Multivariate Pattern Analysis in Python

# mvpa2.measures.nnsearchlight.sphere_m1nnsearchlight

`mvpa2.measures.nnsearchlight.``sphere_m1nnsearchlight`(knn, generator, radius=1, center_ids=None, space='voxel_indices', *args, **kwargs)

  Creates a `M1NNSearchlight` to assess cross-validation classification performance of M1NN on all possible spheres of a certain size within a dataset.

  The idea of taking advantage of naiveness of M1NN 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.

  knn : `kNN`

  > Used to fetch space and dfx settings. TODO

  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`.