wsp-cheval 0.3.0

High-performance discrete-choice (logit) travel demand model evaluation

Cheval (wsp-cheval)

Cheval is a Python package for high-performance evaluation of discrete-choice (logit) models. It's largely built upon the Pandas, NumPy, and NumExpr packages; along with some custom Numba code for performance-critical bottlenecks.

The name is an acronym for "CHoice EVALuator" but has a double-meaning as cheval is the French word for "horse" - and this package has a lot of horsepower! It has been designed for use in travel demand modelling, specifically microsimulated discrete choice models that need to process hundreds of thousands of records through a logit model. It also supports "stochastic" models, where the probabilities are the key outputs.

[!IMPORTANT] As of v0.3, this package is imported using wsp_cheval instead of cheval

Key features

Cheval contains two main components:

• cheval.ChoiceModel which is the main entry point for discrete choice modelling
• cheval.LinkedDataFrame which helps to simplify complex utility calculations.

These components can be used together or separately.

Cheval is compatible with Python 3.7+

Installation

Cheval can be installed with the following command:

``batch pip install wsp-cheval

### With `pip` directly from GitHub

Cheval can be installed directly from GitHub using `pip` by running the following command:

```batch
pip install git+https://github.com/wsp-sag/wsp-cheval.git