#!/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 __future__ import annotations
from logging import Logger
from typing import Any, Dict, List, Optional, Tuple, Type
import torch
from ax.core.types import TCandidateMetadata, TConfig
from ax.models.model_utils import best_in_sample_point
from ax.models.torch.utils import (
_to_inequality_constraints,
pick_best_out_of_sample_point_acqf_class,
predict_from_model,
)
from ax.utils.common.base import Base
from ax.utils.common.constants import Keys
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast, checked_cast_optional, not_none
from botorch.fit import fit_gpytorch_model
from botorch.models.model import Model
from botorch.utils.containers import TrainingData
from gpytorch.kernels import Kernel
from gpytorch.likelihoods.likelihood import Likelihood
from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
from torch import Tensor
NOT_YET_FIT_MSG = (
"Underlying BoTorch `Model` has not yet received its training_data."
"Please fit the model first."
)
logger: Logger = get_logger(__name__)
[docs]class Surrogate(Base):
"""
**All classes in 'botorch_modular' directory are under
construction, incomplete, and should be treated as alpha
versions only.**
Ax wrapper for BoTorch ``Model``, subcomponent of ``BoTorchModel``
and is not meant to be used outside of it.
Args:
botorch_model_class: ``Model`` class to be used as the underlying
BoTorch model.
mll_class: ``MarginalLogLikelihood`` class to use for model-fitting.
model_options: Dictionary of options / kwargs for the BoTorch
``Model`` constructed during ``Surrogate.fit``.
kernel_class: ``Kernel`` class, not yet used. Will be used to
construct custom BoTorch ``Model`` in the future.
kernel_options: Kernel kwargs, not yet used. Will be used to
construct custom BoTorch ``Model`` in the future.
likelihood: ``Likelihood`` class, not yet used. Will be used to
construct custom BoTorch ``Model`` in the future.
"""
botorch_model_class: Type[Model]
mll_class: Type[MarginalLogLikelihood]
model_options: Dict[str, Any]
kernel_class: Optional[Type[Kernel]] = None
_training_data: Optional[TrainingData] = None
_model: Optional[Model] = None
# Special setting for surrogates instantiated via `Surrogate.from_BoTorch`,
# to avoid re-constructing the underlying BoTorch model on `Surrogate.fit`
# when set to `False`.
_constructed_manually: bool = False
def __init__(
self,
# TODO: make optional when BoTorch model factory is checked in.
# Construction will then be possible from likelihood, kernel, etc.
botorch_model_class: Type[Model],
mll_class: Type[MarginalLogLikelihood] = ExactMarginalLogLikelihood,
model_options: Optional[Dict[str, Any]] = None,
kernel_class: Optional[Type[Kernel]] = None, # TODO: use.
kernel_options: Optional[Dict[str, Any]] = None, # TODO: use.
likelihood: Optional[Type[Likelihood]] = None, # TODO: use.
) -> None:
self.botorch_model_class = botorch_model_class
self.mll_class = mll_class
self.model_options = model_options or {}
# Temporary validation while we develop these customizations.
if likelihood is not None:
raise NotImplementedError("Customizing likelihood not yet implemented.")
if kernel_class is not None or kernel_options:
raise NotImplementedError("Customizing kernel not yet implemented.")
@property
def model(self) -> Model:
if self._model is None:
raise ValueError(
"BoTorch `Model` has not yet been constructed, please fit the "
"surrogate first (done via `BoTorchModel.fit`)."
)
return not_none(self._model)
@property
def training_data(self) -> TrainingData:
if self._training_data is None:
raise ValueError(NOT_YET_FIT_MSG)
return not_none(self._training_data)
@property
def training_data_per_outcome(self) -> Dict[str, TrainingData]:
raise NotImplementedError( # pragma: no cover
"`training_data_per_outcome` is only used in `ListSurrogate`."
)
@property
def dtype(self) -> torch.dtype:
return self.training_data.X.dtype
@property
def device(self) -> torch.device:
return self.training_data.X.device
[docs] @classmethod
def from_BoTorch(
cls,
model: Model,
mll_class: Type[MarginalLogLikelihood] = ExactMarginalLogLikelihood,
) -> Surrogate:
"""Instantiate a `Surrogate` from a pre-instantiated Botorch `Model`."""
surrogate = cls(botorch_model_class=model.__class__, mll_class=mll_class)
surrogate._model = model
# Temporarily disallowing `update` for surrogates instantiated from
# pre-made BoTorch `Model` instances to avoid reconstructing models
# that were likely pre-constructed for a reason (e.g. if this setup
# doesn't fully allow to constuct them).
surrogate._constructed_manually = True
return surrogate
[docs] def clone_reset(self) -> Surrogate:
return self.__class__(**self._serialize_attributes_as_kwargs())
[docs] def construct(self, training_data: TrainingData, **kwargs: Any) -> None:
"""Constructs the underlying BoTorch ``Model`` using the training data.
