def make_mode_holdout(data, seed, configuration, num_run, output_dir): global evaluator evaluator = HoldoutEvaluator(data, output_dir, configuration, seed=seed, num_run=num_run, all_scoring_functions=False, **_get_base_dict()) loss, opt_pred, valid_pred, test_pred = evaluator.fit_predict_and_loss() evaluator.finish_up(loss, opt_pred, valid_pred, test_pred)
def make_mode_holdout_iterative_fit(data, seed, configuration, num_run, output_dir): global evaluator evaluator = HoldoutEvaluator(data, output_dir, configuration, seed=seed, num_run=num_run, all_scoring_functions=False, **_get_base_dict()) evaluator.iterative_fit() signal.signal(15, empty_signal_handler) evaluator.finish_up()
def make_mode_holdout_iterative_fit(data, seed, configuration, num_run): global evaluator evaluator = HoldoutEvaluator(data, configuration, seed=seed, num_run=num_run, **_get_base_dict()) evaluator.iterative_fit() signal.signal(15, empty_signal_handler) evaluator.finish_up() backend = Backend(None, os.getcwd()) if os.path.exists(backend.get_model_dir()): backend.save_model(evaluator.model, num_run, seed)