learners : list of Learner
combine_axis : [‘h’, ‘v’]
a: {‘unique’,’drop_nonunique’,’uniques’,’all’} or True or False or None (default: None) :
Indicates which dataset attributes from datasets are stored
in merged_dataset. If an int k, then the dataset attributes from
datasets[k] are taken. If ‘unique’ then it is assumed that any
attribute common to more than one dataset in datasets is unique;
if not an exception is raised. If ‘drop_nonunique’ then as ‘unique’,
except that exceptions are not raised. If ‘uniques’ then, for each
attribute, any unique value across the datasets is stored in a tuple
in merged_datasets. If ‘all’ then each attribute present in any
dataset across datasets is stored as a tuple in merged_datasets;
missing values are replaced by None. If None (the default) then no
attributes are stored in merged_dataset. True is equivalent to
‘drop_nonunique’. False is equivalent to None.
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
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
nodes: list :
mappers : list
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