Args:
training_data: Training data for the model (for one outcome for
the default ``Surrogate``, with the exception of batched
multi-output case, where training data is formatted with just
one X and concatenated Ys).
**kwargs: Optional keyword arguments, expects any of:
- "fidelity_features": Indices of columns in X that represent
fidelity.
"""
if self._constructed_manually:
logger.warning("Reconstructing a manually constructed `Model`.")
if not isinstance(training_data, TrainingData):
raise ValueError( # pragma: no cover
"Base `Surrogate` expects training data for single outcome."
)
kwargs = kwargs or {}
self._training_data = training_data
formatted_model_inputs = self.botorch_model_class.construct_inputs(
training_data=self.training_data,
fidelity_features=kwargs.get(Keys.FIDELITY_FEATURES),
)
# pyre-ignore[45]: Py raises informative msg if `model_cls` abstract.
self._model = self.botorch_model_class(**formatted_model_inputs)
[docs] def fit(
self,
training_data: TrainingData,
bounds: List[Tuple[float, float]],
task_features: List[int],
feature_names: List[str],
metric_names: List[str],
fidelity_features: List[int],
target_fidelities: Optional[Dict[int, float]] = None,
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
state_dict: Optional[Dict[str, Tensor]] = None,
refit: bool = True,
) -> None:
"""Fits the underlying BoTorch ``Model`` to ``m`` outcomes.
NOTE: ``state_dict`` and ``refit`` keyword arguments control how the
undelying BoTorch ``Model`` will be fit: whether its parameters will
be reoptimized and whether it will be warm-started from a given state.
There are three possibilities:
* ``fit(state_dict=None)``: fit model from stratch (optimize model
parameters and set its training data used for inference),
* ``fit(state_dict=some_state_dict, refit=True)``: warm-start refit
with a state dict of parameters (still re-optimize model parameters
and set the training data),
* ``fit(state_dict=some_state_dict, refit=False)``: load model parameters
without refitting, but set new training data (used in cross-validation,
for example).
Args:
training data: BoTorch ``TrainingData`` container with Xs, Ys, and
possibly Yvars, to be passed to ``Model.construct_inputs`` in
BoTorch.
bounds: A list of d (lower, upper) tuples for each column of X.
task_features: Columns of X that take integer values and should be
treated as task parameters.
feature_names: Names of each column of X.
metric_names: Names of each outcome Y in Ys.
fidelity_features: Columns of X that should be treated as fidelity
parameters.
candidate_metadata: Model-produced metadata for candidates, in
the order corresponding to the Xs.
state_dict: Optional state dict to load.
refit: Whether to re-optimize model parameters.
"""
if self._constructed_manually:
logger.debug(
"For manually constructed surrogates (via `Surrogate.from_BoTorch`), "
"`fit` skips setting the training data on model and only reoptimizes "
"its parameters if `refit=True`."
)
if self._model is None and not self._constructed_manually:
self.construct(
training_data=training_data,
fidelity_features=fidelity_features,
# Kwargs below are unused in base `Surrogate`, but used in subclasses.
metric_names=metric_names,
task_features=task_features,
)
if state_dict:
self.model.load_state_dict(not_none(state_dict))
if state_dict is None or refit:
# pyre-ignore[16]: Model has no attribute likelihood.
# All BoTorch `Model`-s expected to work with this setup have likelihood.
mll = self.mll_class(self.model.likelihood, self.model)
fit_gpytorch_model(mll)
[docs] def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]:
"""Predicts outcomes given a model and input tensor.
Args:
model: A botorch Model.
X: A ``n x d`` tensor of input parameters.
Returns:
Tensor: The predicted posterior mean as an ``n x o``-dim tensor.
Tensor: The predicted posterior covariance as a ``n x o x o``-dim tensor.
"""
return predict_from_model(model=self.model, X=X)
[docs] def best_in_sample_point(
self,
bounds: List[Tuple[float, float]],
objective_weights: Optional[Tensor],
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
options: Optional[TConfig] = None,
) -> Tuple[Tensor, float]:
"""Finds the best observed point and the corresponding observed outcome
values.
"""
best_point_and_observed_value = best_in_sample_point(
Xs=[self.training_data.X],
# pyre-ignore[6]: `best_in_sample_point` currently expects a `TorchModel`
# or a `NumpyModel` as `model` kwarg, but only uses them for `predict`
# function, the signature for which is the same on this `Surrogate`.
# TODO: When we move `botorch_modular` directory to OSS, we will extend
# the annotation for `model` kwarg to accept `Surrogate` too.
model=self,
bounds=bounds,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
options=options,
)
if best_point_and_observed_value is None:
raise ValueError("Could not obtain best in-sample point.")
best_point, observed_value = best_point_and_observed_value
return checked_cast(Tensor, best_point), observed_value
[docs] def best_out_of_sample_point(
self,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
fidelity_features: Optional[List[int]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
options: Optional[TConfig] = None,
) -> Tuple[Tensor, Tensor]:
"""Finds the best predicted point and the corresponding value of the
appropriate best point acquisition function.
