BF travis + minor typo fix #534

yarikoptic · 2017-09-07T01:31:07Z

No description provided.

* origin/master: fix spelling

coveralls · 2017-09-07T02:40:15Z

Changes Unknown when pulling ccda075 on yarikoptic:bf-travis into ** on PyMVPA:master**.

nno · 2017-09-09T11:59:28Z

We have some failures at travis - full output below:

Current date:   2017-09-07 01:46
PyMVPA:
 Version:       2.6.1.dev1
 Hash:          $Format:%H$
 Path:          /home/travis/build/PyMVPA/PyMVPA/mvpa2/__init__.py
 Version control (GIT):
  Status:
   HEAD detached at FETCH_HEAD
   Untracked files:
     (use "git add <file>..." to include in what will be committed)
   
   	R-libs/
   	erlang-R14B04-nonroot.tar.bz2
   	report/
   	src/
   
   nothing added to commit but untracked files present (use "git add" to track)
  Reference:
   7fdf8915c81abdcdc12796c6f5dfc64882e2e56d HEAD
   1fe95f5628f351be46bd8defcf39a3743aeb7269 refs/remotes/origin/HEAD
  Difference from last release 2.6.1.dev1:
   fatal: ambiguous argument 'upstream/2.6.1.dev1...': unknown revision or path not in the working tree.
   Use '--' to separate paths from revisions, like this:
   'git <command> [<revision>...] -- [<file>...]'
SYSTEM:
 OS:            posix Linux 4.4.0-83-generic #106~14.04.1-Ubuntu SMP Mon Jun 26 18:10:19 UTC 2017
 Distribution:  Ubuntu/14.04/trusty
EXTERNALS:
 Present:       cPickle, cran-energy, ctypes, elasticnet, good scipy.stats.rv_continuous._reduce_func(floc,fscale), good scipy.stats.rv_discrete.ppf, griddata, gzip, h5py, hdf5, ipython, joblib, lars, liblapack.so, libsvm, libsvm verbosity control, lxml, mass, matplotlib, mdp, mdp ge 2.4, mock, nibabel, nipy, nose, numpy, numpy_correct_unique, pprocess, pylab, pylab plottable, pywt, pywt wp reconstruct, reportlab, rpy2, scipy, scipy.weave, skl, statsmodels
 Absent:        afni-3dinfo, atlas_fsl, atlas_pymvpa, datalad, glmnet, good scipy.stats.rdist, hcluster, nipy.neurospin, numpydoc, openopt, pywt wp reconstruct fixed, running ipython env, sg ge 0.6.4, sg ge 0.6.5, sg_fixedcachesize, shogun, shogun.krr, shogun.lightsvm, shogun.mpd, shogun.svmocas, shogun.svrlight, weave
 Versions of critical externals:
  ctypes      : 1.1.0
  h5py        : 2.2.1
  hdf5        : 1.8.11
  ipython     : 1.2.1
  joblib      : 0.10.3
  lxml        : 3.3.3
  matplotlib  : 1.3.1
  mdp         : 3.5
  mock        : 1.0.1
  nibabel     : 2.1.0
  nipy        : 0.4.1
  numpy       : 1.8.2
  pprocess    : 0.5
  reportlab   :  $Id$ 
  rpy2        : 2.3.9
  scipy       : 0.13.3
  skl         : 0.18.1
 Matplotlib backend: agg
RUNTIME:
 PyMVPA Environment Variables:
  MVPA_DEBUG_OUTPUT   : "/dev/null"
  MVPA_TESTS_VERBOSITY: "2"
  PYTHONPATH          : ".:"
  MVPA_EXTERNALS_RAISE_EXCEPTION: "off"
  MVPA_MATPLOTLIB_BACKEND: "agg"
  PYTHON_CONFIGURE_OPTS: "--enable-unicode=ucs4 --with-wide-unicode --enable-shared --enable-ipv6 --enable-loadable-sqlite-extensions --with-computed-gotos"
  MVPA_TESTS_WTF      : "1"
  PYTHON_CFLAGS       : "-g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security"
  PYTHON              : "python"
  MVPA_DEBUG_METRICS  : "all"
  MVPA_TESTS_LABILE   : "no"
 PyMVPA Runtime Configuration:
  [general]
  verbose = 1
  
