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:
(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_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
disable_ca : None or list of str
null_dist : instance of distribution estimator
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
|
---|
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_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 |
Indicate that this measure doesn’t have to be trained