mvpa2.generators.partition.NGroupPartitioner¶
- class mvpa2.generators.partition.NGroupPartitioner(ngroups=4, **kwargs)¶
Partition a dataset into N-groups of the sample attribute.
For example, NGroupPartitioner(2) is the same as the HalfPartitioner and yields exactly the same partitions and labeling patterns.
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 : ngroups : int
Number of groups to split the attribute into.
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.
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
- ngroups¶
