def main(): # BASIC SETUP logger = set_logger() args = NET_parse_args( main_description= "Training launcher for Neural net classifier on HIGGS benchmark") logger.info(args) flush(logger) # INFO model = build_model(args, -1) os.makedirs(model.results_directory, exist_ok=True) config = Config() config_table = evaluate_config(config) config_table.to_csv( os.path.join(model.results_directory, 'config_table.csv')) # RUN if not args.conditional_only: eval_table = get_eval_table(args, model.results_directory) if not args.estimate_only: eval_conditional = get_eval_conditional(args, model.results_directory) if not args.estimate_only and not args.conditional_only: eval_table = pd.concat([eval_table, eval_conditional], axis=1) # EVALUATION print_line() print_line() print(eval_table) print_line() print_line() eval_table.to_csv( os.path.join(model.results_directory, 'evaluation.csv')) gather_images(model.results_directory)
def main(): # BASIC SETUP logger = set_logger() args = REG_parse_args( main_description= "Training launcher for Marginal Regressor on HIGGS benchmark") logger.info(args) flush(logger) # INFO model = build_model(args, -1) os.makedirs(model.results_directory, exist_ok=True) config = Config() config_table = evaluate_config(config) config_table.to_csv( os.path.join(model.results_directory, 'config_table.csv')) # RUN eval_table = get_eval_table(args, model.results_directory) # EVALUATION print_line() print_line() print(eval_table) print_line() print_line() eval_table.to_csv(os.path.join(model.results_directory, 'evaluation.csv')) gather_images(model.results_directory)
def main(): # BASIC SETUP logger = set_logger() args = REG_parse_args( main_description= "Training launcher for Gradient boosting on S3D2 benchmark") logger.info(args) flush(logger) # INFO model = build_model(args, -1) os.makedirs(model.results_directory, exist_ok=True) config = Config() config_table = evaluate_config(config) config_table.to_csv( os.path.join(model.results_directory, 'config_table.csv')) # RUN results = [run(args, i_cv) for i_cv in range(N_ITER)] results = pd.concat(results, ignore_index=True) results.to_csv(os.path.join(model.results_directory, 'estimations.csv')) # EVALUATION eval_table = evaluate_estimator(config.INTEREST_PARAM_NAME, results) print_line() print_line() print(eval_table) print_line() print_line() eval_table.to_csv(os.path.join(model.results_directory, 'evaluation.csv')) gather_images(model.results_directory)
def main(): # BASIC SETUP logger = set_logger() args = NET_parse_args( main_description= "Training launcher for Gradient boosting on S3D2 benchmark") logger.info(args) flush(logger) # INFO model = build_model(args, -1) os.makedirs(model.results_directory, exist_ok=True) # config = Config() # config_table = evaluate_config(config) # config_table.to_csv(os.path.join(model.results_directory, 'config_table.csv')) # RUN evaluation = [run(args, i_cv) for i_cv in range(N_ITER)] # EVALUATION evaluation = pd.concat(evaluation) evaluation.to_csv(os.path.join(model.results_directory, "evaluation.csv")) plot_auc(evaluation, model_name=model.base_name, directory=model.results_directory) plot_accuracy(evaluation, model_name=model.base_name, directory=model.results_directory) if False: # Temporary removed gather_images(model.results_directory)
def main(): # BASIC SETUP logger = set_logger() args = INFERNO_parse_args( main_description= "Training launcher for Gradient boosting on S3D2 benchmark") logger.info(args) flush(logger) # INFO model = build_model(args, -1) os.makedirs(model.results_directory, exist_ok=True) config = Config() config_table = evaluate_config(config) config_table.to_csv( os.path.join(model.results_directory, 'config_table.csv')) # RUN if args.load_run: logger.info(f'Loading previous runs [{args.start_cv},{args.end_cv}[') directory = model.results_directory estimations = load_estimations(directory, start_cv=args.start_cv, end_cv=args.end_cv) conditional_estimations = load_conditional_estimations( directory, start_cv=args.start_cv, end_cv=args.end_cv) else: logger.info(f'Running runs [{args.start_cv},{args.end_cv}[') results = [ run(args, i_cv) for i_cv in range(args.start_cv, args.end_cv) ] estimations = [e0 for e0, e1 in results] estimations = pd.concat(estimations, ignore_index=True) conditional_estimations = [e1 for e0, e1 in results] conditional_estimations = pd.concat(conditional_estimations) estimations.to_csv(os.path.join(model.results_directory, 'estimations.csv')) conditional_estimations.to_csv( os.path.join(model.results_directory, 'conditional_estimations.csv')) # EVALUATION eval_table = evaluate_estimator(config.INTEREST_PARAM_NAME, estimations) eval_conditional = evaluate_conditional_estimation( conditional_estimations, interest_param_name=config.INTEREST_PARAM_NAME) eval_table = pd.concat([eval_table, eval_conditional], axis=1) print_line() print_line() print(eval_table) print_line() print_line() eval_table.to_csv(os.path.join(model.results_directory, 'evaluation.csv')) gather_images(model.results_directory)
def main(): logger = set_logger() logger.info("Hello world !") os.makedirs(DIRECTORY, exist_ok=True) set_plot_config() args = None config = Config() results = [run(args, i_cv) for i_cv in range(N_ITER)] results = pd.concat(results, ignore_index=True) results.to_csv(os.path.join(DIRECTORY, 'results.csv')) # EVALUATION eval_table = evaluate_estimator(config.TRUE.interest_parameters_names, results) print_line() print_line() print(eval_table) print_line() print_line() eval_table.to_csv(os.path.join(DIRECTORY, 'evaluation.csv')) gather_images(DIRECTORY)
def main(): # BASIC SETUP logger = set_logger() args = INFERNO_parse_args( main_description="Training launcher for Regressor on S3D2 benchmark") logger.info(args) flush(logger) # INFO model = build_model(args, -1) pb_config = Config() # RUN results = [run(args, i_cv) for i_cv in range(N_ITER)] results = pd.concat(results, ignore_index=True) results.to_csv(os.path.join(model.directory, 'results.csv')) # EVALUATION eval_table = evaluate_estimator(pb_config.INTEREST_PARAM_NAME, results) print_line() print_line() print(eval_table) print_line() print_line() eval_table.to_csv(os.path.join(model.directory, 'evaluation.csv')) gather_images(model.directory)