def __init__(self):
self.tokenization = Tokenization(
import_module="jieba",
user_dict=config.USER_DEFINED_DICT_PATH,
chn_stop_words_dir=config.CHN_STOP_WORDS_PATH)
self.database = Database()
self.classifier = Classifier()
def __init__(self, database_name, collection_name):
super(JrjSpyder, self).__init__()
self.db_obj = Database()
self.col = self.db_obj.conn[database_name].get_collection(collection_name)
self.terminated_amount = 0
self.db_name = database_name
self.col_name = collection_name
self.tokenization = Tokenization(import_module="jieba", user_dict=config.USER_DEFINED_DICT_PATH)
def get_all_news_about_specific_stock(self, database_name,
collection_name):
# 获取collection_name的key值，看是否包含RelatedStockCodes，如果没有说明，没有做将新闻中所涉及的
# 股票代码保存在新的一列
_keys_list = list(
next(
self.database.get_collection(database_name,
collection_name).find()).keys())
if "RelatedStockCodes" not in _keys_list:
tokenization = Tokenization(import_module="jieba",
user_dict="./Leorio/financedict.txt")
tokenization.update_news_database_rows(database_name,
collection_name)
# 创建stock_code为名称的collection
stock_symbol_list = self.database.get_data(
config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["symbol"])["symbol"].to_list()
col_names = self.database.connect_database(
config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE).list_collection_names(
session=None)
for symbol in stock_symbol_list:
if symbol not in col_names:
_collection = self.database.get_collection(
config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE, symbol)
_tmp_num_stat = 0
for row in self.database.get_collection(
database_name, collection_name).find(): # 迭代器
if symbol[2:] in row["RelatedStockCodes"].split(" "):
# 返回新闻发布后n天的标签
_tmp_dict = {}
for label_days, key_name in self.label_range.items():
_tmp_res = self._label_news(
datetime.datetime.strptime(
row["Date"].split(" ")[0], "%Y-%m-%d"),
symbol, label_days)
_tmp_dict.update({key_name: _tmp_res})
_data = {
"Date": row["Date"],
"Url": row["Url"],
"Title": row["Title"],
"Article": row["Article"],
"OriDB": database_name,
"OriCOL": collection_name
}
_data.update(_tmp_dict)
_collection.insert_one(_data)
_tmp_num_stat += 1
logging.info(
"there are {} news mentioned {} in {} collection need to be fetched ... "
.format(_tmp_num_stat, symbol, collection_name))
else:
logging.info(
"{} has fetched all related news from {}...".format(
symbol, collection_name))
break
def __init__(self, database_name, collection_name):
super(NbdSpyder, self).__init__()
self.db_obj = Database()
self.col = self.db_obj.conn[database_name].get_collection(collection_name)
self.terminated_amount = 0
self.db_name = database_name
self.col_name = collection_name
self.tokenization = Tokenization(import_module="jieba", user_dict=config.USER_DEFINED_DICT_PATH)
self.redis_client = redis.StrictRedis(host=config.REDIS_IP,
port=config.REDIS_PORT,
db=config.CACHE_NEWS_REDIS_DB_ID)
def get_all_news_about_specific_stock(self, database_name,
collection_name):
# 获取collection_name的key值，看是否包含RelatedStockCodes，如果没有说明，没有做将新闻中所涉及的
# 股票代码保存在新的一列
_keys_list = list(
next(
self.database.get_collection(database_name,
collection_name).find()).keys())
if "RelatedStockCodes" not in _keys_list:
tokenization = Tokenization(import_module="jieba",
user_dict="./Leorio/financedict.txt")
tokenization.update_news_database_rows(database_name,
collection_name)
# 创建stock_code为名称的collection
stock_code_list = self.database.get_data("stock",
"basic_info",
keys=["code"
])["code"].to_list()
for code in stock_code_list:
_collection = self.database.get_collection(
config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE, code)
_tmp_num_stat = 0
for row in self.database.get_collection(
database_name, collection_name).find(): # 迭代器
if code in row["RelatedStockCodes"].split(" "):
_collection.insert_one({
"Date": row["Date"],
"Url": row["Url"],
"Title": row["Title"],
"Article": row["Article"],
"OriDB": database_name,
"OriCOL": collection_name
})
_tmp_num_stat += 1
logging.info(
"there are {} news mentioned {} in {} collection ... ".format(
_tmp_num_stat, code, collection_name))
class NbdSpyder(Spyder):
def __init__(self, database_name, collection_name):
super(NbdSpyder, self).__init__()
self.db_obj = Database()
self.col = self.db_obj.conn[database_name].get_collection(
collection_name)
self.terminated_amount = 0
self.db_name = database_name
self.col_name = collection_name
self.tokenization = Tokenization(
import_module="jieba", user_dict=config.USER_DEFINED_DICT_PATH)
def get_url_info(self, url):
try:
bs = utils.html_parser(url)
except Exception:
return False
span_list = bs.find_all("span")
part = bs.find_all("p")
article = ""
date = ""
for span in span_list:
if "class" in span.attrs and span.text and span["class"] == [
"time"
]:
string = span.text.split()
for dt in string:
if dt.find("-") != -1:
date += dt + " "
elif dt.find(":") != -1:
date += dt
break
for paragraph in part:
chn_status = utils.count_chn(str(paragraph))
possible = chn_status[1]
if possible > self.is_article_prob:
article += str(paragraph)
while article.find("<") != -1 and article.find(">") != -1:
string = article[article.find("<"):article.find(">") + 1]
article = article.replace(string, "")
while article.find("\u3000") != -1:
article = article.replace("\u3000", "")
article = " ".join(re.split(" +|\n+", article)).strip()
return [date, article]
def get_historical_news(self, start_page=684):
date_list = self.db_obj.get_data(self.db_name,
self.col_name,
keys=["Date"])["Date"].to_list()
name_code_df = self.db_obj.get_data(
config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
name_code_dict = dict(name_code_df.values)
if len(date_list) == 0:
# 说明没有历史数据，从头开始爬取
crawled_urls_list = []
page_urls = [
"{}/{}".format(config.WEBSITES_LIST_TO_BE_CRAWLED_NBD, page_id)
for page_id in range(start_page, 0, -1)
]
for page_url in page_urls:
bs = utils.html_parser(page_url)
a_list = bs.find_all("a")
for a in a_list:
if "click-statistic" in a.attrs and a.string \
and a["click-statistic"].find("Article_") != -1 \
and a["href"].find("http://www.nbd.com.cn/articles/") != -1:
if a["href"] not in crawled_urls_list:
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.NBD_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.NBD_MAX_REJECTED_AMOUNTS,
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH
))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(
a.string, a["href"]))
else:
# 有返回但是article为null的情况
date, article = result
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.NBD_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(
a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.
