1061 lines
212 KiB
Plaintext
1061 lines
212 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": "markdown",
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"metadata": {},
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"source": [
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"这段代码是一个函数 time_series_to_supervised，它用于将时间序列数据转换为监督学习问题的数据集。下面是该函数的各个部分的含义：\n",
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"\n",
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"data: 输入的时间序列数据，可以是列表或2D NumPy数组。\n",
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"n_in: 作为输入的滞后观察数，即用多少个时间步的观察值作为输入。默认值为96，表示使用前96个时间步的观察值作为输入。\n",
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"n_out: 作为输出的观测数量，即预测多少个时间步的观察值。默认值为10，表示预测未来10个时间步的观察值。\n",
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"dropnan: 布尔值，表示是否删除具有NaN值的行。默认为True，即删除具有NaN值的行。\n",
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"函数首先检查输入数据的维度，并初始化一些变量。然后，它创建一个新的DataFrame对象 df 来存储输入数据，并保存原始的列名。接着，它创建了两个空列表 cols 和 names，用于存储新的特征列和列名。\n",
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"\n",
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"接下来，函数开始构建特征列和对应的列名。首先，它将原始的观察序列添加到 cols 列表中，并将其列名添加到 names 列表中。然后，它依次将滞后的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t-滞后时间)。这样就创建了输入特征的部分。\n",
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"\n",
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"接着，函数开始构建输出特征的部分。它依次将未来的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t+未来时间)。\n",
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"\n",
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"最后，函数将所有的特征列拼接在一起，构成一个新的DataFrame对象 agg。如果 dropnan 参数为True，则删除具有NaN值的行。最后，函数返回处理后的数据集 agg。"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def time_series_to_supervised(data, n_in=96, n_out=10,dropnan=True):\n",
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" \"\"\"\n",
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" :param data:作为列表或2D NumPy数组的观察序列。需要。\n",
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" :param n_in:作为输入的滞后观察数（X）。值可以在[1..len（数据）]之间可选。默认为1。\n",
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" :param n_out:作为输出的观测数量（y）。值可以在[0..len（数据）]之间。可选的。默认为1。\n",
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" :param dropnan:Boolean是否删除具有NaN值的行。可选的。默认为True。\n",
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" :return:\n",
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" \"\"\"\n",
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" n_vars = 1 if type(data) is list else data.shape[1]\n",
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" df = pd.DataFrame(data)\n",
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" origNames = df.columns\n",
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" cols, names = list(), list()\n",
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" cols.append(df.shift(0))\n",
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" names += [('%s' % origNames[j]) for j in range(n_vars)]\n",
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" n_in = max(0, n_in)\n",
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" for i in range(n_in, 0, -1):\n",
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" time = '(t-%d)' % i\n",
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" cols.append(df.shift(i))\n",
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" names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
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" n_out = max(n_out, 0)\n",
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" for i in range(1, n_out+1):\n",
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" time = '(t+%d)' % i\n",
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" cols.append(df.shift(-i))\n",
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" names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
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" agg = pd.concat(cols, axis=1)\n",
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" agg.columns = names\n",
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" if dropnan:\n",
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" agg.dropna(inplace=True)\n",
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" return agg"
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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": 3,
