#!/usr/bin/env python3
# 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
from logging import Logger
import torch
from ax.core.data import Data
from ax.core.experiment import Experiment
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, Y_trans
from ax.modelbridge.torch import TorchModelBridge
from ax.modelbridge.transforms.base import Transform
from ax.models.torch.botorch import (
TAcqfConstructor,
TModelConstructor,
TModelPredictor,
TOptimizer,
)
from ax.models.torch.botorch_defaults import (
get_and_fit_model,
get_qLogNEI,
scipy_optimizer,
)
from ax.models.torch.utils import predict_from_model
from ax.models.types import TConfig
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
logger: Logger = get_logger(__name__)
DEFAULT_TORCH_DEVICE = torch.device("cpu")
DEFAULT_EHVI_BATCH_LIMIT = 5
"""
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: int | None = None,
deduplicate: bool = False,
init_position: int = 0,
scramble: bool = True,
fallback_to_sample_polytope: bool = False,
) -> 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,
fallback_to_sample_polytope=fallback_to_sample_polytope,
),
)
[docs]
def get_botorch(
experiment: Experiment,
data: Data,
search_space: SearchSpace | None = None,
dtype: torch.dtype = torch.double,
device: torch.device = DEFAULT_TORCH_DEVICE,
transforms: list[type[Transform]] = Cont_X_trans + Y_trans,
transform_configs: dict[str, TConfig] | None = None,
model_constructor: TModelConstructor = get_and_fit_model,
model_predictor: TModelPredictor = predict_from_model,
acqf_constructor: TAcqfConstructor = get_qLogNEI,
acqf_optimizer: TOptimizer = scipy_optimizer, # pyre-ignore[9]
refit_on_cv: bool = False,
optimization_config: OptimizationConfig | None = None,
) -> TorchModelBridge:
"""Instantiates a BotorchModel."""
if data.df.empty:
raise ValueError("`BotorchModel` requires non-empty data.")
return checked_cast(
TorchModelBridge,
Models.LEGACY_BOTORCH(
experiment=experiment,
data=data,
search_space=search_space or experiment.search_space,
torch_dtype=dtype,
torch_device=device,
transforms=transforms,
transform_configs=transform_configs,
model_constructor=model_constructor,
model_predictor=model_predictor,
acqf_constructor=acqf_constructor,
acqf_optimizer=acqf_optimizer,
refit_on_cv=refit_on_cv,
optimization_config=optimization_config,
),
)
[docs]
def get_factorial(search_space: SearchSpace) -> DiscreteModelBridge:
"""Instantiates a factorial generator."""
return checked_cast(
DiscreteModelBridge,
Models.FACTORIAL(search_space=search_space, fit_out_of_design=True),
)
[docs]
def get_empirical_bayes_thompson(
experiment: Experiment,
data: Data,
search_space: SearchSpace | None = None,
num_samples: int = 10000,
min_weight: float | None = None,
uniform_weights: bool = False,
) -> DiscreteModelBridge:
"""Instantiates an empirical Bayes / Thompson sampling model."""
if data.df.empty:
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,
fit_out_of_design=True,
),
)
[docs]
def get_thompson(
experiment: Experiment,
data: Data,
search_space: SearchSpace | None = None,
num_samples: int = 10000,
min_weight: float | None = None,
uniform_weights: bool = False,
) -> DiscreteModelBridge:
"""Instantiates a Thompson sampling model."""
if data.df.empty:
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,
fit_out_of_design=True,
),
)