NBD_MAX_REJECTED_AMOUNTS,
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH
))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(
article, name_code_dict)
data = {
"Date":
date,
# "PageId": page_url.split("/")[-1],
"Url":
a["href"],
"Title":
a.string,
"Article":
article,
"RelatedStockCodes":
" ".join(related_stock_codes_list)
}
# self.col.insert_one(data)
self.db_obj.insert_data(
self.db_name, self.col_name, data)
logging.info("[SUCCESS] {} {} {}".format(
date, a.string, a["href"]))
else:
is_stop = False
start_date = max(date_list)
page_start_id = 1
while not is_stop:
page_url = "{}/{}".format(
config.WEBSITES_LIST_TO_BE_CRAWLED_NBD, page_start_id)
bs = utils.html_parser(page_url)
a_list = bs.find_all("a")
for a in a_list:
if "click-statistic" in a.attrs and a.string \
and a["click-statistic"].find("Article_") != -1 \
and a["href"].find("http://www.nbd.com.cn/articles/") != -1:
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.NBD_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.NBD_MAX_REJECTED_AMOUNTS,
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"], 60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(
a.string, a["href"]))
else:
# 有返回但是article为null的情况
date, article = result
if date > start_date:
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.NBD_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(
a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.
NBD_MAX_REJECTED_AMOUNTS,
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH
))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(
article, name_code_dict)
data = {
"Date":
date,
"Url":
a["href"],
"Title":
a.string,
"Article":
article,
"RelatedStockCodes":
" ".join(related_stock_codes_list)
}
self.db_obj.insert_data(
self.db_name, self.col_name, data)
logging.info("[SUCCESS] {} {} {}".format(
date, a.string, a["href"]))
else:
is_stop = True
break
if not is_stop:
page_start_id += 1
def get_realtime_news(self, interval=60):
page_url = "{}/1".format(config.WEBSITES_LIST_TO_BE_CRAWLED_NBD)
logging.info(
"start real-time crawling of URL -> {}, request every {} secs ... "
.format(page_url, interval))
name_code_df = self.db_obj.get_data(
config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
name_code_dict = dict(name_code_df.values)
crawled_urls = []
date_list = self.db_obj.get_data(self.db_name,
self.col_name,
keys=["Date"])["Date"].to_list()
latest_date = max(date_list)
while True:
# 每隔一定时间轮询该网址
if len(crawled_urls) > 100:
# 防止list过长，内存消耗大，维持list在100条
crawled_urls.pop(0)
bs = utils.html_parser(page_url)
a_list = bs.find_all("a")
for a in a_list:
if "click-statistic" in a.attrs and a.string \
and a["click-statistic"].find("Article_") != -1 \
and a["href"].find("http://www.nbd.com.cn/articles/") != -1:
if a["href"] not in crawled_urls:
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.NBD_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.NBD_MAX_REJECTED_AMOUNTS,
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"], 60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(
a.string, a["href"]))
else:
# 有返回但是article为null的情况
date, article = result
if date > latest_date:
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.NBD_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(
a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.
NBD_MAX_REJECTED_AMOUNTS,
config.
RECORD_NBD_FAILED_URL_TXT_FILE_PATH
))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(
article, name_code_dict)
data = {
"Date":
date,
# "PageId": page_url.split("/")[-1],
"Url":
a["href"],
"Title":
a.string,
"Article":
article,
"RelatedStockCodes":
" ".join(related_stock_codes_list)
}
# self.col.insert_one(data)
self.db_obj.insert_data(
self.db_name, self.col_name, data)
crawled_urls.append(a["href"])
logging.info("[SUCCESS] {} {} {}".format(
date, a.string, a["href"]))
# logging.info("sleep {} secs then request again ... ".format(interval))
time.sleep(interval)
class TopicModelling(object):
def __init__(self):
self.tokenization = Tokenization(
import_module="jieba",
user_dict=config.USER_DEFINED_DICT_PATH,
chn_stop_words_dir=config.CHN_STOP_WORDS_PATH)
self.database = Database()
self.classifier = Classifier()
def create_dictionary(self,
raw_documents_list,
save_path=None,
is_saved=False):
"""
将文中每个词汇关联唯一的ID，因此需要定义词汇表
:param: raw_documents_list, 原始语料列表，每个元素即文本，如["洗尽铅华...", "风雨赶路人...", ...]
