def PrecisionAndRecallAndCoverageAndPopularity(train, test, item_popularity, K, W, N, method):
""" 计算 准确率，召回率，覆盖率，流行度
Desc:
Args:
train
test
item_popularity
K: 取相似度前K个
W: 相似度矩阵
N: TopN
method: recommend 推荐方法
Returns:
precision: 准确率
recall: 召回率
coverage: 覆盖率
popularity: 流行度
"""
hit = 0
num_rank = 0
num_tu = 0
recommend_items = set()
all_items = set()
popularity = 0.0
for user in train: # test / train
if user not in test:
continue
all_items = all_items | set(train[user][0])
tu = test[user][0]
if method == 1:
rank = ItemCF.Recommend1(user, train, W, K, N)
elif method == 2:
rank = ItemCF.Recommend2(user, train, W, K, N)
for item, _ in rank:
if item in tu:
hit += 1
popularity += math.log(1 + item_popularity[item])
recommend_items.add(item)
num_rank += len(rank)
num_tu += len(tu)
print(K, ': ', hit / (num_rank * 1.0), hit / (num_tu * 1.0), len(recommend_items) / (len(all_items) * 1.0), popularity / (num_rank * 1.0))
return hit / (num_rank * 1.0), hit / (
num_tu * 1.0), len(recommend_items) / (len(all_items) * 1.0), popularity / (num_rank * 1.0)
