1198 lines
274 KiB
Plaintext
1198 lines
274 KiB
Plaintext
{
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"cells": [
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{
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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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{
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"# data2= data1.drop(['date','Air_P','RH'], 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": 22,
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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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"high_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\\high_re.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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"high_re = pd.DataFrame(pd.read_csv(high_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": 23,
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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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" column_name\n",
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"0 -1.426824\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]\n"
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]
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}
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],
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"source": [
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"reconstructed_data_high= high_re\n",
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"# # 打印重构的原始数据\n",
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"print(reconstructed_data_high)"
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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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{
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"data": {
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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(reconstructed_data_high[200:1000], 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": 26,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data3=data1.iloc[:,:5]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"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(reconstructed_data_high)\n",
|
||
"\n",
|
||
"# # 合并data3_df和imf1_df\n",
|
||
"merged_df = pd.concat([data3_df, imf1_df], axis=1)\n",
|
||
"\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": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(104256, 6)"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"merged_df.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"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": 30,
|
||
"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 0.245160 0.490360 \n",
|
||
"97 0.564819 0.315350 0.211335 0.044613 0.0 0.264683 0.489088 \n",
|
||
"98 0.576854 0.288321 0.229657 0.047549 0.0 0.283988 0.485824 \n",
|
||
"99 0.581973 0.268243 0.247775 0.053347 0.0 0.303131 0.482997 \n",
|
||
"100 0.586026 0.264586 0.266058 0.057351 0.0 0.322308 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 0.225396 \n",
|
||
"97 0.364859 0.002061 0.001839 ... 0.190042 0.040558 0.0 0.245160 \n",
|
||
"98 0.364886 0.001973 0.001839 ... 0.211335 0.044613 0.0 0.264683 \n",
|
||
"99 0.365615 0.001950 0.001697 ... 0.229657 0.047549 0.0 0.283988 \n",
|
||
"100 0.361965 0.001679 0.001167 ... 0.247775 0.053347 0.0 0.303131 \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 0.264683 \n",
|
||
"97 0.576854 0.288321 0.229657 0.047549 0.0 0.283988 \n",
|
||
"98 0.581973 0.268243 0.247775 0.053347 0.0 0.303131 \n",
|
||
"99 0.586026 0.264586 0.266058 0.057351 0.0 0.322308 \n",
|
||
"100 0.590772 0.258790 0.282900 0.060958 0.0 0.340588 \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": 31,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"# processedData1.to_csv('processedData1.csv', index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']\n",
|
||
"data_y = processedData1.loc[:,'5']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(104159, 576)"
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data_x.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"96 0.245160\n",
|
||
"97 0.264683\n",
|
||
"98 0.283988\n",
|
||
"99 0.303131\n",
|
||
"100 0.322308\n",
|
||
" ... \n",
|
||
"104250 0.000090\n",
|
||
"104251 0.000099\n",
|
||
"104252 0.000109\n",
|
||
"104253 0.000118\n",
|
||
"104254 0.000128\n",
|
||
"Name: 5, Length: 104159, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data_y"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(104159,)"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data_y.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(83328, 96, 6) (83328,) (20831, 96, 6) (20831,)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 7.划分训练集和测试集\n",
|
||
"\n",
|
||
"test_size = int(len(data_x) * 0.2)\n",
|
||
"# 计算训练集和测试集的索引范围\n",
|
||
"train_indices = range(len(data_x) - test_size)\n",
|
||
