Hello again,
Thanks for the quick response!
I'm using pyMVPA version 0.4.2 (which I installed last August). Should
I upgrade to 0.4.3? Is that as simple as " sudo port update py25-
pymvpa" ?
(I'm generally leery of changing versions mid-project, but I'll do it
if it will fix this problem).
As to the problem: your modification of my code (using
datasets['uni4large'] instead of my data ("PyDat")) gave an error, so
I take it that the problem isn't with my data set:
[RFEC] DBG: Step 0: nfeatures=20
[RFEC] DBG: Step 0: nfeatures=20 error=0.0833 best/stop=1/0
[RFEC] DBG: Step 1: nfeatures=3
[RFEC] DBG: Step 1: nfeatures=3 error=0.3333 best/stop=0/0
Traceback (most recent call last):
File "./bug_rfe.py", line 40, in <module>
Err = cvterr(datasets['uni4large'])
File "/opt/local/lib/python2.5/site-packages/mvpa/measures/
base.py", line 105, in __call__
result = self._call(dataset)
File "/opt/local/lib/python2.5/site-packages/mvpa/algorithms/
cvtranserror.py", line 173, in _call
result = transerror(split[1], split[0])
File "/opt/local/lib/python2.5/site-packages/mvpa/clfs/
transerror.py", line 1283, in __call__
self._precall(testdataset, trainingdataset)
File "/opt/local/lib/python2.5/site-packages/mvpa/clfs/
transerror.py", line 1239, in _precall
self.__clf.train(trainingdataset)
File "/opt/local/lib/python2.5/site-packages/mvpa/clfs/base.py",
line 354, in train
result = self._train(dataset)
File "/opt/local/lib/python2.5/site-packages/mvpa/clfs/meta.py",
line 1058, in _train
self.__testdataset)
File "/opt/local/lib/python2.5/site-packages/mvpa/featsel/rfe.py",
line 268, in __call__
wdataset = wdataset.selectFeatures(selected_ids)
File "/opt/local/lib/python2.5/site-packages/mvpa/datasets/
base.py", line 1018, in selectFeatures
new_data['samples'] = self._data['samples'][:, ids]
IndexError: index (4) out of range (0<=index<2) in dimension 1
print PyDat.summary() gives:
Dataset / float32 440 x 649
uniq: 8 chunks 3 labels
stats: mean=-3.86503e-09 std=0.999965 var=0.99993 min=-4.09558
max=4.98693
Counts of labels in each chunk:
chunks\labels 1.0 2.0 3.0
--- --- ---
1.0 19 18 18
2.0 18 19 18
3.0 18 19 18
4.0 18 19 18
5.0 18 19 18
6.0 18 18 19
7.0 18 19 18
8.0 19 18 18
Summary per label across chunks
label mean std min max #chunks
1 18.2 0.433 18 19 8
2 18.6 0.484 18 19 8
3 18.1 0.331 18 19 8
Summary per chunk across labels
chunk mean std min max #labels
1 18.3 0.471 18 19 3
2 18.3 0.471 18 19 3
3 18.3 0.471 18 19 3
4 18.3 0.471 18 19 3
5 18.3 0.471 18 19 3
6 18.3 0.471 18 19 3
7 18.3 0.471 18 19 3
8 18.3 0.471 18 19 3
Any further advice?
Cheers,
Mark
On Nov 24, 2009, at 10:42 AM, Yaroslav Halchenko wrote:
Lets return back to the mailing list ;) might be of interest to public
In general, classifier with RFE should behave as any other
classifier...
See attached your exact code to use RFE-ed classifier (I've just
changed
proportion to be removed to 50%, and enabling debugging output so
we could get some basic progress information from RFE). It runs
fine on
test datasets for binary and multiclass... So the problem might be
indeed in dataset somehow?
What does
print PyDat.summary()
say?
What version of pymvpa do you use btw?
as for
raise UnknownStateError("Unknown yet value of %s" %
(self.name))
mvpa.misc.exceptions.UnknownStateError: Exception: Unknown yet
value of
feature_ids
to access them you should have enabled 'feature_ids' of your
classifier,
not of the RFE per se (just as I've done in the attached script)
;)
--
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
<bug_rfe.py>
~~~~~~~~~~~~~~~~~~~~~~~~~~
Mark Lescroart
(say it LESS-qua)
University of Southern California
Neuroscience Graduate Program
Image Understanding Lab
Email: [email protected]
Cell: (213) 447-0752
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