def build_user_item_sim_CF(self):
from collections import defaultdict
self.user_sim = SimMatrix()
self.item_sim = SimMatrix()
self.user_k_neibor = defaultdict(dict)
self.item_k_neibor = defaultdict(dict)
# compute item-item similarity matrix
print('constructing user-user similarity matrix...')
# self.user_sim = util.load_data('../data/sim/ft_08_uu_tricf.pkl')
for u1 in self.rg.user:
for u2 in self.rg.user:
if u1 != u2:
if self.user_sim.contains(u1, u2):
continue
sim = pearson_sp(self.rg.get_row(u1), self.rg.get_row(u2))
sim = round(sim, 5)
self.user_sim.set(u1, u2, sim)
util.save_data(self.user_sim, '../data/sim/ft_08_uu_tricf_cv0.pkl')
# compute the k neighbors of user
# self.user_k_neibor = util.load_data(
# '../data/neibor/ft_08_uu_' + str(self.config.user_near_num) + '_neibor_tricf.pkl')
for user in self.rg.user:
matchUsers = sorted(self.user_sim[user].items(),
key=lambda x: x[1],
reverse=True)[:self.config.user_near_num]
matchUsers = matchUsers[:self.config.user_near_num]
self.user_k_neibor[user] = dict(matchUsers)
util.save_data(
self.user_k_neibor, '../data/neibor/ft_08_uu_' +
str(self.config.user_near_num) + '_neibor_tricf_cv0.pkl')
# compute item-item similarity matrix
print('constructing item-item similarity matrix...')
# self.item_sim = util.load_data('../data/sim/ft_08_ii_tricf.pkl')
for i1 in self.rg.item:
for i2 in self.rg.item:
if i1 != i2:
if self.item_sim.contains(i1, i2):
continue
sim = pearson_sp(self.rg.get_col(i1), self.rg.get_col(i2))
sim = round(sim, 5)
self.item_sim.set(i1, i2, sim)
util.save_data(self.item_sim, '../data/sim/ft_08_ii_tricf_cv0.pkl')
# compute the k neighbors of item
# self.item_k_neibor = util.load_data(
# '../data/neibor/ft_08_ii_' + str(self.config.item_near_num) + '_neibor_tricf.pkl')
for item in self.rg.item:
matchItems = sorted(self.item_sim[item].items(),
key=lambda x: x[1],
reverse=True)[:self.config.item_near_num]
matchItems = matchItems[:self.config.item_near_num]
self.item_k_neibor[item] = dict(matchItems)
util.save_data(
self.item_k_neibor, '../data/neibor/ft_08_ii_' +
str(self.config.item_near_num) + '_neibor_tricf_cv0.pkl')
pass
def init_model(self):
self.item_sim = SimMatrix()
for i_test in self.rg.testSet_i:
for i_train in self.rg.item:
if i_test != i_train:
if self.item_sim.contains(i_test, i_train):
continue
sim = pearson_sp(self.rg.get_col(i_test),
self.rg.get_col(i_train))
self.item_sim.set(i_test, i_train, sim)
def init_model(self):
self.user_sim = SimMatrix()
for u_test in self.rg.testSet_u:
for u_train in self.rg.user:
if u_test != u_train:
if self.user_sim.contains(u_test, u_train):
continue
sim = pearson_sp(self.rg.get_row(u_test),
self.rg.get_row(u_train))
self.user_sim.set(u_test, u_train, sim)
def get_neighbor(self, user, item):
if user in self.user_item_nei and item in self.user_item_nei[user]:
return self.user_item_nei[user][item]
items = self.rg.user_rated_items(user)
u_item_d = {}
for u_item in items:
if item != u_item:
sim = pearson_sp(self.rg.get_col(item),
self.rg.get_col(u_item))
u_item_d[u_item] = round(sim, 4)
matchItems = sorted(u_item_d.items(), key=lambda x: x[1],
reverse=True)[:self.config.item_near_num]
matchItems = list(zip(*matchItems))
if len(matchItems) > 0:
self.user_item_nei[user][item] = matchItems[0]
return matchItems[0]
else:
return []
def get_sim(self, u, k):
sim = (pearson_sp(self.rg.get_row(u), self.rg.get_row(k)) +
1.0) / 2.0 # fit the value into range [0.0,1.0]
return sim
def build_user_item_sim_CF(self,
kfold,
user_near_num=50,
item_near_num=50,
load_save_sim=False):
self.rg = RatingGetter(kfold)
self.mg = MetaGetter(kfold)
from collections import defaultdict
# compute item-item similarity matrix
print('构建 item-item 相似度矩阵 ...')
if load_save_sim:
self.item_sim = util.load_data(
'../data/sim/%s_08_ii_cucmemf_cv0.pkl' %
self.config.dataset_name)
else:
# 封装 item 相似度计算
self.item_sim = self.mg.getSimMatrix(jaccard_sim)
util.save_data(
self.item_sim, '../data/sim/%s_08_ii_cucmemf_cv0.pkl' %
self.config.dataset_name)
# compute the k neighbors of item
if load_save_sim:
self.item_k_neibor = util.load_data(
'../data/neibor/%s_08_ii_%s_neibor_cucmemf_cv0.pkl' %
(self.config.dataset_name, item_near_num))
for item in self.mg.item:
matchItems = sorted(self.item_sim[item].items(),
key=lambda x: x[1],
reverse=True)[:item_near_num]
matchItems = matchItems[:item_near_num]
self.item_k_neibor[item] = dict(matchItems)
util.save_data(
self.item_k_neibor,
'../data/neibor/%s_08_ii_%s_neibor_cucmemf_cv0.pkl' %
(self.config.dataset_name, item_near_num))
# compute user-user similarity matrix
print('构建 user-user 相似度矩阵 ...')
