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_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.
preallocate_output : bool, optional
If set, the output of each computation block will be pre-allocated.
This can speed up computations if the datameasure returns a large
number of samples and there are many features for which the
datameasure is computed. The user should verify the correct
assignment of sample attributes and feature attributes, since no
hstacking is performed within each computing block.
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 query engine ids (e.g., feature ids, not coordinates, in case
of IndexQueryEngine; and node_indices in case of
SurfaceQueryEngine) that 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 (be careful to use it only with
IndexQueryEngine). By default all query engine ids 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.
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
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