:param: savepath, corpora.Dictionary对象保存路径
"""
documents_token_list = []
for doc in raw_documents_list:
documents_token_list.append(self.tokenization.cut_words(doc))
_dict = corpora.Dictionary(documents_token_list)
# 找到只出现一次的token
once_items = [
_dict[tokenid] for tokenid, docfreq in _dict.dfs.items()
if docfreq == 1
]
# 在documents_token_list的每一条语料中，删除只出现一次的token
for _id, token_list in enumerate(documents_token_list):
documents_token_list[_id] = list(
filter(lambda token: token not in once_items, token_list))
# 极端情况，某一篇语料所有token只出现一次，这样该篇新闻语料的token列表就变为空，因此删除掉
documents_token_list = [
token_list for token_list in documents_token_list
if (len(token_list) != 0)
]
# 找到只出现一次的token对应的id
once_ids = [
tokenid for tokenid, docfreq in _dict.dfs.items() if docfreq == 1
]
# 删除仅出现一次的词
_dict.filter_tokens(once_ids)
# 消除id序列在删除词后产生的不连续的缺口
_dict.compactify()
if is_saved and save_path:
_dict.save(save_path)
logging.info(
"new generated dictionary saved in path -> {} ...".format(
save_path))
return _dict, documents_token_list
def renew_dictionary(self,
old_dict_path,
new_raw_documents_list,
new_dict_path=None,
is_saved=False):
documents_token_list = []
for doc in new_raw_documents_list:
documents_token_list.append(self.tokenization.cut_words(doc))
_dict = corpora.Dictionary.load(old_dict_path)
_dict.add_documents(documents_token_list)
if new_dict_path:
old_dict_path = new_dict_path
if is_saved:
_dict.save(old_dict_path)
logging.info(
"updated dictionary by another raw documents serialized in {} ... "
.format(old_dict_path))
return _dict, documents_token_list
def create_bag_of_word_representation(self,
raw_documents_list,
old_dict_path=None,
new_dict_path=None,
bow_vector_save_path=None,
is_saved_dict=False):
if old_dict_path:
# 如果存在旧的语料词典，就在原先词典的基础上更新，增加未见过的词
corpora_dictionary, documents_token_list = self.renew_dictionary(
old_dict_path, raw_documents_list, new_dict_path=new_dict_path)
else:
# 否则重新创建词典
start_time = time.time()
corpora_dictionary, documents_token_list = self.create_dictionary(
raw_documents_list,
save_path=new_dict_path,
is_saved=is_saved_dict)
end_time = time.time()
logging.info(
"there are {} mins spent to create a new dictionary ... ".
format((end_time - start_time) / 60))
# 根据新词典对文档(或语料)生成对应的词袋向量
start_time = time.time()
bow_vector = [
corpora_dictionary.doc2bow(doc_token)
for doc_token in documents_token_list
]
end_time = time.time()
logging.info(
"there are {} mins spent to calculate bow-vector ... ".format(
(end_time - start_time) / 60))
if bow_vector_save_path:
corpora.MmCorpus.serialize(bow_vector_save_path, bow_vector)
return documents_token_list, corpora_dictionary, bow_vector
@staticmethod
def transform_vectorized_corpus(corpora_dictionary,
bow_vector,
model_type="lda",
model_save_path=None):
# 如何没有保存任何模型，重新训练的情况下，可以选择该函数
model_vector = None
if model_type == "lsi":
# LSI(Latent Semantic Indexing)模型，将文本从词袋向量或者词频向量(更好)，转为一个低维度的latent空间
# 对于现实语料，目标维度在200-500被认为是"黄金标准"
model_tfidf = models.TfidfModel(bow_vector)
# model_tfidf.save("model_tfidf.tfidf")
tfidf_vector = model_tfidf[bow_vector]
model = models.LsiModel(tfidf_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[tfidf_vector]
if model_save_path:
model.save(model_save_path)
elif model_type == "lda":
model = models.LdaModel(bow_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[bow_vector]
if model_save_path:
model.save(model_save_path)
elif model_type == "tfidf":
model = models.TfidfModel(bow_vector) # 初始化
# model = models.TfidfModel.load("model_tfidf.tfidf")
model_vector = model[bow_vector] # 将整个语料进行转换
if model_save_path:
model.save(model_save_path)
return model_vector
def classify_stock_news(self,
unseen_raw_document,
database_name,
collection_name,
label_name="60DaysLabel",
topic_model_type="lda",
classifier_model="svm",
ori_dict_path=None,
bowvec_save_path=None,
is_saved_bow_vector=False):
historical_raw_documents_list = []
Y = []
for row in self.database.get_collection(database_name,
collection_name).find():
if label_name in row.keys():
if row[label_name] != "":
historical_raw_documents_list.append(row["Article"])
Y.append(row[label_name])
logging.info(
"fetch symbol '{}' historical news with label '{}' from [DB:'{}' - COL:'{}'] ... "
.format(collection_name, label_name, database_name,
collection_name))
le = preprocessing.LabelEncoder()
Y = le.fit_transform(Y)
logging.info(
"encode historical label list by sklearn preprocessing for training ... "
)
label_name_list = le.classes_ # ['中性' '利好' '利空'] -> [0, 1, 2]
# 根据历史新闻数据库创建词典，以及计算每个历史新闻的词袋向量；如果历史数据库创建的字典存在，则加载进内存
# 用未见过的新闻tokens去更新该词典
if not os.path.exists(ori_dict_path):
if not os.path.exists(bowvec_save_path):
_, _, historical_bow_vec = self.create_bag_of_word_representation(
historical_raw_documents_list,
new_dict_path=ori_dict_path,
bow_vector_save_path=bowvec_save_path,
is_saved_dict=True)
logging.info(
"create dictionary of historical news, and serialized in path -> {} ... "
.format(ori_dict_path))
logging.info(
"create bow-vector of historical news, and serialized in path -> {} ... "
.format(bowvec_save_path))
else:
_, _, _ = self.create_bag_of_word_representation(
historical_raw_documents_list,
new_dict_path=ori_dict_path,
is_saved_dict=True)
logging.info(
"create dictionary of historical news, and serialized in path -> {} ... "
.format(ori_dict_path))
else:
if not os.path.exists(bowvec_save_path):
_, _, historical_bow_vec = self.create_bag_of_word_representation(
historical_raw_documents_list,
new_dict_path=ori_dict_path,
bow_vector_save_path=bowvec_save_path,
is_saved_dict=True)
logging.info(
"historical news dictionary existed, which saved in path -> {}, but not the historical bow-vector"
" ... ".format(ori_dict_path))
else:
historical_bow_vec_mmcorpus = corpora.MmCorpus(
