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I was interested to see the code more easily test things closer to real source data. I made these changes:
- I added some "pass" values (aside from just "agree" / "disagree")
- I added some "None" values to simulate missing votes (aka sparse matrix calculations)
- I added some moderated OUT (-1) and unmoderated (0) statements to comments.csv, better simulating how source data might arrive.
gist with raw files: https://gist.github.com/patcon/f0bf700378433f339464d91550e3b26c
I'd suggest this be implemented after #3, as we can keep current data as
input.nonsparse.yaml and add this new data as input.sparse.yaml, along with maybe input.comments.csv and input.votes.csv.
Source google sheet: https://docs.google.com/spreadsheets/d/1kxAf7OYH-XDjwkHwx66zJjTPEybJJdXyvD1AsjsSNmM/edit?gid=1565951778#gid=1565951778
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