mvpa2.measures.searchlight.Searchlight

Inheritance diagram of Searchlight

class mvpa2.measures.searchlight.Searchlight(datameasure, queryengine, add_center_fa=False, **kwargs)

The implementation of a generic searchlight measure.

The idea for a searchlight algorithm stems from a paper by Kriegeskorte et al. (2006). As a result it produces a map of measures given a datameasure instance of interest, which is ran at each spatial location.

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • null_prob+: None
  • null_t: None
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • roi_feature_ids: Feature IDs for all generated ROIs.
  • roi_sizes: Number of features in each ROI.
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Parameters :

datameasure : callable

Any object that takes a Dataset and returns some measure when called.

add_center_fa : bool or str

If True or a string, each searchlight ROI dataset will have a boolean vector as a feature attribute that indicates the feature that is the seed (e.g. sphere center) for the respective ROI. If True, the attribute is named ‘roi_seed’, the provided string is used as the name otherwise.

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

queryengine : QueryEngine

Engine to use to discover the “neighborhood” of each feature. See QueryEngine.

roi_ids : None or list(int) or str

List of feature ids (not coordinates) the shall serve as ROI seeds (e.g. sphere centers). Alternatively, this can be the name of a feature attribute of the input dataset, whose non-zero values determine the feature ids. By default all features will be used.

nproc : None or int

How many processes to use for computation. Requires pprocess external module. If None – all available cores will be used.

null_dist : instance of distribution estimator

The estimated distribution is used to assign a probability for a certain value of the computed measure.

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

add_center_fa
datameasure

NeuroDebian

NITRC-listed