Reranker NDCG

Normalized Discounted Cumulative Gain for ranking metrics using graded relevance scores.

• Inputs: list of lists of retrieved items, list of dicts mapping item → relevance score
• Returns: NDCG score (float)

Example

from vero.metrics import RerankerNDCG

#example inputs
#rr is the reranked results from the retriever
#ranks is the relevance scores for the items retrieved by the retriever
rr = [[1,2,3,5,6],[1,2,3,5,6]]
ranks = [{2:2, 3:2},{2:2, 3:2, 6:1}]
rndcg = RerankerNDCG(rr, ranks)
print(rndcg.evaluate())

Output

0.89