#!/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"""
References
.. [Feng2020HDCPS]
Q. Feng, B. Latham, H. Mao and E. Backshy. High-Dimensional Contextual Policy
Search with Unknown Context Rewards using Bayesian Optimization.
Advances in Neural Information Processing Systems 33, NeurIPS 2020.
"""
import warnings
from typing import List, Optional
import torch
from botorch.models.multitask import MultiTaskGP
from botorch.models.transforms.input import InputTransform
from botorch.models.transforms.outcome import OutcomeTransform
from gpytorch.constraints import Interval
from gpytorch.distributions.multivariate_normal import MultivariateNormal
from gpytorch.kernels.rbf_kernel import RBFKernel
from linear_operator.operators import InterpolatedLinearOperator, LinearOperator
from torch import Tensor
from torch.nn import ModuleList
[docs]class LCEMGP(MultiTaskGP):
r"""The Multi-Task GP with the latent context embedding multioutput (LCE-M)
kernel. See [Feng2020HDCPS]_ for a reference on the model and its use in Bayesian
optimization.
"""
def __init__(
self,
train_X: Tensor,
train_Y: Tensor,
task_feature: int,
train_Yvar: Optional[Tensor] = None,
context_cat_feature: Optional[Tensor] = None,
context_emb_feature: Optional[Tensor] = None,
embs_dim_list: Optional[List[int]] = None,
output_tasks: Optional[List[int]] = None,
input_transform: Optional[InputTransform] = None,
outcome_transform: Optional[OutcomeTransform] = None,
) -> None:
r"""
Args:
train_X: (n x d) X training data.
train_Y: (n x 1) Y training data.
task_feature: Column index of train_X to get context indices.
train_Yvar: An optional (n x 1) tensor of observed variances of each
training Y. If None, we infer the noise. Note that the inferred noise
is common across all tasks.
context_cat_feature: (n_contexts x k) one-hot encoded context
features. Rows are ordered by context indices, where k is the
number of categorical variables. If None, task indices will
be used and k = 1.
context_emb_feature: (n_contexts x m) pre-given continuous
embedding features. Rows are ordered by context indices.
embs_dim_list: Embedding dimension for each categorical variable.
The length equals k. If None, the embedding dimension is set to 1
for each categorical variable.
output_tasks: A list of task indices for which to compute model
outputs for. If omitted, return outputs for all task indices.
"""
super().__init__(
train_X=train_X,
train_Y=train_Y,
task_feature=task_feature,
train_Yvar=train_Yvar,
output_tasks=output_tasks,
input_transform=input_transform,
outcome_transform=outcome_transform,
)
self.device = train_X.device
# context indices
all_tasks = train_X[:, task_feature].unique()
self.all_tasks = all_tasks.to(dtype=torch.long).tolist()
self.all_tasks.sort() # unique in python does automatic sort; add for safety
if context_cat_feature is None:
context_cat_feature = all_tasks.unsqueeze(-1).to(device=self.device)
self.context_cat_feature = context_cat_feature # row indices = context indices
self.context_emb_feature = context_emb_feature
# construct emb_dims based on categorical features
if embs_dim_list is None:
# set embedding_dim = 1 for each categorical variable
embs_dim_list = [1 for _i in range(context_cat_feature.size(1))]
n_embs = sum(embs_dim_list)
self.emb_dims = [
(len(context_cat_feature[:, i].unique()), embs_dim_list[i])
for i in range(context_cat_feature.size(1))
]
# contruct embedding layer: need to handle multiple categorical features
self.emb_layers = ModuleList(
[
torch.nn.Embedding(num_embeddings=x, embedding_dim=y, max_norm=1.0)
for x, y in self.emb_dims
]
)
self.task_covar_module = RBFKernel(
ard_num_dims=n_embs,
lengthscale_constraint=Interval(
0.0, 2.0, transform=None, initial_value=1.0
),
)
self.to(train_X)
def _eval_context_covar(self) -> LinearOperator:
"""obtain context covariance matrix (num_contexts x num_contexts)"""
all_embs = self._task_embeddings()
return self.task_covar_module(all_embs)
def _task_embeddings(self) -> Tensor:
"""generate embedding features for all contexts."""
embeddings = [
emb_layer(
self.context_cat_feature[:, i].to(
dtype=torch.long, device=self.device
) # pyre-ignore
)
for i, emb_layer in enumerate(self.emb_layers)
]
embeddings = torch.cat(embeddings, dim=1)
# add given embeddings if any
if self.context_emb_feature is not None:
embeddings = torch.cat(
[embeddings, self.context_emb_feature.to(self.device)],
dim=1, # pyre-ignore
)
return embeddings
[docs] def task_covar_matrix(self, task_idcs: Tensor) -> Tensor:
r"""compute covariance matrix of a list of given context
Args:
task_idcs: (n x 1) or (b x n x 1) task indices tensor
"""
covar_matrix = self._eval_context_covar()
return InterpolatedLinearOperator(
base_linear_op=covar_matrix,
left_interp_indices=task_idcs,
right_interp_indices=task_idcs,
).to_dense()
[docs] def forward(self, x: Tensor) -> MultivariateNormal:
if self.training:
x = self.transform_inputs(x)
x_basic, task_idcs = self._split_inputs(x)
# Compute base mean and covariance
mean_x = self.mean_module(x_basic)
covar_x = self.covar_module(x_basic)
# Compute task covariances
covar_i = self.task_covar_matrix(task_idcs)
covar = covar_x.mul(covar_i)
return MultivariateNormal(mean_x, covar)
[docs]class FixedNoiseLCEMGP(LCEMGP):
r"""The Multi-Task GP the latent context embedding multioutput
(LCE-M) kernel, with known observation noise.
DEPRECATED: Please use `LCEMGP` with `train_Yvar` instead.
Will be removed in a future release (~v0.11).
"""
def __init__(
self,
train_X: Tensor,
train_Y: Tensor,
train_Yvar: Tensor,
task_feature: int,
context_cat_feature: Optional[Tensor] = None,
context_emb_feature: Optional[Tensor] = None,
embs_dim_list: Optional[List[int]] = None,
output_tasks: Optional[List[int]] = None,
) -> None:
r"""
Args:
train_X: (n x d) X training data.
train_Y: (n x 1) Y training data.
train_Yvar: (n x 1) Observed variances of each training Y.
task_feature: Column index of train_X to get context indices.
context_cat_feature: (n_contexts x k) one-hot encoded context
features. Rows are ordered by context indices, where k is the
number of categorical variables. If None, task indices will
be used and k = 1.
context_emb_feature: (n_contexts x m) pre-given continuous
embedding features. Rows are ordered by context indices.
embs_dim_list: Embedding dimension for each categorical variable.
The length equals to k. If None, the embedding dimension is set to
1 for each categorical variable.
output_tasks: A list of task indices for which to compute model
outputs for. If omitted, return outputs for all task indices.
"""
warnings.warn(
"`FixedNoiseLCEMGP` has been deprecated and will be removed in a "
"future release. Please use the `LCEMGP` model instead. "
"When `train_Yvar` is specified, `LCEMGP` behaves the same "
"as the `FixedNoiseLCEMGP`.",
DeprecationWarning,
)
super().__init__(
train_X=train_X,
train_Y=train_Y,
task_feature=task_feature,
train_Yvar=train_Yvar,
context_cat_feature=context_cat_feature,
context_emb_feature=context_emb_feature,
embs_dim_list=embs_dim_list,
output_tasks=output_tasks,
)
