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Folkert van Heusden

Joined: 14 Mar 2008
Posts: 890
Location: Gouda, the Netherlands

 Posted: Thu Nov 23, 2017 10:37 am    Post subject: tuning for the uninformed Hi, What about the following method for tuning evaluation: - run through, say, 1000 positions - generate a (semi-)random set of tuning parameters - get their eval from your program - get the eval from the must-be-good program (I'm using stockfish) - compare those 1000 pairs by using https://en.wikipedia.org/wiki/Pearson_correlation_coefficient - coefficient > previous_coefficient? then remember this tuning parameters set What do you think?_________________https://vanheusden.com/feeks/ https://vanheusden.com/Embla/ https://vanheusden.com/DeepBrutePos/ https://vanheusden.com/pos/
Henk van den Belt

Joined: 27 May 2013
Posts: 4972

Posted: Thu Nov 23, 2017 11:49 am    Post subject: Re: tuning for the uninformed

 flok wrote: Hi, What about the following method for tuning evaluation: - run through, say, 1000 positions - generate a (semi-)random set of tuning parameters - get their eval from your program - get the eval from the must-be-good program (I'm using stockfish) - compare those 1000 pairs by using https://en.wikipedia.org/wiki/Pearson_correlation_coefficient - coefficient > previous_coefficient? then remember this tuning parameters set What do you think?

Something is better then nothing.

Are these 1000 positions representative for every position that may occur when playing games. Probably not.

Don't you want to create something different than Stockfish. I already have a copy of Stockfish running on my machine.

But something is better than nothing. (Skipper is nothing). Or not. For instance. Why waste time on something which won't make it. Maybe only for generating better ideas.

Last edited by Henk van den Belt on Thu Nov 23, 2017 11:52 am; edited 2 times in total
Mehdi Amini

Joined: 05 Jun 2014
Posts: 105
Location: Iran

Posted: Thu Nov 23, 2017 11:49 am    Post subject: Re: tuning for the uninformed

 flok wrote: Hi, What about the following method for tuning evaluation: - run through, say, 1000 positions - generate a (semi-)random set of tuning parameters - get their eval from your program - get the eval from the must-be-good program (I'm using stockfish) - compare those 1000 pairs by using https://en.wikipedia.org/wiki/Pearson_correlation_coefficient - coefficient > previous_coefficient? then remember this tuning parameters set What do you think?

You can use Genetic Algorithm too. See this link for instance:

https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/hmw/article1.html
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Henk van den Belt

Joined: 27 May 2013
Posts: 4972

 Posted: Thu Nov 23, 2017 11:53 am    Post subject: Re: tuning for the uninformed Yes he is doing hill climbing now or perhaps only sampling.
Folkert van Heusden

Joined: 14 Mar 2008
Posts: 890
Location: Gouda, the Netherlands

 Posted: Thu Nov 23, 2017 12:26 pm    Post subject: Re: tuning for the uninformed Only sampling._________________https://vanheusden.com/feeks/ https://vanheusden.com/Embla/ https://vanheusden.com/DeepBrutePos/ https://vanheusden.com/pos/
Henk van den Belt

Joined: 27 May 2013
Posts: 4972

 Posted: Thu Nov 23, 2017 12:44 pm    Post subject: Re: tuning for the uninformed Sampling is ok for tuning two parameters or so. Otherwise it is terribly slow. O wait if you tune it badly it generalizes better. So it will do better on evaluating unseen positions. Somewhere there is an optimum between bad tuning and 'overtuning'. Other constraint is that tuning should not cost too much time so better use hill climbing with restart. Or genetic algorithm (with restart?)
Alexandru Mosoi

Joined: 16 Jan 2015
Posts: 426

 Posted: Thu Nov 23, 2017 2:07 pm    Post subject: Re: tuning for the uninformed I tried pearson correlation in the past, but it's not a good measure. Right now my experiments with evolving the eval parameters use texel tuning on a set of 100K positions (so I can train ~6000 models per day). In terms of the error rate, I got very close to the hand tuned model (stable version). If you want to see the fully automatic trained eval with almost 0 human intervention check [1] or [2]. The evolved version is 100 Elo weaker than the stable version of Zurichess. I need to add back the pawns cache, though. [1] https://bitbucket.org/brtzsnr/zurichess/src/c37cb071903537a543d76607b75ce5d43c192710/engine/eval.go?at=evolve&fileviewer=file-view-default [2] https://bitbucket.org/brtzsnr/zurichess/src/59a02f5ae8b725e6d5442f5ddbb12a439b6381bf/engine/eval.go?at=lmr1&fileviewer=file-view-default_________________zurichess - http://www.zurichess.xyz
Folkert van Heusden

Joined: 14 Mar 2008
Posts: 890
Location: Gouda, the Netherlands

 Posted: Thu Nov 23, 2017 2:16 pm    Post subject: Re: tuning for the uninformed Yeah I looked at texel tuning but it looked rather complicated. This pearson was implemented in an hour during the morning commute _________________https://vanheusden.com/feeks/ https://vanheusden.com/Embla/ https://vanheusden.com/DeepBrutePos/ https://vanheusden.com/pos/
Folkert van Heusden

Joined: 14 Mar 2008
Posts: 890
Location: Gouda, the Netherlands

 Posted: Thu Nov 23, 2017 5:34 pm    Post subject: Re: tuning for the uninformed If anyone is willing to explain the Texel tuning method tht would be great! Sofar I understand I have to let it play (well, run QS + eval on FENs) millions of games and then do something with the evaluation-value. But what? I don't understand the wiki explanation._________________https://vanheusden.com/feeks/ https://vanheusden.com/Embla/ https://vanheusden.com/DeepBrutePos/ https://vanheusden.com/pos/
Sander Maassen vd Brink

Joined: 28 Jan 2017
Posts: 104
Location: The Netherlands

Posted: Thu Nov 23, 2017 6:47 pm    Post subject: Re: tuning for the uninformed

 flok wrote: If anyone is willing to explain the Texel tuning method tht would be great! Sofar I understand I have to let it play (well, run QS + eval on FENs) millions of games and then do something with the evaluation-value. But what? I don't understand the wiki explanation.

The basic idea is pretty simple: calculate the error of the evaluation when it is compared to the actual outcome of the positions. Lower a particular evaluation parameter and check if the error has improved, if not, higher the parameter, if again not improved, keep the original value. Do this for all parameters until you have reached the lowest error.
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