# 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.
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple
import pandas as pd
import torch
from ax.benchmark.benchmark_problem import BenchmarkProblemBase
from ax.core.base_trial import BaseTrial, TrialStatus
from ax.core.data import Data
from ax.core.metric import Metric, MetricFetchE, MetricFetchResult
from ax.core.observation import ObservationFeatures
from ax.core.optimization_config import (
MultiObjectiveOptimizationConfig,
OptimizationConfig,
)
from ax.core.parameter import RangeParameter
from ax.core.runner import Runner
from ax.core.search_space import SearchSpace
from ax.core.types import TParameterization
from ax.modelbridge.transforms.int_to_float import IntToFloat
from ax.modelbridge.transforms.log import Log
from ax.models.torch.botorch_modular.surrogate import Surrogate
from ax.utils.common.base import Base
from ax.utils.common.equality import equality_typechecker
from ax.utils.common.result import Err, Ok
from ax.utils.common.serialization import TClassDecoderRegistry, TDecoderRegistry
from ax.utils.common.typeutils import not_none
from botorch.utils.datasets import SupervisedDataset
[docs]class SurrogateBenchmarkProblemBase(Base, BenchmarkProblemBase):
"""
Base class for SOOSurrogateBenchmarkProblem and MOOSurrogateBenchmarkProblem.
Allows for lazy creation of objects needed to construct a `runner`,
including a surrogate and datasets.
"""
def __init__(
self,
*,
name: str,
search_space: SearchSpace,
optimization_config: OptimizationConfig,
num_trials: int,
infer_noise: bool,
metric_names: List[str],
get_surrogate_and_datasets: Optional[
Callable[[], Tuple[Surrogate, List[SupervisedDataset]]]
] = None,
tracking_metrics: Optional[List[Metric]] = None,
_runner: Optional[Runner] = None,
) -> None:
if get_surrogate_and_datasets is None and _runner is None:
raise ValueError(
"Either `get_surrogate_and_datasets` or `_runner` required."
)
self.name = name
self.search_space = search_space
self.optimization_config = optimization_config
self.num_trials = num_trials
self.infer_noise = infer_noise
self.metric_names = metric_names
self.get_surrogate_and_datasets = get_surrogate_and_datasets
self.tracking_metrics: List[Metric] = (
[] if tracking_metrics is None else tracking_metrics
)
self._runner = _runner
@equality_typechecker
def __eq__(self, other: Base) -> bool:
if type(other) is not type(self):
return False
# Checking the whole datasets' equality here would be too expensive to be
# worth it; just check names instead
return self.name == other.name
[docs] def set_runner(self) -> None:
surrogate, datasets = not_none(self.get_surrogate_and_datasets)()
self._runner = SurrogateRunner(
name=self.name,
surrogate=surrogate,
datasets=datasets,
search_space=self.search_space,
metric_names=self.metric_names,
)
@property
def runner(self) -> Runner:
if self._runner is None:
self.set_runner()
return not_none(self._runner)
def __repr__(self) -> str:
"""
Return a string representation that includes only the attributes that
print nicely and contain information likely to be useful.
"""
return (
f"{self.__class__.__name__}("
f"name={self.name}, "
f"optimization_config={self.optimization_config}, "
f"num_trials={self.num_trials}, "
f"infer_noise={self.infer_noise}, "
f"tracking_metrics={self.tracking_metrics})"
)
[docs]class SOOSurrogateBenchmarkProblem(SurrogateBenchmarkProblemBase):
"""
Has the same attributes/properties as a `SingleObjectiveBenchmarkProblem`,
but allows for constructing from a surrogate.
"""
def __init__(
self,
*,
name: str,
search_space: SearchSpace,
optimization_config: OptimizationConfig,
num_trials: int,
infer_noise: bool,
optimal_value: float,
metric_names: List[str],
get_surrogate_and_datasets: Optional[
Callable[[], Tuple[Surrogate, List[SupervisedDataset]]]
] = None,
tracking_metrics: Optional[List[Metric]] = None,
_runner: Optional[Runner] = None,
) -> None:
super().__init__(
name=name,
search_space=search_space,
optimization_config=optimization_config,
num_trials=num_trials,
infer_noise=infer_noise,
metric_names=metric_names,
get_surrogate_and_datasets=get_surrogate_and_datasets,
tracking_metrics=tracking_metrics,
_runner=_runner,
)
self.optimization_config = optimization_config
self.optimal_value = optimal_value
[docs]class MOOSurrogateBenchmarkProblem(SurrogateBenchmarkProblemBase):
"""
Has the same attributes/properties as a `MultiObjectiveBenchmarkProblem`,
but its runner is not constructed until needed, to allow for deferring
constructing the surrogate.
Simple aspects of the problem problem such as its search space
are defined immediately, while the surrogate is only defined when [TODO]
in order to avoid expensive operations like downloading files and fitting
a model.
