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Doc data co #15

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Jan 19, 2024
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Doc data co #15

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0536435
ADD: Intro to DCM in readme
VincentAuriau Jan 19, 2024
a924c97
ENH: data exampel
VincentAuriau Jan 19, 2024
8e212ec
ADD: SwissMetro example beginning
VincentAuriau Jan 19, 2024
19567da
FIX: possibility to define different weights for the same feature
VincentAuriau Jan 19, 2024
018e0d5
ENH: prints
VincentAuriau Jan 19, 2024
3f58f50
ADD: finalized PyLogit SwissMetro example
VincentAuriau Jan 19, 2024
c3a4573
ADD: init rumnet example
VincentAuriau Jan 19, 2024
ef771ff
ENH: documentation
VincentAuriau Jan 19, 2024
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18 changes: 17 additions & 1 deletion README.md
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Expand Up @@ -23,13 +23,21 @@ This repository contains a private version of the package.
## Table of Contents

- [choice-learn-private](#choice-learn-private)
- [Introduction - Discrete Choice Modelling](#introduction---discrete-choice-modelling)
- [Table of Contents](#table-of-contents)
- [What's in there ?](#whats-in-there)
- [Getting Started](#getting-started---fast-track)
- [Installation](#installation)
- [Usage](#usage)
- [Documentation](#documentation)
- [Citation](#citation)

## Introduction - Discrete Choice Modelling

Discrete choice models aim at explaining or predicting a choice from a set of alternatives. Well known use-cases include analyzing people choice of mean of transport or products purchases in stores.

If you are new to choice modelling, you can check this [resource](https://www.publichealth.columbia.edu/research/population-health-methods/discrete-choice-model-and-analysis). The different notebooks from the [Getting Started](#getting-started---fast-track) section can also help you understand choice modelling and more importantly help you for your usecase.

## What's in there ?

### Data
Expand All @@ -41,7 +49,7 @@ This repository contains a private version of the package.
### Models
- Ready to use models:
- Conditional MultiNomialLogit, Train, K.; McFadden, D.; Ben-Akiva, M. (1987)
- RUMnet, Aouad A.; Désir A. (2022)
- RUMnet, Aouad A.; Désir A. (2022) [1]
- Ready to use models to be implemented:
- Nested MultiNomialLogit
- MultiNomialLogit with latent variables (MixedLogit)
Expand Down Expand Up @@ -100,3 +108,11 @@ A detailed documentation of this project is available [here](https://artefactory
### Contributors

## References

### Papers
[1][Representing Random Utility Choice Models with Neural Networks](https://arxiv.org/abs/2207.12877), Aouad A.; Désir A. (2022)

### Code and Repositories
- [PyLogit](https://github.com/timothyb0912/pylogit)
- [Torch Choice](https://gsbdbi.github.io/torch-choice/)
- [1][RUMnet](https://github.com/antoinedesir/rumnet)
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