Multivariate Pattern Analysis in Python

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# mvpa2.generators.partition.ExcludeTargetsCombinationsPartitioner

class `mvpa2.generators.partition.``ExcludeTargetsCombinationsPartitioner`(k, targets_attr, partitions_attr='partitions', partitions_keep=2, partition_assign=3, **kwargs)

  Exclude combinations for a given partition from other partitions

  Given a pre-generated partitioning this generates new partitions by selecting all possible combinations of k-targets from all targets and excluding samples with the selected k-targets from training partition for each combination.

  A simple example would be:

  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.

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

  Examples

  For a dataset with 3 classes with one sample per class, k=2 gives 3 combination partitions with 2 samples for testing and one sample for training (since it excludes the 2 selected target samples) per partition.

      >>> from mvpa2.base.node import ChainNode
      >>> partitioner = ChainNode([NFoldPartitioner(),
      ...                          ExcludeTargetsCombinationsPartitioner(
      ...                             k=2,
      ...                             targets_attr='targets',
      ...                             space='partitions')],
      ...                         space='partitions')

  While cross-validating across subjects (e.g. working with hyperaligned data), to avoid significant bias due to matching trial-order effects instead of categorical boundaries, it is important to exclude from training chunks with the order matching the ones in testing.

      >>> partitioner = ChainNode([NFoldPartitioner(attr='subject'),
      ...                          ExcludeTargetsCombinationsPartitioner(
      ...                             k=1,
      ...                             targets_attr='chunks',
      ...                             space='partitions')],
      ...                         space='partitions')

  Methods

  `generate`(ds) |
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `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.

  Initialize instance of ExcludeTargetsCombinationsPartitioner

  Parameters:

  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

  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

  Methods

  `generate`(ds) |
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `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.

  `generate`(ds)

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