attr : str or list(str)
Name of the to-be-permuted attribute. This can also be a list of
attribute names, in which case the identical shuffling is applied to
all listed attributes.
count : int
Number of permutations to be yielded by .generate()
limit : None or str or dict
If None all attribute values will be permuted. If an single
attribute name is given, its unique values will be used to define
chunks of data that are permuted individually (i.e. no attributed
values will be replaced across chunks). Finally, if a dictionary is
provided, its keys define attribute names and its values (single value
or sequence thereof) attribute value, where all key-value combinations
across all given items define a “selection” of to-be-permuted samples
or features.
strategy : ‘simple’, ‘uattrs’
‘simple’ strategy is the straightfoward permutation of attributes (given
the limit). In some sense it assumes independence of those samples.
‘uattrs’ strategy looks at unique values of attr (or their unique
combinations in case of attr being a list), and “permutes” those
unique combinations values thus breaking their assignment to the samples
but preserving any dependencies between samples within the same unique
combination.
assure : bool
If set, by-chance non-permutations will be prevented, i.e. it is
checked that at least two items change their position. Since this
check adds a runtime penalty it is off by default.
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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