  [tests]
  verbosity = 2
  wtf = 1
  labile = no
  
  [debug]
  output = /dev/null
  metrics = all
  
  [matplotlib]
  backend = agg
  
  [externals]
  raise exception = off
  have running ipython env = no
  have numpy = yes
  have scipy = yes
  have matplotlib = yes
  have good scipy.stats.rdist = no
  have good scipy.stats.rv_discrete.ppf = yes
  have good scipy.stats.rv_continuous._reduce_func(floc,fscale) = yes
  have pylab = yes
  have weave = no
  have scipy.weave = yes
  have nibabel = yes
  have h5py = yes
  have lxml = yes
  have reportlab = yes
  have rpy2 = yes
  have elasticnet = yes
  have glmnet = no
  have openopt = no
  have lars = yes
  have ctypes = yes
  have mass = yes
  have shogun = no
  have skl = yes
  have libsvm = yes
  have joblib = yes
  have sg ge 0.6.5 = no
  have nipy = yes
  have statsmodels = yes
  have mdp = yes
  have mdp ge 2.4 = yes
  have pywt = yes
  have cran-energy = yes
  have griddata = yes
  have liblapack.so = yes
  have nipy.neurospin = no
  have nose = yes
  have mock = yes
  have pprocess = yes
  have libsvm verbosity control = yes
  have pylab plottable = yes
  have shogun.krr = no
  have atlas_pymvpa = no
  have cpickle = yes
  have shogun.svmocas = no
  have hcluster = no
  have numpy_correct_unique = yes
  have shogun.lightsvm = no
  have pywt wp reconstruct = yes
  have afni-3dinfo = no
  have shogun.svrlight = no
  have shogun.mpd = no
  have datalad = no
  have sg ge 0.6.4 = no
  have pywt wp reconstruct fixed = no
  have ipython = yes
  have atlas_fsl = no
  have hdf5 = yes
  have numpydoc = no
  have sg_fixedcachesize = no
  have gzip = yes
 Process Information:
  Name:	coverage
  State:	R (running)
  Tgid:	18687
  Ngid:	0
  Pid:	18687
  PPid:	2246
  TracerPid:	0
  Uid:	2000	2000	2000	2000
  Gid:	2000	2000	2000	2000
  FDSize:	256
  Groups:	999 2000 
  NStgid:	18687
  NSpid:	18687
  NSpgid:	2246
  NSsid:	2246
  VmPeak:	 1258844 kB
  VmSize:	 1224664 kB
  VmLck:	       0 kB
  VmPin:	       0 kB
  VmHWM:	  448516 kB
  VmRSS:	  417064 kB
  VmData:	  604344 kB
  VmStk:	     204 kB
  VmExe:	    2796 kB
  VmLib:	   67196 kB
  VmPTE:	    2052 kB
  VmPMD:	      16 kB
  VmSwap:	       0 kB
  HugetlbPages:	       0 kB
  Threads:	3
  SigQ:	0/29820
  SigPnd:	0000000000000000
  ShdPnd:	0000000000000000
  SigBlk:	0000000000000000
  SigIgn:	0000000001001000
  SigCgt:	0000000180000002
  CapInh:	0000000000000000
  CapPrm:	0000000000000000
  CapEff:	0000000000000000
  CapBnd:	0000003fffffffff
  CapAmb:	0000000000000000
  Seccomp:	0
  Cpus_allowed:	3
  Cpus_allowed_list:	0-1
  Mems_allowed:	00000000,00000001
  Mems_allowed_list:	0
  voluntary_ctxt_switches:	93571
  nonvoluntary_ctxt_switches:	4198
======================================================================
ERROR: test_diff_len_labels_str (mvpa2.tests.test_clf.ClassifiersTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_clf.py", line 1107, in test_diff_len_labels_str
    clf2.train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[  7.27049407e-01  -3.14431027e-02   6.02131595e-02   2.79460032e-01
    1.17866102e-01   8.64789750e-02]
 [  4.64841223e-01  -1.23850410e-01  -1.56713013e-01   2.18997131e-02
   -1.90082158e-02   1.04372156e-01]
 [  5.66004236e-01   6.71174265e-02  -1.01889157e-02   1.43701210e-01
   -6.24282719e-02   2.21192007e-01]
 [  7.09000334e-01   1.79657072e-03   4.65178216e-02  -2.65332907e-02
   -3.55208797e-02   2.84354341e-01]
 [  7.42477604e-01  -8.98352611e-02   2.19721598e-01   4.42680421e-02
    4.30306269e-02   1.16194564e-01]
 [  6.98241306e-01  -8.43065807e-03   3.34235621e-02   1.77503924e-01
    2.19407773e-01  -1.87116883e-01]
 [  7.61935294e-01  -2.29632444e-01  -2.07297929e-01  -1.74829061e-01
    2.95813513e-01  -3.20415483e-01]
 [  5.38842001e-01  -1.02756243e-01   2.29598646e-02  -1.47135772e-01
   -1.35432868e-01   2.19733189e-02]
 [  4.42309750e-01  -4.08648803e-01   7.57218134e-02  -1.55585585e-01
   -2.42013102e-01   1.38406565e-01]
 [  7.00361290e-01  -1.78462122e-01  -7.27678528e-02   5.80983006e-02
    1.14436898e-01   2.34727669e-02]
 [  5.26373144e-01  -7.76205683e-02   2.45167676e-01   4.34618112e-01
    7.51593756e-02   3.50612913e-01]
 [  5.07052416e-01  -2.29565416e-01  -4.69037586e-02   4.23953498e-02
    2.36662618e-02  -8.48579979e-02]
 [ -3.54593699e-01   8.82109672e-02   1.34536528e-01  -1.13197522e-01
    2.04757628e-02   7.09348341e-01]
 [  1.13471914e-02   4.11017767e-01  -2.75451747e-03   1.49988457e-01
    1.53582723e-01   7.36687037e-01]
 [ -1.84863594e-01   3.69869631e-02   7.29379068e-02  -2.30768510e-01
   -1.14895806e-01   7.81640794e-01]
 [  2.65391997e-01   1.17404934e-01  -1.24668719e-01   1.18079419e-01
   -5.65763480e-02   5.52591925e-01]
 [  3.44705443e-03   1.87533383e-01   9.21659754e-02  -1.00292344e-02
   -2.72695302e-01   7.96624328e-01]
 [ -1.66669049e-02  -1.36524801e-01   2.10764690e-01  -1.65581140e-01
   -2.11737599e-01   6.29122045e-01]
 [  1.38609873e-01  -1.46639161e-01   4.88342656e-02  -8.21697585e-02
    1.97851813e-01   5.54974031e-01]
 [ -9.03260807e-02   1.03286826e-01   2.91877206e-01   1.30212426e-01
    3.46469217e-02   5.95483028e-01]
 [  1.24430056e-02   3.74233535e-01  -1.38202483e-02  -1.75242366e-01
    4.09445746e-02   5.44922619e-01]
 [ -4.16720820e-02   1.56301705e-03   1.04668265e-01   5.77711829e-03
   -2.65990077e-01   5.68677726e-01]
 [ -9.59882285e-02   1.62320626e-02   1.45705785e-02  -3.99536795e-02
   -2.01964450e-01   6.20339155e-01]
 [  3.30868964e-01  -2.18123484e-01  -4.57329898e-02   1.04519526e-02
    1.51670502e-02   3.05499194e-01]
 [ -2.57517410e-02   5.42146467e-01   7.22129268e-02  -3.24978976e-02
    3.61882759e-01   5.59729082e-02]
 [  9.97902100e-02   5.48547666e-01  -1.34859460e-01  -1.71748916e-01
    2.11130300e-01  -3.27612284e-02]
 [  5.79424285e-03   7.62390521e-01  -1.05549143e-01  -1.00478763e-01
   -1.32252521e-01   2.82557409e-02]
 [ -1.75649542e-02   3.61754992e-01  -2.53234215e-01  -2.11247034e-01
    1.17858745e-01  -1.27488185e-01]
 [  3.94420958e-02   7.62021161e-01   1.38514603e-01  -2.03901150e-01
   -2.71707774e-01   2.85385466e-01]
 [  3.57328713e-01   5.73506864e-01  -1.97236178e-01   3.19731352e-01
   -1.41113489e-01  -1.21552949e-02]
 [ -4.68182023e-02   5.39355287e-01  -2.10560067e-01  -1.51368854e-01
    6.84835462e-02  -2.63743890e-01]
 [  2.45973610e-02   7.99750717e-01  -1.78467474e-01  -2.48462410e-01
   -5.77806967e-02  -1.56647099e-02]
 [ -1.67403488e-01   1.00000000e+00   1.21213733e-01  -1.91595908e-01
   -1.62372969e-02   7.52203204e-02]
 [ -1.08578551e-01   4.46694499e-01   2.62390546e-01   1.74061927e-01
   -1.47536980e-01  -5.92814657e-02]
 [ -1.56064226e-01   5.26830970e-01  -8.05810248e-02   1.04791443e-01
   -1.56722635e-01  -9.60448766e-02]
 [  1.87420870e-01   4.48814757e-01   2.94819336e-02  -4.55168744e-01
    9.75978490e-02   1.81333165e-01]
 [  2.61612951e-02  -9.99633409e-02   1.24114130e-01   6.12211083e-01
   -2.28661830e-01   2.36244077e-01]
 [  2.54825702e-01   9.01340559e-02   1.67146360e-02   5.40778638e-01
   -9.46148369e-02   4.66147268e-02]
 [  1.81853440e-01  -2.08247274e-01   6.95029608e-02   6.51496805e-01
    2.05881348e-01   9.51898843e-02]
 [  2.25806945e-02   1.25383261e-01   8.44216766e-02   4.91265153e-01
   -1.98115276e-01   7.18411990e-02]
 [  1.02599400e-02  -2.27362520e-01  -1.11868953e-01   4.43011130e-01
   -2.83012049e-02  -1.58700311e-01]
