Hmm, on second thought, I can't quite seem to get this to work. Trying the example from __test_matthias_question line for line, it throws a TypeError on classifier initialization.
TypeError: Unexpected keyword argument transformer=<ufunc 'absolute'> for BoostedClassifierSensitivityAnalyzer: 4 ca:ca{base_sensitivities raw_results null_t null_prob+}. Valid parameters are ['base_sensitivities', 'raw_results', 'null_t', 'null_prob'] wtf, right? Current date: 2010-11-01 17:43 PyMVPA: Version: 0.5.0.dev Path: /software/python/nipype0.3/lib/python2.6/site-packages/pymvpa-0.5.0.dev-py2.6-linux-x86_64.egg/mvpa/__init__.pyc Version control (GIT): GIT information could not be obtained due "/software/python/nipype0.3/lib/python2.6/site-packages/pymvpa-0.5.0.dev-py2.6-linux-x86_64.egg/mvpa/.. is not under GIT" SYSTEM: OS: posix Linux 2.6.31-22-generic #65-Ubuntu SMP Thu Sep 16 16:21:34 UTC 2010 Distribution: Ubuntu/9.10/karmic EXTERNALS: Present: cPickle, ctypes, good scipy.stats.rdist, good scipy.stats.rv_discrete.ppf, griddata, gzip, libsvm, libsvm verbosity control, lxml, matplotlib, nibabel, nose, numpy, pylab, pylab plottable, pywt, pywt wp reconstruct, running ipython env, scipy Absent: atlas_fsl, atlas_pymvpa, elasticnet, glmnet, h5py, hcluster, lars, mdp, mdp ge 2.4, nifti, nifti ge 0.20090205.1, nipy, openopt, pprocess, pywt wp reconstruct fixed, reportlab, rpy2, sg ge 0.6.4, sg ge 0.6.5, sg_fixedcachesize, shogun, shogun.krr, shogun.lightsvm, shogun.mpd, shogun.svrlight, weave Versions of critical externals: ipython : 0.10.1 scipy : 0.7.2 numpy : 1.4.1 ctypes : 1.1.0 matplotlib : 1.0.0 lxml : 2.3.beta1 numpy : 1.4.1 pywt : 0.2.0 Matplotlib backend: TkAgg RUNTIME: PyMVPA Environment Variables: PyMVPA Runtime Configuration: [externals] have griddata = yes have pprocess = no have good scipy.stats.rdist = yes have pylab plottable = yes have pywt wp reconstruct = yes have mdp = no have lxml = yes have running ipython env = yes have nibabel = yes have sg_fixedcachesize = no have elasticnet = no have shogun.mpd = no have matplotlib = yes have pywt wp reconstruct fixed = no have scipy = yes have reportlab = no have openopt = no have libsvm = yes have h5py = no have shogun.krr = no have nifti ge 0.20090205.1 = no have nose = yes have weave = no have atlas_fsl = no have ctypes = yes have hcluster = no have sg ge 0.6.4 = no have sg ge 0.6.5 = no have good scipy.stats.rv_discrete.ppf = yes have libsvm verbosity control = yes have mdp ge 2.4 = no have shogun.svrlight = no have shogun = no have nipy = no have glmnet = no have lars = no have nifti = no have atlas_pymvpa = no have cpickle = yes have numpy = yes have pylab = yes have rpy2 = no have shogun.lightsvm = no have pywt = yes have gzip = yes [general] verbose = 1 Process Information: Name: ipython State: R (running) Tgid: 18461 Pid: 18461 PPid: 12889 TracerPid: 0 Uid: 85302 85302 85302 85302 Gid: 33368 33368 33368 33368 FDSize: 256 Groups: 1007 1008 33368 33376 68218 68236 68252 777333 VmPeak: 372540 kB VmSize: 372512 kB VmLck: 0 kB VmHWM: 64688 kB VmRSS: 64684 kB VmData: 58408 kB VmStk: 244 kB VmExe: 2108 kB VmLib: 53604 kB VmPTE: 700 kB Threads: 2 SigQ: 0/16382 SigPnd: 0000000000000000 ShdPnd: 0000000000000000 SigBlk: 0000000000000000 SigIgn: 0000000001001000 SigCgt: 0000000180000002 CapInh: 0000000000000000 CapPrm: 0000000000000000 CapEff: 0000000000000000 CapBnd: ffffffffffffffff Cpus_allowed: ff Cpus_allowed_list: 0-7 Mems_allowed: 00000000,00000001 Mems_allowed_list: 0 voluntary_ctxt_switches: 2295 nonvoluntary_ctxt_switches: 33 On Mon, Nov 1, 2010 at 2:10 PM, Michael Waskom <mwas...@mit.edu> wrote: > Hi Yarik, > > Thanks! The pointer to that test is very helpful indeed. > > Best, > Mike > > > On Mon, Nov 1, 2010 at 2:02 PM, Yaroslav Halchenko <y...@dartmouth.edu>wrote: > >> Hi Mike, >> >> sorry that it causes you troble... but RFE is indeed somewhat difficult >> to figure out... >> >> but before anything: >> have you seen >> http://dev.pymvpa.org/featsel.html#recursive-feature-elimination >> >> also look may be at >> mvpa/tests/test_rfe.py: def __test_matthias_question(self): >> which has one of RFE structures >> >> in general classifier with RFE as feature selection should be treated as >> any other classifier >> >> also -- do you really think that RFE is necessary in your case? may be >> some simpler feature selection or SMLR would do? >> >> P.S. it would be of greater benefit to everyone if we continue this >> communication on the mailing list, if you don't mind of cause >> >> On Mon, 01 Nov 2010, Michael Waskom wrote: >> >> > Hey Yarik, >> >> > I'm trying to figure out how to use the RFE class in pymvpa 0.5dev. >> > Sadly, there doesn't seem to be much in the (otherwise >> > super-helpful) documentation about it. I get all of the parameters, >> > but I'm not quite sure how it might fit into a typical analysis, and >> > I'm very interested in trying out some recursive feature selection. >> Do >> > you have a simple use case you could put up as a gist (or elsewhere) >> so >> > I can get the basic idea? Nothing too didactic is required. >> >> > Thanks! >> >> > Mike >> >> -- >> Yaroslav O. Halchenko >> Postdoctoral Fellow, Department of Psychological and Brain Sciences >> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 >> Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 >> WWW: http://www.linkedin.com/in/yarik >> > >
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