CNN_LSTM3.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP1MP3dvjDzVI10udfzQYqA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Swastik200/30DaysOfDSA/blob/main/CNN_LSTM3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "oNB_TO8pZXxu"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import random\n",
        "import time\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWEfadbpZhMv",
        "outputId": "7018bbf1-6cc3-4958-d2cd-f4632ba75728"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp \"/content/drive/MyDrive/CMAPSS.zip\" \"/content\"\n",
        "!unzip CMAPSS.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-lobFW9Zpfa",
        "outputId": "99769031-bacf-45bb-a389-02ab894f292c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  CMAPSS.zip\n",
            "  inflating: CMAPSS/readme.txt       \n",
            "  inflating: CMAPSS/RUL_FD001.txt    \n",
            "  inflating: CMAPSS/RUL_FD002.txt    \n",
            "  inflating: CMAPSS/RUL_FD003.txt    \n",
            "  inflating: CMAPSS/RUL_FD004.txt    \n",
            "  inflating: CMAPSS/test_FD001.txt   \n",
            "  inflating: CMAPSS/test_FD002.txt   \n",
            "  inflating: CMAPSS/test_FD003.txt   \n",
            "  inflating: CMAPSS/test_FD004.txt   \n",
            "  inflating: CMAPSS/train_FD001.txt  \n",
            "  inflating: CMAPSS/train_FD002.txt  \n",
            "  inflating: CMAPSS/train_FD003.txt  \n",
            "  inflating: CMAPSS/train_FD004.txt  \n",
            "  inflating: CMAPSS/x.txt            \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import time\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler"
      ],
      "metadata": {
        "id": "8vwKoe4qZuu6"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Conv1D\n",
        "from tensorflow.keras.layers import BatchNormalization, Dropout\n",
        "from tensorflow.keras.layers import TimeDistributed, Flatten\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau,EarlyStopping"
      ],
      "metadata": {
        "id": "DmDv1YwZZuw7"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index_names = ['unit_number', 'time_cycles']\n",
        "setting_names = ['setting_1', 'setting_2', 'setting_3']\n",
        "sensor_names = ['sensor_{}'.format(i) for i in range(1,22)]\n",
        "col_names = index_names + setting_names + sensor_names"
      ],
      "metadata": {
        "id": "EtmqCY9VZ171"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/CMAPSS/'\n",
        "df_train = pd.read_csv(path+'train_FD003.txt',sep='\\s+',header=None,index_col=False,names=col_names) #\\s表示以空白為間隔\n",
        "df_test = pd.read_csv(path+'test_FD003.txt',sep='\\s+',header=None,index_col=False,names=col_names)\n",
        "y_test = pd.read_csv(path+'RUL_FD003.txt',sep='\\s+',header=None,index_col=False,names=['RUL'])\n",
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgZCnLBoZ2PC",
        "outputId": "b33e494a-d27a-433a-e41f-ac1123d85f37"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = df_train.copy()\n",
        "test = df_test.copy()\n",
        "display(train)\n",
        "display(test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "DFqBq9yFaBHx",
        "outputId": "dd57ce9b-fb3b-478a-f5e7-afa365fa3c0a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    -0.0005     0.0004      100.0    518.67   \n",
              "1                1            2     0.0008    -0.0003      100.0    518.67   \n",
              "2                1            3    -0.0014    -0.0002      100.0    518.67   \n",
              "3                1            4    -0.0020     0.0001      100.0    518.67   \n",
              "4                1            5     0.0016     0.0000      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "24715          100          148    -0.0016    -0.0003      100.0    518.67   \n",
              "24716          100          149     0.0034    -0.0003      100.0    518.67   \n",
              "24717          100          150    -0.0016     0.0004      100.0    518.67   \n",
              "24718          100          151    -0.0023     0.0004      100.0    518.67   \n",
              "24719          100          152     0.0000     0.0003      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        642.36   1583.23   1396.84     14.62  ...     522.31    2388.01   \n",
              "1        642.50   1584.69   1396.89     14.62  ...     522.42    2388.03   \n",
              "2        642.18   1582.35   1405.61     14.62  ...     522.03    2388.00   \n",
              "3        642.92   1585.61   1392.27     14.62  ...     522.49    2388.08   \n",
