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 = REG_parse_args( main_description="Training launcher for Regressor on S3D2 benchmark") logger.info(args) flush(logger) # Setup model logger.info("Setup model") model = build_model(args, 0) os.makedirs(model.results_directory, exist_ok=True) # Setup data logger.info("Setup data") config = Config() config_table = evaluate_config(config) config_table.to_csv( os.path.join(model.results_directory, 'config_table.csv')) seed = SEED + 99999 train_generator, valid_generator, test_generator = get_generators_torch( seed, cuda=args.cuda, GeneratorClass=GeneratorClass) train_generator = GeneratorCPU(train_generator) train_generator = TrainGenerator(param_generator, train_generator) valid_generator = GeneratorCPU(valid_generator) test_generator = GeneratorCPU(test_generator) i_cv = 0 result_row = {'i_cv': i_cv} # TRAINING / LOADING train_or_load_neural_net(model, train_generator, retrain=args.retrain) # CHECK TRAINING result_row.update(evaluate_neural_net(model, prefix='valid')) evaluate_regressor(model, prefix='valid') print_line() result_table = [ run_iter(model, result_row, i, test_config, valid_generator, test_generator) for i, test_config in enumerate(config.iter_test_config()) ] result_table = pd.DataFrame(result_table) result_table.to_csv(os.path.join(model.results_directory, 'results.csv')) logger.info('Plot params') param_names = [CALIB_PARAM_NAME] for name in param_names: plot_params(name, result_table, title=model.full_name, directory=model.results_directory) logger.info('DONE')
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() directory = os.path.join(DIRECTORY, "nll_contour") os.makedirs(directory, exist_ok=True) args = parse_args() i_cv = 0 seed = SEED + i_cv * 5 train_generator, valid_generator, test_generator = get_generators_torch(seed, cuda=args.cuda) train_generator = GeneratorCPU(train_generator) valid_generator = GeneratorCPU(valid_generator) test_generator = GeneratorCPU(test_generator) model = load_some_NN() config = Config() config_table = evaluate_config(config) os.makedirs(os.path.join(directory, model.name), exist_ok=True) config_table.to_csv(os.path.join(directory, model.name, 'config_table.csv')) for i_iter, test_config in enumerate(config.iter_test_config()): do_iter(test_config, model, i_iter, valid_generator, test_generator, directory)
def main(): logger = set_logger() logger.info("Hello world !") os.makedirs(DIRECTORY, exist_ok=True) set_plot_config() args = None config = GGConfig() config_table = evaluate_config(config) config_table.to_csv(os.path.join(DIRECTORY, 'config_table.csv')) 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)