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PyMVPA: Python MultiVariate Pattern Analysis
Package mvpa :: Package featsel :: Module base :: Class FeatureSelectionPipeline
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Class FeatureSelectionPipeline

source code

Feature elimination through the list of FeatureSelection's.

Given as list of FeatureSelections it applies them in turn.

Nested Classes [hide private]

Inherited from misc.state.ClassWithCollections: __metaclass__

Instance Methods [hide private]
 
__init__(self, feature_selections, **kwargs)
Initialize feature selection pipeline		 source code
 
untrain(self)
'Untrain' feature selection		 source code
 
__call__(self, dataset, testdataset=None, **kwargs)
Invocation of the feature selection		 source code

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

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

Class Variables [hide private]
  feature_selections = property(fget= lambda self: self.__featur...

Inherited from FeatureSelection: selected_ids

Inherited from misc.state.ClassWithCollections: _DEV__doc__, descr

Instance Variables [hide private]
  __feature_selections
Selectors to use in turn
  nfeatures
Number of features at each step (before running selection)
Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, feature_selections, **kwargs)
(Constructor)

source code 
Initialize feature selection pipeline
Parameters:
  • feature_selections (lisf of FeatureSelection) - selections which to use. Order matters
Overrides: object.__init__

untrain(self)

source code 

'Untrain' feature selection

Necessary for full 'untraining' of the classifiers. By default does nothing, needs to be overridden in corresponding feature selections to pass to the sensitivities

Overrides: FeatureSelection.untrain
(inherited documentation)

__call__(self, dataset, testdataset=None, **kwargs)
(Call operator)

source code 
Invocation of the feature selection
Parameters:
  • dataset - dataset used to select features
  • testdataset - dataset the might be used to compute a stopping criterion
Overrides: FeatureSelection.__call__

Class Variable Details [hide private]

feature_selections

Value:
property(fget= lambda self: self.__feature_selections, doc= "List of `\
FeatureSelections`")

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PyMVPA: Python MultiVariate Pattern Analysis
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