Source code for botorch.posteriors.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.

Deterministic (degenerate) posteriors. Used in conjunction with deterministic

from __future__ import annotations

from typing import Optional
from warnings import warn

import torch
from botorch.posteriors.posterior import Posterior
from torch import Tensor

[docs] class DeterministicPosterior(Posterior): r"""Deterministic posterior. [DEPRECATED] Use `EnsemblePosterior` instead. """ def __init__(self, values: Tensor) -> None: r""" Args: values: Values of the samples produced by this posterior. """ warn( "`DeterministicPosterior` is marked for deprecation, consider using " "`EnsemblePosterior`.", DeprecationWarning, ) self.values = values @property def device(self) -> torch.device: r"""The torch device of the posterior.""" return self.values.device @property def dtype(self) -> torch.dtype: r"""The torch dtype of the posterior.""" return self.values.dtype def _extended_shape( self, sample_shape: torch.Size = torch.Size() # noqa: B008 ) -> torch.Size: r"""Returns the shape of the samples produced by the posterior with the given `sample_shape`. """ return sample_shape + self.values.shape @property def mean(self) -> Tensor: r"""The mean of the posterior as a `(b) x n x m`-dim Tensor.""" return self.values @property def variance(self) -> Tensor: r"""The variance of the posterior as a `(b) x n x m`-dim Tensor. As this is a deterministic posterior, this is a tensor of zeros. """ return torch.zeros_like(self.values)
[docs] def rsample( self, sample_shape: Optional[torch.Size] = None, ) -> Tensor: r"""Sample from the posterior (with gradients). For the deterministic posterior, this just returns the values expanded to the requested shape. Args: sample_shape: A `torch.Size` object specifying the sample shape. To draw `n` samples, set to `torch.Size([n])`. To draw `b` batches of `n` samples each, set to `torch.Size([b, n])`. Returns: Samples from the posterior, a tensor of shape `self._extended_shape(sample_shape=sample_shape)`. """ if sample_shape is None: sample_shape = torch.Size([1]) return self.values.expand(self._extended_shape(sample_shape))