def start_transaction(self, request):
"""Начинает записывать в БД"""
if self._hotels is None:
raise Exception("Не установлен лист отелей")
else:
self._chat = _User(request)
self._operation = _Operations(self._sql.conn, self._chat)
self._operation.insert(self._hotels, request)
def f_create_data(self, args):
self.m_min_occ = args.min_occ
self.m_max_line = 1e8
self.m_data_dir = args.data_dir
self.m_data_name = args.data_name
self.m_raw_data_file = args.data_file
self.m_raw_data_path = os.path.join(self.m_data_dir, self.m_raw_data_file)
self.m_vocab_file = self.m_data_name+"_vocab.json"
### to save new generated data
self.m_data_file = "tokenized_"+self.m_data_name+"_pro.pickle"
data = pd.read_pickle(self.m_raw_data_path)
train_df = data["train"]
valid_df = data["valid"]
tokenizer = TweetTokenizer(preserve_case=False)
train_reviews = train_df.review
train_item_ids = train_df.itemid
train_user_ids = train_df.userid
valid_reviews = valid_df.review
valid_item_ids = valid_df.itemid
valid_user_ids = valid_df.userid
vocab_obj = _Vocab()
self._create_vocab(vocab_obj, train_reviews)
# i = 0
review_corpus = defaultdict(dict)
item_corpus = defaultdict(dict)
user_corpus = defaultdict(dict)
user2uid = defaultdict()
stop_word_ids = [vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>']) for w in stopwords.words('english')]
punc_ids = [vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>']) for w in string.punctuation]
print("loading train reviews")
ss_time = datetime.datetime.now()
non_informative_words = stop_word_ids + punc_ids
# non_informative_words = stopwords.words()+string.punctuation
print("non informative words num", len(non_informative_words))
# print_index = 0
for index, review in enumerate(train_reviews):
if index > self.m_max_line:
break
item_id = train_item_ids.iloc[index]
user_id = train_user_ids.iloc[index]
words = tokenizer.tokenize(review)
word_ids = [vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>']) for w in words]
word_tf_map = Counter(word_ids)
new_word_tf_map = {}
for word in word_tf_map:
if word in non_informative_words:
continue
new_word_tf_map[word] = word_tf_map[word]
informative_word_num = sum(new_word_tf_map.values())
if informative_word_num < 5:
continue
review_id = len(review_corpus['train'])
review_obj = _Review()
review_obj.f_set_review(review_id, word_ids, new_word_tf_map, informative_word_num)
# print_index += 1
review_corpus["train"][review_id] = review_obj
if user_id not in user_corpus:
user_obj = _User()
user_obj.f_set_user_id(user_id)
user_corpus[user_id] = user_obj
user2uid[user_id] = len(user2uid)
uid = user2uid[user_id]
user_obj = user_corpus[user_id]
user_obj.f_add_review_id(review_id)
if item_id not in item_corpus:
item_obj = _Item()
item_corpus[item_id] = item_obj
item_obj.f_set_item_id(item_id)
review_obj.f_set_user_item(uid, item_id)
item_obj = item_corpus[item_id]
item_obj.f_add_review_id(review_obj, review_id)
e_time = datetime.datetime.now()
print("load training duration", e_time-ss_time)
print("load train review num", len(review_corpus["train"]))
s_time = datetime.datetime.now()
user_num = len(user_corpus)
vocab_obj.f_set_user(user2uid)
# vocab_obj.f_set_user_size(user_num)
save_item_corpus = {}
print("item num", len(item_corpus))
# print_index = 0
# print_review_index = 0
for item_id in item_corpus:
item_obj = item_corpus[item_id]
# s_time = datetime.datetime.now()
item_obj.f_get_item_lm()
for review_id in item_obj.m_review_id_list:
review_obj = review_corpus["train"][review_id]
