diff --git a/config_juxiting.py b/config_juxiting.py
index 79c29d1..20003ff 100644
--- a/config_juxiting.py
+++ b/config_juxiting.py
@@ -211,7 +211,7 @@ upload_data = {
 ### 开关
 is_train = True # 是否训练
 is_debug = False # 是否调试
-is_eta = False # 是否使用eta接口
+is_eta = True # 是否使用eta接口
 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
diff --git a/lib/dataread.py b/lib/dataread.py
index 6599e00..6a7a08a 100644
--- a/lib/dataread.py
+++ b/lib/dataread.py
@@ -1,5 +1,5 @@
-from config_jingbo import *
-# from config_juxiting import *
+# from config_jingbo import *
+from config_juxiting import *
 
 
 # 导入模块
diff --git a/聚烯烃预测值绘图调试.ipynb b/聚烯烃预测值绘图调试.ipynb
index f7bd224..d516188 100644
--- a/聚烯烃预测值绘图调试.ipynb
+++ b/聚烯烃预测值绘图调试.ipynb
@@ -2,107 +2,68 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "id": "7fadc60c-d710-4b8c-89cd-1d889ece1eaf",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "从eta获取数据...\n",
-      "从eta获取数据...\n",
-      "['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']\n",
-      "['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']\n",
-      "ID01385938\n",
-      "ID01385938\n",
-      "lmcads03 lme comdty\n",
-      "lmcads03 lme comdty\n",
-      "GC1 COMB Comdty\n",
-      "GC1 COMB Comdty\n",
-      "C2404171822\n",
-      "C2404171822\n",
-      "dxy curncy\n",
-      "dxy curncy\n",
-      "S5443199 \n",
-      "S5443199 \n",
-      "S5479800\n",
-      "S5479800\n",
-      "S5443108\n",
-      "S5443108\n",
-      "H7358586\n",
-      "H7358586\n",
-      "LC3FM1 INDEX\n",
-      "LC3FM1 INDEX\n",
-      "CNY REGN Curncy\n",
-      "CNY REGN Curncy\n",
-      "s0105897\n",
-      "s0105897\n",
-      "M0067419\n",
-      "M0067419\n",
-      "M0066351\n",
-      "M0066351\n",
-      "S0266372\n",
-      "S0266372\n",
-      "S0266438\n",
-      "S0266438\n",
-      "S0266506\n",
-      "S0266506\n",
-      "         date  PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）   LME铜价  黄金连1合约  Brent-WTI  \\\n",
-      "0  2024-11-19                        7390.0     NaN  2634.7       4.12   \n",
-      "1  2024-11-18                        7380.0  9072.5  2614.6       4.14   \n",
-      "2  2024-11-15                        7380.0  9002.5  2570.1       4.02   \n",
-      "3  2024-11-14                        7380.0  8990.0  2572.9       3.86   \n",
-      "4  2024-11-13                        7380.0  9047.0  2586.5       3.85   \n",
-      "\n",
-      "      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  \\\n",
-      "0  106.206     NaN       NaN      NaN     NaN    615.08   7.2387      NaN   \n",
-      "1  106.275  2340.0       NaN   6850.0     NaN    606.60   7.2320  3727.05   \n",
-      "2  106.687  2310.0       NaN   6930.0     NaN    611.69   7.2294  3708.14   \n",
-      "3  106.673  2310.0    2472.0   6945.0     NaN    618.61   7.2271  3739.76   \n",
-      "4  106.481  2310.0    2480.0   6800.0     NaN    621.88   7.2340  3772.43   \n",
-      "\n",
-      "   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  \n",
-      "0      NaN      NaN      NaN      NaN      NaN  \n",
-      "1   5284.0      NaN      NaN      NaN      NaN  \n",
-      "2   5282.0      NaN      NaN      NaN      NaN  \n",
-      "3   5310.0      NaN      NaN      NaN      NaN  \n",
-      "4   5347.0      NaN      NaN      NaN      NaN  \n",
-      "         date  PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）   LME铜价  黄金连1合约  Brent-WTI  \\\n",
-      "0  2024-11-19                        7390.0     NaN  2634.7       4.12   \n",
-      "1  2024-11-18                        7380.0  9072.5  2614.6       4.14   \n",
-      "2  2024-11-15                        7380.0  9002.5  2570.1       4.02   \n",
-      "3  2024-11-14                        7380.0  8990.0  2572.9       3.86   \n",
-      "4  2024-11-13                        7380.0  9047.0  2586.5       3.85   \n",
-      "\n",
-      "      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  \\\n",
-      "0  106.206     NaN       NaN      NaN     NaN    615.08   7.2387      NaN   \n",
-      "1  106.275  2340.0       NaN   6850.0     NaN    606.60   7.2320  3727.05   \n",
-      "2  106.687  2310.0       NaN   6930.0     NaN    611.69   7.2294  3708.14   \n",
-      "3  106.673  2310.0    2472.0   6945.0     NaN    618.61   7.2271  3739.76   \n",
-      "4  106.481  2310.0    2480.0   6800.0     NaN    621.88   7.2340  3772.43   \n",
-      "\n",
-      "   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  \n",
-      "0      NaN      NaN      NaN      NaN      NaN  \n",
-      "1   5284.0      NaN      NaN      NaN      NaN  \n",
-      "2   5282.0      NaN      NaN      NaN      NaN  \n",
-      "3   5310.0      NaN      NaN      NaN      NaN  \n",
-      "4   5347.0      NaN      NaN      NaN      NaN  \n"
+      "从eta获取数据...\n"
      ]
     },
     {
-     "ename": "KeyError",
-     "evalue": "\"None of [Index(['PP：拉丝：HP550J：市场价：青岛：金能化学（日）'], dtype='object')] are in the [columns]\"",
+     "ename": "Exception",
