High-performance discrete-choice (logit) travel demand model evaluation
Project description
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_chevalinstead ofcheval
Key features
Cheval contains two main components:
cheval.ChoiceModelwhich is the main entry point for discrete choice modellingcheval.LinkedDataFramewhich 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:
pip install wsp-cheval
With pip directly from GitHub
Cheval can be installed directly from GitHub using pip by running the following command:
pip install git+https://github.com/wsp-sag/wsp-cheval.git
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