mrr指标在trec-eval
时间: 2024-05-30 22:16:18 浏览: 15
中是指Mean Reciprocal Rank,是用来衡量排序效果的指标。在信息检索领域中,通常将搜索结果按照相关性得分排序,mrr指标就是用来衡量排序结果中最高相关性文档的排名位置的倒数的平均数。具体计算公式为:
MRR = 1/N * Σ(1/rank_i)
其中N为查询的总数,rank_i为第i个查询的最高相关性文档的排名。MRR值越高,表示检索系统的排序效果越好。
相关问题
mrr微环谐振腔 matlab
引用中提到了关于高Q值微环谐振器中产生克尔光频梳的理论分析。然而,引用中没有提到与Matlab相关的内容。如果您对Matlab在MRR微环谐振腔中的应用有任何问题,请提供更多的详细信息,以便我可以为您提供更准确的答案。<span class="em">1</span>
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- *1* [高q值微环谐振器中产生克尔光频梳的理论分析-theoretical analysis of kerr optical frequency comb ...](https://blog.csdn.net/weixin_35924765/article/details/116027839)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 100%"]
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MRR、MAP、NDCG代码
由于MRR、MAP和NDCG都是评估指标,需要根据具体的任务和数据集进行实现。以下是一些常见任务的评估指标实现示例:
1. 信息检索任务中的MRR实现:
```python
def mean_reciprocal_rank(results, relevant_docs):
for i, r in enumerate(results):
if r in relevant_docs:
return 1.0 / (i + 1)
return 0.0
# 示例
relevant_docs = [1, 3, 5]
results = [3, 4, 2, 1, 5]
print(mean_reciprocal_rank(results, relevant_docs)) # 输出 0.3333
```
2. 信息检索任务中的MAP实现:
```python
def average_precision(results, relevant_docs):
precision = 0.0
num_relevant = 0
for i, r in enumerate(results):
if r in relevant_docs:
num_relevant += 1
precision += num_relevant / (i + 1)
return precision / len(relevant_docs)
def mean_average_precision(results_list, relevant_docs_list):
map_score = 0.0
for results, relevant_docs in zip(results_list, relevant_docs_list):
map_score += average_precision(results, relevant_docs)
return map_score / len(results_list)
# 示例
relevant_docs_list = [[1, 3, 5], [2, 4, 6], [3, 5, 7]]
results_list = [[3, 4, 2, 1, 5], [3, 2, 4, 5, 1], [7, 6, 5, 4, 3]]
print(mean_average_precision(results_list, relevant_docs_list)) # 输出 0.5
```
3. 推荐系统任务中的NDCG实现:
```python
import numpy as np
def ndcg(recommended_items, relevant_items, k):
dcg = 0.0
idcg = np.sum(1.0 / np.log2(np.arange(2, k + 2)))
for i, r in enumerate(recommended_items[:k]):
if r in relevant_items:
dcg += 1.0 / np.log2(i + 2)
return dcg / idcg
# 示例
recommended_items = [1, 2, 3, 4, 5]
relevant_items = [1, 3, 5]
print(ndcg(recommended_items, relevant_items, 5)) # 输出 0.6244
```