石油焦铝用调试
This commit is contained in:
workpc
parent
c9b4673389
commit
313e9e229d
@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -303,7 +303,7 @@
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" 复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则\n",
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" '''\n",
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" if df1.loc[1,'70号沥青开工率'] / 100 > 0.3:\n",
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" a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1\n",
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" a = -(df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1\n",
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" else :\n",
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" a = 0\n",
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" b = df1.loc[1,'资金因素']\n",
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@ -61,7 +61,8 @@ login_push_data = {
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"funcOperation": "获取token"
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}
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read_file_path_name = "液化气数据.xls"
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# read_file_path_name = "液化气数据.xls"
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read_file_path_name = "液化气数据.xlsx"
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one_cols = []
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two_cols = []
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@ -183,6 +184,88 @@ def upload_data_to_system(token_push, date):
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print('预测值：', data['data'][0]['dataValue'])
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def getLogToken():
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login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))
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text = json.loads(login_res.text)
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if text["status"]:
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token = text["data"]["accessToken"]
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else:
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print("获取认证失败")
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token = None
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return token
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def updateYesterdayExcelData(date='', token=None):
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# 使用pandas读取Excel文件
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df = pd.read_excel(read_file_path_name, engine='openpyxl')
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# 获取第二行的数据作为列名
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one_cols = df.iloc[0, :].tolist()
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# 获取当前日期的前一天
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if date == '':
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previous_date = (datetime.now() - timedelta(days=1)
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).strftime('%Y-%m-%d')
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else:
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# 字符串转日期
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previous_date = (datetime.strptime(date, "%Y-%m-%d") -
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timedelta(days=1)).strftime('%Y-%m-%d')
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cur_time, cur_time2 = getNow(previous_date)
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search_data = {
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"data": {
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"date": cur_time,
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"dataItemNoList": one_cols[1:]
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},
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"funcModule": "数据项",
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"funcOperation": "查询"
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}
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headers = {"Authorization": token}
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search_res = requests.post(
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url=search_url, headers=headers, json=search_data, timeout=(3, 5))
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print('数据请求结果：')
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print(search_res.text)
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search_value = json.loads(search_res.text)["data"]
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if search_value:
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datas = search_value
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else:
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datas = None
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append_rows = [cur_time2]
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dataItemNo_dataValue = {}
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for data_value in datas:
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if "dataValue" not in data_value:
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print(data_value)
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dataItemNo_dataValue[data_value["dataItemNo"]] = ""
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else:
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dataItemNo_dataValue[data_value["dataItemNo"]
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] = data_value["dataValue"]
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for value in one_cols[1:]:
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if value in dataItemNo_dataValue:
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append_rows.append(dataItemNo_dataValue[value])
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else:
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append_rows.append("")
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print('更新数据前')
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print(df.tail(1))
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# 检查日期是否已存在于数据中
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if previous_date not in df['日期'].values:
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# 将新的数据添加到DataFrame中
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new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())
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df = pd.concat([df, new_row], ignore_index=True)
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else:
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# 更新现有数据
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print('日期存在，即将更新')
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print('新数据', append_rows[1:])
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df.loc[df['日期'] == previous_date,
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df.columns.tolist()[1:]] = append_rows[1:]
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print('更新数据后')
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print(df.tail(1))
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# 使用pandas保存Excel文件
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df.to_excel("液化气数据.xls", index=False, engine='openpyxl')
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price_list = []
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@ -553,14 +636,12 @@ def save_xls_2(append_rows):
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start_date = datetime(2025, 3, 10)
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end_date = datetime(2025, 3, 20)
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token = getLogToken()
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while start_date < end_date:
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# 更新昨日数据
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start_1(start_date)
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date = start_date.strftime('%Y%m%d')
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# 获取当日数据，预测数据，并上传
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start(date)
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# time.sleep(1)
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start_date += timedelta(days=1)
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date = start_date.strftime('%Y-%m-%d')
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updateYesterdayExcelData(date, token=token)
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# start(date)
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# # time.sleep(1)
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# start_1(start_date)
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# start_date += timedelta(days=1)
