CNN_LSTM.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNBFQXYmYcFICcvHcWAAnBQ",
      "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_LSTM.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": "_iUsYo_ZFqgr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ECUyxaAGA9n",
        "outputId": "c302a787-de20-401e-af30-5236ca38d249"
      },
      "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": "1RDDNIlMGXrP",
        "outputId": "34fd6e6c-bd67-486a-d6e4-f639d15e49d7"
      },
      "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')"
      ],
      "metadata": {
        "id": "DK1-EguiGYwh"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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": "k41YOkJRGYyW"
      },
      "execution_count": 5,
      "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": "FqgaWHCrGY03"
      },
      "execution_count": 6,
      "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": "fZiqf6bhGY37"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/CMAPSS/'\n",
        "df_train = pd.read_csv(path+'train_FD001.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_FD001.txt',sep='\\s+',header=None,index_col=False,names=col_names)\n",
        "y_test = pd.read_csv(path+'RUL_FD001.txt',sep='\\s+',header=None,index_col=False,names=['RUL'])\n",
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCbVqpuqGl3N",
        "outputId": "79ae3820-fc9e-4eac-8db5-d00be6b9b48e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "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": "_3HVY8_qGl4_",
        "outputId": "a872dc4b-e64a-4853-a9fc-1ba4d462ef6a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    -0.0007    -0.0004      100.0    518.67   \n",
              "1                1            2     0.0019    -0.0003      100.0    518.67   \n",
              "2                1            3    -0.0043     0.0003      100.0    518.67   \n",
              "3                1            4     0.0007     0.0000      100.0    518.67   \n",
              "4                1            5    -0.0019    -0.0002      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "20626          100          196    -0.0004    -0.0003      100.0    518.67   \n",
              "20627          100          197    -0.0016    -0.0005      100.0    518.67   \n",
              "20628          100          198     0.0004     0.0000      100.0    518.67   \n",
              "20629          100          199    -0.0011     0.0003      100.0    518.67   \n",
              "20630          100          200    -0.0032    -0.0005      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        641.82   1589.70   1400.60     14.62  ...     521.66    2388.02   \n",
              "1        642.15   1591.82   1403.14     14.62  ...     522.28    2388.07   \n",
              "2        642.35   1587.99   1404.20     14.62  ...     522.42    2388.03   \n",
              "3        642.35   1582.79   1401.87     14.62  ...     522.86    2388.08   \n",
              "4        642.37   1582.85   1406.22     14.62  ...     522.19    2388.04   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "20626    643.49   1597.98   1428.63     14.62  ...     519.49    2388.26   \n",
              "20627    643.54   1604.50   1433.58     14.62  ...     519.68    2388.22   \n",
              "20628    643.42   1602.46   1428.18     14.62  ...     520.01    2388.24   \n",
              "20629    643.23   1605.26   1426.53     14.62  ...     519.67    2388.23   \n",
              "20630    643.85   1600.38   1432.14     14.62  ...     519.30    2388.26   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8138.62     8.4195       0.03        392       2388      100.0   \n",
              "1        8131.49     8.4318       0.03        392       2388      100.0   \n",
              "2        8133.23     8.4178       0.03        390       2388      100.0   \n",
              "3        8133.83     8.3682       0.03        392       2388      100.0   \n",
              "4        8133.80     8.4294       0.03        393       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "20626    8137.60     8.4956       0.03        397       2388      100.0   \n",
              "20627    8136.50     8.5139       0.03        395       2388      100.0   \n",
              "20628    8141.05     8.5646       0.03        398       2388      100.0   \n",
              "20629    8139.29     8.5389       0.03        395       2388      100.0   \n",
              "20630    8137.33     8.5036       0.03        396       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          39.06    23.4190  \n",
              "1          39.00    23.4236  \n",
              "2          38.95    23.3442  \n",
              "3          38.88    23.3739  \n",
              "4          38.90    23.4044  \n",
              "...          ...        ...  \n",
              "20626      38.49    22.9735  \n",
              "20627      38.30    23.1594  \n",
              "20628      38.44    22.9333  \n",
              "20629      38.29    23.0640  \n",
              "20630      38.37    23.0522  \n",
              "\n",
              "[20631 rows x 26 columns]"
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              "      <th></th>\n",
              "      <th>unit_number</th>\n",
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              "      <td>1589.70</td>\n",
              "      <td>1400.60</td>\n",
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              "      <td>2388.02</td>\n",
