ICEEMDAN-Solar_power-forecast/iceemdan-筛选-high-ConvBiGruAttention copy 2.ipynb

{
 "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\\完整的模型代码流程 copy\\data66.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": [
    "# # 获取重构的原始数据\n",
    "# # 获取重构的原始数据\n",
    "# # 获取重构的原始数据\n",
    "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 copy\\t+3\\iceemdan_reconstructed_data_re_high.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",
    "high_re = pd.DataFrame(pd.read_csv(high_re))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        column_name\n",
      "0         -1.460307\n",
      "1         -1.460504\n",
      "2         -1.460698\n",
      "3         -1.460886\n",
      "4         -1.461071\n",
      "...             ...\n",
      "104251    -1.663370\n",
      "104252    -1.664516\n",
      "104253    -1.665650\n",
      "104254    -1.666774\n",
      "104255    -1.667887\n",
      "\n",
      "[104256 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "reconstructed_data_high= high_re\n",
    "# # 打印重构的原始数据\n",
    "print(reconstructed_data_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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[90000:], 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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=data1.iloc[:,:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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    -1.460307\n",
      "1       19.714937  39.605961  3.194991  1.576346       0.0    -1.460504\n",
      "2       19.549330  39.608631  3.070866  1.576157       0.0    -1.460698\n",
      "3       19.405870  39.680702  3.038623  1.482489       0.0    -1.460886\n",
      "4       19.387363  39.319881  2.656474  1.134153       0.0    -1.461071\n",
      "...           ...        ...       ...       ...       ...          ...\n",
      "104251  13.303740  34.212711  1.210789  0.787026       0.0    -1.663370\n",
      "104252  13.120920  34.394939  2.142980  1.582670       0.0    -1.664516\n",
      "104253  12.879215  35.167400  1.926214  1.545889       0.0    -1.665650\n",
      "104254  12.915867  35.359989  1.317695  0.851529       0.0    -1.666774\n",
      "104255  13.134816  34.500034  1.043269  0.597816       0.0    -1.667887\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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104256, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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  0.250386  0.490360   \n",
      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.268375  0.489088   \n",
      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.286165  0.485824   \n",
      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.303808  0.482997   \n",
      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.321484  0.482632   \n",
      "\n",
      "      1(t-96)   2(t-96)   3(t-96)  ...    2(t+2)    3(t+2)  4(t+2)    5(t+2)  \\\n",
      "96   0.369105  0.002088  0.002013  ...  0.229657  0.047549     0.0  0.286165   \n",
      "97   0.364859  0.002061  0.001839  ...  0.247775  0.053347     0.0  0.303808   \n",
      "98   0.364886  0.001973  0.001839  ...  0.266058  0.057351     0.0  0.321484   \n",
      "99   0.365615  0.001950  0.001697  ...  0.282900  0.060958     0.0  0.338338   \n",
      "100  0.361965  0.001679  0.001167  ...  0.299668  0.065238     0.0  0.355108   \n",
      "\n",
      "       0(t+3)    1(t+3)    2(t+3)    3(t+3)  4(t+3)    5(t+3)  \n",
      "96   0.581973  0.268243  0.247775  0.053347     0.0  0.303808  \n",
      "97   0.586026  0.264586  0.266058  0.057351     0.0  0.321484  \n",
      "98   0.590772  0.258790  0.282900  0.060958     0.0  0.338338  \n",
      "99   0.600396  0.249246  0.299668  0.065238     0.0  0.355108  \n",
      "100  0.607019  0.247850  0.313694  0.066189     0.0  0.372185  \n",
      "\n",
      "[5 rows x 600 columns]\n"
     ]
    }
   ],
   "source": [
    "n_steps_in =96 #历史时间长度\n",
    "n_steps_out=3#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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']\n",
    "data_y = processedData1.loc[:,'5(t+3)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104157, 576)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96        0.303808\n",
       "97        0.321484\n",
       "98        0.338338\n",
       "99        0.355108\n",
       "100       0.372185\n",
       "            ...   \n",
       "104248    0.023869\n",
       "104249    0.023687\n",
       "104250    0.023507\n",
       "104251    0.023329\n",
       "104252    0.023153\n",
