| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# featsel.base

## Module: `featsel.base`

Inheritance diagram for `mvpa.featsel.base`:

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

## Classes

### `CombinedFeatureSelection`

class `mvpa.featsel.base.``CombinedFeatureSelection`(feature_selections, combiner, **kwargs)

  Bases: `mvpa.featsel.base.FeatureSelection`

  Meta feature selection utilizing several embedded selection methods.

  Each embedded feature selection method is computed individually. Afterwards all feature sets are combined by either taking the union or intersection of all sets.

  The individual feature sets of all embedded methods are optionally avialable from the selections_ids state variable.

  Note

  Available state variables:

  - selected_ids: State variable
  - selections_ids+: List of feature id sets for each performed method.

  (States enabled by default are listed with +)

  See also

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

  `FeatureSelection`

  Parameters:

  - feature_selections (list) – FeatureSelection instances to run. Order is not important.
  - combiner (‘union’, ‘intersection’) – which method to be used to combine the feature selection set of all computed methods.
  - 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

  `combiner`

    Selection set combination method.

  `feature_selections`

    List of FeatureSelections

  `untrain`()

### `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

  `untrain`()

    ‘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

### `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

  `untrain`()

### `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

  `untrain`()

### Navigation

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