Multivariate Pattern Analysis in Python

# 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+`: Time (in seconds) it took to call the node
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `trained_dataset`: The dataset it has been trained on
  - `trained_nsamples+`: Number of samples it has been trained on
  - `trained_targets+`: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)
  - `training_time+`: Time (in seconds) it took to train the learner

  (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')]))

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon every call.
  `is_trained` | Whether the Learner is currently trained.
  `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) |
  `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_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:

  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

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon every call.
  `is_trained` | Whether the Learner is currently trained.
  `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) |
  `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_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