mvpa2.featsel.base.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)

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.
• trained_dataset: The dataset it has been trained on
• trained_nsamples+: Number of samples it has been trained on
• trained_targets+: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)
• training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Attributes

auto_train	Whether the Learner performs automatic trainingwhen called untrained.
bestdetector
descr	Description of the object if any
fmeasure
force_train	Whether the Learner enforces training upon every call.
fselector
is_trained	Whether the Learner is currently trained.
pass_attr	Which attributes of the dataset or self.ca to pass into result dataset upon call
pmeasure
postproc	Node to perform post-processing of results
slicearg
space	Processing space name of this node
splitter
stopping_criterion
train_pmeasure

Methods

__call__(ds)
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_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_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

Attributes

auto_train	Whether the Learner performs automatic trainingwhen called untrained.
bestdetector
descr	Description of the object if any
fmeasure
force_train	Whether the Learner enforces training upon every call.
fselector
is_trained	Whether the Learner is currently trained.
pass_attr	Which attributes of the dataset or self.ca to pass into result dataset upon call
pmeasure
postproc	Node to perform post-processing of results
slicearg
space	Processing space name of this node
splitter
stopping_criterion
train_pmeasure

Methods

__call__(ds)
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_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