The $\texttt{torch-choice}$ is an open-source library for flexible, fast choice modeling with Python and PyTorch. $\texttt{torch-choice}$ provides a $\texttt{ChoiceDataset}$ data structure to manage databases flexibly and memory-efficiently. The paper demonstrates constructing a $\texttt{ChoiceDataset}$ from databases of various formats and functionalities of $\texttt{ChoiceDataset}$. The package implements two widely used models, namely the multinomial logit and nested logit models, and supports regularization during model estimation. The package incorporates the option to take advantage of GPUs for estimation, allowing it to scale to massive datasets while being computationally efficient. Models can be initialized using either R-style formula strings or Python dictionaries. We conclude with a comparison of the computational efficiencies of $\texttt{torch-choice}$ and $\texttt{mlogit}$ in R as (1) the number of observations increases, (2) the number of covariates increases, and (3) the expansion of item sets. Finally, we demonstrate the scalability of $\texttt{torch-choice}$ on large-scale datasets.

本文介绍了开源的 $	exttt{torch-choice}$ 库及其数据结构 $	exttt{ChoiceDataset}$ ，实现了两种模型（多项式Logit 模型和嵌套Logit 模型）和模型估计中的正则化，支持R-style公式字符串或Python字典作为模型初始化，GPU加速可以应对大型数据集，最后与$	exttt{mlogit}$相比较效率更高

Torch-Choice: 一种用 Python 进行大规模选择模型的 PyTorch 包