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
 
__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__

__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`")