              "4        641.68   1588.63   1397.65     14.62  ...     522.58    2388.03   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "24715    643.78   1596.01   1424.11     14.62  ...     519.66    2388.30   \n",
              "24716    643.29   1596.38   1429.14     14.62  ...     519.91    2388.28   \n",
              "24717    643.84   1604.53   1431.41     14.62  ...     519.44    2388.24   \n",
              "24718    643.94   1597.56   1426.57     14.62  ...     520.01    2388.26   \n",
              "24719    643.64   1599.04   1436.06     14.62  ...     519.48    2388.24   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8145.32     8.4246       0.03        391       2388      100.0   \n",
              "1        8152.85     8.4403       0.03        392       2388      100.0   \n",
              "2        8150.17     8.3901       0.03        391       2388      100.0   \n",
              "3        8146.56     8.3878       0.03        392       2388      100.0   \n",
              "4        8147.80     8.3869       0.03        392       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "24715    8138.08     8.5036       0.03        394       2388      100.0   \n",
              "24716    8144.36     8.5174       0.03        395       2388      100.0   \n",
              "24717    8135.95     8.5223       0.03        396       2388      100.0   \n",
              "24718    8141.24     8.5148       0.03        395       2388      100.0   \n",
              "24719    8136.98     8.5150       0.03        396       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          39.11    23.3537  \n",
              "1          38.99    23.4491  \n",
              "2          38.85    23.3669  \n",
              "3          38.96    23.2951  \n",
              "4          39.14    23.4583  \n",
              "...          ...        ...  \n",
              "24715      38.44    22.9631  \n",
              "24716      38.50    22.9746  \n",
              "24717      38.39    23.0682  \n",
              "24718      38.31    23.0753  \n",
              "24719      38.56    23.0847  \n",
              "\n",
              "[24720 rows x 26 columns]"
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              "      <th>unit_number</th>\n",
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              "      <td>642.50</td>\n",
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              "      <td>1396.89</td>\n",
              "      <td>14.62</td>\n",
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              "      <td>-0.0002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.18</td>\n",
              "      <td>1582.35</td>\n",
              "      <td>1405.61</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.03</td>\n",
              "      <td>2388.00</td>\n",
              "      <td>8150.17</td>\n",
              "      <td>8.3901</td>\n",
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              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.85</td>\n",
              "      <td>23.3669</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>100.0</td>\n",
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              "      <td>641.68</td>\n",
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              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.31</td>\n",
              "      <td>23.0753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24719</th>\n",
              "      <td>100</td>\n",
              "      <td>152</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.64</td>\n",
              "      <td>1599.04</td>\n",
              "      <td>1436.06</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>519.48</td>\n",
              "      <td>2388.24</td>\n",
              "      <td>8136.98</td>\n",
              "      <td>8.5150</td>\n",
              "      <td>0.03</td>\n",
              "      <td>396</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.56</td>\n",
              "      <td>23.0847</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24720 rows × 26 columns</p>\n",
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              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    -0.0017    -0.0004      100.0    518.67   \n",
              "1                1            2     0.0006    -0.0002      100.0    518.67   \n",
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              "...            ...          ...        ...        ...        ...       ...   \n",
              "16591          100          243     0.0011    -0.0003      100.0    518.67   \n",
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              "16594          100          246    -0.0006     0.0001      100.0    518.67   \n",
