trust있으면 만점, distrust있으면 최하점 넣는 방식으로 fr_train.txt 생성

fr_parser_with_injection.py

from collections import defaultdict

users = {}
trust = {}

# 신뢰 관계 특정 개수 이상인 유저
group = {}
group_list = []

def build_users():
    input = open("./user_relation.txt", "r")
    while True:
        line = input.readline()
        if not line: break
        p = line.split(' ')

        uid = int(p[0])
        u = int(p[1])
        cnt = int(p[2])

        if cnt >= 10:
            group[uid] = 1
            group_list.append(uid)

        users[u]=uid
    input.close()

    output = open("./group10.txt", "w")
    group_list.sort()

    for i in range(0, len(group_list)):
        output.write(str(group_list[i])+"\n")

    output.close()

def build_trust():
    input = open("./trust.txt", "r")
    while True:
        line = input.readline()
        if not line: break
        p = line.split('\t')

        u = users[int(p[0])]
        v = users[int(p[1])]
        t = int(p[2])

        trust[(u,v)] = t
    input.close()

# 훈련에 사용할 fr 값
def generate_train():
    input = open("./fr_given.txt", "r")
    output = open("./fr_train.txt", "w")

    train_fr = defaultdict(lambda: 0)

    # 원래 주어진 fr값 채우기
    while True:
        line = input.readline()
        if not line: break
        p = line.split(' ')

        u = users[int(p[0])]
        v = users[int(p[1])]

        r = float(p[2])

        if u in group and v in group and (u,v) in trust:
            train_fr[(u, v)] += r

    # trust 관계로 train_fr에 추가하기
    for key, value in trust.items():
        u = key[0]
        v = key[1]

        t = value

        if u in group and v in group:
            if t == 1:
                if not (u, v) in train_fr:
                    train_fr[(u, v)] = 0.85
            else:
                if not (u, v) in train_fr:
                    train_fr[(u, v)] = 0

    # 출력하기
    for key, value in train_fr.items():
        u = key[0]
        v = key[1]

        r = value

        if u in group and v in group:
            output.write(str(u) + "," + str(v) + "," + str(r) + "\n")

    input.close()
    output.close()


build_users()
build_trust()
generate_train()

print('finish')