Source code for botorch.models.contextual

#!/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.

from typing import Dict, List, Optional

from botorch.models.gp_regression import FixedNoiseGP
from botorch.models.kernels.contextual_lcea import LCEAKernel
from botorch.models.kernels.contextual_sac import SACKernel
from torch import Tensor


[docs]class SACGP(FixedNoiseGP): """The GP uses Structural Additive Contextual(SAC) kernel. Args: train_X: (n x d) X training data. train_Y: (n x 1) Y training data. train_Yvar: (n x 1) Noise variances of each training Y. decomposition: Keys are context names. Values are the indexes of parameters belong to the context. The parameter indexes are in the same order across contexts. """ def __init__( self, train_X: Tensor, train_Y: Tensor, train_Yvar: Tensor, decomposition: Dict[str, List[int]], ) -> None: super().__init__(train_X=train_X, train_Y=train_Y, train_Yvar=train_Yvar) self.covar_module = SACKernel( decomposition=decomposition, batch_shape=self._aug_batch_shape, device=train_X.device, ) self.decomposition = decomposition self.to(train_X)
[docs]class LCEAGP(FixedNoiseGP): r"""The GP with Latent Context Embedding Additive (LCE-A) Kernel. Note that the model does not support batch training. Input training data sets should have dim = 2. Args: train_X: (n x d) X training data. train_Y: (n x 1) Y training data. train_Yvar: (n x 1) Noise variance of Y. decomposition: Keys are context names. Values are the indexes of parameters belong to the context. The parameter indexes are in the same order across contexts. cat_feature_dict: Keys are context names and values are list of categorical features i.e. {"context_name" : [cat_0, ..., cat_k]}. k equals to number of categorical variables. If None, we use context names in the decomposition as the only categorical feature i.e. k = 1 embs_feature_dict: Pre-trained continuous embedding features of each context. embs_dim_list: Embedding dimension for each categorical variable. The length equals to num of categorical features k. If None, emb dim is set to 1 for each categorical variable. context_weight_dict: Known population Weights of each context. """ def __init__( self, train_X: Tensor, train_Y: Tensor, train_Yvar: Tensor, decomposition: Dict[str, List[int]], train_embedding: bool = True, cat_feature_dict: Optional[Dict] = None, embs_feature_dict: Optional[Dict] = None, embs_dim_list: Optional[List[int]] = None, context_weight_dict: Optional[Dict] = None, ) -> None: super().__init__(train_X=train_X, train_Y=train_Y, train_Yvar=train_Yvar) self.covar_module = LCEAKernel( decomposition=decomposition, batch_shape=self._aug_batch_shape, train_embedding=train_embedding, cat_feature_dict=cat_feature_dict, embs_feature_dict=embs_feature_dict, embs_dim_list=embs_dim_list, context_weight_dict=context_weight_dict, device=train_X.device, ) self.decomposition = decomposition self.to(train_X)