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clfs.libsvmc.sens

Module: clfs.libsvmc.sens

Inheritance diagram for mvpa.clfs.libsvmc.sens:

Provide sensitivity measures for libsvm’s SVM.

LinearSVMWeights

class mvpa.clfs.libsvmc.sens.LinearSVMWeights(clf, **kwargs)

Bases: mvpa.measures.base.Sensitivity

SensitivityAnalyzer for the LIBSVM implementation of a linear SVM.

Note

Available state variables:

• base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
• biases+: Offsets of separating hyperplanes
• null_prob+: State variable
• null_t: State variable
• raw_results: Computed results before applying any transformation algorithm

(States enabled by default are listed with +)

See also

Please refer to the documentation of the base class for more information:

Sensitivity

Initialize the analyzer with the classifier it shall use.

Parameters:

• clf (LinearSVM) – classifier to use. Only classifiers sub-classed from LinearSVM may be used.
• split_weights – If binary classification either to sum SVs per each class separately. (Default: False)
• enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
• disable_states (None or list of basestring) – Names of the state variables which should be disabled
• force_training (Bool) – if classifier was already trained – do not retrain
• combiner (Functor) – The combiner is only applied if the computed featurewise dataset measure is more than one-dimensional. This is different from a transformer, which is always applied. By default, the sum of absolute values along the second axis is computed.
• transformer (Functor) – This functor is called in __call__() to perform a final processing step on the to be returned dataset measure. If None, nothing is called
• null_dist (instance of distribution estimator) – The estimated distribution is used to assign a probability for a certain value of the computed measure.