mvpa2.generators.partition.ExcludeTargetsCombinationsPartitioner

class mvpa2.generators.partition.ExcludeTargetsCombinationsPartitioner(k, targets_attr, partitions_attr='partitions', partitions_keep=2, partition_assign=3, **kwargs)

Exclude combinations for a given partition from other partitions

Given a pre-generated partitioning this generates new partitions by selecting all possible combinations of k-targets from all targets and excluding samples with the selected k-targets from training partition for each combination.

A simple example would be:

Notes

Available conditional attributes:

• calling_time+: None
• raw_results: None

(Conditional attributes enabled by default suffixed with +)

Examples

For a dataset with 3 classes with one sample per class, k=2 gives 3 combination partitions with 2 samples for testing and one sample for training (since it excludes the 2 selected target samples) per partition.

>>> from mvpa2.base.node import ChainNode
>>> partitioner = ChainNode([NFoldPartitioner(),
...                          ExcludeTargetsCombinationsPartitioner(
...                             k=2,
...                             targets_attr='targets',
...                             space='partitions')],
...                         space='partitions')

While cross-validating across subjects (e.g. working with hyperaligned data), to avoid significant bias due to matching trial-order effects instead of categorical boundaries, it is important to exclude from training chunks with the order matching the ones in testing.

>>> partitioner = ChainNode([NFoldPartitioner(attr='subject'),
...                          ExcludeTargetsCombinationsPartitioner(
...                             k=1,
...                             targets_attr='chunks',
...                             space='partitions')],
...                         space='partitions')

Methods

generate(ds)

Initialize instance of ExcludeTargetsCombinationsPartitioner

Parameters:

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

Methods

generate(ds)

generate(ds)