Source code for botorch.models.fully_bayesian

# 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.

r"""Gaussian Process Regression models with fully Bayesian inference.

Fully Bayesian models use Bayesian inference over model hyperparameters, such
as lengthscales and noise variance, learning a posterior distribution for the
hyperparameters using the No-U-Turn-Sampler (NUTS). This is followed by
sampling a small set of hyperparameters (often ~16) from the posterior
that we will use for model predictions and for computing acquisition function
values. By contrast, our “standard” models (e.g.
`SingleTaskGP`) learn only a single best value for each hyperparameter using
MAP. The fully Bayesian method generally results in a better and more
well-calibrated model, but is more computationally intensive. For a full
description, see [Eriksson2021saasbo].

We use a lightweight PyTorch implementation of a Matern-5/2 kernel as there are
some performance issues with running NUTS on top of standard GPyTorch models.
The resulting hyperparameter samples are loaded into a batched GPyTorch model
after fitting.


.. [Eriksson2021saasbo]
    D. Eriksson, M. Jankowiak. High-Dimensional Bayesian Optimization
    with Sparse Axis-Aligned Subspaces. Proceedings of the Thirty-
    Seventh Conference on Uncertainty in Artificial Intelligence, 2021.

import math
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Mapping, Optional, Tuple

import pyro
import torch
from botorch.acquisition.objective import PosteriorTransform
from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel
from botorch.models.transforms.input import InputTransform
from botorch.models.transforms.outcome import OutcomeTransform
from botorch.models.utils import validate_input_scaling
from botorch.posteriors.fully_bayesian import FullyBayesianPosterior, MCMC_DIM
from gpytorch.constraints import GreaterThan
from gpytorch.distributions.multivariate_normal import MultivariateNormal
from gpytorch.kernels import MaternKernel, ScaleKernel
from gpytorch.kernels.kernel import Distance, Kernel
from gpytorch.likelihoods.gaussian_likelihood import (
from gpytorch.likelihoods.likelihood import Likelihood
from gpytorch.means.constant_mean import ConstantMean
from gpytorch.means.mean import Mean
from gpytorch.models.exact_gp import ExactGP
from linear_operator import settings
from torch import Tensor


[docs]def matern52_kernel(X: Tensor, lengthscale: Tensor) -> Tensor: """Matern-5/2 kernel.""" nu = 5 / 2 dist = compute_dists(X=X, lengthscale=lengthscale) exp_component = torch.exp(-math.sqrt(nu * 2) * dist) constant_component = (math.sqrt(5) * dist).add(1).add(5.0 / 3.0 * (dist**2)) return constant_component * exp_component
[docs]def compute_dists(X: Tensor, lengthscale: Tensor) -> Tensor: """Compute kernel distances.""" return Distance()._dist( X / lengthscale, X / lengthscale, postprocess=False, x1_eq_x2=True )
[docs]def reshape_and_detach(target: Tensor, new_value: Tensor) -> None: """Detach and reshape `new_value` to match `target`.""" return new_value.detach().clone().view(target.shape).to(target)
def _psd_safe_pyro_mvn_sample( name: str, loc: Tensor, covariance_matrix: Tensor, obs: Tensor ) -> None: r"""Wraps the `pyro.sample` call in a loop to add an increasing series of jitter to the covariance matrix each time we get a LinAlgError. This is modelled after linear_operator's `psd_safe_cholesky`. """ jitter = settings.cholesky_jitter.value(loc.dtype) max_tries = settings.cholesky_max_tries.value() for i in range(max_tries + 1): jitter_matrix = ( torch.eye( covariance_matrix.shape[-1], device=covariance_matrix.device, dtype=covariance_matrix.dtype, ) * jitter ) jittered_covar = ( covariance_matrix if i == 0 else covariance_matrix + jitter_matrix ) try: pyro.sample( name, pyro.distributions.MultivariateNormal( loc=loc, covariance_matrix=jittered_covar, ), obs=obs, ) return except (torch.linalg.LinAlgError, ValueError) as e: if isinstance(e, ValueError) and "satisfy the constraint" not in str(e): # Not-PSD can be also caught in Distribution.