mvpa2.mappers.base.CombinedMapper

Inheritance diagram of CombinedMapper

class mvpa2.mappers.base.CombinedMapper(mappers, combine_axis, **kwargs)

Mapper to pass a dataset on to a set of mappers and combine there output.

Output combination or aggregation is currently done by hstacking or vstacking the resulting datasets.

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Parameters :

mappers : list

combine_axis : [‘h’, ‘v’]

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space: str, optional :

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

Examples

>>> import numpy as np
>>> from mvpa2.mappers.base import CombinedMapper
>>> from mvpa2.featsel.base import StaticFeatureSelection
>>> from mvpa2.datasets import Dataset
>>> mp = CombinedMapper([StaticFeatureSelection([1,2]),
...                      StaticFeatureSelection([2,3])],
...                     combine_axis='h')
>>> mp.is_trained = True
>>> ds = Dataset(np.arange(12).reshape(3,4))
>>> out = mp(ds)
>>> out.samples
array([[ 1,  2,  2,  3],
       [ 5,  6,  6,  7],
       [ 9, 10, 10, 11]])
mappers

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