def create_nevergrad_parameter_from_override(override: Override) -> Any:
val = override.value()
if not override.is_sweep_override():
return val
if override.is_choice_sweep():
assert isinstance(val, ChoiceSweep)
vals = [
x for x in override.sweep_iterator(transformer=Transformer.encode)
]
if "ordered" in val.tags:
return ng.p.TransitionChoice(vals)
else:
return ng.p.Choice(vals)
elif override.is_range_sweep():
vals = [
x for x in override.sweep_iterator(transformer=Transformer.encode)
]
return ng.p.Choice(vals)
elif override.is_interval_sweep():
assert isinstance(val, IntervalSweep)
if "log" in val.tags:
scalar = ng.p.Log(lower=val.start, upper=val.end)
else:
scalar = ng.p.Scalar(lower=val.start,
upper=val.end) # type: ignore
if isinstance(val.start, int):
scalar.set_integer_casting()
return scalar
def create_optuna_distribution_from_override(override: Override) -> Any:
value = override.value()
if not override.is_sweep_override():
return value
if override.is_choice_sweep():
assert isinstance(value, ChoiceSweep)
choices = [
x for x in override.sweep_iterator(transformer=Transformer.encode)
]
return CategoricalDistribution(choices)
if override.is_range_sweep():
choices = [
x for x in override.sweep_iterator(transformer=Transformer.encode)
]
return CategoricalDistribution(choices)
if override.is_interval_sweep():
assert isinstance(value, IntervalSweep)
if "log" in value.tags:
if "int" in value.tags:
return IntLogUniformDistribution(value.start, value.end)
return LogUniformDistribution(value.start, value.end)
else:
if "int" in value.tags:
return IntUniformDistribution(value.start, value.end)
return UniformDistribution(value.start, value.end)
raise NotImplementedError(
"{} is not supported by Optuna sweeper.".format(override))
def create_optuna_distribution_from_override(override: Override) -> Any:
value = override.value()
if not override.is_sweep_override():
return value
choices: List[CategoricalChoiceType] = []
if override.is_choice_sweep():
assert isinstance(value, ChoiceSweep)
for x in override.sweep_iterator(transformer=Transformer.encode):
assert isinstance(
x, (str, int, float, bool)
), f"A choice sweep expects str, int, float, or bool type. Got {type(x)}."
choices.append(x)
return CategoricalDistribution(choices)
if override.is_range_sweep():
assert isinstance(value, RangeSweep)
assert value.start is not None
assert value.stop is not None
if value.shuffle:
for x in override.sweep_iterator(transformer=Transformer.encode):
assert isinstance(
x, (str, int, float, bool)
), f"A choice sweep expects str, int, float, or bool type. Got {type(x)}."
choices.append(x)
return CategoricalDistribution(choices)
return IntUniformDistribution(int(value.start),
int(value.stop),
step=int(value.step))
if override.is_interval_sweep():
assert isinstance(value, IntervalSweep)
assert value.start is not None
assert value.end is not None
if "log" in value.tags:
if isinstance(value.start, int) and isinstance(value.end, int):
return IntLogUniformDistribution(int(value.start),
int(value.end))
return LogUniformDistribution(value.start, value.end)
else:
if isinstance(value.start, int) and isinstance(value.end, int):
return IntUniformDistribution(value.start, value.end)
return UniformDistribution(value.start, value.end)
raise NotImplementedError(
f"{override} is not supported by Optuna sweeper.")
