Wrapper to allow for alternative post-processing of a shared measure.
This class is useful whenever a measure (or for example a trained classifier) shall be utilized in multiple nodes, but each node needs to perform its on post-processing of results. One can simply wrap the measure into this class and assign arbitrary post-processing nodes to the wrapper, instead of the measure itself.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
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() | |
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 |
Initialize instance of ProxyMeasure
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
|
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Methods
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() | |
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 |
Return proxied measure