tuning for the uninformed

[Henk]

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?)

[brtzsnr]

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 ... ew-default
[2] https://bitbucket.org/brtzsnr/zurichess ... ew-default

[flok]

Yeah I looked at texel tuning but it looked rather complicated. This pearson was implemented in an hour during the morning commute

[flok]

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.

[sandermvdb]

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.

[Henk]

I don't know how to get an estimate of the value of a position without using other engines.

Maybe use your own engine and re-search it on shallow depth. But who will say that that gives a right estimate if your current evaluation is bad.

You can not evaluate every test position manually for that is too much work.

[sandermvdb]

I don't know how to get an estimate of the value of a position without using other engines.

Maybe use your own engine and re-search it on shallow depth. But who will say that that gives a right estimate if your current evaluation is bad.

You can not evaluate every test position manually for that is too much work.

quiet-labeled.epd contains the outcome of every position

[Henk]

I don't know how to get an estimate of the value of a position without using other engines.

Maybe use your own engine and re-search it on shallow depth. But who will say that that gives a right estimate if your current evaluation is bad.

You can not evaluate every test position manually for that is too much work.

quiet-labeled.epd contains the outcome of every position

Don't understand. What is quiet-labeled.epd?

[sandermvdb]

Maybe use your own engine and re-search it on shallow depth. But who will say that that gives a right estimate if your current evaluation is bad.

You can not evaluate every test position manually for that is too much work.

quiet-labeled.epd contains the outcome of every position

Don't understand. What is quiet-labeled.epd?

Sorry, that is one of the testsets by Alexandru Mosoi, the author of Zurichess. It conains quiet positions including the outcome of the game.

[flok]

The basic idea is pretty simple: calculate the error of the evaluation when it is compared to the actual outcome of the positions.

What do you mean by that?
Do you mean the following:
- fen as input
- calc move with an eval val
- calc eval of the move that should've been moved
- compare these two (how? percentual difference? or what?)