2024-12-09 13:59:33 +08:00
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{
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"cells": [
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{
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"cell_type": "code",
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2024-12-19 14:05:16 +08:00
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"execution_count": 13,
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2024-12-09 13:59:33 +08:00
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"id": "9daadf20-caa6-4b25-901c-6cc3ef563f58",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2024-12-18 17:51:01 +08:00
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"(85, 28)\n",
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2024-12-19 14:05:16 +08:00
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"(22, 4)\n",
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2024-12-18 17:51:01 +08:00
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"(85, 31)\n",
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" ds NHITS Informer LSTM iTransformer TSMixer \\\n",
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"0 2024-11-25 75.714300 75.523370 73.614220 75.27068 75.03936 \n",
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"1 2024-11-26 76.039635 75.558270 73.692310 75.04110 74.60100 \n",
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"2 2024-11-27 77.375790 75.361885 73.826140 74.99121 74.37731 \n",
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"3 2024-11-28 78.872400 76.339920 73.883484 75.79425 74.04826 \n",
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"4 2024-11-29 79.576970 76.333170 73.876396 75.89008 74.07330 \n",
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2024-12-09 13:59:33 +08:00
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"\n",
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2024-12-18 17:51:01 +08:00
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" TSMixerx PatchTST RNN GRU ... y \\\n",
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"0 74.581190 75.70277 74.721280 74.512060 ... 73.010002 \n",
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"1 73.496025 75.97611 74.588060 74.713425 ... 72.809998 \n",
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"2 73.522026 76.48628 74.486400 74.946010 ... 72.830002 \n",
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"3 73.416306 76.38267 75.195710 74.946014 ... 73.279999 \n",
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"4 73.521570 76.20661 75.089966 74.935165 ... 72.940002 \n",
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2024-12-09 13:59:33 +08:00
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"\n",
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2024-12-18 17:51:01 +08:00
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" min_within_quantile max_within_quantile id CREAT_DATE min_price \\\n",
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"0 74.41491 75.29100 1 2024-11-22 74.414910 \n",
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"1 74.11780 74.95678 2 2024-11-22 73.496025 \n",
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"2 73.93820 74.50395 3 2024-11-22 73.522026 \n",
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"3 73.85808 74.46382 4 2024-11-22 73.416306 \n",
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"4 73.96690 74.81860 5 2024-11-22 73.521570 \n",
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"\n",
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" max_price 序号 LOW_PRICE HIGH_PRICE \n",
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2024-12-19 14:05:16 +08:00
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"0 75.959854 15.0 72.30 74.83 \n",
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"1 77.182580 14.0 71.63 73.80 \n",
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"2 78.378624 13.0 71.71 72.85 \n",
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"3 79.415400 12.0 71.85 72.96 \n",
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"4 79.576970 11.0 71.75 73.34 \n",
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2024-12-18 17:51:01 +08:00
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"\n",
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"[5 rows x 31 columns]\n",
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" ds NHITS Informer LSTM iTransformer TSMixer \\\n",
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"80 2024-12-16 74.53431 73.944080 71.68200 74.022340 74.295820 \n",
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"81 2024-12-17 74.81450 73.830450 71.95232 74.314950 74.167290 \n",
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"82 2024-12-18 75.55861 73.525100 72.00824 74.441380 74.212180 \n",
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"83 2024-12-19 75.36518 74.012215 72.20199 74.397190 74.330130 \n",
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"84 2024-12-20 74.78187 73.929596 72.23908 74.510895 74.208084 \n",
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"\n",
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" TSMixerx PatchTST RNN GRU ... y min_within_quantile \\\n",
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"80 74.41700 74.587390 73.607780 73.747700 ... NaN 74.231680 \n",
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"81 74.36576 74.363060 73.688736 73.833950 ... NaN 73.735420 \n",
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"82 74.29719 74.073555 73.456700 74.146034 ... NaN 74.073555 \n",
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"83 73.79145 74.529945 74.230125 74.144520 ... NaN 74.330130 \n",
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"84 74.59672 74.231255 74.201860 73.996100 ... NaN 74.083810 \n",
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"\n",
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" max_within_quantile id CREAT_DATE min_price max_price 序号 LOW_PRICE \\\n",
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"80 74.621160 81 2024-12-16 72.75007 74.62116 NaN NaN \n",
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"81 74.682365 82 2024-12-16 72.72196 74.81450 NaN NaN \n",
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"82 75.157074 83 2024-12-16 73.12483 75.55861 NaN NaN \n",
