def test_distributions():
# test that the distributions come out right
# XXX: test more distributions
bandit = base.Bandit({
'loss':
hp_loguniform('lu', -2, 2) +
hp_qloguniform('qlu', np.log(1 + 0.01), np.log(20), 2) +
hp_quniform('qu', -4.999, 5, 1) + hp_uniform('u', 0, 10)
})
algo = base.Random(bandit)
trials = base.Trials()
exp = base.Experiment(trials, algo)
exp.catch_bandit_exceptions = False
N = 1000
exp.run(N)
assert len(trials) == N
idxs, vals = base.miscs_to_idxs_vals(trials.miscs)
print idxs.keys()
COUNTMAX = 130
COUNTMIN = 70
# -- loguniform
log_lu = np.log(vals['lu'])
assert len(log_lu) == N
assert -2 < np.min(log_lu)
assert np.max(log_lu) < 2
h = np.histogram(log_lu)[0]
print h
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
# -- quantized log uniform
qlu = vals['qlu']
assert np.all(np.fmod(qlu, 2) == 0)
assert np.min(qlu) == 2
assert np.max(qlu) == 20
bc_qlu = np.bincount(qlu)
assert bc_qlu[2] > bc_qlu[4] > bc_qlu[6] > bc_qlu[8]
# -- quantized uniform
qu = vals['qu']
assert np.min(qu) == -5
assert np.max(qu) == 5
assert np.all(np.fmod(qu, 1) == 0)
bc_qu = np.bincount(np.asarray(qu).astype('int') + 5)
assert np.all(40 < bc_qu), bc_qu # XXX: how to get the distribution flat
# with new rounding rule?
assert np.all(bc_qu < 125), bc_qu
assert np.all(bc_qu < COUNTMAX)
# -- uniform
u = vals['u']
assert np.min(u) > 0
assert np.max(u) < 10
h = np.histogram(u)[0]
print h
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
def test_distributions():
# test that the distributions come out right
# XXX: test more distributions
bandit = base.Bandit({
'loss': hp_loguniform('lu', -2, 2) +
hp_qloguniform('qlu', np.log(1 + 0.01), np.log(20), 2) +
hp_quniform('qu', -4.999, 5, 1) +
hp_uniform('u', 0, 10)})
algo = base.Random(bandit)
trials = base.Trials()
exp = base.Experiment(trials, algo)
exp.catch_bandit_exceptions = False
N = 1000
exp.run(N)
assert len(trials) == N
idxs, vals = base.miscs_to_idxs_vals(trials.miscs)
print idxs.keys()
COUNTMAX = 130
COUNTMIN = 70
# -- loguniform
log_lu = np.log(vals['lu'])
assert len(log_lu) == N
assert -2 < np.min(log_lu)
assert np.max(log_lu) < 2
h = np.histogram(log_lu)[0]
print h
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
# -- quantized log uniform
qlu = vals['qlu']
assert np.all(np.fmod(qlu, 2) == 0)
assert np.min(qlu) == 2
assert np.max(qlu) == 20
bc_qlu = np.bincount(qlu)
assert bc_qlu[2] > bc_qlu[4] > bc_qlu[6] > bc_qlu[8]
# -- quantized uniform
qu = vals['qu']
assert np.min(qu) == -5
assert np.max(qu) == 5
assert np.all(np.fmod(qu, 1) == 0)
bc_qu = np.bincount(np.asarray(qu).astype('int') + 5)
assert np.all(40 < bc_qu), bc_qu # XXX: how to get the distribution flat
# with new rounding rule?
assert np.all(bc_qu < 125), bc_qu
assert np.all(bc_qu < COUNTMAX)
# -- uniform
u = vals['u']
assert np.min(u) > 0
assert np.max(u) < 10
h = np.histogram(u)[0]
print h
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
def many_dists():
a = hp_choice('a', [0, 1, 2])
b = hp_randint('b', 10)
c = hp_uniform('c', 4, 7)
d = hp_loguniform('d', -2, 0)
e = hp_quniform('e', 0, 10, 3)
f = hp_qloguniform('f', 0, 3, 2)
g = hp_normal('g', 4, 7)
h = hp_lognormal('h', -2, 2)
i = hp_qnormal('i', 0, 10, 2)
j = hp_qlognormal('j', 0, 2, 1)
z = a + b + c + d + e + f + g + h + i + j
return {'loss': scope.float(scope.log(1e-12 + z**2))}
def many_dists():
a=hp_choice('a', [0, 1, 2])
b=hp_randint('b', 10)
c=hp_uniform('c', 4, 7)
d=hp_loguniform('d', -2, 0)
e=hp_quniform('e', 0, 10, 3)
f=hp_qloguniform('f', 0, 3, 2)
g=hp_normal('g', 4, 7)
h=hp_lognormal('h', -2, 2)
i=hp_qnormal('i', 0, 10, 2)
j=hp_qlognormal('j', 0, 2, 1)
z = a + b + c + d + e + f + g + h + i + j
return {'loss': scope.float(scope.log(1e-12 + z ** 2))}
def pipeline_extension(prefix, X, n_patches, max_filters):
assert max_filters > 16
f_layer = new_fbncc_layer(prefix, X, n_patches,
n_filters=s_int(
hp_qloguniform('%sfb_nfilters' % prefix,
np.log(8.01), np.log(max_filters), q=16)),
size=rfilter_size('%sfb_size' % prefix, 3, 8),
)
p_layer = partial(slm_lpool,
stride=hp_choice('%sp_stride' % prefix, [1, 2]),
order=hp_choice('%sp_order' % prefix,
[1, 2, hp_lognormal('%sp_order_real' % prefix,
mu=np.log(1), sigma=np.log(3))]),
ker_size=rfilter_size('%sp_size' % prefix, 2, 8))
return [f_layer, p_layer]
def test_distributions():
