mvpa2.generators.partition.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