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"83 75.339240 84 2024-12-16 73.07359 75.36518 NaN NaN \n",
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"84 74.604610 85 2024-12-16 72.93583 74.78187 NaN NaN \n",
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"\n",
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" HIGH_PRICE \n",
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"80 NaN \n",
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"81 NaN \n",
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"82 NaN \n",
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"83 NaN \n",
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"84 NaN \n",
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"\n",
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"[5 rows x 31 columns]\n"
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2024-12-09 13:59:33 +08:00
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]
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}
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],
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"source": [
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"import sqlite3\n",
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"import os\n",
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"import pandas as pd\n",
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"\n",
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2024-12-19 14:05:16 +08:00
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"# dataset = r'D:\\liurui\\dev\\code\\PriceForecast\\yuanyoudataset'\n",
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"\n",
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"dataset = r'C:\\Users\\Administrator\\Desktop' \n",
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"\n",
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2024-12-09 13:59:33 +08:00
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"# 预测价格数据\n",
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2024-12-18 17:51:01 +08:00
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"# dbfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','jbsh_yuanyou.db')\n",
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"# conn = sqlite3.connect(dbfilename)\n",
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"# query = 'SELECT * FROM accuracy'\n",
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"# df1 = pd.read_sql_query(query, conn)\n",
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"# df1['ds'] = df1['PREDICT_DATE']\n",
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"# conn.close()\n",
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"# print(df1.shape)\n",
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2024-12-10 17:06:26 +08:00
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"\n",
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2024-12-18 17:51:01 +08:00
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"# 预测价格数据\n",
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2024-12-19 14:05:16 +08:00
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"dfcsvfilename = os.path.join(dataset,'accuracy_ten.csv')\n",
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2024-12-18 17:51:01 +08:00
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"df1 = pd.read_csv(dfcsvfilename)\n",
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"print(df1.shape)\n",
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2024-12-10 17:06:26 +08:00
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"\n",
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2024-12-09 13:59:33 +08:00
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"# 最高最低价\n",
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2024-12-19 14:05:16 +08:00
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"xlsfilename = os.path.join(dataset,'数据项下载.xls')\n",
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2024-12-09 13:59:33 +08:00
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"df2 = pd.read_excel(xlsfilename)[5:]\n",
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2024-12-10 17:06:26 +08:00
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"df2 = df2.rename(columns = {'数据项名称':'ds','布伦特最低价':'LOW_PRICE','布伦特最高价':'HIGH_PRICE'})\n",
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2024-12-09 13:59:33 +08:00
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"print(df2.shape)\n",
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"\n",
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2024-12-10 17:06:26 +08:00
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"\n",
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"\n",
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"df = pd.merge(df1,df2,on=['ds'],how='left')\n",
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"\n",
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"df['ds'] = pd.to_datetime(df['ds'])\n",
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2024-12-18 17:51:01 +08:00
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"# df['PREDICT_DATE'] = pd.to_datetime(df['PREDICT_DATE'])\n",
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2024-12-09 13:59:33 +08:00
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"df = df.reindex()\n",
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"\n",
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"print(df.shape)\n",
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2024-12-19 14:05:16 +08:00
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"# from datetime import datetime\n",
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"import time\n",
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"df.to_csv(os.path.join(dataset,f'预测数据-{time.time()}.csv'))\n",
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2024-12-09 13:59:33 +08:00
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"# df = df[['ds','min_within_quantile','max_within_quantile']]\n",
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"\n",
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"\n",
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"\n",
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"# 打印数据框的前几行\n",
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"print(df.head())\n",
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"print(df.tail())\n"
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]
