mvpa.measures.searchlight

Implementation of the Searchlight algorithm

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

Searchlight

class mvpa.measures.searchlight.Searchlight(datameasure, radius=1.0, center_ids=None, **kwargs)

Bases: mvpa.measures.base.DatasetMeasure

Runs a scalar DatasetMeasure on all possible spheres of a certain size within a dataset.

The idea for a searchlight algorithm stems from a paper by Kriegeskorte et al. (2006).

See also

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

DatasetMeasure

Note

Available state variables:

• null_prob+: State variable
• null_t: State variable
• raw_result: Computed results before applying any transformation algorithm
• spheresizes: Number of features in each sphere.

(States enabled by default are listed with +)

See also

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

DatasetMeasure

Parameters:

• datameasure (callable) – Any object that takes a Dataset and returns some measure when called.
• radius (float) – All features within the radius around the center will be part of a sphere.
• center_ids (list(int)) – List of feature ids (not coordinates) the shall serve as sphere centers. By default all features will be used.
• enable_states – Names of the state variables which should be enabled additionally to default ones
• disable_states – Names of the state variables which should be disabled
• 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

Note

If Searchlight is used as SensitivityAnalyzer one has to make sure that the specified scalar DatasetMeasure returns large (absolute) values for high sensitivities and small (absolute) values for low sensitivities. Especially when using error functions usually low values imply high performance and therefore high sensitivity. This would in turn result in sensitivity maps that have low (absolute) values indicating high sensitivites and this conflicts with the intended behavior of a SensitivityAnalyzer.

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 Searchlight documentation.

Full API documentation of Searchlight in module mvpa.measures.searchlight.