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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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" Temp Humidity GHI DHI Rainfall Power\n",
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"0 19.779453 40.025826 3.232706 1.690531 0.0 0.0\n",
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"1 19.714937 39.605961 3.194991 1.576346 0.0 0.0\n",
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"2 19.549330 39.608631 3.070866 1.576157 0.0 0.0\n",
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"3 19.405870 39.680702 3.038623 1.482489 0.0 0.0\n",
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"4 19.387363 39.319881 2.656474 1.134153 0.0 0.0\n",
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"(104256, 6)\n"
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]
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}
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],
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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\\data6.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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"datas1 = pd.DataFrame(pd.read_csv(path1))\n",
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"#我只取了data表里的第3、23、16、17、18、19、20、21、27列，如果取全部列的话这一行可以去掉\n",
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"# data1 = datas1.iloc[:,np.r_[3,23,16:22,27]]\n",
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"data1=datas1.interpolate()\n",
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"values1 = data1.values\n",
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"print(data1.head())\n",
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"print(data1.shape)"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# data2= data1.drop(['date'], axis = 1)"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # 获取重构的原始数据\n",
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"# # 获取重构的原始数据\n",
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"# # 获取重构的原始数据\n",
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"path_re = 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\\iceemdan_reconstructed_data_low.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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"data_re = pd.DataFrame(pd.read_csv(path_re))"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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" vertical-align: middle;\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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" <th>0</th>\n",
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" <td>1.426824</td>\n",
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" </tr>\n",
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" <th>1</th>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1.426815</td>\n",
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" <tr>\n",
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" <td>1.426812</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>1.426810</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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" <th>104251</th>\n",
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" <td>1.629381</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>104252</th>\n",
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" <td>1.629328</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>104253</th>\n",
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" <td>1.629271</td>\n",
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" <tr>\n",
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" <th>104254</th>\n",
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" <td>1.629213</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>104255</th>\n",
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" <td>1.629152</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>104256 rows × 1 columns</p>\n",
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],
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" column_name\n",
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"1 1.426819\n",
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"2 1.426815\n",
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"3 1.426812\n",
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"4 1.426810\n",
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"... ...\n",
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"104251 1.629381\n",
