def run_iter(model, result_row, i_iter, i_cv, args, config, test_generator, n_bins=10): logger = logging.getLogger() iter_directory = os.path.join(model.results_path, f'iter_{i_iter}') os.makedirs(iter_directory, exist_ok=True) result_row['i'] = i_iter suffix = f'-mu={config.TRUE.mu:1.2f}_rescale={config.TRUE.rescale}' logger.info('Generate testing data') test_generator.reset() X_test, y_test, w_test = test_generator.generate( *config.TRUE, n_samples=config.N_TESTING_SAMPLES) # PLOT SUMMARIES # evaluate_summary_computer(model, X_test, y_test, w_test, n_bins=n_bins, prefix='', suffix=suffix, directory=iter_directory) # logger.info('Set up NLL computer') # compute_summaries = ClassifierSummaryComputer(model, n_bins=n_bins) # compute_nll = NLLComputer(compute_summaries, valid_generator, X_test, w_test, config=config) compute_nll = NLL(X_test, w_test, i_cv, args, config=config, n_bins=n_bins) # NLL PLOTS plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix) # MINIMIZE NLL logger.info('Prepare minuit minimizer') minimizer = get_minimizer(compute_nll, config.CALIBRATED, config.CALIBRATED_ERROR) result_row.update(evaluate_minuit(minimizer, config.TRUE)) return result_row.copy()
def run_iter(compute_summaries, i_cv, i_iter, config, valid_generator, test_generator, directory): logger = logging.getLogger() result_row = dict(i_cv=i_cv, i=i_iter) iter_directory = os.path.join(directory, f'iter_{i_iter}') os.makedirs(iter_directory, exist_ok=True) logger.info(f"True Parameters = {config.TRUE}") suffix = f'-mu={config.TRUE.mu:1.2f}_rescale={config.TRUE.rescale}' X_test, y_test, w_test = test_generator.generate( *config.TRUE, n_samples=config.N_TESTING_SAMPLES) debug_label(y_test) compute_nll = NLLComputer(compute_summaries, valid_generator, X_test, w_test, config=config) plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix) logger.info('Prepare minuit minimizer') minimizer = get_minimizer(compute_nll, config.CALIBRATED, config.CALIBRATED_ERROR) result_row.update(evaluate_minuit(minimizer, config.TRUE)) return result_row
def run_estimation_iter(model, result_row, i_iter, config, valid_generator, test_generator, calib_rescale, n_bins=10): logger = logging.getLogger() logger.info('-' * 45) logger.info(f'iter : {i_iter}') flush(logger) iter_directory = os.path.join(model.results_path, f'iter_{i_iter}') os.makedirs(iter_directory, exist_ok=True) result_row['i'] = i_iter result_row['n_test_samples'] = config.N_TESTING_SAMPLES suffix = f'-mu={config.TRUE.mu:1.2f}_rescale={config.TRUE.rescale}' logger.info('Generate testing data') test_generator.reset() X_test, y_test, w_test = test_generator.generate( *config.TRUE, n_samples=config.N_TESTING_SAMPLES) # PLOT SUMMARIES evaluate_summary_computer(model, X_test, y_test, w_test, n_bins=n_bins, prefix='', suffix=suffix, directory=iter_directory) # CALIBRATION config = calibrates(calib_rescale, config, X_test, w_test) for name, value in config.FITTED.items(): result_row[name + "_fitted"] = value for name, value in config.FITTED_ERROR.items(): result_row[name + "_fitted_error"] = value logger.info('Set up NLL computer') compute_summaries = lambda X, w: model.compute_summaries( X, w, n_bins=n_bins) compute_nll = NLLComputer(compute_summaries, valid_generator, X_test, w_test, config=config) # NLL PLOTS plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix) # MINIMIZE NLL logger.info('Prepare minuit minimizer') minimizer = get_minimizer(compute_nll, config.FITTED, config.FITTED_ERROR) result_row.update( evaluate_minuit(minimizer, config.TRUE, iter_directory, suffix=suffix)) return result_row.copy()
