1104 lines
41 KiB
Plaintext
1104 lines
41 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 130,
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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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"(50, 9)\n",
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"(20, 4)\n",
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"(50, 12)\n",
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" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x LOW_PRICE_x \\\n",
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"0 1 2024-11-26 2024-11-25 71.071556 76.006900 \n",
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"1 2 2024-11-27 2024-11-25 71.003624 75.580560 \n",
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"2 3 2024-11-28 2024-11-25 72.083850 76.204260 \n",
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"3 4 2024-11-29 2024-11-25 71.329730 75.703950 \n",
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"4 5 2024-12-02 2024-11-25 71.720825 76.264275 \n",
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"\n",
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" RIGHT_ROTE ds 序号 LOW_PRICE_y HIGH_PRICE_y \n",
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"0 2024-11-26 9.0 71.63 73.80 \n",
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"1 2024-11-27 8.0 71.71 72.85 \n",
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"2 2024-11-28 7.0 71.85 72.96 \n",
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"3 2024-11-29 6.0 71.75 73.34 \n",
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"4 2024-12-02 5.0 71.52 72.89 \n",
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" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x \\\n",
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"45 46 2024-12-09 2024-12-06 68.894770 73.909450 \n",
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"46 47 2024-12-10 2024-12-06 68.845604 73.921120 \n",
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"47 48 2024-12-11 2024-12-06 69.181210 73.906260 \n",
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"48 49 2024-12-12 2024-12-06 69.075340 73.862946 \n",
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"49 50 2024-12-13 2024-12-06 69.076550 74.007740 \n",
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"\n",
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" LOW_PRICE_x RIGHT_ROTE ds 序号 LOW_PRICE_y HIGH_PRICE_y \n",
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"45 2024-12-09 NaN NaN NaN \n",
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"46 2024-12-10 NaN NaN NaN \n",
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"47 2024-12-11 NaN NaN NaN \n",
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"48 2024-12-12 NaN NaN NaN \n",
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"49 2024-12-13 NaN NaN NaN \n"
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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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"# 预测价格数据\n",
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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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"\n",
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"\n",
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"\n",
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"\n",
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"# 最高最低价\n",
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"xlsfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','数据项下载.xls')\n",
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"df2 = pd.read_excel(xlsfilename)[5:]\n",
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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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"print(df2.shape)\n",
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"\n",
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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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"df['PREDICT_DATE'] = pd.to_datetime(df['PREDICT_DATE'])\n",
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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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"\n",
