更改聚烯烃绘图注释
This commit is contained in:
workpc
parent
65935fbf6e
commit
80838f7a0b
@ -211,7 +211,7 @@ upload_data = {
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### 开关
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### 开关
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is_train = True # 是否训练
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is_train = True # 是否训练
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is_debug = False # 是否调试
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is_debug = False # 是否调试
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is_eta = False # 是否使用eta接口
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is_eta = True # 是否使用eta接口
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is_timefurture = True # 是否使用时间特征
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbcode = False # 特征使用edbcoding列表中的
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@ -1,5 +1,5 @@
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from config_jingbo import *
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# from config_jingbo import *
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# from config_juxiting import *
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from config_juxiting import *
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# 导入模块
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# 导入模块
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657
聚烯烃预测值绘图调试.ipynb
657
聚烯烃预测值绘图调试.ipynb
@ -2,107 +2,68 @@
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"cells": [
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"cells": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 1,
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"id": "7fadc60c-d710-4b8c-89cd-1d889ece1eaf",
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"id": "7fadc60c-d710-4b8c-89cd-1d889ece1eaf",
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"metadata": {},
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"metadata": {},
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"outputs": [
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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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{
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"name": "stderr",
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"name": "stderr",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"从eta获取数据...\n",
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"从eta获取数据...\n",
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"从eta获取数据...\n",
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"从eta获取数据...\n"
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"['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",
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"['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",
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"ID01385938\n",
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"ID01385938\n",
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"lmcads03 lme comdty\n",
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"lmcads03 lme comdty\n",
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"GC1 COMB Comdty\n",
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"GC1 COMB Comdty\n",
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"C2404171822\n",
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"C2404171822\n",
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"dxy curncy\n",
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"dxy curncy\n",
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"S5443199 \n",
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"S5443199 \n",
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"S5479800\n",
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"S5479800\n",
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"S5443108\n",
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"S5443108\n",
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"H7358586\n",
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"H7358586\n",
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"LC3FM1 INDEX\n",
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"LC3FM1 INDEX\n",
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"CNY REGN Curncy\n",
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"CNY REGN Curncy\n",
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"s0105897\n",
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"s0105897\n",
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"M0067419\n",
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"M0067419\n",
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"M0066351\n",
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"M0066351\n",
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"S0266372\n",
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"S0266372\n",
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"S0266438\n",
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"S0266438\n",
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"S0266506\n",
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"S0266506\n",
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" 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",
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"0 2024-11-19 7390.0 NaN 2634.7 4.12 \n",
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"1 2024-11-18 7380.0 9072.5 2614.6 4.14 \n",
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"2 2024-11-15 7380.0 9002.5 2570.1 4.02 \n",
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"3 2024-11-14 7380.0 8990.0 2572.9 3.86 \n",
