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<p class="MsoNormal">Hello PyMVPA community,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I am currently working on a fMRI data applying an ENET classifier.
<o:p></o:p></p>
<p class="MsoNormal">My PyMVPA is running under Linux environment, with python 2.7.12.
<o:p></o:p></p>
<p class="MsoNormal">The design of the data is a repeated measurement with pre and post conditions. <o:p></o:p></p>
<p class="MsoNormal">In my understanding, in my case, chunk value should store subject ID, target value should store the time conditions.
<o:p></o:p></p>
<p class="MsoNormal">I am using the classifier code that I found in the maillist:
<a href="http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/2009q1/000412.html">
http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/2009q1/000412.html</a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">clf = FeatureSelectionClassifier(\<o:p></o:p></p>
<p class="MsoNormal"> ENET(lm=1.0,max_steps=500,trace=False,normalize=False),\<o:p></o:p></p>
<p class="MsoNormal"> SensitivityBasedFeatureSelection(\<o:p></o:p></p>
<p class="MsoNormal"> CorrStability(),\<o:p></o:p></p>
<p class="MsoNormal"> FixedNElementTailSelector(5000,mode='select',tail='upper')),\<o:p></o:p></p>
<p class="MsoNormal"> descr="ENET on 5K best(CorrStability) features")<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I am also using 1000 times of permutation to test the null hypothesis, which are almost the same as those in the tutorial:
<a href="http://www.pymvpa.org/tutorial_significance.html#the-following-content-is-incomplete-and-experimental">
http://www.pymvpa.org/tutorial_significance.html#the-following-content-is-incomplete-and-experimental</a> (section: Avoiding the trap OR Advanced magic 101)<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">However, errors suspend my program:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-left:.5in">/lustre/work/apps/anaconda/lib/python2.7/site-packages/mvpa2/measures/corrstability.py:94: RuntimeWarning: invalid value encountered in divide<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> covar = (dat1*dat2).mean(0) / (dat1.std(0) * dat2.std(0))<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in">/lustre/work/apps/anaconda/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:185: RRuntimeWarning: Error in y - mu : non-numeric argument to binary operator<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> warnings.warn(x, RRuntimeWarning)<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in">Traceback (most recent call last):<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> File "15subj_enet.py", line 68, in <module><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> err_fds = cv_mc_corr_fds(fds)<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> File "/lustre/work/apps/anaconda/lib/python2.7/site-packages/mvpa2/base/learner.py", line 258, in __call__<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> return super(Learner, self).__call__(ds)<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> File "/lustre/work/apps/anaconda/lib/python2.7/site-packages/mvpa2/base/node.py", line 136, in __call__<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> self._precall(ds)<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> File "/lustre/work/apps/anaconda/lib/python2.7/site-packages/mvpa2/measures/base.py", line 120, in _precall<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> self.__null_dist.fit(measure, ds)<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> File "/lustre/work/apps/anaconda/lib/python2.7/site-packages/mvpa2/clfs/stats.py", line 427, in fit<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"> % (measure, skipped))<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in">RuntimeError: Failed to obtain any value from <CrossValidation>. 1000 measurements were skipped. Check above warnings, and your code/data<o:p></o:p></p>
<p class="MsoNormal" style="margin-left:.5in"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I was wondering if someone could kindly explain what are those mean, and how do I resolve this?<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Thank you very much!<o:p></o:p></p>
<p class="MsoNormal">Carl<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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