This is a package for discrete choice model estimation and simulation, with an emphasis on large choice sets and behavioral refinements to multinomial models. Most of these models are not available in Statsmodels or Scikit-learn.
The underlying estimation routines come from two main places: (1) UrbanSim's urbanchoice codebase, which is being moved into ChoiceModels, and (2) Timothy Brathwaite's PyLogit package, which handles more flexible model specifications.
Clone this repository and run python setup.py develop.
Two required packages should also be installed the same way:
- PyLogit: https://github.com/timothyb0912/pylogit
- UrbanSim: https://github.com/udst/urbansim
UrbanSim won't be a requirement any more after we finish refactoring the estimation code.
choicemodels.tools.MergedChoiceTable()
- Generates a merged long-format table of choosers and alternatives.
choicemodels.MultinomialLogit()
- Fits MNL models, using either the ChoiceModels or PyLogit estimation engines.
chociemodels.MultinomialLogitResults()
- Stores and reports fitted MNL models.
There's documentation in these classes' docstrings, and a usage demo in a Jupyter notebook.
https://github.com/udst/choicemodels/blob/master/choicemodels/tools/interaction.py https://github.com/udst/choicemodels/blob/master/choicemodels/mnl.py
https://github.com/udst/choicemodels/blob/master/notebooks/Destination-choice-models-01.ipynb