Thanks Michael,
At 10:15 AM 10/1/2012, Michael Hanke wrote:
> Errors:
> 'gcc' is not recognized as an internal or external command,
> operable program or batch file.
> C:\Python27\lib\site-packages\scipy\integrate\quadpack.py:288:
You don't have built pymvpa's extensions properly. How did you
install it.
I got it from Christoph Gohlke's excellent build
bot: http://www.lfd.uci.edu/~gohlke/pythonlibs/
He did have a ticket for msvc compile errors earlier:
https://github.com/PyMVPA/PyMVPA/pull/58
and might be amenable to debugging this build if we can help him in
the right direction.
Incidentally, I added gcc on this system, and the only errors are now:
C:\Python27\lib\site-packages\scipy\integrate\quadpack.py:288:
UserWarning: Extremely bad integrand behavior occurs at some points of the
integration interval.
warnings.warn(msg)
C:\Python27\lib\site-packages\mvpa2\misc\errorfx.py:106:
RuntimeWarning: invalid value encountered in divide
([0], np.cumsum(~t)/(~t).sum(dtype=np.float), [1]))
C:\Python27\lib\site-packages\scipy\stats\stats.py:274:
RuntimeWarning: invalid value encountered in double_scalars
return np.mean(x,axis)/factor
C:\Python27\lib\site-packages\mvpa2\misc\errorfx.py:102:
RuntimeWarning: invalid value encountered in divide
([0], np.cumsum(t)/t.sum(dtype=np.float), [1]))
Here is also the usual advice: "Don't do this at home!" On
windows
it is a lot more convenient to get the NeuroDebian virtual machine and
install PyMVPA inside -- shouldn't take you more than 5 min to set up
Downloading now for myself; the issue there is that I write code for
a small group of Windows7 users here, and I'd like to incorporate
mvpa with the usual Python exes I make for them. The 2nd best
alternative is to run it on Amazon's *NIX cloud service we use.
I'll also try the py2.6 version next.
.... <SNIP>
stats['FP'] /
(1.0*stats["P'"])
> C:\Python27\lib\site-packages\mvpa2\measures\anova.py:111:
> RuntimeWarning: invalid value encountered in divide
> msb = ssbn / float(dfbn)
>
Not sure why this happens -- could be specific to you input dataset.
attached, with a revised script
The runtime errors and the mvpa2 warning " Only 1 sets have estimates
assigned from 2 sets" looks like my data setup (?)
> WARNING: Obtained degenerate data with zero norm for training
of
> <LinearCSVMC>. Scaling of C cannot be done.
There seem to be a problem with the dataset. do you have invariant
features in it?
I don't belive so, the features are FFT peaks' power and f.
I added
mvpa2.datasets.miscfx.coarsen_chunks(ds, nchunks=4)
mvpa2.datasets.miscfx.remove_invariant_features(ds)
to the code, with no apparent change.
I do note that CART (tested prior on this set) will _always_ split
data even on junk, there is no guarantee that there are any useful
correlations in the data presented...
I'm looking at mlpy right now as well.
Best,
Ray
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SN15,Normal,9048.362008,2308.421085,408.9271238,160.3851086,82.37352773,13,19,33,48,43,8248.692149,3148.66456,486.8420244,473.8733314,352.9882684,13,20,3,33,38,19668.54338,3988.617486,1197.389779,1008.185212,841.7652758,12,34,2,48,70,40872.49059,3282.539163,1640.500915,985.9437457,985.8431828,12,34,6,2,48
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SN18,Normal,4319.323366,2762.24168,1785.658529,815.7001278,665.8870054,28,38,20,11,49,3340.178724,2003.722088,1495.402202,509.1106411,193.1125021,28,38,18,50,11,1942.409023,1355.560635,1147.915454,256.7677288,255.9024442,22,15,10,56,52,4282.504844,2300.568862,421.196901,310.8636376,304.5124522,22,11,38,57,53
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SN20,Normal,2515.798986,2462.686149,2271.438477,2052.165786,1644.380604,26,30,45,40,49,4736.779188,3524.186969,3394.963339,3049.692155,2328.004149,30,44,25,40,49,8276.826344,7238.918469,2869.583029,2535.953777,709.7072603,24,28,14,33,38,12233.11341,3164.139744,2078.375336,1999.32259,431.2510501,27,14,41,46,54
from mvpa2.tutorial_suite import *
d = file(r"C:\temp\test3_redo_trim.csv").readlines()
lol= [x[:-1].split(",") for x in d]
#print lol[0]
## the list of subject names
subjects = [r[0] for r in lol]
## the feature data
dat = [[float(c) for c in row[2:]] for row in lol]
## the truth values
labels = [r[1] for r in lol]
ds = Dataset(samples=dat)
ds.sa['subject'] = subjects
ds.sa['targets'] = labels
ds.sa['chunks'] = np.arange(len(ds))
mvpa2.datasets.miscfx.coarsen_chunks(ds, nchunks=4)
mvpa2.datasets.miscfx.remove_invariant_features(ds)
print mvpa2.datasets.miscfx.summary(ds), '\n'
clf = LinearCSVMC()
cvte = CrossValidation(clf, HalfPartitioner(count=2,
selection_strategy='random', attr='subject'),
errorfx=lambda p, t: np.mean(p == t), enable_ca=['stats'])
cv_results = cvte(ds)
#print 'cvte.ca.stats', cvte.ca.stats.as_string(description=True)
print cvte.ca.stats.matrix
clf = FeatureSelectionClassifier(
kNN(k=5),
SensitivityBasedFeatureSelection(
SMLRWeights(SMLR(lm=1.0), postproc=maxofabs_sample()),
FixedNElementTailSelector(1, tail='upper', mode='select')))
clf.train(ds)
print '\nkNN', len(clf.mapper.slicearg)
final_dataset = clf.mapper.forward(ds)
print final_dataset
#print 'cvte.ca.stats', cvte.ca.stats.as_string(description=True)
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