def compare_with_default_hyper_params(prj): l = IncrementalLearningModel(prj['name'], 'RF', 30, 1, use_cache=False, hyper_params=prj['params']) l.get_predicted_data() l.print_scores() l = IncrementalLearningModel(prj['name'], 'RF', 30, 1, use_cache=False, hyper_params={}) l.get_predicted_data() l.print_scores() print()
def find_optimum_hyper_params_for_random_forest(prj): for n_estimators in [10, 50, 100, 200, 400]: hyper_params = {'n_estimators': n_estimators} for _ in ['RF']: l = IncrementalLearningModel(prj, _, 30, 1, use_cache=False, hyper_params=hyper_params) l.get_predicted_data() l.print_scores() if prj['params']['n_estimators'] == 10: return for max_depth in [10, 20, 50, 100, 200, None]: hyper_params = {'max_depth': max_depth, 'n_estimators': prj['params']['n_estimators']} for _ in ['RF']: l = IncrementalLearningModel(prj['name'], _, 30, 1, use_cache=False, hyper_params=hyper_params) l.get_predicted_data() l.print_scores() for criterion in ['gini', 'entropy']: hyper_params = {'criterion': criterion, 'n_estimators': prj['params']['n_estimators']} for _ in ['RF']: l = IncrementalLearningModel(prj['name'], _, 30, 1, use_cache=False, hyper_params=hyper_params) l.get_predicted_data() l.print_scores() for min_samples_split in [2, 5, 10, 20, 50, 100]: hyper_params = {'min_samples_split': min_samples_split, 'n_estimators': prj['params']['n_estimators']} for _ in ['RF']: l = IncrementalLearningModel(prj['name'], _, 30, 1, use_cache=False, hyper_params=hyper_params) l.get_predicted_data() l.print_scores() for min_samples_leaf in [1, 2, 5, 10, 20, 50, 100]: hyper_params = {'min_samples_leaf': min_samples_leaf, 'n_estimators': prj['params']['n_estimators']} for _ in ['RF']: l = IncrementalLearningModel(prj['name'], _, 30, 1, use_cache=False, hyper_params=hyper_params) l.get_predicted_data() l.print_scores()
def compare_classifiers(prj): for _ in ['DT', 'RF', 'NB', 'MLP', 'LR', 'SGD']: l = IncrementalLearningModel(prj['name'], _, 30, 1, use_cache=False, hyper_params={}) l.get_predicted_data() l.print_scores()