item_obj.f_get_RRe(review_obj)
if item_id not in save_item_corpus:
save_item_corpus[item_id] = item_obj.m_avg_review_words
print("loading valid reviews")
for index, review in enumerate(valid_reviews):
if index > self.m_max_line:
break
item_id = valid_item_ids.iloc[index]
user_id = valid_user_ids.iloc[index]
if user_id not in user2uid:
continue
if item_id not in save_item_corpus:
continue
words = tokenizer.tokenize(review)
word_ids = [vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>']) for w in words]
word_tf_map = Counter(word_ids)
new_word_tf_map = {}
for word in word_tf_map:
if word in non_informative_words:
continue
new_word_tf_map[word] = word_tf_map[word]
informative_word_num = sum(new_word_tf_map.values())
if informative_word_num < 5:
continue
review_id = len(review_corpus["valid"])
review_obj = _Review()
review_obj.f_set_review(review_id, word_ids, new_word_tf_map, informative_word_num)
review_corpus["valid"][review_id] = review_obj
uid = user2uid[user_id]
review_obj.f_set_user_item(uid, item_id)
item_obj = item_corpus[item_id]
# print(len(item_corpus))
item_obj.f_get_RRe(review_obj)
print("load validate review num", len(review_corpus["valid"]))
save_data = {"item": save_item_corpus, "review": review_corpus, "user":user_num}
print("save data to ", self.m_data_file)
data_pickle_file = os.path.join(self.m_data_dir, self.m_data_file)
f = open(data_pickle_file, "wb")
pickle.dump(save_data, f)
f.close()
vocab = dict(w2i=vocab_obj.m_w2i, i2w=vocab_obj.m_i2w, user2uid=vocab_obj.m_user2uid)
with io.open(os.path.join(self.m_data_dir, self.m_vocab_file), 'wb') as vocab_file:
data = json.dumps(vocab, ensure_ascii=False)
vocab_file.write(data.encode('utf8', 'replace'))
def f_create_data(self, args):
self.m_min_occ = args.min_occ
self.m_max_line = 1e8
self.m_data_dir = args.data_dir
self.m_data_name = args.data_name
self.m_raw_data_file = args.data_file
self.m_raw_data_path = os.path.join(self.m_data_dir,
self.m_raw_data_file)
self.m_output_file = args.output_file
# self.m_vocab_file = self.m_data_name+".vocab.json"
self.m_vocab_file = "vocab.json"
### to save new generated data
self.m_data_file = "tokenized_" + self.m_output_file
# self.m_data_file = "tokenized_"+self.m_data_name+"_"+self.m_output_file
# self.m_data_file = "tokenized_"+self.m_data_name+"_pro_v2.pickle"
data = pd.read_pickle(self.m_raw_data_path)
train_df = data["train"]
valid_df = data["valid"]
tokenizer = TweetTokenizer(preserve_case=False)
train_reviews = train_df.review
train_item_ids = train_df.itemid
train_user_ids = train_df.userid
valid_reviews = valid_df.review
valid_item_ids = valid_df.itemid
valid_user_ids = valid_df.userid
vocab_obj = _Vocab()
self.f_create_vocab(vocab_obj, train_reviews)
# i = 0
review_corpus = defaultdict(dict)
item_corpus = defaultdict(dict)
user_corpus = defaultdict(dict)
global_user2uid = defaultdict()
global_item2iid = defaultdict()
stop_word_ids = [
vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>'])
for w in stopwords.words('english')
]
punc_ids = [
vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>'])
for w in string.punctuation
]
print("loading train reviews")
ss_time = datetime.datetime.now()
non_informative_words = stop_word_ids + punc_ids
# non_informative_words = stopwords.words()+string.punctuation
print("non informative words num", len(non_informative_words))
### load user words
self.m_user_word_file = os.path.join(self.m_data_dir,
args.user_word_file)
self.f_load_user_word_score(vocab_obj, self.m_user_word_file)
