mvpa2.featsel.base.CombinedFeatureSelection

Inheritance diagram of CombinedFeatureSelection

class mvpa2.featsel.base.CombinedFeatureSelection(selectors, method, **kwargs)

Meta feature selection utilizing several embedded selection methods.

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

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.
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Methods

forward(data) Map data from input to output space.
forward1(data) Wrapper method to map single samples.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_space() Query the processing space name of this node.
reset()
reverse(data) Reverse-map data from output back into input space.
reverse1(data)
set_postproc(node) Assigns a post-processing node Set to None to disable postprocessing.
set_space(name) Set the processing space name of this node.
train(ds) The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain() Reverts changes in the state of this node caused by previous training
Parameters:

selectors : list

FeatureSelection instances to run. Order is not important.

method : {‘union’, ‘intersection’}

which method to be used to combine the feature selection set of all computed methods.

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

Methods

forward(data) Map data from input to output space.
forward1(data) Wrapper method to map single samples.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_space() Query the processing space name of this node.
reset()
reverse(data) Reverse-map data from output back into input space.
reverse1(data)
set_postproc(node) Assigns a post-processing node Set to None to disable postprocessing.
set_space(name) Set the processing space name of this node.
train(ds) The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain() Reverts changes in the state of this node caused by previous training
method
selectors

NeuroDebian

NITRC-listed