Multivariate Pattern Analysis in Python

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# mvpa2.generators.partition.NFoldPartitioner

class `mvpa2.generators.partition.``NFoldPartitioner`(cvtype=1, **kwargs)

  Generic N-fold data partitioner.

  Given a dataset with N chunks, with `cvtype` = 1 (which is default), it would generate N partition sets, where each chunk is sequentially taken out (with replacement) to form a second partition, while all other samples together form the first partition. Example, if there are 4 chunks, partition sets for `cvtype` = 1 are:

      [[1, 2, 3], [0]]
      [[0, 2, 3], [1]]
      [[0, 1, 3], [2]]
      [[0, 1, 2], [3]]

  If `cvtype`>1, then all possible combinations of `cvtype` number of chunks are taken out, so for `cvtype` = 2 in previous example yields:

      [[2, 3], [0, 1]]
      [[1, 3], [0, 2]]
      [[1, 2], [0, 3]]
      [[0, 3], [1, 2]]
      [[0, 2], [1, 3]]
      [[0, 1], [2, 3]]

  Note that the “taken-out” partition is always labeled ‘2’ while the remaining elements are labeled ‘1’.

  If `cvtype` is a float in the range from 0 to 1, it specifies the ratio of present unique values to be taken.

  If `cvtype` is large enough generating prohibitively large number of combinations, provide `count` to limit number of combinations and provide `selection_strategy` = ‘random’.

  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:

  cvtype : int, float

  > Type of leave-one-out scheme: N-(cvtype). float value (0..1) specifies ratio of samples to be taken into the combination (e.g. 0.5 for 50%) given a dataset

  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.

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