              "16595          100          247     0.0023     0.0004      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        641.94   1581.93   1396.93     14.62  ...     521.89    2387.94   \n",
              "1        642.02   1584.86   1398.90     14.62  ...     521.85    2388.01   \n",
              "2        641.68   1581.78   1391.92     14.62  ...     522.10    2387.94   \n",
              "3        642.20   1584.53   1395.34     14.62  ...     522.45    2387.96   \n",
              "4        642.46   1589.03   1395.86     14.62  ...     521.91    2387.97   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "16591    643.04   1594.99   1411.28     14.62  ...     529.23    2388.24   \n",
              "16592    642.47   1591.27   1417.64     14.62  ...     529.27    2388.26   \n",
              "16593    642.70   1593.81   1412.70     14.62  ...     529.48    2388.28   \n",
              "16594    642.19   1595.63   1406.58     14.62  ...     529.39    2388.33   \n",
              "16595    643.19   1592.54   1407.20     14.62  ...     529.59    2388.29   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8133.48     8.3760       0.03        391       2388      100.0   \n",
              "1        8137.44     8.4062       0.03        391       2388      100.0   \n",
              "2        8138.25     8.3553       0.03        391       2388      100.0   \n",
              "3        8137.07     8.3709       0.03        392       2388      100.0   \n",
              "4        8134.20     8.4146       0.03        391       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "16591    8162.24     8.2939       0.03        395       2388      100.0   \n",
              "16592    8163.24     8.3247       0.03        394       2388      100.0   \n",
              "16593    8162.12     8.3264       0.03        394       2388      100.0   \n",
              "16594    8164.20     8.2908       0.03        395       2388      100.0   \n",
              "16595    8169.24     8.3191       0.03        392       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          39.07    23.4468  \n",
              "1          39.04    23.4807  \n",
              "2          39.10    23.4244  \n",
              "3          38.97    23.4782  \n",
              "4          39.09    23.3950  \n",
              "...          ...        ...  \n",
              "16591      39.40    23.4949  \n",
              "16592      39.42    23.6011  \n",
              "16593      39.43    23.5482  \n",
              "16594      39.40    23.6687  \n",
              "16595      39.41    23.6536  \n",
              "\n",
              "[16596 rows x 26 columns]"
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              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.10</td>\n",
              "      <td>2387.94</td>\n",
              "      <td>8138.25</td>\n",
              "      <td>8.3553</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.10</td>\n",
              "      <td>23.4244</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
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              "      <td>1395.34</td>\n",
              "      <td>14.62</td>\n",
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              "      <td>522.45</td>\n",
              "      <td>2387.96</td>\n",
              "      <td>8137.07</td>\n",
              "      <td>8.3709</td>\n",
              "      <td>0.03</td>\n",
              "      <td>392</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.97</td>\n",
              "      <td>23.4782</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
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              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.46</td>\n",
              "      <td>1589.03</td>\n",
              "      <td>1395.86</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.91</td>\n",
              "      <td>2387.97</td>\n",
              "      <td>8134.20</td>\n",
              "      <td>8.4146</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.09</td>\n",
              "      <td>23.3950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16591</th>\n",
              "      <td>100</td>\n",
              "      <td>243</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.04</td>\n",
              "      <td>1594.99</td>\n",
              "      <td>1411.28</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.23</td>\n",
              "      <td>2388.24</td>\n",
              "      <td>8162.24</td>\n",
              "      <td>8.2939</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.40</td>\n",