__init__ during parameter # validation, which raises a ValueError. Only catch those errors. raise e jitter = jitter * (10**i) warnings.warn( "Received a linear algebra error while sampling with Pyro. Adding a " f"jitter of {jitter} to the covariance matrix and retrying.", RuntimeWarning, ) class PyroModel: r""" Base class for a Pyro model; used to assist in learning hyperparameters. This class and its subclasses are not a standard BoTorch models; instead the subclasses are used as inputs to a `SaasFullyBayesianSingleTaskGP`, which should then have its hyperparameters fit with `fit_fully_bayesian_model_nuts`. (By default, its subclass `SaasPyroModel` is used). A `PyroModel`’s `sample` method should specify lightweight PyTorch functionality, which will be used for fast model fitting with NUTS. The utility of `PyroModel` is in enabling fast fitting with NUTS, since we would otherwise need to use GPyTorch, which is computationally infeasible in combination with Pyro. :meta private: """ def set_inputs( self, train_X: Tensor, train_Y: Tensor, train_Yvar: Optional[Tensor] = None ): """Set the training data. Args: train_X: Training inputs (n x d) train_Y: Training targets (n x 1) train_Yvar: Observed noise variance (n x 1). Inferred if None. """ self.train_X = train_X self.train_Y = train_Y self.train_Yvar = train_Yvar @abstractmethod def sample(self) -> None: r"""Sample from the model.""" pass # pragma: no cover @abstractmethod def postprocess_mcmc_samples( self, mcmc_samples: Dict[str, Tensor], **kwargs: Any ) -> Dict[str, Tensor]: """Post-process the final MCMC samples.""" pass # pragma: no cover @abstractmethod def load_mcmc_samples( self, mcmc_samples: Dict[str, Tensor] ) -> Tuple[Mean, Kernel, Likelihood]: pass # pragma: no cover
[docs]class SaasPyroModel(PyroModel): r"""Implementation of the sparse axis-aligned subspace priors (SAAS) model. The SAAS model uses sparsity-inducing priors to identify the most important parameters. This model is suitable for high-dimensional BO with potentially hundreds of tunable parameters. See [Eriksson2021saasbo]_ for more details. `SaasPyroModel` is not a standard BoTorch model; instead, it is used as an input to `SaasFullyBayesianSingleTaskGP`. It is used as a default keyword argument, and end users are not likely to need to instantiate or modify a `SaasPyroModel` unless they want to customize its attributes (such as `covar_module`). """
[docs] def set_inputs( self, train_X: Tensor, train_Y: Tensor, train_Yvar: Optional[Tensor] = None ): super().set_inputs(train_X, train_Y, train_Yvar) self.ard_num_dims = self.train_X.shape[-1]
[docs] def sample(self) -> None: r"""Sample from the SAAS model. This samples the mean, noise variance, outputscale, and lengthscales according to the SAAS prior. """ tkwargs = {"dtype": self.train_X.dtype, "device": self.train_X.device} outputscale = self.sample_outputscale(concentration=2.0, rate=0.15, **tkwargs) mean = self.sample_mean(**tkwargs) noise = self.sample_noise(**tkwargs) lengthscale = self.sample_lengthscale(dim=self.ard_num_dims, **tkwargs) k = matern52_kernel(X=self.train_X, lengthscale=lengthscale) k = outputscale * k + noise * torch.eye(self.train_X.shape[0], **tkwargs) _psd_safe_pyro_mvn_sample( name="Y", loc=mean.view(-1).expand(self.train_X.shape[0]), covariance_matrix=k, obs=self.train_Y.squeeze(-1), )
[docs] def sample_outputscale( self, concentration: float = 2.0, rate: float = 0.15, **tkwargs: Any ) -> Tensor: r"""Sample the outputscale.""" return pyro.sample( "outputscale", pyro.distributions.Gamma( torch.tensor(concentration, **tkwargs), torch.tensor(rate, **tkwargs), ), )
