#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from typing import List, Optional, Type
import torch
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.multi_type_experiment import MultiTypeExperiment
from ax.core.observation import ObservationFeatures
from ax.core.optimization_config import OptimizationConfig
from ax.core.search_space import SearchSpace
from ax.modelbridge.discrete import DiscreteModelBridge
from ax.modelbridge.random import RandomModelBridge
from ax.modelbridge.registry import (
Cont_X_trans,
Models,
MT_MTGP_trans,
ST_MTGP_trans,
Y_trans,
)
from ax.modelbridge.torch import TorchModelBridge
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.convert_metric_names import tconfig_from_mt_experiment
from ax.models.torch.botorch import (
BotorchModel,
TAcqfConstructor,
TModelConstructor,
TModelPredictor,
TOptimizer,
)
from ax.models.torch.botorch_defaults import (
get_and_fit_model,
get_NEI,
predict_from_model,
scipy_optimizer,
)
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
logger = get_logger(__name__)
# pyre-fixme[19]: __init__ expects 0 args but got 1.
DEFAULT_TORCH_DEVICE = torch.device("cpu")
"""
Module containing functions that generate standard models, such as Sobol,
GP+EI, etc.
Note: a special case here is a composite generator, which requires an
additional ``GenerationStrategy`` and is able to delegate work to multiple models
(for instance, to a random model to generate the first trial, and to an
optimization model for subsequent trials).
"""
[docs]def get_sobol(
search_space: SearchSpace,
seed: Optional[int] = None,
deduplicate: bool = False,
init_position: int = 0,
scramble: bool = True,
) -> RandomModelBridge:
"""Instantiates a Sobol sequence quasi-random generator.
Args:
search_space: Sobol generator search space.
kwargs: Custom args for sobol generator.
Returns:
RandomModelBridge, with SobolGenerator as model.
"""
return checked_cast(
RandomModelBridge,
Models.SOBOL(
search_space=search_space,
seed=seed,
deduplicate=deduplicate,
init_position=init_position,
scramble=scramble,
),
)
[docs]def get_botorch(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
dtype: torch.dtype = torch.double,
device: torch.device = DEFAULT_TORCH_DEVICE,
transforms: List[Type[Transform]] = Cont_X_trans + Y_trans,
model_constructor: TModelConstructor = get_and_fit_model, # pyre-ignore[9]
model_predictor: TModelPredictor = predict_from_model,
acqf_constructor: TAcqfConstructor = get_NEI, # pyre-ignore[9]
acqf_optimizer: TOptimizer = scipy_optimizer, # pyre-ignore[9]
refit_on_cv: bool = False,
refit_on_update: bool = True,
optimization_config: Optional[OptimizationConfig] = None,
) -> TorchModelBridge:
"""Instantiates a BotorchModel."""
if data.df.empty: # pragma: no cover
raise ValueError("`BotorchModel` requires non-empty data.")
return checked_cast(
TorchModelBridge,
Models.BOTORCH(
experiment=experiment,
data=data,
search_space=search_space or experiment.search_space,
torch_dtype=dtype,
torch_device=device,
transforms=transforms,
model_constructor=model_constructor,
model_predictor=model_predictor,
acqf_constructor=acqf_constructor,
acqf_optimizer=acqf_optimizer,
refit_on_cv=refit_on_cv,
refit_on_update=refit_on_update,
optimization_config=optimization_config,
),
)
[docs]def get_GPEI(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
dtype: torch.dtype = torch.double,
device: torch.device = DEFAULT_TORCH_DEVICE,
) -> TorchModelBridge:
"""Instantiates a GP model that generates points with EI."""
if data.df.empty: # pragma: no cover
raise ValueError("GP+EI BotorchModel requires non-empty data.")
return checked_cast(
TorchModelBridge,
Models.BOTORCH(
experiment=experiment,
data=data,
search_space=search_space or experiment.search_space,
torch_dtype=dtype,
torch_device=device,
),
)
# TODO[Lena]: how to instantiate MTGP through the enum? The Multi-type MTGP requires
# a MultiTypeExperiment, so we would need validation for that, but more importantly,
# we need to create `trial_index_to_type` as in the factory function below.
# Maybe `MultiTypeExperiment` could have that mapping as a property?
[docs]def get_MTGP(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
trial_index: Optional[int] = None,
) -> TorchModelBridge:
"""Instantiates a Multi-task Gaussian Process (MTGP) model that generates
points with EI.
If the input experiment is a MultiTypeExperiment then a
Multi-type Multi-task GP model will be instantiated.
Otherwise, the model will be a Single-type Multi-task GP.
"""
if isinstance(experiment, MultiTypeExperiment):
trial_index_to_type = {
t.index: t.trial_type for t in experiment.trials.values()
}
transforms = MT_MTGP_trans
transform_configs = {
"TrialAsTask": {"trial_level_map": {"trial_type": trial_index_to_type}},
"ConvertMetricNames": tconfig_from_mt_experiment(experiment),
}
else:
# Set transforms for a Single-type MTGP model.
transforms = ST_MTGP_trans
transform_configs = None
# Choose the status quo features for the experiment from the selected trial.
# If trial_index is None, the status quo from the last experiment trial
# will be used as a status quo for the experiment.
if trial_index is None:
trial_index = len(experiment.trials) - 1
elif trial_index >= len(experiment.trials):
raise ValueError("trial_index is bigger than the number of experiment trials")
# pyre-fixme[16]: `ax.core.base_trial.BaseTrial` has no attribute `status_quo`.
status_quo = experiment.trials[trial_index].status_quo
if status_quo is None:
raise ValueError("status_quo is not defined for the selected trial.")
else:
status_quo_features = ObservationFeatures(
parameters=status_quo.parameters, trial_index=trial_index
)
return TorchModelBridge(
experiment=experiment,
search_space=search_space or experiment.search_space,
data=data,
model=BotorchModel(),
transforms=transforms,
transform_configs=transform_configs,
torch_dtype=torch.double,
torch_device=DEFAULT_TORCH_DEVICE,
status_quo_features=status_quo_features,
)
[docs]def get_factorial(search_space: SearchSpace) -> DiscreteModelBridge:
"""Instantiates a factorial generator."""
return checked_cast(
DiscreteModelBridge, Models.FACTORIAL(search_space=search_space)
)
[docs]def get_empirical_bayes_thompson(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
num_samples: int = 10000,
min_weight: Optional[float] = None,
uniform_weights: bool = False,
) -> DiscreteModelBridge:
"""Instantiates an empirical Bayes / Thompson sampling model."""
if data.df.empty: # pragma: no cover
raise ValueError("Empirical Bayes Thompson sampler requires non-empty data.")
return checked_cast(
DiscreteModelBridge,
Models.EMPIRICAL_BAYES_THOMPSON(
experiment=experiment,
data=data,
search_space=search_space or experiment.search_space,
num_samples=num_samples,
min_weight=min_weight,
uniform_weights=uniform_weights,
),
)
[docs]def get_thompson(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
num_samples: int = 10000,
min_weight: Optional[float] = None,
uniform_weights: bool = False,
) -> DiscreteModelBridge:
"""Instantiates a Thompson sampling model."""
if data.df.empty: # pragma: no cover
raise ValueError("Thompson sampler requires non-empty data.")
return checked_cast(
DiscreteModelBridge,
Models.THOMPSON(
experiment=experiment,
data=data,
search_space=search_space or experiment.search_space,
num_samples=num_samples,
min_weight=min_weight,
uniform_weights=uniform_weights,
),
)
