self.gammas.get(bandit.name, TreeParzenEstimator.gamma),
prior_weight=self.prior_weights.get(
bandit.name, TreeParzenEstimator.prior_weight),
n_EI_candidates=self.n_EIs.get(
bandit.name, TreeParzenEstimator.n_EI_candidates),
)
LEN = self.LEN.get(bandit.name, 50)
trials = Trials()
exp = Experiment(trials, algo)
exp.catch_bandit_exceptions = False
exp.run(LEN)
assert len(trials) == LEN
if 1:
rtrials = Trials()
exp = Experiment(rtrials, Random(bandit))
exp.run(LEN)
print 'RANDOM MINS', list(sorted(rtrials.losses()))[:6]
#logx = np.log([s['x'] for s in rtrials.specs])
#print 'RND MEAN', np.mean(logx)
#print 'RND STD ', np.std(logx)
print algo.n_EI_candidates
print algo.gamma
print algo.prior_weight
if 0:
plt.subplot(2, 2, 1)
plt.scatter(range(LEN), trials.losses())
plt.title('TPE losses')
plt.subplot(2, 2, 2)
plt.scatter(range(LEN), ([s['x'] for s in trials.specs]))
plt.title('TPE x')
plt.subplot(2, 2, 3)
plt.title('RND losses')
plt.scatter(range(LEN), rtrials.losses())
plt.subplot(2, 2, 4)
plt.title('RND x')
plt.scatter(range(LEN), ([s['x'] for s in rtrials.specs]))
plt.show()
if 0:
plt.hist([t['x'] for t in self.experiment.trials], bins=20)
#print trials.losses()
print 'TPE MINS', list(sorted(trials.losses()))[:6]
#logx = np.log([s['x'] for s in trials.specs])
#print 'TPE MEAN', np.mean(logx)
#print 'TPE STD ', np.std(logx)
thresh = self.thresholds[bandit.name]
print 'Thresh', thresh
assert min(trials.losses()) < thresh
def test_basic(self):
bandit = self._bandit_cls()
print 'bandit params', bandit.params
algo = Random(bandit)
print 'algo params', algo.vh.params
trials = Trials()
experiment = Experiment(trials, algo, async=False)
experiment.catch_bandit_exceptions = False
experiment.max_queue_len = 50
experiment.run(self._n_steps)
print
print self._bandit_cls
print bandit.loss_target
print trials.average_best_error(bandit)
assert trials.average_best_error(bandit) - bandit.loss_target < .2
print
def work(self):
bandit = self.bandit
assert bandit.name is not None
print 'Bandit', bandit.name
algo = TreeParzenEstimator(bandit,
gamma=self.gammas.get(bandit.name,
TreeParzenEstimator.gamma),
prior_weight=self.prior_weights.get(bandit.name,
TreeParzenEstimator.prior_weight),
n_EI_candidates=self.n_EIs.get(bandit.name,
TreeParzenEstimator.n_EI_candidates),
)
LEN = self.LEN.get(bandit.name, 50)
trials = Trials()
exp = Experiment(trials, algo)
exp.catch_bandit_exceptions = False
exp.run(LEN)
assert len(trials) == LEN
if 1:
rtrials = Trials()
exp = Experiment(rtrials, Random(bandit))
exp.run(LEN)
print 'RANDOM MINS', list(sorted(rtrials.losses()))[:6]
#logx = np.log([s['x'] for s in rtrials.specs])
#print 'RND MEAN', np.mean(logx)
#print 'RND STD ', np.std(logx)
print algo.n_EI_candidates
print algo.gamma
print algo.prior_weight
if 0:
plt.subplot(2, 2, 1)
plt.scatter(range(LEN), trials.losses())
plt.title('TPE losses')
plt.subplot(2, 2, 2)
plt.scatter(range(LEN), ([s['x'] for s in trials.specs]))
plt.title('TPE x')
plt.subplot(2, 2, 3)
plt.title('RND losses')
plt.scatter(range(LEN), rtrials.losses())
plt.subplot(2, 2, 4)
plt.title('RND x')
plt.scatter(range(LEN), ([s['x'] for s in rtrials.specs]))
plt.show()
if 0:
plt.hist(
[t['x'] for t in self.experiment.trials],
bins=20)
#print trials.losses()
print 'TPE MINS', list(sorted(trials.losses()))[:6]
#logx = np.log([s['x'] for s in trials.specs])
#print 'TPE MEAN', np.mean(logx)
#print 'TPE STD ', np.std(logx)
thresh = self.thresholds[bandit.name]
print 'Thresh', thresh
assert min(trials.losses()) < thresh