def run_iter(model, result_row, i_iter, config, valid_generator, test_generator, n_bins=10): logger = logging.getLogger() logger.info('-' * 45) logger.info(f'iter : {i_iter}') flush(logger) iter_directory = os.path.join(model.results_path, f'iter_{i_iter}') os.makedirs(iter_directory, exist_ok=True) result_row['i'] = i_iter result_row['n_test_samples'] = config.N_TESTING_SAMPLES suffix = f'-mu={config.TRUE.mu:1.2f}_rescale={config.TRUE.rescale}' logger.info('Generate testing data') test_generator.reset() X_test, y_test, w_test = test_generator.generate( *config.TRUE, n_samples=config.N_TESTING_SAMPLES) # PLOT SUMMARIES evaluate_summary_computer(model, X_test, y_test, w_test, n_bins=n_bins, prefix='', suffix=suffix, directory=iter_directory) logger.info('Set up NLL computer') compute_summaries = lambda X, w: model.compute_summaries( X, w, n_bins=n_bins) compute_nll = NLLComputer(compute_summaries, valid_generator, X_test, w_test, config=config) # NLL PLOTS plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix) # MEASURE STAT/SYST VARIANCE logger.info('MEASURE STAT/SYST VARIANCE') conditional_results = make_conditional_estimation(compute_nll, config) fname = os.path.join(iter_directory, "no_nuisance.csv") conditional_estimate = pd.DataFrame(conditional_results) conditional_estimate['i'] = i_iter conditional_estimate.to_csv(fname) # MINIMIZE NLL logger.info('Prepare minuit minimizer') minimizer = get_minimizer(compute_nll, config.CALIBRATED, config.CALIBRATED_ERROR) result_row.update(evaluate_minuit(minimizer, config.TRUE)) return result_row.copy(), conditional_estimate
def run_iter(model, result_row, i_iter, config, valid_generator, test_generator, calib_rescale, n_bins=10): logger = logging.getLogger() logger.info('-'*45) logger.info(f'iter : {i_iter}') flush(logger) iter_directory = os.path.join(model.results_path, f'iter_{i_iter}') os.makedirs(iter_directory, exist_ok=True) result_row['i'] = i_iter result_row['n_test_samples'] = config.N_TESTING_SAMPLES suffix = f'-mu={config.TRUE.mu:1.2f}_rescale={config.TRUE.rescale}' logger.info('Generate testing data') test_generator.reset() X_test, y_test, w_test = test_generator.generate(*config.TRUE, n_samples=config.N_TESTING_SAMPLES) # PLOT SUMMARIES evaluate_summary_computer(model, X_test, y_test, w_test, n_bins=n_bins, prefix='', suffix=suffix, directory=iter_directory) # CALIBRATION rescale_mean, rescale_sigma = calib_rescale.predict(X_test, w_test) logger.info('rescale = {} =vs= {} +/- {}'.format(config.TRUE.rescale, rescale_mean, rescale_sigma) ) config.CALIBRATED = Parameter(rescale_mean, config.CALIBRATED.interest_parameters) config.CALIBRATED_ERROR = Parameter(rescale_sigma, config.CALIBRATED_ERROR.interest_parameters) for name, value in config.CALIBRATED.items(): result_row[name+"_calib"] = value for name, value in config.CALIBRATED_ERROR.items(): result_row[name+"_calib_error"] = value logger.info('Set up NLL computer') compute_summaries = ClassifierSummaryComputer(model, n_bins=n_bins) compute_nll = NLLComputer(compute_summaries, valid_generator, X_test, w_test, config=config) # NLL PLOTS plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix) # MEASURE STAT/SYST VARIANCE logger.info('MEASURE STAT/SYST VARIANCE') conditional_results = make_conditional_estimation(compute_nll, config) fname = os.path.join(iter_directory, "no_nuisance.csv") conditional_estimate = pd.DataFrame(conditional_results) conditional_estimate['i'] = i_iter conditional_estimate.to_csv(fname) # MINIMIZE NLL logger.info('Prepare minuit minimizer') minimizer = get_minimizer(compute_nll, config.CALIBRATED, config.CALIBRATED_ERROR) result_row.update(evaluate_minuit(minimizer, config.TRUE)) return result_row.copy(), conditional_estimate
def run_iter(i_cv, i_iter, config, seed, directory): logger = logging.getLogger() result_row = dict(i_cv=i_cv, i=i_iter) iter_directory = os.path.join(directory, f'iter_{i_iter}') os.makedirs(iter_directory, exist_ok=True) logger.info(f"True Parameters = {config.TRUE}") suffix = f'-mu={config.TRUE.mu:1.2f}_rescale={config.TRUE.rescale}' generator = Generator(seed) # test_generator data, label = generator.sample_event(*config.TRUE, size=config.N_TESTING_SAMPLES) debug_label(label) compute_nll = lambda rescale, mu: generator.nll(data, rescale, mu) plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix) logger.info('Prepare minuit minimizer') minimizer = get_minimizer(compute_nll, config.CALIBRATED, config.CALIBRATED_ERROR) result_row.update(evaluate_minuit(minimizer, config.TRUE)) return result_row