Multivariate Pattern Analysis in Python

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# mvpa2.mappers.fxy.FxyMapper

class `mvpa2.mappers.fxy.``FxyMapper`(fx, train_as_1st=True, **kwargs)

  Mapper to execute a callable with two datasets as arguments.

  The first dataset is passed to the mapper during training, the second dataset is passed to forward/call(). This mapper is useful to, for example, compare two datasets regarding particular aspects, merge them, or perform other operations that require the presence of two datasets.

  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.
  - `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:

  fx : callable

  > Functor that is called with the two datasets upon forward-mapping.

  train_as_1st : bool

  > If True, the training dataset is passed to the target callable as the first argument and the other dataset as the second argument. If False, it is vice versa.

  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.

  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

  Examples

      >>> from mvpa2.mappers.fxy import FxyMapper
      >>> from mvpa2.datasets import Dataset
      >>> callable = lambda x,y: len(x) > len(y)
      >>> ds1 = Dataset(range(5))
      >>> ds2 = Dataset(range(3))
      >>> fxy = FxyMapper(callable)
      >>> fxy.train(ds1)
      >>> fxy(ds2).item()
      True
      >>> fxy = FxyMapper(callable, train_as_1st=False)
      >>> fxy.train(ds1)
      >>> fxy(ds2).item()
      False

  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

  `fx`

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