mvpa2.generators.partition.Partitioner

Inheritance diagram of Partitioner

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

Generator node to partition a dataset.

Partitioning is done by adding a sample attribute that assigns samples to an arbitrary number of partitions. Subclasses offer a variety of partitioning technique that are useful in e.g. cross-validation procedures.

it is important to note that other than adding a new sample attribute input datasets are not modified. In particular, there is no splitting of datasets into multiple pieces. If this is desired, a Partitioner can be chained to a Splitter node to achieve this.

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 +)

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

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

attr
generate(ds)
get_partition_specs(ds)

Returns the specs for all to be generated partition sets.

Returns :list(lists) :
get_partitions_attr(ds, specs)

Create a partition attribute array for a particular partion spec.

Parameters :

ds : Dataset

This is this source dataset.

specs : sequence of sequences

Contains ids of a sample attribute that shall go into each partition.

Returns :

array(ints) :

Each partition is represented by a unique integer value.

selection_strategy
splitattr

DEPRECATED: to be removed in PyMVPA 2.1; use .attr instead

NeuroDebian

NITRC-listed