TorchDCM

PyTorch-first discrete choice model estimation and econometric inference.

Quick Start | Model Zoo | Installation | Benchmarks | Development

TorchDCM is the importable Python package for discrete choice model estimation. This repository is intentionally package-first: it keeps the reusable torchdcm implementation, unit tests, examples, and packaging metadata in one small repo that users can install and import directly.

The software-paper repository is separate and contains public benchmark data, validation wrappers, plots, comparison tables, generated results, and LaTeX:

torchdcm-paper: validation, benchmarks, datasets, and manuscript

Why TorchDCM

Installation

python -m pip install torchdcm

For local development:

python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -e ".[dev]"
pytest

Quick Start

import torch

from torchdcm import Beta, ChoiceDataset, MultinomialLogit, UtilitySpec
from torchdcm.datasets import make_swissmetro_like

df = make_swissmetro_like(n_obs=300, seed=7)
data = ChoiceDataset.from_wide(
    df,
    alternatives=["TRAIN", "SM", "CAR"],
    choice="choice",
    variables={
        "time": {"TRAIN": "time_train", "SM": "time_sm", "CAR": "time_car"},
        "cost": {"TRAIN": "cost_train", "SM": "cost_sm", "CAR": "cost_car"},
    },
    availability={
        "TRAIN": "avail_train",
        "SM": "avail_sm",
        "CAR": "avail_car",
    },
    individual_id="person_id",
)

spec = UtilitySpec()
spec.utility(
    "TRAIN",
    Beta("ASC_TRAIN")
    + Beta("B_TIME", init=-0.01) * "time"
    + Beta("B_COST", init=-0.1) * "cost",
)
spec.utility(
    "SM",
    Beta("B_TIME", init=-0.01) * "time"
    + Beta("B_COST", init=-0.1) * "cost",
)
spec.utility(
    "CAR",
    Beta("ASC_CAR")
    + Beta("B_TIME", init=-0.01) * "time"
    + Beta("B_COST", init=-0.1) * "cost",
)

device = "cuda" if torch.cuda.is_available() else "cpu"
result = MultinomialLogit(spec, device=device).fit(data, cov_type="cluster", groups="person_id")
print(result.summary())

summary() renders an organized console report covering the model and data, convergence diagnostics, fit statistics, inference, alternative shares, and parameter estimates. The same structured report can be inspected as tables or saved as a reproducible artifact directory:

report = result.report(cov_type="cluster", confidence_level=0.95)
parameter_table = report.parameters

result.save_report(
    "outputs/swissmetro_mnl",
    formats=["html", "json", "csv", "latex", "text"],
)

The output directory contains a readable HTML report, a machine-readable JSON record, parameter/covariance/correlation CSV files, a LaTeX fragment, and a plain-text summary.

All estimators accept a standard PyTorch-style device argument. Passing device="cuda" moves estimation, prediction, simulated likelihoods, and covariance calculations for that model to CUDA when your PyTorch installation has GPU support.

Model Zoo

The current package includes:

• multinomial logit / conditional logit;
• nested logit and cross-nested logit;
• mixed logit, WTP-space mixed logit, and error-components logit;
• latent-class logit;
• scaled and covariate-scaled multinomial logit;
• ordered logit and ordered probit;
• hybrid choice with latent variables and Gaussian measurement indicators;
• classic, robust, and cluster covariance estimates;
• WTP and elasticity helpers.

Repository Layout

Development

python -m pip install -e ".[dev]"
pytest

Benchmark and manuscript work should happen in the companion repository:

git clone https://github.com/mbc96325/torchdcm-paper.git