[pymvpa] ENET error

Yaroslav Halchenko yoh at onerussian.com
Tue Sep 20 21:42:58 UTC 2016

On September 20, 2016 3:46:40 PM EDT, "Liang, Guangsheng" <guangsheng.liang at ttu.edu> wrote:
>Thank you very much for your kindly response.
>Which function that I can remove them?
>Thanks again!
>From: Pkg-ExpPsy-PyMVPA
>[mailto:pkg-exppsy-pymvpa-bounces+guangsheng.liang=ttu.edu at lists.alioth.debian.org]
>On Behalf Of Richard Dinga
>Sent: Tuesday, September 20, 2016 3:50 AM
>To: Development and support of PyMVPA
><pkg-exppsy-pymvpa at lists.alioth.debian.org>
>Subject: Re: [pymvpa] ENET error
>I guess you have invariant features in your dataset, therefore you will
>get problems when trying to divide by 0. There is a function to remove
>On Fri, Sep 16, 2016 at 8:01 PM, Liang, Guangsheng
><guangsheng.liang at ttu.edu<mailto:guangsheng.liang at ttu.edu>> wrote:
>Hello PyMVPA community,
>I am currently working on a fMRI data applying an ENET classifier.
>My PyMVPA is running under Linux environment, with python 2.7.12.
>The design of the data is a repeated measurement with pre and post
>In my understanding, in my case, chunk value should store subject ID,
>target value should store the time conditions.
>I am using the classifier code that I found in the maillist:
>clf = FeatureSelectionClassifier(\
>               ENET(lm=1.0,max_steps=500,trace=False,normalize=False),\
>                SensitivityBasedFeatureSelection(\
>                                CorrStability(),\
>          FixedNElementTailSelector(5000,mode='select',tail='upper')),\
>                descr="ENET on 5K best(CorrStability) features")
>I am also using 1000 times of permutation to test the null hypothesis,
>which are almost the same as those in the tutorial:
>(section: Avoiding the trap OR Advanced magic 101)
>However, errors suspend my program:
>RuntimeWarning: invalid value encountered in divide
>  covar = (dat1*dat2).mean(0) / (dat1.std(0) * dat2.std(0))
>RRuntimeWarning: Error in y - mu : non-numeric argument to binary
>  warnings.warn(x, RRuntimeWarning)
>Traceback (most recent call last):
>  File "15subj_enet.py", line 68, in <module>
>    err_fds = cv_mc_corr_fds(fds)
>line 258, in __call__
>    return super(Learner, self).__call__(ds)
>line 136, in __call__
>    self._precall(ds)
>line 120, in _precall
>    self.__null_dist.fit(measure, ds)
>line 427, in fit
>    % (measure, skipped))
>RuntimeError: Failed to obtain any value from <CrossValidation>. 1000
>measurements were skipped. Check above warnings, and your code/data
>I was wondering if someone could kindly explain what are those mean,
>and how do I resolve this?
>Thank you very much!
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