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"4 2024-11-13 7380.0 9047.0 2586.5 3.85 \n",
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"\n",
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" 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",
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"0 106.206 NaN NaN NaN NaN 615.08 7.2387 NaN \n",
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"1 106.275 2340.0 NaN 6850.0 NaN 606.60 7.2320 3727.05 \n",
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"2 106.687 2310.0 NaN 6930.0 NaN 611.69 7.2294 3708.14 \n",
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"3 106.673 2310.0 2472.0 6945.0 NaN 618.61 7.2271 3739.76 \n",
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"4 106.481 2310.0 2480.0 6800.0 NaN 621.88 7.2340 3772.43 \n",
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"\n",
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" 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",
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"0 NaN NaN NaN NaN NaN \n",
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"1 5284.0 NaN NaN NaN NaN \n",
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"2 5282.0 NaN NaN NaN NaN \n",
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"3 5310.0 NaN NaN NaN NaN \n",
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"4 5347.0 NaN NaN NaN NaN \n",
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" 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",
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"0 2024-11-19 7390.0 NaN 2634.7 4.12 \n",
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"1 2024-11-18 7380.0 9072.5 2614.6 4.14 \n",
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"2 2024-11-15 7380.0 9002.5 2570.1 4.02 \n",
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"3 2024-11-14 7380.0 8990.0 2572.9 3.86 \n",
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"4 2024-11-13 7380.0 9047.0 2586.5 3.85 \n",
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"\n",
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" 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",
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"0 106.206 NaN NaN NaN NaN 615.08 7.2387 NaN \n",
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"1 106.275 2340.0 NaN 6850.0 NaN 606.60 7.2320 3727.05 \n",
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"2 106.687 2310.0 NaN 6930.0 NaN 611.69 7.2294 3708.14 \n",
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"3 106.673 2310.0 2472.0 6945.0 NaN 618.61 7.2271 3739.76 \n",
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"4 106.481 2310.0 2480.0 6800.0 NaN 621.88 7.2340 3772.43 \n",
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"\n",
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" 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",
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"0 NaN NaN NaN NaN NaN \n",
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"1 5284.0 NaN NaN NaN NaN \n",
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"2 5282.0 NaN NaN NaN NaN \n",
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"3 5310.0 NaN NaN NaN NaN \n",
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"4 5347.0 NaN NaN NaN NaN \n"
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]
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]
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},
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},
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{
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{
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"ename": "KeyError",
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"ename": "Exception",
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"evalue": "\"None of [Index(['PP：拉丝：HP550J：市场价：青岛：金能化学（日）'], dtype='object')] are in the [columns]\"",
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"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')",
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"output_type": "error",
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"output_type": "error",
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"traceback": [
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;31mTimeoutError\u001b[0m Traceback (most recent call last)",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"\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。",
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"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",
|
"\nThe above exception was the direct cause of the following exception:\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;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": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\n",
|
"now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\n",
|
||||||
"ex_Model(df,\n",
|
"# ex_Model(df,\n",
|
||||||
" horizon=horizon,\n",
|
"# horizon=horizon,\n",
|
||||||
" input_size=input_size,\n",
|
"# input_size=input_size,\n",
|
||||||
" train_steps=train_steps,\n",
|
"# train_steps=train_steps,\n",
|
||||||
" val_check_steps=val_check_steps,\n",
|
"# val_check_steps=val_check_steps,\n",
|
||||||
" early_stop_patience_steps=early_stop_patience_steps,\n",
|
"# early_stop_patience_steps=early_stop_patience_steps,\n",
|
||||||
" is_debug=is_debug,\n",
|
"# is_debug=is_debug,\n",
|
||||||
" dataset=dataset,\n",
|
"# dataset=dataset,\n",
|
||||||
" is_train=is_train,\n",
|
"# is_train=is_train,\n",
|
||||||
" is_fivemodels=is_fivemodels,\n",
|
"# is_fivemodels=is_fivemodels,\n",
|
||||||
" val_size=val_size,\n",
|
"# val_size=val_size,\n",
|
||||||
