Multivariate Pattern Analysis in Python

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# mvpa2.generators.partition.OddEvenPartitioner

class `mvpa2.generators.partition.``OddEvenPartitioner`(usevalues=False, **kwargs)

  Create odd and even partitions based on a sample attribute.

  The partitioner yields two datasets. In the first set all odd chunks are labeled ‘1’ and all even runs are labeled ‘2’. In the second set the assignment is reversed (odd: ‘2’, even: ‘1’).

  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:

  usevalues : bool

  > If True the values of the attribute used for partitioning will be used to determine odd and even samples. If False odd and even chunks are defined by the order of attribute values, i.e. first unique attribute is odd, second is even, despite the corresponding values might indicate the opposite (e.g. in case of [2,3].

  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.

  `usevalues`

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