mvpa2.measures.gnbsearchlight.sphere_gnbsearchlight

mvpa2.measures.gnbsearchlight.sphere_gnbsearchlight(gnb, generator, radius=1, center_ids=None, space='voxel_indices', *args, **kwargs)

Creates a GNBSearchlight to assess cross-validation classification performance of GNB on all possible spheres of a certain size within a dataset.

The idea of taking advantage of naiveness of GNB for the sake of quick searchlight-ing stems from Francisco Pereira (paper under review).

Parameters:

radius : float

All features within this radius around the center will be part of a sphere.

center_ids : list of int

List of feature ids (not coordinates) the shall serve as sphere centers. By default all features will be used (it is passed roi_ids argument for Searchlight).

space : str

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

gnb : GNB

GNB classifier as the specification of what GNB parameters to use. Instance itself isn’t used.

generator : Generator

Some Generator to prepare partitions for cross-validation. It must not change “targets”, thus e.g. no AttributePermutator’s

errorfx : func, optional

Functor that computes a scalar error value from the vectors of desired and predicted values (e.g. subclass of ErrorFunction).

indexsum : (‘sparse’, ‘fancy’), optional

What use to compute sums over arbitrary columns. ‘fancy’ corresponds to regular fancy indexing over columns, whenever in ‘sparse’, product of sparse matrices is used (usually faster, so is default if scipy is available).

reuse_neighbors : bool, optional

Compute neighbors information only once, thus allowing for efficient reuse on subsequent calls where dataset’s feature attributes remain the same (e.g. during permutation testing)

splitter : Splitter, optional

Which will be used to split partitioned datasets. If None specified then standard one operating on partitions 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.

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

Notes

If any BaseSearchlight 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.