#!/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.
from __future__ import annotations
import warnings
from copy import deepcopy
from logging import Logger
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from ax.core.search_space import SearchSpaceDigest
from ax.core.types import TCandidateMetadata
from ax.exceptions.core import DataRequiredError
from ax.models.torch.botorch_defaults import (
get_and_fit_model,
get_qLogNEI,
recommend_best_observed_point,
scipy_optimizer,
TAcqfConstructor,
)
from ax.models.torch.utils import (
_datasets_to_legacy_inputs,
_get_X_pending_and_observed,
_to_inequality_constraints,
normalize_indices,
predict_from_model,
subset_model,
)
from ax.models.torch_base import TorchGenResults, TorchModel, TorchOptConfig
from ax.models.types import TConfig
from ax.utils.common.constants import Keys
from ax.utils.common.docutils import copy_doc
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.models import ModelList
from botorch.models.model import Model
from botorch.utils.datasets import SupervisedDataset
from botorch.utils.transforms import is_fully_bayesian
from torch import Tensor
from torch.nn import ModuleList # @manual
logger: Logger = get_logger(__name__)
# pyre-fixme[33]: Aliased annotation cannot contain `Any`.
TModelConstructor = Callable[
[
List[Tensor],
List[Tensor],
List[Tensor],
List[int],
List[int],
List[str],
Optional[Dict[str, Tensor]],
Any,
],
Model,
]
TModelPredictor = Callable[[Model, Tensor], Tuple[Tensor, Tensor]]
# pyre-fixme[33]: Aliased annotation cannot contain `Any`.
TOptimizer = Callable[
[
AcquisitionFunction,
Tensor,
int,
Optional[List[Tuple[Tensor, Tensor, float]]],
Optional[List[Tuple[Tensor, Tensor, float]]],
Optional[Dict[int, float]],
Optional[Callable[[Tensor], Tensor]],
Any,
],
Tuple[Tensor, Tensor],
]
TBestPointRecommender = Callable[
[
TorchModel,
List[Tuple[float, float]],
Tensor,
Optional[Tuple[Tensor, Tensor]],
Optional[Tuple[Tensor, Tensor]],
Optional[Dict[int, float]],
Optional[TConfig],
Optional[Dict[int, float]],
],
Optional[Tensor],
]
[docs]class BotorchModel(TorchModel):
r"""
Customizable botorch model.
By default, this uses a noisy Log Expected Improvement (qLogNEI) acquisition
function on top of a model made up of separate GPs, one for each outcome. This
behavior can be modified by providing custom implementations of the following
components:
- a `model_constructor` that instantiates and fits a model on data
- a `model_predictor` that predicts outcomes using the fitted model
- a `acqf_constructor` that creates an acquisition function from a fitted model
- a `acqf_optimizer` that optimizes the acquisition function
- a `best_point_recommender` that recommends a current "best" point (i.e.,
what the model recommends if the learning process ended now)
Args:
model_constructor: A callable that instantiates and fits a model on data,
with signature as described below.
model_predictor: A callable that predicts using the fitted model, with
signature as described below.
acqf_constructor: A callable that creates an acquisition function from a
fitted model, with signature as described below.
acqf_optimizer: A callable that optimizes the acquisition function, with
signature as described below.
best_point_recommender: A callable that recommends the best point, with
signature as described below.
refit_on_cv: If True, refit the model for each fold when performing
cross-validation.
refit_on_update: If True, refit the model after updating the training
data using the `update` method.
warm_start_refitting: If True, start model refitting from previous
model parameters in order to speed up the fitting process.
prior: An optional dictionary that contains the specification of GP model prior.
Currently, the keys include:
- covar_module_prior: prior on covariance matrix e.g.
{"lengthscale_prior": GammaPrior(3.0, 6.0)}.
- type: type of prior on task covariance matrix e.g.`LKJCovariancePrior`.
- sd_prior: A scalar prior over nonnegative numbers, which is used for the
default LKJCovariancePrior task_covar_prior.
- eta: The eta parameter on the default LKJ task_covar_prior.
Call signatures:
::
model_constructor(
Xs,
Ys,
Yvars,
task_features,
fidelity_features,
metric_names,
state_dict,
**kwargs,
) -> model
Here `Xs`, `Ys`, `Yvars` are lists of tensors (one element per outcome),
`task_features` identifies columns of Xs that should be modeled as a task,
`fidelity_features` is a list of ints that specify the positions of fidelity
parameters in 'Xs', `metric_names` provides the names of each `Y` in `Ys`,
`state_dict` is a pytorch module state dict, and `model` is a BoTorch `Model`.
Optional kwargs are being passed through from the `BotorchModel` constructor.
This callable is assumed to return a fitted BoTorch model that has the same
dtype and lives on the same device as the input tensors.
