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--></style></head><body bgcolor=white lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:11.0pt;font-family:Calibri'>I have four classes, so I can’t just use {0,1}. It looks like no matter what the sample attributes of the input dataset are, MulticlassClassifier will send {-1,1} to the classifier. <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:Calibri'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:Calibri'>It looks like I need to either modify what MulticlassClassifier is sending to PLR, or change PLR to handle other labels.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:Calibri'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:Calibri'><o:p> </o:p></span></p><div style='border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0in 0in 0in'><p class=MsoNormal><b><span style='font-family:Calibri;color:black'>From: </span></b><span style='font-family:Calibri;color:black'>Pkg-ExpPsy-PyMVPA <pkg-exppsy-pymvpa-bounces+mortonne=gmail.com@lists.alioth.debian.org> on behalf of Swaroop Guntupalli <swaroopgj@gmail.com><br><b>Reply-To: </b>Development and support of PyMVPA <pkg-exppsy-pymvpa@lists.alioth.debian.org><br><b>Date: </b>Wednesday, March 1, 2017 at 12:38 PM<br><b>To: </b>Development and support of PyMVPA <pkg-exppsy-pymvpa@lists.alioth.debian.org><br><b>Subject: </b>Re: [pymvpa] Using PLR with multiclass<o:p></o:p></span></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Does it help if you assign targets in your sample attributes to {0,1} instead?<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p><div><p class=MsoNormal>On Wed, Mar 1, 2017 at 1:13 PM, Neal Morton <<a href="mailto:mortonne@gmail.com" target="_blank">mortonne@gmail.com</a>> wrote:<o:p></o:p></p><blockquote style='border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-right:0in'><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>Is there a way to use PLR with a multiclass problem? When I try to use the MulticlassClassifier, that produces binary labels with -1 and 1. But PLR expects 0 and 1.</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>The classifier is created like so:</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>from mvpa2.clfs.plr import PLR</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>from mvpa2.clfs.meta import MulticlassClassifier</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>clf = MulticlassClassifier(PLR(lm=10))</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>When I try to train the classifier on an fMRI dataset with four classes, I get:</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>Traceback (most recent call last):</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/home1/03206/mortonne/analysis/bender/mvpa/loc_xval_diag.py", line 98, in <module></span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> clf.train(ds1)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/base/learner.py", line 137, in train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> self._train(ds)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/clfs/meta.py", line 1165, in _train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> CombinedClassifier._train(self, dataset)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/clfs/meta.py", line 628, in _train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> BoostedClassifier._train(self, dataset)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/clfs/meta.py", line 118, in _train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> clf.train(dataset)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/base/learner.py", line 137, in train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> self._train(ds)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/clfs/meta.py", line 1053, in _train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> self.clf.train(datasetselected)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/base/learner.py", line 137, in train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> self._train(ds)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> File "/work/IRC/ls5/lib/python2.7/site-packages/mvpa2/clfs/plr.py", line 76, in _train</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> %(set(d),)</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>ValueError: Regressors for logistic regression should be [0,1]. Got set([1, -1])</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>I have the latest versions of meta.py and plr.py. There was a <a href="https://github.com/PyMVPA/PyMVPA/issues/37" target="_blank">change</a> in PLRWeights to support different values of labels, but that change doesn’t seem to have affected the PLR classifier.</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>Thanks,</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'>Neal</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-size:11.0pt'> </span><o:p></o:p></p></div></div><p class=MsoNormal><br>_______________________________________________<br>Pkg-ExpPsy-PyMVPA mailing list<br><a href="mailto:Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org">Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org</a><br><a href="http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa" target="_blank">http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa</a><o:p></o:p></p></blockquote></div><p class=MsoNormal><o:p> </o:p></p></div><p class=MsoNormal>_______________________________________________ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa<o:p></o:p></p></div></body></html>