This is the code associated with the paper Representing Random Utility Choice Models with Neural Networks (Aouad and Désir, 2023). The code has two parts.

• Python code base for training RUMnets.
• Python code to run the benchmarks from the paper.

This repository is work-in-progress.

The modules in src/ contain the experiment auxiliary functions (experiment_auxiliary.py) and the Keras model classes (models.py).

The script run_template.py runs experiments for a given dataset (specify test_name = "swiss_metro") and model class (specify model_family = "RUMnet"), which include RUMnet, Tastenet, DeepMNL, random forests (RF), and neural networks (NN).

The configurations of experiments and models should be specified in the config JSON files in the subfolder src/test_name/. These include the general and experiment-specific parameters in the files config_experiment.json and config_general.json as well as model-specific parameters in the files config_modelsArchitecture.json, located in each model subfolder of src/test_name/modelArchitecture/.

For new dataset applications, one need to construct a new data_preparation function under the same template format as in experiment_auxiliary.py and import the function in the script run_template.py.

The results are saved in a folder output/testGroup/test_name/.

The code was tested with the following:

• python 3.8.13
• numpy 1.19.2
• pandas 1.4.2
• tensorflow 2.4.0 (tf-nightly)
• joblib 0.17.0

The full environment dependencies are provided in /env/rumnet_env.yml.

To run the code, the datasets should be copied into a new subfolder data/testname and loaded in the data_preparation function (see above). The Swissmetro dataset is available here and the Expedia dataset is available here.

This project is licensed under the terms of the MIT License.