::
model_predictor(model, X) -> [mean, cov]
Here `model` is a fitted botorch model, `X` is a tensor of candidate points,
and `mean` and `cov` are the posterior mean and covariance, respectively.
::
acqf_constructor(
model,
objective_weights,
outcome_constraints,
X_observed,
X_pending,
**kwargs,
) -> acq_function
Here `model` is a botorch `Model`, `objective_weights` is a tensor of weights
for the model outputs, `outcome_constraints` is a tuple of tensors describing
the (linear) outcome constraints, `X_observed` are previously observed points,
and `X_pending` are points whose evaluation is pending. `acq_function` is a
BoTorch acquisition function crafted from these inputs. For additional
details on the arguments, see `get_qLogNEI`.
::
acqf_optimizer(
acq_function,
bounds,
n,
inequality_constraints,
equality_constraints,
fixed_features,
rounding_func,
**kwargs,
) -> candidates
Here `acq_function` is a BoTorch `AcquisitionFunction`, `bounds` is a tensor
containing bounds on the parameters, `n` is the number of candidates to be
generated, `inequality_constraints` are inequality constraints on parameter
values, `fixed_features` specifies features that should be fixed during
generation, and `rounding_func` is a callback that rounds an optimization
result appropriately. `candidates` is a tensor of generated candidates.
For additional details on the arguments, see `scipy_optimizer`.
::
best_point_recommender(
model,
bounds,
objective_weights,
outcome_constraints,
linear_constraints,
fixed_features,
model_gen_options,
target_fidelities,
) -> candidates
Here `model` is a TorchModel, `bounds` is a list of tuples containing bounds
on the parameters, `objective_weights` is a tensor of weights for the model outputs,
`outcome_constraints` is a tuple of tensors describing the (linear) outcome
constraints, `linear_constraints` is a tuple of tensors describing constraints
on the design, `fixed_features` specifies features that should be fixed during
generation, `model_gen_options` is a config dictionary that can contain
model-specific options, and `target_fidelities` is a map from fidelity feature
column indices to their respective target fidelities, used for multi-fidelity
optimization problems. % TODO: refer to an example.
"""
dtype: Optional[torch.dtype]
device: Optional[torch.device]
Xs: List[Tensor]
Ys: List[Tensor]
Yvars: List[Tensor]
_model: Optional[Model]
_search_space_digest: Optional[SearchSpaceDigest] = None
def __init__(
self,
model_constructor: TModelConstructor = get_and_fit_model,
model_predictor: TModelPredictor = predict_from_model,
acqf_constructor: TAcqfConstructor = get_qLogNEI,
# pyre-fixme[9]: acqf_optimizer declared/used type mismatch
acqf_optimizer: TOptimizer = scipy_optimizer,
best_point_recommender: TBestPointRecommender = recommend_best_observed_point,
refit_on_cv: bool = False,
refit_on_update: bool = True,
warm_start_refitting: bool = True,
use_input_warping: bool = False,
use_loocv_pseudo_likelihood: bool = False,
prior: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> None:
warnings.warn(
"The legacy `BotorchModel` and its subclasses, including the current"
f"class `{self.__class__.__name__}`, slated for deprecation. "
"These models will not be supported going forward and may be "
"fully removed in a future release. Please consider using the "
"Modular BoTorch Model (MBM) setup (ax/models/torch/botorch_modular) "
"instead. If you run into a use case that is not supported by MBM, "
"please raise this with an issue at https://github.com/facebook/Ax",
DeprecationWarning,
)
self.model_constructor = model_constructor
self.model_predictor = model_predictor
self.acqf_constructor = acqf_constructor
self.acqf_optimizer = acqf_optimizer
self.best_point_recommender = best_point_recommender
# pyre-fixme[4]: Attribute must be annotated.
self._kwargs = kwargs
self.refit_on_cv = refit_on_cv
self.refit_on_update = refit_on_update
self.warm_start_refitting = warm_start_refitting
self.use_input_warping = use_input_warping
self.use_loocv_pseudo_likelihood = use_loocv_pseudo_likelihood
self.prior = prior
self._model: Optional[Model] = None
self.Xs = []
self.Ys = []
self.Yvars = []
self.dtype = None
self.device = None
self.task_features: List[int] = []
self.fidelity_features: List[int] = []
self.metric_names: List[str] = []
[docs] @copy_doc(TorchModel.fit)
def fit(
self,
datasets: List[SupervisedDataset],
metric_names: List[str],
search_space_digest: SearchSpaceDigest,
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
) -> None:
if len(datasets) == 0:
raise DataRequiredError("BotorchModel.fit requires non-empty data sets.")