              "      <td>23.4949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16592</th>\n",
              "      <td>100</td>\n",
              "      <td>244</td>\n",
              "      <td>-0.0024</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.47</td>\n",
              "      <td>1591.27</td>\n",
              "      <td>1417.64</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.27</td>\n",
              "      <td>2388.26</td>\n",
              "      <td>8163.24</td>\n",
              "      <td>8.3247</td>\n",
              "      <td>0.03</td>\n",
              "      <td>394</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.42</td>\n",
              "      <td>23.6011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16593</th>\n",
              "      <td>100</td>\n",
              "      <td>245</td>\n",
              "      <td>-0.0053</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.70</td>\n",
              "      <td>1593.81</td>\n",
              "      <td>1412.70</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.48</td>\n",
              "      <td>2388.28</td>\n",
              "      <td>8162.12</td>\n",
              "      <td>8.3264</td>\n",
              "      <td>0.03</td>\n",
              "      <td>394</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.43</td>\n",
              "      <td>23.5482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16594</th>\n",
              "      <td>100</td>\n",
              "      <td>246</td>\n",
              "      <td>-0.0006</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
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              "      <td>1595.63</td>\n",
              "      <td>1406.58</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.39</td>\n",
              "      <td>2388.33</td>\n",
              "      <td>8164.20</td>\n",
              "      <td>8.2908</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.40</td>\n",
              "      <td>23.6687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16595</th>\n",
              "      <td>100</td>\n",
              "      <td>247</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>0.0004</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.19</td>\n",
              "      <td>1592.54</td>\n",
              "      <td>1407.20</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.59</td>\n",
              "      <td>2388.29</td>\n",
              "      <td>8169.24</td>\n",
              "      <td>8.3191</td>\n",
              "      <td>0.03</td>\n",
              "      <td>392</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.41</td>\n",
              "      <td>23.6536</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16596 rows × 26 columns</p>\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Shape of the train dataset : ',train.shape)\n",
        "print('Shape of the test dataset : ',test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71pXl0CGa6Vs",
        "outputId": "1719849f-cb9e-4926-fbc1-e5e5a0f6843b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the train dataset :  (24720, 26)\n",
            "Shape of the test dataset :  (16596, 26)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def process_targets(data_length, early_rul):\n",
        "  early_rul_duration = data_length - early_rul # 壽命比early rul 多了多長\n",
        "  if early_rul_duration <= 0: # 如果壽命小於early rul，targets則不變\n",
        "    return np.arange(data_length-1, -1, -1)\n",
        "  else:\n",
        "    new_early_rul = early_rul * np.ones(early_rul_duration) # 多出多少early rul\n",
        "    origin_rul = np.arange(early_rul-1, -1, -1)\n",
        "    return np.append(new_early_rul,origin_rul)"
      ],
      "metadata": {
        "id": "UKvgtxk6a6Yq"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_input_data_with_targets(input_data, target_data , window_length , shift ):\n",
        "  num_batches = int(np.floor((len(input_data) - window_length)/shift)) + 1 # 共有多少組小資料\n",
        "  num_features = input_data.shape[1] # 每筆資料的特徵數\n",
        "  output = np.repeat(np.nan, repeats = num_batches * window_length * num_features)\n",
        "  output_data = output.reshape(num_batches, window_length , num_features)\n",
        "\n",
        "  if target_data is None: #測試資料無target data\n",
        "    for batch in range(num_batches):\n",
        "      output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
        "    return output_data\n",
        "  else : #處理訓練資料的target data\n",
        "    output_targets = np.repeat(np.nan, repeats = num_batches) #target rul\n",
        "    for batch in range(num_batches):\n",
