def test_opt_qn_normal(f=hp_normal):
bandit = Bandit(
{'loss': -scope.sum([f('v%i' % ii, 0, 1)
for ii in range(25)]) ** 2},
loss_target=0)
algo = TreeParzenEstimator(bandit,
prior_weight=.5,
n_startup_jobs=0,
n_EI_candidates=1,
gamma=0.15)
trials = Trials()
experiment = Experiment(trials, algo, async=False)
experiment.max_queue_len = 1
experiment.run(40)
print list(sorted(trials.losses()))
idxs, vals = miscs_to_idxs_vals(trials.miscs)
if 1:
import hyperopt.plotting
hyperopt.plotting.main_plot_vars(trials, bandit, do_show=1)
else:
import matplotlib.pyplot as plt
begin = [v[:10] for k, v in vals.items()]
end = [v[-10:] for k, v in vals.items()]
plt.subplot(2, 1, 1)
plt.title('before')
plt.hist(np.asarray(begin).flatten())
plt.subplot(2, 1, 2)
plt.title('after')
plt.hist(np.asarray(end).flatten())
plt.show()
def test_basic(self):
bandit = self._bandit_cls()
algo = Random(bandit)
trials = Trials()
experiment = Experiment(trials, algo, async=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 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 setUp(self):
bandit = self.bandit = many_dists()
algo = TreeParzenEstimator(bandit)
trials = Trials()
experiment = Experiment(trials, algo, async=False)
experiment.max_queue_len = 1
N=200
if 0:
import cProfile
stats = cProfile.runctx('experiment.run(N)', globals={},
locals=locals(), filename='fooprof')
import pstats
p = pstats.Stats('fooprof')
p.sort_stats('cumulative').print_stats(10)
p.sort_stats('time').print_stats(10)
else:
experiment.run(N)
self.trials = trials
