mvpa2.measures.irelief.IterativeReliefOnline

class mvpa2.measures.irelief.IterativeReliefOnline(a=10.0, permute=True, max_iter=3, **kwargs)

FeaturewiseMeasure that performs multivariate I-RELIEF algorithm. Online version.

This algorithm is exactly the one in the referenced paper (I-RELIEF-2 online), using weighted 1-norm and Exponential Kernel.

Notes

Available conditional attributes:

• calling_time+: Time (in seconds) it took to call the node
• null_prob+: None
• null_t: None
• 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

compute_M_H(label)	Compute hit/miss dictionaries.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_space()	Query the processing space name of this node.
k(distances)	Exponential kernel.
reset()
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

Constructor of the IRELIEF class.

Parameters:

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

null_dist : instance of distribution estimator

The estimated distribution is used to assign a probability for a certain value of the computed measure.

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

compute_M_H(label)	Compute hit/miss dictionaries.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_space()	Query the processing space name of this node.
k(distances)	Exponential kernel.
reset()
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

is_trained = True

Indicate that this measure doesn’t have to be trained