**Dear Yaroslav,
thank you very much for your reply. I have made several attempts, trying to
guess a solution, but it seems I always get a
'TypeError: 'NoneType' object is not callable'.
Any further advice is greatly appreciated!
Best,
Marco
Case 1:
slght = sphere_gnbsearchlight(clf, partitioner, radius=slradius,
space='voxel_indices', errorfx=None, postproc=mean_sample())
slght_map = slght(fds)
In [70]: slght = sphere_gnbsearchlight(clf, partitioner, radius=slradius,
space='voxel_indices', errorfx=None, postproc=mean_sample())
In [71]: slght_map = slght(fds)
[SLC] DBG: Phase 1. Initializing partitions using
<NFoldPartitioner> on <Dataset: 108x111@float32, <sa:
chunks,targets,time_coords,time_indices>, <fa: voxel_indices>, <a:
imgaffine,imghdr,imgtype,mapper,voxel_dim,voxel_eldim>>
[SLC] DBG: Phase 2. Blocking data for 18 splits and 3 labels
[SLC] DBG: Phase 3. Computing statistics for 54 blocks
[SLC] DBG: Phase 4. Deducing neighbors information for 111 ROIs
[SLC] DBG: Phase 4b. Converting neighbors to sparse matrix
representation
[SLC] DBG: Phase 5. Major loop
[SLC] DBG: Split 0 out of 18
[SLC] DBG: 'Training' is done
[SLC] DBG: Doing 'Searchlight'
[SLC] DBG: Assessing accuracies
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-71-1146d298ca06> in <module>()
----> 1 slght_map = slght(fds)
/usr/lib/python2.7/dist-packages/mvpa2/base/learner.pyc in __call__(self, ds)
257 "used and auto training is
disabled."
258 % str(self))
--> 259 return super(Learner, self).__call__(ds)
260
261
/usr/lib/python2.7/dist-packages/mvpa2/base/node.pyc in __call__(self, ds)
119
120 self._precall(ds)
--> 121 result = self._call(ds)
122 result = self._postcall(ds, result)
123
/usr/lib/python2.7/dist-packages/mvpa2/measures/searchlight.pyc in
_call(self, dataset)
141
142 # pass to subclass
--> 143 results = self._sl_call(dataset, roi_ids, nproc)
144
145 if 'mapper' in dataset.a:
/usr/lib/python2.7/dist-packages/mvpa2/measures/adhocsearchlightbase.pyc in
_sl_call(self, dataset, roi_ids, nproc)
513 # error functions without a chance to screw up
514 for i, fpredictions in enumerate(predictions.T):
--> 515 results[isplit, i] = errorfx(fpredictions,
targets)
516
517
TypeError: 'NoneType' object is not callable
Similarly for other cases and combinations of them:
Case 2:
slght = sphere_gnbsearchlight(clf, partitioner, radius=slradius,
space='voxel_indices', errorfx=ConfusionMatrixError(), postproc=mean_sample())
slght_map = slght(fds)
Case3:
class KeepConfusionMatrix(Node):
def _call(self, fds):
out = np.zeros(1, dtype=object)
out[0] = (fds.samples)
return out
slght = sphere_gnbsearchlight(clf, partitioner, errorfx=None, radius=slradius,
space='voxel_indices', postproc=ChainNode([Confusion(labels=fds.UT)]))
slght.postproc.append(KeepConfusionMatrix())
slght_map = slght(fds)
Case4:
class KeepConfusionMatrix(Node):
def _call(self, fds):
out = np.zeros(1, dtype=object)
out[0] = (fds.samples)
return out
slght = sphere_gnbsearchlight(clf, partitioner, errorfx=None, radius=slradius,
space='voxel_indices', postproc=ChainNode([mean_sample(),Confusion(labels=fds.UT)]))
slght.postproc.append(KeepConfusionMatrix())
slght_map = slght(fds)
Case5:
class KeepConfusionMatrix(Node):
def _call(self, fds):
out = np.zeros(1, dtype=object)
out[0] = (fds.samples)
return out
slght = sphere_gnbsearchlight(clf, partitioner, errorfx=ConfusionMatrixError(),
radius=slradius, space='voxel_indices',
postproc=ChainNode([mean_sample(),Confusion(labels=fds.UT)]))
slght.postproc.append(KeepConfusionMatrix())
slght_map = slght(fds)
Yaroslav Halchenko debian at onerussian.com
Fri Aug 28 13:16:38 UTC 2015
quick an possible partial reply
1. "not sure" -- if it pukes then probably not, although judging from
the code I foresaw arbitrary shape of the errorfx output
2. but you could make sphere_gnbsearchlight to return labels (not
errors) and then post-process to get those confusion matrices. Just
specify errorfx=None to it (not to CV). But you could also try
passing errorfx=ConfusionMatrixError and see how that goes
Please share what you discover/end up with.
mvpa2/tests/test_usecases.py has more of usecase demos for gnb
searchlights which might come handy
--
Yaroslav O. Halchenko, Ph.D.
http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
Research Scientist, Psychological and Brain Sciences Dept.
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
On 08/28/2015 01:48 PM, marco tettamanti wrote:
Dear all,
is it possible to obtain confusion matrices for all nodes with
"sphere_gnbsearchlight", as was suggested before with "sphere_searchlight":
slcvte = CrossValidation(clf, partitioner, errorfx=None,
postproc=ChainNode([Confusion(labels=fds.UT)]))
class KeepConfusionMatrix(Node):
def _call(self, fds):
out = np.zeros(1, dtype=object)
out[0] = (fds.samples)
return out
slcvte.postproc.append(KeepConfusionMatrix())
slght = sphere_searchlight(slcvte, radius=slradius, space='voxel_indices',
nproc=4, postproc=mean_sample())
slght_map = slght(fds)
Thank you and best wishes,
Marco
--
Marco Tettamanti, Ph.D.
Nuclear Medicine Department & Division of Neuroscience
San Raffaele Scientific Institute
Via Olgettina 58
I-20132 Milano, Italy
Phone ++39-02-26434888
Fax ++39-02-26434892
Email:tettamanti.ma...@hsr.it
Skype: mtettamanti
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