+     "evalue": "(\"请求失败,请确认是否为内网环境: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))\", '\\x1b[0m')",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[10], line 41\u001b[0m\n\u001b[0;32m     38\u001b[0m     df_zhibiaoshuju,df_zhibiaoliebiao \u001b[38;5;241m=\u001b[39m  etadata\u001b[38;5;241m.\u001b[39mget_eta_api_pp_data(data_set\u001b[38;5;241m=\u001b[39mdata_set,dataset\u001b[38;5;241m=\u001b[39mdataset)  \u001b[38;5;66;03m# 原始数据，未处理\u001b[39;00m\n\u001b[0;32m     40\u001b[0m     \u001b[38;5;66;03m# 数据处理\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m     df \u001b[38;5;241m=\u001b[39m datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,y \u001b[38;5;241m=\u001b[39m y,dataset\u001b[38;5;241m=\u001b[39mdataset,add_kdj\u001b[38;5;241m=\u001b[39madd_kdj,is_timefurture\u001b[38;5;241m=\u001b[39mis_timefurture,end_time\u001b[38;5;241m=\u001b[39mend_time) \n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     44\u001b[0m     logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m读取本地数据：\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dataset,data_set))\n",
-      "File \u001b[1;32md:\\liurui\\dev\\code\\PriceForecast\\lib\\dataread.py:635\u001b[0m, in \u001b[0;36mdatachuli_juxiting\u001b[1;34m(df_zhibiaoshuju, df_zhibiaoliebiao, datecol, end_time, y, dataset, delweekenday, add_kdj, is_timefurture)\u001b[0m\n\u001b[0;32m    632\u001b[0m df\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{datecol:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mds\u001b[39m\u001b[38;5;124m'\u001b[39m},inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    634\u001b[0m \u001b[38;5;66;03m# 指定列统一减少数值\u001b[39;00m\n\u001b[1;32m--> 635\u001b[0m df[offsite_col] \u001b[38;5;241m=\u001b[39m df[offsite_col]\u001b[38;5;241m-\u001b[39moffsite\n\u001b[0;32m    636\u001b[0m \u001b[38;5;66;03m# 预测列为avg_cols的均值\u001b[39;00m\n\u001b[0;32m    637\u001b[0m df[y] \u001b[38;5;241m=\u001b[39m df[avg_cols]\u001b[38;5;241m.\u001b[39mmean(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3899\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3897\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m   3898\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 3899\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39m_get_indexer_strict(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m   3901\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m   3902\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6115\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   6112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6113\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6115\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[0;32m   6117\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m   6118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m   6119\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6176\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   6174\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m use_interval_msg:\n\u001b[0;32m   6175\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 6176\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6178\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m   6179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['PP：拉丝：HP550J：市场价：青岛：金能化学（日）'], dtype='object')] are in the [columns]\""
+      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:203\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    202\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m     sock \u001b[38;5;241m=\u001b[39m connection\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[0;32m    204\u001b[0m         (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dns_host, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport),\n\u001b[0;32m    205\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout,\n\u001b[0;32m    206\u001b[0m         source_address\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource_address,\n\u001b[0;32m    207\u001b[0m         socket_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msocket_options,\n\u001b[0;32m    208\u001b[0m     )\n\u001b[0;32m    209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\util\\connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 85\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m     86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     87\u001b[0m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\util\\connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[1;32m---> 73\u001b[0m sock\u001b[38;5;241m.\u001b[39mconnect(sa)\n\u001b[0;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
+      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:791\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m    790\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[1;32m--> 791\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m    792\u001b[0m     conn,\n\u001b[0;32m    793\u001b[0m     method,\n\u001b[0;32m    794\u001b[0m     url,\n\u001b[0;32m    795\u001b[0m     timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m    796\u001b[0m     body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m    797\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m    798\u001b[0m     chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    799\u001b[0m     retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[0;32m    800\u001b[0m     response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[0;32m    801\u001b[0m     preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m    802\u001b[0m     decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m    803\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[0;32m    804\u001b[0m )\n\u001b[0;32m    806\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:497\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m    496\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 497\u001b[0m     conn\u001b[38;5;241m.