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"df.to_csv(os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','123.csv'))\n",
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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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"execution_count": 131,
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"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
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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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" ds ACCURACY HIGH_PRICE_y LOW_PRICE_y MIN_PRICE MAX_PRICE\n",
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"0 2024-11-26 1.000000 73.80 71.63 71.071556 76.006900\n",
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"1 2024-11-27 1.000000 72.85 71.71 71.003624 75.580560\n",
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"2 2024-11-28 0.789324 72.96 71.85 72.083850 76.204260\n",
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"3 2024-11-29 1.000000 73.34 71.75 71.329730 75.703950\n",
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"4 2024-12-02 0.853412 72.89 71.52 71.720825 76.264275\n",
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"5 2024-11-27 0.606873 72.85 71.71 72.158165 76.173650\n",
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"6 2024-11-28 0.802144 72.96 71.85 72.069620 76.447000\n",
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"7 2024-11-29 0.608239 73.34 71.75 72.372900 76.082910\n",
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"8 2024-12-02 0.457007 72.89 71.52 72.263900 76.198685\n",
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"9 2024-12-03 0.758302 73.93 71.68 72.223820 76.041725\n",
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"10 2024-11-28 1.000000 72.96 71.85 70.709750 75.208030\n",
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"11 2024-11-29 1.000000 73.34 71.75 69.923110 75.423775\n",
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"12 2024-12-02 1.000000 72.89 71.52 70.810356 75.786860\n",
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"13 2024-12-03 1.000000 73.93 71.68 70.814430 75.791115\n",
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"14 2024-12-04 1.000000 74.28 72.25 70.476650 75.623730\n",
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"15 2024-11-29 1.000000 73.34 71.75 70.432760 75.480620\n",
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"16 2024-12-02 1.000000 72.89 71.52 69.651825 75.270940\n",
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"17 2024-12-03 1.000000 73.93 71.68 70.536285 75.826430\n",
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"18 2024-12-04 1.000000 74.28 72.25 70.568340 75.770290\n",
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"19 2024-12-05 1.000000 72.92 71.80 70.222640 76.113310\n",
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"20 2024-12-02 0.000000 72.89 71.52 81.179990 81.179990\n",
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"21 2024-12-03 0.000000 73.93 71.68 80.791550 80.791550\n",
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"22 2024-12-04 0.000000 74.28 72.25 80.650894 80.650894\n",
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"23 2024-12-05 0.000000 72.92 71.80 80.298000 80.298000\n",
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"24 2024-12-06 0.000000 72.19 70.85 75.508380 80.264275\n",
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"25 2024-12-03 1.000000 73.93 71.68 69.807790 75.837190\n",
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"26 2024-12-04 1.000000 74.28 72.25 71.281296 76.240510\n",
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"27 2024-12-05 1.000000 72.92 71.80 70.526230 75.955410\n",
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"28 2024-12-06 1.000000 72.19 70.85 70.719320 75.997680\n",
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"29 2024-12-09 0.000000 NaN NaN 71.642590 75.828636\n",
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"30 2024-12-04 0.943207 74.28 72.25 72.365290 75.890610\n",
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"31 2024-12-05 0.643920 72.92 71.80 72.198810 75.729620\n",
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"32 2024-12-06 0.040172 72.19 70.85 72.136170 75.977620\n",
