mvpa2.featsel.rfe.Repeater¶
-
class
mvpa2.featsel.rfe.
Repeater
(count, space='repetitons', **kwargs)¶ Node that yields the same dataset for a certain number of repetitions.
Each yielded dataset has a dataset attribute that identifies the iteration (see the
space
setting).Notes
Available conditional attributes:
calling_time+
: Time (in seconds) it took to call the noderaw_results
: Computed results before invoking postproc. Stored only if postproc is not None.
(Conditional attributes enabled by default suffixed with
+
)Attributes
descr
Description of the object if any pass_attr
Which attributes of the dataset or self.ca to pass into result dataset upon call postproc
Node to perform post-processing of results space
Processing space name of this node Methods
__call__
(ds[, _call_kwargs])The default implementation calls _precall()
,_call()
, and finally returns the output of_postcall()
.generate
(ds)Generate the desired number of repetitions. get_postproc
()Returns the post-processing node or None. get_space
()Query the processing space name of this node. reset
()set_postproc
(node)Assigns a post-processing node set_space
(name)Set the processing space name of this node. Parameters: count : int
Positive integer that set the numbed of repetitions.
space : str
The name of the dataset attribute that will hold the actual repetiton in the yielded datasets.
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
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
Attributes
descr
Description of the object if any pass_attr
Which attributes of the dataset or self.ca to pass into result dataset upon call postproc
Node to perform post-processing of results space
Processing space name of this node Methods
__call__
(ds[, _call_kwargs])The default implementation calls _precall()
,_call()
, and finally returns the output of_postcall()
.generate
(ds)Generate the desired number of repetitions. get_postproc
()Returns the post-processing node or None. get_space
()Query the processing space name of this node. reset
()set_postproc
(node)Assigns a post-processing node set_space
(name)Set the processing space name of this node. -
generate
(ds)¶ Generate the desired number of repetitions.