"test_indices = range(len(data_x) - test_size, len(data_x))\n",
|
||
"\n",
|
||
"# 根据索引范围划分数据集\n",
|
||
"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",
|
||
"\n",
|
||
"\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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(83328, 96, 6)"
|
||
]
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"train_X1.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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\">Model: \"functional_1\"</span>\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1mModel: \"functional_1\"\u001b[0m\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\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
|
||
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃<span style=\"font-weight: bold\"> Connected to </span>┃\n",
|
||
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
|
||
"│ input_layer_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ - │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">832</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ max_pooling1d_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">49,920</span> │ max_pooling1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ self_attention_1 │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, │ <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SelfAttention</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, │ │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
|
||
"│ │ <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)] │ │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ global_average_poo… │ (<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\">0</span> │ self_attention_1… │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ dense_9 (<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> │ global_average_p… │\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┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
|
||
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
|
||
"│ input_layer_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m6\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
|
||
"│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m832\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m]… │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ max_pooling1d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv1d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"│ (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m49,920\u001b[0m │ max_pooling1d_1[\u001b[38;5;34m…\u001b[0m │\n",
|
||
"│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ self_attention_1 │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
|
||
"│ (\u001b[38;5;33mSelfAttention\u001b[0m) │ \u001b[38;5;34m128\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
|
||
"│ │ \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)] │ │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ self_attention_1… │\n",
|
||
"│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
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"│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m129\u001b[0m │ global_average_p… │\n",
|
||
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
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},
|
||
{
|
||
"data": {
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"text/html": [
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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\">116,929</span> (456.75 KB)\n",
|
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"</pre>\n"
|
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],
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"text/plain": [
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m116,929\u001b[0m (456.75 KB)\n"
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||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
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"text/html": [
|
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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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">116,929</span> (456.75 KB)\n",
|
||
"</pre>\n"
|
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],
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"text/plain": [
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"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m116,929\u001b[0m (456.75 KB)\n"
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]
|
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},
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||
"metadata": {},
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"output_type": "display_data"
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},
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{
|
||
"data": {
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"text/html": [
|
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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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
