Class MappedClassifier

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ProxyClassifier which uses some mapper prior training/testing.

MaskMapper can be used just a subset of features to train/classify. Having such classifier we can easily create a set of classifiers for BoostedClassifier, where each classifier operates on some set of features, e.g. set of best spheres from SearchLight, set of ROIs selected elsewhere. It would be different from simply applying whole mask over the dataset, since here initial decision is made by each classifier and then later on they vote for the final decision across the set of classifiers.

Nested Classes

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Inherited from misc.state.ClassWithCollections: __metaclass__

Instance Methods

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__init__(self, clf, mapper, **kwargs)
Initialize the instance

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_train(self, dataset)
Train MappedClassifier

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_predict(self, data)
Predict using MappedClassifier

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getSensitivityAnalyzer(self, slave_kwargs, **kwargs)
Return an appropriate SensitivityAnalyzer

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Inherited from ProxyClassifier: __repr__, summary, untrain

Inherited from base.Classifier: __str__, clone, isTrained, predict, repredict, retrain, train, trained

Inherited from base.Classifier (private): _getFeatureIds, _postpredict, _posttrain, _prepredict, _pretrain, _regressionIsBogus, _setRetrainable

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

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

Class Variables

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mapper = property(lambda x: x.__mapper, doc= "Used mapper")

Inherited from ProxyClassifier: clf

Inherited from base.Classifier: _DEV__doc__, feature_ids, predicting_time, predictions, regression, retrainable, trained_dataset, trained_labels, trained_nsamples, training_confusion, training_time, values