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"33 2024-12-09 0.000000 NaN NaN 72.165790 75.880104\n",
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"34 2024-12-10 0.000000 NaN NaN 72.000620 75.906100\n",
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"35 2024-12-05 0.000000 72.92 71.80 82.405370 82.405370\n",
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"36 2024-12-06 0.000000 72.19 70.85 82.072700 82.072700\n",
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"37 2024-12-09 0.000000 NaN NaN 82.652700 82.652700\n",
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"38 2024-12-10 0.000000 NaN NaN 81.920910 81.920910\n",
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"39 2024-12-11 0.000000 NaN NaN 81.091270 81.091270\n",
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"40 2024-12-06 0.000000 72.19 70.85 83.367485 83.367485\n",
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"41 2024-12-09 0.000000 NaN NaN 82.869705 82.869705\n",
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"42 2024-12-10 0.000000 NaN NaN 82.580550 82.580550\n",
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"43 2024-12-11 0.000000 NaN NaN 82.442154 82.442154\n",
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"44 2024-12-12 0.000000 NaN NaN 82.276510 82.276510\n",
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"45 2024-12-09 0.000000 NaN NaN 68.894770 73.909450\n",
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"46 2024-12-10 0.000000 NaN NaN 68.845604 73.921120\n",
|
|
"47 2024-12-11 0.000000 NaN NaN 69.181210 73.906260\n",
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"48 2024-12-12 0.000000 NaN NaN 69.075340 73.862946\n",
|
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"49 2024-12-13 0.000000 NaN NaN 69.076550 74.007740\n"
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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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"def calculate_accuracy(row):\n",
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" if row['HIGH_PRICE_y'] > row['MIN_PRICE']:\n",
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" sorted_prices = sorted([row['LOW_PRICE_y'], row['MIN_PRICE'], row['MAX_PRICE'], row['HIGH_PRICE_y']])\n",
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" middle_diff = sorted_prices[2] - sorted_prices[1]\n",
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" price_range = row['HIGH_PRICE_y'] - row['LOW_PRICE_y']\n",
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" accuracy = middle_diff / price_range\n",
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" return accuracy\n",
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" else:\n",
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" return 0\n",
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"\n",
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"# 使用 apply 函数来应用计算准确率的函数\n",
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"\n",
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"columns = ['HIGH_PRICE_y','LOW_PRICE_y','MIN_PRICE','MAX_PRICE']\n",
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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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"\n",
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"\n",
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"# 打印结果\n",
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"print(df[['ds','ACCURACY',]+columns])\n",
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"\n",
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"df = df[['ds','ACCURACY','PREDICT_DATE','CREAT_DATE']+columns]"
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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": 132,
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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",
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|
"execution_count": 133,
|
|
"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",
|
|
" }\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",
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" <th>ds</th>\n",
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" <th>ACCURACY</th>\n",
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" <th>PREDICT_DATE</th>\n",