"""
if fixed_features:
# When have fixed features, need `FixedFeatureAcquisitionFunction`
# which has peculiar instantiation (wraps another acquisition fn.),
# so need to figure out how to handle.
# TODO (ref: https://fburl.com/diff/uneqb3n9)
raise NotImplementedError("Fixed features not yet supported.")
options = options or {}
acqf_class, acqf_options = pick_best_out_of_sample_point_acqf_class(
outcome_constraints=outcome_constraints,
seed_inner=checked_cast_optional(int, options.get(Keys.SEED_INNER, None)),
qmc=checked_cast(bool, options.get(Keys.QMC, True)),
)
# Avoiding circular import between `Surrogate` and `Acquisition`.
from ax.models.torch.botorch_modular.acquisition import Acquisition
acqf = Acquisition( # TODO: For multi-fidelity, might need diff. class.
surrogate=self,
botorch_acqf_class=acqf_class,
bounds=bounds,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
target_fidelities=target_fidelities,
options=acqf_options,
)
candidates, acqf_values = acqf.optimize(
# pyre-ignore[6]: Exp. Tensor, got List[Tuple[float, float]].
# TODO: Fix typing of `bounds` in `TorchModel`-s.
bounds=bounds,
n=1,
inequality_constraints=_to_inequality_constraints(
linear_constraints=linear_constraints
),
fixed_features=fixed_features,
)
return candidates[0], acqf_values[0]
[docs] def update(
self,
training_data: TrainingData,
bounds: List[Tuple[float, float]],
task_features: List[int],
feature_names: List[str],
metric_names: List[str],
fidelity_features: List[int],
target_fidelities: Optional[Dict[int, float]] = None,
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
state_dict: Optional[Dict[str, Tensor]] = None,
refit: bool = True,
) -> None:
"""Updates the surrogate model with new data. In the base ``Surrogate``,
just calls ``fit`` after checking that this surrogate was not created
via ``Surrogate.from_BoTorch`` (in which case the ``Model`` comes premade,
constructed manually and then supplied to ``Surrogate``).
NOTE: Expects `training_data` to be all available data,
not just the new data since the last time the model was updated.
Args:
training_data: Surrogate training_data containing all the data the model
should use for inference.
bounds: A list of d (lower, upper) tuples for each column of X.
task_features: Columns of X that take integer values and should be
treated as task parameters.
feature_names: Names of each column of X.
metric_names: Names of each outcome Y in Ys.
fidelity_features: Columns of X that should be treated as fidelity
parameters.
target_fidelities: Target values for fidelity parameters, representing
full-fidelity value.
candidate_metadata: Model-produced metadata for candidates, in
the order corresponding to the Xs.
state_dict: Optional state dict to load.
refit: Whether to re-optimize model parameters or just set the training
data used for interence to new training data.
"""
# NOTE: In the future, could have `incremental` kwarg, in which case
# `training_data` could contain just the new data.
if self._constructed_manually:
raise NotImplementedError(
"`update` not yet implemented for models that are "
"constructed manually, but it is possible to create a new "
"surrogate in the same way as the current manually constructed one, "
"via `Surrogate.from_BoTorch`."
)
self.fit(
training_data=training_data,
bounds=bounds,
task_features=task_features,
feature_names=feature_names,
metric_names=metric_names,
fidelity_features=fidelity_features,
target_fidelities=target_fidelities,
candidate_metadata=candidate_metadata,
state_dict=state_dict,
refit=refit,
)
[docs] def pareto_frontier(self) -> Tuple[Tensor, Tensor]:
"""For multi-objective optimization, retrieve Pareto frontier instead
of best point.
Returns: A two-tuple of:
- tensor of points in the feature space,
- tensor of corresponding (multiple) outcomes.
"""
raise NotImplementedError(
"Pareto frontier not yet implemented."
) # pragma: no cover
[docs] def compute_diagnostics(self) -> Dict[str, Any]:
"""Computes model diagnostics like cross-validation measure of fit, etc."""
return {} # pragma: no cover
def _serialize_attributes_as_kwargs(self) -> Dict[str, Any]:
"""Serialize attributes of this surrogate, to be passed back to it
as kwargs on reinstantiation.
"""
return {
"botorch_model_class": self.botorch_model_class,
"mll_class": self.mll_class,
"model_options": self.model_options,
}