" test_size=test_size,\n",
|
"# test_size=test_size,\n",
|
||||||
" settings=settings,\n",
|
"# settings=settings,\n",
|
||||||
" now=now,\n",
|
"# now=now,\n",
|
||||||
" etadata = etadata,\n",
|
"# etadata = etadata,\n",
|
||||||
" modelsindex = modelsindex,\n",
|
"# modelsindex = modelsindex,\n",
|
||||||
" data = data,\n",
|
"# data = data,\n",
|
||||||
" is_eta=is_eta,\n",
|
"# is_eta=is_eta,\n",
|
||||||
" )\n"
|
"# )\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -290,10 +251,10 @@
|
|||||||
"# # 模型评估\n",
|
"# # 模型评估\n",
|
||||||
"\n",
|
"\n",
|
||||||
"pd.set_option('display.max_columns', 100)\n",
|
"pd.set_option('display.max_columns', 100)\n",
|
||||||
|
"\n",
|
||||||
"# 计算预测评估指数\n",
|
"# 计算预测评估指数\n",
|
||||||
"def model_losss_juxiting(sqlitedb):\n",
|
"def model_losss_juxiting(sqlitedb):\n",
|
||||||
" global dataset\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 = [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",
|
" most_model_name = most_model[0]\n",
|
||||||
"\n",
|
"\n",
|
||||||
@ -345,15 +306,9 @@
|
|||||||
" with open(os.path.join(dataset,\"best_modelnames.txt\"), 'w') as f:\n",
|
" with open(os.path.join(dataset,\"best_modelnames.txt\"), 'w') as f:\n",
|
||||||
" f.write(','.join(modelnames) + '\\n')\n",
|
" f.write(','.join(modelnames) + '\\n')\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",
|
" # 预测值与真实值对比图\n",
|
||||||
" plt.rcParams['font.sans-serif'] = ['SimHei']\n",
|
" plt.rcParams['font.sans-serif'] = ['SimHei']\n",
|
||||||
" plt.figure(figsize=(15, 10))\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",
|
" for n,model in enumerate(modelnames[:5]):\n",
|
||||||
" plt.subplot(3, 2, n+1)\n",
|
" plt.subplot(3, 2, n+1)\n",
|
||||||
" plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')\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.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')\n",
|
||||||
" plt.close()\n",
|
" plt.close()\n",
|
||||||
" \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",
|
" # 历史数据+预测数据\n",
|
||||||
" # 拼接未来时间预测\n",
|
" # 拼接未来时间预测\n",
|
||||||
" df_predict = loadcsv(os.path.join(dataset,'predict.csv'))\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",
|
" first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
|
||||||
"\n",
|
"\n",
|
||||||
" # # 将预测结果保存到数据库\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",
|
" 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",
|
" # # 判断 df 的数值列转为float\n",
|
||||||
" for col in df_combined3.columns:\n",
|
" for col in df_combined3.columns:\n",
|
||||||
" try:\n",
|
" try:\n",
|
||||||
@ -405,11 +589,21 @@
|
|||||||
" df_combined3[col] = df_combined3[col].round(2)\n",
|
" df_combined3[col] = df_combined3[col].round(2)\n",
|
||||||
" except ValueError:\n",
|
" except ValueError:\n",
|
||||||
" pass\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",
|
" df_combined3.to_csv(os.path.join(dataset,\"testandpredict_groupby.csv\"),index=False)\n",
|
||||||
" \n",
|
" df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
|
||||||
" \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",
|
" ten_models = allmodelnames\n",
|
||||||
" # 计算每个模型的方差\n",
|
" # 计算每个模型的方差\n",
|
||||||
" variances = df_combined3[ten_models].var()\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.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.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2)\n",
|
||||||
" plt.grid(True)\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",
|
" # 当前日期画竖虚线\n",
|
||||||
" plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')\n",
|
" plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')\n",
|
||||||
" plt.legend()\n",
|
" plt.legend()\n",
|
||||||
" plt.xlabel('日期')\n",
|
" plt.xlabel('日期')\n",
|
||||||
" plt.ylabel('价格')\n",
|
" plt.ylabel('价格')\n",
|
||||||
"\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.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')\n",
|
||||||
" plt.show()\n",
|
|
||||||
" plt.close()\n",
|
" plt.close()\n",
|
||||||
" \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",
|
" # # 历史数据+预测数据\n",
|
||||||
" plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')\n",
|
" # # 拼接未来时间预测\n",
|
||||||
" plt.close()\n",
|
" df_predict = pd.read_csv(os.path.join(dataset,'predict.csv'))\n",
|
||||||
" # plt.show()\n",
|
" df_predict.drop('unique_id',inplace=True,axis=1)\n",
|
||||||
" \n",
|
" df_predict.dropna(axis=1,inplace=True)\n",
|
||||||
" # 可视化评估结果\n",
|
"\n",
|
||||||
" plt.rcParams['font.sans-serif'] = ['SimHei']\n",
|
" try:\n",
|
||||||
" fig, ax = plt.subplots(figsize=(20, 10))\n",
|
" df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')\n",
|
||||||
" ax.axis('off') # 关闭坐标轴\n",
|
" except ValueError :\n",
|
||||||
" table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')\n",
|
" df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')\n",
|
||||||
" # 加宽表格\n",
|
"\n",
|
||||||
" table.auto_set_font_size(False)\n",
|
" def first_row_to_database(df):\n",
|
||||||
" table.set_fontsize(10)\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",
|
"\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",
|
" return model_results3\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
|
||||||
"logger.info('训练数据绘图ing')\n",
|
"logger.info('训练数据绘图ing')\n",
|
||||||
"model_results3 = model_losss_juxiting(sqlitedb)\n",
|
"model_results3 = model_losss_juxiting(sqlitedb)\n",
|
||||||
"\n",
|
"\n",
|
||||||
@ -529,7 +852,7 @@
|
|||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
"display_name": "Python 3 (ipykernel)",
|
"display_name": "base",
|
||||||
"language": "python",
|
"language": "python",
|
||||||
"name": "python3"
|
"name": "python3"
|
||||||
},
|
},
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user