Multivariate Pattern Analysis in Python

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# mvpa2.generators.permutation.AttributePermutator

class `mvpa2.generators.permutation.``AttributePermutator`(attr, count=1, limit=None, assure=False, **kwargs)

  Node to permute one a more attributes in a dataset.

  This node can permute arbitrary sample or feature attributes in a dataset. Moreover, it supports limiting the permutation to a subset of samples or features (see `limit` argument). The node can simply be called with a dataset for a one time permutation, or used as a generator to produce multiple permutations.

  This node only permutes dataset attributes, dataset samples are no affected. The permuted output dataset shares the samples container with the input dataset.

  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) | Generate the desired number of permuted datasets.
  `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:

  attr : str or list(str)

  > Name of the to-be-permuted attribute. This can also be a list of attribute names, in which case the identical shuffling is applied to all listed attributes.

  count : int

  > Number of permutations to be yielded by .generate()

  limit : None or str or dict

  > If `None` all attribute values will be permuted. If an single attribute name is given, its unique values will be used to define chunks of data that are permuted individually (i.e. no attributed values will be replaced across chunks). Finally, if a dictionary is provided, its keys define attribute names and its values (single value or sequence thereof) attribute value, where all key-value combinations across all given items define a “selection” of to-be-permuted samples or features.

  assure : bool

  > If set, by-chance non-permutations will be prevented, i.e. it is checked that at least two items change their position. Since this check adds a runtime penalty it is off by default.

  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) | Generate the desired number of permuted datasets.
  `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.

  `assure`
  `attr`
  `generate`(ds)

    Generate the desired number of permuted datasets.

  `limit`
  `nruns`

    DEPRECATED: to be removed in 2.1 – use .count instead

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