def evaluate(train_set, test_set):
print('Evaluating start ...')
kg_sim = {}
kg_sim_detail = {}
for line in lf.load_file_all("./data/kg_sim1.txt"):
temp = line.split("::")
details = temp[1].replace("{", "").replace("}", "").split(",")
kg_sim.setdefault(int(temp[0]), {})
for items in details:
kv = items.split(":")
kg_sim_detail[int(kv[0])] = kv[1]
kg_sim[temp[0]] = kg_sim_detail
user_sim_matrix = gg.calc_user_sim(train_set)
movie_sim_matrix = ic.calc_movie_sim(train_set)
all_rec_movies = set()
for i in sim_mix:
sim_score = i[0]
sim_kg = i[1]
for j in range(loop_count):
# group generate
group_mem = gg.generateGroup(user_sim_matrix, train_set,
group_count, group_sim)
group_rank_kg = ic.kg_ic_predict(movie_sim_matrix, train_set,
group_mem, kg_sim, sim_score,
sim_kg)
g_rating_kg_lm = gsp.least_strategy(group_rank_kg, N)
precision_kg_lm, recall_kg_lm, sim_score, sim_kg = ev_detail_kg(
group_mem, test_set, g_rating_kg_lm, all_rec_movies, sim_score,
sim_kg)
g_rating_kg_avg = gsp.avg_strategy(group_rank_kg, N)
precision_kg_avg, recall_kg_avg, sim_score, sim_kg = ev_detail_kg(
group_mem, test_set, g_rating_kg_avg, all_rec_movies,
sim_score, sim_kg)
print("执行完毕！")