              "      <td>8138.62</td>\n",
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              "      <td>0.03</td>\n",
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              "      <td>2388</td>\n",
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              "      <td>-0.0003</td>\n",
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              "      <td>518.67</td>\n",
              "      <td>642.15</td>\n",
              "      <td>1591.82</td>\n",
              "      <td>1403.14</td>\n",
              "      <td>14.62</td>\n",
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              "      <td>522.28</td>\n",
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              "      <td>518.67</td>\n",
              "      <td>642.35</td>\n",
              "      <td>1587.99</td>\n",
              "      <td>1404.20</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.42</td>\n",
              "      <td>2388.03</td>\n",
              "      <td>8133.23</td>\n",
              "      <td>8.4178</td>\n",
              "      <td>0.03</td>\n",
              "      <td>390</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.95</td>\n",
              "      <td>23.3442</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2388.08</td>\n",
              "      <td>8133.83</td>\n",
              "      <td>8.3682</td>\n",
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              "      <td>2388</td>\n",
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              "      <td>38.88</td>\n",
              "      <td>23.3739</td>\n",
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              "      <td>-0.0002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.37</td>\n",
              "      <td>1582.85</td>\n",
              "      <td>1406.22</td>\n",
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              "      <td>522.19</td>\n",
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              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.29</td>\n",
              "      <td>23.0640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20630</th>\n",
              "      <td>100</td>\n",
              "      <td>200</td>\n",
              "      <td>-0.0032</td>\n",
              "      <td>-0.0005</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.85</td>\n",
              "      <td>1600.38</td>\n",
              "      <td>1432.14</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>519.30</td>\n",
              "      <td>2388.26</td>\n",
              "      <td>8137.33</td>\n",
              "      <td>8.5036</td>\n",
              "      <td>0.03</td>\n",
              "      <td>396</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.37</td>\n",
              "      <td>23.0522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20631 rows × 26 columns</p>\n",
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            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1     0.0023     0.0003      100.0    518.67   \n",
              "1                1            2    -0.0027    -0.0003      100.0    518.67   \n",
              "2                1            3     0.0003     0.0001      100.0    518.67   \n",
              "3                1            4     0.0042     0.0000      100.0    518.67   \n",
              "4                1            5     0.0014     0.0000      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "13091          100          194     0.0049     0.0000      100.0    518.67   \n",
              "13092          100          195    -0.0011    -0.0001      100.0    518.67   \n",
              "13093          100          196    -0.0006    -0.0003      100.0    518.67   \n",
              "13094          100          197    -0.0038     0.0001      100.0    518.67   \n",
              "13095          100          198     0.0013     0.0003      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        643.02   1585.29   1398.21     14.62  ...     521.72    2388.03   \n",
              "1        641.71   1588.45   1395.42     14.62  ...     522.16    2388.06   \n",
              "2        642.46   1586.94   1401.34     14.62  ...     521.97    2388.03   \n",
              "3        642.44   1584.12   1406.42     14.62  ...     521.38    2388.05   \n",
              "4        642.51   1587.19   1401.92     14.62  ...     522.15    2388.03   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "13091    643.24   1599.45   1415.79     14.62  ...     520.69    2388.00   \n",
              "13092    643.22   1595.69   1422.05     14.62  ...     521.05    2388.09   \n",
              "13093    643.44   1593.15   1406.82     14.62  ...     521.18    2388.04   \n",
              "13094    643.26   1594.99   1419.36     14.62  ...     521.33    2388.08   \n",
              "13095    642.95   1601.62   1424.99     14.62  ...     521.07    2388.05   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8125.55     8.4052       0.03        392       2388      100.0   \n",
              "1        8139.62     8.3803       0.03        393       2388      100.0   \n",
              "2        8130.10     8.4441       0.03        393       2388      100.0   \n",
              "3        8132.90     8.3917       0.03        391       2388      100.0   \n",
              "4        8129.54     8.4031       0.03        390       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "13091    8213.28     8.4715       0.03        394       2388      100.0   \n",
