def SearchSpaceNumberToSMAC(key: str, hp: SearchSpaceNumber) -> Hyperparameter:
"""Returns either a list of values intended to be sampled uniformly or a frozen scipy.stats distribution"""
dist = "uniform"
if hp.distribution:
dist = hp.distribution
if hp.maximum is None:
raise ValueError(
f"maximum not specified for a number with distribution {dist} for {key}"
)
max = hp.getInclusiveMax()
if hp.minimum is None:
raise ValueError(
f"minimum not specified for a number with distribution {dist} for {key}"
)
min = hp.getInclusiveMin()
log: bool
if dist == "uniform" or dist == "integer":
log = False
elif dist == "loguniform":
log = True
else:
raise ValueError(f"unknown/unsupported distribution {dist} for {key}")
if hp.discrete:
return UniformIntegerHyperparameter(key, min, max, log=log)
else:
return UniformFloatHyperparameter(key, min, max, log=log)
def visitSearchSpaceNumber(self, space: SearchSpaceNumber, path: str, counter=None):
label = self.mk_label(path, counter)
if space.pgo is not None:
return scope.pgo_sample(
space.pgo, hp.quniform(label, 0, len(space.pgo) - 1, 1)
)
dist = "uniform"
if space.distribution:
dist = space.distribution
if space.maximum is None:
raise SearchSpaceError(
path, f"maximum not specified for a number with distribution {dist}"
)
max = space.getInclusiveMax()
# if the maximum is not None, the inclusive maximum should not be none
assert max is not None
# These distributions need only a maximum
if dist == "integer":
if not space.discrete:
raise SearchSpaceError(
path,
"integer distribution specified for a non discrete numeric type",
)
return hp.randint(label, max)
if space.minimum is None:
raise SearchSpaceError(
path, f"minimum not specified for a number with distribution {dist}"
)
min = space.getInclusiveMin()
# if the minimum is not None, the inclusive minimum should not be none
assert min is not None
if dist == "uniform":
if space.discrete:
return scope.int(hp.quniform(label, min, max, 1))
else:
return hp.uniform(label, min, max)
elif dist == "loguniform":
# for log distributions, hyperopt requires that we provide the log of the min/max
if min <= 0:
raise SearchSpaceError(
path,
f"minimum of 0 specified with a {dist} distribution. This is not allowed; please set it (possibly using minimumForOptimizer) to be positive",
)
if min > 0:
min = math.log(min)
if max > 0:
max = math.log(max)
if space.discrete:
return scope.int(hp.qloguniform(label, min, max, 1))
else:
return hp.loguniform(label, min, max)
else:
raise SearchSpaceError(path, f"Unknown distribution type: {dist}")
def visitSearchSpaceNumber(self,
space: SearchSpaceNumber,
path: str,
counter=None,
useCounter=True):
label = self.mk_label(path, counter, useCounter=useCounter)
if space.pgo is not None:
self.pgo_dict[label] = space.pgo
return f"scope.pgo_sample(pgo_{label}, hp.quniform('{label}', {0}, {len(space.pgo)-1}, 1))"
dist = "uniform"
if space.distribution:
dist = space.distribution
if space.maximum is None:
SearchSpaceError(
path,
f"maximum not specified for a number with distribution {dist}")
max = space.getInclusiveMax()
# These distributions need only a maximum
if dist == "integer":
if not space.discrete:
raise SearchSpaceError(
path,
"integer distribution specified for a non discrete numeric type....",
)
return f"hp.randint('{label}', {max})"
if space.minimum is None:
raise SearchSpaceError(
path,
f"minimum not specified for a number with distribution {dist}")
min = space.getInclusiveMin()
if dist == "uniform":
if space.discrete:
return f"hp.quniform('{label}', {min}, {max}, 1)"
else:
return f"hp.uniform('{label}', {min}, {max})"
elif dist == "loguniform":
# for log distributions, hyperopt requires that we provide the log of the min/max
if min <= 0:
raise SearchSpaceError(
path,
f"minimum of 0 specified with a {dist} distribution. This is not allowed; please set it (possibly using minimumForOptimizer) to be positive",
)
if min > 0:
min = math.log(min)
if max > 0:
max = math.log(max)
if space.discrete:
return f"hp.qloguniform('{label}', {min}, {max}, 1)"
else:
return f"hp.loguniform('{label}', {min}, {max})"
else:
raise SearchSpaceError(path, f"Unknown distribution type: {dist}")
def SearchSpaceNumberToGSValues(
key: str,
hp: SearchSpaceNumber,
num_samples: Optional[int] = None) -> List[GSValue]:
"""Returns either a list of values intended to be sampled uniformly"""
samples: int
if num_samples is None:
samples = DEFAULT_SAMPLES_PER_DISTRIBUTION
else:
samples = num_samples
# Add preliminary support for PGO
if hp.pgo is not None:
ret = list(hp.pgo.samples(samples))
return ret
# if we are not doing PGO
dist = "uniform"
if hp.distribution:
dist = hp.distribution
if hp.maximum is None:
raise ValueError(
f"maximum not specified for a number with distribution {dist} for {key}"
)
max = hp.getInclusiveMax()
assert max is not None
if hp.minimum is None:
raise ValueError(
f"minimum not specified for a number with distribution {dist} for {key}"
)
min = hp.getInclusiveMin()
assert min is not None
dt: np.dtype
if hp.discrete:
dt = np.dtype(int)
else:
dt = np.dtype(float)
default = hp.default()
if default is not None:
# always use the default as one of the samples
# TODO: ensure that the default is valid according to the schema
if samples <= 1:
return [default]
samples = samples - 1
if dist == "uniform" or dist == "integer":
ret = np.linspace(min, max, num=samples, dtype=dt).tolist()
elif dist == "loguniform":
ret = np.logspace(min, max, num=samples, dtype=dt).tolist()
else:
raise ValueError(f"unknown/unsupported distribution {dist} for {key}")
if default is not None:
ret.append(default)
return ret