||
"</pre>\n"
|
||
],
|
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"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": [
|
||
"import tensorflow as tf\n",
|
||
"from tensorflow.keras.layers import Input, Conv1D, Bidirectional, GlobalAveragePooling1D, Dense, GRU, MaxPooling1D\n",
|
||
"from tensorflow.keras.models import Model\n",
|
||
"class SelfAttention(tf.keras.layers.Layer):\n",
|
||
" def __init__(self, d_model, num_heads):\n",
|
||
" super(SelfAttention, self).__init__()\n",
|
||
" self.num_heads = num_heads\n",
|
||
" self.d_model = d_model\n",
|
||
" assert d_model % self.num_heads == 0\n",
|
||
" self.depth = d_model // self.num_heads\n",
|
||
" self.wq = tf.keras.layers.Dense(d_model)\n",
|
||
" self.wk = tf.keras.layers.Dense(d_model)\n",
|
||
" self.wv = tf.keras.layers.Dense(d_model)\n",
|
||
" self.dense = tf.keras.layers.Dense(d_model)\n",
|
||
"\n",
|
||
" def split_heads(self, x, batch_size):\n",
|
||
" x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n",
|
||
" return tf.transpose(x, perm=[0, 2, 1, 3])\n",
|
||
"\n",
|
||
" def call(self, v, k, q, mask):\n",
|
||
" batch_size = tf.shape(q)[0]\n",
|
||
" q = self.wq(q)\n",
|
||
" k = self.wk(k)\n",
|
||
" v = self.wv(v)\n",
|
||
"\n",
|
||
" q = self.split_heads(q, batch_size)\n",
|
||
" k = self.split_heads(k, batch_size)\n",
|
||
" v = self.split_heads(v, batch_size)\n",
|
||
"\n",
|
||
" scaled_attention, attention_weights = self.scaled_dot_product_attention(q, k, v, mask)\n",
|
||
" scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])\n",
|
||
" concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))\n",
|
||
" output = self.dense(concat_attention)\n",
|
||
" return output, attention_weights\n",
|
||
"\n",
|
||
" def scaled_dot_product_attention(self, q, k, v, mask):\n",
|
||
" matmul_qk = tf.matmul(q, k, transpose_b=True)\n",
|
||
" dk = tf.cast(tf.shape(k)[-1], tf.float32)\n",
|
||
" scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n",
|
||
"\n",
|
||
" if mask is not None:\n",
|
||
" scaled_attention_logits += (mask * -1e9)\n",
|
||
"\n",
|
||
" attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)\n",
|
||
" output = tf.matmul(attention_weights, v)\n",
|
||
" return output, attention_weights\n",
|
||
"\n",
|
||
"class SelfAttentionWithRelativePositionEncoding(tf.keras.layers.Layer):\n",
|
||
" def __init__(self, d_model, num_heads, max_len=5000):\n",
|
||
" super(SelfAttentionWithRelativePositionEncoding, self).__init__()\n",
|
||
" self.num_heads = num_heads\n",
|
||
" self.d_model = d_model\n",
|
||
" self.max_len = max_len\n",
|
||
" self.wq = tf.keras.layers.Dense(d_model)\n",
|
||
" self.wk = tf.keras.layers.Dense(d_model)\n",
|
||
" self.wv = tf.keras.layers.Dense(d_model)\n",
|
||
" self.dense = tf.keras.layers.Dense(d_model)\n",
|
||
" self.relative_position_encoding = AdvancedRelativePositionalEncoding(d_model)\n",
|
||
"\n",
|
||
" def call(self, v, k, q, mask):\n",
|
||
" batch_size = tf.shape(q)[0]\n",
|
||
" q = self.wq(q)\n",
|
||
" k = self.wk(k)\n",
|
||
" v = self.wv(v)\n",
|
||
"\n",
|
||
" # 添加相对位置编码\n",
|
||
" k += self.relative_position_encoding(k)\n",
|
||
" q += self.relative_position_encoding(q)\n",
|
||
"\n",
|
||
" q = self.split_heads(q, batch_size)\n",
|
||
" k = self.split_heads(k, batch_size)\n",
|
||
" v = self.split_heads(v, batch_size)\n",
|
||
"\n",
|
||
" scaled_attention, attention_weights = self.scaled_dot_product_attention(q, k, v, mask)\n",
|
||
" scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])\n",
|
||
" concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))\n",
|
||
" output = self.dense(concat_attention)\n",
|
||
" return output, attention_weights\n",
|
||
"\n",
|
||
" def split_heads(self, x, batch_size):\n",
|
||
" x = tf.reshape(x, (batch_size, -1, self.num_heads, self.d_model // self.num_heads))\n",
|
||
" return tf.transpose(x, perm=[0, 2, 1, 3])\n",
|
||
"\n",
|
||
" def scaled_dot_product_attention(self, q, k, v, mask):\n",
|
||
" matmul_qk = tf.matmul(q, k, transpose_b=True)\n",
|
||
" dk = tf.cast(tf.shape(k)[-1], tf.float32)\n",
|
||
" scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n",
|
||
"\n",
|
||
" if mask is not None:\n",
|
||
" scaled_attention_logits += (mask * -1e9)\n",
|
||
"\n",
|
||
" attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)\n",
|
||
" output = tf.matmul(attention_weights, v)\n",
|
||
" return output, attention_weights\n",
|
||
"\n",
|
||
"import tensorflow as tf\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"import tensorflow as tf\n",
|
||
"\n",
|
||
"class AdvancedRelativePositionalEncoding(tf.keras.layers.Layer):\n",
|
||
" def __init__(self, d_model, max_len=5000):\n",
|
||
" super(AdvancedRelativePositionalEncoding, self).__init__()\n",
|
||
" self.max_len = max_len\n",
|
||
" self.d_model = d_model\n",
|
||
" # #创新点 引入可变化的参数u，v 进行线性变化\n",
|
||
" self.u = tf.Variable(tf.random(self.add_weight(shape=(d_model,), initializer='random_normal', trainable=True)))\n",
|
||
" self.v = tf.Variable(tf.random(self.add_weight(shape=(d_model,), initializer='random_normal', trainable=True)))\n",