 [  2.84107745e-02  -2.59172815e-01  -2.34374997e-02   4.93922674e-01
    1.30909039e-01  -1.85619665e-01]
 [  7.49720534e-02  -9.21983986e-02   4.68002345e-02   7.33062095e-01
    7.68057380e-02   1.07806738e-01]
 [ -4.68844506e-02  -1.46653307e-01  -1.33411754e-01   5.43538958e-01
    1.72944787e-01   2.91106681e-01]
 [ -2.67819385e-01  -1.37154507e-02   2.87390976e-01   7.47020406e-01
   -1.65983041e-01  -1.58631743e-01]
 [ -1.55464566e-02   3.40058848e-02   1.19991338e-01   5.57193761e-01
   -1.36259799e-01   1.13793369e-01]
 [ -6.55393324e-02   3.96379525e-02   5.04909933e-04   5.72162186e-01
   -8.29914107e-03  -6.97919256e-02]
 [  1.25656477e-01  -7.16691945e-02   1.35106704e-02   3.67323904e-01
    1.90938166e-02   9.67694610e-02]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: Test all classifiers for conformant behavior
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_clf.py", line 916, in test_generic_tests
    clf.train(traindata)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 1  0]
 [ 1  1]
 [ 2  0]
 [ 2  1]
 [ 3  0]
 [ 3  1]
 [ 4  0]
 [ 4  1]
 [ 5  0]
 [ 5  1]
 [ 6  0]
 [ 6  1]
 [ 7  0]
 [ 7  1]
 [ 8  0]
 [ 8  1]
 [ 9  0]
 [ 9  1]
 [10  0]
 [10  1]
 [11  0]
 [11  1]
 [12  0]
 [12  1]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: Basic tests of metaclass for using regressions as classifiers
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_clf.py", line 981, in test_regression_as_classifier
    error = cv(ds).samples.squeeze()
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 514, in _call
    return super(CrossValidation, self)._call(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 337, in _call
    result = node(sds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 619, in _call
    measure.train(dstrain)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 137, in train
    self._train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1558, in _train
    ProxyClassifier._train(self, dataset_relabeled)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 285, in _train
    self.__clf.train(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.59461635 -0.13809965  0.02209644  0.16741362 -0.31830869 -0.07136187]
 [ 0.70319992  0.08412928 -0.03697287 -0.0298405  -0.25561327  0.01311791]
 [ 0.3800189  -0.18326445 -0.13616834 -0.17820831 -0.08681077  0.15105049]
 [ 0.67504043 -0.05810402 -0.02971667 -0.14022357 -0.00303484  0.16318454]
 [ 0.74412484 -0.23247494 -0.16943893 -0.05492715 -0.22971687  0.00699149]
 [ 0.87598798  0.0976492  -0.10990257  0.14988048  0.30881278  0.01505723]
 [-0.01522729  0.24262266 -0.2075472  -0.16156989  0.2993139   0.94028619]
 [-0.03864101 -0.15681576  0.3838556  -0.21516427  0.11797299  0.88176212]
 [-0.09134822 -0.15673505 -0.19863361  0.0511498  -0.10922103  0.3952027 ]
 [-0.39212692  0.3077243  -0.17039628 -0.2198078  -0.06630048  0.52950225]
 [-0.23036897 -0.22473935  0.0885206   0.35947322 -0.11444255  0.95737643]
 [-0.3724823  -0.16309134  0.25195478 -0.2373606   0.0949639   0.72535496]] using <_LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: Basic testing of the clf summary
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_clf.py", line 281, in test_summary
    clf.train(datasets[dsname])
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.59461635 -0.13809965  0.02209644  0.16741362 -0.31830869 -0.07136187]
 [ 0.70319992  0.08412928 -0.03697287 -0.0298405  -0.25561327  0.01311791]
 [ 0.3800189  -0.18326445 -0.13616834 -0.17820831 -0.08681077  0.15105049]
 [ 0.87559753  0.18195249 -0.18004391 -0.01489045 -0.1908205  -0.00222793]
 [ 0.3688804  -0.12290667 -0.19492739 -0.22027822 -0.29154681 -0.07900503]
 [ 0.70387528 -0.12049148 -0.17425837  0.14235327 -0.07583576  0.32801754]
 [ 0.67504043 -0.05810402 -0.02971667 -0.14022357 -0.00303484  0.16318454]
 [ 0.74412484 -0.23247494 -0.16943893 -0.05492715 -0.22971687  0.00699149]
 [ 0.87598798  0.0976492  -0.10990257  0.14988048  0.30881278  0.01505723]
 [ 0.6988415  -0.16991705 -0.0202274   0.17744056 -0.07176597 -0.02384043]
 [ 0.63936466 -0.13383244  0.12777981  0.09855071  0.05543115  0.07476608]
 [ 0.63139617 -0.09022002 -0.10342256 -0.28860066  0.1106773   0.14832867]
 [-0.01522729  0.24262266 -0.2075472  -0.16156989  0.2993139   0.94028619]
 [-0.03864101 -0.15681576  0.3838556  -0.21516427  0.11797299  0.88176212]
 [-0.09134822 -0.15673505 -0.19863361  0.0511498  -0.10922103  0.3952027 ]
 [ 0.06648574 -0.10276034  0.06201671 -0.2150808  -0.42597438  0.43277916]
 [ 0.14502873  0.3795922  -0.13640618 -0.12096812  0.08077244  0.60795434]
 [-0.10319908 -0.00881024  0.07464275 -0.28160494  0.1512661   1.        ]
 [-0.39212692  0.3077243  -0.17039628 -0.2198078  -0.06630048  0.52950225]
 [-0.23036897 -0.22473935  0.0885206   0.35947322 -0.11444255  0.95737643]
 [-0.3724823  -0.16309134  0.25195478 -0.2373606   0.0949639   0.72535496]
 [ 0.50854518  0.09175083 -0.11474184 -0.02908687  0.03346806  0.74854449]
 [-0.2800426   0.04067589  0.12297008 -0.12095915  0.11435867  0.64102157]
 [ 0.38700069  0.10823692 -0.03045422 -0.19800566 -0.26264718  0.56380056]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: test_values (mvpa2.tests.test_clf.ClassifiersTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_clf.py", line 663, in test_values
    _ = cv(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 514, in _call
    return super(CrossValidation, self)._call(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 337, in _call
    result = node(sds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 619, in _call
    measure.train(dstrain)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.59461635 -0.13809965  0.02209644  0.16741362 -0.31830869 -0.07136187]
 [ 0.70319992  0.08412928 -0.03697287 -0.0298405  -0.25561327  0.01311791]
 [ 0.3800189  -0.18326445 -0.13616834 -0.17820831 -0.08681077  0.15105049]
 [ 0.67504043 -0.05810402 -0.02971667 -0.14022357 -0.00303484  0.16318454]
 [ 0.74412484 -0.23247494 -0.16943893 -0.05492715 -0.22971687  0.00699149]
 [ 0.87598798  0.0976492  -0.10990257  0.14988048  0.30881278  0.01505723]
 [-0.01522729  0.24262266 -0.2075472  -0.16156989  0.2993139   0.94028619]
 [-0.03864101 -0.15681576  0.3838556  -0.21516427  0.11797299  0.88176212]
 [-0.09134822 -0.15673505 -0.19863361  0.0511498  -0.10922103  0.3952027 ]
 [-0.39212692  0.3077243  -0.17039628 -0.2198078  -0.06630048  0.52950225]
 [-0.23036897 -0.22473935  0.0885206   0.35947322 -0.11444255  0.95737643]
 [-0.3724823  -0.16309134  0.25195478 -0.2373606   0.0949639   0.72535496]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: Test analyzers in split classifier
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_datameasure.py", line 119, in test_analyzer_with_split_classifier
    sens = sana(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 252, in __call__
    self.train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 137, in train
    self._train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 840, in _train
    return clf.train(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 137, in train
    self._train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1299, in _train
    clf.train(split[0])
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.63071771  0.05832965  0.06812112 ...,  0.31904136 -0.00159482
  -0.13503078]
 [ 0.32581411 -0.23538369 -0.08169768 ...,  0.07407642  0.1067602
