AI检测代码解析 importtorchdefdcg_at_k(r,k):"""Compute DCG at k."""# 计算前k个结果的DCG值dcg=torch.zeros(1)foriinrange(min(k,len(r))):dcg+=(2**r[i]-1)/torch.log2(i+2)returndcgdefndcg_at_k(r,k):"""Compute NDCG at k."""# 计算前k个结果的NDCG值dcg_max=dcg_at_k(...
- `k`:一个整数,表示计算 NDCG 的前 k 项。 例如,给定以下真实排序和相关度分数,计算NDCG@3: ```python r = [3, 2, 3, 0, 1, 2] k = 3 print(ndcg_at_k(r, k)) ``` 输出结果为:0.7895 这意味着前三个项目的平均注意度是实际值的 78.9%。©...
importnumpyasnpdefdcg_at_k(relevance_scores,k):"""计算前 k 个文档的 DCG 值"""iflen(relevance_scores)==0:return0.0relevance_scores=relevance_scores[:k]returnnp.sum(relevance_scores/np.log2(np.arange(2,len(relevance_scores)+2)))defidcg_at_k(relevance_scores,k):"""计算前 k 个文档的...
where 4 is highly relevant and 0 is not relevant at all.\n" "- there is no duplicated ...
Normalized Discounted Cumulative Gain (NDCG) at rank n Normalize DCG at rank n by the DCG value at rank n of the ideal ranking The ideal ranking would first return the documents with the highest relevance level, then the next highest relevance level, etc ...
This piece is co-authored by Jianshu Chi, Software Engineer at Arize AI, and Amber Roberts, ML engineer at Arize AI Ranking models underpin many aspects of modern digital life, from search results to…
(r) = 1 if the document at rankris relevant to intentiand letIi(r) = 0 otherwise; letCi(r) =∑k= 1rIi(k). For a given parameterα(typically set to 0.5), let thenovelty-biased gainatrbeng(r)=∑iIi(r)(1−α)Ci(r−1).This reflects the view that (a) the value of a ...
_at_k <- function (r, k=min(5, length(r)) ) { #only coded alternative formulation of DCG (used by kaggle) r <- as.vector(r)[1:k] sum(( 2^r - 1 )/ log2( 2:(length(r)+1)) ) } ndcg_at_k <- function(r, k=min(5, length(r)) ) { r <- as.vector(r)[1:k]...
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