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"104252 1.629328\n",
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"104253 1.629271\n",
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"104254 1.629213\n",
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"104255 1.629152\n",
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"\n",
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"[104256 rows x 1 columns]"
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]
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},
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"execution_count": 6,
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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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"data_re"
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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": 7,
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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 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 假设你已经有了原始数据和重构数据\n",
|
|||
|
|
"# # 原始数据\n",
|
|||
|
|
"original_data = data1['Power'].values\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 创建时间序列（假设时间序列与数据对应）\n",
|
|||
|
|
"time = range(len(original_data))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 创建画布和子图\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 绘制原始数据\n",
|
|||
|
|
"# plt.plot(time, original_data, label='Original Data', color='blue')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 绘制重构数据\n",
|
|||
|
|
"plt.plot( data_re[:], label='Reconstructed Data', color='red')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 添加标题和标签\n",
|
|||
|
|
"plt.title('Comparison between Original and reconstructed_data_high')\n",
|
|||
|
|
"plt.xlabel('Time')\n",
|
|||
|
|
"plt.ylabel('Power')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 显示图形\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data3=data1.iloc[:,:5]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
" Temp Humidity GHI DHI Rainfall column_name\n",
|
|||
|
|
"0 19.779453 40.025826 3.232706 1.690531 0.0 1.426824\n",
|
|||
|
|
"1 19.714937 39.605961 3.194991 1.576346 0.0 1.426819\n",
|
|||
|
|
"2 19.549330 39.608631 3.070866 1.576157 0.0 1.426815\n",
|
|||
|
|
"3 19.405870 39.680702 3.038623 1.482489 0.0 1.426812\n",
|
|||
|
|
"4 19.387363 39.319881 2.656474 1.134153 0.0 1.426810\n",
|
|||
|
|
"... ... ... ... ... ... ...\n",
|
|||
|
|
"104251 13.303740 34.212711 1.210789 0.787026 0.0 1.629381\n",
|
|||
|
|
"104252 13.120920 34.394939 2.142980 1.582670 0.0 1.629328\n",
|
|||
|
|
"104253 12.879215 35.167400 1.926214 1.545889 0.0 1.629271\n",
|
|||
|
|
"104254 12.915867 35.359989 1.317695 0.851529 0.0 1.629213\n",
|
|||
|
|
"104255 13.134816 34.500034 1.043269 0.597816 0.0 1.629152\n",
|
|||
|
|
"\n",
|
|||
|
|
"[104256 rows x 6 columns]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 创建data3和imf1_array对应的DataFrame\n",
|
|||
|
|
"data3_df = pd.DataFrame(data3)\n",
|
|||
|
|
"imf1_df = pd.DataFrame(data_re)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 合并data3_df和imf1_df\n",
|
|||
|
|
"merged_df = pd.concat([data3_df, imf1_df], axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 设置行数为35040行\n",
|
|||
|
|
"merged_df = merged_df.iloc[:104256]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# # 打印合并后的表\n",
|
|||
|
|
"print(merged_df)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"(104256, 6)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"merged_df.shape"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"(104256, 6)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# 使用MinMaxScaler进行归一化\n",
|
|||
|
|
"scaler = MinMaxScaler(feature_range=(0, 1))\n",
|
|||
|
|
"scaledData1 = scaler.fit_transform(merged_df)\n",
|
|||
|
|
"print(scaledData1.shape)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
" 0 1 2 3 4 5 0(t-96) \\\n",
|
|||
|
|
"96 0.555631 0.349673 0.190042 0.040558 0.0 0.836699 0.490360 \n",
|
|||
|
|
"97 0.564819 0.315350 0.211335 0.044613 0.0 0.836762 0.489088 \n",
|
|||
|
|
"98 0.576854 0.288321 0.229657 0.047549 0.0 0.836826 0.485824 \n",
|
|||
|
|
"99 0.581973 0.268243 0.247775 0.053347 0.0 0.836891 0.482997 \n",
|
|||
|
|
"100 0.586026 0.264586 0.266058 0.057351 0.0 0.836956 0.482632 \n",
|
|||
|
|
"\n",
|
|||
|
|
" 1(t-96) 2(t-96) 3(t-96) ... 2(t-1) 3(t-1) 4(t-1) 5(t-1) \\\n",
|
|||
|
|
"96 0.369105 0.002088 0.002013 ... 0.166009 0.036794 0.0 0.836635 \n",
|
|||
|
|