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" <th>CREAT_DATE</th>\n",
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" <th>HIGH_PRICE_y</th>\n",
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" <th>LOW_PRICE_y</th>\n",
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" <th>MIN_PRICE</th>\n",
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" <th>MAX_PRICE</th>\n",
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" <th>Ds_Week</th>\n",
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" <th>Pre_Week</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
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" <tr>\n",
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|
" <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",
|
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" <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",
|
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" <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",
|
|
" <th>5</th>\n",
|
|
" <td>2024-11-27</td>\n",
|
|
" <td>0.606873</td>\n",
|
|
" <td>2024-11-27</td>\n",
|
|
" <td>2024-11-26</td>\n",
|
|
" <td>72.85</td>\n",
|
|
" <td>71.71</td>\n",
|
|
" <td>72.158165</td>\n",
|
|
" <td>76.173650</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>47</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>0.802144</td>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>2024-11-26</td>\n",
|
|
" <td>72.96</td>\n",
|
|
" <td>71.85</td>\n",
|
|
" <td>72.069620</td>\n",
|
|
" <td>76.447000</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>47</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>0.608239</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>2024-11-26</td>\n",
|
|
" <td>73.34</td>\n",
|
|
" <td>71.75</td>\n",
|
|
" <td>72.372900</td>\n",
|
|
" <td>76.082910</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>47</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>0.457007</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>2024-11-26</td>\n",
|
|
" <td>72.89</td>\n",
|
|
" <td>71.52</td>\n",
|
|
" <td>72.263900</td>\n",
|
|
" <td>76.198685</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>0.758302</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>2024-11-26</td>\n",
|
|
" <td>73.93</td>\n",
|
|
" <td>71.68</td>\n",
|
|
" <td>72.223820</td>\n",
|
|
" <td>76.041725</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>2024-11-27</td>\n",
|
|
" <td>72.96</td>\n",
|
|
" <td>71.85</td>\n",
|
|
" <td>70.709750</td>\n",
|
|
" <td>75.208030</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>47</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>2024-11-27</td>\n",
|
|
" <td>73.34</td>\n",
|
|
" <td>71.75</td>\n",
|
|
" <td>69.923110</td>\n",
|
|
" <td>75.423775</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>47</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>2024-11-27</td>\n",
|
|
" <td>72.89</td>\n",
|
|
" <td>71.52</td>\n",
|
|
" <td>70.810356</td>\n",
|
|
" <td>75.786860</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>2024-11-27</td>\n",
|
|
" <td>73.93</td>\n",
|
|
" <td>71.68</td>\n",
|
|
" <td>70.814430</td>\n",
|
|
" <td>75.791115</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>2024-11-27</td>\n",
|
|
" <td>74.28</td>\n",
|
|
" <td>72.25</td>\n",
|
|
" <td>70.476650</td>\n",
|
|
" <td>75.623730</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>73.34</td>\n",
|
|
" <td>71.75</td>\n",
|
|
" <td>70.432760</td>\n",
|
|
" <td>75.480620</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>47</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>72.89</td>\n",
|
|
" <td>71.52</td>\n",
|
|
" <td>69.651825</td>\n",
|
|
" <td>75.270940</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>73.93</td>\n",
|
|
" <td>71.68</td>\n",
|
|
" <td>70.536285</td>\n",
|
|
" <td>75.826430</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>74.28</td>\n",
|
|
" <td>72.25</td>\n",
|
|
" <td>70.568340</td>\n",
|
|
" <td>75.770290</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>2024-11-28</td>\n",
|
|
" <td>72.92</td>\n",
|
|
" <td>71.80</td>\n",
|
|
" <td>70.222640</td>\n",
|
|
" <td>76.113310</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>72.89</td>\n",
|
|
" <td>71.52</td>\n",
|
|
" <td>81.179990</td>\n",
|
|
