[pymvpa] Error when using pearsonr with ridgeregression

[Thomas Nickson]

Hello,

I'm trying to use pearsonr to crossvalidate a ridge regression on some
structural data.

My code is:

#define the classifier
from mvpa2.clfs.gnb import GNB
from mvpa2.clfs.svm import LinearCSVMC, RbfCSVMC, RbfNuSVMC, LinearNuSVMC
from mvpa2.clfs.meta import FeatureSelectionClassifier
from mvpa2.clfs.warehouse import SensitivityBasedFeatureSelection,
FractionTailSelector, OneWayAnova
from mvpa2.clfs.ridge import RidgeReg


clf = RidgeReg()#LinearCSVMC()


#If some sort of feature selection is required use this to get a certain
percentage of the tail of an anova
"""
CLF = LinearCSVMC()
clf = FeatureSelectionClassifier(
        CLF,
        SensitivityBasedFeatureSelection(
            OneWayAnova(),
            FractionTailSelector(0.05, mode='select', tail='upper'))
        )
"""

#Create a partitioner that generates equal sized testing and training sets
that are balanced
from mvpa2.generators.partition import HalfPartitioner
from mvpa2.generators.resampling import Balancer
from mvpa2.generators.base import Sifter
from mvpa2.base.node import ChainNode

metaPartitioner = HalfPartitioner()#NFoldPartitioner(cvtype=2)
partitioner = ChainNode([metaPartitioner,
                         Sifter([('partitions', 2),
                         ('targets', [1, 2])]),
                         Balancer(attr='targets',
                                  count=1, # can set > 1 if you dont have
"enough"
                                  limit='partitions',
                                  apply_selection=True
                                 )],
                            space='partitions')

#Train the classifier

from mvpa2.generators.partition import NFoldPartitioner, HalfPartitioner

from mvpa2.measures.base import CrossValidation
from mvpa2.misc.errorfx import mean_match_accuracy, corr_error,
corr_error_prob, pearsonr
from mvpa2.mappers.fx import mean_sample

#use correlation error when using ridge regression
cv = CrossValidation(clf, partitioner, postproc=mean_sample(),
errorfx=pearsonr, enable_ca=['stats'])


the error is:

---------------------------------------------------------------------------ValueError
                               Traceback (most recent call
last)<ipython-input-22-0cdf8de46914> in <module>()      3 for dataset
in datasets:      4     print "Partition: " + dataset[0]----> 5
cv_results = cv(dataset[1]).samples      6     print cv_results      7
    resTup = (dataset[0],cv_results)
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-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
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-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
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/measures/base.pyc
in _call(self, ds)    495         # always untrain to wipe out
previous stats    496         self.untrain()--> 497         return
super(CrossValidation, self)._call(ds)    498     499
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/measures/base.pyc
in _call(self, ds)    324                 ca.datasets.append(sds)
325             # run the beast--> 326             result = node(sds)
  327             # callback    328             if not self._callback
is None:
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-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
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/base/node.pyc
in __call__(self, ds)    120         self._precall(ds)    121
result = self._call(ds)--> 122         result = self._postcall(ds,
result)    123     124         self.ca.calling_time = time.time() - t0
# set the calling_time
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/measures/base.pyc
in _postcall(self, dataset, result)    124         if self.__null_dist
is None:    125             # do base-class postcall and be done-->
126             result = super(Measure, self)._postcall(dataset,
result)    127         else:    128             # don't do a full
base-class postcall, only do the
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/base/node.pyc
in _postcall(self, ds, result)    164         """    165
result = self._pass_attr(ds, result)--> 166         result =
self._apply_postproc(ds, result)    167         return result    168
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/base/node.pyc
in _apply_postproc(self, ds, result)    238
"Applying post-processing node %s", (self.__postproc,))    239
    self.ca.raw_results = result--> 240             result =
self.__postproc(result)    241         return result    242
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-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
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/mappers/fx.pyc
in _call(self, ds)    582         if np.isscalar(err):    583
   err = np.array(err, ndmin=2)--> 584         return Dataset(err)
/home/orkney_01/tnickson/Programming/pyVirtualEnv/lib/python2.6/site-packages/mvpa2/base/dataset.pyc
in __init__(self, samples, sa, fa, a)    213         if not
hasattr(samples, 'dtype'):    214             raise ValueError(--> 215
                "AttrDataset only supports dtypes as samples that have
a "    216                 "`dtype` attribute that behaves similar to
the one of an "    217                 "array-like.")
ValueError: AttrDataset only supports dtypes as samples that have a
`dtype` attribute that behaves similar to the one of an array-like.


Anybody know what this error is?

Thanks,

Tom
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