Hello Everyone, I have a question about MaximalVote. I'm getting an error on:
voting=MaximalVote(enable_ca=['estimates']) TypeError: __init__() got an unexpected keyword argument 'enable_ca' The documentation page : http://www.pymvpa.org/generated/mvpa.clfs.meta.MaximalVote.html#mvpa.clfs.meta.MaximalVote shows "enable_ca" as a valid argument. I am using python-pymvpa-snapshot from neuro-debian in ubuntu 10.10, 64bit. I am using this object in the following context: # inner_part is a custom partitioner which provides random subsets of the training set voting = MaximalVote() # voting=MaximalVote(enable_ca=['estimates']) # desired but produces error comb_clf = SplitClassifier(LinearCSVMC(C=1), partitioner=inner_part, splitter=splitter, combiner=voting) spartitions = partitions[:, indices] tm = TransferMeasure(comb_clf, splitter, postproc=BinaryFxNode(mean_mismatch_error, space='targets'), enable_ca=['stats','raw_results']) tm(spartitions) I would like to see the "estimates: Estimates keep counts across classifiers for each label/sample" As you might have guessed, this intends to be example-wise bagging. Any suggestions on how to circumvent this issue or make sure the voting procedure operates as expected? Many thanks! Andrei
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