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time.sleep(5)
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# print(price_list)
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@ -2,9 +2,17 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From C:\\Users\\EDY\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
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"\n"
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]
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},
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{
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"data": {
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"text/html": [
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@ -79,9 +87,6 @@
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"import random\n",
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"import time\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"from plotly import __version__\n",
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"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
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"\n",
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@ -501,7 +506,70 @@
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" pickle.dump(grid_search_XGB, file)\n",
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"\n",
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"\n",
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" \n",
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"def updateYesterdayExcelData(date='', token=None):\n",
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" # 使用pandas读取Excel文件\n",
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" df = pd.read_excel(read_file_path_name, engine='openpyxl')\n",
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"\n",
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" # 获取第二行的数据作为列名\n",
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" one_cols = df.iloc[0,:].tolist()\n",
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"\n",
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" # 获取当前日期的前一天\n",
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" if date == '':\n",
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" previous_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')\n",
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" else:\n",
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" # 字符串转日期\n",
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" previous_date = (datetime.strptime(date, \"%Y-%m-%d\")-timedelta(days=1)).strftime('%Y-%m-%d')\n",
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" \n",
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"\n",
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" cur_time, cur_time2 = getNow(previous_date)\n",
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" search_data = {\n",
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" \"data\": {\n",
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" \"date\": cur_time,\n",
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" \"dataItemNoList\": one_cols[1:]\n",
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" },\n",
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" \"funcModule\": \"数据项\",\n",
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" \"funcOperation\": \"查询\"\n",
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" }\n",
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" headers = {\"Authorization\": token}\n",
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" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
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" search_value = json.loads(search_res.text)[\"data\"]\n",
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" if search_value:\n",
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" datas = search_value\n",
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" else:\n",
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" datas = None\n",
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"\n",
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" append_rows = [cur_time2]\n",
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" dataItemNo_dataValue = {}\n",
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" for data_value in datas:\n",
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" if \"dataValue\" not in data_value:\n",
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" print(data_value)\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
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" else:\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" for value in one_cols[1:]:\n",
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" if value in dataItemNo_dataValue:\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" else:\n",
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" append_rows.append(\"\")\n",
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"\n",
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" print('更新数据前')\n",
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" print(df.tail(1))\n",
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" # 检查日期是否已存在于数据中\n",
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" if previous_date not in df['日期'].values:\n",
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" # 将新的数据添加到DataFrame中\n",
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" new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
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" df = pd.concat([df, new_row], ignore_index=True)\n",
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" else:\n",
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" # 更新现有数据\n",
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" print('日期存在，即将更新')\n",
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" print('新数据',append_rows[1:])\n",
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" df.loc[df['日期'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]\n",
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"\n",
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" print('更新数据后')\n",
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" print(df.tail(1))\n",
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" # 使用pandas保存Excel文件\n",
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" df.to_excel(\"液化气数据.xls\", index=False, engine='openpyxl')\n",
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"\n",
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"\n",
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"def read_xls_data_bak():\n",
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" global one_cols, two_cols\n",
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@ -772,7 +840,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 1,
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"metadata": {
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"scrolled": true
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},
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@ -818,43 +886,32 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"获取到的数据项ID['YHQMXBB|C01100008|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']\n",
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"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0MjMzMDEyMSwianRpIjoiMmI5ZmUzNTA5YjNmNGU4OTkzMjRiNzU1MzQ4ODlkNTQifQ.nezcKMQq4GnNoHKwvIOEe-1pK0Oz3LliiM8yYjOMG8c\n",
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"补充20250228数据\n",
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"数据项查询参数search_data：\n",
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"{'data': {'date': '20250228', 'dataItemNoList': ['C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
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"数据项查询结果search_res：\n",
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"{\"confirmFlg\":false,\"data\":[{\"dataDate\":\"20250228\",\"dataItemNo\":\"100028046|LISTING_PRICE\",\"dataValue\":8441.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"370500|ISOBUTANE|LIST_PRICE\",\"dataValue\":5380.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91110000710926094P|C01100008|SUPPLY_MERE\",\"dataValue\":1300.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91110000710932515R|C01100008|SUPPLY_MERE\"},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91110304102767480H|C01100008|STRIKE_PRICE\",\"dataValue\":5150.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91130193670310403L|C01100008|STRIKE_PRICE\",\"dataValue\":5150.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"912102117169477344|C01100008|STRIKE_PRICE\",\"dataValue\":4670.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370200163576944B|C01100008|STRIKE_PRICE\",\"dataValue\":5300.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370305773165341A|C01100008|STRIKE_PRICE\",\"dataValue\":5600.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370321164425136B|C01100008|STRIKE_PRICE\",\"dataValue\":5500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370321164425136B|C01100008|SUPPLY_MERE\",\"dataValue\":200.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370500674526498A|C01100008|STRIKE_PRICE\",\"dataValue\":5488.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370500674526498A|C01100008|SUPPLY_MERE\",\"dataValue\":175.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370521164880008P|C01100008|STRIKE_PRICE\",\"dataValue\":5455.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|AMOUNT\",\"dataValue\":342.72000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|AUCTION_MAX_PRICE\",\"dataValue\":5500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|CORTED_VALUE\",\"dataValue\":5500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|EFFECTIVE_STOCK\",\"dataValue\":-550.20000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|OTHER|SUPPLY_MERE\",\"dataValue\":5000.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|PLAN_SALE\",\"dataValue\":500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|SUY_DED_DAP\",\"dataValue\":-50.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"HD|LPG|IMPORT_PRICE\",\"dataValue\":5400.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"ICE_CL0_LAST_YEDAY_PRICE\",\"dataValue\":73.35000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|GC|ZDW|LIST_PRICE\",\"dataValue\":5250.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|WJH|DEMANDS\",\"dataValue\":8500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|WJH|SALES_PRICE\",\"dataValue\":8400.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|YT|SG|LIST_PRICE\",\"dataValue\":6050.00000000}],\"status\":true}\n",
|
||||
"数据项查询结果: [{'dataDate': '20250228', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8441.0}, {'dataDate': '20250228', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5380.0}, {'dataDate': '20250228', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250228', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250228', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250228', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250228', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4670.0}, {'dataDate': '20250228', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250228', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250228', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250228', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5488.0}, {'dataDate': '20250228', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250228', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5455.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|AMOUNT', 'dataValue': 342.72}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': -550.2}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250228', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250228', 'dataItemNo': 'ICE_CL0_LAST_YEDAY_PRICE', 'dataValue': 73.35}, {'dataDate': '20250228', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8400.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6050.0}]\n",
|
||||
"{'dataDate': '20250228', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
|
||||
"添加的行： ['20250228', '', 5500.0, 5500.0, 342.72, 73.35, 8441.0, 500.0, 5300.0, '', 5488.0, 5600.0, 5455.0, 5500.0, 5250.0, 5380.0, 6050.0, 1300.0, '', 175.0, 200.0, 5000.0, 8500.0, -50.0, -550.2, 4670.0, 5150.0, 5150.0, 5400.0, 8400.0]\n",
|
||||
"Index(['Date', 'Price', '修正价', '竞拍最高价', '液化石油气|发货量', '昨日布伦特价格', '昨日92#汽油价格',\n",
|
||||
" '计划出货量', '青岛石化', '中化工-昌邑', '海科瑞林', '鑫泰石化|液化石油气|成交价', '垦利价格', '汇丰价格',\n",
|
||||
" '正丁烷', '异丁烷价格', '顺酐', '中石化供应量', '中化工供应量', '海科供应量', '汇丰供应量', '京博和其他供应量',\n",
|
||||
" '烷基化需求量', '昨日烷基化价差', '我司库存', '东北-大连石化', '华北-燕山石化', '华北-石家庄炼化',\n",
|
||||
" '昨日原料气价格', '烷基化油销售价格'],\n",
|
||||
" dtype='object')\n",
|
||||
"保存数据时发生错误: 'Date'\n"
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'datetime' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m start_date \u001b[38;5;241m=\u001b[39m datetime(\u001b[38;5;241m2025\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 2\u001b[0m end_date \u001b[38;5;241m=\u001b[39m datetime(\u001b[38;5;241m2025\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m12\u001b[39m)\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m start_date \u001b[38;5;241m<\u001b[39m end_date:\n",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'datetime' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"start_date = datetime(2025, 3, 1)\n",
|
||||
"end_date = datetime(2025, 3, 2)\n",
|
||||
"end_date = datetime(2025, 3, 12)\n",
|
||||
"\n",
|
||||
"while start_date < end_date:\n",
|
||||
" date = start_date.strftime('%Y%m%d')\n",
|
||||
" updateYesterdayExcelData(date)\n",
|
||||
" # start(date)\n",
|
||||
" # time.sleep(1)\n",
|
||||
" start_1(start_date)\n",
|
||||
" start_date += timedelta(days=1)\n",
|
||||
" # # time.sleep(1)\n",
|
||||
" # start_1(start_date)\n",
|
||||
" # start_date += timedelta(days=1)\n",
|
||||
" time.sleep(5)\n",
|
||||
"\n",
|
||||
"# print(price_list)"
|
||||
|
||||
BIN
aisenzhecode/液化石油气/液化气数据.xlsx
Normal file
BIN
aisenzhecode/液化石油气/液化气数据.xlsx
Normal file
Binary file not shown.
@ -1,320 +0,0 @@
|
||||
import logging
|
||||
import os
|
||||
import logging.handlers
|
||||
import datetime
|
||||
from lib.tools import MySQLDB,SQLiteHandler
|
||||
|
||||
|
||||
# eta 接口token
|
||||
APPID = "XNLDvxZHHugj7wJ7"
|
||||
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
|
||||
|
||||
# eta 接口url
|
||||
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
|
||||
classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
|
||||
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
|
||||
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
|
||||
edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
|
||||
edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
|
||||
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
|
||||
edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
|
||||
edbcodelist = ['ID01385938','lmcads03 lme comdty',
|
||||
'GC1 COMB Comdty',
|
||||
'C2404171822',
|
||||
'dxy curncy',
|
||||
'S5443199 ',
|
||||
'S5479800',
|
||||
'S5443108',
|
||||
'H7358586',
|
||||
'LC3FM1 INDEX',
|
||||
'CNY REGN Curncy',
|
||||
's0105897',
|
||||
'M0067419',
|
||||
'M0066351',
|
||||
'S0266372',
|
||||
'S0266438',
|
||||
'S0266506',
|
||||
'ID01384463']
|
||||
|
||||
# 临时写死用指定的列,与上面的edbcode对应，后面更改
|
||||
edbnamelist = [
|
||||
'ds','y',
|
||||
'LME铜价',
|
||||
'黄金连1合约',
|
||||
'Brent-WTI',
|
||||
'美元指数',
|
||||
'甲醇鲁南价格',
|
||||
'甲醇太仓港口价格',
|
||||
'山东丙烯主流价',
|
||||
'丙烷（山东）',
|
||||
'FEI丙烷 M1',
|
||||
'在岸人民币汇率',
|
||||
'南华工业品指数',
|
||||
'PVC期货主力',
|
||||
'PE期货收盘价',
|
||||
'PP连续-1月',
|
||||
'PP连续-5月',
|
||||
'PP连续-9月',
|
||||
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）'
|
||||
]
|
||||
|
||||
edbcodenamedict = {
|
||||
'ID01385938':'PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）',
|
||||
'ID01384463':'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
|
||||
'lmcads03 lme comdty':'LME铜价',
|
||||
'GC1 COMB Comdty':'黄金连1合约',
|
||||
'C2404171822':'Brent-WTI',
|
||||
'dxy curncy':'美元指数',
|
||||
'S5443199 ':'甲醇鲁南价格',
|
||||
'S5479800':'甲醇太仓港口价格',
|
||||
'S5443108':'山东丙烯主流价',
|
||||
'H7358586':'丙烷（山东）',
|
||||
'LC3FM1 INDEX':'FEI丙烷 M1',
|
||||
'CNY REGN Curncy':'在岸人民币汇率',
|
||||
's0105897':'南华工业品指数',
|
||||
'M0067419':'PVC期货主力',
|
||||
'M0066351':'PE期货收盘价',
|
||||
'S0266372':'PP连续-1月',
|
||||
'S0266438':'PP连续-5月',
|
||||
'S0266506':'PP连续-9月',
|
||||
|
||||
}
|
||||
|
||||
# eta自有数据指标编码
|
||||
modelsindex = {
|
||||
'NHITS': 'SELF0000077',
|
||||
'Informer':'SELF0000078',
|
||||
'LSTM':'SELF0000079',
|
||||
'iTransformer':'SELF0000080',
|
||||
'TSMixer':'SELF0000081',
|
||||
'TSMixerx':'SELF0000082',
|
||||
'PatchTST':'SELF0000083',
|
||||
'RNN':'SELF0000084',
|
||||
'GRU':'SELF0000085',
|
||||
'TCN':'SELF0000086',
|
||||
'BiTCN':'SELF0000087',
|
||||
'DilatedRNN':'SELF0000088',
|
||||
'MLP':'SELF0000089',
|
||||
'DLinear':'SELF0000090',
|
||||
'NLinear':'SELF0000091',
|
||||
'TFT':'SELF0000092',
|
||||
'FEDformer':'SELF0000093',
|
||||
'StemGNN':'SELF0000094',
|
||||
'MLPMultivariate':'SELF0000095',
|
||||
'TiDE':'SELF0000096',
|
||||
'DeepNPTS':'SELF0000097'
|
||||
}
|
||||
|
||||
|
||||
|
||||
# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
|
||||
data = {
|
||||
"IndexCode": "",
|
||||
"IndexName": "价格预测模型",
|
||||
"Unit": "无",
|
||||
"Frequency": "日度",
|
||||
"SourceName": f"价格预测",
|
||||
"Remark": 'ddd',
|
||||
"DataList": [
|
||||
{
|
||||
"Date": "2024-05-02",
|
||||
"Value": 333444
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# eta 分类
|
||||
# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
|
||||
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
|
||||
#ParentId ":1160, 能源化工
|
