Datasets:
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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() # L
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)
# replace nan with 1
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]
# fill missing value with 0 for evaluation
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") |