Source code for

#!/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.

Utilities for model fitting.

from __future__ import annotations

import logging
import warnings
from copy import deepcopy
from typing import Any, Callable

from botorch.exceptions.errors import UnsupportedError
from botorch.exceptions.warnings import BotorchWarning, OptimizationWarning
from botorch.models.converter import batched_to_model_list, model_list_to_batched
from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel
from import fit_gpytorch_scipy
from botorch.optim.utils import sample_all_priors
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood

    "Failed to convert ModelList to batched model. "
    "Performing joint instead of sequential fitting."

[docs]def fit_gpytorch_model( mll: MarginalLogLikelihood, optimizer: Callable = fit_gpytorch_scipy, **kwargs: Any ) -> MarginalLogLikelihood: r"""Fit hyperparameters of a GPyTorch model. On optimizer failures, a new initial condition is sampled from the hyperparameter priors and optimization is retried. The maximum number of retries can be passed in as a `max_retries` kwarg (default is 5). Optimizer functions are in Args: mll: MarginalLogLikelihood to be maximized. optimizer: The optimizer function. kwargs: Arguments passed along to the optimizer function, including `max_retries` and `sequential` (controls the fitting of `ModelListGP` and `BatchedMultiOutputGPyTorchModel` models) or `approx_mll` (whether to use gpytorch's approximate MLL computation). Returns: MarginalLogLikelihood with optimized parameters. Example: >>> gp = SingleTaskGP(train_X, train_Y) >>> mll = ExactMarginalLogLikelihood(gp.likelihood, gp) >>> fit_gpytorch_model(mll) """ sequential = kwargs.pop("sequential", True) max_retries = kwargs.pop("max_retries", 5) if isinstance(mll, SumMarginalLogLikelihood) and sequential: for mll_ in mll.mlls: fit_gpytorch_model( mll=mll_, optimizer=optimizer, max_retries=max_retries, **kwargs ) return mll elif ( isinstance(mll.model, BatchedMultiOutputGPyTorchModel) and mll.model._num_outputs > 1 and sequential ): tf = None try: # check if backwards-conversion is possible # remove the outcome transform since the training targets are already # transformed and the outcome transform cannot currently be split. # TODO: support splitting outcome transforms. if hasattr(mll.model, "outcome_transform"): tf = mll.model.outcome_transform mll.model.outcome_transform = None model_list = batched_to_model_list(mll.model) model_ = model_list_to_batched(model_list) mll_ = SumMarginalLogLikelihood(model_list.likelihood, model_list) fit_gpytorch_model( mll=mll_, optimizer=optimizer, sequential=True, max_retries=max_retries, **kwargs, ) model_ = model_list_to_batched(mll_.model) mll.model.load_state_dict(model_.state_dict()) # setting the transformed inputs is necessary because gpytorch # stores the raw training inputs on the ExactGP in the # ExactGP.__init__ call. At evaluation time, the test inputs will # already be in the transformed space if some transforms have # transform_on_eval set to False. ExactGP.__call__ will # concatenate the test points with the training inputs. Therefore, # it is important to set the ExactGP's train_inputs to also be # transformed data using all transforms (including the transforms # with transform_on_train set to True). mll.train() if tf is not None: mll.model.outcome_transform = tf return mll.eval() # NotImplementedError is omitted since it derives from RuntimeError except (UnsupportedError, RuntimeError, AttributeError): warnings.warn(FAILED_CONVERSION_MSG, BotorchWarning) if tf is not None: mll.model.outcome_transform = tf return fit_gpytorch_model( mll=mll, optimizer=optimizer, sequential=False, max_retries=max_retries ) # retry with random samples from the priors upon failure mll.train() original_state_dict = deepcopy(mll.model.state_dict()) retry = 0 while retry < max_retries: with warnings.catch_warnings(record=True) as ws: if retry > 0: # use normal initial conditions on first try mll.model.load_state_dict(original_state_dict) sample_all_priors(mll.model) mll, _ = optimizer(mll, track_iterations=False, **kwargs) has_optwarning = False for w in ws: has_optwarning |= issubclass(w.category, OptimizationWarning) warnings.warn(w.message, w.category) # TODO: this counts hitting `maxiter` as an optimization failure! if not has_optwarning: mll.eval() return mll retry += 1 logging.log(logging.DEBUG, f"Fitting failed on try {retry}.") warnings.warn("Fitting failed on all retries.", OptimizationWarning) return mll.eval()