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[Feature Request] custom fusion method in optimize_fusion

See original GitHub issue

Is your feature request related to a problem? Please describe. Hi, you’ve done a great job implementing plenty of different fusion algorithms, but I think it will always be a bottleneck. What would you think about letting the user define their own training function?

Describe the solution you’d like For example, in optimize_fusion, allow method to be a callable and in this case, do not call has_hyperparams and optimization_switch.

Describe alternatives you’ve considered

  • Open a feature request every time I want to try out something new 😃
  • Fork ranx and implement new fusion methods there

My use case/ Ma et al. By the way, at the moment, my use case is to use the default-minimum trick of Ma et al.: when combining results from systems A and B, it consists in giving the minimum score of A’s results if a given document was only retrieved by system B, and vice-versa.

Maybe this is already possible in ranx via some option/method named differently? Or maybe you’d like to add it in the core ranx fusion algorithms?

Issue Analytics

  • State:closed
  • Created 10 months ago
  • Comments:14 (6 by maintainers)

github_iconTop GitHub Comments

AmenRacommented, Nov 30, 2022

I never used ZMUV, to be honest. I implemented it for completeness and tried it for comparison purposes but never got better results than min-max, max, or sum, which sometimes works the best.

In general, I prefer local normalization schemes because they are “unsupervised” and can be used out of the box. Without strong empirical evidence that default-minimum (w/ or w/o ZMUV) works better than min-max, max, or sum, I would not use it.

Also, without a standardized way of normalizing/fusing results is often difficult to understand what brings improvements over the state-of-the-art. Conducting in-depth ablation studies is costly, and we often lack enough space on conference papers to write about them.

AmenRacommented, Nov 29, 2022

Thank you very much, Paul!

I am happy to see that max-norm outperforms default-minimum. To give you some context, I added/invented max norm because the minimum score is often unknown. We usually fuse only the top retrieved documents from each model, which makes min-max (in this specific context) not very sound to me. I did not do extensive experimentation but from my experience max norm outperforms min-max very often.

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