I am running through the hyperalignment tutorial (http://www.pymvpa.org/examples/hyperalignment.html), but am getting errors while running Searchlight Hyperalignment code. The error output is pasted below. To clarify, I am using the given tutorial dataset (hyperalignment_tutorial_data.hdf5.gz) and went through the first few parts up until “Comparing the Results”.
In addition, I am getting a similar error with the “Regularized Hyperalignment” code. And a value error is thrown with “Similarity Structures” and the error output is pasted below as well.
Is there part of the setup I’m missing? Thank you in advance!
Error output from “Searchlight Hyperalignment” and “Regularized Hyperalignment” (https://docs.google.com/document/d/1Z8LKwz4795mNmZTK07GhLfgziB0Qd3OILeheLRzRQt4/edit?usp=sharing). An excerpt is included below:
Performing classification analyses...
between-subject (searchlight hyperaligned)...
---------------------------------------------------------------------------
DegenerateInputError Traceback (most recent call last)
<ipython-input-175-b8ec4e405550> in <module>()
25 # Searchlight Hyperalignment returns a list of mappers corresponding to
26 # subjects in the same order as the list of datasets we passed in.
---> 27 slhypmaps = slhyper(ds_train)
global slhypmaps = undefined
global slhyper = SearchlightHyperalignment(sparse_radius=3, featsel=0.4, exclude_from_model=[], nblocks=1)
…
/anaconda2/envs/pymvaenv/lib/python2.7/site-packages/mvpa2/base/learner.pyc in train(self=ZScoreMapper(), ds=Dataset(array([], shape=(0, 24), dtype=float64),...ems=[]), a=DatasetAttributesCollection(items=[])))
120 if got_ds and (ds.nfeatures == 0 or len(ds) == 0):
121 raise DegenerateInputError(
--> 122 "Cannot train learner on degenerate data %s" % ds)
ds = Dataset(array([], shape=(0, 24), dtype=float64), sa=SampleAttributesCollection(items=[]), fa=FeatureAttributesCollection(items=[]), a=DatasetAttributesCollection(items=[]))
123 if __debug__:
124 debug(
DegenerateInputError: Cannot train learner on degenerate data <Dataset: 0x24@float64>
Error output for “Similarity Structures”:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-174-42943870f9c3> in <module>()
2 anova = OneWayAnova()
3 fscores = [anova(sd) for sd in ds_all]
----> 4 fscores = np.mean(np.asarray(vstack(fscores)), axis=0)
global fscores = [Dataset(array([[ inf, inf, inf, ..., inf, -inf, inf]]), sa=SampleAttributesCollection(items=[]), fa=FeatureAttributesCollection(items=[ArrayCollectable(name='fprob', doc=None, value=array([nan, nan, nan, ..., nan, nan, nan]), length=58861)]), a=DatasetAttributesCollection(items=[])), Dataset(array([[ inf, inf, -inf, ..., -inf, inf, inf]]), sa=SampleAttributesCollection(items=[]), fa=FeatureAttributesCollection(items=[ArrayCollectable(name='fprob', doc=None, value=array([nan, nan, nan, ..., nan, nan, nan]), length=60327)]), a=DatasetAttributesCollection(items=[]))]
global np.mean = <function mean at 0x10d9b4b90>
global np.asarray = <function asarray at 0x10d9a3b18>
global vstack = <function vstack at 0x10e06b7d0>
global axis = undefined
5 # apply to full datasets
6 ds_fs = [sd[:, fselector(fscores)] for sd in ds_all]
/anaconda2/envs/pymvaenv/lib/python2.7/site-packages/mvpa2/base/dataset.pyc in vstack(datasets=[Dataset(array([[ inf, inf, inf, ..., inf, -in...8861)]), a=DatasetAttributesCollection(items=[])), Dataset(array([[ inf, inf, -inf, ..., -inf, in...0327)]), a=DatasetAttributesCollection(items=[]))], a=None, fa='drop_nonunique')
744 "datasets have varying attributes.")
745 # will puke if not equal number of features
--> 746 stacked_samp = np.concatenate([ds.samples for ds in datasets], axis=0)
stacked_samp = undefined
global np.concatenate = <built-in function concatenate>
ds.samples = array([[ inf, inf, -inf, ..., -inf, inf, inf]])
ds = Dataset(array([[ inf, inf, -inf, ..., -inf, inf, inf]]), sa=SampleAttributesCollection(items=[]), fa=FeatureAttributesCollection(items=[ArrayCollectable(name='fprob', doc=None, value=array([nan, nan, nan, ..., nan, nan, nan]), length=60327)]), a=DatasetAttributesCollection(items=[]))
datasets = [Dataset(array([[ inf, inf, inf, ..., inf, -inf, inf]]), sa=SampleAttributesCollection(items=[]), fa=FeatureAttributesCollection(items=[ArrayCollectable(name='fprob', doc=None, value=array([nan, nan, nan, ..., nan, nan, nan]), length=58861)]), a=DatasetAttributesCollection(items=[])), Dataset(array([[ inf, inf, -inf, ..., -inf, inf, inf]]), sa=SampleAttributesCollection(items=[]), fa=FeatureAttributesCollection(items=[ArrayCollectable(name='fprob', doc=None, value=array([nan, nan, nan, ..., nan, nan, nan]), length=60327)]), a=DatasetAttributesCollection(items=[]))]
global axis = undefined
747
748 stacked_sa = {}
ValueError: all the input array dimensions except for the concatenation axis must match exactly