常⽤的Top-N产品推荐评估指标
Top-N产品推荐与传统的评分预测相⽐，更符合实际的业务需求，在对推荐算法产⽣的Top-K推荐列表进⾏评估时，有⼀些常⽤的指标，如Hit_Rate@k、Precision@k、Recall@k、Map@k、NDCG@k和MRR@k，下⾯基于Python实现这6种评估指标。
代码和⽰例如下：收发文
import numpy as np
import sys
def hit(rank, ground_truth):
# HR is equal to Recall when datat is loo split.
last_idx = sys.maxsize
医患关系心得体会for idx, item in enumerate(rank):
if item in ground_truth:
last_idx = idx
break
result = np.zeros(len(rank), dtype=np.float32)
result[last_idx:] = 1.0
return result
def precision(rank, ground_truth):
# Precision is meaningless when datat is loo split.
hits = [1 if item in ground_truth el 0 for item in rank]
result = np.cumsum(hits, dtype=np.float32)/np.arange(1, len(rank)+1)
return result
def recall(rank, ground_truth):
# Recall is equal to HR when datat is loo split.
hits = [1 if item in ground_truth el 0 for item in rank]
result = np.cumsum(hits, dtype=np.float32) / len(ground_truth)
return result
def map(rank, ground_truth):
pre = precision(rank, ground_truth)
pre = [pre[idx] if item in ground_truth el 0 for idx, item in enumerate(rank)]
sum_pre = np.cumsum(pre, dtype=np.float32)
# relevant_num = np.cumsum([1 if item in ground_truth el 0 for item in rank])
relevant_num = np.cumsum([min(idx+1, len(ground_truth)) for idx, _ in enumerate(rank)])
result = [p/r_num if r_num!=0 el 0 for p, r_num in zip(sum_pre, relevant_num)]
return result
浓睡不消残酒def ndcg(rank, ground_truth):
马铃薯的营养价值
len_rank = len(rank)
idcg_len = min(len(ground_truth), len_rank)
idcg = np.cumsum(1.0 / np.log2(np.arange(2, len_rank + 2)))
纷纷出发
idcg[idcg_len:] = idcg[idcg_len - 1]
dcg = np.cumsum([1.0/np.log2(idx+2) if item in ground_truth el 0.0 for idx, item in enumerate(rank)])
result = dcg/idcg
return result
def mrr(rank, ground_truth):
# MRR is equal to MAP when datat is loo split.
last_idx = sys.maxsize
for idx, item in enumerate(rank):
if item in ground_truth:
last_idx = idx
break
break
result = np.zeros(len(rank), dtype=np.float32)
result[last_idx:] = 1.0/(last_idx+1)
return result
def top_k_eval(ranks, ground_truths, k):
hit_k_list = []
precision_k_list = []
recall_k_list = []
map_k_list = []
ndcg_k_list = []
mrr_k_list = []
ranks_k = [rank[:k] for rank in ranks]
for i in range(0, len(ranks)):
rank_i = ranks_k[i]
hit_i_k = hit(rank_i, ground_truths[i])[-1]
precision_i_k = precision(rank_i, ground_truths[i])[-1]
recall_i_k = recall(rank_i, ground_truths[i])[-1]
map_i_k = map(rank_i, ground_truths[i])[-1]
ndcg_i_k = ndcg(rank_i, ground_truths[i])[-1]
mrr_i_k = mrr(rank_i, ground_truths[i])[-1]
徐小平>音乐机器人hit_k_list.append(hit_i_k)
precision_k_list.append(precision_i_k)
recall_k_list.append(recall_i_k)
map_k_list.append(map_i_k)
ndcg_k_list.append(ndcg_i_k)
mrr_k_list.append(mrr_i_k)
hit_k = np.round(np.average(np.array(hit_k_list)), 4)
precision_k = np.round(np.average(np.array(precision_k_list)), 4)
recall_k = np.round(np.average(np.array(recall_k_list)), 4)
map_k = np.round(np.average(np.array(map_k_list)), 4)
ndcg_k = np.round(np.average(np.array(ndcg_k_list)), 4)
mrr_k = np.round(np.average(np.array(mrr_k_list)), 4)
return hit_k, precision_k, recall_k, map_k, ndcg_k, mrr_k
if __name__ == '__main__':
ranks = [[5, 7, 8, 9, 3], [4, 6, 2, 1, 10]]
ground_truths = [[7, 3, 5], [4, 2, 8, 7]]
k = 3
hit_k, precision_k, recall_k, map_k, ndcg_k, mrr_k = top_k_eval(ranks, ground_truths, k=k) print('hit_rate@%d:' % k, hit_k)
print('precision@%d:' % k, precision_k)
print('recall@%d:' % k, recall_k)
print('map@%d:' % k, map_k)
print('ndcg@%d:' % k, ndcg_k)
print('mrr@%d:' % k, mrr_k)
通过⽰例看⼀下效果：
⾸先取k=1：
hit_rate@1: 1.0
precision@1: 1.0
一直想对你说
recall@1: 0.2917
map@1: 1.0
ndcg@1: 1.0
mrr@1: 1.0
取k=3：
hit_rate@3: 1.0 precision@3: 0.6667 recall@3: 0.5833 map@3: 0.3056 ndcg@3: 0.7346 mrr@3: 1.0
取k=5：
hit_rate@5: 1.0 precision@5: 0.5 recall@5: 0.75 map@5: 0.1679 ndcg@5: 0.7662 mrr@5: 1.0
