mvpa2.featsel.base.IterativeFeatureSelection

Inheritance diagram of IterativeFeatureSelection

class mvpa2.featsel.base.IterativeFeatureSelection(fmeasure, pmeasure, splitter, fselector, stopping_criterion=<mvpa2.featsel.helpers.NBackHistoryStopCrit object at 0xb28418c>, bestdetector=<mvpa2.featsel.helpers.BestDetector object at 0xb284fcc>, train_pmeasure=True, **kwargs)

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • errors+: History of errors
  • nfeatures+: History of # of features left
  • 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:

fmeasure : Measure

Computed for each candidate feature selection. The measure has to compute a scalar value.

pmeasure : Measure

Compute against a test dataset for each incremental feature set.

splitter: Splitter :

This splitter instance has to generate at least one dataset split when called with the input dataset that is used to compute the per-feature criterion for feature selection.

bestdetector : Functor

Given a list of error values it has to return a boolean that signals whether the latest error value is the total minimum.

stopping_criterion : Functor

Given a list of error values it has to return whether the criterion is fulfilled.

fselector : Functor

train_clf : bool

Flag whether the classifier in transfer_error should be trained before computing the error. In general this is required, but if the sensitivity_analyzer and transfer_error share and make use of the same classifier it can be switched off to save CPU cycles. Default None checks if sensitivity_analyzer is based on a classifier and doesn’t train if so.

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
bestdetector
fmeasure
fselector
pmeasure
splitter
stopping_criterion
train_pmeasure

NeuroDebian

NITRC-listed