Abstract classifier class to be inherited by all classifiers
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
clone() | Create full copy of the classifier. |
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_sensitivity_analyzer(**kwargs) | Factory method to return an appropriate sensitivity analyzer for the respective classifier. |
get_space() | Query the processing space name of this node. |
is_trained([dataset]) | Either classifier was already trained. |
predict(obj, data, *args, **kwargs) | |
repredict(obj, data, *args, **kwargs) | |
reset() | |
retrain(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training. |
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. |
summary() | Providing summary over the classifier |
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 Classifier
Parameters: | enable_ca : None or list of str
disable_ca : None or list of str
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
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Methods
clone() | Create full copy of the classifier. |
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_sensitivity_analyzer(**kwargs) | Factory method to return an appropriate sensitivity analyzer for the respective classifier. |
get_space() | Query the processing space name of this node. |
is_trained([dataset]) | Either classifier was already trained. |
predict(obj, data, *args, **kwargs) | |
repredict(obj, data, *args, **kwargs) | |
reset() | |
retrain(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training. |
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. |
summary() | Providing summary over the classifier |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Create full copy of the classifier.
It might require classifier to be untrained first due to present SWIG bindings.
TODO: think about proper re-implementation, without enrollment of deepcopy
Factory method to return an appropriate sensitivity analyzer for the respective classifier.
Either classifier was already trained.
MUST BE USED WITH CARE IF EVER
Helper to avoid check if data was changed actually changed
Useful if just some aspects of classifier were changed since its previous training. For instance if dataset wasn’t changed but only classifier parameters, then kernel matrix does not have to be computed.
Words of caution: classifier must be previously trained, results always should first be compared to the results on not ‘retrainable’ classifier (without calling retrain). Some additional checks are enabled if debug id ‘CHECK_RETRAIN’ is enabled, to guard against obvious mistakes.
Parameters: | kwargs :
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Providing summary over the classifier
Either classifier was already trained