Table Of Contents

Previous topic

mvpa.datasets.masked

Next topic

mvpa.datasets.miscfx

This Page

Quick search

mvpa.datasets.meta

Dataset container

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.datasets.meta (for developers).

MetaDataset

class mvpa.datasets.meta.MetaDataset(datasets)

Bases: object

Dataset container

The class is useful to combine several Datasets with different origin and type and bind them together. Such a combined dataset can then by used to e.g. pass it to a classifier.

MetaDataset does not permanently duplicate data stored in the dataset it contains. The combined samples matrix is build on demand and samples attribute access is redirected to the first dataset in the container.

Currently operations other than samples or feature selection are not fully supported, e.g. passing a MetaDataset to detrend() will initially result in a detrended MetaDataset, but the combined and detrended samples matrix will be lost after the next call to selectSamples() or selectFeatures(), which freshly pulls samples from all datasets in the container.

Initialize dataset instance

Parameters:
  • datasets (list) –
applyMapper(*args, **kwargs)
Apply a mapper on all underlying datasets.
datasets
getNFeatures()
Number of features per sample.
getNSamples()
Currently available number of samples.
getRandomSamples(nperlabel)
Return a MetaDataset with a random subset of samples.
mapReverse(val)
nfeatures
Number of features per sample.
nsamples
Currently available number of samples.
permuteLabels(*args, **kwargs)
Toggle label permutation.
rebuildSamples()
Update the combined samples matrix from all underlying datasets.
selectFeatures(ids, sort=True)
Do feature selection on all underlying datasets at once.
selectSamples(*args, **kwargs)
Select samples from all underlying datasets at once.
setSamplesDType(dtype)
Set the data type of the samples array.

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the MetaDataset documentation.

Full API documentation of MetaDataset in module mvpa.datasets.meta.