bowvec_save_path
) # type -> <gensim.corpora.mmcorpus.MmCorpus>
historical_bow_vec = []
for _bow in historical_bow_vec_mmcorpus:
historical_bow_vec.append(_bow)
logging.info(
"both historical news dictionary and bow-vector existed, load historical bow-vector to memory ... "
)
start_time = time.time()
updated_dictionary_with_old_and_unseen_news, unssen_documents_token_list = self.renew_dictionary(
ori_dict_path, [unseen_raw_document], is_saved=True)
end_time = time.time()
logging.info(
"renew dictionary with unseen news tokens, and serialized in path -> {}, "
"which took {} mins ... ".format(ori_dict_path,
(end_time - start_time) / 60))
unseen_bow_vector = [
updated_dictionary_with_old_and_unseen_news.doc2bow(doc_token)
for doc_token in unssen_documents_token_list
]
updated_bow_vector_with_old_and_unseen_news = []
updated_bow_vector_with_old_and_unseen_news.extend(historical_bow_vec)
updated_bow_vector_with_old_and_unseen_news.extend(unseen_bow_vector)
# 原先updated_bow_vector_with_old_and_unseen_news是list类型，
# 但是经过下面序列化后重新加载进来的类型是gensim.corpora.mmcorpus.MmCorpus
if is_saved_bow_vector and bowvec_save_path:
corpora.MmCorpus.serialize(
bowvec_save_path, updated_bow_vector_with_old_and_unseen_news
) # 保存更新后的bow向量，即包括新旧新闻的bow向量集
logging.info(
"combined bow vector(type -> 'list') generated by historical news with unseen bow "
"vector to create a new one ... ")
if topic_model_type == "lsi":
start_time = time.time()
updated_tfidf_model_vector = self.transform_vectorized_corpus(
updated_dictionary_with_old_and_unseen_news,
updated_bow_vector_with_old_and_unseen_news,
model_type="tfidf"
) # type -> <gensim.interfaces.TransformedCorpus object>
end_time = time.time()
logging.info(
"regenerated TF-IDF model vector by updated dictionary and updated bow-vector, "
"which took {} mins ... ".format((end_time - start_time) / 60))
start_time = time.time()
model = models.LsiModel(
updated_tfidf_model_vector,
id2word=updated_dictionary_with_old_and_unseen_news,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[
updated_tfidf_model_vector] # type -> <gensim.interfaces.TransformedCorpus object>
end_time = time.time()
logging.info(
"regenerated LSI model vector space by updated TF-IDF model vector space, "
"which took {} mins ... ".format((end_time - start_time) / 60))
elif topic_model_type == "lda":
start_time = time.time()
model_vector = self.transform_vectorized_corpus(
updated_dictionary_with_old_and_unseen_news,
updated_bow_vector_with_old_and_unseen_news,
model_type="lda")
end_time = time.time()
logging.info(
"regenerated LDA model vector space by updated dictionary and bow-vector, "
"which took {} mins ... ".format((end_time - start_time) / 60))
# 将gensim.interfaces.TransformedCorpus类型的lsi模型向量转为numpy矩阵
start_time = time.time()
latest_matrix = corpus2dense(model_vector,
num_terms=model_vector.obj.num_terms).T
end_time = time.time()
logging.info(
"transform {} model vector space to numpy.adarray, "
"which took {} mins ... ".format(topic_model_type.upper(),
(end_time - start_time) / 60))
# 利用历史数据的话题模型向量(或特征)，进一步训练新闻分类器
start_time = time.time()
train_x, train_y, test_x, test_y = utils.generate_training_set(
latest_matrix[:-1, :], Y)
clf = self.classifier.train(train_x,
train_y,
test_x,
test_y,
model_type=classifier_model)
end_time = time.time()
logging.info(
"finished training by sklearn {} using latest {} model vector space, which took {} mins ... "
.format(classifier_model.upper(), topic_model_type.upper(),
(end_time - start_time) / 60))
label_id = clf.predict(latest_matrix[-1, :].reshape(1, -1))[0]
return label_name_list[label_id]
class TopicModelling(object):
def __init__(self):
self.tokenization = Tokenization(
import_module="jieba",
user_dict=config.USER_DEFINED_DICT_PATH,
chn_stop_words_dir=config.CHN_STOP_WORDS_PATH)
def create_dictionary(self, raw_documents_list, savepath=None):
"""
将文中每个词汇关联唯一的ID，因此需要定义词汇表
:param: raw_documents_list, 原始语料列表，每个元素即文本，如["洗尽铅华...", "风雨赶路人...", ...]
:param: savepath, corpora.Dictionary对象保存路径
"""
documents_token_list = []
for doc in raw_documents_list:
documents_token_list.append(self.tokenization.cut_words(doc))
_dict = corpora.Dictionary(documents_token_list)
# 找到只出现一次的token
once_items = [
_dict[tokenid] for tokenid, docfreq in _dict.dfs.items()
if docfreq == 1
]
# 在documents_token_list的每一条语料中，删除只出现一次的token
for _id, token_list in enumerate(documents_token_list):
documents_token_list[_id] = list(
filter(lambda token: token not in once_items, token_list))
# 极端情况，某一篇语料所有token只出现一次，这样该篇新闻语料的token列表就变为空，因此删除掉
documents_token_list = [
token_list for token_list in documents_token_list
if (len(token_list) != 0)
]
# 找到只出现一次的token对应的id
once_ids = [
tokenid for tokenid, docfreq in _dict.dfs.items() if docfreq == 1
]
# 删除仅出现一次的词
_dict.filter_tokens(once_ids)
# 消除id序列在删除词后产生的不连续的缺口
_dict.compactify()
if savepath:
_dict.save(savepath)
return _dict, documents_token_list
def create_bag_of_word_representation(self,
raw_documents_list,
dict_save_path=None,
bow_vector_save_path=None):
corpora_dictionary, documents_token_list = self.create_dictionary(
raw_documents_list, savepath=dict_save_path)
bow_vector = [
corpora_dictionary.doc2bow(doc_token)
for doc_token in documents_token_list
]
if bow_vector_save_path:
corpora.MmCorpus.serialize(bow_vector_save_path, bow_vector)
return documents_token_list, corpora_dictionary, bow_vector
def transform_vectorized_corpus(self,
corpora_dictionary,
bow_vector,
model_type="lda",
model_save_path=None):
# 如何没有保存任何模型，重新训练的情况下，可以选择该函数
model_vector = None
if model_type == "lsi":
# LSI(Latent Semantic Indexing)模型，将文本从词袋向量或者词频向量(更好)，转为一个低维度的latent空间
# 对于现实语料，目标维度在200-500被认为是"黄金标准"
tfidf_vector = models.TfidfModel(bow_vector)[bow_vector]