" <td>81.179990</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21</th>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>73.93</td>\n",
|
|
" <td>71.68</td>\n",
|
|
" <td>80.791550</td>\n",
|
|
" <td>80.791550</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>22</th>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>74.28</td>\n",
|
|
" <td>72.25</td>\n",
|
|
" <td>80.650894</td>\n",
|
|
" <td>80.650894</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>72.92</td>\n",
|
|
" <td>71.80</td>\n",
|
|
" <td>80.298000</td>\n",
|
|
" <td>80.298000</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24</th>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>2024-11-29</td>\n",
|
|
" <td>72.19</td>\n",
|
|
" <td>70.85</td>\n",
|
|
" <td>75.508380</td>\n",
|
|
" <td>80.264275</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25</th>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>73.93</td>\n",
|
|
" <td>71.68</td>\n",
|
|
" <td>69.807790</td>\n",
|
|
" <td>75.837190</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>26</th>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>74.28</td>\n",
|
|
" <td>72.25</td>\n",
|
|
" <td>71.281296</td>\n",
|
|
" <td>76.240510</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>72.92</td>\n",
|
|
" <td>71.80</td>\n",
|
|
" <td>70.526230</td>\n",
|
|
" <td>75.955410</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>72.19</td>\n",
|
|
" <td>70.85</td>\n",
|
|
" <td>70.719320</td>\n",
|
|
" <td>75.997680</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>2024-12-02</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>71.642590</td>\n",
|
|
" <td>75.828636</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30</th>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>0.943207</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>74.28</td>\n",
|
|
" <td>72.25</td>\n",
|
|
" <td>72.365290</td>\n",
|
|
" <td>75.890610</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>31</th>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>0.643920</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>72.92</td>\n",
|
|
" <td>71.80</td>\n",
|
|
" <td>72.198810</td>\n",
|
|
" <td>75.729620</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32</th>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>0.040172</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>72.19</td>\n",
|
|
" <td>70.85</td>\n",
|
|
" <td>72.136170</td>\n",
|
|
" <td>75.977620</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>33</th>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>72.165790</td>\n",
|
|
" <td>75.880104</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>34</th>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>2024-12-03</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>72.000620</td>\n",
|
|
" <td>75.906100</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>35</th>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>72.92</td>\n",
|
|
" <td>71.80</td>\n",
|
|
" <td>82.405370</td>\n",
|
|
" <td>82.405370</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>72.19</td>\n",
|
|
" <td>70.85</td>\n",
|
|
" <td>82.072700</td>\n",
|
|
" <td>82.072700</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>82.652700</td>\n",
|
|
" <td>82.652700</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>38</th>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>81.920910</td>\n",
|
|
" <td>81.920910</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>2024-12-11</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-11</td>\n",
|
|
" <td>2024-12-04</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>81.091270</td>\n",
|
|
" <td>81.091270</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>72.19</td>\n",
|
|
" <td>70.85</td>\n",
|
|
" <td>83.367485</td>\n",
|
|
" <td>83.367485</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>48</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>41</th>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>82.869705</td>\n",
|
|
" <td>82.869705</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>82.580550</td>\n",
|
|
" <td>82.580550</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>43</th>\n",
|
|
" <td>2024-12-11</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-11</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>82.442154</td>\n",
