Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with
Python
- URL: http://arxiv.org/abs/2304.01906v3
- Date: Fri, 14 Jul 2023 21:42:04 GMT
- Title: Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with
Python
- Authors: Tianyu Du, Ayush Kanodia and Susan Athey
- Abstract summary: $texttttorch-choice$ is an open-source library for flexible, fast choice modeling with Python and PyTorch.
$textttChoiceDataset$ provides a $textttChoiceDataset$ data structure to manage databases flexibly and memory-efficiently.
- Score: 11.566791864440262
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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