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},
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{
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"cell_type": "code",
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2024-12-19 14:05:16 +08:00
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"execution_count": 27,
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"id": "0d77ab7d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 模型评估前五均值 \n",
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"df['min_price'] = df.iloc[:,1:11].mean(axis=1) -2\n",
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"df['max_price'] = df.iloc[:,1:11].mean(axis=1) +2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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2024-12-09 13:59:33 +08:00
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"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
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"metadata": {},
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2024-12-10 17:06:26 +08:00
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"outputs": [
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{
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2024-12-19 14:05:16 +08:00
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-08 2024-11-15 0\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-15 2024-11-22 0\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-15 2024-11-22 0\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-15 2024-11-22 0\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-15 2024-11-22 0\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-15 2024-11-22 0\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-22 2024-11-29 0.808456\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-22 2024-11-29 0.808456\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-22 2024-11-29 0.808456\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-22 2024-11-29 0.808456\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-22 2024-11-29 0.808456\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-29 2024-12-06 0.955061\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-29 2024-12-06 0.955061\n",
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-29 2024-12-06 0.955061\n",
|
|
|
|
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-29 2024-12-06 0.955061\n",
|
|
|
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" 开始日期 结束日期 准确率\n",
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"0 2024-11-29 2024-12-06 0.955061\n",
|
|
|
|
|
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" 开始日期 结束日期 准确率\n",
|
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"0 2024-12-06 2024-12-13 0.905554\n"
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2024-12-10 17:06:26 +08:00
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]
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}
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],
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"source": [
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"# 定义一个函数来计算准确率\n",
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2024-12-19 14:05:16 +08:00
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"# 比较真实最高最低，和预测最高最低 计算准确率\n",
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2024-12-10 17:06:26 +08:00
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"def calculate_accuracy(row):\n",
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2024-12-19 14:05:16 +08:00
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" # 全子集情况：\n",
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" if (row['max_price'] >= row['HIGH_PRICE'] and row['min_price'] <= row['LOW_PRICE']) or \\\n",
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" (row['max_price'] <= row['HIGH_PRICE'] and row['min_price'] >= row['LOW_PRICE']):\n",
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" return 1 \n",
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" # 无交集情况：\n",
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" if row['max_price'] < row['LOW_PRICE'] or \\\n",
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" row['min_price'] > row['HIGH_PRICE']:\n",
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" return 0\n",
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" # 有交集情况：\n",
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" else:\n",
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" sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']])\n",
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2024-12-10 17:06:26 +08:00
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" middle_diff = sorted_prices[2] - sorted_prices[1]\n",
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2024-12-19 14:05:16 +08:00
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" price_range = row['HIGH_PRICE'] - row['LOW_PRICE']\n",
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2024-12-10 17:06:26 +08:00
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" accuracy = middle_diff / price_range\n",
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" return accuracy\n",
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2024-12-19 14:05:16 +08:00
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"import datetime\n",
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"weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重\n",
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2024-12-10 17:06:26 +08:00
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"\n",
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2024-12-19 14:05:16 +08:00
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"columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price']\n",
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2024-12-10 17:06:26 +08:00
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"df[columns] = df[columns].astype(float)\n",
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"df['ACCURACY'] = df.apply(calculate_accuracy, axis=1)\n",
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2024-12-19 14:05:16 +08:00
|
|
|
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"# df['ACCURACY'] = df.apply(is_within_range, axis=1)\n",
|
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|
|
|
"# 取结束日期上一周的日期\n",
|
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|
