#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its 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, List, Optional
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.registry import Cont_X_trans, Models, Y_trans
from ax.modelbridge.transforms.winsorize import Winsorize
from ax.utils.common.logger import get_logger
from botorch.fit import fit_gpytorch_model
from botorch.models.gp_regression import FixedNoiseGP, SingleTaskGP
from botorch.models.model import Model
from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
from torch import Tensor
logger = get_logger(__name__)
ACQF_MODEL_MAP = {
"NEI": Models.BOTORCH,
"KG": Models.GPKG,
"MES": Models.GPMES,
"Sobol": Models.SOBOL,
"RND": Models.UNIFORM,
}
# ------------------------- Standard model constructors ------------------------
[docs]def fixed_noise_gp_model_constructor(
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
task_features: List[int],
fidelity_features: List[int],
metric_names: List[str],
state_dict: Optional[Dict[str, Tensor]] = None,
refit_model: bool = True,
**kwargs: Any,
) -> Model:
gp = FixedNoiseGP(train_X=Xs[0], train_Y=Ys[0], train_Yvar=Yvars[0], **kwargs)
gp.to(Xs[0])
if state_dict is not None:
gp.load_state_dict(state_dict)
if state_dict is None or refit_model:
fit_gpytorch_model(ExactMarginalLogLikelihood(gp.likelihood, gp))
return gp
[docs]def singletask_gp_model_constructor(
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
task_features: List[int],
fidelity_features: List[int],
metric_names: List[str],
state_dict: Optional[Dict[str, Tensor]] = None,
refit_model: bool = True,
**kwargs: Any,
) -> Model:
gp = SingleTaskGP(train_X=Xs[0], train_Y=Ys[0], **kwargs)
gp.to(Xs[0])
if state_dict is not None:
gp.load_state_dict(state_dict)
if state_dict is None or refit_model:
fit_gpytorch_model(ExactMarginalLogLikelihood(gp.likelihood, gp))
return gp
# ----------------- Generation strategy constructor ----------------------------
[docs]def make_basic_generation_strategy(
name: str,
acquisition: str,
num_initial_trials: int = 14,
surrogate_model_constructor: Callable = singletask_gp_model_constructor,
) -> GenerationStrategy:
if acquisition not in ACQF_MODEL_MAP:
acquisition = "Sobol"
logger.warning(
f"{acquisition} is not a supported "
"acquisition function. Defaulting to Sobol."
)
return GenerationStrategy(
name=name,
steps=[
GenerationStep(
model=Models.SOBOL,
num_trials=num_initial_trials,
min_trials_observed=num_initial_trials,
),
GenerationStep(
model=ACQF_MODEL_MAP[acquisition],
num_trials=-1,
model_kwargs={
"model_constructor": surrogate_model_constructor,
"transforms": Cont_X_trans + Y_trans,
},
),
],
)
# ----------------- Standard methods (as generation strategies) ----------------
# examples
winsorized_fixed_noise_NEI = GenerationStrategy(
name="Sobol+fixed_noise_NEI",
steps=[
GenerationStep(model=Models.SOBOL, num_trials=5, min_trials_observed=3),
GenerationStep(
model=Models.BOTORCH, # Note: can use FBModels, like FBModels.GPKG
num_trials=-1,
model_kwargs={
"model_constructor": fixed_noise_gp_model_constructor,
"transforms": [Winsorize] + Cont_X_trans + Y_trans, # pyre-ignore[6]
"transform_configs": {
"Winsorize": {
f"winsorization_{t}": v
for t, v in zip(("lower", "upper"), (0.2, None))
}
},
},
),
],
)
singletask_RND = make_basic_generation_strategy(
name="RND + SingleTaskGP", acquisition="RND", num_initial_trials=14
)
singletask_NEI = make_basic_generation_strategy(
name="NEI + SingleTaskGP", acquisition="NEI", num_initial_trials=14
)
singletask_KG = make_basic_generation_strategy(
name="KG + SingleTaskGP", acquisition="KG", num_initial_trials=14
)
singletask_MES = make_basic_generation_strategy(
name="MES + SingleTaskGP", acquisition="MES", num_initial_trials=14
)
fixednoise_NEI = make_basic_generation_strategy(
name="NEI + SingleTaskGP",
acquisition="NEI",
num_initial_trials=14,
surrogate_model_constructor=fixed_noise_gp_model_constructor,
)