       "Name: 5(t+3), Length: 104157, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104157,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(83325, 96, 6) (83325,) (10417, 96, 6) (10417,) (10415, 96, 6) (10415,)\n"
     ]
    }
   ],
   "source": [
    "# 计算训练集、验证集和测试集的大小\n",
    "train_size = int(len(data_x) * 0.8)\n",
    "test_size = int(len(data_x) * 0.1)\n",
    "val_size = len(data_x) - train_size - test_size\n",
    "\n",
    "# 计算训练集、验证集和测试集的索引范围\n",
    "train_indices = range(train_size)\n",
    "val_indices = range(train_size, train_size + val_size)\n",
    "test_indices = range(train_size + val_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",
    "val_X1 = data_x.iloc[val_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",
    "val_y = data_y.iloc[val_indices].values\n",
    "test_y = data_y.iloc[test_indices].values\n",
    "\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",
    "val_X = val_X1.reshape((val_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",
    "\n",
    "print(train_X.shape, train_y.shape, val_X.shape, val_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83325, 96, 6)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "│ attention_with_imp… │ [(<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\">66,304</span> │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AttentionWithImpr…</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> │ attention_with_i… │\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",
       "│ attention_with_imp… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m,     │     \u001b[38;5;34m66,304\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mAttentionWithImpr…\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 │ attention_with_i… │\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\">117,185</span> (457.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m117,185\u001b[0m (457.75 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\">117,185</span> (457.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m117,185\u001b[0m (457.75 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",
    "\n",
    "class AttentionWithImproveRelativePositionEncoding(tf.keras.layers.Layer):\n",
    "    def __init__(self, d_model, num_heads, max_len=5000):\n",
    "        super(AttentionWithImproveRelativePositionEncoding, 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.position_encoding = ImproveRelativePositionEncoding(d_model)\n",
    "\n",
    "    def call(self, v, k, q, mask=None):\n",
    "        batch_size = tf.shape(q)[0]\n",
    "        q = self.wq(q)\n",
    "        k = self.wk(k)\n",
    "        v = self.wv(v)\n",
    "\n",
    "        # Adding position encoding\n",
    "        k += self.position_encoding(k)\n",
    "        q += self.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",
    "class ImproveRelativePositionEncoding(tf.keras.layers.Layer):\n",
    "    def __init__(self, d_model, max_len=5000):\n",
    "        super(ImproveRelativePositionEncoding, self).__init__()\n",
    "        self.max_len = max_len\n",
    "        self.d_model = d_model\n",
    "        # Introduce learnable parameters u and v\n",
    "        self.u = self.add_weight(shape=(self.d_model,), initializer=tf.keras.initializers.HeNormal(), trainable=True)\n",
    "        self.v = self.add_weight(shape=(self.d_model,), initializer=tf.keras.initializers.HeNormal(), trainable=True)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        super(ImproveRelativePositionEncoding, self).build(input_shape)\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",
    "        # Adjusting relative position encoding with parameters\n",
    "        pe_with_params = pos_encoding * self.u + pos_encoding * self.v\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",
    "        angle_rads_sin = tf.sin(angle_rads[:, 0::2])\n",
    "        angle_rads_cos = tf.cos(angle_rads[:, 1::2])\n",
    "\n",
    "        pos_encoding = tf.stack([angle_rads_sin, angle_rads_cos], axis=2)\n",
    "        pos_encoding = tf.reshape(pos_encoding, [1, position, d_model])\n",
    "\n",
    "        return pos_encoding\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 = AttentionWithImproveRelativePositionEncoding(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",
    "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": 19,
   "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[1m114s\u001b[0m 86ms/step - loss: 0.0116 - val_loss: 0.0025\n",
      "Epoch 2/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 84ms/step - loss: 0.0016 - val_loss: 0.0024\n",
