generator : Generator
Some Generator to prepare partitions for cross-validation.
qe : QueryEngine
Query engine which would provide neighborhood information
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.
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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