def run(self):
print('/1')
index, r_index = 0, 0
dict_locid_catid,dict_catid_to_index,dict_r_catid_to_index= {},{},{}
for ny_vcat_i in self.ny_vcat[1:]:
ny_vcat_i = ny_vcat_i.split(',')
locid_ = ny_vcat_i[0]
catid_ = ny_vcat_i[1]
if locid_ not in dict_locid_catid:
dict_locid_catid[locid_] = (catid_, int(catid_))
if catid_ not in dict_catid_to_index:
dict_catid_to_index[catid_] = index
index += 1
if int(catid_) not in dict_r_catid_to_index:
dict_r_catid_to_index[int(catid_)] = r_index
r_index += 1
set_locid_name = set(dict_locid_catid.keys())
locid_name = list(set(dict_locid_catid.keys()))
catid_set = list(set(dict_catid_to_index.values()))
relation_catid_set = list(set(dict_r_catid_to_index.values()))
print('/2')
for i in range(len(locid_name)):
locid_catid_ = dict_locid_catid[locid_name[i]]
index_ = dict_catid_to_index[locid_catid_[0]]
r_index_ = dict_r_catid_to_index[locid_catid_[1]]
dict_locid_catid[locid_name[i]] = (index_, r_index_)
print('/3')
dict_uid_category,uid_category_set,gender_set,race_set = {},[],[],[]
for ny_demo_i in self.ny_demo[1:]:
ny_demo_i = ny_demo_i.split(',')
uid_ = ny_demo_i[0]
gender_ = int(ny_demo_i[1]) - 1
race_ = int(ny_demo_i[3]) - 1
category_ = gender_ * race_ + race_
dict_uid_category[uid_] = (category_, (gender_, race_))
uid_category_set.append(category_)
gender_set.append(gender_)
race_set.append(race_)
uid_category_set = list(set(uid_category_set))
uid_name = set(dict_uid_category.keys())
self.gender_set = list(set(gender_set))
self.race_set = list(set(race_set))
print('/4')
dict_uid_time_locid = {}
for i in range(len(self.ny_checkin) - 1):
ny_checkin_i = self.ny_checkin[i + 1].split(',')
uid_, time_, locid_ = ny_checkin_i[1], ny_checkin_i[
2], ny_checkin_i[5].strip('\n')
time_day, time_hms = time_.split()[0], time_.split()[1]
if uid_ in uid_name and locid_ in set_locid_name:
year_ = int(time_day.split('-')[0])
if year_ in self.dict_restrict['year']:
if uid_ not in dict_uid_time_locid:
dict_uid_time_locid[uid_] = [(time_day, time_hms,
locid_)]
else:
dict_uid_time_locid[uid_].append(
(time_day, time_hms, locid_))
print('/5')
uid_name = list(set(dict_uid_time_locid.keys()))
dict_uid_locid_relation, dict_locid_to_entity, index = {}, {}, 0
for u_i in range(len(uid_name)):
uid_time_locid_ = dict_uid_time_locid[uid_name[u_i]]
if len(uid_time_locid_) >= self.dict_restrict['num_checkin']:
sorted_uid_time_locid_ = sorted(uid_time_locid_)
for i in range(len(sorted_uid_time_locid_)):
time_day_, time_hms_, locid_ = sorted_uid_time_locid_[i][
0], sorted_uid_time_locid_[i][
1], sorted_uid_time_locid_[i][2]
daynum_time_day_ = int(time_day_.split('-')[1]) - 1
time_h = int(time_hms_.split('-')[0].split(':')[0])
if time_h > 11:
time_h = 1
else:
time_h = 0
relation_time = time_h * daynum_time_day_ + daynum_time_day_
sorted_uid_time_locid_[i] = (locid_, relation_time)
if locid_ not in dict_locid_to_entity:
dict_locid_to_entity[locid_] = index
index += 1
dict_uid_locid_relation[uid_name[u_i]] = sorted_uid_time_locid_
uid_name = list(set(dict_uid_locid_relation.keys()))
locid_name = list(set(dict_locid_to_entity.keys()))
print('/6')
test_X_num = self.dict_restrict['test_X_num']
test_Y_num = self.dict_restrict['test_Y_num']
train_set,test_set,test_X_set,test_Y_set,relation_time_set,data_set = [],[],[],[],[],[]
for i in range(len(uid_name)):
uid_locid_relation_ = dict_uid_locid_relation[uid_name[i]]
data_set_, catid_set_ = [], []
for j in range(len(uid_locid_relation_)):
locid_ = uid_locid_relation_[j][0]
catid_ = dict_locid_catid[locid_][0]
entity_locid_ = dict_locid_to_entity[locid_]
relation_time_ = uid_locid_relation_[j][1]
data_set_.append(entity_locid_)
relation_time_set.append(relation_time_)
catid_set_.append(catid_)
data_set.append(data_set_)
train_set_ = data_set_[:len(data_set_) - test_X_num - test_Y_num]
test_X_set_ = data_set_[len(data_set_) - test_X_num -
test_Y_num:len(data_set_) - test_Y_num]
test_Y_set_ = data_set_[len(data_set_) - test_Y_num:]
