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Neurips24 #1970

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Neurips24 #1970

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lenhoanglnh
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Description

Initially the goal was mostly data analysis and experiments for NeurIPS 24 submissions.
However, this has spurred two changes to Solidago source files:

  • Added import of other csv files of Tournesol public dataset.
  • Modified qr_quantile, which was previously flawed. More precisely, qr_quantile had the undesirable property that the addition of a new user with (near) infinite uncertainty decreased the qr_quantile estimate. With the new version of qr_quantile, infinite uncertainty is equivalent to zero voting right.

Checklist

  • I described my changes and my decisions in the PR description
  • I read the development guidelines of the CONTRIBUTING.md
  • The tests pass and have been updated if relevant
  • The code quality check pass

❤️ Thank you for your contribution!

Fixed tiny_tournesol.zip file for testing.
Added data_analysis for dataset submission.
WIP Runtime error on icml24 experiments to be fixed
…than additional term.

This implies that the addition of a new user with huge uncertainties will not affect the quantile much.
Comment on lines +92 to +104
if quantile == 0.5:
return regularization + forces.sum()

left_strength = min(1.0, quantile / (1-quantile))
right_strength = min(1.0, (1-quantile) / quantile)

forces = np.where(
forces < 0,
forces * left_strength,
forces * right_strength,
)

return regularization + quantile_term + forces.sum()
return regularization + forces.sum()
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@lenhoanglnh This change seems to change significantly the behaviour of the "zero shift" on current Tournesol data. Is it expected? Should we adjust the quantile parameter?

On "main", after applying the shift with score_shift_quantile = 0.15, about 13% of the individual scores are negative. On this branch "neurips24", that would be 37%.

As a consequence the distribution of Tournesol would be modified, with fewer videos reaching the recommendability threshold (1238 instead of 3013).

(I used the "legacy2023" pipeline, currently deployed on production. But I expect it would similar with the new pipeline).

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This is unsatisfactory indeed.
I'm a bit disturbed. It feels like the quantile is now poorly estimated.
Maybe this is because videos with lower scores have higher uncertainty? Or less trust?

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OK I looked at the data and indeed, the uncertainties for bad videos are smaller than for good videos, which explains why the quantile increased with the new quantile definition. I see two simple fixes:

  • Reduce score_shift_quantile = 0.15 to score_shift_quantile = 0.05.
  • Remove uncertainties in quantile estimation.

The former is much more satisfactory.

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