||||
# ClassifyId ":1214,原油 3912 石油焦
|
||||
#ParentId ":1214,"，就是原油下所有的数据。
|
||||
ClassifyId = 3707
|
||||
|
||||
|
||||
|
||||
############################################################################################################### 变量定义--测试环境
|
||||
server_host = '192.168.100.53'
|
||||
|
||||
login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
|
||||
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
upload_data = {
|
||||
"funcModule":'研究报告信息',
|
||||
"funcOperation":'上传聚烯烃PP价格预测报告',
|
||||
"data":{
|
||||
"groupNo":'000128', # 用户组编号
|
||||
"ownerAccount":'arui', #报告所属用户账号
|
||||
"reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称
|
||||
"fileBase64": '' ,#文件内容base64
|
||||
"categoryNo":'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode":'JXTJGYCBG', #分析报告分类编码
|
||||
"reportEmployeeCode":"E40116", # 报告人
|
||||
"reportDeptCode" :"D0044" ,# 报告部门
|
||||
"productGroupCode":"RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo":'000128', # 用户组编号
|
||||
"funcModule":'原油特征停更预警',
|
||||
"funcOperation":'原油特征停更预警',
|
||||
"data":{
|
||||
'WARNING_TYPE_NAME':'特征数据停更预警',
|
||||
'WARNING_CONTENT':'',
|
||||
'WARNING_DATE':''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart":"20200101",
|
||||
"dateEnd":"20241231",
|
||||
"dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername ='root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
### 开关
|
||||
is_train = False # 是否训练
|
||||
is_debug = True # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = False # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
is_del_tow_month = True # 是否删除两个月不更新的特征
|
||||
|
||||
|
||||
|
||||
# 连接到数据库
|
||||
db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
|
||||
db_mysql.connect()
|
||||
print("数据库连接成功",host,dbname,dbusername)
|
||||
|
||||
|
||||
# 数据截取日期
|
||||
start_year = 2020 # 数据开始年份
|
||||
end_time = '' # 数据截取日期
|
||||
freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
|
||||
delweekenday = True if freq == 'B' else False # 是否删除周末数据
|
||||
is_corr = False # 特征是否参与滞后领先提升相关系数
|
||||
add_kdj = False # 是否添加kdj指标
|
||||
if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K','D','J']
|
||||
|
||||
### 模型参数
|
||||
y = 'AVG-金能大唐久泰青州'
|
||||
avg_cols = [
|
||||
'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
|
||||
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
|
||||
'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
|
||||
'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
|
||||
]
|
||||
offsite = 80
|
||||
offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
|
||||
horizon =5 # 预测的步长
|
||||
input_size = 40 # 输入序列长度
|
||||
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
|
||||
val_check_steps = 30 # 评估频率
|
||||
early_stop_patience_steps = 5 # 早停的耐心步数
|
||||
# --- 交叉验证用的参数
|
||||
test_size = 200 # 测试集大小，定义100，后面使用的时候重新赋值
|
||||
val_size = test_size # 验证集大小,同测试集大小
|
||||
|
||||
### 特征筛选用到的参数
|
||||
k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
|
||||
corr_threshold = 0.6 # 相关性大于0.6的特征
|
||||
rote = 0.06 # 绘图上下界阈值
|
||||
|
||||
### 计算准确率
|
||||
weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
|
||||
|
||||
|
||||
### 文件
|
||||
data_set = '石油焦指标数据.xlsx' # 数据集文件
|
||||
dataset = 'shiyoujiaodataset' # 数据集文件夹
|
||||
|
||||
# 数据库名称
|
||||
db_name = os.path.join(dataset,'jbsh_juxiting.db')
|
||||
sqlitedb = SQLiteHandler(db_name)
|
||||
sqlitedb.connect()
|
||||
|
||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
||||
# 获取日期时间
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
|
||||
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
|
||||
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
if end_time == '':
|
||||
end_time = now
|
||||
### 邮件配置
|
||||
username='1321340118@qq.com'
|
||||
passwd='wgczgyhtyyyyjghi'
|
||||
# recv=['liurui_test@163.com','52585119@qq.com']
|
||||
recv=['liurui_test@163.com']
|
||||
# recv=['liurui_test@163.com']
|
||||
title='reportname'
|
||||
content=y+'预测报告请看附件'
|
||||
file=os.path.join(dataset,'reportname')
|
||||
# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
|
||||
ssl=True
|
||||
|
||||
|
||||
### 日志配置
|
||||
|
||||
# 创建日志目录（如果不存在）
|
||||
log_dir = 'logs'
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir)
|
||||
|
||||
# 配置日志记录器
|
||||
logger = logging.getLogger('my_logger')
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
# 配置文件处理器，将日志记录到文件
|
||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||
|
||||
# 配置控制台处理器，将日志打印到控制台
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setFormatter(logging.Formatter('%(message)s'))
|
||||
|
||||
# 将处理器添加到日志记录器
|
||||
logger.addHandler(file_handler)
|
||||
logger.addHandler(console_handler)
|
||||
|
||||
# logger.info('当前配置：'+settings)
|
||||
|
||||
408
config_shiyoujiao_lvyong.py
Normal file
408
config_shiyoujiao_lvyong.py
Normal file
@ -0,0 +1,408 @@
|
||||
import logging
|
||||
import os
|
||||
import logging.handlers
|
||||
import datetime
|
||||
from lib.tools import MySQLDB, SQLiteHandler
|
||||
|
||||
|
||||
# eta 接口token
|
||||
APPID = "XNLDvxZHHugj7wJ7"
|
||||
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
|
||||
|
||||
# eta 接口url
|
||||
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
|
||||
classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
|
||||
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
|
||||
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
|
||||
edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
|
||||
edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
|
||||
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
|
||||
edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
|
||||
|
||||
edbcodenamedict = {
|
||||
'C2403283369': '预赔阳极加工利润（高端）',
|
||||
'C2403285560': '预培阳极加工利润（低端）',
|
||||
'C2403288616': '低硫石油焦煅烧利润',
|
||||
'S6949656': '平均价：氧化铝：一级：全国',
|
||||
'S5807052': '氧化铝：一级：贵阳',
|
||||
'S5443355': '市场价：煤沥青：河北地区',
|
||||
'S5443357': '市场价：煤沥青：山西地区',
|
||||
'W000294': '国内主要港口石油焦出货量（隆重）',
|
||||
'W000293': '日照港库存（隆重）',
|
||||
'W000292': '港口总库存（隆重）',
|
||||
'W000283': '主营石油焦产量（隆重）',
|
||||
'W000282': '地炼石油焦产量（隆重）',
|
||||
'W000281': '中国石油焦产量（隆重）',
|
||||
'W000280': '主营石油焦开工负荷率（隆重）',
|
||||
'W000279': '地炼石油焦开工负荷率（隆重）',
|
||||
'ID00150273': '石油焦：1 # ：市场低端价：东北地区（日）',
|
||||
'ID00150281': '石油焦：1 # ：市场主流价：东北地区（日）',
|
||||
'ID00150277': '石油焦：1 # ：市场高端价：东北地区（日）',
|
||||
'ID00150289': '石油焦：2 # ：市场低端价：华东地区（日）',
|
||||
'ID00150285': '石油焦：2 # ：市场低端价：西北地区（日）',
|
||||
'ID00150313': '石油焦：2 # ：市场主流价：华东地区（日）',
|
||||
'ID00150309': '石油焦：2 # ：市场主流价：西北地区（日）',
|
||||
'ID00150301': '石油焦：2 # ：市场高端价：华东地区（日）',
|
||||
'ID00150297': '石油焦：2 # ：市场高端价：西北地区（日）',
|
||||
'ID00150321': '石油焦：2 # A：市场低端价：山东（日）',
|
||||
'ID00150329': '石油焦：2 # A：市场主流价：山东（日）',
|
||||
'ID00150325': '石油焦：2 # A：市场高端价：山东（日）',
|
||||
'ID00150337': '石油焦：2 # B：市场低端价：华南地区（日）',
|
||||
'ID00150341': '石油焦：2 # B：市场低端价：华中地区（日）',
|
||||
'ID00150361': '石油焦：2 # B：市场主流价：华南地区（日）',
|
||||
'ID00150365': '石油焦：2 # B：市场主流价：华中地区（日）',
|
||||
'ID00150349': '石油焦：2 # B：市场高端价：华南地区（日）',
|
||||
'ID00150353': '石油焦：2 # B：市场高端价：华中地区（日）',
|
||||
'ID00150333': '石油焦：2 # B：市场低端价：山东（日）',
|
||||
'ID00150357': '石油焦：2 # B：市场主流价：山东（日）',
|
||||
'ID00150345': '石油焦：2 # B：市场高端价：山东（日）',
|
||||
'ID00150369': '石油焦：3 # ：市场低端价：华中地区（日）',
|
||||
'ID00150373': '石油焦：3 # ：市场高端价：华中地区（日）',
|
||||
'ID00150385': '石油焦：3 # A：市场高端价：山东（日）',
|
||||
'ID00150393': '石油焦：3 # B：市场低端价：华东地区（日）',
|
||||
'ID00150409': '石油焦：3 # B：市场主流价：华东地区（日）',
|
||||
'ID00150401': '石油焦：3 # B：市场高端价：华东地区（日）',
|
||||
'ID00146589': '海绵焦：4 # ：出厂价：华中地区：洛阳石化（日）',
|
||||
'ID01242846': '石油焦：4 # B：挂牌价：华北地区：中石化燕山（日）',
|
||||
'ID01300358': '石油焦：3 # C：市场低端价：山东（日）',
|
||||
'ID01300357': '石油焦：3 # C：市场高端价：山东（日）',
|
||||
'ID00150377': '石油焦：3 # ：市场主流价：华中地区（日）',
|
||||
'ID01387643': '煅烧焦：低硫：0.5 % S：市场价：东北地区（日）',
|
||||
'ID01387646': '煅烧焦：低硫：3.5 % S：市场价：东北地区（日）',
|
||||
'ID01387660': '煅烧焦：中硫：3 % S，400V：市场价：山东（日）',
|
||||
'ID00150381': '石油焦：3 # A：市场低端价：山东（日）',
|
||||
'ID00150397': '石油焦：3 # B：市场低端价：山东（日）',
|
||||
'ID00150405': '石油焦：3 # B：市场高端价：山东（日）',
|
||||
'ID00146545': '海绵焦：3B：出厂价：山东：山东东明（日）',
|
||||
'B3e90b34e4b9e7a6ea3': '石油焦市场均价(元/吨)',
|
||||
'B6b5c53b270a3af12ac': '石油焦1 # 市场均价(元/吨)',
|
||||
'B10721189a11c209a20': '石油焦2 # 市场均价(元/吨)',
|
||||
'B6accfa9d2bf4735a50': '石油焦3 # 市场均价(元/吨)',
|
||||
'B8a0ab5357569c385a9': '石油焦海绵焦市场均价(元/吨)',
|
||||
'B19dcf45e22fbfd3e43': '石油焦海绵焦东北1 # A焦（低端）(元/吨)（百川）',
|
||||
'B5832a62d1e0fba50b6': '石油焦海绵焦东北1 # A焦（高端）(元/吨)（百川）',
|
||||
'B1de4fba026d4609cc7': '石油焦海绵焦东北1 # B焦（低端）(元/吨)（百川）',
|
||||
'B38f89180736172490d': '石油焦海绵焦东北1 # B焦（高端）(元/吨)（百川）',
|
||||
'B4f847871674c3d77f2': '石油焦海绵焦山东地炼1 # -3#焦（低端）(元/吨)',
|
||||
'B1aefb8a64a5200adbd': '石油焦海绵焦山东地炼1 # -3#焦（高端）(元/吨)',
|
||||
'B1df7d0afbfedfb628a': '煅烧焦东北低硫(高端S < 0.5)(元/吨)',
|
||||
'B5f8f9859635876da28': '煅烧焦东北低硫(低端S < 0.5)(元/吨)',
|
||||