if load_save_sim:
# if True:
self.user_sim = util.load_data(
'../data/sim/%s_08_uu_cucmemf_cv0.pkl' %
self.config.dataset_name)
else:
for u1 in tqdm(self.rg.user):
for u2 in self.rg.user:
if u1 != u2:
if self.user_sim.contains(u1, u2):
continue
# 皮尔逊相似度？ 修改为余弦相似度?；
sim = pearson_sp(self.rg.get_row(u1),
self.rg.get_row(u2))
sim = round(sim, 5)
self.user_sim.set(u1, u2, sim)
if not os.path.exists('../data/sim'):
os.makedirs('../data/sim')
print('../data/sim folder has been established.')
util.save_data(
self.user_sim, '../data/sim/%s_08_uu_cucmemf_cv0.pkl' %
self.config.dataset_name)
# compute the k neighbors of user
if load_save_sim:
self.user_k_neibor = util.load_data(
'../data/neibor/%s_08_uu_%s_neibor_cucmemf_cv0.pkl' %
(self.config.dataset_name, user_near_num))
for user in self.rg.user:
matchUsers = sorted(self.user_sim[user].items(),
key=lambda x: x[1],
reverse=True)[kfold:user_near_num]
matchUsers = matchUsers[:user_near_num]
self.user_k_neibor[user] = dict(matchUsers)
if not os.path.exists('../data/neibor'):
os.makedirs('../data/neibor')
print('../data/neibor folder has been established.')
util.save_data(
self.user_k_neibor,
'../data/neibor/%s_08_uu_%s_neibor_cucmemf_cv0.pkl' %
(self.config.dataset_name, user_near_num))
def build_user_item_sim_CF(self,
kfold,
user_near_num=50,
item_near_num=50,
load_save_sim=False):
self.rg = RatingGetter(kfold)
from collections import defaultdict
# compute item-item similarity matrix
print('构建 user-user 相似度矩阵 ...')
if load_save_sim:
self.user_sim = util.load_data(
'../data/sim/db_08_uu_tricf_cv0.pkl')
else:
for u1 in self.rg.user:
for u2 in self.rg.user:
if u1 != u2:
if self.user_sim.contains(u1, u2):
continue
# 皮尔逊相似度？ 修改为余弦相似度；
sim = pearson_sp(self.rg.get_row(u1),
self.rg.get_row(u2))
sim = round(sim, 5)
self.user_sim.set(u1, u2, sim)
if not os.path.exists('../data/sim'):
os.makedirs('../data/sim')
print('../data/sim folder has been established.')
util.save_data(self.user_sim, '../data/sim/db_08_uu_tricf_cv0.pkl')
# compute the k neighbors of user
# self.user_k_neibor = util.load_data(
# '../data/neibor/db_08_uu_' + str(user_near_num) + '_neibor_tricf.pkl')
for user in self.rg.user:
matchUsers = sorted(self.user_sim[user].items(),
key=lambda x: x[1],
reverse=True)[kfold:user_near_num]
matchUsers = matchUsers[:user_near_num]
self.user_k_neibor[user] = dict(matchUsers)
if not os.path.exists('../data/neibor'):
os.makedirs('../data/neibor')
print('../data/neibor folder has been established.')
util.save_data(
self.user_k_neibor, '../data/neibor/db_08_uu_' +
str(user_near_num) + '_neibor_tricf_cv0.pkl')
# compute item-item similarity matrix
print('构建 item-item 相似度矩阵 ...')
if load_save_sim:
self.item_sim = util.load_data(
'../data/sim/db_08_ii_tricf_cv0.pkl')
else:
for i1 in self.rg.item:
for i2 in self.rg.item:
if i1 != i2:
if self.item_sim.contains(i1, i2):
continue
# 皮尔逊相似度？ 修改为余弦相似度；
sim = pearson_sp(self.rg.get_col(i1),
self.rg.get_col(i2))
sim = round(sim, 5)
self.item_sim.set(i1, i2, sim)
util.save_data(self.item_sim, '../data/sim/db_08_ii_tricf_cv0.pkl')
# compute the k neighbors of item
# self.item_k_neibor = util.load_data(
# '../data/neibor/db_08_ii_' + str(item_near_num) + '_neibor_tricf.pkl')
for item in self.rg.item:
matchItems = sorted(self.item_sim[item].items(),
key=lambda x: x[1],
reverse=True)[:item_near_num]
matchItems = matchItems[:item_near_num]
self.item_k_neibor[item] = dict(matchItems)
util.save_data(
self.item_k_neibor, '../data/neibor/db_08_ii_' +
str(item_near_num) + '_neibor_tricf_cv0.pkl')
pass
def get_sim(self, u, k):
sim = (pearson_sp(self.rg.get_row(u), self.rg.get_row(k)) + 1.0) / 2.0 # 为了让范围在[0,1] +1.0)/2.0 0.83626
return sim
