mvpa2.datasets.sources.skl_data.skl_mlcomp(name_or_id, set_='raw', mlcomp_root=None, **kwargs)

Load a datasets as downloaded from


name_or_id : the integer id or the string name metadata of the MLComp

dataset to load

set_ : select the portion to load: ‘train’, ‘test’ or ‘raw’

mlcomp_root : the filesystem path to the root folder where MLComp datasets

are stored, if mlcomp_root is None, the MLCOMP_DATASETS_HOME environment variable is looked up instead.

**kwargs : domain specific kwargs to be passed to the dataset loader.


data : Bunch

Dictionary-like object, the interesting attributes are: ‘filenames’, the files holding the raw to learn, ‘target’, the classification labels (integer index), ‘target_names’, the meaning of the labels, and ‘DESCR’, the full description of the dataset.

Note on the lookup process: depending on the type of name_or_id, :

will choose between integer id lookup or metadata name lookup by :

looking at the unzipped archives and metadata file. :

TODO: implement zip dataset loading too :


This function has been auto-generated by wrapping load_mlcomp() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.