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discrete-choice's Introduction

choice_model Build Status

A Python package for defining discrete choice models and solving them using numerous back ends through a single interface.

This module is a result of the ALOGIT in Python project, part of the Research Engineering programme at the Alan Turing Institute.

Currently supported back ends

Currently supported models

  • Multinomial logit

Installation

Install the packages and dependencies with pip install .

Testing

The pytest module (pip install pytest) is required to run the tests. The tests can be run with python -m pytest.

Examples

Example validation and benchmarking scripts are given in the examples directory.

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discrete-choice's Issues

Implement scalable synthetic data / model creation

A function to create models and associated data of arbitrary size (number of records, number of choices, number of parameters) would be helpful for benchmarking purposes.

To simplify this in the fist instance perhaps assert that utility functions for all alternatives depend on all variables and there are no choice dependent variables.

Don't make model a component of interfaces

This could make it easy to accidentally modify the model and cause unexpected errors when creating a new interface (or recreating the same interface).

The models should be passed as an argument only to methods that require it.

Unify interface creation

The constructor for interfaces should be uniform to aid in creating benchmarking tools. Additional parameters which may be required (i.e. the ALOGIT path ) could be passed in the generic **kwargs manner.

No magic strings

Some column labels are input multiple times as literal strings. It would be better to create a module level variable

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