"97 0.364859 0.002061 0.001839 ... 0.190042 0.040558 0.0 0.836699 \n",
|
|||
|
|
"98 0.364886 0.001973 0.001839 ... 0.211335 0.044613 0.0 0.836762 \n",
|
|||
|
|
"99 0.365615 0.001950 0.001697 ... 0.229657 0.047549 0.0 0.836826 \n",
|
|||
|
|
"100 0.361965 0.001679 0.001167 ... 0.247775 0.053347 0.0 0.836891 \n",
|
|||
|
|
"\n",
|
|||
|
|
" 0(t+1) 1(t+1) 2(t+1) 3(t+1) 4(t+1) 5(t+1) \n",
|
|||
|
|
"96 0.564819 0.315350 0.211335 0.044613 0.0 0.836762 \n",
|
|||
|
|
"97 0.576854 0.288321 0.229657 0.047549 0.0 0.836826 \n",
|
|||
|
|
"98 0.581973 0.268243 0.247775 0.053347 0.0 0.836891 \n",
|
|||
|
|
"99 0.586026 0.264586 0.266058 0.057351 0.0 0.836956 \n",
|
|||
|
|
"100 0.590772 0.258790 0.282900 0.060958 0.0 0.837022 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 588 columns]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"n_steps_in =96 #历史时间长度\n",
|
|||
|
|
"n_steps_out=1#预测时间长度\n",
|
|||
|
|
"processedData1 = time_series_to_supervised(scaledData1,n_steps_in,n_steps_out)\n",
|
|||
|
|
"print(processedData1.head())"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"# processedData1.to_csv('processedData1.csv', index=False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']\n",
|
|||
|
|
"data_y = processedData1.loc[:,'5']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"(104159, 576)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data_x.shape"
|
|||
|
|
]
|
|||
|
|
},
|
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"96 0.836699\n",
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"97 0.836762\n",
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"98 0.836826\n",
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"99 0.836891\n",
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"100 0.836956\n",
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" ... \n",
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"104250 0.989547\n",
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"104251 0.989508\n",
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"104252 0.989466\n",
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"104253 0.989423\n",
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"104254 0.989378\n",
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"Name: 5, Length: 104159, dtype: float64"
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"(104159,)"
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"execution_count": 17,
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"source": [
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(83328, 96, 6) (83328,) (20831, 96, 6) (20831,)\n"
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]
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}
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],
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"source": [
|
|||
|
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"# 7.划分训练集和测试集\n",
|
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"\n",
|
|||
|
|
"test_size = int(len(data_x) * 0.2)\n",
|
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|
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"# 计算训练集和测试集的索引范围\n",
|
|||
|
|
"train_indices = range(len(data_x) - test_size)\n",
|
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|
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"test_indices = range(len(data_x) - test_size, len(data_x))\n",
|
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"\n",
|
|||
|
|
"# 根据索引范围划分数据集\n",
|
|||
|
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"train_X1 = data_x.iloc[train_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
|
|||
|
|
"test_X1 = data_x.iloc[test_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
|
|||
|
|
"train_y = data_y.iloc[train_indices].values\n",
|
|||
|
|
"test_y = data_y.iloc[test_indices].values\n",
|
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"\n",
|
|||
|
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"\n",
|
|||
|
|
"# # 多次运行代码时希望得到相同的数据分割，可以设置 random_state 参数为一个固定的整数值\n",
|
|||
|
|
"# train_X1,test_X1, train_y, test_y = train_test_split(data_x.values, data_y.values, test_size=0.2, random_state=343)\n",
|
|||
|
|
"# reshape input to be 3D [samples, timesteps, features]\n",
|
|||
|
|
"train_X = train_X1.reshape((train_X1.shape[0], n_steps_in, scaledData1.shape[1]))\n",
|
|||
|
|
"test_X = test_X1.reshape((test_X1.shape[0], n_steps_in,scaledData1.shape[1]))\n",
|
|||
|
|
"print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)\n",
