def _run_classification_and_evaluation(
database_class: DataBase, classifier: str,
is_classifier_bootstrap: bool, **kwargs: dict) -> DataBase:
"""
Runs active learning classification and evaluation methods
Parameters
----------
database_class
An instance of DataBase class
classifier
Machine Learning algorithm.
Currently 'RandomForest', 'GradientBoostedTrees',
'KNN', 'MLP', 'SVM' and 'NB' are implemented.
Default is 'RandomForest'.
is_classifier_bootstrap
if tp apply a machine learning classifier by bootstrapping
kwargs
All keywords required by the classifier function.
"""
if is_classifier_bootstrap:
database_class.classify_bootstrap(method=classifier, **kwargs)
else:
database_class.classify(method=classifier, **kwargs)
database_class.evaluate_classification()
return database_class
def run_classification(database_class: DataBase, classifier: str,
is_classifier_bootstrap: bool, prediction_dir: str,
is_save_prediction: bool, iteration_step: int,
**kwargs: dict) -> DataBase:
"""
Run active learning classification model
Parameters
----------
database_class
An instance of DataBase class
classifier
Machine Learning algorithm.
Currently implemented options are 'RandomForest', 'GradientBoostedTrees',
'KNNclassifier','MLPclassifier','SVMclassifier' and 'NBclassifier'.
Default is 'RandomForest'.
is_classifier_bootstrap
if tp apply a machine learning classifier by bootstrapping
prediction_dir
Output directory to store prediction file for each loop.
Only used if `save_predictions==True
is_save_prediction
if predictions should be saved
iteration_step
active learning iteration number
kwargs
All keywords required by the classifier function.
-------
"""
if is_classifier_bootstrap:
database_class.classify_bootstrap(method=classifier,
loop=iteration_step,
pred_dir=prediction_dir,
save_predictions=is_save_prediction,
**kwargs)
else:
database_class.classify(method=classifier,
pred_dir=prediction_dir,
loop=iteration_step,
save_predictions=is_save_prediction,
**kwargs)
return database_class
