attr : str
Typically the sample or feature attribute used to determine splits.
attr_values : tuple
If not None, this is a list of values of the attr used to determine
the splits. The order of values in this list defines the order of the
resulting splits. It is possible to specify a particular value
multiple times. All dataset samples with values that are not listed
are going to be ignored.
count : None or int
Desired number of generated splits. If None, all splits are output
(default), otherwise the number of splits is limited to the given
count or the maximum number of possible split (whatever is less).
noslicing : bool
If True, dataset splitting is not done by slicing (causing
shared data between source and split datasets) even if it would
be possible. By default slicing is performed whenever possible
to reduce the memory footprint.
reverse : bool
If True, the order of datasets in the split is reversed, e.g.
instead of (training, testing), (training, testing) will be spit
out.
ignore_values : tuple
If not None, this is a list of value of the attr the shall be
ignored when determining the splits. This settings also affects
any specified attr_values.
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
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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