  -0.05794896]
 [ 0.48682025 -0.16651595  0.04682456 ..., -0.3044885  -0.10972023
   0.10564117]
 ..., 
 [ 0.13724176 -0.09087151  0.18993398 ..., -0.00663708 -0.2557711
  -0.05821187]
 [-0.20665093  0.12715484  0.21730297 ..., -0.20513215  0.06383597
   0.22646379]
 [ 0.05658417  0.08833059  0.22745048 ..., -0.03318372  0.01923356
   0.15996534]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: Test sensitivity of the mapped classifier
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_datameasure.py", line 283, in test_mapped_classifier_sensitivity_analyzer
    sens = sana(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 252, in __call__
    self.train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 137, in train
    self._train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 840, in _train
    return clf.train(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 137, in train
    self._train(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1396, in _train
    ProxyClassifier._train(self, wdataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 285, in _train
    self.__clf.train(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.59461635  0.02209644 -0.07136187]
 [ 0.70319992 -0.03697287  0.01311791]
 [ 0.3800189  -0.13616834  0.15105049]
 [ 0.87559753 -0.18004391 -0.00222793]
 [ 0.3688804  -0.19492739 -0.07900503]
 [ 0.70387528 -0.17425837  0.32801754]
 [ 0.67504043 -0.02971667  0.16318454]
 [ 0.74412484 -0.16943893  0.00699149]
 [ 0.87598798 -0.10990257  0.01505723]
 [ 0.6988415  -0.0202274  -0.02384043]
 [ 0.63936466  0.12777981  0.07476608]
 [ 0.63139617 -0.10342256  0.14832867]
 [-0.01522729 -0.2075472   0.94028619]
 [-0.03864101  0.3838556   0.88176212]
 [-0.09134822 -0.19863361  0.3952027 ]
 [ 0.06648574  0.06201671  0.43277916]
 [ 0.14502873 -0.13640618  0.60795434]
 [-0.10319908  0.07464275  1.        ]
 [-0.39212692 -0.17039628  0.52950225]
 [-0.23036897  0.0885206   0.95737643]
 [-0.3724823   0.25195478  0.72535496]
 [ 0.50854518 -0.11474184  0.74854449]
 [-0.2800426   0.12297008  0.64102157]
 [ 0.38700069 -0.03045422  0.56380056]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: mvpa2.tests.test_hdf5_clf.test_h5py_clfs
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/usr/lib/python2.7/dist-packages/nose/util.py", line 620, in newfunc
    return func(*arg, **kw)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_hdf5_clf.py", line 71, in test_h5py_clfs
    error = te(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 619, in _call
    measure.train(dstrain)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.59566998 -0.05677176  0.33870819 ..., -0.15570113 -0.07310967
   0.11722311]
 [ 0.45370439  0.05663013  0.12894265 ..., -0.19377984 -0.14254118
   0.25113296]
 [ 0.58514559 -0.14572059 -0.15821309 ...,  0.13484351  0.11269039
   0.35107816]
 ..., 
 [ 0.13724176 -0.09087151  0.18993398 ..., -0.00663708 -0.2557711
  -0.05821187]
 [-0.20665093  0.12715484  0.21730297 ..., -0.20513215  0.06383597
   0.22646379]
 [ 0.05658417  0.08833059  0.22745048 ..., -0.03318372  0.01923356
   0.15996534]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: test_lars (mvpa2.tests.test_lars.LARSTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_lars.py", line 37, in test_lars
    pre = clf.predict(data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 49, in wrap_samples
    return fx(obj, Dataset(data), *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.17203398 -0.03318741 -0.2492734  ..., -0.20513438  0.00539535
   0.63717143]
 [ 0.05447122 -0.25044026  0.48357937 ...,  0.2983194  -0.08111362
   0.21472919]
 [-0.20995298 -0.03100369  0.31065692 ..., -0.11021801 -0.340312
  -0.24937861]
 ..., 
 [-0.75823564 -0.52311338  0.08014782 ...,  0.25993758  0.03772175
  -0.05702924]
 [-0.59138238 -1.10121687 -0.12112219 ..., -0.17277496 -0.19693973
   0.09687207]
 [-0.09662372 -0.16809965 -0.28461039 ..., -0.00693318  0.21342123
   0.10494994]] using <LARS>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: test_lars_state (mvpa2.tests.test_lars.LARSTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_lars.py", line 54, in test_lars_state
    p = clf.predict(data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 49, in wrap_samples
    return fx(obj, Dataset(data), *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.17203398 -0.03318741 -0.2492734  ..., -0.20513438  0.00539535
   0.63717143]
 [ 0.05447122 -0.25044026  0.48357937 ...,  0.2983194  -0.08111362
   0.21472919]
 [-0.20995298 -0.03100369  0.31065692 ..., -0.11021801 -0.340312
  -0.24937861]
 ..., 
 [-0.75823564 -0.52311338  0.08014782 ...,  0.25993758  0.03772175
  -0.05702924]
 [-0.59138238 -1.10121687 -0.12112219 ..., -0.17277496 -0.19693973
   0.09687207]
 [-0.09662372 -0.16809965 -0.28461039 ..., -0.00693318  0.21342123
   0.10494994]] using <LARS>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: Simple tests on regressions
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_regr.py", line 53, in test_regressions
    corr = np.asscalar(cve(ds).samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 514, in _call
    return super(CrossValidation, self)._call(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 337, in _call
    result = node(sds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 619, in _call
    measure.train(dstrain)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.0629521  -0.17172779  0.49882374 ...,  0.30788024  0.27570862
  -0.06147558]
 [ 0.12505164 -0.2738747   0.08274413 ...,  0.27210744 -0.58704225
  -0.05137067]
 [ 0.39616526  0.28310221  0.31460581 ...,  0.08652326  0.06394402
   0.04597285]
 ..., 
 [-0.75823564 -0.52311338  0.08014782 ...,  0.25993758  0.03772175
  -0.05702924]
 [-0.59138238 -1.10121687 -0.12112219 ..., -0.17277496 -0.19693973
   0.09687207]
 [-0.09662372 -0.16809965 -0.28461039 ..., -0.00693318  0.21342123
   0.10494994]] using <_LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
ERROR: Test AUC computation
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/testing/sweep.py", line 69, in do_sweep
    method(*args_, **kwargs_)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_transerror.py", line 304, in test_auc
    cverror = cv(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 514, in _call
    return super(CrossValidation, self)._call(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 337, in _call
    result = node(sds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 258, in __call__
    return super(Learner, self).__call__(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/node.py", line 137, in __call__
    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/measures/base.py", line 619, in _call
    measure.train(dstrain)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/learner.py", line 152, in train
    self._posttrain(ds)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 254, in _posttrain
    predictions = self.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 1565, in _predict
    = ProxyClassifier._predict(self, dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/meta.py", line 303, in _predict
    result = clf.predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 47, in wrap_samples
    return fx(obj, data, *args, **kwargs)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/base.py", line 405, in predict
