mvpa2.measures.irelief.IterativeReliefOnline_Devel

Inheritance diagram of IterativeReliefOnline_Devel

class mvpa2.measures.irelief.IterativeReliefOnline_Devel(a=5.0, permute=True, max_iter=3, **kwargs)

FeaturewiseMeasure that performs multivariate I-RELIEF algorithm. Online version.

UNDER DEVELOPMENT

Online version with complexity O(T*N*I), where N is the number of instances and I the number of features.

See: Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007. http://plaza.ufl.edu/sunyijun/Paper/PAMI_1.pdf

Note that this implementation is not fully online, since hit and miss dictionaries (H,M) are computed once at the beginning using full access to all labels. This can be easily corrected to a full online implementation. But this is not mandatory now since the major goal of this current online implementation is reduction of computational complexity.

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • null_prob+: None
  • null_t: None
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Constructor of the IRELIEF class.

Parameters :

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

null_dist : instance of distribution estimator

The estimated distribution is used to assign a probability for a certain value of the computed measure.

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.

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

is_trained

Indicate that this measure doesn’t have to be trained

NeuroDebian

NITRC-listed