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
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.
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