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mvpa2.measures.gnbsearchlight.BaseSearchlight

class mvpa2.measures.gnbsearchlight.BaseSearchlight(queryengine, roi_ids=None, nproc=None, **kwargs)

Base class for searchlights.

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

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_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 :

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.

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

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

is_trained = True

Indicate that this measure is always trained.

queryengine

roi_ids