mvpa2.base.learner.ChainNode¶

Inheritance diagram of ChainNode

class mvpa2.base.learner.ChainNode(nodes, **kwargs)¶

This class allows to concatenate a list of nodes into a processing chain. When called with a dataset, it is sequentially fed through nodes in the chain. A ChainNode may also be used as a generator. In this case, all nodes in the chain are treated as generators too, and the ChainNode behaves as a single big generator that recursively calls all embedded generators and yield the results.

Notes

Available conditional attributes:

calling_time+: Time (in seconds) it took to call the node
raw_results: Computed results before invoking postproc. Stored only if postproc is not None.

(Conditional attributes enabled by default suffixed with +)

Methods

append(node) Append a node to the chain.

generate(ds[, startnode])

Parameters:

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:

nodes: list :

Node instances.

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