Add W&B logging - #761
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desilinguist
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@tamarl08 this looks pretty elegant for the most part! I like that you added what you needed to the learner result dictionaries and then just use those for logging most of the time.
I made some minor suggestions based on the code itself but without testing what the various W&B outputs look like. I will do that next.
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@tamarl08 any idea why gitlab is failing and Azure is passing? |
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Will check. I didn't expect anything to pass! |
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This is why I tried to log to summary instead, but I couldn't always get rid of these charts. I'll try some more and let's talk next week. |
…in tests and fix some tests. Some code fixes and improvement.
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Hello @tamarl08! Thanks for updating this PR.
Comment last updated at 2024-01-26 21:47:38 UTC |
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@desilinguist @mulhod @damien2012eng @Frost45 See the updated evaluation logging here - look only at the most recent run. Changes to tests are due to: a change I made to the job name/output file names; data added to the result dict; bug fixes in some tests. |
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #761 +/- ##
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+ Coverage 95.33% 95.44% +0.11%
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Files 30 30
Lines 3598 3688 +90
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+ Hits 3430 3520 +90
Misses 168 168 ☔ View full report in Codecov by Sentry. |
desilinguist
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Looks really good! I ran some test experiments and W&B logging now looks much better than before. I made some minor suggestions. I am assuming that you are going to add documentation in a separate MR?
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Thanks for the review @desilinguist! All suggestions applied. I did add a section in the docs, and also a comment about the output file names. Will add more examples later. |
desilinguist
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Just some minor tweaks for the documentation.
Co-authored-by: Nitin Madnani <nmadnani@ets.org>

This PR adds W&B logging for all task types.
Changes include:
Examples:
train: currently only logs the configuration, model file path and train set size (in run summary).
predict: logs configuration, train and test sizes and predictions file
learning curve: logs config, plots and some raw data from the learning curve results (in run summary)
evaluate: logs config and all evaluation metrics and confusion matrix as a chart.
cross_validate: logs evaluation per fold + average of folds
TODO: