441 lines
319 KiB
Plaintext
441 lines
319 KiB
Plaintext
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{
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"cells": [
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n",
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" from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
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"C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
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" from pandas.core import (\n"
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]
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}
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],
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"source": [
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"from math import sqrt\n",
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"from numpy import concatenate\n",
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"from matplotlib import pyplot\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.metrics import mean_squared_error\n",
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"from tensorflow.keras import Sequential\n",
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"\n",
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"from tensorflow.keras.layers import Dense\n",
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"from tensorflow.keras.layers import LSTM\n",
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"from tensorflow.keras.layers import Dropout\n",
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"from sklearn.model_selection import train_test_split\n",
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"import matplotlib.pyplot as plt"
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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": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 加载数据\n",
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"path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\低频_forecast.csv\"#数据所在路径\n",
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"#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
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"f_low= pd.DataFrame(pd.read_csv(path1))"
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 加载数据\n",
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"path2 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\高频re_forecast.csv\"#数据所在路径\n",
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"#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
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"f_high= pd.DataFrame(pd.read_csv(path2))"
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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": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"path3= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\低频_test.csv\"#数据所在路径\n",
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"#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
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"true_low= pd.DataFrame(pd.read_csv(path3))"
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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": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"path4= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\高频re_test.csv\"#数据所在路径\n",
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"#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
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"true_high= pd.DataFrame(pd.read_csv(path4))"
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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": 32,
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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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" <td>2.104709</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2.144592</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1.375800</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1.751718</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1.977364</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20826</th>\n",
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" <td>0.012646</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20827</th>\n",
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" <td>0.012262</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20828</th>\n",
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" <td>0.011389</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20829</th>\n",
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" <td>0.014322</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20830</th>\n",
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" <td>0.011895</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"... ...\n",
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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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"pre_data=f_low+f_high\n",
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"pre_data"
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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": 33,
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"metadata": {},
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{
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" <th>0</th>\n",
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" <td>2.186333e+00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1.377467e+00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1.452000e+00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1.846867e+00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2.793334e+00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20826</th>\n",
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" <td>6.661338e-16</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20827</th>\n",
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" <td>0.000000e+00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20828</th>\n",
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" <td>2.220446e-16</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20829</th>\n",
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" <td>2.220446e-16</td>\n",
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" </tr>\n",
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" <tr>\n",
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|||
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" <th>20830</th>\n",
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" <td>4.440892e-16</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>20831 rows × 1 columns</p>\n",
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"</div>"
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"... ...\n",
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"20830 4.440892e-16\n",
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"\n",
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"[20831 rows x 1 columns]"
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]
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},
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"execution_count": 33,
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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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"true=true_low+true_high\n",
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"true"
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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": 53,
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"metadata": {},
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"outputs": [
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{
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"data": {
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|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(16,8))\n",
|
|||
|
|
"plt.plot(true, label='true')\n",
|
|||
|
|
"plt.plot(pre_data, label='pre')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"mean_squared_error: 0.049185981148514475\n",
|
|||
|
|
"mean_absolute_error: 0.07759843372042166\n",
|
|||
|
|
"rmse: 0.22177912694506324\n",
|
|||
|
|
"r2 score: 0.9988074259067585\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.metrics import mean_squared_error, mean_absolute_error # 评价指标\n",
|
|||
|
|
"# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
|
|||
|
|
"from math import sqrt\n",
|
|||
|
|
"from sklearn.metrics import mean_absolute_error\n",
|
|||
|
|
"from sklearn.metrics import mean_squared_error\n",
|
|||
|
|
"from sklearn.metrics import r2_score\n",
|
|||
|
|
"print('mean_squared_error:', mean_squared_error(pre_data, true)) # mse)\n",
|
|||
|
|
"print(\"mean_absolute_error:\", mean_absolute_error(pre_data, true)) # mae\n",
|
|||
|
|
"print(\"rmse:\", sqrt(mean_squared_error(pre_data, true)))\n",
|
|||
|
|
"print(\"r2 score:\", r2_score(pre_data[:], true[:]))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 46,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"(20831, 1)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# 使用MinMaxScaler进行归一化\n",
|
|||
|
|
"from sklearn.preprocessing import MinMaxScaler\n",
|
|||
|
|
"scaler = MinMaxScaler(feature_range=(0, 1))\n",
|
|||
|
|
"pre = scaler.fit_transform(pre_data)\n",
|
|||
|
|
"print(pre.shape)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 47,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"(20831, 1)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.preprocessing import MinMaxScaler\n",
|
|||
|
|
"scaler = MinMaxScaler(feature_range=(0, 1))\n",
|
|||
|
|
"true_data = scaler.fit_transform(true)\n",
|
|||
|
|
"print(true_data.shape)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 50,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"mean_squared_error: 0.0026778377010073626\n",
|
|||
|
|
"mean_absolute_error: 0.027468762691519367\n",
|
|||
|
|
"rmse: 0.05174782798347543\n",
|
|||
|
|
"r2 score: 0.9988074259067585\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.metrics import mean_squared_error, mean_absolute_error # 评价指标\n",
|
|||
|
|
"# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
|
|||
|
|
"from math import sqrt\n",
|
|||
|
|
"from sklearn.metrics import mean_absolute_error\n",
|
|||
|
|
"from sklearn.metrics import mean_squared_error\n",
|
|||
|
|
"from sklearn.metrics import r2_score\n",
|
|||
|
|
"print('mean_squared_error:', mean_squared_error(pre, true_data)) # mse)\n",
|
|||
|
|
"print(\"mean_absolute_error:\", mean_absolute_error(pre, true_data)) # mae\n",
|
|||
|
|
"print(\"rmse:\", sqrt(mean_squared_error(pre, true_data)))\n",
|
|||
|
|
"print(\"r2 score:\", r2_score(pre_data, true))"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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": "3.9.13"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|