### load item words
self.m_item_word_file = os.path.join(self.m_data_dir,
args.item_word_file)
self.f_load_item_word_score(vocab_obj, self.m_item_word_file)
print("user word", len(self.m_user_word_score_map))
print("item word", len(self.m_item_word_score_map))
for index, review in enumerate(train_reviews):
if index > self.m_max_line:
break
item_id = train_item_ids.iloc[index]
user_id = train_user_ids.iloc[index]
words = tokenizer.tokenize(review)
word_ids = [
vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>']) for w in words
]
new_review_user, new_review_item, new_review_local = self.f_get_perturb(
vocab_obj, item_id, words, word_ids)
review_id = len(review_corpus['train'])
review_obj = _Review()
review_obj.f_set_review(review_id, word_ids)
# review_obj.f_set_review(review_id, word_ids, new_word_tf_map, informative_word_num)
review_obj.f_set_pertub_review(new_review_user, new_review_item,
new_review_local)
review_corpus["train"][review_id] = review_obj
if user_id not in user_corpus:
user_obj = _User()
user_obj.f_set_user_id(user_id)
user_corpus[user_id] = user_obj
global_user2uid[user_id] = len(global_user2uid)
uid = global_user2uid[user_id]
user_obj = user_corpus[user_id]
user_obj.f_add_review_id(review_id)
if item_id not in item_corpus:
item_obj = _Item()
item_corpus[item_id] = item_obj
item_obj.f_set_item_id(item_id)
global_item2iid[item_id] = len(global_item2iid)
iid = global_item2iid[item_id]
item_obj = item_corpus[item_id]
item_obj.f_add_review_id(review_obj, review_id)
review_obj.f_set_user_item(uid, iid)
e_time = datetime.datetime.now()
print("load training duration", e_time - ss_time)
print("load train review num", len(review_corpus["train"]))
s_time = datetime.datetime.now()
user_num = len(user_corpus)
vocab_obj.f_set_user(global_user2uid)
print("item num", len(item_corpus))
print("loading valid reviews")
for index, review in enumerate(valid_reviews):
if index > self.m_max_line:
break
item_id = valid_item_ids.iloc[index]
user_id = valid_user_ids.iloc[index]
if user_id not in global_user2uid:
continue
if item_id not in item_corpus:
continue
words = tokenizer.tokenize(review)
word_ids = [
vocab_obj.m_w2i.get(w, vocab_obj.m_w2i['<unk>']) for w in words
]
new_review_user, new_review_item, new_review_local = self.f_get_perturb(
vocab_obj, item_id, words, word_ids)
review_id = len(review_corpus["valid"])
review_obj = _Review()
review_obj.f_set_review(review_id, word_ids)
# review_obj.f_set_review(review_id, word_ids, new_word_tf_map, informative_word_num)
review_obj.f_set_pertub_review(new_review_user, new_review_item,
new_review_local)
review_corpus["valid"][review_id] = review_obj
uid = global_user2uid[user_id]
iid = global_item2iid[item_id]
review_obj.f_set_user_item(uid, iid)
item_obj = item_corpus[item_id]
# print(len(item_corpus))
# item_obj.f_get_RRe(review_obj)
# item_obj.f_get_ARe(review_obj)
print("load validate review num", len(review_corpus["valid"]))
save_data = {
"item": global_item2iid,
"review": review_corpus,
"user": global_user2uid
}
print("save data to ", self.m_data_file)
data_pickle_file = os.path.join(self.m_data_dir, self.m_data_file)
f = open(data_pickle_file, "wb")
pickle.dump(save_data, f)
f.close()
vocab = dict(w2i=vocab_obj.m_w2i,
i2w=vocab_obj.m_i2w,
user2uid=vocab_obj.m_user2uid)
with io.open(os.path.join(self.m_data_dir, self.m_vocab_file),
'wb') as vocab_file:
data = json.dumps(vocab, ensure_ascii=False)
vocab_file.write(data.encode('utf8', 'replace'))