mvpa2.generators.resampling.Balancer

class mvpa2.generators.resampling.Balancer(amount='equal', attr='targets', count=1, limit='chunks', apply_selection=False, include_offlimit=False, space='balanced_set', **kwargs)

Generator to (repeatedly) select subsets of a dataset.

The Balancer can equalize the number of samples/features in a dataset, or select an absolute number or fraction of all available data. Selection is performed given a particular attribute and additionally can be limited to a subset of the dataset defined by more complex criteria (see limit argument). The node can either “mark” elements as selected by adding a corresponding attribute to the output dataset, or actually apply the selection by returning a new dataset with only selected elements.

Notes

Available conditional attributes:

• calling_time+: None
• raw_results: None

(Conditional attributes enabled by default suffixed with +)

Methods

generate(ds)

Generate the desired number of balanced datasets datasets.

Parameters:

amount : {‘equal’} or int or float

Specify the amount of elements to be selected (within the current limit). The amount can be given as an integer value corresponding to the absolute number of elements per unique attribute (see attr) value, as a float corresponding to the fraction of elements, or with the keyword ‘equal’. In the latter case the number of to be selected elements is determined by the least number of available elements for any given unique attribute value within the current limit.

attr : str

Dataset attribute whose unique values define element classes that are to be balanced in number.

count : int

How many iterations to perform on generate().

limit : None or str or dict

If None the whole dataset is considered as one. If a single attribute name is given, its unique values will be used to define chunks of data that are balanced individually. Finally, if a dictionary is provided, its keys define attribute names and its values (single value or sequence thereof) attribute value, where all key-value combinations across all given items define a “selection” of to-be-balanced samples or features.

apply_selection : bool

Flag whether the balanced selection shall be applied, i.e. the output dataset only contains selected elements. If False, the selection is instead added as an attribute that merely marks selected elements (see space argument).

include_offlimit : bool

If True, all samples that were off limit (i.e. not included in the balancing input are included in the balanced selection. If False (default) they are excluded.

space : str

Name of the selection marker attribute in the output dataset that is created if the balanced selection is not applied to the output dataset (see apply_selection argument).

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

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 the desired number of balanced datasets datasets.

generate(ds)

Generate the desired number of balanced datasets datasets.