def PrecisionAndRecallAndCoverageAndPopularity(train, test, item_popularity, K, W, N, method=1):
hit = 0
num_rank = 0
num_tu = 0
recommend_items = set()
all_items = set()
popularity = 0.0
if method == 1:
for user in train: # test / train
if user not in test:
continue
all_items = all_items | set(train[user][0])
tu = test[user][0]
rank = UserCF.Recommend(user, train, W, K, N)
recommend_items = recommend_items | set(rank)
# hit += len(np.intersect1d(rank, tu))
for item in rank:
if item in tu:
hit += 1
popularity += math.log(1 + item_popularity[item])
# for item, value in rank.items():
# if item in tu:
# hit += 1
num_rank += len(rank)
num_tu += len(tu)
# print('Hit: ', hit)
# print('Rank num: ', num_rank)
# print('Test user\'s item num:', num_tu)
# print(len(all_items), len(recommend_items))
return hit / (num_rank * 1.0), hit / (
num_tu * 1.0), len(recommend_items) / (len(all_items) * 1.0), popularity / (num_rank * 1.0)
elif method == 2:
for user in train: # test / train
if user not in test:
continue
all_items = all_items | set(train[user][0])
tu = test[user][0]
rank = ItemCF.Recommend(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('Hit: ', hit)
# print('Rank num: ', num_rank)
# print('Test user\'s item num:', num_tu)
# print(len(all_items), len(recommend_items))
return hit / (num_rank * 1.0), hit / (
num_tu * 1.0), len(recommend_items) / (len(all_items) * 1.0), popularity / (num_rank * 1.0)
