Source code for botorch.models.deterministic

#!/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.

r"""
Deterministic Models: Simple wrappers that allow the usage of deterministic
mappings via the BoTorch Model and Posterior APIs.

Deterministic models are useful for expressing known input-output relationships
within the BoTorch Model API. This is useful e.g. for multi-objective
optimization with known objective functions (e.g. the number of parameters of a
Neural Network in the context of Neural Architecture Search is usually a known
function of the architecture configuration), or to encode cost functions for
cost-aware acquisition utilities. Cost-aware optimization is desirable when
evaluations have a cost that is heterogeneous, either in the inputs `X` or in a
particular fidelity parameter that directly encodes the fidelity of the
observation. `GenericDeterministicModel` supports arbitrary deterministic
functions, while `AffineFidelityCostModel` is a particular cost model for
multi-fidelity optimization. Other use cases of deterministic models include
representing approximate GP sample paths, e.g. Matheron paths obtained
with `get_matheron_path_model`, which allows them to be substituted in acquisition
functions or in other places where a `Model` is expected.
"""

from __future__ import annotations

from abc import abstractmethod
from typing import Callable, List, Optional, Union

import torch
from botorch.models.ensemble import EnsembleModel
from botorch.models.model import Model
from torch import Tensor


[docs] class DeterministicModel(EnsembleModel): """Abstract base class for deterministic models."""
[docs] @abstractmethod def forward(self, X: Tensor) -> Tensor: r"""Compute the (deterministic) model output at X. Args: X: A `batch_shape x n x d`-dim input tensor `X`. Returns: A `batch_shape x n x m`-dimensional output tensor (the outcome dimension `m` must be explicit if `m=1`). """ pass # pragma: no cover
def _forward(self, X: Tensor) -> Tensor: r"""Compatibilizes the `DeterministicModel` with `EnsemblePosterior`""" return self.forward(X=X).unsqueeze(-3)
[docs] class GenericDeterministicModel(DeterministicModel): r"""A generic deterministic model constructed from a callable. Example: >>> f = lambda x: x.sum(dim=-1, keep_dims=True) >>> model = GenericDeterministicModel(f) """ def __init__(self, f: Callable[[Tensor], Tensor], num_outputs: int = 1) -> None: r""" Args: f: A callable mapping a `batch_shape x n x d`-dim input tensor `X` to a `batch_shape x n x m`-dimensional output tensor (the outcome dimension `m` must be explicit, even if `m=1`). num_outputs: The number of outputs `m`. """ super().__init__() self._f = f self._num_outputs = num_outputs
[docs] def subset_output(self, idcs: List[int]) -> GenericDeterministicModel: r"""Subset the model along the output dimension. Args: idcs: The output indices to subset the model to. Returns: The current model, subset to the specified output indices. """ def f_subset(X: Tensor) -> Tensor: return self._f(X)[..., idcs] return self.__class__(f=f_subset, num_outputs=len(idcs))
[docs] def forward(self, X: Tensor) -> Tensor: r"""Compute the (deterministic) model output at X. Args: X: A `batch_shape x n x d`-dim input tensor `X`. Returns: A `batch_shape x n x m`-dimensional output tensor. """ return self._f(X)
[docs] class AffineDeterministicModel(DeterministicModel): r"""An affine deterministic model.""" def __init__(self, a: Tensor, b: Union[Tensor, float] = 0.01) -> None: r"""Affine deterministic model from weights and offset terms. A simple model of the form y[..., m] = b[m] + sum_{i=1}^d a[i, m] * X[..., i] Args: a: A `d x m`-dim tensor of linear weights, where `m` is the number of outputs (must be explicit if `m=1`) b: The affine (offset) term. Either a float (for single-output models or if the offset is shared), or a `m`-dim tensor (with different offset values for for the `m` different outputs). """ if not a.ndim == 2: raise ValueError("a must be two-dimensional") if not torch.is_tensor(b): b = torch.tensor([b]) if not b.ndim == 1: raise ValueError("b nust be one-dimensional") super().__init__() self.register_buffer("a", a) self.register_buffer("b", b.expand(a.size(-1))) self._num_outputs = a.size(-1)
[docs] def subset_output(self, idcs: List[int]) -> AffineDeterministicModel: r"""Subset the model along the output dimension. Args: idcs: The output indices to subset the model to. Returns: The current model, subset to the specified output indices. """ a_sub = self.a.detach()[..., idcs].clone() b_sub = self.b.detach()[..., idcs].clone() return self.__class__(a=a_sub, b=b_sub)
[docs] def forward(self, X: Tensor) -> Tensor: return self.b + torch.einsum("...d,dm", X, self.a)
[docs] class PosteriorMeanModel(DeterministicModel): """A deterministic model that always returns the posterior mean.""" def __init__(self, model: Model) -> None: r""" Args: model: The base model. """ super().__init__() self.model = model
[docs] def forward(self, X: Tensor) -> Tensor: return self.model.posterior(X).mean
[docs] class FixedSingleSampleModel(DeterministicModel): r""" A deterministic model defined by a single sample `w`. Given a base model `f` and a fixed sample `w`, the model always outputs y = f_mean(x) + f_stddev(x) * w We assume the outcomes are uncorrelated here. """ def __init__( self, model: Model, w: Optional[Tensor] = None, dim: Optional[int] = None, jitter: Optional[float] = 1e-8, dtype: Optional[torch.dtype] = None, device: Optional[torch.dtype] = None, ) -> None: r""" Args: model: The base model. w: A 1-d tensor with length model.num_outputs. If None, draw it from a standard normal distribution. dim: dimensionality of w. If None and w is not provided, draw w samples of size model.num_outputs. jitter: jitter value to be added for numerical stability, 1e-8 by default. dtype: dtype for w if specified device: device for w if specified """ super().__init__() self.model = model self._num_outputs = model.num_outputs self.jitter = jitter if w is None: self.w = ( torch.randn(model.num_outputs, dtype=dtype, device=device) if dim is None else torch.randn(dim, dtype=dtype, device=device) ) else: self.w = w
[docs] def forward(self, X: Tensor) -> Tensor: post = self.model.posterior(X) return post.mean + torch.sqrt(post.variance + self.jitter) * self.w.to(X)