From 42b1ed9afd654b10e832e450071be23558fc9b84 Mon Sep 17 00:00:00 2001
From: hanyp <hanyp@qibebt.ac.cn>
Date: Thu, 1 Aug 2024 10:45:08 +0800
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20'ConvBigru=5FIRPE=5FAttent?=
 =?UTF-8?q?ion.ipynb'?=
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 ConvBigru_IRPE_Attention.ipynb | 998 ---------------------------------
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 delete mode 100644 ConvBigru_IRPE_Attention.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "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",
-      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
-      "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",
-      "  from pandas.core import (\n"
-     ]
-    }
-   ],
-   "source": [
-    "from math import sqrt\n",
-    "from numpy import concatenate\n",
-    "from matplotlib import pyplot\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from tensorflow.keras import Sequential\n",
-    "\n",
-    "from tensorflow.keras.layers import Dense\n",
-    "from tensorflow.keras.layers import LSTM\n",
-    "from tensorflow.keras.layers import Dropout\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "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",
-    "\n",
-    "data: 输入的时间序列数据，可以是列表或2D NumPy数组。\n",
-    "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",
-    "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",
-    "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",
-    "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",
-    "\n",
-    "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",
-    "\n",
-    "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",
-    "\n",
-    "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。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def time_series_to_supervised(data, n_in=96, n_out=10,dropnan=True):\n",
-    "    \"\"\"\n",
-    "    :param data:作为列表或2D NumPy数组的观察序列。需要。\n",
-    "    :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",
-    "    :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",
-    "    :param dropnan:Boolean是否删除具有NaN值的行。可选的。默认为True。\n",
-    "    :return:\n",
-    "    \"\"\"\n",
-    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
-    "    df = pd.DataFrame(data)\n",
-    "    origNames = df.columns\n",
-    "    cols, names = list(), list()\n",
-    "    cols.append(df.shift(0))\n",
-    "    names += [('%s' % origNames[j]) for j in range(n_vars)]\n",
-    "    n_in = max(0, n_in)\n",
-    "    for i in range(n_in, 0, -1):\n",
-    "        time = '(t-%d)' % i\n",
-    "        cols.append(df.shift(i))\n",
-    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
-    "    n_out = max(n_out, 0)\n",
-    "    for i in range(1, n_out+1):\n",
-    "        time = '(t+%d)' % i\n",
-    "        cols.append(df.shift(-i))\n",
-    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
-    "    agg = pd.concat(cols, axis=1)\n",
-    "    agg.columns = names\n",
-    "    if dropnan:\n",
-    "        agg.dropna(inplace=True)\n",
-    "    return agg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        Temp   Humidity       GHI       DHI  Rainfall  Power\n",
-      "0  19.779453  40.025826  3.232706  1.690531       0.0    0.0\n",
-      "1  19.714937  39.605961  3.194991  1.576346       0.0    0.0\n",
-      "2  19.549330  39.608631  3.070866  1.576157       0.0    0.0\n",
-      "3  19.405870  39.680702  3.038623  1.482489       0.0    0.0\n",
-      "4  19.387363  39.319881  2.656474  1.134153       0.0    0.0\n",
-      "(104256, 6)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 加载数据\n",
-    "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",
-    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
-    "datas1 = pd.DataFrame(pd.read_csv(path1))\n",
-    "#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",
-    "# data1 = datas1.iloc[:,np.r_[3,23,16:22,27]]\n",
-    "data1=datas1.interpolate()\n",
-    "values1 = data1.values\n",
-    "print(data1.head())\n",
-    "print(data1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# data2= data1.drop(['date','Air_P','RH'], axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "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(data1)\n",
-    "print(scaledData1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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  0.236302  0.490360   \n",
-      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.258204  0.489088   \n",
-      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.279860  0.485824   \n",
-      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.301336  0.482997   \n",
-      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.322851  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  0.214129   \n",
-      "97   0.364859  0.002061  0.001839  ...  0.190042  0.040558     0.0  0.236302   \n",
-      "98   0.364886  0.001973  0.001839  ...  0.211335  0.044613     0.0  0.258204   \n",
-      "99   0.365615  0.001950  0.001697  ...  0.229657  0.047549     0.0  0.279860   \n",
-      "100  0.361965  0.001679  0.001167  ...  0.247775  0.053347     0.0  0.301336   \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  0.258204  \n",
-      "97   0.576854  0.288321  0.229657  0.047549     0.0  0.279860  \n",
-      "98   0.581973  0.268243  0.247775  0.053347     0.0  0.301336  \n",
-      "99   0.586026  0.264586  0.266058  0.057351     0.0  0.322851  \n",
-      "100  0.590772  0.258790  0.282900  0.060958     0.0  0.343360  \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": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']#去除power剩下的做标签列\n",
-    "data_y = processedData1.loc[:,'5']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(104159, 576)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "96        0.236302\n",
-       "97        0.258204\n",
-       "98        0.279860\n",
-       "99        0.301336\n",
-       "100       0.322851\n",
-       "            ...   \n",
-       "104250    0.000000\n",
-       "104251    0.000000\n",
-       "104252    0.000000\n",
-       "104253    0.000000\n",
-       "104254    0.000000\n",
-       "Name: 5, Length: 104159, dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(104159,)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_y.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "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": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(83328, 96, 6)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_X1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From d:\\Anaconda3\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py:192: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
-      "\n"
-     ]
-    },
-    {
-     "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\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"functional\"\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         │ (<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 (<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[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ max_pooling1d       │ (<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[<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       │ (<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[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ self_attention      │ [(<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[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
