from math import sqrt
from scipy.stats import kendalltau
def rmse(true, predicted):
true=list(true)
predicted=list(predicted)
error=list()
error=[true[i]-predicted[i] for i in range(len(true))]
mse=sum([error[i]*error[i] for i in range(len(error))])/len(error)
value=sqrt(mse)
return value
def kendalltau(rankA, rankB):
if len(rankA) != len(rankB):
raise TypeError("The two rank lists must be of the same length.")
N = len(rankA)
if isinstance(rankA[0], tuple):
rankA = [rankA[i][0] for i in range(N)]
if isinstance(rankB[0], tuple):
rankB = [rankB[i][0] for i in range(N)]
listA = [i for i in range(N)]
listB = [rankB.index(rankA[i]) for i in range(N)]
return kendalltau(listA, listB)[0]
