On Tue, 22 Feb 2011, Nynke van der Laan wrote: > However, in the first example, the best performing sphere is the > sphere with the highest error (the best performing sphere is retrieved > by max(sl_map). I would expect that the best performing sphere is the > one with the lowest error.... (i.e., the best sphere would be the one > with the highest accuracy and thus the lowest error) SORRY ABOUT THE MESS: indeed it should have been 'min' in there and issue was reported/fixed quite a while ago, but only in 0.5 branch :-/
So, I pushed this fix now also into 0.4 (what you are using) $> git cherry-pick 0747f55db1399b343e54b9dc20f0490a24d1db2d Finished one cherry-pick. [maint/0.4 3ea1324] BF: The best error is the lowest ;-) Author: Michael W. Cole <[email protected]> 1 files changed, 1 insertions(+), 1 deletions(-) > I was wondering if there is a way to get a sl_map with the accuracies > instead of the errors? I couldn't find a proper errorfx argument for > that. Or should I than just do that myself by for each datapoint doing > 1 minus the value I get now? it would be the easiest, since should be accomplished with just sl_map = 1.0 - sl_map ;-) alternatively, yes, you should be able to provide your custom errorfx... iirc it could be just a function, e.g.: import numpy as np cv = CrossValidatedTransferError( TransferError(LinearCSVMC(), errorfx=lambda predicted, target: np.mean( predicted == target )), OddEvenSplitter()) -- =------------------------------------------------------------------= Keep in touch www.onerussian.com Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
