def run(args, i_cv): logger = logging.getLogger() print_line() logger.info('Running iter n°{}'.format(i_cv)) print_line() result_row = {'i_cv': i_cv} # LOAD/GENERATE DATA logger.info('Set up data generator') config = Config() seed = SEED + i_cv * 5 train_generator = Generator(seed) valid_generator = Generator(seed + 1) test_generator = Generator(seed + 2) train_generator = TrainGenerator(param_generator, train_generator) # SET MODEL logger.info('Set up regressor') model = build_model(args, i_cv) os.makedirs(model.results_path, exist_ok=True) flush(logger) # TRAINING / LOADING train_or_load_neural_net(model, train_generator, retrain=args.retrain) # CHECK TRAINING logger.info('Generate validation data') X_valid, y_valid, w_valid = valid_generator.generate( *config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES) result_row.update(evaluate_neural_net(model, prefix='valid')) evaluate_regressor(model, prefix='valid') # MEASUREMENT result_row['nfcn'] = NCALL iter_results = [ run_iter(model, result_row, i, test_config, valid_generator, test_generator) for i, test_config in enumerate(config.iter_test_config()) ] result_table = [e0 for e0, e1 in iter_results] result_table = pd.DataFrame(result_table) result_table.to_csv(os.path.join(model.results_path, 'estimations.csv')) logger.info('Plot params') param_names = config.PARAM_NAMES for name in param_names: plot_params(name, result_table, title=model.full_name, directory=model.results_path) conditional_estimate = pd.concat([e1 for e0, e1 in iter_results]) conditional_estimate['i_cv'] = i_cv fname = os.path.join(model.results_path, "conditional_estimations.csv") conditional_estimate.to_csv(fname) logger.info('DONE') return result_table, conditional_estimate
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 run_estimation(args, i_cv): logger = logging.getLogger() print_line() logger.info('Running iter n°{}'.format(i_cv)) print_line() result_row = {'i_cv': i_cv} # LOAD/GENERATE DATA logger.info('Set up data generator') config = Config() seed = SEED + i_cv * 5 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) # SET MODEL logger.info('Set up classifier') model = build_model(args, i_cv) os.makedirs(model.results_path, exist_ok=True) flush(logger) # TRAINING / LOADING train_or_load_neural_net(model, train_generator, retrain=args.retrain) # CHECK TRAINING logger.info('Generate validation data') X_valid, y_valid, w_valid = valid_generator.generate( *config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES, no_grad=True) result_row.update(evaluate_neural_net(model, prefix='valid')) evaluate_regressor(model, prefix='valid') # MEASUREMENT calibs = {} calibs['tes'] = load_calib_tes(DATA_NAME, BENCHMARK_NAME) calibs['jes'] = load_calib_jes(DATA_NAME, BENCHMARK_NAME) calibs['les'] = load_calib_les(DATA_NAME, BENCHMARK_NAME) result_row['nfcn'] = NCALL iter_results = [ run_estimation_iter(model, result_row, i, test_config, valid_generator, test_generator, calibs) for i, test_config in enumerate(config.iter_test_config()) ] result_table = pd.DataFrame(iter_results) result_table.to_csv(os.path.join(model.results_path, 'estimations.csv')) logger.info('Plot params') param_names = config.PARAM_NAMES for name in param_names: plot_params(name, result_table, title=model.full_name, directory=model.results_path) logger.info('DONE') return result_table