Source code for botorch.models.model_list_gp_regression

#! /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.

Model List GP Regression models.

from copy import deepcopy
from typing import Any, List

from gpytorch.models import IndependentModelList
from torch import Tensor

from ..exceptions.errors import BotorchTensorDimensionError
from .gpytorch import GPyTorchModel, ModelListGPyTorchModel

[docs]class ModelListGP(IndependentModelList, ModelListGPyTorchModel): r"""A multi-output GP model with independent GPs for the outputs. This model supports different-shaped training inputs for each of its sub-models. It can be used with any BoTorch models. Internally, this model is just a list of individual models, but it implements the same input/output interface as all other BoTorch models. This makes it very flexible and convenient to work with. The sequential evaluation comes at a performance cost though - if you are using a block design (i.e. the same number of training example for each output, and a similar model structure, you should consider using a batched GP model instead). """ def __init__(self, *gp_models: GPyTorchModel) -> None: r"""A multi-output GP model with independent GPs for the outputs. Args: *gp_models: An variable number of single-output BoTorch models. If models have input/output transforms, these are honored individually for each model. Example: >>> model1 = SingleTaskGP(train_X1, train_Y1) >>> model2 = SingleTaskGP(train_X2, train_Y2) >>> model = ModelListGP(model1, model2) """ super().__init__(*gp_models)
[docs] def condition_on_observations( self, X: Tensor, Y: Tensor, **kwargs: Any ) -> "ModelListGP": 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`, its is assumed that the missing batch dimensions are the same for all `Y`. Returns: A `ModelListGPyTorchModel` representing the original model conditioned on the new observations `(X, Y)` (and possibly noise observations passed in via kwargs). Here the `i`-th model has `n_i + n'` training examples, where the `n'` training examples have been added and all test-time caches have been updated. """ self._validate_tensor_args( X=X, Y=Y, Yvar=kwargs.get("noise", None), strict=False ) inputs = [X] * self.num_outputs if Y.shape[-1] != self.num_outputs: raise BotorchTensorDimensionError( "Incorrect number of outputs for observations. Received " f"{Y.shape[-1]} observation outputs, but model has " f"{self.num_outputs} outputs." ) targets = [Y[..., i] for i in range(Y.shape[-1])] if "noise" in kwargs: noise = kwargs.pop("noise") # Note: dimension checks were performed in _validate_tensor_args kwargs_ = {**kwargs, "noise": [noise[..., i] for i in range(Y.shape[-1])]} else: kwargs_ = kwargs return super().get_fantasy_model(inputs, targets, **kwargs_)
[docs] def subset_output(self, idcs: List[int]) -> "ModelListGP": 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. """ return self.__class__(*[deepcopy(self.models[i]) for i in idcs])