# 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.
r"""
Model List GP Regression models.
"""
from __future__ import annotations
from copy import deepcopy
from typing import Any, List
from botorch.exceptions.errors import BotorchTensorDimensionError
from botorch.models.gpytorch import GPyTorchModel, ModelListGPyTorchModel
from gpytorch.models import IndependentModelList
from torch import Tensor
[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])
```