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Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python

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Listed:
  • Du, Tianyu

    (Stanford U)

  • Kanodia, Ayush

    (Stanford U)

  • Athey, Susan

    (Stanford U)

Abstract

The torch-choice is an open-source library for flexible, fast choice modeling with Python and PyTorch. torch-choice provides a ChoiceDataset data structure to manage databases flexibly and memory-efficiently. The paper demonstrates constructing a ChoiceDataset from databases of various formats and functionalities of 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 torch-choice and 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 torch-choice on large-scale datasets.

Suggested Citation

  • Du, Tianyu & Kanodia, Ayush & Athey, Susan, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Research Papers 4106, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:4106
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    File URL: https://arxiv.org/abs/2304.01906
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    1. Rudolph, Maja & Ruiz, Francisco & Athey, Susan & Blei, David, 2017. "Structured Embedding Models for Grouped Data," Research Papers repec:ecl:stabus:3597, Stanford University, Graduate School of Business.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    3. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    4. Bonoma, Thomas V & Johnston, Wesley J, 1979. "Decision Making under Uncertainty: A Direct Measurement Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 6(2), pages 177-191, Se.
    5. Robert Donnelly & Francisco J. R. Ruiz & David Blei & Susan Athey, 2021. "Correction to: Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 409-409, December.
    6. Arthur van Soest, 1995. "Structural Models of Family Labor Supply: A Discrete Choice Approach," Journal of Human Resources, University of Wisconsin Press, vol. 30(1), pages 63-88.
    7. Christopher Conlon & Jeff Gortmaker, 2023. "Incorporating Micro Data into Differentiated Products Demand Estimation with PyBLP," NBER Working Papers 31605, National Bureau of Economic Research, Inc.
    8. Tom Kornstad & Thor Thoresen, 2007. "A discrete choice model for labor supply and childcare," Journal of Population Economics, Springer;European Society for Population Economics, vol. 20(4), pages 781-803, October.
    9. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    10. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    11. Chintagunta, Pradeep & Kyriazidou, Ekaterini & Perktold, Josef, 2001. "Panel data analysis of household brand choices," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 111-153, July.
    12. Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 64-67, May.
    13. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
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