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We had to set this threshold, after trial and error. Because the generalized nested logit model has so many exponential terms that need to be evaluated for getting the final choice probabilities, that it used to run in some memory issues and for larger datasets. Since we cannot remove the features, we had to subsample the instances.
This threshold is library dependent. Maybe we need to talk about using a different library for it.
Or we can recheck if the model works with larger datasets in new versions of pymc3.
Peculiar constant - this should rather be something passed into
__init__
as parameter. But this is something for a separate issue.Originally posted by @kiudee in #157 (comment)
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