def make_big_space(backend='ConfigSpace'):
def make_subspace(sspace):
sspace.uniform('uniform_float', 0, 1)
sspace.loguniform('loguniform_float', 2, 3)
sspace.normal('normal_float', 0, 1)
sspace.lognormal('lognormal_float', 2, 3)
sspace.uniform('uniform_float_q', 0, 1, quantization=0.01)
sspace.loguniform('loguniform_float_q', 2, 3, quantization=0.01)
sspace.normal('normal_float_q', 0, 1, quantization=0.01)
sspace.lognormal('lognormal_float_q', 2, 1, quantization=0.01)
sspace.uniform('uniform_int', 0, 1, discrete=True)
sspace.loguniform('loguniform_int', 2, 3, discrete=True)
sspace.normal('normal_int', 0, 1, discrete=True)
sspace.lognormal('lognormal_int', 2, 3, discrete=True)
sspace.choices('choices', ['a', 'b', 'c', 'd'])
sspace.ordinal('ordinal', ['a', 'b', 'c', 'd'])
sspace.variable('epoch')
sspace.identity('uid')
return sspace
space = Space(backend=backend)
make_subspace(space)
make_subspace(space.subspace('sub'))
return space
def make_space(backend='ConfigSapce'):
space = Space(backend=backend)
optim = space.categorical('optimizer', ['sgd', 'adam'])
sgd_lr = space.loguniform('optimizer.lr', 1, 2, quantization=0.01)
sgd_lr.enable_if(either(eq(optim, 'adam'), eq(optim, 'sgd')))
sgd_lr.forbid_equal(1)
return space
def test_conditions_or():
space = Space('ConfigSpace')
a = space.normal('a', 1, 2, quantization=0.01)
b = space.normal('b', 1, 2, quantization=0.01)
b.enable_if(either(eq(a, 1), ne(a, 2)))
print(space.sample())
def test_forbid_and():
space = Space('ConfigSpace')
a = space.uniform('a', 1, 2, quantization=0.01)
a.forbid_equal(1)
a.forbid_in([1, 2])
print(space.sample())
def test_conditions_in():
space = Space('ConfigSpace')
a = space.normal('a', 1, 2, quantization=0.01)
b = space.normal('b', 1, 2, quantization=0.01)
b.enable_if(contains(a, [1, 1.5, 2]))
print(space.sample())
def test_conditions_and():
space = Space('ConfigSpace')
a = space.normal('a', 1, 2, quantization=0.01)
b = space.normal('b', 1, 2, quantization=0.01)
b.enable_if(both(gt(a, 1), lt(a, 2)))
print(space.sample())
def test_conditions(condition):
space = Space('ConfigSpace')
a = space.normal('a', 1, 2, quantization=0.01)
b = space.normal('b', 1, 2, quantization=0.01)
b.enable_if(condition(a, 1.5))
print(space.sample())
def test_subspace(backend):
space = Space(backend=backend)
space.normal('a', 1, 2, quantization=0.01)
subspace = space.subspace('b')
subspace.normal('a', 1, 2, quantization=0.01)
print(space.sample())
def test_categorical(backend):
space = Space(backend=backend)
space.categorical('cat', ['a', 'b', 'c'])
space.categorical('caw', a=0.2, b=0.1, c=0.7)
space.categorical('cad', dict(a=0.2, b=0.1, c=0.7))
print(space.sample())
def test_ordinal(backend):
space = Space(backend=backend)
try:
space.ordinal('ord', ['a', 'b', 'c'])
print(space.sample())
print(space.sample())
print(space.sample())
except NotImplementedError:
assert backend == 'Orion'
def test_uniform(backend):
space = Space(backend=backend)
for discrete in [True, False]:
for log in [True, False]:
for q in [None, 0.01, 1]:
space.uniform(f'a_{discrete}_{log}_{q}', 1, 2,
discrete=discrete,
log=log,
quantization=q)
print(space.sample())
def test_normal(backend):
for discrete in [True, False]:
for log in [True, False]:
for q in [None, 0.01, 1]:
space = Space(backend=backend)
space.normal(f'a_{discrete}_{log}_{q}',
loc=1, scale=2,
discrete=discrete,
log=log,
quantization=q)
try:
print(space.sample())
except NotImplementedError:
assert backend == 'Orion' and log is True
from sspace import Space, either, eq
import json
if __name__ == '__main__':
space = Space(backend='ConfigSpace')
optim = space.categorical('optimizer', ['sgd', 'adam'])
sgd_lr = space.loguniform('optimizer.lr', 1, 2, quantization=0.01)
sgd_lr.enable_if(either(eq(optim, 'adam'), eq(optim, 'sgd')))
sgd_lr.forbid_equal(1)
for sample in space.sample(2):
print(sample)
print(json.dumps(space.serialize(), indent=2))
from sspace import Space
space = Space()
space.uniform('lr', 0, 1)
space.ordinal('epoch', [1, 2, 3])
s1 = space.sample(seed=0)
s2 = space.sample(seed=1)
s1p = space.sample(seed=0)
print(s1)
print(s2)
print(s1p)
# [OrderedDict([('epoch', 1), ('optimizer', 'adam')])]
# [OrderedDict([('epoch', 2), ('optimizer', 'adam')])]
# [OrderedDict([('epoch', 1), ('optimizer', 'adam')])]