\u001b[39mrequest(\n\u001b[0;32m    498\u001b[0m         method,\n\u001b[0;32m    499\u001b[0m         url,\n\u001b[0;32m    500\u001b[0m         body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m    501\u001b[0m         headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m    502\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    503\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m    504\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m    505\u001b[0m         enforce_content_length\u001b[38;5;241m=\u001b[39menforce_content_length,\n\u001b[0;32m    506\u001b[0m     )\n\u001b[0;32m    508\u001b[0m \u001b[38;5;66;03m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[0;32m    509\u001b[0m \u001b[38;5;66;03m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[0;32m    510\u001b[0m \u001b[38;5;66;03m# With this behaviour, the received response is still readable.\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:395\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers, chunked, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m    394\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mputheader(header, value)\n\u001b[1;32m--> 395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendheaders()\n\u001b[0;32m    397\u001b[0m \u001b[38;5;66;03m# If we're given a body we start sending that in chunks.\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:1289\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1288\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[1;32m-> 1289\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_output(message_body, encode_chunked\u001b[38;5;241m=\u001b[39mencode_chunked)\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:1048\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1047\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[1;32m-> 1048\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(msg)\n\u001b[0;32m   1050\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1051\u001b[0m \n\u001b[0;32m   1052\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:986\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    985\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[1;32m--> 986\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[0;32m    987\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:243\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    242\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 243\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_conn()\n\u001b[0;32m    244\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n\u001b[0;32m    245\u001b[0m         \u001b[38;5;66;03m# If we're tunneling it means we're connected to our proxy.\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:212\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[0;32m    213\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    214\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    215\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[1;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)')",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m    487\u001b[0m         method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m    488\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m    489\u001b[0m         body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m    490\u001b[0m         headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m    491\u001b[0m         redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    492\u001b[0m         assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    493\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    494\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    495\u001b[0m         retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m    496\u001b[0m         timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m    497\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    498\u001b[0m     )\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:845\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m    843\u001b[0m     new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[1;32m--> 845\u001b[0m retries \u001b[38;5;241m=\u001b[39m retries\u001b[38;5;241m.\u001b[39mincrement(\n\u001b[0;32m    846\u001b[0m     method, url, error\u001b[38;5;241m=\u001b[39mnew_e, _pool\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, _stacktrace\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]\n\u001b[0;32m    847\u001b[0m )\n\u001b[0;32m    848\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\util\\retry.py:515\u001b[0m, in \u001b[0;36mRetry.increment\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    514\u001b[0m     reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[1;32m--> 515\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m    517\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
+      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
+      "File \u001b[1;32md:\\code\\PriceForecast\\lib\\dataread.py:1273\u001b[0m, in \u001b[0;36mEtaReader.get_eta_api_pp_data\u001b[1;34m(self, data_set, dataset)\u001b[0m\n\u001b[0;32m   1271\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1272\u001b[0m     \u001b[38;5;66;03m# 发送GET请求  获取指标分类列表\u001b[39;00m\n\u001b[1;32m-> 1273\u001b[0m     response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclassifylisturl, headers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheaders)  \n\u001b[0;32m   1274\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\api.py:73\u001b[0m, in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \n\u001b[0;32m     65\u001b[0m \u001b[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;124;03m:rtype: requests.Response\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, params\u001b[38;5;241m=\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\adapters.py:507\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[1;32m--> 507\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m    509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n",
