# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
import numpy as np
from ax.core.experiment import Experiment
from ax.core.formatting_utils import DataType
from ax.core.parameter import (
ChoiceParameter,
FixedParameter,
Parameter,
ParameterType as CoreParameterType,
RangeParameter,
)
from ax.core.parameter_constraint import validate_constraint_parameters
from ax.core.search_space import HierarchicalSearchSpace, SearchSpace
from ax.exceptions.core import UserInputError
from ax.preview.api.configs import (
ChoiceParameterConfig,
ExperimentConfig,
ParameterScaling,
ParameterType,
RangeParameterConfig,
)
from ax.preview.api.utils.instantiation.from_string import parse_parameter_constraint
[docs]
def parameter_from_config(
config: RangeParameterConfig | ChoiceParameterConfig,
) -> Parameter:
"""
Create a RangeParameter, ChoiceParameter, or FixedParameter from a ParameterConfig.
"""
if isinstance(config, RangeParameterConfig):
lower, upper = config.bounds
# TODO[mpolson64] Add support for RangeParameterConfig.step_size native to
# RangeParameter instead of converting to ChoiceParameter
if (step_size := config.step_size) is not None:
if not (
config.scaling == ParameterScaling.LINEAR or config.scaling is None
):
raise UserInputError(
"Non-linear parameter scaling is not supported when using "
"step_size."
)
if (upper - lower) % step_size != 0:
raise UserInputError(
"The range of the parameter must be evenly divisible by the "
"step size."
)
return ChoiceParameter(
name=config.name,
parameter_type=_parameter_type_converter(config.parameter_type),
values=[*np.arange(lower, upper + step_size, step_size)],
is_ordered=True,
)
return RangeParameter(
name=config.name,
parameter_type=_parameter_type_converter(config.parameter_type),
lower=lower,
upper=upper,
log_scale=config.scaling == ParameterScaling.LOG,
)
else:
# If there is only one value, create a FixedParameter instead of a
# ChoiceParameter
if len(config.values) == 1:
return FixedParameter(
name=config.name,
parameter_type=_parameter_type_converter(config.parameter_type),
value=config.values[0],
# pyre-fixme[6] Variance issue caused by FixedParameter.dependents
# using List instead of immutable container type.
dependents=config.dependent_parameters,
)
return ChoiceParameter(
name=config.name,
parameter_type=_parameter_type_converter(config.parameter_type),
# pyre-fixme[6] Variance issue caused by ChoiceParameter.value using List
# instead of immutable container type.
values=config.values,
is_ordered=config.is_ordered,
# pyre-fixme[6] Variance issue caused by ChoiceParameter.dependents using
# List instead of immutable container type.
dependents=config.dependent_parameters,
)
[docs]
def experiment_from_config(config: ExperimentConfig) -> Experiment:
"""Create an Experiment from an ExperimentConfig."""
parameters = [
parameter_from_config(config=parameter_config)
for parameter_config in config.parameters
]
constraints = [
parse_parameter_constraint(constraint_str=constraint_str)
for constraint_str in config.parameter_constraints
]
# Ensure that all ParameterConstraints are valid and acting on existing parameters
for constraint in constraints:
validate_constraint_parameters(
parameters=[
parameter
for parameter in parameters
if parameter.name in constraint.constraint_dict.keys()
]
)
if any(p.is_hierarchical for p in parameters):
search_space = HierarchicalSearchSpace(
parameters=parameters, parameter_constraints=constraints
)
else:
search_space = SearchSpace(
parameters=parameters, parameter_constraints=constraints
)
return Experiment(
search_space=search_space,
name=config.name,
description=config.description,
experiment_type=config.experiment_type,
properties={"owners": [config.owner]},
default_data_type=DataType.MAP_DATA,
)
def _parameter_type_converter(parameter_type: ParameterType) -> CoreParameterType:
"""
Convert from an API ParameterType to a core Ax ParameterType.
"""
if parameter_type == ParameterType.BOOL:
return CoreParameterType.BOOL
elif parameter_type == ParameterType.FLOAT:
return CoreParameterType.FLOAT
elif parameter_type == ParameterType.INT:
return CoreParameterType.INT
elif parameter_type == ParameterType.STRING:
return CoreParameterType.STRING
else:
raise UserInputError(f"Unsupported parameter type {parameter_type}.")
