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：

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：

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：

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