Bi_LSTM3.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP3sZRlsuXkOlxxQEBjLLZN",
      "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/Bi_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": 2,
      "metadata": {
        "id": "fogoEGk01Q-S"
      },
      "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": "qb4LkWoU1utM",
        "outputId": "a5da38ab-3276-45c3-dc27-803ddd95d558"
      },
      "execution_count": 3,
      "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": "TXVEG7N91uu4",
        "outputId": "b349a224-b6b8-40e0-d754-b82f2afa1ef4"
      },
      "execution_count": 4,
      "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": "u92nONle153L"
      },
      "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": "kA5ESjWo1ux6"
      },
      "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": "2QZM3OvK1uz1"
      },
      "execution_count": 7,
      "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": "uDpXjs6w2B0e",
        "outputId": "90a2818e-8ae0-408d-bc5f-78c17c565a32"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "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": "wFAbEkQ_2B2I",
        "outputId": "a84b628a-bfbf-4231-eb19-a74f5b22d44b"
      },
      "execution_count": 27,
      "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></th>\n",
              "      <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",
              "      <td>0.03</td>\n",
              "      <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>642.92</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",
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              "<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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              "4                1            5    -0.0001     0.0001      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "16591          100          243     0.0011    -0.0003      100.0    518.67   \n",
              "16592          100          244    -0.0024     0.0002      100.0    518.67   \n",
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              "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>1391.92</td>\n",
              "      <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>100.0</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",
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              "      <td>392</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.97</td>\n",
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
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              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.46</td>\n",
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              "      <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",
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              "    <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",
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              "    <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",
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              "    <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",
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              "    <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",
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              "      <td>0.03</td>\n",
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              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.40</td>\n",
              "      <td>23.6687</td>\n",
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              "    <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",
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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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              "    </button>\n",
              "    <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": "fjCxWL532B5K",
        "outputId": "c875bd90-1020-40fa-ea22-f15acc61e466"
      },
      "execution_count": 28,
      "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": "mgMAtSQR2B8G"
      },
      "execution_count": 29,
      "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": "9EJMcbXy1u2b"
      },
      "execution_count": 30,
      "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": "W78wmD4c2X3x"
      },
      "execution_count": 31,
      "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": "vClQJLWj2X7T"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "window_length = 30\n",
        "shift = 1\n",
        "early_rul = 125\n",
        "num_test_windows = 5"
      ],
      "metadata": {
        "id": "fL3KNiqT2X9V"
      },
      "execution_count": 33,
      "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": "dsJTUXVx2X_a"
      },
      "execution_count": 34,
      "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": "ySS7fM5h2kEL"
      },
      "execution_count": 35,
      "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": "4fsm7VwY2kIK",
        "outputId": "c4ab3a15-e3f6-42ea-fe5a-c2bad2db2b6b"
      },
      "execution_count": 36,
      "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": "qvH5W5cz2kNk",
        "outputId": "30181085-cfcc-4e03-dcfe-7c6cbbfdc064"
      },
      "execution_count": 37,
      "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": [
        "def Bi_LSTM_model():\n",
        "  input_shape = (window_length, 15)\n",
        "  model = Sequential([\n",
        "    layers.Bidirectional( LSTM(128, return_sequences = True, activation = \"tanh\") ),\n",
        "    layers.Bidirectional( LSTM(64, activation = \"tanh\", return_sequences = True) ),\n",
        "    layers.Bidirectional( 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"
      ],
      "metadata": {
        "id": "hnrwcaPG2YBu"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def scheduler(epoch):\n",
        "  if epoch < 10:\n",
        "    return 0.001\n",
        "  else:\n",
        "    return 0.0001"
      ],
      "metadata": {
        "id": "lXAnIylK1u5h"
      },
      "execution_count": 39,
      "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": "dVGXBYjs25XB"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bi_lstm_model = Bi_LSTM_model()\n",
        "models = { 'Bi-LSTM': bi_lstm_model}"
      ],
      "metadata": {
        "id": "1F-HK-xX25dc"
      },
      "execution_count": 41,
      "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": "Nvycudd225lB"
      },
      "execution_count": 42,
      "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": "fMCTV7fU1psO",
        "outputId": "ca8a2788-208a-45a4-b0e2-3b65c48001d2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================ Bi-LSTM ================\n",
            "Training : loss =  107.94491577148438\n",
            "Validation : loss =  109.00006103515625\n",
            "Testing : R-square =  0.8287868617437836 MAE =  12.738575309753417 RMSE =  17.128643343952277\n",
            "Run Time : 1598 sec\n"
          ]
        }
      ]
    }
  ]
}