model = models.LsiModel(tfidf_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[tfidf_vector]
if model_save_path:
model.save(model_save_path)
elif model_type == "lda":
model = models.LdaModel(bow_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[bow_vector]
if model_save_path:
model.save(model_save_path)
elif model_type == "tfidf":
model = models.TfidfModel(bow_vector) # 初始化
model_vector = model[bow_vector] # 将整个语料进行转换
if model_save_path:
model.save(model_save_path)
return model_vector
def add_documents_to_serialized_model(self,
old_model_path,
another_raw_documents_list,
latest_model_path=None,
model_type="lsi"):
# 加载已有的模型，Gensim提供在线学习的模式，不断基于新的documents训练新的模型
if not os.path.exists(old_model_path):
raise Exception(
"the file path {} does not exist ... ".format(old_model_path))
if model_type == "lsi":
loaded_model = models.LsiModel.load(old_model_path)
elif model_type == "lda":
loaded_model = models.LdaModel.load(old_model_path)
# loaded_model.add_documents(another_tfidf_corpus)
if latest_model_path:
old_model_path = latest_model_path
loaded_model.save(old_model_path)
def load_transform_model(self, model_path):
if ".tfidf" in model_path:
return models.TfidfModel.load(model_path)
elif ".lsi" in model_path:
return models.LsiModel.load(model_path)
elif ".lda" in model_path:
return models.LdaModel.load(model_path)
# for url_to_be_crawled, type_chn in config.WEBSITES_LIST_TO_BE_CRAWLED_CNSTOCK.items():
# logging.info("start crawling {} ...".format(url_to_be_crawled))
# cnstock_spyder.get_historical_news(url_to_be_crawled, category_chn=type_chn)
# logging.info("finished ...")
# time.sleep(30)
#
# jrj_spyder = JrjSpyder(config.DATABASE_NAME, config.COLLECTION_NAME_JRJ)
# jrj_spyder.get_historical_news(config.WEBSITES_LIST_TO_BE_CRAWLED_JRJ, "2020-12-04", "2020-12-08")
#
# nbd_spyder = NbdSpyder(config.DATABASE_NAME, config.COLLECTION_NAME_NBD)
# nbd_spyder.get_historical_news(684)
# 2. 抽取出新闻中所涉及的股票，并保存其股票代码在collection中新的一列
from Leorio.tokenization import Tokenization
tokenization = Tokenization(import_module="jieba",
user_dict="./Leorio/financedict.txt")
tokenization.update_news_database_rows(config.DATABASE_NAME, "cnstock")
# tokenization.update_news_database_rows(config.DATABASE_NAME, "nbd")
# tokenization.update_news_database_rows(config.DATABASE_NAME, "jrj")
# 3. 针对历史数据进行去重清洗
from Killua.deduplication import Deduplication
Deduplication("finnewshunter", "cnstock").run()
# Deduplication("finnewshunter", "nbd").run()
# Deduplication("finnewshunter", "jrj").run() # 暂时只有jrj需要去重
# 4. 将历史数据中包含null值的行去掉
from Killua.denull import DeNull
# DeNull("finnewshunter", "cnstock").run()
class CnStockSpyder(Spyder):
def __init__(self, database_name, collection_name):
super(CnStockSpyder, self).__init__()
self.db_obj = Database()
self.col = self.db_obj.conn[database_name].get_collection(
collection_name)
self.terminated_amount = 0
self.db_name = database_name
self.col_name = collection_name
self.tokenization = Tokenization(
import_module="jieba", user_dict=config.USER_DEFINED_DICT_PATH)
self.redis_client = redis.StrictRedis(host=config.REDIS_IP,
port=config.REDIS_PORT,
db=config.CACHE_NEWS_REDIS_DB_ID)
def get_url_info(self, url):
try:
bs = utils.html_parser(url)
except Exception:
return False
span_list = bs.find_all("span")
part = bs.find_all("p")
article = ""
date = ""
for span in span_list:
if "class" in span.attrs and span["class"] == ["timer"]:
date = span.text
break
for paragraph in part:
chn_status = utils.count_chn(str(paragraph))
possible = chn_status[1]
if possible > self.is_article_prob:
article += str(paragraph)
while article.find("<") != -1 and article.find(">") != -1:
string = article[article.find("<"):article.find(">") + 1]
article = article.replace(string, "")
while article.find("\u3000") != -1:
article = article.replace("\u3000", "")
article = " ".join(re.split(" +|\n+", article)).strip()
return [date, article]
def get_historical_news(self, url, category_chn=None, start_date=None):
"""
:param url: 爬虫网页
:param category_chn: 所属类别, 中文字符串, 包括'公司聚焦', '公告解读', '公告快讯', '利好公告'
:param start_date: 数据库中category_chn类别新闻最近一条数据的时间
"""
assert category_chn is not None
driver = webdriver.Chrome(executable_path=config.CHROME_DRIVER)
btn_more_text = ""
crawled_urls_list = self.extract_data(["Url"])[0]
logging.info("historical data length -> {} ... ".format(
len(crawled_urls_list)))
# crawled_urls_list = []
driver.get(url)
name_code_df = self.db_obj.get_data(
config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
name_code_dict = dict(name_code_df.values)
if start_date is None:
while btn_more_text != "没有更多":
more_btn = driver.find_element_by_id('j_more_btn')
btn_more_text = more_btn.text
logging.info("1-{}".format(more_btn.text))
if btn_more_text == "加载更多":
more_btn.click()
time.sleep(random.random()) # sleep random time less 1s
elif btn_more_text == "加载中...":
time.sleep(random.random() + 2)
more_btn = driver.find_element_by_id('j_more_btn')
btn_more_text = more_btn.text
logging.info("2-{}".format(more_btn.text))
if btn_more_text == "加载更多":
more_btn.click()
else:
more_btn.click()
break
bs = BeautifulSoup(driver.page_source, "html.parser")
for li in bs.find_all("li", attrs={"class": ["newslist"]}):
a = li.find_all("h2")[0].find("a")
if a["href"] not in crawled_urls_list:
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.CNSTOCK_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.CNSTOCK_MAX_REJECTED_AMOUNTS,
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(a["href"], 60 *
self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(
a["title"], a["href"]))
else:
# 有返回但是article为null的情况
date, article = result
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.CNSTOCK_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.
