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[S] Can I avoid Optimize Parameters algorithms' subparameters incorrect testing?
Contributor II
For sure the title does not explain itself, I tried!
The problem is very simple, for example, if I set a k-NN with a grid search for:
k: 1-100 (10 steps).
measure_types: mixed, numerical and bregman.
numerical measure: 10 of them.
The process executes 10 x 3 x 10 (300) instead of 10 x 2 + 10 x 10 (120), because it tries out the 10 different numerical measures for mixed and bregman measure types, even though they do not apply to them.
Is there a way to avoid this without programming X different optimization nodes, where X is the number of instances this error occurs? X can get quite large, and the 300/120 ratio even larger!
If there is not, any insight on how to better approach my quite-brute solution space search? ;D
Thanks in advance!!
The problem is very simple, for example, if I set a k-NN with a grid search for:
k: 1-100 (10 steps).
measure_types: mixed, numerical and bregman.
numerical measure: 10 of them.
The process executes 10 x 3 x 10 (300) instead of 10 x 2 + 10 x 10 (120), because it tries out the 10 different numerical measures for mixed and bregman measure types, even though they do not apply to them.
Is there a way to avoid this without programming X different optimization nodes, where X is the number of instances this error occurs? X can get quite large, and the 300/120 ratio even larger!
If there is not, any insight on how to better approach my quite-brute solution space search? ;D
Thanks in advance!!
0
Answers
the easiest way would be to add two optimization operators, one for each type of measures.
Best regards,
Marius