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"def get_week_date(end_time):\n",
|
|
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|
|
" endtime = end_time\n",
|
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|
|
|
" endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')\n",
|
|
|
|
|
|
" up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)\n",
|
|
|
|
|
|
" up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]\n",
|
|
|
|
|
|
" up_week_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates]\n",
|
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" return up_week_dates\n",
|
2024-12-10 17:06:26 +08:00
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"\n",
|
2024-12-19 14:05:16 +08:00
|
|
|
|
"# 计算准确率并保存结果\n",
|
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|
|
|
"def _get_accuracy_rate(df,up_week_dates,endtime):\n",
|
|
|
|
|
|
" df3 = df.copy()\n",
|
|
|
|
|
|
" df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]\n",
|
|
|
|
|
|
" df3 = df3[df3['ds'].isin(up_week_dates)]\n",
|
|
|
|
|
|
" accuracy_rote = 0\n",
|
|
|
|
|
|
" for i,group in df3.groupby('ds'):\n",
|
|
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|
|
|
" # print('权重：',weight_dict[len(group)-1])\n",
|
|
|
|
|
|
" # print('准确率：',(group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1])\n",
|
|
|
|
|
|
" accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]\n",
|
|
|
|
|
|
" df3.to_csv(os.path.join(dataset,f'accuracy_{endtime}.csv'),index=False)\n",
|
|
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|
|
" df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])\n",
|
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|
|
|
" df4.loc[len(df4)] = {'开始日期':up_week_dates[0],'结束日期':up_week_dates[-1],'准确率':accuracy_rote}\n",
|
|
|
|
|
|
" df4.to_csv(os.path.join(dataset,f'accuracy_rote_{endtime}.csv'),index=False)\n",
|
|
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|
|
" print(df4)\n",
|
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|
|
" # df4.to_sql(\"accuracy_rote\", con=sqlitedb.connection, if_exists='append', index=False)\n",
|
2024-12-10 17:06:26 +08:00
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"\n",
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"\n",
|
2024-12-19 14:05:16 +08:00
|
|
|
|
"end_times = df['CREAT_DATE'].unique()\n",
|
|
|
|
|
|
"for endtime in end_times:\n",
|
|
|
|
|
|
" up_week_dates = get_week_date(endtime)\n",
|
|
|
|
|
|
" _get_accuracy_rate(df,up_week_dates,end_time)\n",
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"\n",
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|
|
"# 打印结果\n",
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"\n"
|
2024-12-10 17:06:26 +08:00
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]
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},
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{
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"cell_type": "code",
|
2024-12-19 14:05:16 +08:00
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"execution_count": null,
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2024-12-10 17:06:26 +08:00
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"id": "0f942c69",
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"metadata": {},
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"outputs": [],
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"source": [
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"import datetime\n",
|
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|
|
|
"# ds 按周取\n",
|
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|
|
"df['Ds_Week'] = df['ds'].apply(lambda x: x.strftime('%U'))\n",
|
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|
"df['Pre_Week'] = df['PREDICT_DATE'].apply(lambda x: x.strftime('%U'))"
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]
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},
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{
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"cell_type": "code",
|
2024-12-19 14:05:16 +08:00
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"execution_count": null,
|
2024-12-10 17:06:26 +08:00
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"id": "a7b05510",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
|
|
|
|
|
" <th>ds</th>\n",
|
|
|
|
|
|
" <th>ACCURACY</th>\n",
|
|
|
|
|
|
" <th>PREDICT_DATE</th>\n",
|
|
|
|
|
|
" <th>CREAT_DATE</th>\n",
|
|
|
|
|
|
" <th>HIGH_PRICE_y</th>\n",
|
|
|
|
|
|
" <th>LOW_PRICE_y</th>\n",
|
|
|
|
|
|
" <th>MIN_PRICE</th>\n",
|
|
|
|
|
|
" <th>MAX_PRICE</th>\n",
|
|
|
|
|
|
" <th>Ds_Week</th>\n",
|
|
|
|
|
|
" <th>Pre_Week</th>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>0</th>\n",
|
|
|
|
|
|
" <td>2024-11-26</td>\n",
|
|
|
|
|
|
" <td>1.000000</td>\n",
|
|
|
|
|
|
" <td>2024-11-26</td>\n",
|
|
|
|
|
|
" <td>2024-11-25</td>\n",
|
|
|
|
|
|
" <td>73.80</td>\n",
|
|
|
|
|
|
" <td>71.63</td>\n",
|
|
|
|
|
|
" <td>71.071556</td>\n",
|
|
|
|
|
|
" <td>76.006900</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>1</th>\n",
|
|
|
|
|
|
" <td>2024-11-27</td>\n",
|
|
|
|
|
|
" <td>1.000000</td>\n",
|
|
|
|
|
|
" <td>2024-11-27</td>\n",
|
|
|
|
|
|
" <td>2024-11-25</td>\n",
|
|
|
|
|
|
" <td>72.85</td>\n",
|
|
|
|
|
|
" <td>71.71</td>\n",
|
|
|
|
|
|
" <td>71.003624</td>\n",
|
|
|
|
|
|
" <td>75.580560</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>2</th>\n",
|
|
|
|
|
|
" <td>2024-11-28</td>\n",
|
|
|
|
|
|
" <td>0.789324</td>\n",
|
|
|
|
|
|
" <td>2024-11-28</td>\n",
|
|
|
|
|
|
" <td>2024-11-25</td>\n",
|
|
|
|
|
|
" <td>72.96</td>\n",
|
|
|
|
|
|
" <td>71.85</td>\n",
|
|
|
|
|
|
" <td>72.083850</td>\n",
|
|
|
|
|
|
" <td>76.204260</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>3</th>\n",
|
|
|
|
|
|
" <td>2024-11-29</td>\n",
|
|
|
|
|
|
" <td>1.000000</td>\n",
|
|
|
|
|
|
" <td>2024-11-29</td>\n",
|
|
|
|
|
|
" <td>2024-11-25</td>\n",
|
|
|
|
|
|
" <td>73.34</td>\n",
|
|
|
|
|
|
" <td>71.75</td>\n",
|
|
|
|
|
|
" <td>71.329730</td>\n",
|
|
|
|
|
|
" <td>75.703950</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>4</th>\n",
|
|
|
|
|
|
" <td>2024-12-02</td>\n",
|
|
|
|
|
|
" <td>0.853412</td>\n",
|
|
|
|
|
|
" <td>2024-12-02</td>\n",
|
|
|
|
|
|
" <td>2024-11-25</td>\n",
|
|
|
|
|
|
" <td>72.89</td>\n",
|
|
|
|
|
|
" <td>71.52</td>\n",
|
|
|
|
|
|
" <td>71.720825</td>\n",
|
|
|
|
|
|
" <td>76.264275</td>\n",
|
|
