def f_load_data(self, args):
self.m_data_name = args.data_name
# self.m_vocab_file = self.m_data_name+"_vocab.json"
self.m_vocab_file = "vocab.json"
print("data_dir", args.data_dir)
with open(os.path.join(args.data_dir, args.data_file), 'rb') as file:
data = pickle.load(file)
with open(os.path.join(args.data_dir, self.m_vocab_file), 'r') as file:
vocab = json.load(file)
review_corpus = data['review']
global_user2iid = data['user']
global_item2iid = data['item']
vocab_obj = _Vocab()
vocab_obj.f_set_vocab(vocab['w2i'], vocab['i2w'])
# item_num = len(item_corpus)
vocab_obj.f_set_user(global_user2iid)
vocab_obj.f_set_item(global_item2iid)
# print("vocab size", vocab_obj.m_vocab_size)
# train_data = _CLOTHING(args, vocab_obj, review_corpus['train'])
# # valid_data = Amazon(args, vocab_obj, review_corpus['valid'])
# valid_data = _CLOTHING_TEST(args, vocab_obj, review_corpus['valid'])
train_data = _MOVIE(args, vocab_obj, review_corpus['train'])
valid_data = _MOVIE_TEST(args, vocab_obj, review_corpus['valid'])
return train_data, valid_data, vocab_obj
def f_load_data_movie(self, args):
self.m_data_name = args.data_name
# self.m_vocab_file = self.m_data_name+".vocab.json"
self.m_vocab_file = args.vocab_file
self.m_item_boa_file = args.item_boa_file
self.m_user_boa_file = args.user_boa_file
train_data_file = args.data_dir+"/new_train.pickle"
valid_data_file = args.data_dir+"/new_valid.pickle"
test_data_file = args.data_dir+"/new_valid.pickle"
# train_data_file = args.data_dir+"/train.pickle"
# valid_data_file = args.data_dir+"/valid.pickle"
# test_data_file = args.data_dir+"/test.pickle"
train_df = pd.read_pickle(train_data_file)
valid_df = pd.read_pickle(valid_data_file)
test_df = pd.read_pickle(test_data_file)
user_num = train_df.userid.nunique()
print("user num", user_num)
with open(os.path.join(args.data_dir, self.m_vocab_file), 'r',encoding='utf8') as f:
vocab = json.loads(f.read())
with open(os.path.join(args.data_dir, self.m_item_boa_file), 'r',encoding='utf8') as f:
item_boa_dict = json.loads(f.read())
with open(os.path.join(args.data_dir, self.m_user_boa_file), 'r', encoding='utf8') as f:
user_boa_dict = json.loads(f.read())
vocab_obj = _Vocab()
vocab_obj.f_set_vocab(vocab['t2i'], vocab['i2t'])
vocab_obj.f_set_user_num(user_num)
global_user2iid = vocab['user_index']
global_item2iid = vocab['item_index']
vocab_obj.f_set_user(global_user2iid)
vocab_obj.f_set_item(global_item2iid)
print("vocab size", vocab_obj.m_vocab_size)
train_data = _MOVIE(args, vocab_obj, train_df, item_boa_dict, user_boa_dict)
valid_data = _MOVIE_TEST(args, vocab_obj, valid_df, item_boa_dict, user_boa_dict)
batch_size = args.batch_size
if args.parallel:
train_sampler = DistributedSampler(dataset=train_data)
train_loader = DataLoader(dataset=train_data, batch_size=batch_size, sampler=train_sampler, num_workers=8, collate_fn=train_data.collate)
else:
train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True, num_workers=8, collate_fn=train_data.collate)
test_loader = DataLoader(dataset=valid_data, batch_size=batch_size, shuffle=False, num_workers=4, collate_fn=valid_data.collate)
return train_loader, test_loader, vocab_obj
def f_load_data_yelp(self, args):
self.m_data_name = args.data_name
train_data_file = args.data_dir + "/train_100.pickle"
valid_data_file = args.data_dir + "/valid.pickle"
test_data_file = args.data_dir + "/test.pickle"
# train_data_file = args.data_dir+"/train_100.pickle"
# valid_data_file = args.data_dir+"/valid.pickle"
# test_data_file = args.data_dir+"/valid.pickle"
# train_data_file = args.data_dir+"/self_attn_train_100.pickle"
# valid_data_file = args.data_dir+"/self_attn_valid.pickle"
# test_data_file = args.data_dir+"/self_attn_valid.pickle"
train_df = pd.read_pickle(train_data_file)
valid_df = pd.read_pickle(valid_data_file)
test_df = pd.read_pickle(test_data_file)
self.m_vocab_file = args.vocab_file
with open(os.path.join(args.data_dir, self.m_vocab_file),
'r',
encoding='utf8') as f:
vocab = json.loads(f.read())
vocab_obj = _Vocab()
vocab_obj.f_set_vocab(vocab['t2i'], vocab['i2t'])
vocab_obj.f_set_user(vocab['u2i'])
vocab_obj.f_set_item(vocab['i2i'])
train_user_num = train_df.userid.nunique()
print("train user num", train_user_num)
train_item_num = train_df.itemid.nunique()
print("train item num", train_item_num)
train_pos_tag_num = train_df.pos_tagid.nunique()
print("train tag num", train_pos_tag_num)
train_data = _MOVIE(args, train_df)
valid_data = _MOVIE_TEST(args, valid_df)
batch_size = args.batch_size
if args.parallel:
train_sampler = DistributedSampler(dataset=train_data)
train_loader = DataLoader(dataset=train_data,
batch_size=batch_size,
sampler=train_sampler,
num_workers=0,
collate_fn=train_data.collate)
else:
train_loader = DataLoader(dataset=train_data,
batch_size=batch_size,
shuffle=True,
num_workers=8,
collate_fn=train_data.collate)
test_loader = DataLoader(dataset=valid_data,
batch_size=batch_size,
shuffle=False,
num_workers=4,
collate_fn=valid_data.collate)
return train_loader, test_loader, vocab_obj
