Multivariate Pattern Analysis in Python

# mvpa2.generators.permutation.AttributePermutator

class `mvpa2.generators.permutation.``AttributePermutator`(attr, count=1, limit=None, assure=False, strategy='simple', chunk_attr=None, rng=<module 'numpy.random' from '/usr/lib/python2.7/dist-packages/numpy/random/__init__.pyc'>, **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+`: None
  - `raw_results`: None

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

  Attributes

  `assure` |
  `attr` |
  `descr` | Description of the object if any
  `limit` |
  `nruns` | DEPRECATED: to be removed in 2.1 – use .count instead
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `space` | Processing space name of this node

  Methods

  `generate`(ds) | Generate the desired number of permuted datasets.

  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.

  strategy : ‘simple’, ‘uattrs’, ‘chunks’

  > ‘simple’ strategy is the straightforward permutation of attributes (given the limit). In some sense it assumes independence of those samples. ‘uattrs’ strategy looks at unique values of attr (or their unique combinations in case of `attr` being a list), and “permutes” those unique combinations values thus breaking their assignment to the samples but preserving any dependencies between samples within the same unique combination. The ‘chunks’ strategy swaps attribute values of entire chunks. Naturally, this will only work if there is the same number of samples in all chunks.

  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.

  pass_attr : str, list of str|tuple, optional

  > Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see `Dataset.get_attr()`), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

  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

  Attributes

  `assure` |
  `attr` |
  `descr` | Description of the object if any
  `limit` |
  `nruns` | DEPRECATED: to be removed in 2.1 – use .count instead
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `space` | Processing space name of this node

  Methods

  `generate`(ds) | Generate the desired number of permuted datasets.

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

    Generate the desired number of permuted datasets.

  `limit`
  `nruns`

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