Comments (4)
Mirroring this comment, I wanted something better than ELO and this library has been super useful with only minor gotchas.
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Hi @Kobold and @sethtroisi thanks for the kind words!
Is there anything you'd find useful to have in the library / any ideas for improvements?
If you're using the library for player rating, you might potentially be interested in kickscore, another lib I'm working on that handles time-varying parameters:
https://github.com/lucasmaystre/kickscore
(Though it's a bit less stable and there's no documentation for now)
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@lucasmaystre
One of the strong points of choix is the good documentation. I also appreciated your support In #9
I'm using choix for AI ratings in Go, we break out each version as a seperate "player" so there should be no time varying component.
For useful features:
- I have lots of games ~300K if I don't pass good starting points it takes ~~2 minutes which could always be faster but certainly isn't a blocking anything right now
- Most of my pairs have 20+ games played, maybe something could be improved here
- I've written a dynamic pairing program a couple of times and it always has issues. input is all games played (and potentially a estimate of rating for players without many games) and output is a set of matchups that will reduce uncertainty.
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@sethtroisi thanks for the input. Point 1 (computational efficiency) is on my TODO list, I hope to be able to get to this soon. Will create an issue to track this.
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Related Issues (20)
- where can I find the function of "partial ranking"
- Understanding the islr_pairwise behaviour HOT 1
- Using features as extra evidence for preference selection HOT 2
- Unexpected argument 'alpha' for opt_pairwise HOT 2
- How to provide probabilities as well for pairwise to list ? HOT 1
- partial ranking aggression HOT 4
- Citation? HOT 1
- Dynamically choosing the pairs to compare in an experiment to limit the number of comparisons HOT 10
- Use Choix for table-like heterogenous data for comparison? HOT 6
- Using probabilistic comparisons as training data HOT 2
- Speed up parameter inference HOT 1
- Is "Team" ranking possible with LSR and I-LSR? HOT 1
- Handling ties HOT 1
- Probability of win given pairwise probabilities HOT 5
- Missing link to Bayesian example network HOT 2
- Type Error in choicerank-tutorial.ipynb HOT 1
- Using Python sets to represent top-1 data leads to TypeError HOT 1
- Weights for the importance of each observation HOT 2
- Ties in rankings
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