mvpa2.clfs.meta.NFoldPartitioner

class mvpa2.clfs.meta.NFoldPartitioner(cvtype=1, **kwargs)

Generic N-fold data partitioner.

Given a dataset with N chunks, with cvtype = 1 (which is default), it would generate N partition sets, where each chunk is sequentially taken out (with replacement) to form a second partition, while all other samples together form the first partition. Example, if there are 4 chunks, partition sets for cvtype = 1 are:

[[1, 2, 3], [0]]
[[0, 2, 3], [1]]
[[0, 1, 3], [2]]
[[0, 1, 2], [3]]

If cvtype>1, then all possible combinations of cvtype number of chunks are taken out, so for cvtype = 2 in previous example yields:

[[2, 3], [0, 1]]
[[1, 3], [0, 2]]
[[1, 2], [0, 3]]
[[0, 3], [1, 2]]
[[0, 2], [1, 3]]
[[0, 1], [2, 3]]

Note that the “taken-out” partition is always labeled ‘2’ while the remaining elements are labeled ‘1’.

If cvtype is a float in the range from 0 to 1, it specifies the ratio of present unique values to be taken.

If cvtype is large enough generating prohibitively large number of combinations, provide count to limit number of combinations and provide selection_strategy = ‘random’.

Notes

Available conditional attributes:

• calling_time+: None
• raw_results: None

(Conditional attributes enabled by default suffixed with +)

Methods

Parameters:

cvtype : int, float

Type of leave-one-out scheme: N-(cvtype). float value (0..1) specifies ratio of samples to be taken into the combination (e.g. 0.5 for 50%) given a dataset

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

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.

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