Initialize the analyzer with the classifier it shall use.

:Parameters:
  clf : :class:`Classifier`
    classifier to use.
  force_training : Bool
    if classifier was already trained -- do not retrain

Parameters:

combiner - 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.

Overrides: object.__init__

repr(self, prefixes=None)
(Representation operator)

source code

String representation of DatasetMeasure

Includes only arguments which differ from default ones

Parameters:

fullname - Either to include full name of the module
prefixes - What other prefixes to prepend to list of arguments

Overrides: object.__repr__

(inherited documentation)

call(self, dataset=None)
(Call operator)

source code

Train classifier on dataset and then compute actual sensitivity.

If the classifier is already trained it is possible to extract the sensitivities without passing a dataset.

Overrides: DatasetMeasure.__call__

untrain(self)

source code

Untrain corresponding classifier for Sensitivity

Overrides: DatasetMeasure.untrain

feature_ids(self)

source code

Return feature_ids used by the underlying classifier

Decorators:

@property

Class Variable Details

[hide private]

clf

Value:

property(fget= lambda self: self.__clf, fset= _setClassifier)

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Class Sensitivity

__init__(self, clf, force_training=True, **kwargs) (Constructor)

__repr__(self, prefixes=None) (Representation operator)

__call__(self, dataset=None) (Call operator)

untrain(self)

feature_ids(self)

clf

init(self, clf, force_training=True, **kwargs)
(Constructor)

repr(self, prefixes=None)
(Representation operator)

call(self, dataset=None)
(Call operator)