    result = self._predict(dataset)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/base/types.py", line 35, in extract_samples
    return fx(obj, data.samples)
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/clfs/lars.py", line 217, in _predict
    % (data, self, e)
FailedToPredictError: Failed to predict on [[ 0.59461635 -0.13809965  0.02209644  0.16741362 -0.31830869 -0.07136187]
 [ 0.70319992  0.08412928 -0.03697287 -0.0298405  -0.25561327  0.01311791]
 [ 0.3800189  -0.18326445 -0.13616834 -0.17820831 -0.08681077  0.15105049]
 [ 0.67504043 -0.05810402 -0.02971667 -0.14022357 -0.00303484  0.16318454]
 [ 0.74412484 -0.23247494 -0.16943893 -0.05492715 -0.22971687  0.00699149]
 [ 0.87598798  0.0976492  -0.10990257  0.14988048  0.30881278  0.01505723]
 [-0.01522729  0.24262266 -0.2075472  -0.16156989  0.2993139   0.94028619]
 [-0.03864101 -0.15681576  0.3838556  -0.21516427  0.11797299  0.88176212]
 [-0.09134822 -0.15673505 -0.19863361  0.0511498  -0.10922103  0.3952027 ]
 [-0.39212692  0.3077243  -0.17039628 -0.2198078  -0.06630048  0.52950225]
 [-0.23036897 -0.22473935  0.0885206   0.35947322 -0.11444255  0.95737643]
 [-0.3724823  -0.16309134  0.25195478 -0.2373606   0.0949639   0.72535496]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
FAIL: Doctest: mvpa2.support.utils.deprecated
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.7/doctest.py", line 2226, in runTest
    raise self.failureException(self.format_failure(new.getvalue()))
AssertionError: Failed doctest test for mvpa2.support.utils.deprecated
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/support/utils.py", line 22, in deprecated
----------------------------------------------------------------------
File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/support/utils.py", line 33, in mvpa2.support.utils.deprecated
Failed example:
    deprecated() # doctest: +ELLIPSIS
Expected:
    <sklearn.utils.deprecated object at ...>
Got:
    <sklearn.utils.deprecation.deprecated object at 0x7f279cf57910>
======================================================================
FAIL: Simple test if classifiers can generalize ok on simple data
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_clf.py", line 190, in test_classifier_generalization
    self.fail("Failed with %s" % e)
AssertionError: 
 Single scenario lead to failures of unittest test_classifier_generalization:
  on
    clf=<LARS(model_type='lasso') classifier> :
     Failed with Failed to predict on [[  1.67768145e-01   3.67223255e-02  -1.48970279e-01  -9.70237123e-02
    9.90600989e-03   1.82884356e-01  -1.09536901e-02  -1.29391579e-01
   -5.31329379e-02  -5.79283887e-02   5.31618684e-02   6.02777902e-02
    6.61916002e-02   7.78629470e-02]
 [  5.45727490e-01   1.95272454e-01  -1.66091315e-01  -1.44332140e-01
    1.30915842e-02   1.22076735e-01  -1.92549290e-01   1.10203013e-01
   -8.96974451e-02   1.50766138e-01   2.95952292e-01  -2.02681272e-03
    5.68530829e-02   1.65095432e-02]
 [  2.69359891e-01  -3.82031351e-02   4.92579233e-02  -2.49562891e-01
   -2.32238971e-01  -1.41844952e-01  -6.95316577e-02  -8.08933650e-02
   -2.48006964e-01   1.30931235e-01   2.78942138e-02  -8.17170393e-02
    1.36871924e-01   6.39744399e-02]
 [  4.28720711e-01  -3.47981809e-02  -1.51152053e-01  -8.83306464e-02
   -8.52674887e-02  -1.90156292e-02   5.59522213e-02  -9.66336084e-02
   -3.17871076e-02   6.85290106e-02  -2.28414997e-01   1.50879818e-01
   -1.28270185e-01  -2.68906446e-01]
 [ -2.07318358e-01   1.67369039e-01  -1.51470122e-01   1.24186993e-01
    3.16014300e-01   4.97028085e-01   9.16426638e-02   3.23163490e-01
   -1.97405600e-01  -3.18832463e-01  -3.14115004e-03   5.63009292e-02
    3.36017872e-02  -6.18648110e-02]
 [ -2.01876669e-01  -1.55924567e-01  -5.77809320e-02  -1.37223864e-01
    3.70019881e-03   6.31102472e-01  -1.80274447e-01  -1.05208355e-01
   -6.61997888e-02   2.19138593e-01  -1.03525070e-01   1.81366972e-01
    1.00639874e-01   1.02521582e-01]
 [ -1.66468396e-01   1.20315857e-01  -6.05713275e-02   7.75364258e-02
   -6.67847698e-02   4.05638464e-01   2.42618528e-01   8.84730205e-03
    3.29982473e-01  -4.49970977e-02   3.03739772e-01  -3.54137082e-01
    1.04446524e-01   1.57437476e-01]
 [ -2.47209068e-01   1.53999261e-01  -3.28390220e-03  -3.63767858e-02
    2.29562238e-01   3.85172040e-01   2.84890462e-03   2.08072320e-01
    1.27009928e-01  -5.12605806e-02   7.37260978e-02   8.26101005e-02
   -8.73204926e-03   5.02555926e-02]
 [ -3.40173426e-01   6.10481267e-01   7.80272290e-02  -6.73351887e-04
    6.60981633e-02   1.57992049e-01   1.77365024e-01   1.41118026e-01
   -1.83268457e-01  -3.10519834e-01   1.47838671e-01   1.47633138e-01
   -1.73537375e-01  -4.31738613e-01]
 [  1.44266981e-01   4.13409308e-01   4.05885927e-02   2.61168141e-02
   -1.12723147e-01   4.81684046e-02   4.23085754e-03   1.52913588e-01
   -2.49694942e-01  -9.60966144e-02   2.02204979e-01  -1.88657182e-01
    9.94091684e-02  -5.66458742e-03]
 [  7.53992618e-02   8.21102109e-01  -1.49383531e-01  -1.52705404e-01
    2.60189674e-02   1.26572406e-02  -2.49469512e-01  -1.84023304e-01
   -9.57212268e-02  -2.89566706e-02   3.85867683e-02   9.23289532e-02
   -1.48974825e-01  -5.51040163e-02]
 [  2.44912023e-01   2.88128953e-01   1.18162721e-02  -1.27800884e-01
   -1.74625675e-01  -8.09759506e-02  -1.65306058e-01  -1.43617317e-01
    9.37256911e-02   2.29707938e-01  -1.11417226e-01   4.09719874e-02
   -1.57837800e-01  -5.78635120e-02]
 [ -9.52130429e-02  -1.98236332e-02   1.13405411e-02   4.68088101e-01
   -4.99232666e-02   5.67083320e-02   1.49166059e-01  -1.15352146e-01
    7.14198436e-03   1.58759102e-01  -1.15677769e-01   1.54369739e-01
    1.90564467e-01  -2.82438773e-01]
 [ -1.20183782e-02  -2.35463227e-01   1.29135185e-01   3.72235759e-01
    1.68831351e-01   1.90496521e-01   1.60368536e-01  -2.56842504e-02
    3.26061349e-01  -1.88709042e-01  -2.15368007e-01  -4.32388717e-02
   -7.37731727e-02   1.82104354e-02]
 [ -1.28632728e-01  -6.37405793e-02   1.01456656e-01   5.41818856e-01
    6.75501316e-02  -1.18838943e-01   2.11759312e-01  -9.83608009e-02
    6.35550809e-02  -1.35701927e-01   5.07542871e-02   7.34317580e-02
   -2.90549172e-01   2.56703809e-01]
 [  1.57924982e-01   4.59337716e-01   2.24541162e-01   5.92839616e-01
   -8.31545150e-03  -6.85963273e-02  -1.37042535e-02   2.15332929e-03
    1.30001904e-01  -5.50374379e-02  -1.81691758e-01  -1.17466176e-01
    4.28379401e-02   5.61091967e-02]] using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
    clf=<LARS(model_type='stepwise') classifier> :
     Failed with Failed to predict on [[  1.67768145e-01   3.67223255e-02  -1.48970279e-01  -9.70237123e-02
    9.90600989e-03   1.82884356e-01  -1.09536901e-02  -1.29391579e-01
   -5.31329379e-02  -5.79283887e-02   5.31618684e-02   6.02777902e-02
    6.61916002e-02   7.78629470e-02]
 [  5.45727490e-01   1.95272454e-01  -1.66091315e-01  -1.44332140e-01
    1.30915842e-02   1.22076735e-01  -1.92549290e-01   1.10203013e-01
   -8.96974451e-02   1.50766138e-01   2.95952292e-01  -2.02681272e-03
    5.68530829e-02   1.65095432e-02]
 [  2.69359891e-01  -3.82031351e-02   4.92579233e-02  -2.49562891e-01
   -2.32238971e-01  -1.41844952e-01  -6.95316577e-02  -8.08933650e-02
   -2.48006964e-01   1.30931235e-01   2.78942138e-02  -8.17170393e-02
    1.36871924e-01   6.39744399e-02]