"""
optimization_config: MultiObjectiveOptimizationConfig
def __init__(
self,
*,
name: str,
search_space: SearchSpace,
optimization_config: MultiObjectiveOptimizationConfig,
num_trials: int,
infer_noise: bool,
maximum_hypervolume: float,
reference_point: List[float],
metric_names: List[str],
get_surrogate_and_datasets: Optional[
Callable[[], Tuple[Surrogate, List[SupervisedDataset]]]
] = None,
tracking_metrics: Optional[List[Metric]] = None,
_runner: Optional[Runner] = None,
) -> None:
super().__init__(
name=name,
search_space=search_space,
optimization_config=optimization_config,
num_trials=num_trials,
infer_noise=infer_noise,
metric_names=metric_names,
get_surrogate_and_datasets=get_surrogate_and_datasets,
tracking_metrics=tracking_metrics,
_runner=_runner,
)
self.reference_point = reference_point
self.maximum_hypervolume = maximum_hypervolume
@property
def optimal_value(self) -> float:
return self.maximum_hypervolume
[docs]class SurrogateMetric(Metric):
def __init__(
self, name: str, lower_is_better: bool, infer_noise: bool = True
) -> None:
super().__init__(name=name, lower_is_better=lower_is_better)
self.infer_noise = infer_noise
# pyre-fixme[2]: Parameter must be annotated.
[docs] def fetch_trial_data(self, trial: BaseTrial, **kwargs) -> MetricFetchResult:
try:
prediction = [
trial.run_metadata[self.name][name]
for name, arm in trial.arms_by_name.items()
]
df = pd.DataFrame(
{
"arm_name": [name for name, _ in trial.arms_by_name.items()],
"metric_name": self.name,
"mean": prediction,
"sem": None if self.infer_noise else 0,
"trial_index": trial.index,
}
)
return Ok(value=Data(df=df))
except Exception as e:
return Err(
MetricFetchE(
message=f"Failed to predict for trial {trial}", exception=e
)
)
[docs]class SurrogateRunner(Runner):
def __init__(
self,
name: str,
surrogate: Surrogate,
datasets: List[SupervisedDataset],
search_space: SearchSpace,
metric_names: List[str],
) -> None:
self.name = name
self.surrogate = surrogate
self.metric_names = metric_names
self.datasets = datasets
self.search_space = search_space
self.results: Dict[int, float] = {}
self.statuses: Dict[int, TrialStatus] = {}
# If there are log scale parameters, these need to be transformed.
if any(
isinstance(p, RangeParameter) and p.log_scale
for p in search_space.parameters.values()
):
int_to_float_tf = IntToFloat(search_space=search_space)
log_tf = Log(
search_space=int_to_float_tf.transform_search_space(
search_space.clone()
)
)
self.transforms: Optional[Tuple[IntToFloat, Log]] = (
int_to_float_tf,
log_tf,
)
else:
self.transforms = None
def _get_transformed_parameters(
self, parameters: TParameterization
) -> TParameterization:
if self.transforms is None:
return parameters
obs_ft = ObservationFeatures(parameters=parameters)
for t in not_none(self.transforms):
obs_ft = t.transform_observation_features([obs_ft])[0]
return obs_ft.parameters
[docs] def run(self, trial: BaseTrial) -> Dict[str, Any]:
self.statuses[trial.index] = TrialStatus.COMPLETED
preds = { # Cache predictions for each arm
arm.name: self.surrogate.predict(
X=torch.tensor(
[*self._get_transformed_parameters(arm.parameters).values()]
).reshape([1, len(arm.parameters)])
)[0].squeeze(0)
for arm in trial.arms
}
return {
metric_name: {arm_name: float(pred[i]) for arm_name, pred in preds.items()}
for i, metric_name in enumerate(self.metric_names)
}
[docs] def poll_trial_status(
self, trials: Iterable[BaseTrial]
) -> Dict[TrialStatus, Set[int]]:
return {TrialStatus.COMPLETED: {t.index for t in trials}}
[docs] @classmethod
# pyre-fixme[2]: Parameter annotation cannot be `Any`.
def serialize_init_args(cls, obj: Any) -> Dict[str, Any]:
"""Serialize the properties needed to initialize the runner.
Used for storage.
WARNING: Because of issues with consistently saving and loading BoTorch and
GPyTorch modules the SurrogateRunner cannot be serialized at this time. At load
time the runner will be replaced with a SyntheticRunner.
"""
return {}
[docs] @classmethod
def deserialize_init_args(
cls,
args: Dict[str, Any],
decoder_registry: Optional[TDecoderRegistry] = None,
class_decoder_registry: Optional[TClassDecoderRegistry] = None,
) -> Dict[str, Any]:
return {}