'B2342a8c5a39fa00348': '煅烧焦华北中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'B051f27900397c6a35f': '煅烧焦山东中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'Be2a8050a48e86cae1f': '煅烧焦华东中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'B4a1811938f85065f6a': '煅烧焦华中中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'Bc197d4834ef7fb98ec': '煅烧焦华东高硫(高端S < 3.5，钒 < 400)(元/吨)',
|
||||
'B62be5dbdb8c6454530': '煅烧焦低硫参考价格(元/吨)（百川）',
|
||||
'Bdd813140bffc4edfa6': '煅烧焦中硫微量市场均价(元/吨)（百川）',
|
||||
'B185a597decfc71915a': '预焙阳极山东低端(元/吨)（百川）',
|
||||
'B1bcde6130de031bd42': '山西 改质沥青(元/吨)',
|
||||
'Bb9f4a1f6dd32b4ad8a': '山东 改质沥青(元/吨)',
|
||||
'C2411261557491549': '石油焦市场均价(元/吨)/4DMA',
|
||||
'C2411271143174617': '石油焦市场均价(元/吨)/9DMA',
|
||||
'ID01387649': '煅烧焦：中硫：3 % S，350V：市场价：华东地区（日）',
|
||||
'ID01387655': '煅烧焦：中硫：3 % S，350V：市场价：山东（日）',
|
||||
'RE00010076': '煅烧焦：低硫：生产毛利：东北地区（周）',
|
||||
'B9d1acaf80383683da3': '石油焦总产量（周）(吨)',
|
||||
'Bdaa719a38936c8dd76': '石油焦开工率（周）( % )',
|
||||
'B9459d549a332b200e7': '石油焦行业总库存（周）(吨)',
|
||||
'Bce6e098b9518370cff': '石油焦工厂库存（周）(吨)',
|
||||
'B577ce2809772779710': '石油焦市场库存（周）(吨)',
|
||||
'B5d8c564c62f3e6b77f': '石油焦成本（周）(吨)',
|
||||
'B43baa98bcaa06c11a5': '石油焦利润（周）(吨)',
|
||||
'Bdd0c1361d94081211c': '煅烧石油焦总产量（周）(吨)',
|
||||
'B65315111fa28951b1e': '煅烧石油焦开工率（周）( % )',
|
||||
'B2aff5f2632a20027d0': '煅烧石油焦行业总库存（周）(吨)',
|
||||
'B29fbd31128cd71b212': '煅烧石油焦工厂库存（周）(吨)',
|
||||
'B7a88313a89d1261c53': '煅烧石油焦成本（周）(吨)',
|
||||
'Bd4fa36b4decec0aafa': '煅烧石油焦利润（周）(吨)',
|
||||
'B9bd80eac7df81ffbd4': '预焙阳极总产量（周）(吨)',
|
||||
'B27074786605f4660d2': '预焙阳极开工率（周）( % )',
|
||||
'Bdc2a5985ecb56b6a0c': '预焙阳极行业总库存（周）(吨)',
|
||||
'Bce8511f899e487e5b6': '预焙阳极工厂库存（周）(吨)',
|
||||
'B13ec89105bd866a2bd': '预焙阳极成本（周）(吨)',
|
||||
'B66c3abcfa15a2e611c': '预焙阳极利润（周）(吨)',
|
||||
'Bf7efe3200f9abc0453': '电解铝开工率（周）( % )',
|
||||
'Be193166f347267b1a7': '电解铝行业总库存（周）(吨)',
|
||||
'Baa744fc97769353175': '电解铝工厂库存（周）(吨)',
|
||||
'Bf9654603913cfc5282': '电解铝市场库存（周）(吨)',
|
||||
'Bef1535c96da0d70fbc': '电解铝利润（周）(吨)',
|
||||
'B7d1d0b24316d49cbdc': '煤沥青总产量（周）(吨)',
|
||||
'B4303fb002ea1c214da': '煤沥青开工率（周）( % )',
|
||||
'Be9a470c97e9efe660c': '煤沥青行业总库存（周）(吨)',
|
||||
'B50d4d87f6b78bca587': '煤沥青工厂库存（周）(吨)',
|
||||
'B46cc7d0a90155b5bfd': '煅烧焦山东高硫(高端S < 3.5，普货)(元/吨)'
|
||||
}
|
||||
|
||||
edbcodelist = edbcodenamedict.keys()
|
||||
# 临时写死用指定的列,与上面的edbcode对应，后面更改
|
||||
edbnamelist = ['ds', 'y']+[edbcodenamedict[edbcodename]
|
||||
for edbcodename in edbcodelist]
|
||||
|
||||
# eta自有数据指标编码，石油焦铝用还没新增，暂且留空
|
||||
modelsindex = {
|
||||
}
|
||||
|
||||
# 百川数据指标编码
|
||||
baicangidnamedict = {
|
||||
'1588348470396480000': '石油焦滨州-友泰',
|
||||
'1588348470396480000.00': '石油焦东营-海科瑞林',
|
||||
'1588348470396480000.00': '石油焦东营-华联2',
|
||||
'1588348470396480000.00': '石油焦东营-华联3',
|
||||
'1588348470396480000.00': '石油焦东营-联合',
|
||||
'1588348470396480000.00': '石油焦东营-联合3',
|
||||
'1588348470396480915': '石油焦淄博-汇丰',
|
||||
'1588348470396480888': '石油焦沧州-鑫海',
|
||||
'1588348470396480917': '石油焦东营-万通',
|
||||
'1588348470396480925': '石油焦东营-齐润',
|
||||
'1588348470396481084': '石油焦东营-尚能4',
|
||||
'1588348470396480930': '石油焦潍坊-寿光鲁清',
|
||||
'1588348470396480929': '石油焦滨州-鑫岳',
|
||||
}
|
||||
|
||||
|
||||
# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
|
||||
data = {
|
||||
"IndexCode": "",
|
||||
"IndexName": "价格预测模型",
|
||||
"Unit": "无",
|
||||
"Frequency": "日度",
|
||||
"SourceName": f"价格预测",
|
||||
"Remark": 'ddd',
|
||||
"DataList": [
|
||||
{
|
||||
"Date": "2024-05-02",
|
||||
"Value": 333444
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# eta 分类
|
||||
# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
|
||||
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
|
||||
# ParentId ":1160, 能源化工
|
||||
# ClassifyId ":1214,原油 3912 石油焦
|
||||
# ParentId ":1214,"，就是原油下所有的数据。
|
||||
ClassifyId = 3707
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
server_host = '192.168.100.53' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
|
||||
# 上传报告
|
||||
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# 停更预警
|
||||
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
# 查询数据项编码
|
||||
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
upload_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
# 'ciri': 'yyycbdwdcr',
|
||||
# 'benzhou': 'yyycbdwdbz',
|
||||
# 'cizhou': 'yyycbdwdcz',
|
||||
# 'gezhou': 'yyycbdwdgz',
|
||||
# 'ciyue': 'yyycbdwdcy',
|
||||
# 'cieryue': 'yyycbdwdcey',
|
||||
# 'cisanyue': 'yyycbdwdcsy',
|
||||
# 'cisiyue': 'yyycbdwdcsiy',
|
||||
}
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
baichuan_table_name = 'V_TBL_BAICHUAN_YINGFU_VALUE'
|
||||
# select BAICHUAN_ID, DATA_DATE, DATA_VALUE from V_TBL_BAICHUAN_YINGFU_VALUE where BAICHUAN_ID in ('1588348470396475286', '1666')
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = False # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
is_del_tow_month = True # 是否删除两个月不更新的特征
|
||||
|
||||
|
||||
# 连接到数据库
|
||||
db_mysql = MySQLDB(host=host, user=dbusername,
|
||||
password=password, database=dbname)
|
||||
db_mysql.connect()
|
||||
print("数据库连接成功", host, dbname, dbusername)
|
||||
|
||||
|
||||
# 数据截取日期
|
||||
start_year = 2020 # 数据开始年份
|
||||
end_time = '' # 数据截取日期
|
||||
freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
|
||||
delweekenday = True if freq == 'B' else False # 是否删除周末数据
|
||||
is_corr = False # 特征是否参与滞后领先提升相关系数
|
||||
add_kdj = False # 是否添加kdj指标
|
||||
if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K', 'D', 'J']
|
||||
|
||||
# 模型参数
|
||||
y = 'B46cc7d0a90155b5bfd'
|
||||
avg_cols = [
|
||||
|
||||
]
|
||||
offsite = 80
|
||||
offsite_col = []
|
||||
horizon = 5 # 预测的步长
|
||||
input_size = 40 # 输入序列长度
|
||||
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
|
||||
val_check_steps = 30 # 评估频率
|
||||
early_stop_patience_steps = 5 # 早停的耐心步数
|
||||
# --- 交叉验证用的参数
|
||||
test_size = 200 # 测试集大小，定义100，后面使用的时候重新赋值
|
||||
val_size = test_size # 验证集大小,同测试集大小
|
||||
|
||||
# 特征筛选用到的参数
|
||||
k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
|
||||
corr_threshold = 0.6 # 相关性大于0.6的特征
|
||||
rote = 0.06 # 绘图上下界阈值
|
||||
|
||||
# 计算准确率
|
||||
weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25] # 权重
|
||||
|
||||
|
||||
# 文件
|
||||
data_set = '石油焦铝用指标数据.xlsx' # 数据集文件
|
||||
dataset = 'shiyoujiaolvyongdataset' # 数据集文件夹
|
||||
|
||||
# 数据库名称
|
||||
db_name = os.path.join(dataset, 'jbsh_shiyoujiao_lvyong.db')
|
||||
sqlitedb = SQLiteHandler(db_name)
|
||||
sqlitedb.connect()
|
||||
|
||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
||||
# 获取日期时间
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
|
||||
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
|
||||
reportname = f'石油焦铝用大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
if end_time == '':
|
||||
end_time = now
|
||||
# 邮件配置
|
||||
username = '1321340118@qq.com'
|
||||
passwd = 'wgczgyhtyyyyjghi'
|
||||
# recv=['liurui_test@163.com','52585119@qq.com']
|
||||
recv = ['liurui_test@163.com']
|
||||
# recv=['liurui_test@163.com']
|
||||
title = 'reportname'
|
||||
content = y+'预测报告请看附件'
|
||||
file = os.path.join(dataset, 'reportname')
|
||||
# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
|
||||
ssl = True
|
||||
|
||||
|
||||
# 日志配置
|
||||
|
||||
# 创建日志目录（如果不存在）
|
||||
log_dir = 'logs'
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir)
|
||||
|
||||
# 配置日志记录器
|
||||
logger = logging.getLogger('my_logger')
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
# 配置文件处理器，将日志记录到文件
|
||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(
|
||||
log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||
file_handler.setFormatter(logging.Formatter(
|
||||
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||
|
||||
# 配置控制台处理器，将日志打印到控制台
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setFormatter(logging.Formatter('%(message)s'))
|
||||
|
||||
# 将处理器添加到日志记录器
|
||||
logger.addHandler(file_handler)
|
||||
logger.addHandler(console_handler)
|
||||
|
||||
# logger.info('当前配置：'+settings)
|
||||
319
config_shiyoujiao_puhuo.py
Normal file
319
config_shiyoujiao_puhuo.py
Normal file
@ -0,0 +1,319 @@
|
||||
import logging
|
||||
import os
|
||||
import logging.handlers
|
||||
import datetime
|
||||
from lib.tools import MySQLDB, SQLiteHandler
|
||||
|
||||
|
||||
# eta 接口token
|
||||
APPID = "XNLDvxZHHugj7wJ7"
|
||||
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
|
||||
|
||||
# eta 接口url
|
||||
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
|
||||
classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
|
||||
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
|
||||
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
|
||||
edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
|
||||
edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
|
||||
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
|
||||
edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
|
||||
edbcodelist = ['ID01385938', 'lmcads03 lme comdty',
|
||||
'GC1 COMB Comdty',
|
||||
'C2404171822',
|
||||
'dxy curncy',
|
||||
'S5443199 ',
|
||||
'S5479800',
|
||||
'S5443108',
|
||||
'H7358586',
|
||||
'LC3FM1 INDEX',
|
||||
'CNY REGN Curncy',
