Source code for botorch.models.model

#!/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.

r"""
Abstract base module for all BoTorch models.
"""

from __future__ import annotations

from abc import ABC, abstractmethod
from typing import Any, List, Optional

from torch import Tensor
from torch.nn import Module

from .. import settings
from ..posteriors import Posterior
from ..sampling.samplers import MCSampler


[docs]class Model(Module, ABC): r"""Abstract base class for BoTorch models."""
[docs] @abstractmethod def posterior( self, X: Tensor, output_indices: Optional[List[int]] = None, observation_noise: bool = False, **kwargs: Any, ) -> Posterior: r"""Computes the posterior over model outputs at the provided points. Args: X: A `b x q x d`-dim Tensor, where `d` is the dimension of the feature space, `q` is the number of points considered jointly, and `b` is the batch dimension. 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 observation noise to the posterior. Returns: A `Posterior` object, representing a batch of `b` joint distributions over `q` points and `m` outputs each. """ pass # pragma: no cover
@property def num_outputs(self) -> int: r"""The number of outputs of the model.""" cls_name = self.__class__.__name__ raise NotImplementedError(f"{cls_name} does not define num_outputs property")
[docs] def subset_output(self, idcs: List[int]) -> Model: r"""Subset the model along the output dimension. Args: idcs: The output indices to subset the model to. Returns: A `Model` object of the same type and with the same parameters as the current model, subset to the specified output indices. """ raise NotImplementedError
[docs] def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Model: r"""Condition the model on new observations. Args: X: A `batch_shape x n' x d`-dim Tensor, where `d` is the dimension of the feature space, `n'` is the number of points per batch, and `batch_shape` is the batch shape (must be compatible with the batch shape of the model). Y: A `batch_shape' x n' x m`-dim Tensor, where `m` is the number of model outputs, `n'` is the number of points per batch, and `batch_shape'` is the batch shape of the observations. `batch_shape'` must be broadcastable to `batch_shape` using standard broadcasting semantics. If `Y` has fewer batch dimensions than `X`, it is assumed that the missing batch dimensions are the same for all `Y`. Returns: A `Model` object of the same type, representing the original model conditioned on the new observations `(X, Y)` (and possibly noise observations passed in via kwargs). """ raise NotImplementedError
[docs] def fantasize( self, X: Tensor, sampler: MCSampler, observation_noise: bool = True, **kwargs: Any, ) -> Model: r"""Construct a fantasy model. Constructs a fantasy model in the following fashion: (1) compute the model posterior at `X` (including observation noise if `observation_noise=True`). (2) sample from this posterior (using `sampler`) to generate "fake" observations. (3) condition the model on the new fake observations. Args: X: A `batch_shape x n' x d`-dim Tensor, where `d` is the dimension of the feature space, `n'` is the number of points per batch, and `batch_shape` is the batch shape (must be compatible with the batch shape of the model). sampler: The sampler used for sampling from the posterior at `X`. observation_noise: If True, include observation noise. Returns: The constructed fantasy model. """ propagate_grads = kwargs.pop("propagate_grads", False) with settings.propagate_grads(propagate_grads): post_X = self.posterior(X, observation_noise=observation_noise) Y_fantasized = sampler(post_X) # num_fantasies x batch_shape x n' x m return self.condition_on_observations(X=X, Y=Y_fantasized, **kwargs)