        "      window_start = shift * batch\n",
        "      window_end = window_start + window_length\n",
        "\n",
        "      output_data[batch,:,:] = input_data[window_start:window_end,:]\n",
        "      output_targets[batch] = target_data[window_end-1]\n",
        "    return output_data, output_targets"
      ],
      "metadata": {
        "id": "o2VgH6tTZ2RB"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_test_data(test_data_for_an_engine, window_length, shift, num_test_windows = 1):\n",
        "\n",
        "    max_num_test_batches = int(np.floor((len(test_data_for_an_engine) - window_length)/shift)) + 1 #共有多少組小資料\n",
        "\n",
        "    if max_num_test_batches < num_test_windows:\n",
        "        required_len = (max_num_test_batches -1)* shift + window_length\n",
        "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
        "                                          target_data = None,\n",
        "                                          window_length = window_length, shift = shift)\n",
        "        return batched_test_data_for_an_engine, max_num_test_batches\n",
        "    else:\n",
        "        required_len = (num_test_windows - 1) * shift + window_length\n",
        "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
        "                                          target_data = None,\n",
        "                                          window_length = window_length, shift = shift)\n",
        "        return batched_test_data_for_an_engine, num_test_windows"
      ],
      "metadata": {
        "id": "-C7p3MjoZ2U1"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drop_labels = ['sensor_1', 'sensor_5','sensor_6','sensor_10', 'sensor_16', 'sensor_18', 'sensor_19']\n",
        "train_data_first_column = train[\"unit_number\"]\n",
        "test_data_first_column = test[\"unit_number\"]\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "train_data = scaler.fit_transform(train.drop(columns = ['unit_number'] + setting_names + drop_labels ))\n",
        "test_data = scaler.transform(test.drop(columns = ['unit_number'] + setting_names+ drop_labels ))\n",
        "\n",
        "train_data = pd.DataFrame(data = np.c_[train_data_first_column, train_data])\n",
        "test_data = pd.DataFrame(data = np.c_[test_data_first_column, test_data])\n",
        "\n",
        "num_train_machines = len(train_data[0].unique())\n",
        "num_test_machines = len(test_data[0].unique())"
      ],
      "metadata": {
        "id": "fBfUOMvycOs8"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "window_length = 30\n",
        "shift = 1\n",
        "early_rul = 125\n",
        "num_test_windows = 5"
      ],
      "metadata": {
        "id": "lSU1qhHYcVSq"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_train_data = []\n",
        "processed_train_targets = []\n",
        "\n",
        "for i in np.arange(1, num_train_machines + 1):\n",
        "    temp_train_data = train_data[train_data[0] == i].drop(columns = [0]).values #每台機器的資料\n",
        "\n",
        "    temp_train_targets = process_targets(data_length = temp_train_data.shape[0], early_rul = early_rul)\n",
        "    data_for_a_machine, targets_for_a_machine = process_input_data_with_targets(temp_train_data, temp_train_targets,window_length= window_length, shift = shift)\n",
        "\n",
        "    processed_train_data.append(data_for_a_machine)\n",
        "    processed_train_targets.append(targets_for_a_machine)\n",
        "\n",
        "processed_train_data = np.concatenate(processed_train_data)\n",
        "processed_train_targets = np.concatenate(processed_train_targets)"
      ],
      "metadata": {
        "id": "UzqH0uVecYsJ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_test_data = []\n",
        "num_test_windows_list = []\n",
        "\n",
        "for i in range(1, num_test_machines + 1):  # Use range instead of np.arange\n",
        "    temp_test_data = test_data[test_data[0] == i].drop(columns=[0]).values\n",
        "\n",
        "    # Prepare test data\n",
        "    if temp_test_data.shape[0] >= window_length:  # Ensure temp_test_data has enough rows\n",
        "        test_data_for_an_engine, num_windows = process_test_data(temp_test_data, window_length=window_length, shift=shift, num_test_windows=num_test_windows)\n",
        "        processed_test_data.append(test_data_for_an_engine)\n",
        "        num_test_windows_list.append(num_windows)\n",
        "    else:\n",
        "        print(f\"Skipping engine {i} due to insufficient data for windowing.\")\n",
        "\n",
        "if processed_test_data:  # Check if processed_test_data is not empty\n",