      "Epoch 3/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 96ms/step - loss: 0.0016 - val_loss: 0.0023\n",
      "Epoch 4/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 87ms/step - loss: 0.0016 - val_loss: 0.0025\n",
      "Epoch 5/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 79ms/step - loss: 0.0015 - val_loss: 0.0025\n",
      "Epoch 6/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 85ms/step - loss: 0.0015 - val_loss: 0.0025\n",
      "Epoch 7/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 84ms/step - loss: 0.0014 - val_loss: 0.0027\n",
      "Epoch 8/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 83ms/step - loss: 0.0015 - val_loss: 0.0024\n",
      "Epoch 9/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 109ms/step - loss: 0.0014 - val_loss: 0.0023\n",
      "Epoch 10/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m182s\u001b[0m 140ms/step - loss: 0.0014 - val_loss: 0.0025\n",
      "Epoch 11/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 110ms/step - loss: 0.0014 - val_loss: 0.0026\n",
      "Epoch 12/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 89ms/step - loss: 0.0014 - val_loss: 0.0023\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: 0.0014 - val_loss: 0.0023\n",
      "Epoch 14/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 104ms/step - loss: 0.0014 - val_loss: 0.0024\n",
      "Epoch 15/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 86ms/step - loss: 0.0014 - val_loss: 0.0024\n",
      "Epoch 16/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 81ms/step - loss: 0.0013 - val_loss: 0.0024\n",
      "Epoch 17/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 84ms/step - loss: 0.0013 - val_loss: 0.0024\n",
      "Epoch 18/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 96ms/step - loss: 0.0013 - val_loss: 0.0024\n",
      "Epoch 19/100\n",
      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m179s\u001b[0m 137ms/step - loss: 0.0013 - val_loss: 0.0025\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=(val_X, val_y), \n",
    "                    callbacks=[early_stopping])\n",
    "\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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m326/326\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 25ms/step\n"
     ]
    }
   ],
   "source": [
    "# 预测\n",
    "lstm_pred = model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10415, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10415,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y1=test_y.reshape(10415,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.06037087],\n",
       "       [0.06032172],\n",
       "       [0.06027242],\n",
       "       ...,\n",
       "       [0.02350742],\n",
       "       [0.0233294 ],\n",
       "       [0.02315312]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "results1 = np.broadcast_to(lstm_pred, (10415, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y2 = np.broadcast_to(test_y1, (10415, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 反归一化\n",
    "inv_forecast_y = scaler.inverse_transform(results1)\n",
    "inv_test_y = scaler.inverse_transform(test_y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03686661,  9.49929284, 85.31799419, 40.07645259,  1.43682734,\n",
       "        -1.43294754],\n",
       "       [-2.03936077,  9.49443222, 85.2487593 , 40.04411781,  1.43565736,\n",
       "        -1.43325785],\n",
       "       [-2.04186187,  9.48955805, 85.17933142, 40.0116929 ,  1.43448413,\n",
       "        -1.43356904],\n",
       "       ...,\n",
       "       [-3.90720611,  5.85436487, 33.39945635, 15.82893159,  0.5594767 ,\n",
       "        -1.66565038],\n",
       "       [-3.91623795,  5.83676359, 33.14874276, 15.71184079,  0.55523999,\n",
       "        -1.6667741 ],\n",
       "       [-3.92518186,  5.81933364, 32.90046971, 15.5958898 ,  0.55104453,\n",
       "        -1.66788688]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 0.217\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[900:2100,5], label='true')\n",
    "plt.plot(inv_forecast_y[900:2100,5], label='pre')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error: 0.0011780920849826654\n",
      "mean_absolute_error: 0.013530156512489254\n",
      "rmse: 0.03432334606332351\n",
      "r2 score: 0.9966738024269023\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[900:2100], inv_forecast_y[900:2100]))"
   ]
  },
  {
   "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('xin99939高频re_test(t+3).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('xin99939高频re_forecast(t+3).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
}