Sorry, my mistake.flok wrote: ↑Wed Jun 26, 2019 5:34 pmAre you sure? Because this is what the wiki says about it:Robert Pope wrote: ↑Wed Jun 26, 2019 5:18 pmIt looks to me like you have a scale mismatch:
value_from_fen is in [0,1]
calculateSigmoid(eval_score) is in [1,1].
"Ri is the result of the game corresponding to position i; 0 for black win, 0.5 for draw and 1 for white win."
(https://www.chessprogramming.org/Texel% ... ing_Method)
tuning for the uninformed
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 Posts: 510
 Joined: Sat Mar 25, 2006 7:27 pm
Re: tuning for the uninformed
Re: tuning for the uninformed
* Use score cp wpov in sigmoid
* Result, 1: white wins, 0: black wins, 0.5: even
Given a bad position for black and black to move and result is 1 or white wins.
Try this code for data tests.
* Result, 1: white wins, 0: black wins, 0.5: even
Given a bad position for black and black to move and result is 1 or white wins.
Code: Select all
Input score cp side pov? 1000
Input side to move, [w or b]? b
Input result [1: white_wins, 0: black_wins, 0.5: even]? 1
Input 1 to exit data entry? 1
score cp wpov: 1000
res: 1.0
sig: 0.92414
error = res  sig = 1.0  0.92414 = 0.07586
error squared: 0.00575
E = 0.00575
Code: Select all
#!/usr/bin/env python3
"""
Return mean of sum of error squared
"""
import math
def sigmoid(cp):
""" Returns scoring rate based on centipawn eval (wpov) of positon
cp:
centipawn score
"""
K = 1.0
return 1 / (1 + math.exp(K*cp/400))
def E(pos_score, result):
""" Returns mean of sum of error squared
pos_score:
A list with cp score in white pov
result:
A list of either 1, 0, 0.5
1: white wins, 0: black wins, 0.5: even
"""
sum_error_squared = 0
# Use python 3
for cp, res in zip(pos_score, result):
print('score cp wpov: {}'.format(cp))
print('res: {:0.1f}'.format(res))
sig = sigmoid(cp)
print('sig: {:0.5f}'.format(sig))
error = res  sig
print('error = res  sig = {:0.1f}  {:0.5f} = {:0.5f}'.format(res, sig, error))
error_squared = error * error
print('error squared: {:0.5f}'.format(error_squared))
sum_error_squared += error_squared
num_pos = len(result)
return sum_error_squared/num_pos
def main():
pos_score = []
result = []
# User input of pos score and result
while True:
score_cp_side_pov = int(input('Input score cp side pov? '))
stm = input('Input side to move, [w or b]? ')
# Convert score side pov to white pov
if stm == 'b':
score_cp_white_pov = 1 * score_cp_side_pov
else:
score_cp_white_pov = score_cp_side_pov
pos_score.append(score_cp_white_pov)
res = float(input('Input result [1: white_wins, 0: black_wins, 0.5: even]? '))
result.append(res)
try:
e = int(input('Input 1 to exit data entry? '))
if e == 1:
break
except:
pass
value = E(pos_score, result)
print('E = {:0.5f}'.format(value))
if __name__ == '__main__':
main()
Re: tuning for the uninformed
Ok found that at least the way I determine K was (is?) incorrect.
Now implemented https://github.com/flok99/Micah/blob/ma ... h.cpp#L177 <= that binary search for it.
Will now try  that'll keep at least a day.
Now implemented https://github.com/flok99/Micah/blob/ma ... h.cpp#L177 <= that binary search for it.
Will now try  that'll keep at least a day.
www.vanheusden.com: Micah / Embla / PuppetMaster / DeepBrutePos / Pos / Feeks
Re: tuning for the uninformed
I saw 2 issues in Micah.cpp:
Code: Select all
double K = calculate_error(&work, &results, positions, 1.0);
double min_err = 1000.0;
 Juse use a constant K for now, i'm using 1.4 on Pirarucu.
 Set your min_err to the error from your current used variables.
Code: Select all
double K = 1.4;
double min_err = calculate_error(&work, &results, positions, K);
Re: tuning for the uninformed
Ratosh wrote: ↑Thu Jun 27, 2019 5:29 pmI saw 2 issues in Micah.cpp:Code: Select all
double K = calculate_error(&work, &results, positions, 1.0); double min_err = 1000.0;
Yes that's indeed the case!