              "13092    8210.85     8.4512       0.03        395       2388      100.0   \n",
              "13093    8217.24     8.4569       0.03        395       2388      100.0   \n",
              "13094    8220.48     8.4711       0.03        395       2388      100.0   \n",
              "13095    8214.64     8.4903       0.03        396       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          38.86    23.3735  \n",
              "1          39.02    23.3916  \n",
              "2          39.08    23.4166  \n",
              "3          39.00    23.3737  \n",
              "4          38.99    23.4130  \n",
              "...          ...        ...  \n",
              "13091      38.65    23.1974  \n",
              "13092      38.57    23.2771  \n",
              "13093      38.62    23.2051  \n",
              "13094      38.66    23.2699  \n",
              "13095      38.70    23.1855  \n",
              "\n",
              "[13096 rows x 26 columns]"
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              "      <td>...</td>\n",
              "      <td>521.97</td>\n",
              "      <td>2388.03</td>\n",
              "      <td>8130.10</td>\n",
              "      <td>8.4441</td>\n",
              "      <td>0.03</td>\n",
              "      <td>393</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.08</td>\n",
              "      <td>23.4166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
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              "      <td>1584.12</td>\n",
              "      <td>1406.42</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.38</td>\n",
              "      <td>2388.05</td>\n",
              "      <td>8132.90</td>\n",
              "      <td>8.3917</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.00</td>\n",
              "      <td>23.3737</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>0.0000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.51</td>\n",
              "      <td>1587.19</td>\n",
              "      <td>1401.92</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.15</td>\n",
              "      <td>2388.03</td>\n",
              "      <td>8129.54</td>\n",
              "      <td>8.4031</td>\n",
              "      <td>0.03</td>\n",
              "      <td>390</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.99</td>\n",
              "      <td>23.4130</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>13091</th>\n",
              "      <td>100</td>\n",
              "      <td>194</td>\n",
              "      <td>0.0049</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.24</td>\n",
              "      <td>1599.45</td>\n",
              "      <td>1415.79</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>520.69</td>\n",
              "      <td>2388.00</td>\n",
              "      <td>8213.28</td>\n",
              "      <td>8.4715</td>\n",
              "      <td>0.03</td>\n",
              "      <td>394</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.65</td>\n",
              "      <td>23.1974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13092</th>\n",
              "      <td>100</td>\n",
              "      <td>195</td>\n",
              "      <td>-0.0011</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.22</td>\n",
              "      <td>1595.69</td>\n",
              "      <td>1422.05</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.05</td>\n",
              "      <td>2388.09</td>\n",
              "      <td>8210.85</td>\n",
              "      <td>8.4512</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.57</td>\n",
              "      <td>23.2771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13093</th>\n",
              "      <td>100</td>\n",
              "      <td>196</td>\n",
              "      <td>-0.0006</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.44</td>\n",
              "      <td>1593.15</td>\n",
              "      <td>1406.82</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.18</td>\n",
              "      <td>2388.04</td>\n",
              "      <td>8217.24</td>\n",
              "      <td>8.4569</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.62</td>\n",
              "      <td>23.2051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13094</th>\n",
              "      <td>100</td>\n",
              "      <td>197</td>\n",
              "      <td>-0.0038</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.26</td>\n",
              "      <td>1594.99</td>\n",
              "      <td>1419.36</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.33</td>\n",
              "      <td>2388.08</td>\n",
              "      <td>8220.48</td>\n",
              "      <td>8.4711</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.66</td>\n",
              "      <td>23.2699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13095</th>\n",
              "      <td>100</td>\n",
              "      <td>198</td>\n",
              "      <td>0.0013</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.95</td>\n",
              "      <td>1601.62</td>\n",
              "      <td>1424.99</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.07</td>\n",
              "      <td>2388.05</td>\n",
              "      <td>8214.64</td>\n",
              "      <td>8.4903</td>\n",
              "      <td>0.03</td>\n",
