Inheritance diagram of FeatureSelection

class mvpa2.featsel.base.FeatureSelection(filler=0, **kwargs)

Mapper to select a subset of features.

Depending on the actual slicing two FeatureSelections can be merged in a number of ways: incremental selection (+=), union (&=) and intersection (|=). Were the former assumes that two feature selections are applied subsequently, and the latter two assume that both slicings operate on the set of input features.


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 +)


>>> from mvpa2.datasets import *
>>> ds = Dataset([[1,2,3,4,5]])
>>> fs0 = StaticFeatureSelection([0,1,2,3])
>>> fs0(ds).samples
array([[1, 2, 3, 4]])

Merge two incremental selections: the resulting mapper performs a selection that is equivalent to first applying one slicing and subsequently the next slicing. In this scenario the slicing argument of the second mapper is relative to the output feature space of the first mapper.

>>> fs1 = StaticFeatureSelection([0,2])
>>> fs0 += fs1
>>> fs0(ds).samples
array([[1, 3]])



filler : optional

Value to fill empty entries upon reverse operation

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

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