NDCG and its implementation

NDCG implementation

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