Package mvpa :: Package clfs :: Module meta :: Class MappedClassifier

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# Class MappedClassifier

source code

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 | [hide private]

Inherited from `misc.state.ClassWithCollections`: `__metaclass__`

Instance Methods | [hide private]

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

_train(self, dataset) Train MappedClassifier | source code

_predict(self, data) Predict using MappedClassifier | source code

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

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 | [hide private]

| 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` Inherited from `base.Classifier` (private): `_clf_internals` Inherited from `misc.state.ClassWithCollections`: `descr`

Instance Variables | [hide private]

| __mapper mapper to help us our with prepping data to training/classification

Properties | [hide private]

Inherited from `object`: `__class__`

Method Details | [hide private]

### __init__(self, clf, mapper, **kwargs)
(Constructor)

source code

Initialize the instance

Parameters:
  - `clf` (Classifier) - classifier based on which mask classifiers is created
  - `mapper` - whatever Mapper comes handy
Overrides: object.__init__

### _train(self, dataset)

source code

Train MappedClassifier

Overrides: base.Classifier._train

### _predict(self, data)

source code

Predict using MappedClassifier

Overrides: base.Classifier._predict

### getSensitivityAnalyzer(self, slave_kwargs, **kwargs)

source code

Return an appropriate SensitivityAnalyzer

Decorators:
  - `@group_kwargs(prefixes= ['slave_'], passthrough= True)`
Overrides: base.Classifier.getSensitivityAnalyzer