def main(arg, f):
# 数据集按均匀分布划分为M份，M-1份均为训练集，剩下1份为测试集
M = 8
k = 0
seed = 42 # 随机数种子
data = [
tuple(line.split('::')[:2]) for line in open(
'G:/master/python/PycharmProjects/RecommendationSystem/ItemCF/MovieLens/data/ml-1m/ratings.dat'
).readlines()
] # win10上的ml-1m数据集
# data = [tuple(line.split(',')[:2]) for line in open('G:/Recommend/User-CF/MovieLens/ml-latest-small/ratings_test.csv').readlines()] # ml-latest-small数据集
train, test = SplitData(data, M, k, seed)
# 基于训练集计算物品相似度
W = ItemCF.ItemSimilarity(train)
# 离线指标计算
precision, recall = PrecisionRecall(train, test, W, arg[1], arg[0])
f.write(str(precision))
f.write(',')
f.write(str(recall))
f.write(',')
coverage = Coverge(train, W, arg[1], arg[0])
f.write(str(coverage))
f.write(',')
novelty = Novelty(train, W, arg[1], arg[0])
f.write(str(novelty))
f.write(',')
F1 = 2 * precision * recall / (precision + recall)
f.write(str(F1))
f.write(',')
# print(f'precision:{precision}\trecall:{recall}\tcoverage:{coverage}\tpopularity:{novelty}\tF1:{F1}')
'''