|
||
"\n",
|
||
" def call(self, inputs):\n",
|
||
" seq_length = tf.shape(inputs)[1]\n",
|
||
" pos_encoding = self.relative_positional_encoding(seq_length, self.d_model)\n",
|
||
"\n",
|
||
" # 保留Sinusoidal生成方案\n",
|
||
" angle_rads_sin = pos_encoding[:, :, 0]\n",
|
||
" angle_rads_cos = pos_encoding[:, :, 1]\n",
|
||
"\n",
|
||
" # 线性维度转换层\n",
|
||
" ti = tf.expand_dims(inputs, axis=1) # shape: [batch_size, 1, seq_length, d_model]\n",
|
||
" tj = tf.expand_dims(inputs, axis=2) # shape: [batch_size, seq_length, 1, d_model]\n",
|
||
"\n",
|
||
" # 计算表征 t_i * W_q * W_k^T * t_j\n",
|
||
" t_wq_wk_t = tf.einsum('bijd,d->bij', tf.einsum('bijd,d->bijd', ti, self.u), tf.transpose(tj, perm=[0, 1, 3, 2]))\n",
|
||
"\n",
|
||
" # 计算基于全局的偏置 t_i * W_q * W_k^T * R_(i-j)^T\n",
|
||
" t_wq_wk_r = tf.einsum('bijd,d->bij', tf.einsum('bijd,d->bijd', ti, self.u), angle_rads_sin)\n",
|
||
"\n",
|
||
" # 计算基于表征的偏置 u * W_q * W_k^T * t_j\n",
|
||
" E_u = tf.einsum('bd,bijd->bij', self.u, ti)\n",
|
||
"\n",
|
||
" # 计算基于表征的局部偏置 v * W_q * W_k^T * R_(i-j)^T\n",
|
||
" R_v = tf.einsum('bd,bijd->bij', self.v, angle_rads_cos)\n",
|
||
"\n",
|
||
" \n",
|
||
" pe_with_params = t_wq_wk_t + R_v + t_wq_wk_r + E_u\n",
|
||
"\n",
|
||
" return inputs + pe_with_params\n",
|
||
"\n",
|
||
" def relative_positional_encoding(self, position, d_model):\n",
|
||
" pos = tf.range(position, dtype=tf.float32)\n",
|
||
" i = tf.range(d_model, dtype=tf.float32)\n",
|
||
"\n",
|
||
" angles = 1 / tf.pow(10000.0, (2 * (i // 2)) / tf.cast(d_model, tf.float32))\n",
|
||
" angle_rads = tf.einsum('i,j->ij', pos, angles)\n",
|
||
"\n",
|
||
" pos_encoding = tf.stack([tf.sin(angle_rads[:, 0::2]), tf.cos(angle_rads[:, 1::2])], axis=-1)\n",
|
||
" pos_encoding = tf.pad(pos_encoding, [[0, 0], [0, 0], [0, 0]]) #embbing维度嵌入层\n",
|
||
"\n",
|
||
" return pos_encoding\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"def PosConv1biGRUWithSelfAttention(input_shape, gru_units, num_heads):\n",
|
||
" inputs = Input(shape=input_shape)\n",
|
||
" # CNN layer\n",
|
||
" cnn_layer = Conv1D(filters=64, kernel_size=2, activation='relu')(inputs)\n",
|
||
" cnn_layer = MaxPooling1D(pool_size=1)(cnn_layer)\n",
|
||
" gru_output = Bidirectional(GRU(gru_units, return_sequences=True))(cnn_layer)\n",
|
||
" \n",
|
||
" # Apply Self-Attention\n",
|
||
" self_attention = SelfAttention(d_model=gru_units*2, num_heads=num_heads)\n",
|
||
" gru_output, _ = self_attention(gru_output, gru_output, gru_output, mask=None)\n",
|
||
" \n",
|
||
" pool1 = GlobalAveragePooling1D()(gru_output)\n",
|
||
" output = Dense(1)(pool1)\n",
|
||
" \n",
|
||
" return Model(inputs=inputs, outputs=output)\n",
|
||
"\n",
|
||
"\n",
|
||
"input_shape = (96, 6)\n",
|
||
"gru_units = 64\n",
|
||
"num_heads = 8\n",
|
||
"\n",
|
||
"# Create model\n",
|
||
"model = PosConv1biGRUWithSelfAttention(input_shape, gru_units, num_heads)\n",
|
||
"model.compile(optimizer='adam', loss='mse')\n",
|
||
"model.summary()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"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[1m71s\u001b[0m 53ms/step - loss: 0.0196 - val_loss: 0.0018\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 59ms/step - loss: 0.0013 - val_loss: 0.0019\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 61ms/step - loss: 0.0012 - val_loss: 0.0017\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 58ms/step - loss: 0.0010 - val_loss: 0.0015\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 57ms/step - loss: 9.7760e-04 - val_loss: 0.0018\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 59ms/step - loss: 9.9108e-04 - val_loss: 0.0017\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 69ms/step - loss: 9.7381e-04 - val_loss: 0.0016\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 63ms/step - loss: 9.1248e-04 - val_loss: 0.0015\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 66ms/step - loss: 9.4959e-04 - val_loss: 0.0016\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 58ms/step - loss: 9.3746e-04 - val_loss: 0.0016\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 68ms/step - loss: 9.1358e-04 - val_loss: 0.0015\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 61ms/step - loss: 8.8907e-04 - val_loss: 0.0016\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 67ms/step - loss: 9.0822e-04 - val_loss: 0.0015\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 68ms/step - loss: 8.9505e-04 - val_loss: 0.0015\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 68ms/step - loss: 8.9855e-04 - val_loss: 0.0015\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 68ms/step - loss: 9.4414e-04 - val_loss: 0.0015\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 70ms/step - loss: 8.8443e-04 - val_loss: 0.0014\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 68ms/step - loss: 8.7323e-04 - val_loss: 0.0015\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 69ms/step - loss: 8.7132e-04 - val_loss: 0.0014\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 66ms/step - loss: 8.7265e-04 - val_loss: 0.0015\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 63ms/step - loss: 8.4318e-04 - val_loss: 0.0015\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 67ms/step - loss: 7.9306e-04 - val_loss: 0.0015\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 65ms/step - loss: 8.1019e-04 - val_loss: 0.0015\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 70ms/step - loss: 7.8526e-04 - val_loss: 0.0015\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 72ms/step - loss: 8.6874e-04 - val_loss: 0.0014\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 69ms/step - loss: 8.0322e-04 - val_loss: 0.0015\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 69ms/step - loss: 8.3907e-04 - val_loss: 0.0014\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 71ms/step - loss: 8.2911e-04 - val_loss: 0.0015\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 71ms/step - loss: 8.1428e-04 - val_loss: 0.0015\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 69ms/step - loss: 8.1292e-04 - val_loss: 0.0015\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 69ms/step - loss: 8.2787e-04 - val_loss: 0.0015\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 76ms/step - loss: 7.9780e-04 - val_loss: 0.0015\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 75ms/step - loss: 7.9815e-04 - val_loss: 0.0015\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 74ms/step - loss: 7.9996e-04 - val_loss: 0.0016\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 71ms/step - loss: 7.5751e-04 - val_loss: 0.0016\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 65ms/step - loss: 8.1121e-04 - val_loss: 0.0015\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 71ms/step - loss: 7.6797e-04 - val_loss: 0.0015\n",
|
||
"\u001b[1m651/651\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\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": 40,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(history.history['loss'], label='train')\n",
|
||
"plt.plot(history.history['val_loss'], label='test')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(20831, 1)"
|
||
]
|
||
},
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"lstm_pred.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(20831,)"
|
||
]
|
||
},
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"test_y.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"test_y1=test_y.reshape(20831,1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[4.52189913e-01],\n",
|
||
" [3.12516873e-01],\n",
|
||
" [3.25310588e-01],\n",
|
||
" ...,\n",
|
||
" [1.08522631e-04],\n",
|
||
" [1.18219088e-04],\n",
|
||
" [1.28327022e-04]])"
|
||
]
|
||
},
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"test_y1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"results1 = np.broadcast_to(lstm_pred, (20831, 6))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"test_y2 = np.broadcast_to(test_y1, (20831, 6))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"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": 48,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[ 1.78428369e+01, 4.82409691e+01, 6.37156385e+02,\n",
|
||
" 2.97801603e+02, 1.07621239e+01, 9.90052500e-01],\n",
|
||
" [ 1.07562527e+01, 3.44305945e+01, 4.40440713e+02,\n",
|
||
" 2.05929459e+02, 7.43790432e+00, 1.80780551e-01],\n",
|
||
" [ 1.14053667e+01, 3.56955916e+01, 4.58459395e+02,\n",
|
||
" 2.14344726e+02, 7.74239484e+00, 2.54907916e-01],\n",
|
||
" ...,\n",
|
||
" [-5.09439462e+00, 3.54076535e+00, 4.44428011e-01,\n",
|
||
" 4.37940726e-01, 2.58283957e-03, -1.62932764e+00],\n",
|
||
" [-5.09390265e+00, 3.54172410e+00, 4.58084512e-01,\n",
|
||
" 4.44318723e-01, 2.81361533e-03, -1.62927146e+00],\n",
|
||
" [-5.09338980e+00, 3.54272354e+00, 4.72320538e-01,\n",
|
||
" 4.50967376e-01, 3.05418424e-03, -1.62921289e+00]])"
|
||
]
|
||
},
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"inv_test_y"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Test RMSE: 0.222\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"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[300:3000,5], label='true')\n",
|
||
"plt.plot(inv_forecast_y[300:3000,5], label='pre')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"mean_squared_error: 0.0014630274318863602\n",
|
||
"mean_absolute_error: 0.013232284805068965\n",
|
||
"rmse: 0.03824954159053884\n",
|
||
"r2 score: 0.9900756487103545\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[480:850,5], inv_forecast_y[480:850,5]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 107,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 108,
|
||
"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('高频_test.csv', index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 109,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df2 = pd.DataFrame(inv_forecast_y[:,5], columns=['column_name'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 110,
|
||
"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('高频_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
|
||
}
|