[pymvpa] regression analysis to predict subject-specific score
David V. Smith
david.v.smith at duke.edu
Thu Mar 8 20:19:44 UTC 2012
Hi,
I was trying to apply this code to one of my datasets, but it fails with a FailedToPredictError. I can get the code to work fine with the testing_dataset below, so I'm not sure what's wrong with my data. The data sets seem comparable, but I suspect I am missing something small...
Thanks,
David
In [90]: print summary(dataset_roi)
Dataset: 217x81 at float32, <sa: chunks,targets,time_coords,time_indices>, <fa: voxel_indices>, <a: imghdr,imgtype,mapper,voxel_dim,voxel_eldim>
stats: mean=-0.0284916 std=0.949083 var=0.900759 min=-3.14012 max=3.75654
No details due to large number of targets or chunks. Increase maxc and maxt if desiredNumber of unique targets > 20 thus no sequence statistics
In [87]: clf = SVM(svm_impl='NU_SVR',kernel= RbfLSKernel())
In [88]: cv = CrossValidation(clf, NFoldPartitioner(), postproc=mean_sample(), errorfx=corr_error, enable_ca=['training_stats', 'stats'])
In [89]: print cv(dataset_roi)
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (114, 0))
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (7, 0))
---------------------------------------------------------------------------
FailedToPredictError Traceback (most recent call last)
/mnt/BIAC/.users/smith/munin.dhe.duke.edu/Huettel/Imagene.02/Analysis/Framing/MVPA/<ipython-input-89-fa2a8d3342c0> in <module>()
----> 1 print cv(dataset_roi)
/usr/lib64/python2.6/site-packages/mvpa2/base/learner.pyc in __call__(self, ds)
235 "used and auto training is disabled."
236 % str(self))
--> 237 return super(Learner, self).__call__(ds)
238
239
/usr/lib64/python2.6/site-packages/mvpa2/base/node.pyc in __call__(self, ds)
74
75 self._precall(ds)
---> 76 result = self._call(ds)
77 result = self._postcall(ds, result)
78
/usr/lib64/python2.6/site-packages/mvpa2/measures/base.pyc in _call(self, ds)
470 # always untrain to wipe out previous stats
471 self.untrain()
--> 472 return super(CrossValidation, self)._call(ds)
473
474
/usr/lib64/python2.6/site-packages/mvpa2/measures/base.pyc in _call(self, ds)
303 ca.datasets.append(sds)
304 # run the beast
--> 305 result = node(sds)
306 # callback
307 if not self._callback is None:
/usr/lib64/python2.6/site-packages/mvpa2/base/learner.pyc in __call__(self, ds)
235 "used and auto training is disabled."
236 % str(self))
--> 237 return super(Learner, self).__call__(ds)
238
239
/usr/lib64/python2.6/site-packages/mvpa2/base/node.pyc in __call__(self, ds)
74
75 self._precall(ds)
---> 76 result = self._call(ds)
77 result = self._postcall(ds, result)
78
/usr/lib64/python2.6/site-packages/mvpa2/measures/base.pyc in _call(self, ds)
558 for i in dstrain.get_attr(splitter.get_space())[0].unique])
559 # ask splitter for first part
--> 560 measure.train(dstrain)
561 # cleanup to free memory
562 del dstrain
/usr/lib64/python2.6/site-packages/mvpa2/base/learner.pyc in train(self, ds)
135
136 # and post-proc
--> 137 result = self._posttrain(ds)
138
139 # finally flag as trained
/usr/lib64/python2.6/site-packages/mvpa2/clfs/base.pyc in _posttrain(self, dataset)
265 # training_stats... sad
266 self.__changedData_isset = False
--> 267 predictions = self.predict(dataset)
268 self.ca.reset_changed_temporarily()
269 self.ca.training_stats = self.__summary_class__(
/usr/lib64/python2.6/site-packages/mvpa2/clfs/base.pyc in wrap_samples(obj, data, *args, **kwargs)
46 def wrap_samples(obj, data, *args, **kwargs):
47 if is_datasetlike(data):
---> 48 return fx(obj, data, *args, **kwargs)
49 else:
50 return fx(obj, Dataset(data), *args, **kwargs)
/usr/lib64/python2.6/site-packages/mvpa2/clfs/base.pyc in predict(self, dataset)
422 raise FailedToPredictError, \
423 "Failed to convert predictions from numeric into " \
--> 424 "literals: %s" % e
425
426 self._postpredict(dataset, result)
FailedToPredictError: Failed to convert predictions from numeric into literals: 89.333273242895899
On Aug 11, 2011, at 12:00 PM, Yaroslav Halchenko wrote:
> indeed we have no good tutorial/example for regressions yet besides one
> for GPR
>
> doc/examples/gpr.py
>
> also we interface to SVR regressions (from libsvm and shogun) and started
> to add interfaces to scikits.learn. Some samples of them are available from
> regrswh, so on my system:
>
> *In [5]: print '\n'.join([str(x) for x in regrswh[:]])
> <libsvm epsilon-SVR>
> <libsvm nu-SVR>
> <sg.LinSVMR()/libsvr>
> <skl.PLSRegression_1d()>
> <skl.LARS()>
> <skl.LassoLARS()>
> <GPR(kernel='linear')>
> <GPR(kernel='sqexp')>
>
> As for "howto" -- just the same way you use classifiers -- then ConfusionMatrix
> in .stats would be replaced with RegressionStatistics. e.g.
>
> In [5]: from mvpa.suite import *
>
> In [6]: from mvpa.testing.datasets import datasets as testing_datasets
>
> In [7]: cve = CrossValidation(regrswh[:][0], NFoldPartitioner(), postproc=mean_sample(), errorfx=corr_error, enable_ca=['training_stats', 'stats'])
>
> In [8]: print cve(testing_datasets['chirp_linear'])
> <Dataset: 1x1 at float64, <sa: cvfolds>>
>
> In [9]: print cve.ca.stats
> Statistics Mean Std Min Max
> ---------- ----- ----- ----- -----
> Data:
> RMP_t 0.668 0.015 0.639 0.681
> STD_t 0.661 0.015 0.631 0.675
> RMP_p 0.644 0.043 0.593 0.731
> STD_p 0.637 0.042 0.583 0.721
> Results:
> CCe 0.06 0.016 0.036 0.084
> RMSE 0.232 0.027 0.184 0.266
> RMSE/RMP_t 0.348 0.043 0.27 0.4
> Summary:
> CCe 0.06 p= 3.65268e-137
> RMSE 0.23
> RMSE/RMP_t 0.35
> # of sets 6
>
>
> On Mon, 08 Aug 2011, Zhen Zonglei wrote:
>
>> Hi Guys:
>
>
>
>> I am trying to do multivariate regression analysis to predict
>> subject-specific score with pymvpa 0.6. But, I did not find some
>> examples about this in the manual. What regressions are implemented in
>> the toolbox? Could you please show me how to do regression analysis in
>> the toolbox?
>
>
>> Best
>
>
>> Zonglei Zhen
>
>> _______________________________________________
>> Pkg-ExpPsy-PyMVPA mailing list
>> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
>> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
>
>
> --
> =------------------------------------------------------------------=
> Keep in touch www.onerussian.com
> Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic
>
> _______________________________________________
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