Dear all,
is there any manner to obtain a sensitivity map with RFE, similar to what can be
done with feature selection?
I am trying to use the following code:
#-------------------------------------------------
clfsvm = LinearCSVMC()
rfesvm = RFE(clfsvm.get_sensitivity_analyzer(postproc=maxofabs_sample()),
CrossValidation(clfsvm, NFoldPartitioner(), errorfx=mean_mismatch_error,
postproc=mean_sample()), Repeater(2), fselector=FractionTailSelector(0.30,
mode='select', tail='upper'),
stopping_criterion=NBackHistoryStopCrit(BestDetector(), 10),
update_sensitivity=True)
fclfsvm = FeatureSelectionClassifier(clfsvm, rfesvm)
sensanasvm = fclfsvm.get_sensitivity_analyzer(postproc=maxofabs_sample())
cv_sensana_svm = RepeatedMeasure(sensanasvm, NFoldPartitioner())
senssvm = cv_sensana_svm(fds)
#-------------------------------------------------
However, after a while I get the following error:
RuntimeError: Cannot reverse-map data since the original data shape is unknown.
Either set `dshape` in the constructor, or call train().
Thank you in advance for any help!
Best wishes,
Marco
--
Marco Tettamanti, Ph.D.
Nuclear Medicine Department & Division of Neuroscience
San Raffaele Scientific Institute
Via Olgettina 58
I-20132 Milano, Italy
Phone ++39-02-26434888
Fax ++39-02-26434892
Email: [email protected]
Skype: mtettamanti
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