Class TScoredFeaturewiseMeasure

SplitFeaturewiseMeasure computing featurewise t-score of sensitivities across splits.

Nested Classes

Inherited from misc.state.ClassWithCollections: __metaclass__

Instance Methods

__init__(self, sensana, splitter, noise_level=0.0, **kwargs)
Cheap initialization.

source code

_call(self, dataset, callables=[])
Compute sensitivity maps for all dataset splits and return the featurewise t-score of them.

source code

Inherited from base.FeaturewiseDatasetMeasure: __repr__, combiner

Inherited from base.FeaturewiseDatasetMeasure (private): _postcall

Inherited from base.DatasetMeasure: __call__, null_dist, transformer, untrain

Inherited from misc.state.ClassWithCollections: __getattribute__, __new__, __setattr__, __str__, reset

Inherited from object: __delattr__, __format__, __hash__, __reduce__, __reduce_ex__, __sizeof__, __subclasshook__

Class Variables

Inherited from base.FeaturewiseDatasetMeasure: base_sensitivities

Inherited from base.DatasetMeasure: __doc__, null_prob, null_t, raw_results

Inherited from misc.state.ClassWithCollections: _DEV__doc__, descr

Instance Variables

__noise_level
Output of the sensitivity analyzer when there is no signal.

Properties

Inherited from object: __class__

Method Details

Cheap initialization.

Parameters:

sensana (SensitivityAnalyzer) - that shall be run on the Dataset splits.
splitter (Splitter) - used to split the Dataset. By convention the first dataset in the tuple returned by the splitter on each iteration is used to compute the sensitivity map.
noise_level, float - Theoretical output of the respective SensitivityAnalyzer for a pure noise pattern. For most algorithms this is probably zero, hence the default.

Overrides: object.__init__

Compute sensitivity maps for all dataset splits and return the featurewise t-score of them.

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