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()