def __init__(self, time=None):
if time is None:
time = datetime.datetime.now().strftime('%Y-%m-%d')
self.time = time
#time = pd.to_datetime(time)
self.conn = connectDB()
self.update(time)
def __init__(self,
target,
PushDate=None,
Title='測試推播',
Content='無內文',
PushFile='NULL',
FileName='NULL'):
now = datetime.datetime.now()
if PushDate is None:
PushDate = now.strftime('%Y/%m/%d')
Object = f'、{target}、'
CreateAccount = target
PT_no = f'{PushDate} {Title} {now.strftime("%H%M%S")}'
CreateDate = now.strftime('%Y/%m/%d %H:%M:%S')
Title = f'{Title}[{now.strftime("%Y/%m/%d")}]'
self.conn = connectDB()
self.Notification(PushDate, Title, Content, PushFile, FileName, Object,
CreateAccount, PT_no, CreateDate)
self.NotificationUnread(PushDate, Title, CreateAccount, PT_no,
CreateDate)
if foot.text == '':
try:
newslist = driver.find_element(By.ID, 'rso')
newslist = newslist.find_elements(By.CSS_SELECTOR,
'div[class="g"]')
count = len(newslist)
except:
count = 0
else:
res = foot.find_element(By.TAG_NAME,
'tr').find_elements(By.TAG_NAME, 'td')
count = (len(res) - 2) * 9
df.loc[ii]['次數'] = count
conn = connectDB()
TIME = today.strftime('%Y-%m-%d')
sql = f'''SELECT TOP (1000) a.ID, a.ChemicalChnName, c.theme_name
FROM [ChemiBigData].[dbo].[網路擷取分析_國內proc1_新聞對應化學物質] a
inner join [ChemiBigData].[dbo].[新聞分群_國內新聞_結果] b
on a.id=b.id
inner join [ChemiBigData].[dbo].[新聞分群_主題] c
on b.label = c.theme_id
where cast(updatetime as date) = '{TIME}'
'''
sql = ' '.join(sql.split())
chemilist = conn.fetch(sql)
chemilist = chemilist.drop_duplicates(subset=['ID', 'ChemicalChnName'])
chemilist = chemilist.pivot_table(
index='ChemicalChnName',
aggfunc=lambda i: ','.join(set((','.join(i)).split(','))))
data = data.drop_duplicates(subset='news_content')
data.update_time = datetime.datetime.now()
return data
if __name__ == '__main__':
if len(sys.argv) > 1:
UPDATE_TIME = sys.argv[1]
print(f' >> Condition 1: 讀取到使用者輸入的「日期」：{UPDATE_TIME}')
elif len(sys.argv) > 0:
UPDATE_TIME = datetime.datetime.today().strftime('%Y-%m-%d')
print(f' >> Condition 2: 處理日期：{UPDATE_TIME}')
print(f' 0. 讀取資料庫資料')
c = connectDB()
data = c.fetch(makesql())
print(f' >>> 0.1 資料前處理')
data = preproc(data)
print(f' >>> 0.2 讀取到{len(data)}筆新聞資料，將資料寫入待預測資料 "DATA/pred_set.xlsx"')
data.to_excel(f'DATA/pred_set_aboard.xlsx', index=False)
print(f' 1. 尋找新聞內出現之化學物質')
exec(open('./6.findChemistry_aboard.py').read())
print(f' 2. 開始預測新聞分類')
exec(open('./7.NewsClassify_aboard.py').read())
print(f' >>> 2.1 將新聞預測結果存入每日之新聞資料')