mvpa.measures.splitmeasure

This is a FeaturewiseDatasetMeasure that uses another FeaturewiseDatasetMeasure and runs it multiple times on differents splits of a Dataset.

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

SplitFeaturewiseMeasure

class mvpa.measures.splitmeasure.SplitFeaturewiseMeasure(sensana, splitter=<class 'mvpa.datasets.splitters.NoneSplitter'>, combiner=<function FirstAxisMean at 0x56c18c6c>, **kwargs)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

This is a FeaturewiseDatasetMeasure that uses another FeaturewiseDatasetMeasure and runs it multiple times on differents splits of a Dataset.

When called with a Dataset it returns the mean sensitivity maps of all data splits.

Additonally this class supports the State interface. Several postprocessing functions can be specififed to the constructor. The results of the functions specified in the postproc dictionary will be available via their respective keywords.

Note

Available state variables:

• base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
• maps: To store maps per each split
• null_prob+: State variable
• null_t: State variable
• raw_result: Computed results before applying any transformation algorithm

(States enabled by default are listed with +)

See also

Please refer to the documentation of the base class for more information:

FeaturewiseDatasetMeasure

Cheap initialization.

Parameters:

• sensana (FeaturewiseDatasetMeasure) – that shall be run on the Dataset splits.
• splitter (Splitter) – used to split the Dataset. By convention the first dataset in the tuple returned by the splitter on each iteration is used to compute the sensitivity map.
• combiner – This functor will be called on an array of sensitivity maps and the result will be returned by __call__(). The result of a combiner must be an 1d ndarray.
• enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
• disable_states (None or list of basestring) – Names of the state variables which should be disabled

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 SplitFeaturewiseMeasure documentation.

Full API documentation of SplitFeaturewiseMeasure in module mvpa.measures.splitmeasure.