Inheritance diagram of SplitSamplesProbabilityMapper

class mvpa2.featsel.base.SplitSamplesProbabilityMapper(sensitivity_analyzer, split_by_labels, select_common_features=True, probability_label=None, probability_combiner=None, selector=FractionTailSelector() fraction=0.050000, **kwargs)

Mapper to select features & samples based on some sensitivity value.

A use case is feature selection across participants, where either the same features are selected in all participants or not (see select_common_features parameter).


Available conditional attributes:

  • calling_time+: None
  • raw_results: None
  • trained_dataset: None
  • trained_nsamples+: None
  • trained_targets+: None
  • training_time+: None

(Conditional attributes enabled by default suffixed with +)


>>> nf = 10
>>> ns = 100
>>> nsubj = 5
>>> nchunks = 5
>>> data = np.random.normal(size=(ns, nf))
>>> from mvpa2.base.dataset import AttrDataset
>>> from mvpa2.measures.anova import OneWayAnova
>>> ds = AttrDataset(data,
...                sa=dict(sidx=np.arange(ns),
...                        targets=np.arange(ns) % nchunks,
...                        chunks=np.floor(np.arange(ns) * nchunks / ns),
...                        subjects=np.arange(ns) / (ns / nsubj / nchunks) % nsubj),
...                fa=dict(fidx=np.arange(nf)))
>>> analyzer=OneWayAnova()
>>> element_selector=FractionTailSelector(.4, mode='select', tail='upper')
>>> common=True
>>> m=SplitSamplesProbabilityMapper(analyzer, 'subjects',
...                                 probability_label='fprob',
...                                 select_common_features=common,
...                                 selector=element_selector)
>>> m.train(ds)
>>> y=m(ds)
>>> z=m(ds.samples)
>>> np.all(np.equal(z, y.samples))
>>> y.shape
(100, 4)



sensitivity_analyzer: FeaturewiseMeasure :

Sensitivity analyzer to come up with sensitivity.

split_by_labels: str or list of str :

Sample labels on which input datasets are split before data is selected.

select_common_features: bool :

True means that the same features are selected after the split.

probablity_label: None or str :

If None, then the output dataset ds from the sensitivity_analyzer is taken to select the samples. If not None it takes[‘probablity_label’]. For example if sensitivity_analyzer=OneWayAnova then probablity_label=’fprob’ is a sensible value.

probability_combiner: function :

If select_common_features is True, then this function is applied to the feature scores across splits. If None, it uses lambda x:np.sum(-np.log(x)) which is sensible if the scores are probability values

selector: Selector :

function that returns the indices to keep.

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

slicearg :

Argument for slicing

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

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



Function used to do selection


Measure which was used to do selection