mvpa2.mappers.mdp_adaptor.MDPFlowMapper

class mvpa2.mappers.mdp_adaptor.MDPFlowMapper(flow, node_arguments=None, **kwargs)

Mapper encapsulating an arbitray MDP flow.

This mapper wraps an MDP flow and uses it for forward and reverse data mapping (reverse is only available if the underlying MDP flow supports it). It is possible to specify arbitrary arguments for the training of the MDP flow.

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

Notes

It is not possible to perform incremental training of the MDP flow.

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 +)

Examples

>>> import mdp
>>> from mvpa2.mappers.mdp_adaptor import MDPFlowMapper
>>> from mvpa2.base.dataset import DAE
>>> flow = (mdp.nodes.PCANode() + mdp.nodes.IdentityNode() +
...         mdp.nodes.FDANode())
>>> mapper = MDPFlowMapper(flow,
...                        node_arguments=(None, None,
...                        [DAE('sa', 'targets')]))

Methods

Parameters:

flow : mdp.Flow instance

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

node_arguments : tuple, list

A tuple or a list the same length as the flow. Each item is a list of arguments for the training of the corresponding node in the flow. If a node does not require additional arguments, None can be provided instead. Keyword arguments are currently not supported by mdp.Flow.

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

Methods