NDCG实现
import numpy as np
def getDCG(scores):
return np.sum(
np.divide(np.power(2, scores) - 1, np.log2(np.arange(scores.shape[0], dtype=np.float32) + 2)),
dtype=np.float32)
def getNDCG(rank_list, pos_items):
relevance = np.ones_like(pos_items)
it2rel = {
it: r for it, r in zip(pos_items, relevance)}
rank_scores = np.asarray([it2rel.get(it, 0.0) for it in rank_list], dtype=np.float32)
idcg = getDCG(relevance)
dcg = getDCG(rank_scores)
if dcg == 0.0:
return 0.0
ndcg = dcg / idcg
return ndcg
l1 = [1, 4, 5]
l2 = [1, 2, 3]
a = getNDCG(l1, l2)
print(a)
# 0.4692787