+      "\u001b[1;31mConnectTimeout\u001b[0m: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 37\u001b[0m\n\u001b[0;32m     27\u001b[0m signature \u001b[38;5;241m=\u001b[39m BinanceAPI(APPID, SECRET)\n\u001b[0;32m     28\u001b[0m etadata \u001b[38;5;241m=\u001b[39m EtaReader(signature\u001b[38;5;241m=\u001b[39msignature,\n\u001b[0;32m     29\u001b[0m                             classifylisturl \u001b[38;5;241m=\u001b[39m classifylisturl,\n\u001b[0;32m     30\u001b[0m                             classifyidlisturl\u001b[38;5;241m=\u001b[39mclassifyidlisturl,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     35\u001b[0m                             edbbusinessurl\u001b[38;5;241m=\u001b[39medbbusinessurl,\n\u001b[0;32m     36\u001b[0m                             )\n\u001b[1;32m---> 37\u001b[0m df_zhibiaoshuju,df_zhibiaoliebiao \u001b[38;5;241m=\u001b[39m  etadata\u001b[38;5;241m.\u001b[39mget_eta_api_pp_data(data_set\u001b[38;5;241m=\u001b[39mdata_set,dataset\u001b[38;5;241m=\u001b[39mdataset)  \u001b[38;5;66;03m# 原始数据，未处理\u001b[39;00m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;66;03m# 数据处理\u001b[39;00m\n\u001b[0;32m     40\u001b[0m df \u001b[38;5;241m=\u001b[39m datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,y \u001b[38;5;241m=\u001b[39m y,dataset\u001b[38;5;241m=\u001b[39mdataset,add_kdj\u001b[38;5;241m=\u001b[39madd_kdj,is_timefurture\u001b[38;5;241m=\u001b[39mis_timefurture,end_time\u001b[38;5;241m=\u001b[39mend_time) \n",
+      "File \u001b[1;32md:\\code\\PriceForecast\\lib\\dataread.py:1275\u001b[0m, in \u001b[0;36mEtaReader.get_eta_api_pp_data\u001b[1;34m(self, data_set, dataset)\u001b[0m\n\u001b[0;32m   1273\u001b[0m     response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclassifylisturl, headers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheaders)  \n\u001b[0;32m   1274\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m-> 1275\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m请求失败,请确认是否为内网环境: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1277\u001b[0m \u001b[38;5;66;03m# 检查响应状态码  \u001b[39;00m\n\u001b[0;32m   1278\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m200\u001b[39m: \n\u001b[0;32m   1279\u001b[0m     \u001b[38;5;66;03m# 获取成功， 处理响应内容\u001b[39;00m\n",
+      "\u001b[1;31mException\u001b[0m: (\"请求失败,请确认是否为内网环境: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))\", '\\x1b[0m')"
      ]
     }
    ],
@@ -258,25 +219,25 @@
    "outputs": [],
    "source": [
     "now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\n",
-    "ex_Model(df,\n",
-    "        horizon=horizon,\n",
-    "        input_size=input_size,\n",
-    "        train_steps=train_steps,\n",
-    "        val_check_steps=val_check_steps,\n",
-    "        early_stop_patience_steps=early_stop_patience_steps,\n",
-    "        is_debug=is_debug,\n",
-    "        dataset=dataset,\n",
-    "        is_train=is_train,\n",
-    "        is_fivemodels=is_fivemodels,\n",
-    "        val_size=val_size,\n",
-    "        test_size=test_size,\n",
-    "        settings=settings,\n",
-    "        now=now,\n",
-    "        etadata = etadata,\n",
-    "        modelsindex = modelsindex,\n",
-    "        data = data,\n",
-    "        is_eta=is_eta,\n",
-    "        )\n"
+    "# ex_Model(df,\n",
+    "#         horizon=horizon,\n",
+    "#         input_size=input_size,\n",
+    "#         train_steps=train_steps,\n",
+    "#         val_check_steps=val_check_steps,\n",
+    "#         early_stop_patience_steps=early_stop_patience_steps,\n",
+    "#         is_debug=is_debug,\n",
+    "#         dataset=dataset,\n",
+    "#         is_train=is_train,\n",
+    "#         is_fivemodels=is_fivemodels,\n",
+    "#         val_size=val_size,\n",
+    "#         test_size=test_size,\n",
+    "#         settings=settings,\n",
+    "#         now=now,\n",
+    "#         etadata = etadata,\n",
+    "#         modelsindex = modelsindex,\n",
+    "#         data = data,\n",
+    "#         is_eta=is_eta,\n",
+    "#         )\n"
    ]
   },
   {
@@ -290,10 +251,10 @@
     "# # 模型评估\n",
     "\n",
     "pd.set_option('display.max_columns', 100)\n",
+    "\n",
     "# 计算预测评估指数\n",
     "def model_losss_juxiting(sqlitedb):\n",
     "    global dataset\n",
-    "    # 数据库查询最佳模型名称\n",
     "    most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]\n",
     "    most_model_name = most_model[0]\n",
     "\n",
@@ -345,15 +306,9 @@
     "    with open(os.path.join(dataset,\"best_modelnames.txt\"), 'w') as f:\n",
     "        f.write(','.join(modelnames) + '\\n')\n",
     "\n",
-    "\n",
-    "    # 去掉方差最大的模型，其余模型预测最大最小值确定通道边界\n",
-    "    best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()\n",
-    "   \n",
-    "\n",
     "    # 预测值与真实值对比图\n",
     "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
     "    plt.figure(figsize=(15, 10))\n",
-    "    # 设置有5个子图的画布\n",
     "    for n,model in enumerate(modelnames[:5]):\n",
     "        plt.subplot(3, 2, n+1)\n",
     "        plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')\n",
@@ -366,6 +321,249 @@
     "    plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')\n",