self.Xs, self.Ys, self.Yvars = _datasets_to_legacy_inputs(datasets=datasets)
self.metric_names = metric_names
# Store search space info for later use (e.g. during generation)
self._search_space_digest = search_space_digest
self.dtype = self.Xs[0].dtype
self.device = self.Xs[0].device
self.task_features = normalize_indices(
search_space_digest.task_features, d=self.Xs[0].size(-1)
)
self.fidelity_features = normalize_indices(
search_space_digest.fidelity_features, d=self.Xs[0].size(-1)
)
extra_kwargs = {} if self.prior is None else {"prior": self.prior}
self._model = self.model_constructor( # pyre-ignore [28]
Xs=self.Xs,
Ys=self.Ys,
Yvars=self.Yvars,
task_features=self.task_features,
fidelity_features=self.fidelity_features,
metric_names=self.metric_names,
use_input_warping=self.use_input_warping,
use_loocv_pseudo_likelihood=self.use_loocv_pseudo_likelihood,
**extra_kwargs,
**self._kwargs,
)
[docs] @copy_doc(TorchModel.predict)
def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]:
return self.model_predictor(model=self.model, X=X) # pyre-ignore [28]
[docs] @copy_doc(TorchModel.gen)
def gen(
self,
n: int,
search_space_digest: SearchSpaceDigest,
torch_opt_config: TorchOptConfig,
) -> TorchGenResults:
options = torch_opt_config.model_gen_options or {}
acf_options = options.get(Keys.ACQF_KWARGS, {})
optimizer_options = options.get(Keys.OPTIMIZER_KWARGS, {})
if search_space_digest.fidelity_features:
raise NotImplementedError(
"Base BotorchModel does not support fidelity_features."
)
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
objective_weights=torch_opt_config.objective_weights,
bounds=search_space_digest.bounds,
pending_observations=torch_opt_config.pending_observations,
outcome_constraints=torch_opt_config.outcome_constraints,
linear_constraints=torch_opt_config.linear_constraints,
fixed_features=torch_opt_config.fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization 357
if options.get(Keys.SUBSET_MODEL, True):
subset_model_results = subset_model(
model=model,
objective_weights=torch_opt_config.objective_weights,
outcome_constraints=torch_opt_config.outcome_constraints,
)
model = subset_model_results.model
objective_weights = subset_model_results.objective_weights
outcome_constraints = subset_model_results.outcome_constraints
else:
objective_weights = torch_opt_config.objective_weights
outcome_constraints = torch_opt_config.outcome_constraints
bounds_ = torch.tensor(
search_space_digest.bounds, dtype=self.dtype, device=self.device
)
bounds_ = bounds_.transpose(0, 1)
botorch_rounding_func = get_rounding_func(torch_opt_config.rounding_func)
from botorch.exceptions.errors import UnsupportedError
# pyre-fixme[53]: Captured variable `X_observed` is not annotated.
# pyre-fixme[53]: Captured variable `X_pending` is not annotated.
# pyre-fixme[53]: Captured variable `acf_options` is not annotated.
# pyre-fixme[53]: Captured variable `botorch_rounding_func` is not annotated.
# pyre-fixme[53]: Captured variable `bounds_` is not annotated.
# pyre-fixme[53]: Captured variable `model` is not annotated.
# pyre-fixme[53]: Captured variable `objective_weights` is not annotated.
# pyre-fixme[53]: Captured variable `optimizer_options` is not annotated.
# pyre-fixme[53]: Captured variable `outcome_constraints` is not annotated.
def make_and_optimize_acqf(override_qmc: bool = False) -> Tuple[Tensor, Tensor]:
add_kwargs = {"qmc": False} if override_qmc else {}
acquisition_function = self.acqf_constructor(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
**acf_options,
**add_kwargs,
)
acquisition_function = checked_cast(
AcquisitionFunction, acquisition_function
)
# pyre-ignore: [28]
candidates, expected_acquisition_value = self.acqf_optimizer(
acq_function=checked_cast(AcquisitionFunction, acquisition_function),
bounds=bounds_,
n=n,
inequality_constraints=_to_inequality_constraints(
linear_constraints=torch_opt_config.linear_constraints
),
fixed_features=torch_opt_config.fixed_features,
rounding_func=botorch_rounding_func,
**optimizer_options,
)
return candidates, expected_acquisition_value
try:
candidates, expected_acquisition_value = make_and_optimize_acqf()
except UnsupportedError as e: # untested
if "SobolQMCSampler only supports dimensions" in str(e):
# dimension too large for Sobol, let's use IID
candidates, expected_acquisition_value = make_and_optimize_acqf(
override_qmc=True
)
else:
raise e
gen_metadata = {}
if expected_acquisition_value.numel() > 0:
gen_metadata[
"expected_acquisition_value"
] = expected_acquisition_value.tolist()
return TorchGenResults(
points=candidates.detach().cpu(),
weights=torch.ones(n, dtype=self.dtype),
gen_metadata=gen_metadata,
)
[docs] @copy_doc(TorchModel.best_point)
def best_point(
self,
search_space_digest: SearchSpaceDigest,
torch_opt_config: TorchOptConfig,
) -> Optional[Tensor]:
if torch_opt_config.is_moo:
raise NotImplementedError(
"Best observed point is incompatible with MOO problems."