        "    processed_test_data = np.concatenate(processed_test_data)\n",
        "    true_rul = y_test.values\n",
        "\n",
        "    # Shuffle training data\n",
        "    index = np.random.permutation(len(processed_train_targets))\n",
        "    processed_train_data, processed_train_targets = processed_train_data[index], processed_train_targets[index]\n",
        "else:\n",
        "    print(\"No valid data available after processing.\")\n"
      ],
      "metadata": {
        "id": "OkZxpZ1Kcbn3"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Processed trianing data shape: \", processed_train_data.shape)\n",
        "print(\"Processed training ruls shape: \", processed_train_targets.shape)\n",
        "print(\"Processed test data shape: \", processed_test_data.shape)\n",
        "print(\"True RUL shape: \", true_rul.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9Bcsdv0cc1k",
        "outputId": "414ea2cd-988f-4cfc-de3f-0f485c9207f7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed trianing data shape:  (21820, 30, 15)\n",
            "Processed training ruls shape:  (21820,)\n",
            "Processed test data shape:  (500, 30, 15)\n",
            "True RUL shape:  (100, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_train_data, processed_val_data, processed_train_targets, processed_val_targets = train_test_split(processed_train_data,\n",
        "                                                        processed_train_targets,\n",
        "                                                        test_size = 0.2,\n",
        "                                                        random_state = 666)\n",
        "print(\"Processed train data shape: \", processed_train_data.shape)\n",
        "print(\"Processed validation data shape: \", processed_val_data.shape)\n",
        "print(\"Processed train targets shape: \", processed_train_targets.shape)\n",
        "print(\"Processed validation targets shape: \", processed_val_targets.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pmn0KfNUcgiy",
        "outputId": "9a6a585c-5240-4b1b-f829-21e0b69e65c2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed train data shape:  (17456, 30, 15)\n",
            "Processed validation data shape:  (4364, 30, 15)\n",
            "Processed train targets shape:  (17456,)\n",
            "Processed validation targets shape:  (4364,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def CNN_LSTM_model(kernel_size,pool_size):\n",
        "  input_shape = (window_length, 15)\n",
        "  model = Sequential([\n",
        "    layers.Conv1D(128, kernel_size = kernel_size, padding = \"same\", activation=\"relu\", input_shape = input_shape),\n",
        "    layers.MaxPooling1D(pool_size= pool_size, padding='same'),\n",
        "    layers.Conv1D(128,kernel_size = kernel_size, padding = \"same\", activation=\"relu\"),\n",
        "    layers.MaxPooling1D(pool_size= pool_size, padding='same'),\n",
        "    layers.TimeDistributed(Flatten()),\n",
        "    layers.LSTM(128, return_sequences = True, activation = \"tanh\"),\n",
        "    layers.LSTM(64, activation = \"tanh\", return_sequences = True),\n",
        "    layers.LSTM(32, activation = \"tanh\"),\n",
        "    layers.Dense(96, activation = \"relu\"),\n",
        "    layers.Dense(128, activation = \"relu\"),\n",
        "    layers.Dense(1)\n",
        "  ])\n",
        "  model.compile(loss = \"mse\", optimizer = tf.keras.optimizers.Adam(learning_rate=0.001))\n",
        "  return model\n",
        "\n",
        "CNN_LSTM_model(kernel_size=3,pool_size=2).summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "of0AVHZcckWM",
        "outputId": "bacb8556-1684-49c1-df62-61ccbb46fd54"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_4 (Conv1D)           (None, 30, 128)           5888      \n",
            "                                                                 \n",
            " max_pooling1d_4 (MaxPoolin  (None, 15, 128)           0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " conv1d_5 (Conv1D)           (None, 15, 128)           49280     \n",
            "                                                                 \n",
            " max_pooling1d_5 (MaxPoolin  (None, 8, 128)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " time_distributed_2 (TimeDi  (None, 8, 128)            0         \n",
            " stributed)                                                      \n",
            "                                                                 \n",
            " lstm_6 (LSTM)               (None, 8, 128)            131584    \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 8, 64)             49408     \n",