|
||||
's0105897',
|
||||
'M0067419',
|
||||
'M0066351',
|
||||
'S0266372',
|
||||
'S0266438',
|
||||
'S0266506',
|
||||
'ID01384463']
|
||||
|
||||
# 临时写死用指定的列,与上面的edbcode对应，后面更改
|
||||
edbnamelist = [
|
||||
'ds', 'y',
|
||||
'LME铜价',
|
||||
'黄金连1合约',
|
||||
'Brent-WTI',
|
||||
'美元指数',
|
||||
'甲醇鲁南价格',
|
||||
'甲醇太仓港口价格',
|
||||
'山东丙烯主流价',
|
||||
'丙烷（山东）',
|
||||
'FEI丙烷 M1',
|
||||
'在岸人民币汇率',
|
||||
'南华工业品指数',
|
||||
'PVC期货主力',
|
||||
'PE期货收盘价',
|
||||
'PP连续-1月',
|
||||
'PP连续-5月',
|
||||
'PP连续-9月',
|
||||
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）'
|
||||
]
|
||||
|
||||
edbcodenamedict = {
|
||||
'ID01385938': 'PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）',
|
||||
'ID01384463': 'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
|
||||
'lmcads03 lme comdty': 'LME铜价',
|
||||
'GC1 COMB Comdty': '黄金连1合约',
|
||||
'C2404171822': 'Brent-WTI',
|
||||
'dxy curncy': '美元指数',
|
||||
'S5443199 ': '甲醇鲁南价格',
|
||||
'S5479800': '甲醇太仓港口价格',
|
||||
'S5443108': '山东丙烯主流价',
|
||||
'H7358586': '丙烷（山东）',
|
||||
'LC3FM1 INDEX': 'FEI丙烷 M1',
|
||||
'CNY REGN Curncy': '在岸人民币汇率',
|
||||
's0105897': '南华工业品指数',
|
||||
'M0067419': 'PVC期货主力',
|
||||
'M0066351': 'PE期货收盘价',
|
||||
'S0266372': 'PP连续-1月',
|
||||
'S0266438': 'PP连续-5月',
|
||||
'S0266506': 'PP连续-9月',
|
||||
|
||||
}
|
||||
|
||||
# eta自有数据指标编码
|
||||
modelsindex = {
|
||||
'NHITS': 'SELF0000077',
|
||||
'Informer': 'SELF0000078',
|
||||
'LSTM': 'SELF0000079',
|
||||
'iTransformer': 'SELF0000080',
|
||||
'TSMixer': 'SELF0000081',
|
||||
'TSMixerx': 'SELF0000082',
|
||||
'PatchTST': 'SELF0000083',
|
||||
'RNN': 'SELF0000084',
|
||||
'GRU': 'SELF0000085',
|
||||
'TCN': 'SELF0000086',
|
||||
'BiTCN': 'SELF0000087',
|
||||
'DilatedRNN': 'SELF0000088',
|
||||
'MLP': 'SELF0000089',
|
||||
'DLinear': 'SELF0000090',
|
||||
'NLinear': 'SELF0000091',
|
||||
'TFT': 'SELF0000092',
|
||||
'FEDformer': 'SELF0000093',
|
||||
'StemGNN': 'SELF0000094',
|
||||
'MLPMultivariate': 'SELF0000095',
|
||||
'TiDE': 'SELF0000096',
|
||||
'DeepNPTS': 'SELF0000097'
|
||||
}
|
||||
|
||||
|
||||
# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
|
||||
data = {
|
||||
"IndexCode": "",
|
||||
"IndexName": "价格预测模型",
|
||||
"Unit": "无",
|
||||
"Frequency": "日度",
|
||||
"SourceName": f"价格预测",
|
||||
"Remark": 'ddd',
|
||||
"DataList": [
|
||||
{
|
||||
"Date": "2024-05-02",
|
||||
"Value": 333444
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# eta 分类
|
||||
# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
|
||||
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
|
||||
# ParentId ":1160, 能源化工
|
||||
# ClassifyId ":1214,原油 3912 石油焦
|
||||
# ParentId ":1214,"，就是原油下所有的数据。
|
||||
ClassifyId = 3707
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
server_host = '192.168.100.53'
|
||||
|
||||
login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
|
||||
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
upload_data = {
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传聚烯烃PP价格预测报告',
|
||||
"data": {
|
||||
"groupNo": '000128', # 用户组编号
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'JXTJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '000128', # 用户组编号
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
baichuan_table_name = 'V_TBL_BAICHUAN_YINGFU_VALUE'
|
||||
# select BAICHUAN_ID, DATA_DATE, DATA_VALUE from V_TBL_BAICHUAN_YINGFU_VALUE where BAICHUAN_ID in ('1588348470396475286', '1666')
|
||||
# 开关
|
||||
is_train = False # 是否训练
|
||||
is_debug = True # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = False # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
is_del_tow_month = True # 是否删除两个月不更新的特征
|
||||
|
||||
|
||||
# 连接到数据库
|
||||
db_mysql = MySQLDB(host=host, user=dbusername,
|
||||
password=password, database=dbname)
|
||||
db_mysql.connect()
|
||||
print("数据库连接成功", host, dbname, dbusername)
|
||||
|
||||
|
||||
# 数据截取日期
|
||||
start_year = 2020 # 数据开始年份
|
||||
end_time = '' # 数据截取日期
|
||||
freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
|
||||
delweekenday = True if freq == 'B' else False # 是否删除周末数据
|
||||
is_corr = False # 特征是否参与滞后领先提升相关系数
|
||||
add_kdj = False # 是否添加kdj指标
|
||||
if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K', 'D', 'J']
|
||||
|
||||
# 模型参数
|
||||
y = 'AVG-金能大唐久泰青州'
|
||||
avg_cols = [
|
||||
'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
|
||||
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
|
||||
'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
|
||||
'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
|
||||
]
|
||||
offsite = 80
|
||||
offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
|
||||
horizon = 5 # 预测的步长
|
||||
input_size = 40 # 输入序列长度
|
||||
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
|
||||
val_check_steps = 30 # 评估频率
|
||||
early_stop_patience_steps = 5 # 早停的耐心步数
|
||||
# --- 交叉验证用的参数
|
||||
test_size = 200 # 测试集大小，定义100，后面使用的时候重新赋值
|
||||
val_size = test_size # 验证集大小,同测试集大小
|
||||
|
||||
# 特征筛选用到的参数
|
||||
k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
|
||||
corr_threshold = 0.6 # 相关性大于0.6的特征
|
||||
rote = 0.06 # 绘图上下界阈值
|
||||
|
||||
# 计算准确率
|
||||
weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25] # 权重
|
||||
|
||||
|
||||
# 文件
|
||||
data_set = '石油焦指标数据.xlsx' # 数据集文件
|
||||
dataset = 'shiyoujiaodataset' # 数据集文件夹
|
||||
|
||||
# 数据库名称
|
||||
db_name = os.path.join(dataset, 'jbsh_juxiting.db')
|
||||
sqlitedb = SQLiteHandler(db_name)
|
||||
sqlitedb.connect()
|
||||
|
||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
||||
# 获取日期时间
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
|
||||
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
|
||||
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
if end_time == '':
|
||||
end_time = now
|
||||
# 邮件配置
|
||||
username = '1321340118@qq.com'
|
||||
passwd = 'wgczgyhtyyyyjghi'
|
||||
# recv=['liurui_test@163.com','52585119@qq.com']
|
||||
recv = ['liurui_test@163.com']
|
||||
# recv=['liurui_test@163.com']
|
||||
title = 'reportname'
|
||||
content = y+'预测报告请看附件'
|
||||
file = os.path.join(dataset, 'reportname')
|
||||
# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
|
||||
ssl = True
|
||||
|
||||
|
||||
# 日志配置
|
||||
|
||||
# 创建日志目录（如果不存在）
|
||||
log_dir = 'logs'
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir)
|
||||
|
||||
# 配置日志记录器
|
||||
logger = logging.getLogger('my_logger')
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
# 配置文件处理器，将日志记录到文件
|
||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(
|
||||
log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||
file_handler.setFormatter(logging.Formatter(
|
||||
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||
|
||||
# 配置控制台处理器，将日志打印到控制台
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setFormatter(logging.Formatter('%(message)s'))
|
||||
|
||||
# 将处理器添加到日志记录器
|
||||
logger.addHandler(file_handler)
|
||||
logger.addHandler(console_handler)
|
||||
|
||||
# logger.info('当前配置：'+settings)
|
||||
@ -797,14 +797,14 @@ def datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y
|
||||
if config.is_del_tow_month:
|
||||
current_date = datetime.datetime.now()
|
||||
two_months_ago = current_date - timedelta(days=180)
|
||||
config.logger.info(f'删除两月不更新特征前数据量：{df.shape}')
|
||||
config.logger.info(f'删除半年不更新特征前数据量：{df.shape}')
|
||||
columns_to_drop = []
|
||||
for clo in df.columns:
|
||||
if check_column(df, clo, two_months_ago):
|
||||
columns_to_drop.append(clo)
|
||||
df = df.drop(columns=columns_to_drop)
|
||||
|
||||
config.logger.info(f'删除两月不更新特征后数据量：{df.shape}')
|
||||
config.logger.info(f'删除半年不更新特征后数据量：{df.shape}')
|
||||
|
||||
# 衍生时间特征
|
||||
if is_timefurture:
|
||||
@ -1604,6 +1604,7 @@ class EtaReader():
|
||||
f'Error: {response.status_code}, {response.text}')
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
# 原油数据获取
|
||||
|
||||
def get_eta_api_yuanyou_data(self, data_set, dataset=''):
|
||||
'''
|
||||
@ -1790,6 +1791,7 @@ class EtaReader():
|
||||
df_zhibiaoshuju = df1.copy()
|
||||
df_zhibiaoliebiao = df.copy()
|
||||
return df_zhibiaoshuju, df_zhibiaoliebiao
|
||||
# 聚烯烃PP数据获取
|
||||
|
||||
def get_eta_api_pp_data(self, data_set, dataset=''):
|
||||
today = datetime.date.today().strftime("%Y-%m-%d")
|
||||
@ -2012,6 +2014,71 @@ class EtaReader():
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
# 石油焦铝用数据获取
|
||||
def get_eta_api_shiyoujiao_lvyong_data(self, data_set, dataset=''):
|
||||
today = datetime.date.today().strftime("%Y-%m-%d")
|
||||
|
||||
# 定义你的headers，这里可以包含多个参数
|
||||
self.headers = {
|
||||
'nonce': self.signature.nonce, # 例如，一个认证令牌
|
||||
# 自定义的header参数
|
||||
'timestamp': str(self.signature.timestamp),
|
||||
'appid': self.signature.APPID, # 另一个自定义的header参数
|
||||
'signature': self.signature.signature
|
||||
}
|
||||
|
||||
# 从列表数据中获取指标名称，判断指标名称频度是否为日 ，如果是，则获取UniqueCode，然后获取指标数据，保存到xlat文件中的sheet表。
|
||||
|
||||
'''
|
||||
df = sheetname 指标列表，存储 指标分类-指标名称-指标id-频度
|
||||
df1 = sheetname 指标数据 ,存储 时间-指标名称1-指标名称2...