|
|
|
|
" <td>48</td>\n",
|
|
|
|
|
|
" <td>48</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <th>70</th>\n",
|
|
|
|
|
|
" <td>2024-11-25</td>\n",
|
|
|
|
|
|
" <td>0.118328</td>\n",
|
|
|
|
|
|
" <td>2024-11-25</td>\n",
|
|
|
|
|
|
" <td>2024-11-22</td>\n",
|
|
|
|
|
|
" <td>74.83</td>\n",
|
|
|
|
|
|
" <td>72.30</td>\n",
|
|
|
|
|
|
" <td>74.530630</td>\n",
|
|
|
|
|
|
" <td>76.673140</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <th>71</th>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>2024-11-26</td>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
|
" <td>2024-11-26</td>\n",
|
|
|
|
|
|
" <td>2024-11-22</td>\n",
|
|
|
|
|
|
" <td>73.80</td>\n",
|
|
|
|
|
|
" <td>71.63</td>\n",
|
|
|
|
|
|
" <td>74.440430</td>\n",
|
|
|
|
|
|
" <td>76.874565</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <th>72</th>\n",
|
|
|
|
|
|
" <td>2024-11-27</td>\n",
|
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
|
" <td>2024-11-27</td>\n",
|
|
|
|
|
|
" <td>2024-11-22</td>\n",
|
|
|
|
|
|
" <td>72.85</td>\n",
|
|
|
|
|
|
" <td>71.71</td>\n",
|
|
|
|
|
|
" <td>74.663180</td>\n",
|
|
|
|
|
|
" <td>76.734130</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <th>73</th>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>2024-11-28</td>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <td>0.000000</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>2024-11-28</td>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <td>2024-11-22</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>72.96</td>\n",
|
|
|
|
|
|
" <td>71.85</td>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <td>74.708410</td>\n",
|
|
|
|
|
|
" <td>77.141050</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <th>74</th>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>2024-11-29</td>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <td>0.000000</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>2024-11-29</td>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <td>2024-11-22</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>73.34</td>\n",
|
|
|
|
|
|
" <td>71.75</td>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
" <td>74.703210</td>\n",
|
|
|
|
|
|
" <td>77.746170</td>\n",
|
2024-12-10 17:06:26 +08:00
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
2024-12-11 14:52:45 +08:00
|
|
|
|
"<p>75 rows × 10 columns</p>\n",
|
2024-12-10 17:06:26 +08:00
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|
"</div>"
|
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],
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|
"text/plain": [
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" ds ACCURACY PREDICT_DATE CREAT_DATE HIGH_PRICE_y LOW_PRICE_y \\\n",
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"0 2024-11-26 1.000000 2024-11-26 2024-11-25 73.80 71.63 \n",
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"1 2024-11-27 1.000000 2024-11-27 2024-11-25 72.85 71.71 \n",
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"2 2024-11-28 0.789324 2024-11-28 2024-11-25 72.96 71.85 \n",
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"3 2024-11-29 1.000000 2024-11-29 2024-11-25 73.34 71.75 \n",
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"4 2024-12-02 0.853412 2024-12-02 2024-11-25 72.89 71.52 \n",
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2024-12-11 14:52:45 +08:00
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".. ... ... ... ... ... ... \n",
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"70 2024-11-25 0.118328 2024-11-25 2024-11-22 74.83 72.30 \n",
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"71 2024-11-26 0.000000 2024-11-26 2024-11-22 73.80 71.63 \n",
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"72 2024-11-27 0.000000 2024-11-27 2024-11-22 72.85 71.71 \n",
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"73 2024-11-28 0.000000 2024-11-28 2024-11-22 72.96 71.85 \n",
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"74 2024-11-29 0.000000 2024-11-29 2024-11-22 73.34 71.75 \n",
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2024-12-10 17:06:26 +08:00
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"\n",
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" MIN_PRICE MAX_PRICE Ds_Week Pre_Week \n",
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"0 71.071556 76.006900 47 47 \n",
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"1 71.003624 75.580560 47 47 \n",
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"2 72.083850 76.204260 47 47 \n",
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"3 71.329730 75.703950 47 47 \n",
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"4 71.720825 76.264275 48 48 \n",
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2024-12-11 14:52:45 +08:00
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".. ... ... ... ... \n",
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"70 74.530630 76.673140 47 47 \n",
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"71 74.440430 76.874565 47 47 \n",
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"72 74.663180 76.734130 47 47 \n",
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"73 74.708410 77.141050 47 47 \n",
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"74 74.703210 77.746170 47 47 \n",
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"\n",
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"[75 rows x 10 columns]"
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]
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},
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"execution_count": 139,
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2024-12-10 17:06:26 +08:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "code",
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2024-12-19 14:05:16 +08:00
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"execution_count": null,
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2024-12-10 17:06:26 +08:00
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"id": "1374e354",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2024-12-11 14:52:45 +08:00
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"['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n"
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]
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},
