Hi Folks:
I've sort of tracked down a bug in regression classifiers when used as
part of a feature selection classifier wherein the code that saves the
results as the values and then converts the results into predictions
gets called twice, which means that the results (which are values for
regressions) get turned into predictions and then copied to the
values, so that there are no values left. You can get reproduce the
error using this classifier:
# define some classifiers
anova_keep = 1000
tokeep = 25
# ANOVA feature selection
anova_select = SensitivityBasedFeatureSelection(
OneWayAnova(),
FixedNElementTailSelector(anova_keep,mode='select',tail='upper'))
# LARS classifier
lars = LARS(trace=False, max_steps=tokeep)
lars.states.enable('values')
#lars.states.disable('trained_labels')
anova_lars = FeatureSelectionClassifier(
lars,
anova_select,
descr="LinSVM on %d(ANOVA)" % (tokeep))
anova_lars.states.enable('values')
I'm hoping someone knows a good way to fix this...
Thanks,
Per
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