CNSTOCK_MAX_REJECTED_AMOUNTS,
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH
))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(
article, name_code_dict)
data = {
"Date":
date,
"Category":
category_chn,
"Url":
a["href"],
"Title":
a["title"],
"Article":
article,
"RelatedStockCodes":
" ".join(related_stock_codes_list)
}
# self.col.insert_one(data)
self.db_obj.insert_data(self.db_name,
self.col_name, data)
logging.info("[SUCCESS] {} {} {}".format(
date, a["title"], a["href"]))
else:
# 当start_date不为None时，补充历史数据
is_click_button = True
start_get_url_info = False
tmp_a = None
while is_click_button:
bs = BeautifulSoup(driver.page_source, "html.parser")
for li in bs.find_all("li", attrs={"class": ["newslist"]}):
a = li.find_all("h2")[0].find("a")
if tmp_a is not None and a["href"] != tmp_a:
continue
elif tmp_a is not None and a["href"] == tmp_a:
start_get_url_info = True
if start_get_url_info:
date, _ = self.get_url_info(a["href"])
if date <= start_date:
is_click_button = False
break
tmp_a = a["href"]
if is_click_button:
more_btn = driver.find_element_by_id('j_more_btn')
more_btn.click()
# 从一开始那条新闻到tmp_a都是新增新闻，不包括tmp_a
bs = BeautifulSoup(driver.page_source, "html.parser")
for li in bs.find_all("li", attrs={"class": ["newslist"]}):
a = li.find_all("h2")[0].find("a")
if a["href"] != tmp_a:
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.CNSTOCK_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.CNSTOCK_MAX_REJECTED_AMOUNTS,
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(a["href"], 60 *
self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(
a["title"], a["href"]))
else:
# 有返回但是article为null的情况
date, article = result
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.CNSTOCK_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.
CNSTOCK_MAX_REJECTED_AMOUNTS,
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH
))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(
article, name_code_dict)
data = {
"Date":
date,
"Category":
category_chn,
"Url":
a["href"],
"Title":
a["title"],
"Article":
article,
"RelatedStockCodes":
" ".join(related_stock_codes_list)
}
# self.col.insert_one(data)
self.db_obj.insert_data(self.db_name,
self.col_name, data)
logging.info("[SUCCESS] {} {} {}".format(
date, a["title"], a["href"]))
else:
break
driver.quit()
def get_realtime_news(self, url, category_chn=None, interval=60):
logging.info(
"start real-time crawling of URL -> {}, request every {} secs ... "
.format(url, interval))
assert category_chn is not None
# TODO: 由于cnstock爬取的数据量并不大，这里暂时是抽取历史所有数据进行去重，之后会修改去重策略
name_code_df = self.db_obj.get_data(
config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
name_code_dict = dict(name_code_df.values)
crawled_urls = self.db_obj.get_data(self.db_name,
self.col_name,
keys=["Url"])["Url"].to_list()
while True:
# 每隔一定时间轮询该网址
bs = utils.html_parser(url)
for li in bs.find_all("li", attrs={"class": ["newslist"]}):
a = li.find_all("h2")[0].find("a")
if a["href"] not in crawled_urls: # latest_3_days_crawled_href
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.CNSTOCK_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.CNSTOCK_MAX_REJECTED_AMOUNTS,
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(a["href"], 60 *
self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(
a["title"], a["href"]))
else:
# 有返回但是article为null的情况
date, article = result
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"])
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.CNSTOCK_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH,
"a+") as file:
file.write("{}\n".format(a["href"]))
logging.info(
"rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(
config.
CNSTOCK_MAX_REJECTED_AMOUNTS,
config.
RECORD_CNSTOCK_FAILED_URL_TXT_FILE_PATH
))
break
logging.info(
"rejected by remote server, request {} again after "
"{} seconds...".format(
a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"])
date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(
article, name_code_dict)
self.db_obj.insert_data(
self.db_name, self.col_name, {
"Date":
date,
"Category":
category_chn,
"Url":
a["href"],
"Title":
a["title"],
"Article":
article,
"RelatedStockCodes":
" ".join(related_stock_codes_list)
})
self.redis_client.lpush(
config.CACHE_NEWS_LIST_NAME,
json.dumps({
"Date":
date,
"Category":
category_chn,
"Url":
a["href"],
"Title":
a["title"],
"Article":
article,
"RelatedStockCodes":
" ".join(related_stock_codes_list),
"OriDB":
config.DATABASE_NAME,
"OriCOL":
config.COLLECTION_NAME_CNSTOCK
}))
logging.info("[SUCCESS] {} {} {}".format(
date, a["title"], a["href"]))
crawled_urls.append(a["href"])
# logging.info("sleep {} secs then request {} again ... ".format(interval, url))
time.sleep(interval)
class JrjSpyder(Spyder):
def __init__(self, database_name, collection_name):
super(JrjSpyder, self).__init__()
self.db_obj = Database()
self.col = self.db_obj.conn[database_name].get_collection(collection_name)
self.terminated_amount = 0
self.db_name = database_name
self.col_name = collection_name
self.tokenization = Tokenization(import_module="jieba", user_dict=config.USER_DEFINED_DICT_PATH)
def get_url_info(self, url, specific_date):
try:
bs = utils.html_parser(url)
except Exception:
return False
date = ""
for span in bs.find_all("span"):
if span.contents[0] == "jrj_final_date_start":
date = span.text.replace("\r", "").replace("\n", "")
break
if date == "":
date = specific_date
article = ""
for p in bs.find_all("p"):
if not p.find_all("jrj_final_daohang_start") and p.attrs == {} and \
not p.find_all("input") and not p.find_all("a", attrs={"class": "red"}) and not p.find_all("i") and not p.find_all("span"):