test_set_ = data_set_[len(data_set_) - test_X_num - test_Y_num:]
train_set.append(train_set_)
test_set.append(test_set_)
test_X_set.append(test_X_set_)
test_Y_set.append(test_Y_set_)
self.test_X_set = np.array(test_X_set)
relation_time_set = list(set(relation_time_set))
entity_set = list(set(dict_locid_to_entity.values()))
print('/7')
dict_uid_category_to_entity = {}
for i in range(len(uid_category_set)):
dict_uid_category_to_entity[
uid_category_set[i]] = uid_category_set[i] + len(entity_set)
dict_catid_to_entity = {}
for i in range(len(catid_set)):
dict_catid_to_entity[catid_set[i]] = len(entity_set) + len(
uid_category_set) + catid_set[i]
dict_relation_catid_to_r = {}
for i in range(len(relation_catid_set)):
dict_relation_catid_to_r[relation_catid_set[i]] = len(
relation_time_set) + relation_catid_set[i]
print('/8')
self.dict_KG, self.uid_attribute_category = {}, []
for i in range(len(uid_name)):
uid_category_, uid_category_detail_ = dict_uid_category[
uid_name[i]]
entity_uid_category_ = dict_uid_category_to_entity[uid_category_]
uid_locid_relation_ = dict_uid_locid_relation[uid_name[i]]
gender_, race_ = uid_category_detail_[0], uid_category_detail_[1]
self.uid_attribute_category.append((gender_, race_))
for j in range(len(uid_locid_relation_)):
locid_ = uid_locid_relation_[j][0]
relation_time_ = uid_locid_relation_[j][1]
entity_locid_ = dict_locid_to_entity[locid_]
catid_, relation_catid_ = dict_locid_catid[locid_][
0], dict_locid_catid[locid_][1]
entity_catid_ = dict_catid_to_entity[catid_]
relation_catid_ = dict_relation_catid_to_r[relation_catid_]
if entity_locid_ not in self.dict_KG:
self.dict_KG[entity_locid_] = {}
for k in range(len(self.gender_set)):
self.dict_KG[entity_locid_]['gender' + '-' + str(
self.gender_set[k])] = []
if self.gender_set[k] == gender_:
self.dict_KG[entity_locid_]['gender' + '-' + str(
self.gender_set[k])].append([
entity_locid_, relation_time_,
entity_uid_category_
])
self.dict_KG[entity_locid_]['gender' + '-' + str(
self.gender_set[k])].append([
entity_locid_, relation_catid_,
entity_catid_
])
for k in range(len(self.race_set)):
self.dict_KG[entity_locid_]['race' + '-' + str(
self.race_set[k])] = []
if self.race_set[k] == race_:
self.dict_KG[entity_locid_]['race' + '-' + str(
self.race_set[k])].append([
entity_locid_, relation_time_,
entity_uid_category_
])
self.dict_KG[entity_locid_]['race' + '-' + str(
self.race_set[k])].append([
entity_locid_, relation_catid_,
entity_catid_
])
else:
for k in range(len(self.gender_set)):
if self.gender_set[k] == gender_:
self.dict_KG[entity_locid_]['gender' + '-' + str(
self.gender_set[k])].append([
entity_locid_, relation_time_,
entity_uid_category_
])
self.dict_KG[entity_locid_]['gender' + '-' + str(
self.gender_set[k])].append([
entity_locid_, relation_catid_,
entity_catid_
])
for k in range(len(self.race_set)):
if self.race_set[k] == race_:
self.dict_KG[entity_locid_]['race' + '-' + str(
self.race_set[k])].append([
entity_locid_, relation_time_,
entity_uid_category_
])
self.dict_KG[entity_locid_]['race' + '-' + str(
self.race_set[k])].append([
entity_locid_, relation_catid_,
entity_catid_
])
self.dict_itemid_upfdf = dict()
for i in range(len(uid_name)):
uid_upf_ = self.uid_attribute_category[i]
uid_data_set_ = data_set[i]
for j in range(len(uid_data_set_)):
if uid_data_set_[j] not in self.dict_itemid_upfdf:
self.dict_itemid_upfdf[uid_data_set_[j]] = list()
self.dict_itemid_upfdf[uid_data_set_[j]].append(uid_upf_)
itemid_name = list(set(self.dict_itemid_upfdf.keys()))
for i in range(len(itemid_name)):
itemid_upfdf_ = self.dict_itemid_upfdf[itemid_name[i]]
b_0, b_1, b_2, g_0, g_1, g_2 = 0, 0, 0, 0, 0, 0
for j in range(len(itemid_upfdf_)):
if itemid_upfdf_[j] == (0, 0):
b_0 += 1
elif itemid_upfdf_[j] == (0, 1):
b_1 += 1
elif itemid_upfdf_[j] == (0, 2):
b_2 += 1
elif itemid_upfdf_[j] == (1, 0):
g_0 += 1
elif itemid_upfdf_[j] == (1, 1):
g_1 += 1
elif itemid_upfdf_[j] == (1, 2):
g_2 += 1
else:
print('Error!!')