 [  4.28720711e-01  -3.47981809e-02  -1.51152053e-01  -8.83306464e-02
   -8.52674887e-02  -1.90156292e-02   5.59522213e-02  -9.66336084e-02
   -3.17871076e-02   6.85290106e-02  -2.28414997e-01   1.50879818e-01
   -1.28270185e-01  -2.68906446e-01]
 [ -2.07318358e-01   1.67369039e-01  -1.51470122e-01   1.24186993e-01
    3.16014300e-01   4.97028085e-01   9.16426638e-02   3.23163490e-01
   -1.97405600e-01  -3.18832463e-01  -3.14115004e-03   5.63009292e-02
    3.36017872e-02  -6.18648110e-02]
 [ -2.01876669e-01  -1.55924567e-01  -5.77809320e-02  -1.37223864e-01
    3.70019881e-03   6.31102472e-01  -1.80274447e-01  -1.05208355e-01
   -6.61997888e-02   2.19138593e-01  -1.03525070e-01   1.81366972e-01
    1.00639874e-01   1.02521582e-01]
 [ -1.66468396e-01   1.20315857e-01  -6.05713275e-02   7.75364258e-02
   -6.67847698e-02   4.05638464e-01   2.42618528e-01   8.84730205e-03
    3.29982473e-01  -4.49970977e-02   3.03739772e-01  -3.54137082e-01
    1.04446524e-01   1.57437476e-01]
 [ -2.47209068e-01   1.53999261e-01  -3.28390220e-03  -3.63767858e-02
    2.29562238e-01   3.85172040e-01   2.84890462e-03   2.08072320e-01
    1.27009928e-01  -5.12605806e-02   7.37260978e-02   8.26101005e-02
   -8.73204926e-03   5.02555926e-02]
 [ -3.40173426e-01   6.10481267e-01   7.80272290e-02  -6.73351887e-04
    6.60981633e-02   1.57992049e-01   1.77365024e-01   1.41118026e-01
   -1.83268457e-01  -3.10519834e-01   1.47838671e-01   1.47633138e-01
   -1.73537375e-01  -4.31738613e-01]
 [  1.44266981e-01   4.13409308e-01   4.05885927e-02   2.61168141e-02
   -1.12723147e-01   4.81684046e-02   4.23085754e-03   1.52913588e-01
   -2.49694942e-01  -9.60966144e-02   2.02204979e-01  -1.88657182e-01
    9.94091684e-02  -5.66458742e-03]
 [  7.53992618e-02   8.21102109e-01  -1.49383531e-01  -1.52705404e-01
    2.60189674e-02   1.26572406e-02  -2.49469512e-01  -1.84023304e-01
   -9.57212268e-02  -2.89566706e-02   3.85867683e-02   9.23289532e-02
   -1.48974825e-01  -5.51040163e-02]
 [  2.44912023e-01   2.88128953e-01   1.18162721e-02  -1.27800884e-01
   -1.74625675e-01  -8.09759506e-02  -1.65306058e-01  -1.43617317e-01
    9.37256911e-02   2.29707938e-01  -1.11417226e-01   4.09719874e-02
   -1.57837800e-01  -5.78635120e-02]
 [ -9.52130429e-02  -1.98236332e-02   1.13405411e-02   4.68088101e-01
   -4.99232666e-02   5.67083320e-02   1.49166059e-01  -1.15352146e-01
    7.14198436e-03   1.58759102e-01  -1.15677769e-01   1.54369739e-01
    1.90564467e-01  -2.82438773e-01]
 [ -1.20183782e-02  -2.35463227e-01   1.29135185e-01   3.72235759e-01
    1.68831351e-01   1.90496521e-01   1.60368536e-01  -2.56842504e-02
    3.26061349e-01  -1.88709042e-01  -2.15368007e-01  -4.32388717e-02
   -7.37731727e-02   1.82104354e-02]
 [ -1.28632728e-01  -6.37405793e-02   1.01456656e-01   5.41818856e-01
    6.75501316e-02  -1.18838943e-01   2.11759312e-01  -9.83608009e-02
    6.35550809e-02  -1.35701927e-01   5.07542871e-02   7.34317580e-02
   -2.90549172e-01   2.56703809e-01]
 [  1.57924982e-01   4.59337716e-01   2.24541162e-01   5.92839616e-01
   -8.31545150e-03  -6.85963273e-02  -1.37042535e-02   2.15332929e-03
    1.30001904e-01  -5.50374379e-02  -1.81691758e-01  -1.17466176e-01
    4.28379401e-02   5.61091967e-02]] using <LARS(stepwise)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
    clf=<LARS(model_type='lar') classifier> :
     Failed with Failed to predict on [[  1.67768145e-01   3.67223255e-02  -1.48970279e-01  -9.70237123e-02
    9.90600989e-03   1.82884356e-01  -1.09536901e-02  -1.29391579e-01
   -5.31329379e-02  -5.79283887e-02   5.31618684e-02   6.02777902e-02
    6.61916002e-02   7.78629470e-02]
 [  5.45727490e-01   1.95272454e-01  -1.66091315e-01  -1.44332140e-01
    1.30915842e-02   1.22076735e-01  -1.92549290e-01   1.10203013e-01
   -8.96974451e-02   1.50766138e-01   2.95952292e-01  -2.02681272e-03
    5.68530829e-02   1.65095432e-02]
 [  2.69359891e-01  -3.82031351e-02   4.92579233e-02  -2.49562891e-01
   -2.32238971e-01  -1.41844952e-01  -6.95316577e-02  -8.08933650e-02
   -2.48006964e-01   1.30931235e-01   2.78942138e-02  -8.17170393e-02
    1.36871924e-01   6.39744399e-02]
 [  4.28720711e-01  -3.47981809e-02  -1.51152053e-01  -8.83306464e-02
   -8.52674887e-02  -1.90156292e-02   5.59522213e-02  -9.66336084e-02
   -3.17871076e-02   6.85290106e-02  -2.28414997e-01   1.50879818e-01
   -1.28270185e-01  -2.68906446e-01]
 [ -2.07318358e-01   1.67369039e-01  -1.51470122e-01   1.24186993e-01
    3.16014300e-01   4.97028085e-01   9.16426638e-02   3.23163490e-01
   -1.97405600e-01  -3.18832463e-01  -3.14115004e-03   5.63009292e-02
    3.36017872e-02  -6.18648110e-02]
 [ -2.01876669e-01  -1.55924567e-01  -5.77809320e-02  -1.37223864e-01
    3.70019881e-03   6.31102472e-01  -1.80274447e-01  -1.05208355e-01
   -6.61997888e-02   2.19138593e-01  -1.03525070e-01   1.81366972e-01
    1.00639874e-01   1.02521582e-01]
 [ -1.66468396e-01   1.20315857e-01  -6.05713275e-02   7.75364258e-02
   -6.67847698e-02   4.05638464e-01   2.42618528e-01   8.84730205e-03
    3.29982473e-01  -4.49970977e-02   3.03739772e-01  -3.54137082e-01
    1.04446524e-01   1.57437476e-01]
 [ -2.47209068e-01   1.53999261e-01  -3.28390220e-03  -3.63767858e-02
    2.29562238e-01   3.85172040e-01   2.84890462e-03   2.08072320e-01
    1.27009928e-01  -5.12605806e-02   7.37260978e-02   8.26101005e-02
   -8.73204926e-03   5.02555926e-02]
 [ -3.40173426e-01   6.10481267e-01   7.80272290e-02  -6.73351887e-04
    6.60981633e-02   1.57992049e-01   1.77365024e-01   1.41118026e-01
   -1.83268457e-01  -3.10519834e-01   1.47838671e-01   1.47633138e-01
   -1.73537375e-01  -4.31738613e-01]
 [  1.44266981e-01   4.13409308e-01   4.05885927e-02   2.61168141e-02
   -1.12723147e-01   4.81684046e-02   4.23085754e-03   1.52913588e-01
   -2.49694942e-01  -9.60966144e-02   2.02204979e-01  -1.88657182e-01
    9.94091684e-02  -5.66458742e-03]
 [  7.53992618e-02   8.21102109e-01  -1.49383531e-01  -1.52705404e-01
    2.60189674e-02   1.26572406e-02  -2.49469512e-01  -1.84023304e-01
   -9.57212268e-02  -2.89566706e-02   3.85867683e-02   9.23289532e-02
   -1.48974825e-01  -5.51040163e-02]
 [  2.44912023e-01   2.88128953e-01   1.18162721e-02  -1.27800884e-01
   -1.74625675e-01  -8.09759506e-02  -1.65306058e-01  -1.43617317e-01
    9.37256911e-02   2.29707938e-01  -1.11417226e-01   4.09719874e-02
   -1.57837800e-01  -5.78635120e-02]
 [ -9.52130429e-02  -1.98236332e-02   1.13405411e-02   4.68088101e-01
   -4.99232666e-02   5.67083320e-02   1.49166059e-01  -1.15352146e-01
    7.14198436e-03   1.58759102e-01  -1.15677769e-01   1.54369739e-01
    1.90564467e-01  -2.82438773e-01]
 [ -1.20183782e-02  -2.35463227e-01   1.29135185e-01   3.72235759e-01
    1.68831351e-01   1.90496521e-01   1.60368536e-01  -2.56842504e-02
    3.26061349e-01  -1.88709042e-01  -2.15368007e-01  -4.32388717e-02
   -7.37731727e-02   1.82104354e-02]
 [ -1.28632728e-01  -6.37405793e-02   1.01456656e-01   5.41818856e-01
    6.75501316e-02  -1.18838943e-01   2.11759312e-01  -9.83608009e-02
    6.35550809e-02  -1.35701927e-01   5.07542871e-02   7.34317580e-02
   -2.90549172e-01   2.56703809e-01]
 [  1.57924982e-01   4.59337716e-01   2.24541162e-01   5.92839616e-01
   -8.31545150e-03  -6.85963273e-02  -1.37042535e-02   2.15332929e-03
    1.30001904e-01  -5.50374379e-02  -1.81691758e-01  -1.17466176e-01
    4.28379401e-02   5.61091967e-02]] using <LARS(lar)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
