Benchmark gap encoder early stopping - #681
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jovan-stojanovic merged 11 commits intoAug 8, 2023
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Benchmark looks good, thanks for the contribution!
Maybe one minor modif before merging (ping me when you think this is ready): remove the print statements (and pass verbose=False).
Co-authored-by: Lilian <lilian@boulard.fr>
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@LilianBoulard Running with the same batch size, the speedup is smaller, but I think still useful. 
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@LilianBoulard I think it's ready, WDYT? |
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* benchmark * fix bug due to mixed type * verbose * test * fix bug * add benchmark results * add balenced accuracy * Update benchmarks/bench_gap_es_score.py Co-authored-by: Lilian <lilian@boulard.fr> * remove prints * run with the same batch size * benchmark results with the same batch size --------- Co-authored-by: Lilian <lilian@boulard.fr>
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Benchmark to evaluate the early stopping change made in #680, compared to the previous version of the gap encoder.
Based on #663 by @simonamaggio and #593 by @LilianBoulard
Compared to #663 , we don't compute the full score regularly, as this operation is very slow. Instead, we use an exponentially weighted average of each batch score. This is based on sklearn's MinibatchNMF's code.
Results
It seems that the new version is faster, while the KL score and balanced accuracy don't change. I think the speed can be improved further by tuning hyperparameters, this is the subject of another benchmark in #680. Furthermore, id columns like
seqidtake a long time to fit even with early stopping, as the score keeps decreasing, so #585 is still relevant even after this speedup.