| from arch.unitroot import ADF |
| from scipy.stats import entropy |
| import numpy as np |
| import torch |
| import argparse |
| from datasets import load_from_disk |
|
|
| def adf_evaluator(x): |
| return ADF(x).stat |
|
|
|
|
| def forecastability_evaluator(x, seq_len=256): |
| x = torch.tensor(x).squeeze() |
| forecastability_list = [] |
| for i in range(max(x.shape[0]-seq_len, 0) // seq_len + 1): |
| start_idx = i * seq_len |
| end_idx = min(start_idx + seq_len, x.shape[0]) |
| window = x[start_idx:end_idx] |
| amps = torch.abs(torch.fft.rfft(window)) |
| amp = torch.sum(amps) |
| forecastability = 1 - entropy(amps/amp, base=len(amps)) |
| forecastability_list.append(forecastability) |
| np_forecastability_list = np.array(forecastability_list) |
| |
| np_forecastability_list[np.isnan(np_forecastability_list)] = 1 |
| return np.mean(np_forecastability_list) |
|
|
|
|
| def save_log(path, content): |
| with open(path, 'a') as f: |
| f.write(content) |
| |
|
|
| if __name__ == '__main__': |
| parser = argparse.ArgumentParser(description='Dataset Evaluation') |
| parser.add_argument('--root_path', type=str, required=True, help='Root path of the dataset, e.g. ./data/bdg-2_bear') |
| parser.add_argument('--log_path', type=str, required=False, default='log.txt', help='Path to save the log file') |
| args = parser.parse_args() |
| print("Evaluate dataset at ", args.root_path) |
| |
| dataset = load_from_disk(args.root_path) |
| print(dataset) |
| series_list = dataset['target'] |
| |
| if not isinstance(series_list[0][0], list): |
| series_list = [series_list] |
| |
| time_point_list = [] |
| adf_stat_list = [] |
| forecastability_list = [] |
| |
| for i in range(len(series_list)): |
| for j in range(len(series_list[i])): |
| try: |
| series = series_list[i][j] |
| |
| series = [0 if np.isnan(x) else x for x in series] |
| adf_stat = adf_evaluator(series) |
| forecastability = forecastability_evaluator(series) |
| forecastability_list.append(forecastability) |
| adf_stat_list.append(adf_stat) |
| time_point_list.append(len(series)) |
| except Exception as e: |
| save_log(args.log_path, f'Error: {args.root_path} {i} {j}\n'+str(e)+'\n') |
| continue |
| |
| time_point_list = np.array(time_point_list) |
| adf_stat_list = np.array(adf_stat_list) |
| forecastability_list = np.array(forecastability_list) |
| |
| time_points = np.sum(time_point_list) |
| weighted_adf = np.sum(adf_stat_list * time_point_list) / time_points |
| weighted_forecastability = np.sum(forecastability_list * time_point_list) / time_points |
| |
| print("Weighted ADF:", weighted_adf) |
| print("Weighted Forecastability:", weighted_forecastability) |
| print("Total Time Points:", time_points) |
| print("Finish evaluation ", args.root_path) |
| save_log(args.log_path, f"root_path: {args.root_path}\n Weighted ADF: {weighted_adf}\n Weighted Forecastability: {weighted_forecastability}\n Total Time Points: {time_points}\n\n") |