|
||||
|
||||
'''
|
||||
|
||||
# 构建新的DataFrame df df1
|
||||
df = pd.DataFrame(columns=['指标分类', '指标名称', '指标id', '频度'])
|
||||
df1 = pd.DataFrame(columns=['DataTime'])
|
||||
|
||||
# 外网环境无法访问，请确认是否为内网环境
|
||||
try:
|
||||
# 发送GET请求 获取指标分类列表
|
||||
response = requests.get(self.classifylisturl, headers=self.headers)
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise Exception(f"请求失败,请确认是否为内网环境: {e}", "\033[0m")
|
||||
# 找到列表中不在指标列中的指标id，保存成新的list
|
||||
new_list = [
|
||||
item for item in self.edbcodelist if item not in df['指标id'].tolist()]
|
||||
config.logger.info(new_list)
|
||||
# 遍历new_list，获取指标数据，保存到df1
|
||||
for item in new_list:
|
||||
config.logger.info(item)
|
||||
# 将item 加入到 df['指标id']中
|
||||
try:
|
||||
itemname = config.edbcodenamedict[item]
|
||||
except:
|
||||
itemname = item
|
||||
|
||||
df1 = self.edbcodegetdata(df1, item, itemname)
|
||||
df = pd.concat([df, pd.DataFrame(
|
||||
{'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他'}, index=[0])])
|
||||
|
||||
# 按时间排序
|
||||
df1.sort_values('DataTime', inplace=True, ascending=False)
|
||||
df1.rename(columns={'DataTime': 'date'}, inplace=True)
|
||||
# df1.dropna(inplace=True)
|
||||
# 去掉大于今天日期的行
|
||||
df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')]
|
||||
config.logger.info(df1.head())
|
||||
# config.logger.info(f'{df1.head()}')
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df1.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
df_zhibiaoshuju = df1.copy()
|
||||
df_zhibiaoliebiao = df.copy()
|
||||
return df_zhibiaoshuju, df_zhibiaoliebiao
|
||||
|
||||
|
||||
def get_market_data(end_time, df):
|
||||
"""
|
||||
|
||||
@ -340,7 +340,8 @@ def predict_main():
|
||||
logger.info('模型训练完成')
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss_juxiting(sqlitedb, end_time=global_config['end_time'],is_fivemodels=global_config['is_fivemodels'])
|
||||
model_results3 = model_losss_juxiting(
|
||||
sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
# # 模型报告
|
||||
|
||||
445
main_shiyoujiao_lvyong.py
Normal file
445
main_shiyoujiao_lvyong.py
Normal file
@ -0,0 +1,445 @@
|
||||
# 读取配置
|
||||
|
||||
from lib.dataread import *
|
||||
from config_shiyoujiao_lvyong import *
|
||||
from lib.tools import SendMail, exception_logger
|
||||
from models.nerulforcastmodels import ex_Model, model_losss, model_losss_juxiting, brent_export_pdf, tansuanli_export_pdf, pp_export_pdf, model_losss_juxiting
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
'logger': logger,
|
||||
'dataset': dataset,
|
||||
'y': y,
|
||||
'is_debug': is_debug,
|
||||
'is_train': is_train,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'settings': settings,
|
||||
'weight_dict': weight_dict,
|
||||
|
||||
|
||||
# 模型参数
|
||||
'data_set': data_set,
|
||||
'input_size': input_size,
|
||||
'horizon': horizon,
|
||||
'train_steps': train_steps,
|
||||
'val_check_steps': val_check_steps,
|
||||
'val_size': val_size,
|
||||
'test_size': test_size,
|
||||
'modelsindex': modelsindex,
|
||||
'rote': rote,
|
||||
'bdwd_items': bdwd_items,
|
||||
|
||||
# 特征工程开关
|
||||
'is_del_corr': is_del_corr,
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'is_update_predict_value': is_update_predict_value,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
'start_year': start_year,
|
||||
'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||
'freq': freq, # 保持列表结构
|
||||
|
||||
# 接口配置
|
||||
'login_pushreport_url': login_pushreport_url,
|
||||
'login_data': login_data,
|
||||
'upload_url': upload_url,
|
||||
'upload_warning_url': upload_warning_url,
|
||||
'warning_data': warning_data,
|
||||
|
||||
# 查询接口
|
||||
'query_data_list_item_nos_url': query_data_list_item_nos_url,
|
||||
'query_data_list_item_nos_data': query_data_list_item_nos_data,
|
||||
|
||||
# 上传数据项
|
||||
'push_data_value_list_url': push_data_value_list_url,
|
||||
'push_data_value_list_data': push_data_value_list_data,
|
||||
|
||||
# eta 配置
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
})
|
||||
|
||||
|
||||
def push_market_value():
|
||||
logger.info('发送预测结果到市场信息平台')
|
||||
# 读取预测数据和模型评估数据
|
||||
predict_file_path = os.path.join(config.dataset, 'predict.csv')
|
||||
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
|
||||
try:
|
||||
predictdata_df = pd.read_csv(predict_file_path)
|
||||
top_models_df = pd.read_csv(model_eval_file_path)
|
||||
except FileNotFoundError as e:
|
||||
logger.error(f"文件未找到: {e}")
|
||||
return
|
||||
|
||||
predictdata = predictdata_df.copy()
|
||||
|
||||
# 取模型前十
|
||||
top_models = top_models_df['模型(Model)'].head(10).tolist()
|
||||
|
||||
# 计算前十模型的均值
|
||||
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
|
||||
|
||||
# 打印日期和前十模型均值
|
||||
print(predictdata_df[['ds', 'top_models_mean']])
|
||||
|
||||
# 准备要推送的数据
|
||||
first_mean = predictdata_df['top_models_mean'].iloc[0]
|
||||
last_mean = predictdata_df['top_models_mean'].iloc[-1]
|
||||
# 保留两位小数
|
||||
first_mean = round(first_mean, 2)
|
||||
last_mean = round(last_mean, 2)
|
||||
|
||||
predictdata = [
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['ciri'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": first_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['benzhou'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": last_mean
|
||||
}
|
||||
]
|
||||
|
||||
print(predictdata)
|
||||
|
||||
# 推送数据到市场信息平台
|
||||
try:
|
||||
push_market_data(predictdata)
|
||||
except Exception as e:
|
||||
logger.error(f"推送数据失败: {e}")
|
||||
|
||||
|
||||
def predict_main():
|
||||
"""
|
||||
主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
|
||||
|
||||
参数:
|
||||
signature (BinanceAPI): Binance API 实例。
|
||||
etadata (EtaReader): ETA 数据读取器实例。
|
||||
is_eta (bool): 是否从 ETA 获取数据。
|
||||
data_set (str): 数据集名称。
|
||||
dataset (str): 数据集路径。
|
||||
add_kdj (bool): 是否添加 KDJ 指标。
|
||||
is_timefurture (bool): 是否添加时间衍生特征。
|
||||
end_time (str): 结束时间。
|
||||
is_edbnamelist (bool): 是否使用 EDB 名称列表。
|
||||
edbnamelist (list): EDB 名称列表。
|
||||
y (str): 预测目标列名。
|
||||
sqlitedb (SQLiteDB): SQLite 数据库实例。
|
||||
is_corr (bool): 是否进行相关性分析。
|
||||
horizon (int): 预测时域。
|
||||
input_size (int): 输入数据大小。
|
||||
train_steps (int): 训练步数。
|
||||
val_check_steps (int): 验证检查步数。
|
||||
early_stop_patience_steps (int): 早停耐心步数。
|
||||
is_debug (bool): 是否调试模式。
|
||||
dataset (str): 数据集名称。
|
||||
is_train (bool): 是否训练模型。
|
||||
is_fivemodels (bool): 是否使用五个模型。
|
||||
val_size (float): 验证集大小。
|
||||
test_size (float): 测试集大小。
|
||||
settings (dict): 模型设置。
|
||||
now (str): 当前时间。
|
||||
etadata (EtaReader): ETA 数据读取器实例。
|
||||
modelsindex (list): 模型索引列表。
|
||||
data (str): 数据类型。
|
||||
is_eta (bool): 是否从 ETA 获取数据。
|
||||
|
||||
返回:
|
||||
None
|
||||
"""
|
||||
end_time = global_config['end_time']
|
||||
# 获取数据
|
||||
if is_eta:
|
||||
logger.info('从eta获取数据...')
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=global_config['classifylisturl'],
|
||||
classifyidlisturl=global_config['classifyidlisturl'],
|
||||
edbcodedataurl=global_config['edbcodedataurl'],
|
||||
edbcodelist=global_config['edbcodelist'],
|
||||
edbdatapushurl=global_config['edbdatapushurl'],
|
||||
edbdeleteurl=global_config['edbdeleteurl'],
|
||||
edbbusinessurl=global_config['edbbusinessurl'],
|
||||
classifyId=global_config['ClassifyId'],
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_data(
|
||||
data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
try:
|
||||
# 如果是测试环境，最高价最低价取excel文档
|
||||
if server_host == '192.168.100.53':
|
||||
logger.info('从excel文档获取最高价最低价')
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(
|
||||
end_time, df_zhibiaoshuju)
|
||||
|
||||
except:
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
# 数据处理
|
||||
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
end_time=end_time)
|
||||
|
||||
else:
|
||||
# 读取数据
|
||||
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
|
||||
# 更改预测列名称
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
|
||||
if is_edbnamelist:
|
||||
df = df[edbnamelist]
|
||||
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# 保存最新日期的y值到数据库
|
||||
# 取第一行数据存储到数据库中
|
||||
first_row = df[['ds', 'y']].tail(1)
|
||||
# 判断y的类型是否为float
|
||||
if not isinstance(first_row['y'].values[0], float):
|
||||
logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
return None
|
||||
|
||||
# 将最新真实值保存到数据库
|
||||
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
else:
|
||||
for row in first_row.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
config.logger.info(f'要保存的真实值：{row_dict}')
|
||||
# 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||
if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
elif not isinstance(row_dict['ds'], str):
|
||||
try:
|
||||
row_dict['ds'] = pd.to_datetime(
|
||||
row_dict['ds']).strftime('%Y-%m-%d')
|
||||
except:
|
||||
logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
check_query = sqlitedb.select_data(
|
||||
'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
if len(check_query) > 0:
|
||||
set_clause = ", ".join(
|
||||
[f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data(
|
||||
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
continue
|
||||
sqlitedb.insert_data('trueandpredict', tuple(
|
||||
row_dict.values()), columns=row_dict.keys())
|
||||
|
||||
# 更新accuracy表的y值
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
pass
|
||||
else:
|
||||
update_y = sqlitedb.select_data(
|
||||
'accuracy', where_condition="y is null")
|
||||
if len(update_y) > 0:
|
||||
logger.info('更新accuracy表的y值')
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds'] <= end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
# try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
try:
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data(
|
||||
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except:
|
||||
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data(
|
||||
'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(
|
||||
model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table(
|
||||
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
try:
|
||||
if is_weekday:
|
||||
# if True:
|
||||
logger.info('今天是周一，发送特征预警')
|
||||
# 上传预警信息到数据库
|
||||
warning_data_df = df_zhibiaoliebiao.copy()
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||
# 重命名列名
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
from sqlalchemy import create_engine
|
||||
import urllib
|
||||
global password
|
||||
if '@' in password:
|
||||
password = urllib.parse.quote_plus(password)
|
||||
|
||||
engine = create_engine(
|
||||
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||
"%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# 插入数据之前查询表数据然后新增id列
|
||||
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
if not existing_data.empty:
|
||||
max_id = existing_data['ID'].astype(int).max()
|
||||
warning_data_df['ID'] = range(
|
||||
max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
else:
|
||||
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
warning_data_df.to_sql(
|
||||
table_name, con=engine, if_exists='append', index=False)
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
except:
|
||||
logger.info('上传预警信息到数据库失败')
|
||||
|
||||
if is_corr:
|
||||
df = corr_feature(df=df)
|
||||
|
||||
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
logger.info(f"开始训练模型...")