b_0_p, b_1_p, b_2_p = b_0 / len(itemid_upfdf_), b_1 / len(
itemid_upfdf_), b_2 / len(itemid_upfdf_)
g_0_p, g_1_p, g_2_p = g_0 / len(itemid_upfdf_), g_1 / len(
itemid_upfdf_), g_2 / len(itemid_upfdf_)
self.dict_itemid_upfdf[itemid_name[i]] = [
b_0_p, b_1_p, b_2_p, g_0_p, g_1_p, g_2_p
]
num_user = len(uid_name)
num_item = len(entity_set)
n_entities = len(entity_set) + len(uid_category_set) + len(catid_set)
n_relation = len(relation_time_set) + len(relation_catid_set)
train = Interactions(train_set, num_user, num_item)
train.to_sequence(self.L_hgn, self.T_hgn)
sequences_np = train.sequences.sequences
targets_np = train.sequences.targets
users_np = train.sequences.user_ids
train_matrix = train.tocsr()
param_ = [self.args, num_user, num_item, n_entities, n_relation]
data_ = [users_np, sequences_np, targets_np, train_matrix]
test_ = [
train, self.test_X_set, test_Y_set, self.uid_attribute_category
]
train_data, test_data = list(), list()
entity_set = set(entity_set)
for i in range(len(train_set)):
uid_ = i
neg_entity_set_ = list(entity_set - set(train_set[i]))
neg_train_set_ = random.sample(neg_entity_set_, len(train_set[i]))
for j in range(len(train_set[i])):
train_data.append([uid_, train_set[i][j], 1])
train_data.append([uid_, neg_train_set_[j], 0])
for i in range(len(test_set)):
uid_ = i
neg_entity_set_ = list(entity_set - set(test_set[i]))
neg_test_set_ = random.sample(neg_entity_set_, len(test_set[i]))
for j in range(len(test_set[i])):
test_data.append([uid_, test_set[i][j], 1])
test_data.append([uid_, neg_test_set_[j], 0])
train_data = np.array(train_data)
eval_data = train_data
test_data = np.array(test_data)
user_history_dict = dict()
for i in range(len(data_set)):
uid_ = i
user_history_dict[uid_] = data_set[i]
pickle_data = {
'train_data': train_data,
'eval_data': eval_data,
'test_data': test_data,
'n_entity': n_entities,
'n_relation': n_relation,
'kg_np': self.KG_random_generator(),
'user_history_dict': user_history_dict,
}
return param_, data_, test_
model_parser.add_argument('--nv', type=int, default=4)
model_parser.add_argument('--nh', type=int, default=16)
model_parser.add_argument('--drop', type=float, default=0.5)
model_parser.add_argument('--ac_conv', type=str, default='relu')
model_parser.add_argument('--ac_fc', type=str, default='relu')
model_config = model_parser.parse_args()
model_config.L = config.L
# set seed
set_seed(config.seed,
cuda=config.use_cuda)
# load dataset
train = Interactions(config.train_root)
train.tocsr()
# transform triplets to sequence representation
train.to_sequence(config.L, config.T)
test = Interactions(config.test_root,
user_map=train.user_map,
item_map=train.item_map)
test.tocsr()
print(config)
print(model_config)
# fit model
model = Recommender(n_iter=config.n_iter,
batch_size=config.batch_size,
learning_rate=config.learning_rate,
