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December 28, 2021 09:13
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Fast Kendall Tau calculation with pytorch.
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import torch | |
import time | |
from scipy.stats import kendalltau | |
def kendall(x, y): | |
n = x.shape[0] | |
def sub_pairs(x): | |
return x.expand(n,n).T.sub(x).sign_() | |
return sub_pairs(x).mul_(sub_pairs(y)).sum().div(n*(n-1)) | |
d = torch.empty(10) | |
for i in range(10): | |
x, y = torch.randperm(4000), torch.randperm(4000) | |
t = time.time_ns() | |
m = kendall(x,y) | |
d[i] = (time.time_ns() - t)*1e-6 | |
print(f'{d[i]:.2f}ms') | |
print(f'{abs(kendalltau(x,y).correlation - m):.9f}') | |
print(f'AVG {d.mean():.2f}ms') |
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