 Juse use a constant K for now, i'm using 1.4 on Pirarucu.
 Set your min_err to the error from your current used variables.
Only the 1.4 value I cannot reproduce: I've now implemented a binary search for finding the K value and I always up with 0.002557039260864258
Please note that I merged the tuning code into master.
www.vanheusden.com: Micah / Embla / PuppetMaster / DeepBrutePos / Pos / Feeks

 Posts: 510
 Joined: Sat Mar 25, 2006 7:27 pm
Re: tuning for the uninformed
K has to be positive or sigmoid goes the wrong direction, so that's a critical problem.
I would suggest picking a dozen positions and checking your calculation to get K. You should be able to do that by hand and see where it is going wrong.
I would suggest picking a dozen positions and checking your calculation to get K. You should be able to do that by hand and see where it is going wrong.
 Ronald
 Posts: 90
 Joined: Tue Jan 23, 2018 9:18 am
 Location: Rotterdam
 Full name: Ronald Friederich
 Contact:
Re: tuning for the uninformed
Hi Folkert,
The sigmoid function is used to map the score of the evaluation (which is f.i. in the range of 100 to +100) to a value in the range of 0 to 1.
A score of infinity results in a value of 0, a score of +infinity in a value of 1. A score of 0 results in a value of the sigmoid of 0.5.
The constant K defines the "sensitivity" of the sigmoid, ie how fast it goes to the border values.
You should map the outcome of the positions in your learningset absolute not relative to the side to move, which is: value 0 is a win for black, 1 is win for white and 0.5 is a draw. The file "quietlabeled.epd" is also absolute (10 win for white, 01 win for black). If I read the source correctly, you are also doing that.
So you need an absolute score for the position in order to calculate the error for the position. In error_calc_thread the score of the position is calculated via qs(). I suppose the result of qs() is calculated relative to the side to move. If that is the case I think you should invert the calculated score of qs in case the side to move is black.
The sigmoid function is used to map the score of the evaluation (which is f.i. in the range of 100 to +100) to a value in the range of 0 to 1.
A score of infinity results in a value of 0, a score of +infinity in a value of 1. A score of 0 results in a value of the sigmoid of 0.5.
The constant K defines the "sensitivity" of the sigmoid, ie how fast it goes to the border values.
You should map the outcome of the positions in your learningset absolute not relative to the side to move, which is: value 0 is a win for black, 1 is win for white and 0.5 is a draw. The file "quietlabeled.epd" is also absolute (10 win for white, 01 win for black). If I read the source correctly, you are also doing that.
So you need an absolute score for the position in order to calculate the error for the position. In error_calc_thread the score of the position is calculated via qs(). I suppose the result of qs() is calculated relative to the side to move. If that is the case I think you should invert the calculated score of qs in case the side to move is black.
Re: tuning for the uninformed
Ah! If I do that, then suddenly the Kvalue makes much more sense: 1.516882Ronald wrote: ↑Thu Jun 27, 2019 7:21 pmSo you need an absolute score for the position in order to calculate the error for the position. In error_calc_thread the score of the position is calculated via qs(). I suppose the result of qs() is calculated relative to the side to move. If that is the case I think you should invert the calculated score of qs in case the side to move is black.
error is then 25,8%
Thanks a lot!!
www.vanheusden.com: Micah / Embla / PuppetMaster / DeepBrutePos / Pos / Feeks

 Posts: 3830
 Joined: Thu May 15, 2008 7:57 pm
 Location: Berlin, Germany
 Full name: Sven Schüle
 Contact:
Re: tuning for the uninformed
No, why? I said "same viewpoint" and if the stored value is from white POV then eval must be white POV, too. My example was more general and did not assume white POV, it was only made to show what would happen on a viewpoint mismatch.
Sven Schüle (engine author: Jumbo, KnockOut, Surprise)
Re: tuning for the uninformed
So instead I could also do (score + 10000) / 20000 ? (assuming eval score range is 10k ... 10k)Ronald wrote: ↑Thu Jun 27, 2019 7:21 pmHi Folkert,
The sigmoid function is used to map the score of the evaluation (which is f.i. in the range of 100 to +100) to a value in the range of 0 to 1.
A score of infinity results in a value of 0, a score of +infinity in a value of 1. A score of 0 results in a value of the sigmoid of 0.5.
www.vanheusden.com: Micah / Embla / PuppetMaster / DeepBrutePos / Pos / Feeks