     "    plt.close()\n",
     "    \n",
+    "\n",
+    "    # # 根据最佳模型的绝对误差的平均比例，最佳模型乘平均比例的百分比，确定最大最小值\n",
+    "    import heapq          # 使用堆来找到最大和最小的值\n",
+    "    def find_min_max_within_quantile(row):\n",
+    "        true_value = row['y']\n",
+    "        row.drop(['ds','y'], inplace=True)\n",
+    "        row = row.astype(float).round(2)\n",
+    "\n",
+    "        max_heap = []\n",
+    "        min_heap = []\n",
+    "        for col in row.index:\n",
+    "            # 对比真实值进行分类\n",
+    "            if row[col] < true_value:\n",
+    "                heapq.heappush(min_heap, row[col])\n",
+    "            elif row[col] > true_value:\n",
+    "                heapq.heappush(max_heap, -row[col])  # 使用负号来实现最大堆\n",
+    "\n",
+    "        if len(max_heap) == 1:\n",
+    "            max_y = max_heap[0]\n",
+    "        elif len(max_heap) == 0:\n",
+    "            max_y = -min_heap[-1]\n",
+    "        else:\n",
+    "            max_y = heapq.nsmallest(2, max_heap)[1]\n",
+    "\n",
+    "        if len(min_heap) < 2 :\n",
+    "            min_y = -max_heap[-1]\n",
+    "        else:\n",
+    "            min_y = heapq.nsmallest(2, min_heap)[-1]\n",
+    "\n",
+    "\n",
+    "        # 获取最大和最小的值\n",
+    "        q10 = min_y \n",
+    "        q90 = -max_y\n",
+    "\n",
+    "        # 获取最大和最小的模型名称\n",
+    "        min_model = row[row == q10].idxmin()\n",
+    "        max_model = row[row == q90].idxmax()\n",
+    "\n",
+    "        # 设置上下界比例\n",
+    "        rote = 1\n",
+    "\n",
+    "        q10 = q10 * rote\n",
+    "        q90 = q90 * rote\n",
+    "\n",
+    "        logger.info(min_model,q10,max_model,q90)\n",
+    "\n",
+    "        return pd.Series([q10, q90, min_model, max_model], index=['min_within_quantile', 'max_within_quantile', 'min_model', 'max_model'])\n",
+    "    # # 遍历行\n",
+    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
+    "    df_combined = df_combined.round(4)\n",
+    "    print(df_combined3)\n",
+    "\n",
+    "\n",
+    "    '''\n",
+    "    # # 根据真实值y确定最大最小值,去掉最高最低的预测值\n",
+    "    import heapq          # 使用堆来找到最大和最小的值\n",
+    "    def find_min_max_within_quantile(row):\n",
+    "        true_value = row['y']\n",
+    "        row.drop(['ds','y'], inplace=True)\n",
+    "        row = row.astype(float).round(2)\n",
+    "\n",
+    "        max_heap = []\n",
+    "        min_heap = []\n",
+    "        for col in row.index:\n",
+    "            # 对比真实值进行分类\n",
+    "            if row[col] < true_value:\n",
+    "                heapq.heappush(min_heap, row[col])\n",
+    "            elif row[col] > true_value:\n",
+    "                heapq.heappush(max_heap, -row[col])  # 使用负号来实现最大堆\n",
+    "\n",
+    "        if len(max_heap) == 1:\n",
+    "            max_y = max_heap[0]\n",
+    "        elif len(max_heap) == 0:\n",
+    "            max_y = -min_heap[-1]\n",
+    "        else:\n",
+    "            max_y = heapq.nsmallest(2, max_heap)[1]\n",
+    "\n",
+    "        if len(min_heap) < 2 :\n",
+    "            min_y = -max_heap[-1]\n",
+    "        else:\n",
+    "            min_y = heapq.nsmallest(2, min_heap)[-1]\n",
+    "\n",
+    "\n",
+    "        # 获取最大和最小的值\n",
+    "        q10 = min_y \n",
+    "        q90 = -max_y\n",
+    "\n",
+    "        # 获取最大和最小的模型名称\n",
+    "        min_model = row[row == q10].idxmin()\n",
+    "        max_model = row[row == q90].idxmax()\n",
+    "\n",
+    "        # 设置上下界比例\n",
+    "        rote = 1\n",
+    "\n",
+    "        q10 = q10 * rote\n",
+    "        q90 = q90 * rote\n",
+    "\n",
+    "        logger.info(min_model,q10,max_model,q90)\n",
+    "\n",
+    "        return pd.Series([q10, q90, min_model, max_model], index=['min_within_quantile', 'max_within_quantile', 'min_model', 'max_model'])\n",
+    "    # # 遍历行\n",
+    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
+    "    df_combined = df_combined.round(4)\n",
+    "    print(df_combined3)\n",
+    "    '''\n",
+    "\n",
+    "    '''\n",
+    "    #使用最佳五个模型进行绘图\n",
+    "    best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()\n",
+    "    def find_min_max_within_quantile(row):\n",
+    "        row = row[best_models]\n",
+    "        q10 = row.min()\n",
+    "        q90 = row.max()\n",
+    "        # 获取 row行最大最小值模型名称\n",
+    "        min_model = row[row == q10].idxmin()\n",
+    "        max_model = row[row == q90].idxmin()\n",
+    "        \n",
+    "        # # 判断flot值是否为空值\n",
+    "        # if pd.isna(q10) or pd.isna(q90):\n",
+    "        return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
+    "\n",
+    "    # 遍历行\n",
+    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
+    "    df_combined = df_combined.round(4)\n",
+    "    print(df_combined3)\n",
+    "    '''\n",
+    "    \n",
+    "    '''\n",
+    "    # 通道使用模型评估前80%作为置信度\n",
+    "    def find_min_max_within_quantile(row):\n",
+    "        row.drop(['ds','y'], inplace=True)\n",
+    "        row = row.astype(float).round(2)\n",
+    "\n",
+    "        row_sorted = row\n",
+    "        # 计算 10% 和 90% 位置的索引\n",
+    "        index_10 = 0\n",
+    "        index_90 = int(len(row_sorted) * 0.8)\n",
+    "        q10 = row_sorted[index_10]\n",
+    "        q90 = row_sorted[index_90]\n",
+    "        # 获取模型名称\n",
+    "        min_model = row[row == q10].idxmin()\n",
+    "        max_model = row[row == q90].idxmin()\n",
+    "\n",
+    "        \n",
+    "        # # 判断flot值是否为空值\n",
+    "        # if pd.isna(q10) or pd.isna(q90):\n",