    clf=<LARS(model_type='forward.stagewise') classifier> :
     Failed with Failed to predict on [[  1.67768145e-01   3.67223255e-02  -1.48970279e-01  -9.70237123e-02
    9.90600989e-03   1.82884356e-01  -1.09536901e-02  -1.29391579e-01
   -5.31329379e-02  -5.79283887e-02   5.31618684e-02   6.02777902e-02
    6.61916002e-02   7.78629470e-02]
 [  5.45727490e-01   1.95272454e-01  -1.66091315e-01  -1.44332140e-01
    1.30915842e-02   1.22076735e-01  -1.92549290e-01   1.10203013e-01
   -8.96974451e-02   1.50766138e-01   2.95952292e-01  -2.02681272e-03
    5.68530829e-02   1.65095432e-02]
 [  2.69359891e-01  -3.82031351e-02   4.92579233e-02  -2.49562891e-01
   -2.32238971e-01  -1.41844952e-01  -6.95316577e-02  -8.08933650e-02
   -2.48006964e-01   1.30931235e-01   2.78942138e-02  -8.17170393e-02
    1.36871924e-01   6.39744399e-02]
 [  4.28720711e-01  -3.47981809e-02  -1.51152053e-01  -8.83306464e-02
   -8.52674887e-02  -1.90156292e-02   5.59522213e-02  -9.66336084e-02
   -3.17871076e-02   6.85290106e-02  -2.28414997e-01   1.50879818e-01
   -1.28270185e-01  -2.68906446e-01]
 [ -2.07318358e-01   1.67369039e-01  -1.51470122e-01   1.24186993e-01
    3.16014300e-01   4.97028085e-01   9.16426638e-02   3.23163490e-01
   -1.97405600e-01  -3.18832463e-01  -3.14115004e-03   5.63009292e-02
    3.36017872e-02  -6.18648110e-02]
 [ -2.01876669e-01  -1.55924567e-01  -5.77809320e-02  -1.37223864e-01
    3.70019881e-03   6.31102472e-01  -1.80274447e-01  -1.05208355e-01
   -6.61997888e-02   2.19138593e-01  -1.03525070e-01   1.81366972e-01
    1.00639874e-01   1.02521582e-01]
 [ -1.66468396e-01   1.20315857e-01  -6.05713275e-02   7.75364258e-02
   -6.67847698e-02   4.05638464e-01   2.42618528e-01   8.84730205e-03
    3.29982473e-01  -4.49970977e-02   3.03739772e-01  -3.54137082e-01
    1.04446524e-01   1.57437476e-01]
 [ -2.47209068e-01   1.53999261e-01  -3.28390220e-03  -3.63767858e-02
    2.29562238e-01   3.85172040e-01   2.84890462e-03   2.08072320e-01
    1.27009928e-01  -5.12605806e-02   7.37260978e-02   8.26101005e-02
   -8.73204926e-03   5.02555926e-02]
 [ -3.40173426e-01   6.10481267e-01   7.80272290e-02  -6.73351887e-04
    6.60981633e-02   1.57992049e-01   1.77365024e-01   1.41118026e-01
   -1.83268457e-01  -3.10519834e-01   1.47838671e-01   1.47633138e-01
   -1.73537375e-01  -4.31738613e-01]
 [  1.44266981e-01   4.13409308e-01   4.05885927e-02   2.61168141e-02
   -1.12723147e-01   4.81684046e-02   4.23085754e-03   1.52913588e-01
   -2.49694942e-01  -9.60966144e-02   2.02204979e-01  -1.88657182e-01
    9.94091684e-02  -5.66458742e-03]
 [  7.53992618e-02   8.21102109e-01  -1.49383531e-01  -1.52705404e-01
    2.60189674e-02   1.26572406e-02  -2.49469512e-01  -1.84023304e-01
   -9.57212268e-02  -2.89566706e-02   3.85867683e-02   9.23289532e-02
   -1.48974825e-01  -5.51040163e-02]
 [  2.44912023e-01   2.88128953e-01   1.18162721e-02  -1.27800884e-01
   -1.74625675e-01  -8.09759506e-02  -1.65306058e-01  -1.43617317e-01
    9.37256911e-02   2.29707938e-01  -1.11417226e-01   4.09719874e-02
   -1.57837800e-01  -5.78635120e-02]
 [ -9.52130429e-02  -1.98236332e-02   1.13405411e-02   4.68088101e-01
   -4.99232666e-02   5.67083320e-02   1.49166059e-01  -1.15352146e-01
    7.14198436e-03   1.58759102e-01  -1.15677769e-01   1.54369739e-01
    1.90564467e-01  -2.82438773e-01]
 [ -1.20183782e-02  -2.35463227e-01   1.29135185e-01   3.72235759e-01
    1.68831351e-01   1.90496521e-01   1.60368536e-01  -2.56842504e-02
    3.26061349e-01  -1.88709042e-01  -2.15368007e-01  -4.32388717e-02
   -7.37731727e-02   1.82104354e-02]
 [ -1.28632728e-01  -6.37405793e-02   1.01456656e-01   5.41818856e-01
    6.75501316e-02  -1.18838943e-01   2.11759312e-01  -9.83608009e-02
    6.35550809e-02  -1.35701927e-01   5.07542871e-02   7.34317580e-02
   -2.90549172e-01   2.56703809e-01]
 [  1.57924982e-01   4.59337716e-01   2.24541162e-01   5.92839616e-01
   -8.31545150e-03  -6.85963273e-02  -1.37042535e-02   2.15332929e-03
    1.30001904e-01  -5.50374379e-02  -1.81691758e-01  -1.17466176e-01
    4.28379401e-02   5.61091967e-02]] using <LARS(forward.stagewise)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0
======================================================================
FAIL: Simple test if a learner could cope with custom sa not targets
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/tests/test_clf.py", line 243, in test_custom_targets
    self.fail("Failed with %r" % e)
AssertionError: 
 Single scenario lead to failures of unittest test_custom_targets:
  on
    lrn=<_LARS(lasso)> :
     Failed with FailedToPredictError("Failed to predict on [[ 1.6895046 ]\n [ 2.74929595]\n [ 0.40862106]\n [ 0.95196314]\n [ 0.41735062]\n [ 2.89424568]\n [ 0.14488834]\n [ 2.1714167 ]\n [ 1.34425912]\n [ 1.16542723]\n [ 0.04177171]\n [ 0.27194343]\n [ 1.06156178]\n [ 0.53595995]\n [ 0.19515038]\n [ 2.09943478]\n [ 1.49369585]\n [ 1.84898885]\n [ 0.10539123]\n [ 2.11930665]\n [ 1.77274424]\n [ 2.21308989]\n [ 2.0111167 ]\n [ 3.04572752]\n [ 2.44446129]\n [ 1.72359739]\n [ 2.91761509]\n [ 2.86969121]\n [ 2.10291086]\n [ 1.01988029]] using <_LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : \n  'dims' cannot be of length 0\n",)
    lrn=<_LARS(stepwise)> :
     Failed with FailedToPredictError("Failed to predict on [[ 1.6895046 ]\n [ 2.74929595]\n [ 0.40862106]\n [ 0.95196314]\n [ 0.41735062]\n [ 2.89424568]\n [ 0.14488834]\n [ 2.1714167 ]\n [ 1.34425912]\n [ 1.16542723]\n [ 0.04177171]\n [ 0.27194343]\n [ 1.06156178]\n [ 0.53595995]\n [ 0.19515038]\n [ 2.09943478]\n [ 1.49369585]\n [ 1.84898885]\n [ 0.10539123]\n [ 2.11930665]\n [ 1.77274424]\n [ 2.21308989]\n [ 2.0111167 ]\n [ 3.04572752]\n [ 2.44446129]\n [ 1.72359739]\n [ 2.91761509]\n [ 2.86969121]\n [ 2.10291086]\n [ 1.01988029]] using <_LARS(stepwise)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : \n  'dims' cannot be of length 0\n",)
    lrn=<_LARS(lar)> :
     Failed with FailedToPredictError("Failed to predict on [[ 1.6895046 ]\n [ 2.74929595]\n [ 0.40862106]\n [ 0.95196314]\n [ 0.41735062]\n [ 2.89424568]\n [ 0.14488834]\n [ 2.1714167 ]\n [ 1.34425912]\n [ 1.16542723]\n [ 0.04177171]\n [ 0.27194343]\n [ 1.06156178]\n [ 0.53595995]\n [ 0.19515038]\n [ 2.09943478]\n [ 1.49369585]\n [ 1.84898885]\n [ 0.10539123]\n [ 2.11930665]\n [ 1.77274424]\n [ 2.21308989]\n [ 2.0111167 ]\n [ 3.04572752]\n [ 2.44446129]\n [ 1.72359739]\n [ 2.91761509]\n [ 2.86969121]\n [ 2.10291086]\n [ 1.01988029]] using <_LARS(lar)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : \n  'dims' cannot be of length 0\n",)
    lrn=<_LARS(forward.stagewise)> :
     Failed with FailedToPredictError("Failed to predict on [[ 1.6895046 ]\n [ 2.74929595]\n [ 0.40862106]\n [ 0.95196314]\n [ 0.41735062]\n [ 2.89424568]\n [ 0.14488834]\n [ 2.1714167 ]\n [ 1.34425912]\n [ 1.16542723]\n [ 0.04177171]\n [ 0.27194343]\n [ 1.06156178]\n [ 0.53595995]\n [ 0.19515038]\n [ 2.09943478]\n [ 1.49369585]\n [ 1.84898885]\n [ 0.10539123]\n [ 2.11930665]\n [ 1.77274424]\n [ 2.21308989]\n [ 2.0111167 ]\n [ 3.04572752]\n [ 2.44446129]\n [ 1.72359739]\n [ 2.91761509]\n [ 2.86969121]\n [ 2.10291086]\n [ 1.01988029]] using <_LARS(forward.stagewise)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : \n  'dims' cannot be of length 0\n",)
----------------------------------------------------------------------
Ran 632 tests in 559.927s
FAILED (SKIP=16, errors=12, failures=3)