              "      <td>396</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.70</td>\n",
              "      <td>23.1855</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13096 rows × 26 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\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": "YmwUPBuWHHsj",
        "outputId": "ea54ac37-9ba8-4ebe-9d5e-f083c2366d95"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the train dataset :  (20631, 26)\n",
            "Shape of the test dataset :  (13096, 26)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "AMQUYtOOGl78"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "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": "sb5WkdSOGl_E"
      },
      "execution_count": 12,
      "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": "S8yGeRC_Hcnp"
      },
      "execution_count": 13,
      "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": "RTUK6hn5Hdjs"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "window_length = 30\n",
        "shift = 1\n",
        "early_rul = 125\n",
        "num_test_windows = 5"
      ],
      "metadata": {
        "id": "U5IaBj07Hdmm"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "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": "JLK0ABy0IDti"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "processed_test_data = []\n",
        "num_test_windows_list = []\n",
        "\n",
        "for i in np.arange(1, num_test_machines + 1):\n",
        "    temp_test_data = test_data[test_data[0] == i].drop(columns = [0]).values\n",
        "\n",
        "    # Prepare test data\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",
        "\n",
        "    processed_test_data.append(test_data_for_an_engine)\n",
        "    num_test_windows_list.append(num_windows)\n",
        "\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]"
      ],
      "metadata": {
        "id": "8O0kPj4WIH0-"
      },
      "execution_count": 17,
      "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": "-aH9xaK6IK--",
        "outputId": "883f7879-5d48-4349-da56-a1415a69bed5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed trianing data shape:  (17731, 30, 15)\n",
            "Processed training ruls shape:  (17731,)\n",
            "Processed test data shape:  (497, 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": "ZNK1XYOAIOIo",
        "outputId": "7b5cd6f9-7f15-4686-f07b-5442a37b124b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed train data shape:  (14184, 30, 15)\n",
            "Processed validation data shape:  (3547, 30, 15)\n",
            "Processed train targets shape:  (14184,)\n",
            "Processed validation targets shape:  (3547,)\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": "f9Nbm-5sIRaS",
        "outputId": "7dda7da4-176f-47a9-f998-dc8826d20e6c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 30, 128)           5888      \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1  (None, 15, 128)           0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 15, 128)           49280     \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPoolin  (None, 8, 128)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " time_distributed (TimeDist  (None, 8, 128)            0         \n",
            " ributed)                                                        \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 8, 128)            131584    \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 8, 64)             49408     \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 32)                12416     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 96)                3168      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               12416     \n",
            "                                                                 \n",
            " dense_2 (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"
      ],
      "metadata": {
        "id": "uMDgx3j2Icc1"
      },
      "execution_count": 21,
      "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\n"
      ],
      "metadata": {
        "id": "My4DjdVYIgKb"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn_lstm_model = CNN_LSTM_model(kernel_size= kernel_size ,pool_size = pool_size)"
      ],
      "metadata": {
        "id": "KB8O81sDIoal"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\"CNN+LSTM\":cnn_lstm_model}"
      ],
      "metadata": {
        "id": "fvENXDVXIqNk"
      },
      "execution_count": 24,
      "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": "_DxtKzTdIyaT"
      },
      "execution_count": 25,
      "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,\n",
        "                                                                                  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 length\n",
        "    true_rul_trimmed = true_rul[:len(mean_pred_for_each_engine)]\n",
        "    evaluate(true_rul_trimmed, 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": "Xyr63gZZI2YL",
        "outputId": "f3d46783-c1a2-4958-ff2a-5f766d25566c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================ CNN+LSTM ================\n",
            "Training : loss =  108.47956848144531\n",
            "Validation : loss =  124.47291564941406\n",
            "Testing : R-square =  0.8784976015225233 MAE =  11.086820710182192 RMSE =  14.48512334285907\n",
            "Run Time : 333 sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I9u39JKrIgPn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}