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mvpa2.clfs.warehouse.SensitivityBasedFeatureSelection

class mvpa2.clfs.warehouse.SensitivityBasedFeatureSelection(sensitivity_analyzer, feature_selector=FractionTailSelector() fraction=0.050000, train_analyzer=True, **kwargs)

Feature elimination.

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

Notes

Available conditional attributes:

• calling_time+: Time (in seconds) it took to call the node
• raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
• sensitivity: None
• training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Initialize feature selection

Parameters :

sensitivity_analyzer : FeaturewiseMeasure

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.

train_analyzer : bool

Flag whether to train the sensitivity analyzer on the input dataset during train(). If False, the employed sensitivity measure has to be already trained before.

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

filler : optional

Value to fill empty entries upon reverse operation

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space: str, optional :

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

sensitivity_analyzer

Measure which was used to do selection