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sytemize the process of retraining/saving recommenders #319

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hjwilli opened this issue Feb 26, 2021 · 0 comments
Open
3 tasks

sytemize the process of retraining/saving recommenders #319

hjwilli opened this issue Feb 26, 2021 · 0 comments

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@hjwilli
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hjwilli commented Feb 26, 2021

The serialized recommenders will need to be periodically retrained- for example as we update python packages, add new experiment configurations, or update what information the serialized recs contain. We want to be able to easily rebuild the files and update github.

  • Add command line option to ai.py that just trains and saves recommenders
  • Add instructions/utility script that documents how to retrain the SVD recommenders for web and PennAIpy experiment configurations
  • If feasible, create github or jenkins action to regenerate the files in a new branch and create a pr (possible complication is git lfs)
hjwilli added a commit that referenced this issue Mar 5, 2021
@hjwilli
R2 score UI changes
a94fca7 
* R2 -> R^2 label update on datasets and results pages
* best results bar on datasets page for regression changed to max at 1

Ref #319
hjwilli added a commit that referenced this issue Apr 9, 2021
@hjwilli
retrain and save recommenders
f66e2fb 
env param to train and save the recommender with the web environment settings

Ref #319
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