Inheritance diagram of IterativeFeatureSelection

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


Available conditional attributes:

  • calling_time+: None
  • errors+: History of errors
  • nfeatures+: History of # of features left
  • raw_results: None
  • trained_dataset: None
  • trained_nsamples+: None
  • trained_targets+: None
  • training_time+: None

(Conditional attributes enabled by default suffixed with +)



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_pmeasure : bool

Flag whether the pmeasure should be trained before computing the error. In general this is required, but if the fmeasure and pmeasure share and make use of the same classifier AND pmeasure does not really need training, it can be switched off to save CPU cycles.

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.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

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