|
|||
|
|
"# 使用train_test_split函数划分训练集和测试集，测试集的比重是40%。\n",
|
|||
|
|
"# 然后将train_X1、test_X1进行一个升维，变成三维，维数分别是[samples,timesteps,features]。\n",
|
|||
|
|
"# 打印一下他们的shape：\\\n"
|
|||
|
|
]
|
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|
|
},
|
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{
|
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|
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"cell_type": "code",
|
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|
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"execution_count": 19,
|
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"metadata": {},
|
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"outputs": [
|
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|
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"(83328, 96, 6)"
|
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|
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|
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|
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|
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"execution_count": 19,
|
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|
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|
|
"output_type": "execute_result"
|
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|
|
}
|
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|
|
],
|
|||
|
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"source": [
|
|||
|
|
"train_X1.shape"
|
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|
|
]
|
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},
|
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{
|
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"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
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"metadata": {},
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"outputs": [
|
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|
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"name": "stderr",
|
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"output_type": "stream",
|
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|
|
"text": [
|
|||
|
|
"d:\\Anaconda3\\lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
|
|||
|
|
" super().__init__(**kwargs)\n"
|
|||
|
|
]
|
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|
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|
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|
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|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
|
|||
|
|
"</pre>\n"
|
|||
|
|
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|
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|
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|
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|
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"\u001b[1mModel: \"sequential\"\u001b[0m\n"
|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
|||
|
|
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
|
|||
|
|
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
|||
|
|
"│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">69,120</span> │\n",
|
|||
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|||
|
|
"│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
|
|||
|
|
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
|
|||
|
|
"</pre>\n"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
|||
|
|
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
|
|||
|
|
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
|||
|
|
"│ lstm (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m69,120\u001b[0m │\n",
|
|||
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|||
|
|
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m129\u001b[0m │\n",
|
|||
|
|
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
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|
|||
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,249</span> (270.50 KB)\n",
|
|||
|
|
"</pre>\n"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m69,249\u001b[0m (270.50 KB)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
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|
|
{
|
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|
|
"data": {
|
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|
|
"text/html": [
|
|||
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,249</span> (270.50 KB)\n",
