Multivariate Pattern Analysis in Python

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# 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+`: Time (in seconds) it took to call the node
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `training_time+`: Time (in seconds) it took to train the learner

  (Conditional attributes enabled by default suffixed with `+`)

  Methods

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  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.

  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

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

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