def run(args, i_cv): logger = logging.getLogger() print_line() logger.info('Running iter n°{}'.format(i_cv)) print_line() directory = os.path.join(DIRECTORY, f'cv_{i_cv}') os.makedirs(directory, exist_ok=True) config = Config() seed = SEED + i_cv * 5 train_generator = Generator(seed) valid_generator = Generator(seed + 1) test_generator = Generator(seed + 2) N_BINS = 10 X_train, y_train, w_train = train_generator.generate( *config.CALIBRATED, n_samples=config.N_TRAINING_SAMPLES) compute_summaries = HistogramSummaryComputer(n_bins=N_BINS).fit(X_train) result_table = [ run_iter(compute_summaries, i_cv, i, test_config, valid_generator, test_generator, directory) for i, test_config in enumerate(config.iter_test_config()) ] result_table = pd.DataFrame(result_table) result_table.to_csv(os.path.join(directory, 'results.csv')) logger.info('Plot params') param_names = config.PARAM_NAMES for name in param_names: plot_params(name, result_table, title='Likelihood fit', directory=directory) return result_table
def run(args, i_cv): logger = logging.getLogger() print_line() logger.info('Running iter n°{}'.format(i_cv)) print_line() directory = os.path.join(DIRECTORY, f'cv_{i_cv}') os.makedirs(directory, exist_ok=True) config = GGConfig() seed = SEED + i_cv * 5 test_seed = seed + 2 result_table = [ run_iter(i_cv, i, test_config, test_seed, directory) for i, test_config in enumerate(config.iter_test_config()) ] result_table = pd.DataFrame(result_table) result_table.to_csv(os.path.join(directory, 'results.csv')) logger.info('Plot params') param_names = config.PARAM_NAMES for name in param_names: plot_params(name, result_table, title='Likelihood fit', directory=directory) return result_table
def run(args, i_cv): logger = logging.getLogger() print_line() logger.info('Running iter n°{}'.format(i_cv)) print_line() # LOAD/GENERATE DATA logger.info('Set up data generator') config = Config() seed = SEED + i_cv * 5 train_generator = GeneratorTorch(seed, cuda=args.cuda) valid_generator = Generator(seed+1) test_generator = Generator(seed+2) # 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) # MEASUREMENT result_row = {'i_cv': i_cv} results = [] for test_config in config.iter_test_config(): logger.info(f"Running test set : {test_config.TRUE}, {test_config.N_TESTING_SAMPLES} samples") for threshold in np.linspace(0, 1, 500): result_row = {'i_cv': i_cv} result_row['threshold'] = threshold result_row.update(test_config.TRUE.to_dict(prefix='true_')) result_row['n_test_samples'] = test_config.N_TESTING_SAMPLES X, y, w = valid_generator.generate(*config.TRUE, n_samples=config.N_VALIDATION_SAMPLES) proba = model.predict_proba(X) decision = proba[:, 1] selected = decision > threshold beta = np.sum(y[selected] == 0) gamma = np.sum(y[selected] == 1) result_row['beta'] = beta result_row['gamma'] = gamma X, y, w = test_generator.generate(*config.TRUE, n_samples=config.N_VALIDATION_SAMPLES) proba = model.predict_proba(X) decision = proba[:, 1] selected = decision > threshold n_selected = np.sum(selected) n_selected_bkg = np.sum(y[selected] == 0) n_selected_sig = np.sum(y[selected] == 1) result_row['n'] = n_selected result_row['b'] = n_selected_bkg result_row['s'] = n_selected_sig result_row['s_sqrt_n'] = n_selected_sig / np.sqrt(n_selected) result_row['s_sqrt_b'] = n_selected_sig / np.sqrt(n_selected) results.append(result_row.copy()) results = pd.DataFrame(results) print(results) return results
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) # SET MODEL # logger.info('Set up classifier') model = build_model(args, i_cv) # flush(logger) # TRAINING / LOADING # train_or_load_classifier(model, train_generator, config.CALIBRATED, config.N_TRAINING_SAMPLES, 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_classifier(model, X_valid, y_valid, w_valid, prefix='valid')) # MEASUREMENT N_BINS = 10 # evaluate_summary_computer(model, X_valid, y_valid, w_valid, n_bins=N_BINS, prefix='valid_', suffix='') result_table = [ run_iter(model, result_row, i, i_cv, args, test_config, test_generator, n_bins=N_BINS) 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_path, 'results.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.path) logger.info('DONE') return result_table
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 = 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 calib_rescale = load_calib_rescale(DATA_NAME, BENCHMARK_NAME) result_row['nfcn'] = NCALL iter_results = [ run_estimation_iter(model, result_row, i, test_config, valid_generator, test_generator, calib_rescale) 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
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) train_generator = TrainGenerator(param_generator, train_generator) valid_generator = Generator(seed+1) test_generator = Generator(seed+2) # 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_pivot(model, train_generator, config.N_TRAINING_SAMPLES*N_AUGMENT, 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')) result_row.update(evaluate_classifier(model, X_valid, y_valid, w_valid, prefix='valid')) # MEASUREMENT N_BINS = 10 evaluate_summary_computer(model, X_valid, y_valid, w_valid, n_bins=N_BINS, prefix='valid_', suffix='') iter_results = [run_iter(model, result_row, i, test_config, valid_generator, test_generator, n_bins=N_BINS) 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 = TrainGenerator(param_generator, Generator(seed)) valid_generator = Generator(seed + 1) test_generator = Generator(seed + 2) 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(args, i_cv): logger = logging.getLogger() print_line() logger.info('Running iter n°{}'.format(i_cv)) print_line() # LOAD/GENERATE DATA logger.info('Set up data generator') config = Config() seed = SEED + i_cv * 5 train_generator = Generator(seed) train_generator = TrainGenerator(param_generator, train_generator) valid_generator = Generator(seed+1) test_generator = Generator(seed+2) # 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_pivot(model, train_generator, config.N_TRAINING_SAMPLES*N_AUGMENT, retrain=args.retrain) some_fisher = compute_fisher(*compute_bins(model, valid_generator, config, n_bins=3), config.TRUE.mu) some_fisher_bis = compute_fisher(*compute_bins(model, valid_generator, config, n_bins=3), config.TRUE.mu) assert some_fisher == some_fisher_bis, f"Fisher info should be deterministic but found : {some_fisher} =/= {some_fisher_bis}" # MEASUREMENT result_row = {'i_cv': i_cv} results = [] for test_config in config.iter_test_config(): logger.info(f"Running test set : {test_config.TRUE}, {test_config.N_TESTING_SAMPLES} samples") for n_bins in range(1, 30): result_row = {'i_cv': i_cv} gamma_array, beta_array = compute_bins(model, valid_generator, test_config, n_bins=n_bins) fisher = compute_fisher(gamma_array, beta_array, test_config.TRUE.mu) result_row.update({f'gamma_{i}' : gamma for i, gamma in enumerate(gamma_array, 1)}) result_row.update({f'beta_{i}' : beta for i, beta in enumerate(beta_array, 1)}) result_row.update(test_config.TRUE.to_dict(prefix='true_')) result_row['n_test_samples'] = test_config.N_TESTING_SAMPLES result_row['fisher'] = fisher result_row['n_bins'] = n_bins results.append(result_row.copy()) results = pd.DataFrame(results) print(results) return results
def run_conditional_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 = 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_conditional_estimation_iter(model, result_row, i, test_config, valid_generator, test_generator) for i, test_config in enumerate(config.iter_test_config()) ] conditional_estimate = pd.concat(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