|
|||
|
|
"</pre>\n"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m69,249\u001b[0m (270.50 KB)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
|||
|
|
"</pre>\n"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from keras.layers import GRU, Bidirectional\n",
|
|||
|
|
"from keras.models import Model\n",
|
|||
|
|
"from keras.layers import Input, Conv1D, MaxPooling1D, LSTM, Dense, Attention, Flatten\n",
|
|||
|
|
"import keras\n",
|
|||
|
|
"from keras.models import Sequential\n",
|
|||
|
|
"from keras.layers import LSTM, Dense\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 创建模型\n",
|
|||
|
|
"model = Sequential()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 添加单层 LSTM\n",
|
|||
|
|
"model.add(LSTM(units=128, input_shape=(96, 6)))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 添加输出层\n",
|
|||
|
|
"model.add(Dense(1))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 编译模型\n",
|
|||
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 查看模型结构\n",
|
|||
|
|
"model.summary()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch 1/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m122s\u001b[0m 92ms/step - loss: 0.0156 - val_loss: 1.0318e-05\n",
|
|||
|
|
"Epoch 2/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 85ms/step - loss: 1.2280e-05 - val_loss: 2.9811e-06\n",
|
|||
|
|
"Epoch 3/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 87ms/step - loss: 9.1935e-06 - val_loss: 2.5579e-06\n",
|
|||
|
|
"Epoch 4/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.0443e-05 - val_loss: 8.4623e-06\n",
|
|||
|
|
"Epoch 5/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 90ms/step - loss: 1.1108e-05 - val_loss: 8.1167e-06\n",
|
|||
|
|
"Epoch 6/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 85ms/step - loss: 5.3451e-06 - val_loss: 2.4689e-06\n",
|
|||
|
|
"Epoch 7/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 90ms/step - loss: 1.5962e-05 - val_loss: 2.2134e-06\n",
|
|||
|
|
"Epoch 8/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 5.3290e-06 - val_loss: 3.5285e-07\n",
|
|||
|
|
"Epoch 9/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 97ms/step - loss: 4.5184e-06 - val_loss: 1.2596e-07\n",
|
|||
|
|
"Epoch 10/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.6976e-06 - val_loss: 7.1095e-06\n",
|
|||
|
|
"Epoch 11/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m123s\u001b[0m 95ms/step - loss: 6.6386e-06 - val_loss: 1.0392e-07\n",
|
|||
|
|
"Epoch 12/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 97ms/step - loss: 2.3165e-06 - val_loss: 8.4822e-07\n",
|
|||
|
|
"Epoch 13/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 92ms/step - loss: 3.5823e-06 - val_loss: 4.9285e-08\n",
|
|||
|
|
"Epoch 14/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 90ms/step - loss: 3.1791e-06 - val_loss: 2.2294e-07\n",
|
|||
|
|
"Epoch 15/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 2.9977e-06 - val_loss: 3.9852e-06\n",
|
|||
|
|
"Epoch 16/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 2.3874e-06 - val_loss: 1.3594e-07\n",
|
|||
|
|
"Epoch 17/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 103ms/step - loss: 3.1801e-07 - val_loss: 1.6932e-07\n",
|
|||
|
|
"Epoch 18/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.5647e-06 - val_loss: 2.1397e-08\n",
|
|||
|
|
"Epoch 19/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 104ms/step - loss: 1.4188e-06 - val_loss: 1.4569e-07\n",
|
|||
|
|
"Epoch 20/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 99ms/step - loss: 1.1043e-06 - val_loss: 5.9704e-07\n",
|
|||
|
|
"Epoch 21/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 103ms/step - loss: 2.0067e-06 - val_loss: 2.0218e-06\n",
|
|||
|
|
"Epoch 22/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 111ms/step - loss: 1.9982e-06 - val_loss: 2.2618e-07\n",
|
|||
|
|
"Epoch 23/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 104ms/step - loss: 1.4178e-06 - val_loss: 1.3009e-06\n",
|
|||
|
|
"Epoch 24/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 100ms/step - loss: 2.7170e-06 - val_loss: 1.2247e-08\n",
|
|||
|
|
"Epoch 25/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 1.8664e-06 - val_loss: 5.6499e-07\n",
|
|||
|
|
"Epoch 26/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 68ms/step - loss: 1.3434e-06 - val_loss: 1.2509e-08\n",
|
|||
|
|
"Epoch 27/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 65ms/step - loss: 1.8632e-06 - val_loss: 5.3179e-07\n",
|
|||
|
|
