mvpa2.featsel.ifs.IFS

Inheritance diagram of IFS

class mvpa2.featsel.ifs.IFS(fmeasure, pmeasure, splitter, fselector=FixedNElementTailSelector() number=1.000000, **kwargs)

Incremental feature search.

A scalar Measure is computed multiple times on variations of a certain dataset. These measures are in turn used to incrementally select important features. Starting with an empty feature set the dataset measure is first computed for each single feature. A number of features is selected based on the resulting data measure map (using an ElementSelector).

Next the dataset measure is computed again using each feature in addition to the already selected feature set. Again the ElementSelector is used to select more features.

For each feature selection the transfer error on some testdatset is computed. This procedure is repeated until a given StoppingCriterion is reached.

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 +)

Initialize incremental feature search

Parameters :

feature_measure : Measure

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

performance_measure : Measure

Compute against a test dataset for each incremental feature set.

splitter: Splitter :

This splitter instance has to generate at least two dataset splits when called with the input dataset. The first split serves as the training dataset and the second as the evaluation dataset.

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

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.

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.

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

NeuroDebian

NITRC-listed