[pymvpa] SL on an artificial dataset

[Matthias Ekman]

Hi,

sometimes I still encounter some problems with the transition from mvpa 
to mvpa2. This time, I would like to run a Searchlight on an artificial 
dataset. Previously that could be done like this:

from sklearn.datasets import make_classification
X,y = make_classification(n_samples=55, n_features=630, n_classes=2)
ds = MaskedDataset(samples=X, labels=y, chunks=np.arange(y.size))
cv = CrossValidatedTransferError(
          TransferError(LinearCSVMC()),
          OddEvenSplitter())
sl = Searchlight(cv, radius=5)

In mvpa2 I could do the same

ds = Dataset(X)
ds.targets = y
ds.chunks = np.arange(55)

but the sphere_searchlight requires a 'space' parameter. Here I don't 
have real neighbor-space relations. It can be completely random or 
neighbors in the np.array. How would you define that for a synthetic 
dataset?

Thanks in advance,
  Matthias
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