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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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2024-12-10 17:06:26 +08:00
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"(15, 10)\n"
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]
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}
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],
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"source": [
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"# 取结束日期上一周的日期\n",
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2024-12-11 14:52:45 +08:00
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"endtime = '2024-12-3'\n",
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2024-12-10 17:06:26 +08:00
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"endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')\n",
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"up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)\n",
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"up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]\n",
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"up_week_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates]\n",
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"print(up_week_dates)\n",
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"\n",
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"\n",
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"df3 = df.copy()\n",
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"df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]\n",
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"df3 = df3[df3['PREDICT_DATE'].isin(up_week_dates)]\n",
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"print(df3.shape)\n",
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"df3.to_csv('up_week_dates.csv',index=False)"
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]
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},
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{
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"cell_type": "code",
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2024-12-19 14:05:16 +08:00
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"execution_count": null,
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2024-12-10 17:06:26 +08:00
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"id": "8aa47e90",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2024-12-11 14:52:45 +08:00
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"2024-11-25 00:00:00\n",
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2024-12-10 17:06:26 +08:00
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"权重： 0.07\n",
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2024-12-11 14:52:45 +08:00
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"准确率： 1.7749209486165771\n",
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"2024-11-26 00:00:00\n",
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2024-12-10 17:06:26 +08:00
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"权重： 0.13\n",
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"准确率： 7.5\n",
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2024-12-11 14:52:45 +08:00
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"2024-11-27 00:00:00\n",
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2024-12-10 17:06:26 +08:00
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"权重： 0.2\n",
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2024-12-11 14:52:45 +08:00
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"准确率： 8.034364035087705\n",
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"2024-11-28 00:00:00\n",
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2024-12-10 17:06:26 +08:00
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"权重： 0.27\n",
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2024-12-11 14:52:45 +08:00
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"准确率： 9.718006756756724\n",
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"2024-11-29 00:00:00\n",
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2024-12-10 17:06:26 +08:00
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"权重： 0.33\n",
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2024-12-11 14:52:45 +08:00
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"准确率： 10.824716981132076\n",
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"37.85200872159308\n"
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2024-12-10 17:06:26 +08:00
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]
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}
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],
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"source": [
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"total = len(df3)\n",
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"accuracy_rote = 0\n",
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"# for i,group in df3.groupby('CREAT_DATE'):\n",
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"for i,group in df3.groupby('ds'):\n",
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" print(i)\n",
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" print('权重：',round(len(group)/total,2))\n",
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" print('准确率：',group['ACCURACY'].sum()/(len(group)/total))\n",
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" accuracy_rote += group['ACCURACY'].sum()/(len(group)/total)\n",
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"\n",
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"print(accuracy_rote)"
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]
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2024-12-09 13:59:33 +08:00
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}
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],
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"metadata": {
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"kernelspec": {
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2024-12-10 17:06:26 +08:00
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"display_name": "base",
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2024-12-09 13:59:33 +08:00
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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