Class GPRLinearWeights

SensitivityAnalyzer that reports the weights GPR trained on a given Dataset.

In case of KernelLinear compute explicitly the coefficients of the linear regression, together with their variances (if requested).

Note that the intercept is not computed.

Nested Classes

Inherited from misc.state.ClassWithCollections: __metaclass__

Instance Methods

_call(self, dataset)
Extract weights from GPR

source code

Inherited from measures.base.Sensitivity: __call__, __init__, __repr__, feature_ids, untrain

Inherited from measures.base.Sensitivity (private): _setClassifier

Inherited from measures.base.FeaturewiseDatasetMeasure: combiner

Inherited from measures.base.FeaturewiseDatasetMeasure (private): _postcall

Inherited from measures.base.DatasetMeasure: null_dist, transformer

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

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

Class Variables

variances = StateVariable(enabled= False, doc= "Variances of t...

_LEGAL_CLFS = [GPR]
If Sensitivity is classifier specific, classes of classifiers should be listed in the list

Inherited from measures.base.Sensitivity: clf

Inherited from measures.base.FeaturewiseDatasetMeasure: base_sensitivities

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

Inherited from misc.state.ClassWithCollections: _DEV__doc__, descr

Instance Variables

Inherited from measures.base.Sensitivity (private): _force_training

Properties

Inherited from object: __class__

Method Details

Extract weights from GPR

Class Variable Details

Value:

StateVariable(enabled= False, doc= "Variances of the weights (for Kern
elLinear)")

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