Multivariate Pattern Analysis in Python

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# mvpa2.generators.partition.NGroupPartitioner

class `mvpa2.generators.partition.``NGroupPartitioner`(ngroups=4, **kwargs)

  Partition a dataset into N-groups of the sample attribute.

  For example, NGroupPartitioner(2) is the same as the HalfPartitioner and yields exactly the same partitions and labeling patterns.

  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 `+`)

  Methods

  `generate`(ds) |
  `get_partition_specs`(ds) | Returns the specs for all to be generated partition sets.
  `get_partitions_attr`(ds, specs) | Create a partition attribute array for a particular partion spec.
  `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.

  Parameters:

  ngroups : int

  > Number of groups to split the attribute into.

  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

  count : None or int

  > Desired number of splits to be output. It is limited by the number of splits possible for a given splitter (e.g. `OddEvenSplitter` can have only up to 2 splits). If None, all splits are output (default).

  selection_strategy : str

  > If `count` is not None, possible strategies are possible: ‘first’: First `count` splits are chosen; ‘random’: Random (without replacement) `count` splits are chosen; ‘equidistant’: Splits which are equidistant from each other.

  attr : str

  > Sample attribute used to determine splits.

  space : str

  > Name of the to be created sample attribute defining the partitions. In addition, a dataset attribute named ‘`space`_set’ will be added to each output dataset, indicating the number of the partition set it corresponds to.

  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_partition_specs`(ds) | Returns the specs for all to be generated partition sets.
  `get_partitions_attr`(ds, specs) | Create a partition attribute array for a particular partion spec.
  `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.

  `ngroups`

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