|
||||
row, col = df.shape
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model(df,
|
||||
horizon=global_config['horizon'],
|
||||
input_size=global_config['input_size'],
|
||||
train_steps=global_config['train_steps'],
|
||||
val_check_steps=global_config['val_check_steps'],
|
||||
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
is_debug=global_config['is_debug'],
|
||||
dataset=global_config['dataset'],
|
||||
is_train=global_config['is_train'],
|
||||
is_fivemodels=global_config['is_fivemodels'],
|
||||
val_size=global_config['val_size'],
|
||||
test_size=global_config['test_size'],
|
||||
settings=global_config['settings'],
|
||||
now=now,
|
||||
etadata=global_config['etadata'],
|
||||
modelsindex=global_config['modelsindex'],
|
||||
data=data,
|
||||
is_eta=global_config['is_eta'],
|
||||
end_time=global_config['end_time'],
|
||||
)
|
||||
|
||||
logger.info('模型训练完成')
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
# 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'Brent原油大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
|
||||
push_market_value()
|
||||
|
||||
# # LSTM 单变量模型
|
||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||
|
||||
# # lstm 多变量模型
|
||||
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
|
||||
|
||||
# # GRU 模型
|
||||
# # ex_GRU(df)
|
||||
|
||||
# 发送邮件
|
||||
# m = SendMail(
|
||||
# username=username,
|
||||
# passwd=passwd,
|
||||
# recv=recv,
|
||||
# title=title,
|
||||
# content=content,
|
||||
# file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
# ssl=ssl,
|
||||
# )
|
||||
# m.send_mail()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2024-12-1', '2025-2-26', freq='W'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
|
||||
predict_main()
|
||||
@ -1,12 +1,80 @@
|
||||
# 读取配置
|
||||
from lib.dataread import *
|
||||
from lib.tools import SendMail,exception_logger
|
||||
from models.nerulforcastmodels import ex_Model_Juxiting,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
|
||||
|
||||
import glob
|
||||
from lib.dataread import *
|
||||
from config_shiyoujiao_puhuo import *
|
||||
from lib.tools import SendMail, exception_logger
|
||||
from models.nerulforcastmodels import ex_Model, model_losss_juxiting, tansuanli_export_pdf, pp_export_pdf
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
'logger': logger,
|
||||
'dataset': dataset,
|
||||
'y': y,
|
||||
'offsite_col': offsite_col,
|
||||
'avg_cols': avg_cols,
|
||||
'offsite': offsite,
|
||||
'edbcodenamedict': edbcodenamedict,
|
||||
'is_debug': is_debug,
|
||||
'is_train': is_train,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'settings': settings,
|
||||
|
||||
|
||||
# 模型参数
|
||||
'data_set': data_set,
|
||||
'input_size': input_size,
|
||||
'horizon': horizon,
|
||||
'train_steps': train_steps,
|
||||
'val_check_steps': val_check_steps,
|
||||
'val_size': val_size,
|
||||
'test_size': test_size,
|
||||
'modelsindex': modelsindex,
|
||||
'rote': rote,
|
||||
|
||||
# 特征工程开关
|
||||
'is_del_corr': is_del_corr,
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
'start_year': start_year,
|
||||
'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||
'freq': freq, # 保持列表结构
|
||||
|
||||
# 接口配置
|
||||
'login_pushreport_url': login_pushreport_url,
|
||||
'login_data': login_data,
|
||||
'upload_url': upload_url,
|
||||
'upload_warning_url': upload_warning_url,
|
||||
'warning_data': warning_data,
|
||||
|
||||
# 查询接口
|
||||
'query_data_list_item_nos_url': query_data_list_item_nos_url,
|
||||
'query_data_list_item_nos_data': query_data_list_item_nos_data,
|
||||
|
||||
# eta 配置
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
})
|
||||
|
||||
|
||||
def predict_main():
|
||||
@ -72,7 +140,8 @@ def predict_main():
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl,
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_data(
|
||||
data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
@ -83,26 +152,26 @@ def predict_main():
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
||||
|
||||
except :
|
||||
df_zhibiaoshuju = get_market_data(
|
||||
end_time, df_zhibiaoshuju)
|
||||
|
||||
except:
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
|
||||
|
||||
# 数据处理
|
||||
df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
end_time=end_time)
|
||||
end_time=end_time)
|
||||
|
||||
else:
|
||||
# 读取数据
|
||||
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
df,df_zhibiaoliebiao = getdata_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
df, df_zhibiaoliebiao = getdata_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
|
||||
# 更改预测列名称
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
@ -125,31 +194,37 @@ def predict_main():
|
||||
for row in first_row.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
check_query = sqlitedb.select_data(
|
||||
'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
if len(check_query) > 0:
|
||||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
set_clause = ", ".join(
|
||||
[f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data(
|
||||
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
continue
|
||||
sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
||||
sqlitedb.insert_data('trueandpredict', tuple(
|
||||
row_dict.values()), columns=row_dict.keys())
|
||||
|
||||
# 更新accuracy表的y值
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
pass
|
||||
else:
|
||||
update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
||||
update_y = sqlitedb.select_data(
|
||||
'accuracy', where_condition="y is null")
|
||||
if len(update_y) > 0:
|
||||
logger.info('更新accuracy表的y值')
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds']<=end_time]
|
||||
update_y = update_y[update_y['ds'] <= end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
# try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
try:
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data(
|
||||
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except:
|
||||
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# except Exception as e:
|
||||
@ -161,10 +236,12 @@ def predict_main():
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
|
||||
model_results = sqlitedb.select_data(
|
||||
'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
|
||||
model_results = model_results.dropna(
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
@ -172,47 +249,59 @@ def predict_main():
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
|
||||
model_results[f'{model}_abs_error_rate'] = abs(
|
||||
model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
min_abs_error_rate_values = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
min_abs_error_rate_column_name = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
sqlitedb.create_table(
|
||||
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
try:
|
||||
if is_weekday:
|
||||
# if True:
|
||||
# if True:
|
||||
logger.info('今天是周一，发送特征预警')
|
||||
# 上传预警信息到数据库
|
||||
warning_data_df = df_zhibiaoliebiao.copy()
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||
# 重命名列名
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
from sqlalchemy import create_engine
|
||||
import urllib
|
||||
global password
|
||||
if '@' in password:
|
||||
password = urllib.parse.quote_plus(password)
|
||||
|
||||
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
engine = create_engine(
|
||||
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||
"%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# 插入数据之前查询表数据然后新增id列
|
||||
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
if not existing_data.empty:
|
||||
max_id = existing_data['ID'].astype(int).max()
|
||||
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
warning_data_df['ID'] = range(
|
||||
max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
else:
|
||||
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
|
||||
warning_data_df.to_sql(
|
||||
table_name, con=engine, if_exists='append', index=False)
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
except:
|
||||
@ -227,50 +316,49 @@ def predict_main():
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model_Juxiting(df,
|
||||
horizon=horizon,
|
||||
input_size=input_size,
|
||||
train_steps=train_steps,
|
||||
val_check_steps=val_check_steps,
|
||||
early_stop_patience_steps=early_stop_patience_steps,
|
||||
is_debug=is_debug,
|
||||
dataset=dataset,
|
||||
is_train=is_train,
|
||||
is_fivemodels=is_fivemodels,
|
||||
val_size=val_size,
|
||||
test_size=test_size,
|
||||
settings=settings,
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=modelsindex,
|
||||
data=data,
|
||||
is_eta=is_eta,
|
||||
end_time=end_time,
|
||||
)
|
||||
|
||||
horizon=horizon,
|
||||
input_size=input_size,
|
||||
train_steps=train_steps,
|
||||
val_check_steps=val_check_steps,
|
||||
early_stop_patience_steps=early_stop_patience_steps,
|
||||
is_debug=is_debug,
|
||||
dataset=dataset,
|
||||
is_train=is_train,
|
||||
is_fivemodels=is_fivemodels,
|
||||
val_size=val_size,
|
||||
test_size=test_size,
|
||||
settings=settings,
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=modelsindex,
|
||||
data=data,
|
||||
is_eta=is_eta,
|
||||
end_time=end_time,
|
||||
)
|
||||
|
||||
logger.info('模型训练完成')
|
||||
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss_juxiting(sqlitedb)
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
|
||||
# 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
|
||||
reportname=reportname,sqlitedb=sqlitedb),
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
|
||||
# # LSTM 单变量模型
|
||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||
|
||||
|
||||
# # lstm 多变量模型
|
||||
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
|
||||
|
||||
|
||||
# # GRU 模型
|
||||
# # ex_GRU(df)
|
||||
|
||||
@ -281,10 +369,11 @@ def predict_main():
|
||||
recv=recv,
|
||||
title=title,
|
||||
content=content,
|
||||
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
file=max(glob.glob(os.path.join(dataset, '*.pdf')),
|
||||
key=os.path.getctime),
|
||||
ssl=ssl,
|
||||
)
|
||||
# m.send_mail()
|
||||
# m.send_mail()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
@ -298,4 +387,4 @@ if __name__ == '__main__':
|
||||
# except:
|
||||
# pass
|
||||
|
||||
predict_main()
|
||||
predict_main()
|
||||
Loading…
Reference in New Issue
Block a user