|
|
" <td>82.442154</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>44</th>\n",
|
|
" <td>2024-12-12</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-12</td>\n",
|
|
" <td>2024-12-05</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>82.276510</td>\n",
|
|
" <td>82.276510</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>45</th>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-09</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>68.894770</td>\n",
|
|
" <td>73.909450</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46</th>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-10</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>68.845604</td>\n",
|
|
" <td>73.921120</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>47</th>\n",
|
|
" <td>2024-12-11</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-11</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>69.181210</td>\n",
|
|
" <td>73.906260</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>48</th>\n",
|
|
" <td>2024-12-12</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-12</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>69.075340</td>\n",
|
|
" <td>73.862946</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>49</th>\n",
|
|
" <td>2024-12-13</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2024-12-13</td>\n",
|
|
" <td>2024-12-06</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>69.076550</td>\n",
|
|
" <td>74.007740</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>49</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" ds ACCURACY PREDICT_DATE CREAT_DATE HIGH_PRICE_y LOW_PRICE_y \\\n",
|
|
"0 2024-11-26 1.000000 2024-11-26 2024-11-25 73.80 71.63 \n",
|
|
"1 2024-11-27 1.000000 2024-11-27 2024-11-25 72.85 71.71 \n",
|
|
"2 2024-11-28 0.789324 2024-11-28 2024-11-25 72.96 71.85 \n",
|
|
"3 2024-11-29 1.000000 2024-11-29 2024-11-25 73.34 71.75 \n",
|
|
"4 2024-12-02 0.853412 2024-12-02 2024-11-25 72.89 71.52 \n",
|
|
"5 2024-11-27 0.606873 2024-11-27 2024-11-26 72.85 71.71 \n",
|
|
"6 2024-11-28 0.802144 2024-11-28 2024-11-26 72.96 71.85 \n",
|
|
"7 2024-11-29 0.608239 2024-11-29 2024-11-26 73.34 71.75 \n",
|
|
"8 2024-12-02 0.457007 2024-12-02 2024-11-26 72.89 71.52 \n",
|
|
"9 2024-12-03 0.758302 2024-12-03 2024-11-26 73.93 71.68 \n",
|
|
"10 2024-11-28 1.000000 2024-11-28 2024-11-27 72.96 71.85 \n",
|
|
"11 2024-11-29 1.000000 2024-11-29 2024-11-27 73.34 71.75 \n",
|
|
"12 2024-12-02 1.000000 2024-12-02 2024-11-27 72.89 71.52 \n",
|
|
"13 2024-12-03 1.000000 2024-12-03 2024-11-27 73.93 71.68 \n",
|
|
"14 2024-12-04 1.000000 2024-12-04 2024-11-27 74.28 72.25 \n",
|
|
"15 2024-11-29 1.000000 2024-11-29 2024-11-28 73.34 71.75 \n",
|
|
"16 2024-12-02 1.000000 2024-12-02 2024-11-28 72.89 71.52 \n",
|
|
"17 2024-12-03 1.000000 2024-12-03 2024-11-28 73.93 71.68 \n",
|
|
"18 2024-12-04 1.000000 2024-12-04 2024-11-28 74.28 72.25 \n",
|
|
"19 2024-12-05 1.000000 2024-12-05 2024-11-28 72.92 71.80 \n",
|
|
"20 2024-12-02 0.000000 2024-12-02 2024-11-29 72.89 71.52 \n",
|
|
"21 2024-12-03 0.000000 2024-12-03 2024-11-29 73.93 71.68 \n",
|
|
"22 2024-12-04 0.000000 2024-12-04 2024-11-29 74.28 72.25 \n",
|
|
"23 2024-12-05 0.000000 2024-12-05 2024-11-29 72.92 71.80 \n",
|
|
"24 2024-12-06 0.000000 2024-12-06 2024-11-29 72.19 70.85 \n",
|
|
"25 2024-12-03 1.000000 2024-12-03 2024-12-02 73.93 71.68 \n",
|
|
"26 2024-12-04 1.000000 2024-12-04 2024-12-02 74.28 72.25 \n",
|
|
"27 2024-12-05 1.000000 2024-12-05 2024-12-02 72.92 71.80 \n",
|
|
"28 2024-12-06 1.000000 2024-12-06 2024-12-02 72.19 70.85 \n",
|
|
"29 2024-12-09 0.000000 2024-12-09 2024-12-02 NaN NaN \n",
|
|
"30 2024-12-04 0.943207 2024-12-04 2024-12-03 74.28 72.25 \n",
|
|
"31 2024-12-05 0.643920 2024-12-05 2024-12-03 72.92 71.80 \n",
|
|
"32 2024-12-06 0.040172 2024-12-06 2024-12-03 72.19 70.85 \n",
|
|
"33 2024-12-09 0.000000 2024-12-09 2024-12-03 NaN NaN \n",
|
|
"34 2024-12-10 0.000000 2024-12-10 2024-12-03 NaN NaN \n",
|
|
"35 2024-12-05 0.000000 2024-12-05 2024-12-04 72.92 71.80 \n",
|
|
"36 2024-12-06 0.000000 2024-12-06 2024-12-04 72.19 70.85 \n",
|
|
"37 2024-12-09 0.000000 2024-12-09 2024-12-04 NaN NaN \n",
|
|
"38 2024-12-10 0.000000 2024-12-10 2024-12-04 NaN NaN \n",
|
|
"39 2024-12-11 0.000000 2024-12-11 2024-12-04 NaN NaN \n",
|
|
"40 2024-12-06 0.000000 2024-12-06 2024-12-05 72.19 70.85 \n",