nno · 2017-09-09T12:02:53Z

All errors (except a doc test one) seem to be of this form:

using <LARS(lasso)>. Exceptions was: Error in (function (data = NA, dim = length(data), dimnames = NULL)  : 
  'dims' cannot be of length 0

yarikoptic · 2017-09-09T13:07:08Z

Doh... Thanks of reminding me of this one... Left it not finished, laptop was rebooted, lost a terminal removing me that I was doing it ;-) need to either look wtf with Lars or rpy2 or just disable it

codecov-io · 2017-09-11T00:31:52Z

Codecov Report

Merging #534 into master will decrease coverage by 1.08%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #534      +/-   ##
==========================================
- Coverage   76.66%   75.58%   -1.09%     
==========================================
  Files         364      363       -1     
  Lines       41341    41299      -42     
  Branches     6667     6662       -5     
==========================================
- Hits        31695    31215     -480     
- Misses       7751     8234     +483     
+ Partials     1895     1850      -45

Impacted Files	Coverage Δ
mvpa2/mappers/__init__.py	`100% <ø> (ø)`	⬆️
mvpa2/support/utils.py	`62.79% <ø> (ø)`	⬆️
mvpa2/tests/test_svmkernels.py	`5.94% <0%> (-94.06%)`	⬇️
mvpa2/tests/test_lars.py	`10% <0%> (-83.34%)`	⬇️
mvpa2/clfs/sg/sens.py	`20% <0%> (-66.16%)`	⬇️
mvpa2/clfs/lars.py	`29.06% <0%> (-58.14%)`	⬇️
mvpa2/support/scipy/signal.py	`11.36% <0%> (-47.73%)`	⬇️
mvpa2/clfs/sg/svm.py	`15.71% <0%> (-45.36%)`	⬇️
mvpa2/kernels/sg.py	`47% <0%> (-44%)`	⬇️
mvpa2/tests/test_kernel.py	`62.02% <0%> (-34.82%)`	⬇️
... and 40 more

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coveralls · 2017-09-11T00:47:53Z

Changes Unknown when pulling dd7d23c on yarikoptic:bf-travis into ** on PyMVPA:master**.

nno · 2017-09-11T14:32:28Z

There's a (what seems trivial) doc test that fails on travis:

======================================================================
FAIL: Doctest: mvpa2.support.utils.deprecated
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.7/doctest.py", line 2226, in runTest
    raise self.failureException(self.format_failure(new.getvalue()))
AssertionError: Failed doctest test for mvpa2.support.utils.deprecated
  File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/support/utils.py", line 22, in deprecated
----------------------------------------------------------------------
File "/home/travis/build/PyMVPA/PyMVPA/mvpa2/support/utils.py", line 33, in mvpa2.support.utils.deprecated
Failed example:
    deprecated() # doctest: +ELLIPSIS
Expected:
    <sklearn.utils.deprecated object at ...>
Got:
    <sklearn.utils.deprecation.deprecated object at 0x7f08c8588b50>

coveralls · 2017-10-02T14:40:24Z

Changes Unknown when pulling 701bc46 on yarikoptic:bf-travis into ** on PyMVPA:master**.

nno · 2017-10-02T15:25:10Z

Thanks @yarikoptic

yarikoptic added 4 commits August 24, 2017 13:20

BF(DOC): fixed URLs to ESLII book etc (thanks JT Ryan for the buzz)

b562e36

DOC: Typo fix, thanks Usman

57fa4dc

Merge remote-tracking branch 'origin/master' into bf-travis

8364af5

* origin/master: fix spelling

BF: do not try installing shogun-modular not present in trusty

ccda075

disable testing lars for now

dd7d23c

DOC: fixup the doctest, thanks @nno for pointing it out

701bc46

nno mentioned this pull request Oct 2, 2017

travis: shogun-python-modular not available #540

Closed

nno merged commit 65ae410 into PyMVPA:master Oct 2, 2017

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BF travis + minor typo fix #534

BF travis + minor typo fix #534

yarikoptic commented Sep 7, 2017

coveralls commented Sep 7, 2017

nno commented Sep 9, 2017

nno commented Sep 9, 2017

yarikoptic commented Sep 9, 2017

codecov-io commented Sep 11, 2017 •

edited

coveralls commented Sep 11, 2017

nno commented Sep 11, 2017

coveralls commented Oct 2, 2017 •

edited

nno commented Oct 2, 2017

BF travis + minor typo fix #534

BF travis + minor typo fix #534

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yarikoptic commented Sep 7, 2017

coveralls commented Sep 7, 2017

nno commented Sep 9, 2017

nno commented Sep 9, 2017

yarikoptic commented Sep 9, 2017

codecov-io commented Sep 11, 2017 • edited

Codecov Report

coveralls commented Sep 11, 2017

nno commented Sep 11, 2017

coveralls commented Oct 2, 2017 • edited

nno commented Oct 2, 2017

codecov-io commented Sep 11, 2017 •

edited

coveralls commented Oct 2, 2017 •

edited