queryengine : QueryEngine
Engine to use to discover the “neighborhood” of each feature.
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.
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.
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