mvpa2.measures.searchlight.sphere_searchlight(datameasure, radius=1, center_ids=None, space='voxel_indices', **kwargs)

Creates a Searchlight to run a scalar Measure 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).


datameasure : callable

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

radius : int

All features within this radius around the center will be part of a sphere. Radius is in grid-indices, i.e. 1 corresponds to all immediate neighbors, regardless of the physical distance.

center_ids : list of int

List of feature ids (not coordinates) the shall serve as 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 (it is passed as roi_ids argument of Searchlight).

space : str

Name of a feature attribute of the input dataset that defines the spatial coordinates of all features.

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.

preallocate_output : bool, optional

If set, the output of each computation block will be pre-allocated. This can speed up computations if the datameasure returns a large number of samples and there are many features for which the datameasure is computed. The user should verify the correct assignment of sample attributes and feature attributes, since no hstacking is performed within each computing block.

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

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.

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


If Searchlight is used as SensitivityAnalyzer one has to make sure that the specified scalar Measure 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 sensitivities and this conflicts with the intended behavior of a SensitivityAnalyzer.