Multivariate Pattern Analysis in Python

# mvpa2.generators.partition.HalfPartitioner

class `mvpa2.generators.partition.``HalfPartitioner`(count=None, selection_strategy='equidistant', attr='chunks', space='partitions', **kwargs)

  Partition a dataset into two halves of the sample attribute.

  The partitioner yields two datasets. In the first set second half of chunks are labeled ‘1’ and the first half labeled ‘2’. In the second set the assignment is reversed (1st half: ‘1’, 2nd half: ‘2’).

  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 `+`)

  Attributes

  `attr` |
  `descr` | Description of the object if any
  `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
  `selection_strategy` |
  `space` | Processing space name of this node
  `splitattr` | DEPRECATED: to be removed in PyMVPA 2.1; use .attr instead

  Methods

  `__call__`(ds[, _call_kwargs]) | The default implementation calls `_precall()`, `_call()`, and finally returns the output of `_postcall()`.
  `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 partition spec.
  `get_postproc`() | Returns the post-processing node or None.
  `get_selected_indexes`(n_cfgs) | A naive selection of indexes according to strategy and count
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `set_postproc`(node) | Assigns a post-processing node
  `set_space`(name) | Set the processing space name of this node.

  Parameters:

  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.

  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

  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

  `attr` |
  `descr` | Description of the object if any
  `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
  `selection_strategy` |
  `space` | Processing space name of this node
  `splitattr` | DEPRECATED: to be removed in PyMVPA 2.1; use .attr instead

  Methods

  `__call__`(ds[, _call_kwargs]) | The default implementation calls `_precall()`, `_call()`, and finally returns the output of `_postcall()`.
  `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 partition spec.
  `get_postproc`() | Returns the post-processing node or None.
  `get_selected_indexes`(n_cfgs) | A naive selection of indexes according to strategy and count
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `set_postproc`(node) | Assigns a post-processing node
  `set_space`(name) | Set the processing space name of this node.