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mvpa.measures.irelief

FeaturewiseDatasetMeasure performing multivariate Iterative RELIEF (I-RELIEF) algorithm. 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.

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.measures.irelief (for developers).

Classes

IterativeRelief

class mvpa.measures.irelief.IterativeRelief(threshold=0.01, kernel_width=1.0, w_guess=None, **kwargs)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

FeaturewiseDatasetMeasure that performs multivariate I-RELIEF algorithm. Batch version.

Batch I-RELIEF-2 feature weighting algorithm. Works for binary or multiclass class-labels. Batch version with complexity O(T*N^2*I), where T is the number of iterations, N the number of instances, 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 current implementation allows to use only exponential-like kernels. Support for linear kernel will be added later.

Constructor of the IRELIEF class.

compute_M_H(label)

Compute hit/miss dictionaries.

For each instance compute the set of indices having the same class label and different class label.

Note that this computation is independent of the number of features.

XXX should it be some generic function since it doesn’t use self

k(distances)
Exponential kernel.

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the IterativeRelief documentation.

Full API documentation of IterativeRelief in module mvpa.measures.irelief.

IterativeReliefOnline

class mvpa.measures.irelief.IterativeReliefOnline(a=10.0, permute=True, max_iter=3, **kwargs)

Bases: mvpa.measures.irelief.IterativeRelief

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

This algorithm is exactly the one in the referenced paper (I-RELIEF-2 online), using weighted 1-norm and Exponential Kernel.

Constructor of the IRELIEF class.

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the IterativeReliefOnline documentation.

Full API documentation of IterativeReliefOnline in module mvpa.measures.irelief.

IterativeReliefOnline_Devel

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

Bases: mvpa.measures.irelief.IterativeRelief_Devel

FeaturewiseDatasetMeasure 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.

Constructor of the IRELIEF class.

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the IterativeReliefOnline_Devel documentation.

Full API documentation of IterativeReliefOnline_Devel in module mvpa.measures.irelief.

IterativeRelief_Devel

class mvpa.measures.irelief.IterativeRelief_Devel(threshold=0.01, kernel=None, kernel_width=1.0, w_guess=None, **kwargs)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

FeaturewiseDatasetMeasure that performs multivariate I-RELIEF algorithm. Batch version allowing various kernels.

UNDER DEVELOPEMNT.

Batch I-RELIEF-2 feature weighting algorithm. Works for binary or multiclass class-labels. Batch version with complexity O(T*N^2*I), where T is the number of iterations, N the number of instances, 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 current implementation allows to use only exponential-like kernels. Support for linear kernel will be added later.

Constructor of the IRELIEF class.

compute_M_H(label)

Compute hit/miss dictionaries.

For each instance compute the set of indices having the same class label and different class label.

Note that this computation is independent of the number of features.

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the IterativeRelief_Devel documentation.

Full API documentation of IterativeRelief_Devel in module mvpa.measures.irelief.