mvpa2.measures.searchlight.Searchlight¶
- class mvpa2.measures.searchlight.Searchlight(datameasure, queryengine, add_center_fa=False, 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.
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- null_prob+: None
- null_t: None
- raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
- roi_feature_ids: Feature IDs for all generated ROIs.
- roi_sizes: Number of features in each ROI.
- training_time+: Time (in seconds) it took to train the learner
(Conditional attributes enabled by default suffixed with +)
Parameters : 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_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.
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
- add_center_fa¶
- datameasure¶