+    "        return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
+    "\n",
+    "    # 重新排列\n",
+    "    df_combined3 = df_combined3[['ds','y'] + allmodelnames]\n",
+    "    # 遍历行\n",
+    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
+    "    df_combined = df_combined.round(4)\n",
+    "    print(df_combined3)\n",
+    "\n",
+    "    '''\n",
+    "    \n",
+    "    '''\n",
+    "    # 通道使用预测模型的80%置信度\n",
+    "    def find_min_max_within_quantile(row):\n",
+    "        row.drop(['ds','y'], inplace=True)\n",
+    "        row = row.astype(float).round(2)\n",
+    "\n",
+    "        row_sorted = row.sort_values(ascending=True).reset_index(drop=True)\n",
+    "        # 计算 10% 和 90% 位置的索引\n",
+    "        index_10 = int(len(row_sorted) * 0.1)\n",
+    "        index_90 = int(len(row_sorted) * 0.9)\n",
+    "        q10 = row_sorted[index_10]\n",
+    "        q90 = row_sorted[index_90]\n",
+    "        # 获取模型名称\n",
+    "        min_model = row[row == q10].idxmin()\n",
+    "        max_model = row[row == q90].idxmin()\n",
+    "\n",
+    "        \n",
+    "        # # 判断flot值是否为空值\n",
+    "        # if pd.isna(q10) or pd.isna(q90):\n",
+    "        return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
+    "\n",
+    "    # 遍历行\n",
+    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
+    "    df_combined = df_combined.round(4)\n",
+    "    print(df_combined3)\n",
+    "    '''\n",
+    "    \n",
+    "\n",
+    "    '''\n",
+    "    # 计算波动率,取近60日波动率的10%和90%分位数确定通道上下界\n",
+    "    df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)\n",
+    "    # 计算近60日的波动率 10% 90%分位数\n",
+    "    df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1)\n",
+    "    df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9)\n",
+    "    df_combined3 = df_combined3.round(4)\n",
+    "    # 计算分位数对应的价格\n",
+    "    df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10'])\n",
+    "    df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90'])\n",
+    "\n",
+    "    # 遍历行\n",
+    "    def find_min_max_within_quantile(row):\n",
+    "        # 获取分位数10%和90%的值\n",
+    "        q10 = row['quantile_10_price']\n",
+    "        q90 = row['quantile_90_price']\n",
+    "        \n",
+    "        # 判断flot值是否为空值\n",
+    "        if pd.isna(q10) or pd.isna(q90):\n",
+    "            return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
+    "        \n",
+    "        # 初始化最小和最大值为None\n",
+    "        min_value = None\n",
+    "        max_value = None\n",
+    "        min_value_model = ''\n",
+    "        max_value_model = ''\n",
+    "\n",
+    "        # 遍历指定列，找出在分位数范围内的最大最小值\n",
+    "        for model in modelnames:\n",
+    "            value = row[model]\n",
+    "            if value >= q10 and value <= q90:\n",
+    "                if min_value is None or value < min_value:\n",
+    "                    min_value = value\n",
+    "                    min_value_model = model\n",
+    "\n",
+    "                if max_value is None or value > max_value:\n",
+    "                    max_value = value\n",
+    "                    max_value_model = model\n",
+    "        \n",
+    "        # 返回最大最小值\n",
+    "        return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model'])\n",
+    "\n",
+    "    # 应用函数到每一行\n",
+    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
+    "\n",
+    "    \n",
+    "    '''\n",
+    "    \n",
+    "    # 去除有空值的行\n",
+    "    df_combined3.dropna(inplace=True)\n",
+    "    # 保存到数据库\n",
+    "    df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)\n",
+    "    df_combined3.to_csv(os.path.join(dataset,\"testandpredict_groupby.csv\"),index=False)\n",
+    "\n",
+    "    '''\n",
+    "   # 去掉方差最大的模型，其余模型预测最大最小值确定通道边界\n",
+    "    \n",
+    "    \n",
     "    # 历史数据+预测数据\n",
     "    # 拼接未来时间预测\n",
     "    df_predict  = loadcsv(os.path.join(dataset,'predict.csv'))\n",
@@ -382,21 +580,7 @@
     "    first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
     "\n",
     "    # # 将预测结果保存到数据库\n",
-    "    # # 判断表存在\n",
-    "    if not sqlitedb.check_table_exists('testandpredict_groupby'):\n",
-    "        df_predict2.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)\n",
-    "    else:\n",
-    "        for row in df_predict2.itertuples(index=False):\n",
-    "            row_dict = row._asdict()\n",
-    "            check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f\"ds = '{row.ds}'\")\n",
-    "            if len(check_query) > 0:\n",
-    "                set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
-    "                sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
-    "                continue\n",
-    "            sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())\n",
-    "\n",
     "    df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)\n",
-    "\n",
     "    # # 判断 df 的数值列转为float\n",
     "    for col in df_combined3.columns:\n",
     "        try:\n",
@@ -405,11 +589,21 @@
     "                df_combined3[col] = df_combined3[col].round(2)\n",
     "        except ValueError:\n",
     "            pass\n",
-    "    df_combined3.to_csv(os.path.join(dataset,\"df_combined3.csv\"),index=False) \n",
-    "    df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)\n",
     "    df_combined3.to_csv(os.path.join(dataset,\"testandpredict_groupby.csv\"),index=False)\n",
-    "    \n",
-    "    \n",
+    "    df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