)
target_fidelities = {
k: v
for k, v in search_space_digest.target_values.items()
if k in search_space_digest.fidelity_features
}
return self.best_point_recommender( # pyre-ignore [28]
model=self,
bounds=search_space_digest.bounds,
objective_weights=torch_opt_config.objective_weights,
outcome_constraints=torch_opt_config.outcome_constraints,
linear_constraints=torch_opt_config.linear_constraints,
fixed_features=torch_opt_config.fixed_features,
model_gen_options=torch_opt_config.model_gen_options,
target_fidelities=target_fidelities,
)
[docs] @copy_doc(TorchModel.cross_validate)
def cross_validate( # pyre-ignore [14]: `search_space_digest` arg not needed here
self,
datasets: List[SupervisedDataset],
X_test: Tensor,
**kwargs: Any,
) -> Tuple[Tensor, Tensor]:
if self._model is None:
raise RuntimeError("Cannot cross-validate model that has not been fitted.")
if self.refit_on_cv:
state_dict = None
else:
state_dict = deepcopy(self.model.state_dict())
Xs, Ys, Yvars = _datasets_to_legacy_inputs(datasets=datasets)
model = self.model_constructor( # pyre-ignore: [28]
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
task_features=self.task_features,
state_dict=state_dict,
fidelity_features=self.fidelity_features,
metric_names=self.metric_names,
refit_model=self.refit_on_cv,
use_input_warping=self.use_input_warping,
use_loocv_pseudo_likelihood=self.use_loocv_pseudo_likelihood,
**self._kwargs,
)
return self.model_predictor(model=model, X=X_test) # pyre-ignore: [28]
[docs] def feature_importances(self) -> np.ndarray:
return get_feature_importances_from_botorch_model(model=self._model)
@property
def search_space_digest(self) -> SearchSpaceDigest:
if self._search_space_digest is None:
raise RuntimeError(
"`search_space_digest` is not initialized. Please fit the model first."
)
return self._search_space_digest
@search_space_digest.setter
def search_space_digest(self, value: SearchSpaceDigest) -> None:
raise RuntimeError("Setting search_space_digest manually is disallowed.")
@property
def model(self) -> Model:
if self._model is None:
raise RuntimeError(
"`model` is not initialized. Please fit the model first."
)
return self._model
@model.setter
def model(self, model: Model) -> None:
self._model = model # there are a few places that set model directly
[docs]def get_rounding_func(
rounding_func: Optional[Callable[[Tensor], Tensor]]
) -> Optional[Callable[[Tensor], Tensor]]:
if rounding_func is None:
botorch_rounding_func = rounding_func
else:
# make sure rounding_func is properly applied to q- and t-batches
def botorch_rounding_func(X: Tensor) -> Tensor:
batch_shape, d = X.shape[:-1], X.shape[-1]
X_round = torch.stack(
[rounding_func(x) for x in X.view(-1, d)] # pyre-ignore: [16]
)
return X_round.view(*batch_shape, d)
return botorch_rounding_func
[docs]def get_feature_importances_from_botorch_model(
model: Union[Model, ModuleList, None],
) -> np.ndarray:
"""Get feature importances from a list of BoTorch models.
Args:
models: BoTorch model to get feature importances from.
Returns:
The feature importances as a numpy array where each row sums to 1.
"""
if model is None:
raise RuntimeError(
"Cannot calculate feature_importances without a fitted model."
"Call `fit` first."
)
elif isinstance(model, ModelList):
models = model.models
else:
models = [model]
lengthscales = []
for m in models:
try:
ls = m.covar_module.base_kernel.lengthscale
except AttributeError:
ls = None
if ls is None or ls.shape[-1] != m.train_inputs[0].shape[-1]:
# TODO: We could potentially set the feature importances to NaN in this
# case, but this require knowing the batch dimension of this model.
# Consider supporting in the future.
raise NotImplementedError(
"Failed to extract lengthscales from `m.covar_module.base_kernel`"
)
if ls.ndim == 2:
ls = ls.unsqueeze(0)
if is_fully_bayesian(m): # Take the median over the MCMC samples
ls = torch.quantile(ls, q=0.5, dim=0, keepdim=True)
lengthscales.append(ls)
lengthscales = torch.cat(lengthscales, dim=0)
feature_importances = (1 / lengthscales).detach().cpu() # pyre-ignore
# Make sure the sum of feature importances is 1.0 for each metric
feature_importances /= feature_importances.sum(dim=-1, keepdim=True)
return feature_importances.numpy()
