Package mvpa :: Package measures :: Module noiseperturbation :: Class NoisePerturbationSensitivity
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Class NoisePerturbationSensitivity

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This is a FeaturewiseDatasetMeasure that uses a scalar DatasetMeasure and selective noise perturbation to compute a sensitivity map.

First the scalar DatasetMeasure computed using the original dataset. Next the data measure is computed multiple times each with a single feature in the dataset perturbed by noise. The resulting difference in the scalar DatasetMeasure is used as the sensitivity for the respective perturbed feature. Large differences are treated as an indicator of a feature having great impact on the scalar DatasetMeasure.

The computed sensitivity map might have positive and negative values!

Nested Classes [hide private]

Inherited from misc.state.Stateful: __metaclass__

Instance Methods [hide private]
 
__init__(self, datameasure, noise=<built-in method normal of mtrand.RandomState object at 0x8433...)
Cheap initialization.
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_call(self, dataset)
Compute the sensitivity map.
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Inherited from base.FeaturewiseDatasetMeasure (private): _postcall

Inherited from base.DatasetMeasure: __call__, __str__

Inherited from misc.state.Stateful: __getattribute__, __repr__, __setattr__, reset

Inherited from object: __delattr__, __hash__, __new__, __reduce__, __reduce_ex__

Class Variables [hide private]
  _collections_template = {'states': <mvpa.misc.state.StateColle...

Inherited from base.FeaturewiseDatasetMeasure: base_sensitivities

Inherited from base.DatasetMeasure: __doc__, null_prob, raw_result

Inherited from misc.state.Stateful (private): _initargs

Properties [hide private]

Inherited from misc.state.Stateful: descr

Inherited from object: __class__

Method Details [hide private]

__init__(self, datameasure, noise=<built-in method normal of mtrand.RandomState object at 0x8433...)
(Constructor)

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Cheap initialization.

Parameters

  • datameasure: Datameasure that is used to quantify the effect of

    noise perturbation.

  • noise: Functor to generate noise. The noise generator has to return

    an 1d array of n values when called the size=n keyword argument. This is the default interface of the random number generators in NumPy's random module.

Parameters:
  • combiner - If _call returned value is 2d -- combines along 2nd dimension as well as sets base_sensitivities TODO change combiner's default
Overrides: object.__init__

_call(self, dataset)

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Compute the sensitivity map.

Returns a 1d array of sensitivities for all features in dataset.

Overrides: base.DatasetMeasure._call

Class Variable Details [hide private]

_collections_template

Value:
{'states': <mvpa.misc.state.StateCollection object at 0x8caa5ec>}