mvpa2.mappers.mdp_adaptor.MDPNodeMapper

class mvpa2.mappers.mdp_adaptor.MDPNodeMapper(node, nodeargs=None, **kwargs)

Mapper encapsulating an arbitray MDP node.

This mapper wraps an MDP node and uses it for forward and reverse data mapping (reverse is only available if the underlying MDP node supports it). It is possible to specify arbitrary arguments for all processing steps of an MDP node (training, training stop, execution, and inverse).

Because MDP does not allow to ‘reset’ a node and (re)train it from scratch the mapper uses a copy of the initially wrapped node for the actual processing. Upon subsequent training attempts a new copy of the original node is made and replaces the previous one.

Notes

MDP nodes requiring multiple training phases are not supported. Use a MDPFlowWrapper for that. Moreover, it is not possible to perform incremental training of a node.

Available conditional attributes:

• calling_time+: None
• raw_results: None
• trained_dataset: None
• trained_nsamples+: None
• trained_targets+: None
• training_time+: None

(Conditional attributes enabled by default suffixed with +)

Methods

Parameters:

node : mdp.Node instance

This node instance is taken as the pristine source of which a copy is made for actual processing upon each training attempt.

nodeargs : dict

Dictionary for additional arguments for all calls to the MDP node. The dictionary key’s meaning is as follows:

‘train’

Arguments for calls to Node.train()

‘stoptrain’

Arguments for calls to Node.stop_training()

‘exec’

Arguments for calls to Node.execute()

‘inv’

Arguments for calls to Node.inverse()

The value for each item is always a 2-tuple, consisting of a tuple (for the arguments), and a dictionary (for keyword arguments), i.e. ((), {}). Both, tuple and dictionary have to be provided even if they are empty.

space : see base class

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.

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

Methods