mvpa2.measures.gnbsearchlight.GNBSearchlight

class mvpa2.measures.gnbsearchlight.GNBSearchlight(gnb, generator, qe, **kwargs)

Efficient implementation of Gaussian Naive Bayes Searchlight.

This implementation takes advantage that GNB is “naive” in its reliance on massive univariate conditional probabilities of each feature given a target class. Plain Searchlight analysis approach asks for the same information over again and over again for the same feature in multiple “lights”. So it becomes possible to drastically cut running time of a Searchlight by pre-computing basic statistics necessary used by GNB beforehand and then doing their subselection for a given split/feature set.

Kudos for the idea and showing that it indeed might be beneficial over generic Searchlight with GNB go to Francisco Pereira.

Notes

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 +)

Methods

Initialize a GNBSearchlight

Parameters:

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)

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.

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

Methods

gnb