+    "    # # 判断表存在\n",
+    "    if not sqlitedb.check_table_exists('testandpredict_groupby'):\n",
+    "        df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)\n",
+    "    else:\n",
+    "        for row in df_combined3.itertuples(index=False):\n",
+    "            row_dict = row._asdict()\n",
+    "            check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f\"ds = '{row.ds}'\")\n",
+    "            if len(check_query) > 0:\n",
+    "                set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
+    "                sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
+    "                continue\n",
+    "            sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())\n",
+    "\n",
     "    ten_models = allmodelnames\n",
     "    # 计算每个模型的方差\n",
     "    variances = df_combined3[ten_models].var()\n",
@@ -431,55 +625,184 @@
     "    plt.plot(df_combined3['ds'], df_combined3[most_model], label=most_model_name)\n",
     "    plt.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2)\n",
     "    plt.grid(True)\n",
+    "    # # 显示历史值\n",
+    "    for i, j in zip(df_combined3['ds'][:-5], df_combined3['y'][:-5]):\n",
+    "        plt.text(i, j, str(j), ha='center', va='bottom')\n",
     "    # 当前日期画竖虚线\n",
     "    plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')\n",
     "    plt.legend()\n",
     "    plt.xlabel('日期')\n",
     "    plt.ylabel('价格')\n",
     "\n",
-    "    # # 显示历史值\n",
-    "    for i, j in zip(df_combined3['ds'][:-5], df_combined3['y'][:-5]):\n",
-    "        plt.text(i, j, str(j), ha='center', va='bottom')\n",
     "    plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')\n",
-    "    plt.show()\n",
     "    plt.close()\n",
-    "       \n",
-    "    # 预测值表格\n",
-    "    fig, ax = plt.subplots(figsize=(20, 6))\n",
-    "    ax.axis('off')  # 关闭坐标轴\n",
-    "    # 数值保留2位小数\n",
-    "    df_combined3 = df_combined3.round(2)\n",
-    "    df_combined3 = df_combined3[-horizon:]\n",
-    "    df_combined3['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]\n",
-    "    # Day列放到最前面\n",
-    "    df_combined3 = df_combined3[['Day'] + list(df_combined3.columns[:-1])]\n",
-    "    table = ax.table(cellText=df_combined3.values, colLabels=df_combined3.columns, loc='center')\n",
-    "    #加宽表格\n",
-    "    table.auto_set_font_size(False)\n",
-    "    table.set_fontsize(10)\n",
+    "    '''\n",
     "\n",
-    "    # 设置表格样式，列数据最小的用绿色标识\n",
-    "    plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')\n",
-    "    plt.close()\n",
-    "    # plt.show()\n",
-    "       \n",
-    "    # 可视化评估结果\n",
-    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
-    "    fig, ax = plt.subplots(figsize=(20, 10))\n",
-    "    ax.axis('off')  # 关闭坐标轴\n",
-    "    table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')\n",
-    "    # 加宽表格\n",
-    "    table.auto_set_font_size(False)\n",
-    "    table.set_fontsize(10)\n",
+    "    # # 历史数据+预测数据\n",
+    "    # # 拼接未来时间预测\n",
+    "    df_predict  = pd.read_csv(os.path.join(dataset,'predict.csv'))\n",
+    "    df_predict.drop('unique_id',inplace=True,axis=1)\n",
+    "    df_predict.dropna(axis=1,inplace=True)\n",
+    "\n",
+    "    try:\n",
+    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')\n",
+    "    except ValueError :\n",
+    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')\n",
+    "\n",
+    "    def first_row_to_database(df):\n",
+    "        # # 取第一行数据存储到数据库中\n",
+    "        first_row = df.head(1)\n",
+    "        first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
+    "        # 将预测结果保存到数据库\n",
+    "        if not sqlitedb.check_table_exists('trueandpredict'):\n",
+    "            first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)\n",
+    "        else:\n",
+    "            for col in first_row.columns:\n",
+    "                    sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')\n",
+    "            for row in first_row.itertuples(index=False):\n",
+    "                row_dict = row._asdict()\n",
+    "                columns=row_dict.keys()\n",
+    "                check_query = sqlitedb.select_data('trueandpredict',where_condition = f\"ds = '{row.ds}'\")\n",
+    "                if len(check_query) > 0:\n",
+    "                    set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
+    "                    sqlitedb.update_data('trueandpredict',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
+    "                    continue\n",
+    "                sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)\n",
+    "\n",
+    "    first_row_to_database(df_predict)\n",
+    "    def find_most_common_model():\n",
+    "        # 最多频率的模型名称\n",
+    "        min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()\n",
+    "        max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax()\n",
+    "        if min_model_max_frequency_model == max_model_max_frequency_model:\n",
+    "            # 取20天第二多的模型\n",
+    "            max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1]\n",
+    "\n",
+    "        df_predict['min_model'] = min_model_max_frequency_model\n",
+    "        df_predict['max_model'] = max_model_max_frequency_model\n",
+    "        df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]\n",
+    "        df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]\n",
+    "\n",
+    "\n",
+    "    # find_most_common_model()\n",
+    "\n",
+    "    df_predict2 = df_predict.copy()\n",