-       "│                     │ <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]      │            │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ dense_4 (<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         │ (\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 (\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[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ max_pooling1d       │ (\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[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
-       "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m)      │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bidirectional       │ (\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[\u001b[38;5;34m0\u001b[0m]… │\n",
-       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ self_attention      │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m,     │     \u001b[38;5;34m66,048\u001b[0m │ bidirectional[\u001b[38;5;34m0\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[\u001b[38;5;34m0\u001b[0m]… │\n",
-       "│                     │ \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)]      │            │ bidirectional[\u001b[38;5;34m0\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[\u001b[38;5;34m0\u001b[0m… │\n",
-       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ dense_4 (\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"
-    },
-    {
-     "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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">116,929</span> (456.75 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m116,929\u001b[0m (456.75 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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">116,929</span> (456.75 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m116,929\u001b[0m (456.75 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": [
-    "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": 17,
-   "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[1m90s\u001b[0m 68ms/step - loss: 0.0267 - val_loss: 0.0024\n",
-      "Epoch 2/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 66ms/step - loss: 0.0015 - val_loss: 0.0026\n",
-      "Epoch 3/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 76ms/step - loss: 0.0013 - val_loss: 0.0020\n",
-      "Epoch 4/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 73ms/step - loss: 0.0013 - val_loss: 0.0020\n",
-      "Epoch 5/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 78ms/step - loss: 0.0012 - val_loss: 0.0018\n",
-      "Epoch 6/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 74ms/step - loss: 0.0012 - val_loss: 0.0019\n",
-      "Epoch 7/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 75ms/step - loss: 0.0012 - val_loss: 0.0018\n",
-      "Epoch 8/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 84ms/step - loss: 0.0011 - val_loss: 0.0018\n",
-      "Epoch 9/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 87ms/step - loss: 0.0012 - val_loss: 0.0018\n",
-      "Epoch 10/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 88ms/step - loss: 0.0012 - val_loss: 0.0019\n",
-      "Epoch 11/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 90ms/step - loss: 0.0011 - val_loss: 0.0019\n",
-      "Epoch 12/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 89ms/step - loss: 0.0011 - val_loss: 0.0018\n",
-      "Epoch 13/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 89ms/step - loss: 0.0011 - val_loss: 0.0019\n",
-      "Epoch 14/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 86ms/step - loss: 0.0011 - val_loss: 0.0018\n",
-      "Epoch 15/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 87ms/step - loss: 0.0011 - val_loss: 0.0019\n",
-      "Epoch 16/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 108ms/step - loss: 0.0011 - val_loss: 0.0020\n",
-      "Epoch 17/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 106ms/step - loss: 0.0011 - val_loss: 0.0019\n",
-      "Epoch 18/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 92ms/step - loss: 0.0011 - val_loss: 0.0019\n",
-      "Epoch 19/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m122s\u001b[0m 94ms/step - loss: 0.0010 - val_loss: 0.0021\n",
-      "\u001b[1m651/651\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 19ms/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",
-    "model_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": 18,
-   "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": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20831, 1)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_pred.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20831,)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_y.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_y1=test_y.reshape(20831,1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.42300856],\n",
-       "       [0.26651022],\n",
-       "       [0.28093082],\n",
-       "       ...,\n",
-       "       [0.        ],\n",
-       "       [0.        ],\n",
-       "       [0.        ]])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_y1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results1 = np.broadcast_to(model_pred, (20831, 6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_y2 = np.broadcast_to(test_y1, (20831, 6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "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": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.63622638e+01,  4.53556200e+01,  5.96057328e+02,\n",
-       "         2.78607105e+02,  1.00676074e+01,  2.18633342e+00],\n",
-       "       [ 8.42201514e+00,  2.98816195e+01,  3.75644883e+02,\n",
-       "         1.75667855e+02,  6.34294548e+00,  1.37746668e+00],\n",
-       "       [ 9.15367247e+00,  3.13074773e+01,  3.95954874e+02,\n",
-       "         1.85153232e+02,  6.68615591e+00,  1.45200002e+00],\n",
-       "       ...,\n",
-       "       [-5.09990072e+00,  3.53003502e+00,  2.91584611e-01,\n",
-       "         3.66558254e-01,  0.00000000e+00,  0.00000000e+00],\n",
-       "       [-5.09990072e+00,  3.53003502e+00,  2.91584611e-01,\n",
-       "         3.66558254e-01,  0.00000000e+00,  0.00000000e+00],\n",
-       "       [-5.09990072e+00,  3.53003502e+00,  2.91584611e-01,\n",
-       "         3.66558254e-01,  0.00000000e+00,  0.00000000e+00]])"
-      ]
-     },
-     "execution_count": 26,
-     "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: 0.234\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[:,5], label='true')\n",
-    "plt.plot(inv_forecast_y[:,5], label='pre')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean_squared_error: 0.0020579791273450843\n",
-      "mean_absolute_error: 0.017256367420885\n",
-      "rmse: 0.04536495483680199\n",
-      "r2 score: 0.9906498316747805\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(model_pred, test_y))  # mse)\n",
-    "print(\"mean_absolute_error:\", mean_absolute_error(model_pred, test_y))  # mae\n",
-    "print(\"rmse:\", sqrt(mean_squared_error(model_pred,test_y)))\n",
-    "#r2对比区域\n",
-    "print(\"r2 score:\", r2_score(inv_test_y[:], inv_forecast_y[:]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "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('test.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2 = pd.DataFrame(inv_forecast_y[:,5], columns=['column_name'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "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('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
-}