[docs] def sample_mean(self, **tkwargs: Any) -> Tensor: r"""Sample the mean constant.""" return pyro.sample( "mean", pyro.distributions.Normal( torch.tensor(0.0, **tkwargs), torch.tensor(1.0, **tkwargs), ), )
[docs] def sample_noise(self, **tkwargs: Any) -> Tensor: r"""Sample the noise variance.""" if self.train_Yvar is None: return pyro.sample( "noise", pyro.distributions.Gamma( torch.tensor(0.9, **tkwargs), torch.tensor(10.0, **tkwargs), ), ) else: return self.train_Yvar
[docs] def sample_lengthscale( self, dim: int, alpha: float = 0.1, **tkwargs: Any ) -> Tensor: r"""Sample the lengthscale.""" tausq = pyro.sample( "kernel_tausq", pyro.distributions.HalfCauchy(torch.tensor(alpha, **tkwargs)), ) inv_length_sq = pyro.sample( "_kernel_inv_length_sq", pyro.distributions.HalfCauchy(torch.ones(dim, **tkwargs)), ) inv_length_sq = pyro.deterministic( "kernel_inv_length_sq", tausq * inv_length_sq ) lengthscale = pyro.deterministic( "lengthscale", (1.0 / inv_length_sq).sqrt(), ) return lengthscale
[docs] def postprocess_mcmc_samples( self, mcmc_samples: Dict[str, Tensor] ) -> Dict[str, Tensor]: r"""Post-process the MCMC samples. This computes the true lengthscales and removes the inverse lengthscales and tausq (global shrinkage). """ inv_length_sq = ( mcmc_samples["kernel_tausq"].unsqueeze(-1) * mcmc_samples["_kernel_inv_length_sq"] ) mcmc_samples["lengthscale"] = (1.0 / inv_length_sq).sqrt() # Delete `kernel_tausq` and `_kernel_inv_length_sq` since they aren't loaded # into the final model. del mcmc_samples["kernel_tausq"], mcmc_samples["_kernel_inv_length_sq"] return mcmc_samples
[docs] def load_mcmc_samples( self, mcmc_samples: Dict[str, Tensor] ) -> Tuple[Mean, Kernel, Likelihood]: r"""Load the MCMC samples into the mean_module, covar_module, and likelihood.""" tkwargs = {"device": self.train_X.device, "dtype": self.train_X.dtype} num_mcmc_samples = len(mcmc_samples["mean"]) batch_shape = torch.Size([num_mcmc_samples]) mean_module = ConstantMean(batch_shape=batch_shape).to(**tkwargs) covar_module = ScaleKernel( base_kernel=MaternKernel( ard_num_dims=self.ard_num_dims, batch_shape=batch_shape, ), batch_shape=batch_shape, ).to(**tkwargs) if self.train_Yvar is not None: likelihood = FixedNoiseGaussianLikelihood( # Reshape to shape `num_mcmc_samples x N` noise=self.train_Yvar.squeeze(-1).expand( num_mcmc_samples, len(self.train_Yvar) ), batch_shape=batch_shape, ).to(**tkwargs) else: likelihood = GaussianLikelihood( batch_shape=batch_shape, noise_constraint=GreaterThan(MIN_INFERRED_NOISE_LEVEL), ).to(**tkwargs) likelihood.noise_covar.noise = reshape_and_detach( target=likelihood.noise_covar.noise, new_value=mcmc_samples["noise"].clamp_min(MIN_INFERRED_NOISE_LEVEL), ) covar_module.base_kernel.lengthscale = reshape_and_detach( target=covar_module.base_kernel.lengthscale, new_value=mcmc_samples["lengthscale"], ) covar_module.outputscale = reshape_and_detach( target=covar_module.outputscale, new_value=mcmc_samples["outputscale"], ) = reshape_and_detach(, new_value=mcmc_samples["mean"], ) return mean_module, covar_module, likelihood
[docs]class SaasFullyBayesianSingleTaskGP(ExactGP, BatchedMultiOutputGPyTorchModel): r"""A fully Bayesian single-task GP model with the SAAS prior. This model assumes that the inputs have been normalized to [0, 1]^d and that the output has been standardized to have zero mean and unit variance. You can either normalize and standardize the data before constructing the model or use an `input_transform` and `outcome_transform`. The SAAS model [Eriksson2021saasbo]_ with a Matern-5/2 kernel is used by default. You are expected to use `fit_fully_bayesian_model_nuts` to fit this model as it isn't compatible with `fit_gpytorch_model`. Example: >>> saas_gp = SaasFullyBayesianSingleTaskGP(train_X, train_Y) >>> fit_fully_bayesian_model_nuts(saas_gp) >>> posterior = saas_gp.posterior(test_X) """ def __init__( self, train_X: Tensor, train_Y: Tensor, train_Yvar: Optional[Tensor] = None, outcome_transform: Optional[OutcomeTransform] = None, input_transform: Optional[InputTransform] = None, pyro_model: Optional[PyroModel] = None, ) -> None: r"""Initialize the fully Bayesian single-task GP model. Args: train_X: Training inputs (n x d) train_Y: Training targets (n x 1) train_Yvar: Observed noise variance (n x 1). Inferred if None. outcome_transform: An outcome transform that is applied to the training data during instantiation and to the posterior during inference (that is, the `Posterior` obtained by calling `.posterior` on the model will be on the original scale). input_transform: An input transform that is applied in the model's forward pass. pyro_model: Optional `PyroModel`, defaults to `SaasPyroModel`. """ if not ( train_X.ndim == train_Y.ndim == 2 and len(train_X) == len(train_Y) and train_Y.shape[-1] == 1 ): raise ValueError( "Expected train_X to have shape n x d and train_Y to have shape n x 1" ) if train_Yvar is not None: if train_Y.shape != train_Yvar.shape: raise ValueError( "Expected train_Yvar to be None or have the same shape as train_Y" ) with torch.no_grad(): transformed_X = self.transform_inputs( X=train_X, input_transform=input_transform ) if outcome_transform is not None: train_Y, train_Yvar = outcome_transform(train_Y, train_Yvar) self._validate_tensor_args(X=transformed_X, Y=train_Y) validate_input_scaling( train_X=transformed_X, train_Y=train_Y, train_Yvar=train_Yvar ) self._num_outputs = train_Y.shape[-1] self._input_batch_shape = train_X.shape[:-2] if train_Yvar is not None: # Clamp after transforming train_Yvar = train_Yvar.clamp(MIN_INFERRED_NOISE_LEVEL) X_tf, Y_tf, _ = self._transform_tensor_args(X=train_X, Y=train_Y) super().__init__( train_inputs=X_tf, train_targets=Y_tf, likelihood=GaussianLikelihood() ) self.mean_module = None self.covar_module = None self.likelihood = None if pyro_model is None: pyro_model = SaasPyroModel() pyro_model.set_inputs( train_X=transformed_X, train_Y=train_Y, train_Yvar=train_Yvar ) self.pyro_model = pyro_model if outcome_transform is not None: self.outcome_transform = outcome_transform if input_transform is not None: self.input_transform = input_transform def _check_if_fitted(self): r"""Raise an exception if the model hasn't been fitted.""" if self.covar_module is None: raise RuntimeError( "Model has not been fitted. You need to call " "`fit_fully_bayesian_model_nuts` to fit the model." ) @property def median_lengthscale(self) -> Tensor: r"""Median lengthscales across the MCMC samples.""" self._check_if_fitted() lengthscale = self.covar_module.base_kernel.lengthscale.clone() return lengthscale.median(0).values.squeeze(0) @property def num_mcmc_samples(self) -> int: r"""Number of MCMC samples in the model.""" self._check_if_fitted() return len(self.covar_module.outputscale) @property def batch_shape(self) -> torch.Size: r"""Batch shape of the model, equal to the number of MCMC samples. Note that `SaasFullyBayesianSingleTaskGP` does not support batching over input data at this point.""" return torch.Size([self.num_mcmc_samples]) @property def _aug_batch_shape(self) -> torch.Size: r"""The batch shape of the model, augmented to include the output dim.""" aug_batch_shape = self.batch_shape if self.num_outputs > 1: aug_batch_shape += torch.Size([self.num_outputs]) return aug_batch_shape
[docs] def train(self, mode: bool = True) -> None: r"""Puts the model in `train` mode.""" super().train(mode=mode) if mode: self.mean_module = None self.covar_module = None self.likelihood = None
[docs] def load_mcmc_samples(self, mcmc_samples: Dict[str, Tensor]) -> None: r"""Load the MCMC hyperparameter samples into the model. This method will be called by `fit_fully_bayesian_model_nuts` when the model has been fitted in order to create a batched SingleTaskGP model. """ ( self.mean_module, self.covar_module, self.likelihood, ) = self.pyro_model.load_mcmc_samples(mcmc_samples=mcmc_samples)
[docs] def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True): r"""Custom logic for loading the state dict. The standard approach of calling `load_state_dict` currently doesn't play well with the `SaasFullyBayesianSingleTaskGP` since the `mean_module`, `covar_module` and `likelihood` aren't initialized until the model has been fitted. The reason for this is that we don't know the number of MCMC samples until NUTS is called. Given the state dict, we can initialize a new model with some dummy samples and then load the state dict into this model. This currently only works for a `SaasPyroModel` and supporting more Pyro models likely requires moving the model construction logic into the Pyro model itself. """ if not isinstance(self.pyro_model, SaasPyroModel): raise NotImplementedError("load_state_dict only works for SaasPyroModel") raw_mean = state_dict["mean_module.raw_constant"] num_mcmc_samples = len(raw_mean) dim = self.pyro_model.train_X.shape[-1] tkwargs = {"device": raw_mean.device, "dtype": raw_mean.dtype} # Load some dummy samples mcmc_samples = { "mean": torch.ones(num_mcmc_samples, **tkwargs), "lengthscale": torch.ones(num_mcmc_samples, dim, **tkwargs), "outputscale": torch.ones(num_mcmc_samples, **tkwargs), } if self.pyro_model.train_Yvar is None: mcmc_samples["noise"] = torch.ones(num_mcmc_samples, **tkwargs) ( self.mean_module, self.covar_module, self.likelihood, ) = self.pyro_model.load_mcmc_samples(mcmc_samples=mcmc_samples) # Load the actual samples from the state dict super().load_state_dict(state_dict=state_dict, strict=strict)
[docs] def forward(self, X: Tensor) -> MultivariateNormal: """ Unlike in other classes' `forward` methods, there is no `if` block, because it ought to be unreachable: If `self.train()` has been called, then `self.covar_module` will be None, `check_if_fitted()` will fail, and the rest of this method will not run. """ self._check_if_fitted() x = X.unsqueeze(MCMC_DIM) mean_x = self.mean_module(x) covar_x = self.covar_module(x) return MultivariateNormal(mean_x, covar_x)
# pyre-ignore[14]: Inconsistent override
[docs] def posterior( self, X: Tensor, output_indices: Optional[List[int]] = None, observation_noise: bool = False, posterior_transform: Optional[PosteriorTransform] = None, **kwargs: Any, ) -> FullyBayesianPosterior: r"""Computes the posterior over model outputs at the provided points. Args: X: A `(batch_shape) x q x d`-dim Tensor, where `d` is the dimension of the feature space and `q` is the number of points considered jointly. output_indices: A list of indices, corresponding to the outputs over which to compute the posterior (if the model is multi-output). Can be used to speed up computation if only a subset of the model's outputs are required for optimization. If omitted, computes the posterior over all model outputs. observation_noise: If True, add the observation noise from the likelihood to the posterior. If a Tensor, use it directly as the observation noise (must be of shape `(batch_shape) x q x m`). posterior_transform: An optional PosteriorTransform. Returns: A `FullyBayesianPosterior` object. Includes observation noise if specified. """ self._check_if_fitted() posterior = super().posterior( X=X, output_indices=output_indices, observation_noise=observation_noise, posterior_transform=posterior_transform, **kwargs, ) posterior = FullyBayesianPosterior(distribution=posterior.distribution) return posterior