+    "    df_predict2['ds'] = pd.to_datetime(df_predict2['ds'])\n",
+    "    df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
+    "\n",
+    "\n",
+    "    def _add_abs_error_rate():\n",
+    "        # 计算每个预测值与真实值之间的偏差率\n",
+    "        for model in allmodelnames:\n",
+    "            df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y']\n",
+    "\n",
+    "        # 获取每行对应的最小偏差率值\n",
+    "        min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].min(), axis=1)\n",
+    "        # 获取每行对应的最小偏差率值对应的列名\n",
+    "        min_abs_error_rate_column_name = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].idxmin(), axis=1) \n",
+    "        # 将列名索引转换为列名\n",
+    "        min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])\n",
+    "        # 获取最小偏差率对应的模型的预测值\n",
+    "        min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)\n",
+    "        # 将最小偏差率对应的模型的预测值添加到DataFrame中\n",
+    "        df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions\n",
+    "        df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name\n",
+    "\n",
+    "    _add_abs_error_rate()\n",
+    "\n",
+    "    df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)\n",
+    "    # 判断 df 的数值列转为float\n",
+    "    for col in df_combined3.columns:\n",
+    "        try:\n",
+    "            if col != 'ds':\n",
+    "                df_combined3[col] = df_combined3[col].astype(float)\n",
+    "                df_combined3[col] = df_combined3[col].round(2)\n",
+    "        except ValueError:\n",
+    "            pass\n",
+    "    df_combined3.to_csv(os.path.join(dataset,\"df_combined3.csv\"),index=False) \n",
+    "    \n",
+    "     # 历史价格+预测价格\n",
+    "        # 将预测结果保存到数据库\n",
+    "    # 判断表存在\n",
+    "    # if not sqlitedb.check_table_exists('testandpredict_groupby'):\n",
+    "    #     df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)\n",
+    "    # else:\n",
+    "    #     for row in df_combined3.itertuples(index=False):\n",
+    "    #         row_dict = row._asdict()\n",
+    "    #         check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f\"ds = '{row.ds}'\")\n",
+    "    #         if len(check_query) > 0:\n",
+    "    #             set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
+    "    #             sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
+    "    #             continue\n",
+    "    #         sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())\n",
+    "    \n",
+    "    \n",
+    "    def _plt_predict_ture(df):\n",
+    "        df = df[-50:] # 取50个数据点画图\n",
+    "        # 历史价格\n",
+    "        plt.figure(figsize=(20, 10))\n",
+    "        plt.plot(df['ds'], df['y'], label='真实值')\n",
+    "        # 颜色填充\n",
+    "        plt.fill_between(df['ds'], df['min_within_quantile'], df['max_within_quantile'], alpha=0.2)\n",
+    "        # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')\n",
+    "        # 网格\n",
+    "        plt.grid(True)\n",
+    "        # 显示历史值\n",
+    "        for i, j in zip(df['ds'], df['y']):\n",
+    "            plt.text(i, j, str(j), ha='center', va='bottom')\n",
+    "\n",
+    "        for model in most_model:\n",
+    "            plt.plot(df['ds'], df[model], label=model,marker='o')\n",
+    "        # 当前日期画竖虚线\n",
+    "        plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')\n",
+    "        plt.legend()\n",
+    "        plt.xlabel('日期')\n",
+    "        plt.ylabel('价格')\n",
+    "        \n",
+    "        plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')\n",
+    "        plt.close()\n",
+    "\n",
+    "    def _plt_predict_table(df): \n",
+    "        # 预测值表格\n",
+    "        fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "        ax.axis('off')  # 关闭坐标轴\n",
+    "        # 数值保留2位小数\n",
+    "        df = df.round(2)\n",
+    "        df = df[-horizon:]\n",
+    "        df['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]\n",
+    "        # Day列放到最前面\n",
+    "        df = df[['Day'] + list(df.columns[:-1])]\n",
+    "        table = ax.table(cellText=df.values, colLabels=df.columns, loc='center')\n",
+    "        #加宽表格\n",
+    "        table.auto_set_font_size(False)\n",
+    "        table.set_fontsize(10)\n",
+    "\n",
+    "        # 设置表格样式，列数据最小的用绿色标识\n",
+    "        plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')\n",
+    "        plt.close()\n",
+    "       \n",
+    "    def _plt_model_results3():\n",
+    "        # 可视化评估结果\n",
+    "        plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "        fig, ax = plt.subplots(figsize=(20, 10))\n",
+    "        ax.axis('off')  # 关闭坐标轴\n",
+    "        table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')\n",
+    "        # 加宽表格\n",
+    "        table.auto_set_font_size(False)\n",
+    "        table.set_fontsize(10)\n",
+    "\n",
+    "        # 设置表格样式，列数据最小的用绿色标识\n",
+    "        plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')\n",
+    "        plt.close()\n",
+    "\n",
+    "    _plt_predict_ture(df_combined3)\n",
+    "    _plt_predict_table(df_combined3)\n",
+    "    _plt_model_results3()\n",
     "\n",
-    "    # 设置表格样式，列数据最小的用绿色标识\n",
-    "    plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')\n",
-    "    plt.close()\n",
     "    return model_results3\n",
     "\n",
     "\n",
     "\n",
-    "\n",
     "logger.info('训练数据绘图ing')\n",
     "model_results3 = model_losss_juxiting(sqlitedb)\n",
     "\n",
@@ -529,7 +852,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },