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| import tqdm | |
| import numpy as np | |
| def baseline_predict(metric_function, eval_xs, eval_ys, categorical_feats, metric_used=None, eval_pos=2, max_time=300, **kwargs): | |
| """ | |
| Baseline prediction interface. | |
| :param metric_function: | |
| :param eval_xs: | |
| :param eval_ys: | |
| :param categorical_feats: | |
| :param metric_used: | |
| :param eval_pos: | |
| :param max_time: Scheduled maximum time | |
| :param kwargs: | |
| :return: list [np.array(metrics), np.array(outputs), best_configs] or [None, None, None] if failed | |
| """ | |
| metrics = [] | |
| outputs = [] | |
| best_configs = [] | |
| eval_splits = list(zip(eval_xs.transpose(0, 1), eval_ys.transpose(0, 1))) | |
| for eval_x, eval_y in tqdm.tqdm(eval_splits, desc='Calculating splits'+str(metric_function)+' '+str(eval_pos)): | |
| try: | |
| metric, output, best_config = metric_function(eval_x[:eval_pos], | |
| eval_y[:eval_pos], | |
| eval_x[eval_pos:], | |
| eval_y[eval_pos:], | |
| categorical_feats, | |
| metric_used=metric_used | |
| , max_time=max_time) | |
| metrics += [metric] | |
| outputs += [output] | |
| best_configs += [best_config] | |
| return np.array(metrics), np.array(outputs), best_configs | |
| except Exception as e: | |
| print(f'There was an exception in {metric_function}') | |
| print(e) | |
| return None, None, None |