def test_repeatable():
u = scope.uniform(0, 1)
aa = as_apply(dict(u=u, n=scope.normal(5, 0.1), l=[0, 1, scope.one_of(2, 3), u]))
dd1 = sample(aa, np.random.RandomState(3))
dd2 = sample(aa, np.random.RandomState(3))
dd3 = sample(aa, np.random.RandomState(4))
assert dd1 == dd2
assert dd1 != dd3
def test_repeatable():
u = scope.uniform(0, 1)
aa = as_apply(
dict(u=u, n=scope.normal(5, 0.1), l=[0, 1, scope.one_of(2, 3), u]))
dd1 = sample(aa, np.random.RandomState(3))
dd2 = sample(aa, np.random.RandomState(3))
dd3 = sample(aa, np.random.RandomState(4))
assert dd1 == dd2
assert dd1 != dd3
def test_sample():
u = scope.uniform(0, 1)
aa = as_apply(dict(u=u, n=scope.normal(5, 0.1), l=[0, 1, scope.one_of(2, 3), u]))
print aa
dd = sample(aa, np.random.RandomState(3))
assert 0 < dd["u"] < 1
assert 4 < dd["n"] < 6
assert dd["u"] == dd["l"][3]
assert dd["l"][:2] == (0, 1)
assert dd["l"][2] in (2, 3)
def test_sample():
u = scope.uniform(0, 1)
aa = as_apply(
dict(u=u, n=scope.normal(5, 0.1), l=[0, 1, scope.one_of(2, 3), u]))
print aa
dd = sample(aa, np.random.RandomState(3))
assert 0 < dd['u'] < 1
assert 4 < dd['n'] < 6
assert dd['u'] == dd['l'][3]
assert dd['l'][:2] == (0, 1)
assert dd['l'][2] in (2, 3)
def n_arms(N=2):
"""
Each arm yields a reward from a different Gaussian.
The correct arm is arm 0.
"""
x = hp_choice('x', [0, 1])
reward_mus = as_apply([-1] + [0] * (N - 1))
reward_sigmas = as_apply([1] * N)
return {'loss': scope.normal(reward_mus[x], reward_sigmas[x]),
'loss_variance': 1.0}
def n_arms(N=2):
"""
Each arm yields a reward from a different Gaussian.
The correct arm is arm 0.
"""
x = hp_choice('x', [0, 1])
reward_mus = as_apply([-1] + [0] * (N - 1))
reward_sigmas = as_apply([1] * N)
return {
'loss': scope.normal(reward_mus[x], reward_sigmas[x]),
'loss_variance': 1.0
}
def n_arms(N=2):
"""
Each arm yields a reward from a different Gaussian.
The correct arm is arm 0.
"""
rng = np.random.RandomState(123)
x = hp_choice('x', [0, 1])
reward_mus = as_apply([-1] + [0] * (N - 1))
reward_sigmas = as_apply([1] * N)
return {
'loss': scope.normal(reward_mus[x], reward_sigmas[x], rng=rng),
'loss_variance': 1.0,
'status': base.STATUS_OK
}
def gauss_wave2():
"""
Variant of the GaussWave problem in which noise is added to the score
function, and there is an option to either have no sinusoidal variation, or
a negative cosine with variable amplitude.
Immediate local max is to sample x from spec and turn off the neg cos.
Better solution is to move x a bit to the side, turn on the neg cos and turn
up the amp to 1.
"""
var = .1
x = hp_uniform('x', -20, 20)
amp = hp_uniform('amp', 0, 1)
t = (scope.normal(0, var) + 2 * scope.exp(-(x / 5.0) ** 2))
return {'loss': - hp_choice('hf', [t, t + scope.sin(x) * amp]),
'loss_variance': var}
def gauss_wave2():
"""
Variant of the GaussWave problem in which noise is added to the score
function, and there is an option to either have no sinusoidal variation, or
a negative cosine with variable amplitude.
Immediate local max is to sample x from spec and turn off the neg cos.
Better solution is to move x a bit to the side, turn on the neg cos and turn
up the amp to 1.
"""
var = .1
x = hp_uniform('x', -20, 20)
amp = hp_uniform('amp', 0, 1)
t = (scope.normal(0, var) + 2 * scope.exp(-(x / 5.0)**2))
return {
'loss': -hp_choice('hf', [t, t + scope.sin(x) * amp]),
'loss_variance': var
}
def hp_normal(label, *args, **kwargs):
if not isinstance(label, basestring):
raise TypeError("require string label")
return scope.float(scope.hyperopt_param(label, scope.normal(*args, **kwargs)))
def hp_normal(label, *args, **kwargs):
return scope.float(
scope.hyperopt_param(label, scope.normal(*args, **kwargs)))
def hp_normal(label, *args, **kwargs):
if not isinstance(label, basestring):
raise TypeError('require string label')
return scope.float(
scope.hyperopt_param(label, scope.normal(*args, **kwargs)))
def hp_normal(label, *args, **kwargs):
return scope.float(
scope.hyperopt_param(label,
scope.normal(*args, **kwargs)))
def opt_q_uniform(target):
x = hp_quniform('x', 1.01, 10, 1)
return {'loss': (x - target)**2 + scope.normal(0, 1)}
def opt_q_uniform(target):
x = hp_quniform('x', 1.01, 10, 1)
return {'loss': (x - target) ** 2 + scope.normal(0, 1)}