"Epoch 28/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 63ms/step - loss: 1.2746e-06 - val_loss: 9.0354e-08\n",
|
|||
|
|
"Epoch 29/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 63ms/step - loss: 1.5440e-06 - val_loss: 1.2604e-07\n",
|
|||
|
|
"Epoch 30/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 68ms/step - loss: 1.2646e-06 - val_loss: 2.5639e-07\n",
|
|||
|
|
"Epoch 31/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 62ms/step - loss: 1.3377e-06 - val_loss: 4.0479e-08\n",
|
|||
|
|
"Epoch 32/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 37ms/step - loss: 7.9140e-07 - val_loss: 1.1824e-06\n",
|
|||
|
|
"Epoch 33/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 37ms/step - loss: 2.1865e-06 - val_loss: 4.2140e-06\n",
|
|||
|
|
"Epoch 34/100\n",
|
|||
|
|
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 39ms/step - loss: 1.4884e-06 - val_loss: 1.8359e-06\n",
|
|||
|
|
"\u001b[1m651/651\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Compile and train the model\n",
|
|||
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')\n",
|
|||
|
|
"from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 定义早停机制\n",
|
|||
|
|
"early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 拟合模型，并添加早停机制和模型检查点\n",
|
|||
|
|
"history = model.fit(train_X, train_y, epochs=100, batch_size=64, validation_data=(test_X, test_y), \n",
|
|||
|
|
" callbacks=[early_stopping])\n",
|
|||
|
|
"# 预测\n",
|
|||
|
|
"lstm_pred = model.predict(test_X)\n",
|
|||
|
|
"# 将预测结果的形状修改为与原始数据相同的形状"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"(20831, 1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"lstm_pred.shape"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"(20831,)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"test_y.shape"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"test_y1=test_y.reshape(20831,1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([[0.65620206],\n",
|
|||
|
|
" [0.6565139 ],\n",
|
|||
|
|
" [0.65682633],\n",
|
|||
|
|
" ...,\n",
|
|||
|
|
" [0.98946626],\n",
|
|||
|
|
" [0.98942303],\n",
|
|||
|
|
" [0.98937795]])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"test_y1"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"results1 = np.broadcast_to(lstm_pred, (20831, 6))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"test_y2 = np.broadcast_to(test_y1, (20831, 6))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# 反归一化\n",
|
|||
|
|
"inv_forecast_y = scaler.inverse_transform(results1)\n",
|
|||
|
|
"inv_test_y = scaler.inverse_transform(test_y2)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([[2.81937911e+01, 6.84129659e+01, 9.24487326e+02, 4.31993807e+02,\n",
|
|||
|
|
" 1.56176147e+01, 1.19628092e+00],\n",
|
|||
|
|
" [2.82096130e+01, 6.84437997e+01, 9.24926524e+02, 4.32198926e+02,\n",
|
|||
|
|
" 1.56250366e+01, 1.19668613e+00],\n",
|
|||
|
|
" [2.82254649e+01, 6.84746920e+01, 9.25366555e+02, 4.32404434e+02,\n",
|
|||
|
|
" 1.56324725e+01, 1.19709211e+00],\n",
|
|||
|
|
" ...,\n",
|
|||
|
|
" [4.51026009e+01, 1.01364948e+02, 1.39385702e+03, 6.51203592e+02,\n",
|
|||
|
|
" 2.35493057e+01, 1.62932764e+00],\n",
|
|||
|
|
" [4.51004072e+01, 1.01360673e+02, 1.39379613e+03, 6.51175153e+02,\n",
|
|||
|
|
" 2.35482767e+01, 1.62927146e+00],\n",
|
|||
|
|
" [4.50981204e+01, 1.01356216e+02, 1.39373265e+03, 6.51145506e+02,\n",
|
|||
|
|
" 2.35472040e+01, 1.62921289e+00]])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"inv_test_y"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Test RMSE: 0.002\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABQsAAAKTCAYAAAC+UV5jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gc1fXw8e+dbeq9S+69914wvfcECCGEGhJCT4Mk1CSQQN7QfrRA6IQOphfj3rvcm4plyeq9a9u8f4wxEGysMrsjrc7nefw80mrm3iNZ2t05c+85Std1HSGEEEIIIYQQQgghRK+nWR2AEEIIIYQQQgghhBCie5BkoRBCCCGEEEIIIYQQApBkoRBCCCGEEEIIIYQQ4hBJFgohhBBCCCGEEEIIIQBJFgohhBBCCCGEEEIIIQ6RZKEQQgghhBBCCCGEEAKQZKEQQgghhBBCCCGEEOIQu9UBtIff76e4uJjo6GiUUlaHI4QQQgghhBBCCCFEj6LrOg0NDWRkZKBpR18/2COShcXFxfTp08fqMIQQQgghhBBCCCGE6NEKCwvJyso66td7RLIwOjoaML6ZmJgYi6MRQgghhBBCCCGEEKJnqa+vp0+fPofzbEfTI5KFX289jomJkWShEEIIIYQQQgghhBCddKwSf9LgRAghhBBCCCGEEEIIAUiyUAghhBBCCCGEEEIIcYgkC4UQQgghhBBCCCGEEEAPqVnYXj6fD4/HY3UYPYLT6fzBNtlCCCGEEEIIIYQQovcJiWShruuUlpZSW1trdSg9hqZpDBgwAKfTaXUoQgghhBBCCCGEEKKbCIlk4deJwpSUFCIiIo7Z1aW38/v9FBcXU1JSQt++feXnJYQQQgghhBBCCCGAEEgW+ny+w4nCxMREq8PpMZKTkykuLsbr9eJwOKwORwghhBBCCCGEEEJ0Az2+aN3XNQojIiIsjqRn+Xr7sc/nszgSIYQQQgghhBBCCNFd9Phk4ddkK23HyM9LCCGEEEIIIYQQQvyvkEkWCiGEEEIIIYQQQgghukaShUIIIYQQQgghhBBCCECShUIIIYQQQgghhBBCiEMkWWihefPmccstt1gdhhBCCCGEEEIIIYQQgCQLuzVd1/F6vVaHIYQQQgghhBBCCCF6iZBLFuq6TrPba8k/XdfbHecVV1zB0qVLefTRR1FKoZTixRdfRCnFZ599xqRJk3C5XKxYsYIrrriC88477zvn33LLLcybN+/w536/nwceeIABAwYQHh7OuHHjeOedd0z6qQohhBBCCCGEEEKI3sBudQBma/H4GHnXF5bMvfO+U4lwtu9H+uijj7J3715Gjx7NfffdB8COHTsAuP322/nnP//JwIEDiY+Pb9d4DzzwAK+++ipPP/00Q4YMYdmyZVx22WUkJydz3HHHde4bEkIIIYQQQgghhBC9SsglC3uK2NhYnE4nERERpKWlAbB7924A7rvvPk4++eR2j9XW1sb999/PV199xYwZMwAYOHAgK1as4JlnnpFkoRBCCCGEEEIIIYRol5BLFoY7bOy871TL5jbD5MmTO3R8Tk4Ozc3N30swut1uJkyYYEpMQgghhBBCCCGEECL0hVyyUCnV7q3A3VVkZOR3Ptc07Xv1ED0ez+GPGxsbAfjkk0/IzMz8znEulytAUQohhBBCCCGEEEKIUNOzs2o9nNPpxOfzHfO45ORktm/f/p3HsrOzcTgcAIwcORKXy8WBAwdky7EQQgghhBBCCCGE6DRJFlqof//+rF27lv379xMVFYXf7z/icSeccAIPPfQQL7/8MjNmzODVV19l+/bth7cYR0dH89vf/pZbb70Vv9/P7NmzqaurY+XKlcTExPDzn/88mN+WEEIIIYQQQgghhOihNKsD6M1++9vfYrPZGDlyJMnJyRw4cOCIx5166qnceeed/P73v2fKlCk0NDRw+eWXf+eYv/zlL9x555088MADjBgxgtNOO41PPvmEAQMGBONbEUIIIYQQQgghhBAhQOn/WwyvG6qvryc2Npa6ujpiYmK+87XW1lby8/MZMGAAYWFhFkXY88jPTQghhBBCCCGEEKL3+KH82rfJykIhhBBCCCGEEEIIIQQgNQuFEEIIIYQQQgghRC+k6zoLdpbx/uaDNLt9zB2azKVT+xLutFkdmqUkWSiEEEIIIYQQQgghehWfX+dP729j54YlnGZbj4af9/dNZ13+DJ752WSrw7OUJAuFEEIIIYQQQgghRK/y4Oc7GbD57/zd9cnhx35h/4RS19VA704WSs1CIYQQQgghhBBCCNFrrMqppGrlS1xnP5QoHHUBDD8LDZ2MgaOtDa4bkJWFQgghhBBCCCGEEKJXcHv93D0/m+ds7xsPzL4VTrrH+Lg4G1IlWSjJQiGEEEIIIYQQQgjRK7y9sZDxNV/Qz1GOPyIJbe7vvvlixnjL4upOJFkohBBCCCGEEEIIIUKe1+fn2SV7eMk2HwBt1s3gjLQ2qG5IahYKIYQQQgghhBBCiJD38dYSptQvoJ9Wjh6RDFOutjqkbkmShUIIIYQQQgghhBAi5L2+Jo8bDq0qVLNlVeHRSLKwh3C73VaHIIQQQgghhBBCCNEj5ZQ3El64jH5aOX5XHEy+yuqQui1JFlpk3rx53HDDDdxwww3ExsaSlJTEnXfeia7rAPTv35+//OUvXH755cTExPCLX/wCgBUrVjBnzhzCw8Pp06cPN910E01NTVZ+K0IIIYQQQgghhBDd2lsbCvmJbREA2vhLZFXhDwi9ZKGug7vJmn+HEn3t9dJLL2G321m3bh2PPvoo//rXv3juuecOf/2f//wn48aNY/Pmzdx5553k5uZy2mmnceGFF7J161befPNNVqxYwQ033GD2T1EIIYQQQgghhBAiJPj9Oos37eQEbbPxwKQrLI2nuwu9bsieZrg/w5q5/1jcocx0nz59ePjhh1FKMWzYMLZt28bDDz/MtddeC8AJJ5zAb37zm8PHX3PNNfz0pz/llltuAWDIkCE89thjHHfccTz11FOEhYWZ+u0IIYQQQgghhBBC9HQbD9Qwu2UxDocPf/oEtJQRVofUrYXeysIeZPr06SilDn8+Y8YM9u3bh8/nA2Dy5MnfOX7Lli28+OKLREVFHf536qmn4vf7yc/PD2rsQgghhBBCCCGEED3Bp9tKuNC2HABt/KUWR9P9hd7KQkeEscLPqrlNFBn53VWKjY2NXHfdddx0003fO7Zv376mzi2EEEIIIYQQQgjR0/n9Ovu2rmW0th+/cqCN+ZHVIXV7HV5ZuGzZMs4++2wyMjJQSjF//vxjntPW1saf/vQn+vXrh8vlon///jz//POdiffYlDK2Alvx71urBNtj7dq13/l8zZo1DBkyBJvNdsTjJ06cyM6dOxk8ePD3/jmdzk7/yIQQQgghhBBCCCFC0ZaiWua2fAWAPvRUiEiwOKLur8PJwqamJsaNG8cTTzzR7nMuuugiFi5cyH/+8x/27NnD66+/zrBhwzo6dcg5cOAAt9122+GfyeOPP87NN9981OP/8Ic/sGrVKm644Qays7PZt28fH3zwgTQ4EUIIIYQQQgghhDiCJbuKOd+2AgDbBNmC3B4d3oZ8+umnc/rpp7f7+M8//5ylS5eSl5dHQoKRve3fv39Hpw1Jl19+OS0tLUydOhWbzcbNN9/ML37xi6MeP3bsWJYuXcqf/vQn5syZg67rDBo0iIsvvjiIUQshhBBCCCGEEEL0DPU7FpCs6mh1xhM2+GSrw+kRAl6z8MMPP2Ty5Mk8+OCDvPLKK0RGRnLOOefwl7/8hfDw8COe09bWRltb2+HP6+vrAx2mJRwOB4888ghPPfXU9762f//+I54zZcoUvvzyywBHJoQQQgghhBBCCNGz1TS5GVKzFGzgG3Ee2KWEW3sEPFmYl5fHihUrCAsL4/3336eyspLrr7+eqqoqXnjhhSOe88ADD3DvvfcGOjQhhBBCCCGEEEIIEaKW7yvnBG0zAJGjz7I4mp6jwzULO8rv96OU4rXXXmPq1KmcccYZ/Otf/+Kll16ipaXliOfccccd1NXVHf5XWFgY6DCFEEIIIYQQQgghRAj
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# 计算均方根误差\n",
|
|||
|
|
"rmse = sqrt(mean_squared_error(inv_test_y[:,5], inv_forecast_y[:,5]))\n",
|
|||
|
|
"print('Test RMSE: %.3f' % rmse)\n",
|
|||
|
|
"#画图\n",
|
|||
|
|
"plt.figure(figsize=(16,8))\n",
|
|||
|
|
"plt.plot(inv_test_y[:,5], label='true')\n",
|
|||
|
|
"plt.plot(inv_forecast_y[:,5], label='pre')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"mean_squared_error: 1.8358609523038586e-06\n",
|
|||
|
|
"mean_absolute_error: 0.0012240899816947145\n",
|
|||
|
|
"rmse: 0.0013549394644425479\n",
|
|||
|
|
"r2 score: 0.9998451201868883\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(lstm_pred, test_y)) # mse)\n",
|
|||
|
|
"print(\"mean_absolute_error:\", mean_absolute_error(lstm_pred, test_y)) # mae\n",
|
|||
|
|
"print(\"rmse:\", sqrt(mean_squared_error(lstm_pred,test_y)))\n",
|
|||
|
|
"print(\"r2 score:\", r2_score(inv_test_y[:], inv_forecast_y[:]))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
|
|||
|
|
"df1.to_csv('低频_test.csv', index=False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"df2 = pd.DataFrame(inv_forecast_y[:,5], columns=['column_name'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
|
|||
|
|
"df2.to_csv('低频_forecast.csv', index=False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"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": "3.9.13"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|