print('Incorrect dataset') continue train_file, test_file = get_penn_case_data_paths(name_of_dataset) config_models_data = get_models_hyperparameters() case_name = f'penn_ml_{name_of_dataset}' try: result_metrics = CaseExecutor(params=ExecutionParams( train_file=train_file, test_file=test_file, task=problem_class, target_name='target', case_label=case_name), models=[ BenchmarkModelTypesEnum.tpot, BenchmarkModelTypesEnum.baseline, BenchmarkModelTypesEnum.fedot ], metric_list=metric_names).execute() except Exception as ex: print(f'Exception on {name_of_dataset}: {ex}') continue result_metrics['hyperparameters'] = config_models_data save_metrics_result_file( result_metrics, file_name=f'penn_ml_metrics_for_{name_of_dataset}') convert_json_stats_to_csv(dataset)
from benchmark.benchmark_model_types import BenchmarkModelTypesEnum from benchmark.benchmark_utils import get_models_hyperparameters, get_scoring_case_data_paths, \ save_metrics_result_file from benchmark.executor import CaseExecutor, ExecutionParams from core.repository.tasks import TaskTypesEnum if __name__ == '__main__': train_file, test_file = get_scoring_case_data_paths() result_metrics = CaseExecutor( params=ExecutionParams(train_file=train_file, test_file=test_file, task=TaskTypesEnum.classification, target_name='default', case_label='scoring'), models=[ BenchmarkModelTypesEnum.baseline, BenchmarkModelTypesEnum.tpot, BenchmarkModelTypesEnum.fedot ], metric_list=['roc_auc', 'f1']).execute() result_metrics['hyperparameters'] = get_models_hyperparameters() save_metrics_result_file(result_metrics, file_name='scoring_metrics')
from benchmark.benchmark_model_types import BenchmarkModelTypesEnum from benchmark.benchmark_utils import get_cancer_case_data_paths, save_metrics_result_file, get_models_hyperparameters from benchmark.executor import CaseExecutor from core.repository.task_types import MachineLearningTasksEnum if __name__ == '__main__': train_file, test_file = get_cancer_case_data_paths() result_metrics = CaseExecutor(train_file=train_file, test_file=test_file, task=MachineLearningTasksEnum.classification, case_label='cancer', target_name='target', models=[ BenchmarkModelTypesEnum.tpot, BenchmarkModelTypesEnum.h2o, BenchmarkModelTypesEnum.autokeras, BenchmarkModelTypesEnum.mlbox, BenchmarkModelTypesEnum.baseline ]).execute() result_metrics['hyperparameters'] = get_models_hyperparameters() save_metrics_result_file(result_metrics, file_name='cancer_metrics')