mvpa2.featsel.ifs.IterativeFeatureSelection¶
- class mvpa2.featsel.ifs.IterativeFeatureSelection(fmeasure, pmeasure, splitter, fselector, stopping_criterion=<mvpa2.featsel.helpers.NBackHistoryStopCrit object at 0xc5e94ac>, bestdetector=<mvpa2.featsel.helpers.BestDetector object at 0xc5e91cc>, 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 +)
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
- bestdetector¶
- fmeasure¶
- fselector¶
- pmeasure¶
- splitter¶
- stopping_criterion¶
- train_pmeasure¶
