learner : Learner
Any trainable node that shall be run on the dataset folds.
generator : Node, optional
Generator used to resample the input dataset into multiple instances
(i.e. partitioning it). The number of datasets yielded by this
generator determines the number of cross-validation folds.
IMPORTANT: The space of this generator determines the attribute
that will be used to split all generated datasets into training and
testing sets. If None provided, a single original dataset will be
passed to the splitter as is
errorfx : Node or callable
Custom implementation of an error function. The callable needs to
accept two arguments (1. predicted values, 2. target values). If not
a Node, it gets wrapped into a BinaryFxNode.
splitter : Splitter or None
A Splitter instance to split the dataset into training and testing
part. The first split will be used for training and the second for
testing – all other splits will be ignored. If None, a default
splitter is auto-generated using the space setting of the
generator. If no generator provided, splitter uses ‘partitions’
sample attribute. The default splitter is configured to return the
1-labeled partition of the input dataset at first, and the
2-labeled partition second. This behavior corresponds to most
Partitioners that label the taken-out portion 2 and the remainder
with 1.
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
node : Node
Node or Measure implementing the procedure that is supposed to be run
multiple times.
callback : functor, optional
Optional callback to extract information from inside the main loop of
the measure. The callback is called with the input ‘data’, the ‘node’
instance that is evaluated repeatedly and the ‘result’ of a single
evaluation – passed as named arguments (see labels in quotes) for
every iteration, directly after evaluating the node.
concat_as : {‘samples’, ‘features’}, optional
Along which axis to concatenate result dataset from all iterations.
By default, results are ‘vstacked’ as multiple samples in the output
dataset. Setting this argument to ‘features’ will change this to
‘hstacking’ along the feature axis.
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.
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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