            "                                                                 \n",
            " lstm_8 (LSTM)               (None, 32)                12416     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 96)                3168      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 128)               12416     \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 264289 (1.01 MB)\n",
            "Trainable params: 264289 (1.01 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def scheduler(epoch):\n",
        "  if epoch < 10:\n",
        "    return 0.001\n",
        "  else:\n",
        "    return 0.0001\n",
        ""
      ],
      "metadata": {
        "id": "g1jZvAmBcttm"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf_callback = tf.keras.callbacks.TensorBoard(log_dir=\"./logs\")\n",
        "callback = tf.keras.callbacks.LearningRateScheduler(scheduler, verbose = 0)\n",
        "batch_size = 50\n",
        "epoch = 20\n",
        "kernel_size = 3\n",
        "pool_size = 2"
      ],
      "metadata": {
        "id": "aVtySiducwAN"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn_lstm_model = CNN_LSTM_model(kernel_size= kernel_size ,pool_size = pool_size)\n",
        "models = {\"CNN+LSTM\":cnn_lstm_model}"
      ],
      "metadata": {
        "id": "GneA4LgsczeK"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(true_rul ,pred_rul, model):\n",
        "  MAE = mean_absolute_error(true_rul, pred_rul)\n",
        "  RMSE = np.sqrt(mean_squared_error(true_rul, pred_rul))\n",
        "  r2 = r2_score(true_rul, pred_rul)\n",
        "  print(\"Testing : R-square = \",r2,'MAE = ',MAE,\"RMSE = \", RMSE)"
      ],
      "metadata": {
        "id": "vQtmkYj2c56G"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_train_loss = []\n",
        "history_val_loss = []\n",
        "\n",
        "for model_name, model in models.items():\n",
        "    print(\"================\", model_name, \"================\")\n",
        "    start_time = time.time()\n",
        "    model_history = model.fit(processed_train_data, processed_train_targets, epochs=epoch,\n",
        "                              validation_data=(processed_val_data, processed_val_targets),\n",
        "                              callbacks=[tf_callback, callback],\n",
        "                              batch_size=batch_size, verbose=0)\n",
        "    rul_pred = model.predict(processed_test_data, verbose=0).reshape(-1)\n",
        "    end_time = time.time()\n",
        "\n",
        "    preds_for_each_engine = np.split(rul_pred, np.cumsum(num_test_windows_list)[:-1])\n",
        "    mean_pred_for_each_engine = [np.average(ruls_for_each_engine, weights=np.repeat(1/num_windows, num_windows))\n",
        "                                  for ruls_for_each_engine, num_windows in zip(preds_for_each_engine, num_test_windows_list)]\n",
        "\n",
        "    print('Training : loss = ', model_history.history['loss'][-1])\n",
        "    print('Validation : loss = ', model_history.history['val_loss'][-1])\n",
        "\n",
        "    # Ensure true_rul and mean_pred_for_each_engine have the same number of samples\n",
        "    min_samples = min(len(true_rul), len(mean_pred_for_each_engine))\n",
        "    true_rul = true_rul[:min_samples]\n",
        "    mean_pred_for_each_engine = mean_pred_for_each_engine[:min_samples]\n",
        "\n",
        "    evaluate(true_rul, mean_pred_for_each_engine, model_name)\n",
        "\n",
        "    history_train_loss.append(model_history.history['loss'])\n",
        "    history_val_loss.append(model_history.history['val_loss'])\n",
        "    print('Run Time :', int(end_time - start_time), 'sec')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYCh3jRKc9D0",
        "outputId": "8040446c-ce46-4bce-e530-3483d85cc8c4"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================ CNN+LSTM ================\n",
            "Training : loss =  58.638160705566406\n",
            "Validation : loss =  62.71104431152344\n",
            "Testing : R-square =  0.8539803089835372 MAE =  11.603391613960271 RMSE =  15.818311922533084\n",
            "Run Time : 388 sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5F3aZToCcOuz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J2iVfsCncOx7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yiPwxaLucO0B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Wh3vqfA6cO3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mh2FwQzRZ2X2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "liKjW2jrZu0Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9y0pPoNDZu3P"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}