mvpa.featsel.base

Feature selection base class and related stuff base classes and helpers.

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.featsel.base (for developers).

Classes

FeatureSelection

class mvpa.featsel.base.FeatureSelection(**kwargs)

Bases: mvpa.misc.state.ClassWithCollections

Base class for any feature selection

Base class for Functors which implement feature selection on the datasets.

Note

Available state variables:

• selected_ids: State variable

(States enabled by default are listed with +)

See also

Please refer to the documentation of the base class for more information:

ClassWithCollections

Initialize instance of FeatureSelection

Parameters:

• enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
• disable_states (None or list of basestring) – Names of the state variables which should be disabled

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the FeatureSelection documentation.

Full API documentation of FeatureSelection in module mvpa.featsel.base.

FeatureSelectionPipeline

class mvpa.featsel.base.FeatureSelectionPipeline(feature_selections, **kwargs)

Bases: mvpa.featsel.base.FeatureSelection

Feature elimination through the list of FeatureSelection’s.

Given as list of FeatureSelections it applies them in turn.

Note

Available state variables:

• nfeatures+: Number of features before each step in pipeline
• selected_ids: State variable

(States enabled by default are listed with +)

See also

Please refer to the documentation of the base class for more information:

FeatureSelection

Initialize feature selection pipeline

Parameters:

• feature_selections (lisf of FeatureSelection) – selections which to use. Order matters
• enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
• disable_states (None or list of basestring) – Names of the state variables which should be disabled

feature_selections

List of FeatureSelections

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the FeatureSelectionPipeline documentation.

Full API documentation of FeatureSelectionPipeline in module mvpa.featsel.base.

SensitivityBasedFeatureSelection

class mvpa.featsel.base.SensitivityBasedFeatureSelection(sensitivity_analyzer, feature_selector=FractionTailSelector() fraction=0.050000, **kwargs)

Bases: mvpa.featsel.base.FeatureSelection

Feature elimination.

A FeaturewiseDatasetMeasure is used to compute sensitivity maps given a certain dataset. These sensitivity maps are in turn used to discard unimportant features.

Note

Available state variables:

• selected_ids: State variable
• sensitivity: State variable

(States enabled by default are listed with +)

See also

Please refer to the documentation of the base class for more information:

FeatureSelection

Initialize feature selection

Parameters:

• sensitivity_analyzer (FeaturewiseDatasetMeasure) – sensitivity analyzer to come up with sensitivity
• feature_selector (Functor) – Given a sensitivity map it has to return the ids of those features that should be kept.
• enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
• disable_states (None or list of basestring) – Names of the state variables which should be disabled

sensitivity_analyzer

Measure which was used to do selection

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the SensitivityBasedFeatureSelection documentation.

Full API documentation of SensitivityBasedFeatureSelection in module mvpa.featsel.base.