Inheritance diagram of Searchlight

class mvpa2.measures.searchlight.Searchlight(datameasure, queryengine, add_center_fa=False, results_postproc_fx=None, results_backend='native', results_fx=None, tmp_prefix='tmpsl', nblocks=None, **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.


Available conditional attributes:

  • calling_time+: None
  • null_prob+: None
  • null_t: None
  • raw_results: None
  • roi_center_ids+: Center ID for all generated ROIs.
  • roi_feature_ids: Feature IDs for all generated ROIs.
  • roi_sizes: Number of features in each ROI.
  • trained_dataset: None
  • trained_nsamples+: None
  • trained_targets+: None
  • training_time+: None

(Conditional attributes enabled by default suffixed with +)



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.

results_postproc_fx : callable

Called with all the results computed in a block for possible post-processing which needs to be done in parallel instead of serial aggregation in results_fx.

results_backend : (‘native’, ‘hdf5’), optional

Specifies the way results are provided back from a processing block in case of nproc > 1. ‘native’ is pickling/unpickling of results by pprocess, while ‘hdf5’ would use h5save/h5load functionality. ‘hdf5’ might be more time and memory efficient in some cases.

results_fx : callable, optional

Function to process/combine results of each searchlight block run. By default it would simply append them all into the list. It receives as keyword arguments sl, dataset, roi_ids, and results (iterable of lists). It is the one to take care of assigning roi_* ca’s

tmp_prefix : str, optional

If specified – serves as a prefix for temporary files storage if results_backend == ‘hdf5’. Thus can specify the directory to use (trailing file path separator is not added automagically).

nblocks : None or int

Into how many blocks to split the computation (could be larger than nproc). If None – nproc is 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

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.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

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