# if p.contents[0] != "jrj_final_daohang_start1" and p.attrs == {} and \
# not p.find_all("input") and not p.find_all("a", attrs={"class": "red"}) and not p.find_all("i"):
article += p.text.replace("\r", "").replace("\n", "").replace("\u3000", "")
return [date, article]
def get_historical_news(self, url, start_date=None, end_date=None):
name_code_df = self.db_obj.get_data(config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
name_code_dict = dict(name_code_df.values)
crawled_urls_list = []
if end_date is None:
end_date = datetime.datetime.now().strftime("%Y-%m-%d")
if start_date is None:
# 如果start_date是None，则从历史数据库最新的日期补充爬取到最新日期
# e.g. history_latest_date_str -> "2020-12-08"
# history_latest_date_dt -> datetime.date(2020, 12, 08)
# start_date -> "2020-12-09"
history_latest_date_list = self.db_obj.get_data(self.db_name,
self.col_name,
keys=["Date"])["Date"].to_list()
if len(history_latest_date_list) != 0:
history_latest_date_str = max(history_latest_date_list).split(" ")[0]
history_latest_date_dt = datetime.datetime.strptime(history_latest_date_str, "%Y-%m-%d").date()
offset = datetime.timedelta(days=1)
start_date = (history_latest_date_dt + offset).strftime('%Y-%m-%d')
else:
start_date = config.JRJ_REQUEST_DEFAULT_DATE
dates_list = utils.get_date_list_from_range(start_date, end_date)
dates_separated_into_ranges_list = utils.gen_dates_list(dates_list, config.JRJ_DATE_RANGE)
for dates_range in dates_separated_into_ranges_list:
for date in dates_range:
first_url = "{}/{}/{}_1.shtml".format(url, date.replace("-", "")[0:6], date.replace("-", ""))
max_pages_num = utils.search_max_pages_num(first_url, date)
for num in range(1, max_pages_num + 1):
_url = "{}/{}/{}_{}.shtml".format(url, date.replace("-", "")[0:6], date.replace("-", ""), str(num))
bs = utils.html_parser(_url)
a_list = bs.find_all("a")
for a in a_list:
if "href" in a.attrs and a.string and \
a["href"].find("/{}/{}/".format(date.replace("-", "")[:4],
date.replace("-", "")[4:6])) != -1:
if a["href"] not in crawled_urls_list:
# 如果标题不包含"收盘","报于"等字样，即可写入数据库，因为包含这些字样标题的新闻多为机器自动生成
if a.string.find("收盘") == -1 and a.string.find("报于") == -1 and \
a.string.find("新三板挂牌上市") == -1:
result = self.get_url_info(a["href"], date)
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.JRJ_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH, "a+") as file:
file.write("{}\n".format(a["href"]))
logging.info("rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(config.JRJ_MAX_REJECTED_AMOUNTS,
config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH))
break
logging.info("rejected by remote server, request {} again after "
"{} seconds...".format(a["href"], 60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"], date)
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(a.string, a["href"]))
else:
# 有返回但是article为null的情况
article_specific_date, article = result
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"], date)
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.JRJ_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH, "a+") as file:
file.write("{}\n".format(a["href"]))
logging.info("rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(config.JRJ_MAX_REJECTED_AMOUNTS,
config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH))
break
logging.info("rejected by remote server, request {} again after "
"{} seconds...".format(a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"], date)
article_specific_date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(article,
name_code_dict)
data = {"Date": article_specific_date,
"Url": a["href"],
"Title": a.string,
"Article": article,
"RelatedStockCodes": " ".join(related_stock_codes_list)}
# self.col.insert_one(data)
self.db_obj.insert_data(self.db_name, self.col_name, data)
logging.info("[SUCCESS] {} {} {}".format(article_specific_date,
a.string,
a["href"]))
self.terminated_amount = 0 # 爬取结束后重置该参数
else:
logging.info("[QUIT] {}".format(a.string))
def get_realtime_news(self, interval=60):
name_code_df = self.db_obj.get_data(config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
name_code_dict = dict(name_code_df.values)
crawled_urls_list = []
is_change_date = False
last_date = datetime.datetime.now().strftime("%Y-%m-%d")
while True:
today_date = datetime.datetime.now().strftime("%Y-%m-%d")
if today_date != last_date:
is_change_date = True
last_date = today_date
if is_change_date:
crawled_urls_list = []
is_change_date = False
_url = "{}/{}/{}_1.shtml".format(config.WEBSITES_LIST_TO_BE_CRAWLED_JRJ,
today_date.replace("-", "")[0:6],
today_date.replace("-", ""))
max_pages_num = utils.search_max_pages_num(_url, today_date)
for num in range(1, max_pages_num + 1):
_url = "{}/{}/{}_{}.shtml".format(config.WEBSITES_LIST_TO_BE_CRAWLED_JRJ,
today_date.replace("-", "")[0:6],
today_date.replace("-", ""),
str(num))
bs = utils.html_parser(_url)
a_list = bs.find_all("a")
for a in a_list:
if "href" in a.attrs and a.string and \
a["href"].find("/{}/{}/".format(today_date.replace("-", "")[:4],
today_date.replace("-", "")[4:6])) != -1:
if a["href"] not in crawled_urls_list:
# 如果标题不包含"收盘","报于"等字样，即可写入数据库，因为包含这些字样标题的新闻多为机器自动生成
if a.string.find("收盘") == -1 and a.string.find("报于") == -1 and \
a.string.find("新三板挂牌上市") == -1:
result = self.get_url_info(a["href"], today_date)
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.JRJ_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH, "a+") as file:
file.write("{}\n".format(a["href"]))
logging.info("rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(config.JRJ_MAX_REJECTED_AMOUNTS,