# test that the distributions come out right
# XXX: test more distributions
space = {
'loss': (
hp_loguniform('lu', -2, 2) +
hp_qloguniform('qlu', np.log(1 + 0.01), np.log(20), 2) +
hp_quniform('qu', -4.999, 5, 1) +
hp_uniform('u', 0, 10)),
'status': 'ok'}
trials = base.Trials()
N = 1000
fmin(lambda x: x,
space=space,
algo=rand.suggest,
trials=trials,
max_evals=N,
rstate=np.random.RandomState(124),
catch_eval_exceptions=False)
assert len(trials) == N
idxs, vals = base.miscs_to_idxs_vals(trials.miscs)
print(list(idxs.keys()))
COUNTMAX = 130
COUNTMIN = 70
# -- loguniform
log_lu = np.log(vals['lu'])
assert len(log_lu) == N
assert -2 < np.min(log_lu)
assert np.max(log_lu) < 2
h = np.histogram(log_lu)[0]
print(h)
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
# -- quantized log uniform
qlu = vals['qlu']
assert np.all(np.fmod(qlu, 2) == 0)
assert np.min(qlu) == 2
assert np.max(qlu) == 20
bc_qlu = np.bincount(qlu)
assert bc_qlu[2] > bc_qlu[4] > bc_qlu[6] > bc_qlu[8]
# -- quantized uniform
qu = vals['qu']
assert np.min(qu) == -5
assert np.max(qu) == 5
assert np.all(np.fmod(qu, 1) == 0)
bc_qu = np.bincount(np.asarray(qu).astype('int') + 5)
assert np.all(40 < bc_qu), bc_qu # XXX: how to get the distribution flat
# with new rounding rule?
assert np.all(bc_qu < 125), bc_qu
assert np.all(bc_qu < COUNTMAX)
# -- uniform
u = vals['u']
assert np.min(u) > 0
assert np.max(u) < 10
h = np.histogram(u)[0]
print(h)
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
def test_distributions():
# test that the distributions come out right
# XXX: test more distributions
space = {
'loss': (hp_loguniform('lu', -2, 2) +
hp_qloguniform('qlu', np.log(1 + 0.01), np.log(20), 2) +
hp_quniform('qu', -4.999, 5, 1) + hp_uniform('u', 0, 10)),
'status':
'ok'
}
trials = base.Trials()
N = 1000
fmin(lambda x: x,
space=space,
algo=rand.suggest,
trials=trials,
max_evals=N,
rstate=np.random.RandomState(124),
catch_eval_exceptions=False)
assert len(trials) == N
idxs, vals = base.miscs_to_idxs_vals(trials.miscs)
print(list(idxs.keys()))
COUNTMAX = 130
COUNTMIN = 70
# -- loguniform
log_lu = np.log(vals['lu'])
assert len(log_lu) == N
assert -2 < np.min(log_lu)
assert np.max(log_lu) < 2
h = np.histogram(log_lu)[0]
print(h)
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
# -- quantized log uniform
qlu = vals['qlu']
assert np.all(np.fmod(qlu, 2) == 0)
assert np.min(qlu) == 2
assert np.max(qlu) == 20
bc_qlu = np.bincount(qlu)
assert bc_qlu[2] > bc_qlu[4] > bc_qlu[6] > bc_qlu[8]
# -- quantized uniform
qu = vals['qu']
assert np.min(qu) == -5
assert np.max(qu) == 5
assert np.all(np.fmod(qu, 1) == 0)
bc_qu = np.bincount(np.asarray(qu).astype('int') + 5)
assert np.all(40 < bc_qu), bc_qu # XXX: how to get the distribution flat
# with new rounding rule?
assert np.all(bc_qu < 125), bc_qu
assert np.all(bc_qu < COUNTMAX)
# -- uniform
u = vals['u']
assert np.min(u) > 0
assert np.max(u) < 10
h = np.histogram(u)[0]
print(h)
assert np.all(COUNTMIN < h)
assert np.all(h < COUNTMAX)
import copy
from collections import OrderedDict
import numpy as np
try:
from hyperopt.pyll import scope
except ImportError:
print 'Trying standalone pyll'
from pyll import scope
from hyperopt.pyll_utils import hp_uniform, hp_loguniform, hp_quniform, hp_qloguniform
from hyperopt.pyll_utils import hp_normal, hp_lognormal, hp_qnormal, hp_qlognormal
from hyperopt.pyll_utils import hp_choice
num_filters1 = scope.int(hp_qloguniform('num_filters1',np.log(16), np.log(96), q=16))
filter1_size = scope.int(hp_quniform('filter1_shape', 2, 12, 1))
num_filters2 = scope.int(hp_qloguniform('num_filters2',np.log(16), np.log(96), q=16))
filter2_size = scope.int(hp_quniform('filter2_shape', 2, 12, 1))
num_filters3 = scope.int(hp_qloguniform('num_filters3',np.log(16), np.log(96), q=16))
filter3_size = scope.int(hp_quniform('filter3_shape', 2, 9, 1))
num_filters4 = scope.int(hp_qloguniform('num_filters4',np.log(16), np.log(64), q=16))
filter4_size = scope.int(hp_quniform('filter4_shape', 2, 9, 1))
pool1_sizex = scope.int(hp_quniform('pool1_sizex', 2, 5, 1))
pool1_type = hp_choice('pool1_type', ['max', 'avg', hp_uniform('pool_order_1', 1, 12)])
pool2_sizex = scope.int(hp_quniform('pool2_sizex', 2, 5, 1))
pool2_type = hp_choice('pool2_type', ['max', 'avg', hp_uniform('pool_order_2', 1, 4)])