|
|
"41 2024-12-09 0.000000 2024-12-09 2024-12-05 NaN NaN \n",
|
|
"42 2024-12-10 0.000000 2024-12-10 2024-12-05 NaN NaN \n",
|
|
"43 2024-12-11 0.000000 2024-12-11 2024-12-05 NaN NaN \n",
|
|
"44 2024-12-12 0.000000 2024-12-12 2024-12-05 NaN NaN \n",
|
|
"45 2024-12-09 0.000000 2024-12-09 2024-12-06 NaN NaN \n",
|
|
"46 2024-12-10 0.000000 2024-12-10 2024-12-06 NaN NaN \n",
|
|
"47 2024-12-11 0.000000 2024-12-11 2024-12-06 NaN NaN \n",
|
|
"48 2024-12-12 0.000000 2024-12-12 2024-12-06 NaN NaN \n",
|
|
"49 2024-12-13 0.000000 2024-12-13 2024-12-06 NaN NaN \n",
|
|
"\n",
|
|
" MIN_PRICE MAX_PRICE Ds_Week Pre_Week \n",
|
|
"0 71.071556 76.006900 47 47 \n",
|
|
"1 71.003624 75.580560 47 47 \n",
|
|
"2 72.083850 76.204260 47 47 \n",
|
|
"3 71.329730 75.703950 47 47 \n",
|
|
"4 71.720825 76.264275 48 48 \n",
|
|
"5 72.158165 76.173650 47 47 \n",
|
|
"6 72.069620 76.447000 47 47 \n",
|
|
"7 72.372900 76.082910 47 47 \n",
|
|
"8 72.263900 76.198685 48 48 \n",
|
|
"9 72.223820 76.041725 48 48 \n",
|
|
"10 70.709750 75.208030 47 47 \n",
|
|
"11 69.923110 75.423775 47 47 \n",
|
|
"12 70.810356 75.786860 48 48 \n",
|
|
"13 70.814430 75.791115 48 48 \n",
|
|
"14 70.476650 75.623730 48 48 \n",
|
|
"15 70.432760 75.480620 47 47 \n",
|
|
"16 69.651825 75.270940 48 48 \n",
|
|
"17 70.536285 75.826430 48 48 \n",
|
|
"18 70.568340 75.770290 48 48 \n",
|
|
"19 70.222640 76.113310 48 48 \n",
|
|
"20 81.179990 81.179990 48 48 \n",
|
|
"21 80.791550 80.791550 48 48 \n",
|
|
"22 80.650894 80.650894 48 48 \n",
|
|
"23 80.298000 80.298000 48 48 \n",
|
|
"24 75.508380 80.264275 48 48 \n",
|
|
"25 69.807790 75.837190 48 48 \n",
|
|
"26 71.281296 76.240510 48 48 \n",
|
|
"27 70.526230 75.955410 48 48 \n",
|
|
"28 70.719320 75.997680 48 48 \n",
|
|
"29 71.642590 75.828636 49 49 \n",
|
|
"30 72.365290 75.890610 48 48 \n",
|
|
"31 72.198810 75.729620 48 48 \n",
|
|
"32 72.136170 75.977620 48 48 \n",
|
|
"33 72.165790 75.880104 49 49 \n",
|
|
"34 72.000620 75.906100 49 49 \n",
|
|
"35 82.405370 82.405370 48 48 \n",
|
|
"36 82.072700 82.072700 48 48 \n",
|
|
"37 82.652700 82.652700 49 49 \n",
|
|
"38 81.920910 81.920910 49 49 \n",
|
|
"39 81.091270 81.091270 49 49 \n",
|
|
"40 83.367485 83.367485 48 48 \n",
|
|
"41 82.869705 82.869705 49 49 \n",
|
|
"42 82.580550 82.580550 49 49 \n",
|
|
"43 82.442154 82.442154 49 49 \n",
|
|
"44 82.276510 82.276510 49 49 \n",
|
|
"45 68.894770 73.909450 49 49 \n",
|
|
"46 68.845604 73.921120 49 49 \n",
|
|
"47 69.181210 73.906260 49 49 \n",
|
|
"48 69.075340 73.862946 49 49 \n",
|
|
"49 69.076550 74.007740 49 49 "
|
|
]
|
|
},
|
|
"execution_count": 133,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 134,
|
|
"id": "1374e354",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n",
|
|
"(15, 10)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 取结束日期上一周的日期\n",
|
|
"endtime = '2024-12-9'\n",
|
|
"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",
|
|
"print(up_week_dates)\n",
|
|
"\n",
|
|
"\n",
|
|
"df3 = df.copy()\n",
|
|
"df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]\n",
|
|
"df3 = df3[df3['PREDICT_DATE'].isin(up_week_dates)]\n",
|
|
"print(df3.shape)\n",
|
|
"df3.to_csv('up_week_dates.csv',index=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 135,
|
|
"id": "8aa47e90",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2024-12-02 00:00:00\n",
|
|
"权重： 0.07\n",
|
|
"准确率： 0.0\n",
|
|
"2024-12-03 00:00:00\n",
|
|
"权重： 0.13\n",
|
|
"准确率： 7.5\n",
|
|
"2024-12-04 00:00:00\n",
|
|
"权重： 0.2\n",
|
|
"准确率： 9.716034482758616\n",
|
|
"2024-12-05 00:00:00\n",
|
|
"权重： 0.27\n",
|
|
"准确率： 6.1646986607143\n",
|
|
"2024-12-06 00:00:00\n",
|
|
"权重： 0.33\n",
|
|
"准确率： 3.1205149253731137\n",
|
|
"26.50124806884603\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"total = len(df3)\n",
|
|
"accuracy_rote = 0\n",
|
|
"# for i,group in df3.groupby('CREAT_DATE'):\n",
|
|
"for i,group in df3.groupby('ds'):\n",
|
|
" print(i)\n",
|
|
" print('权重：',round(len(group)/total,2))\n",
|
|
" print('准确率：',group['ACCURACY'].sum()/(len(group)/total))\n",
|
|
" accuracy_rote += group['ACCURACY'].sum()/(len(group)/total)\n",
|
|
"\n",
|
|
"print(accuracy_rote)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3e69cacc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "base",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|