config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH))
break
logging.info("rejected by remote server, request {} again after "
"{} seconds...".format(a["href"], 60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"], today_date)
if not result:
# 爬取失败的情况
logging.info("[FAILED] {} {}".format(a.string, a["href"]))
else:
# 有返回但是article为null的情况
article_specific_date, article = result
while article == "" and self.is_article_prob >= .1:
self.is_article_prob -= .1
result = self.get_url_info(a["href"], today_date)
while not result:
self.terminated_amount += 1
if self.terminated_amount > config.JRJ_MAX_REJECTED_AMOUNTS:
# 始终无法爬取的URL保存起来
with open(config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH, "a+") as file:
file.write("{}\n".format(a["href"]))
logging.info("rejected by remote server longer than {} minutes, "
"and the failed url has been written in path {}"
.format(config.JRJ_MAX_REJECTED_AMOUNTS,
config.RECORD_JRJ_FAILED_URL_TXT_FILE_PATH))
break
logging.info("rejected by remote server, request {} again after "
"{} seconds...".format(a["href"],
60 * self.terminated_amount))
time.sleep(60 * self.terminated_amount)
result = self.get_url_info(a["href"], today_date)
article_specific_date, article = result
self.is_article_prob = .5
if article != "":
related_stock_codes_list = self.tokenization.find_relevant_stock_codes_in_article(article,
name_code_dict)
data = {"Date": article_specific_date,
"Url": a["href"],
"Title": a.string,
"Article": article,
"RelatedStockCodes": " ".join(related_stock_codes_list)}
# self.col.insert_one(data)
self.db_obj.insert_data(self.db_name, self.col_name, data)
logging.info("[SUCCESS] {} {} {}".format(article_specific_date,
a.string,
a["href"]))
self.terminated_amount = 0 # 爬取结束后重置该参数
else:
logging.info("[QUIT] {}".format(a.string))
crawled_urls_list.append(a["href"])
# logging.info("sleep {} secs then request again ... ".format(interval))
time.sleep(interval)
from Kite.database import Database
from Kite import config
from concurrent import futures
import threading
obj = Database()
df = obj.get_data(config.DATABASE_NAME, config.COLLECTION_NAME_CNSTOCK, keys=["Date", "Category"])
cnstock_spyder = CnStockSpyder(config.DATABASE_NAME, config.COLLECTION_NAME_CNSTOCK)
# 先补充历史数据，比如已爬取数据到2020-12-01，但是启动实时爬取程序在2020-12-23，则先
# 自动补充爬取2020-12-02至2020-12-23的新闻数据
for url_to_be_crawled, type_chn in config.WEBSITES_LIST_TO_BE_CRAWLED_CNSTOCK.items():
# 查询type_chn的最近一条数据的时间
latets_date_in_db = max(df[df.Category == type_chn]["Date"].to_list())
cnstock_spyder.get_historical_news(url_to_be_crawled, category_chn=type_chn, start_date=latets_date_in_db)
tokenization = Tokenization(import_module="jieba", user_dict=config.USER_DEFINED_DICT_PATH)
tokenization.update_news_database_rows(config.DATABASE_NAME, config.COLLECTION_NAME_CNSTOCK)
Deduplication(config.DATABASE_NAME, config.COLLECTION_NAME_CNSTOCK).run()
DeNull(config.DATABASE_NAME, config.COLLECTION_NAME_CNSTOCK).run()
# 开启多线程并行实时爬取
thread_list = []
for url, type_chn in config.WEBSITES_LIST_TO_BE_CRAWLED_CNSTOCK.items():
thread = threading.Thread(target=cnstock_spyder.get_realtime_news, args=(url, type_chn, 60))
thread_list.append(thread)
for thread in thread_list:
thread.start()
for thread in thread_list:
thread.join()
def __init__(self):
self.tokenization = Tokenization(import_module="jieba",
user_dict="../Leorio/financedict.txt",
chn_stop_words_dir="../Leorio/chnstopwords.txt")
class TopicModelling(object):
def __init__(self):
self.tokenization = Tokenization(import_module="jieba",
user_dict="../Leorio/financedict.txt",
chn_stop_words_dir="../Leorio/chnstopwords.txt")
def create_dictionary(self, raw_documents_list, savepath=None):
"""
将文中每个词汇关联唯一的ID，因此需要定义词汇表
:param: raw_documents_list, 原始语料列表，每个元素即文本，如["洗尽铅华...", "风雨赶路人...", ...]
:param: savepath, corpora.Dictionary对象保存路径
"""
documents_token_list = []
for doc in raw_documents_list:
documents_token_list.append(self.tokenization.cut_words(doc))
_dict = corpora.Dictionary(documents_token_list)
if savepath:
_dict.save(savepath)
return _dict, documents_token_list
def create_bag_of_word_representation(self, raw_documents_list, dict_save_path=None, bow_vector_save_path=None):
corpora_dictionary, documents_token_list = self.create_dictionary(raw_documents_list, savepath=dict_save_path)
bow_vector = [corpora_dictionary.doc2bow(doc_token) for doc_token in documents_token_list]
if bow_vector_save_path:
corpora.MmCorpus.serialize(bow_vector_save_path, bow_vector)
return documents_token_list, corpora_dictionary, bow_vector
def transform_vectorized_corpus(self, corpora_dictionary, bow_vector, model_type="lda", model_save_path=None):
if model_type == "lsi":
tfidf_vector = models.TfidfModel(bow_vector)[bow_vector]
model = models.LsiModel(tfidf_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[tfidf_vector]
if model_save_path:
model.save(model_save_path)
return tfidf_vector, model_vector
elif model_type == "lda":
tfidf_vector = models.TfidfModel(bow_vector)[bow_vector]
model = models.LdaModel(tfidf_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[tfidf_vector]
if model_save_path:
model.save(model_save_path)
return tfidf_vector, model_vector
elif model_type == "tfidf":
model = models.TfidfModel(bow_vector) # 初始化
tfidf_vector = model[bow_vector] # 将整个语料进行转换
if model_save_path:
model.save(model_save_path)
return tfidf_vector
def load_transform_model(self, model_path):
if ".tfidf" in model_path:
return models.TfidfModel.load(model_path)
elif ".lsi" in model_path:
return models.LsiModel.load(model_path)
elif ".lda" in model_path:
return models.LdaModel.load(model_path)
