Bi_LSTM4.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMjCYa0IzrU7ZEKxgGQj2KR",
      "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_LSTM4.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_FD004.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_FD004.txt',sep='\\s+',header=None,index_col=False,names=col_names)\n",
        "y_test = pd.read_csv(path+'RUL_FD004.txt',sep='\\s+',header=None,index_col=False,names=['RUL'])\n",
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDpXjs6w2B0e",
        "outputId": "649cec63-2011-4974-fc75-1db49ef48656"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(248, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "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": "a1dc51a8-fd1e-4235-fb20-827b1c72d5d8"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    42.0049     0.8400      100.0    445.00   \n",
              "1                1            2    20.0020     0.7002      100.0    491.19   \n",
              "2                1            3    42.0038     0.8409      100.0    445.00   \n",
              "3                1            4    42.0000     0.8400      100.0    445.00   \n",
              "4                1            5    25.0063     0.6207       60.0    462.54   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "61244          249          251     9.9998     0.2500      100.0    489.05   \n",
              "61245          249          252     0.0028     0.0015      100.0    518.67   \n",
              "61246          249          253     0.0029     0.0000      100.0    518.67   \n",
              "61247          249          254    35.0046     0.8400      100.0    449.44   \n",
              "61248          249          255    42.0030     0.8400      100.0    445.00   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        549.68   1343.43   1112.93      3.91  ...     129.78    2387.99   \n",
              "1        606.07   1477.61   1237.50      9.35  ...     312.59    2387.73   \n",
              "2        548.95   1343.12   1117.05      3.91  ...     129.62    2387.97   \n",
              "3        548.70   1341.24   1118.03      3.91  ...     129.80    2388.02   \n",
              "4        536.10   1255.23   1033.59      7.05  ...     164.11    2028.08   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "61244    605.33   1516.36   1315.28     10.52  ...     380.16    2388.73   \n",
              "61245    643.42   1598.92   1426.77     14.62  ...     535.02    2388.46   \n",
              "61246    643.68   1607.72   1430.56     14.62  ...     535.41    2388.48   \n",
              "61247    555.77   1381.29   1148.18      5.48  ...     187.92    2388.83   \n",
              "61248    549.85   1369.75   1147.45      3.91  ...     134.32    2388.66   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8074.83     9.3335       0.02        330       2212     100.00   \n",
              "1        8046.13     9.1913       0.02        361       2324     100.00   \n",
              "2        8066.62     9.4007       0.02        329       2212     100.00   \n",
              "3        8076.05     9.3369       0.02        328       2212     100.00   \n",
              "4        7865.80    10.8366       0.02        305       1915      84.93   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "61244    8185.69     8.4541       0.03        372       2319     100.00   \n",
              "61245    8185.47     8.2221       0.03        396       2388     100.00   \n",
              "61246    8193.94     8.2525       0.03        395       2388     100.00   \n",
              "61247    8125.64     9.0515       0.02        337       2223     100.00   \n",
              "61248    8144.33     9.1207       0.02        333       2212     100.00   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          10.62     6.3670  \n",
              "1          24.37    14.6552  \n",
              "2          10.48     6.4213  \n",
              "3          10.54     6.4176  \n",
              "4          14.03     8.6754  \n",
              "...          ...        ...  \n",
              "61244      29.11    17.5234  \n",
              "61245      39.38    23.7151  \n",
              "61246      39.78    23.8270  \n",
              "61247      15.26     9.0774  \n",
              "61248      10.66     6.4341  \n",
              "\n",
              "[61249 rows x 26 columns]"
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              "      <th></th>\n",
              "      <th>unit_number</th>\n",
              "      <th>time_cycles</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>42.0049</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.68</td>\n",
              "      <td>1343.43</td>\n",
              "      <td>1112.93</td>\n",
              "      <td>3.91</td>\n",
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              "      <td>129.78</td>\n",
              "      <td>2387.99</td>\n",
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              "      <td>9.3335</td>\n",
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              "      <th>1</th>\n",
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              "      <td>20.0020</td>\n",
              "      <td>0.7002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>491.19</td>\n",
              "      <td>606.07</td>\n",
              "      <td>1477.61</td>\n",
              "      <td>1237.50</td>\n",
              "      <td>9.35</td>\n",
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              "      <td>312.59</td>\n",
              "      <td>2387.73</td>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>42.0038</td>\n",
              "      <td>0.8409</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>548.95</td>\n",
              "      <td>1343.12</td>\n",
              "      <td>1117.05</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>129.62</td>\n",
              "      <td>2387.97</td>\n",
              "      <td>8066.62</td>\n",
              "      <td>9.4007</td>\n",
              "      <td>0.02</td>\n",
              "      <td>329</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.48</td>\n",
              "      <td>6.4213</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0000</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>548.70</td>\n",
              "      <td>1341.24</td>\n",
              "      <td>1118.03</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.6207</td>\n",
              "      <td>60.0</td>\n",
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              "      <td>536.10</td>\n",
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              "      <td>1033.59</td>\n",
              "      <td>7.05</td>\n",
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              "      <td>0.02</td>\n",
              "      <td>337</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>15.26</td>\n",
              "      <td>9.0774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61248</th>\n",
              "      <td>249</td>\n",
              "      <td>255</td>\n",
              "      <td>42.0030</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.85</td>\n",
              "      <td>1369.75</td>\n",
              "      <td>1147.45</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>134.32</td>\n",
              "      <td>2388.66</td>\n",
              "      <td>8144.33</td>\n",
              "      <td>9.1207</td>\n",
              "      <td>0.02</td>\n",
              "      <td>333</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.66</td>\n",
              "      <td>6.4341</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>61249 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    20.0072     0.7000      100.0    491.19   \n",
              "1                1            2    24.9984     0.6200       60.0    462.54   \n",
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              "...            ...          ...        ...        ...        ...       ...   \n",
              "41209          248          277    41.9991     0.8401      100.0    445.00   \n",
              "41210          248          278    20.0026     0.7005      100.0    491.19   \n",
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              "41212          248          280    20.0027     0.7000      100.0    491.19   \n",
              "41213          248          281    35.0075     0.8402      100.0    449.44   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        606.67   1481.04   1227.81      9.35  ...     313.03    2387.78   \n",
              "1        536.22   1256.17   1031.48      7.05  ...     163.61    2028.09   \n",
              "2        549.23   1340.13   1105.88      3.91  ...     129.98    2387.95   \n",
              "3        549.19   1339.70   1107.26      3.91  ...     129.48    2387.90   \n",
              "4        555.10   1353.04   1117.80      5.48  ...     181.82    2387.87   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "41209    550.30   1364.40   1129.17      3.91  ...     130.87    2388.50   \n",
              "41210    608.00   1494.75   1260.88      9.35  ...     314.51    2388.33   \n",
              "41211    555.92   1370.65   1130.97      5.48  ...     182.76    2388.64   \n",
              "41212    608.19   1489.11   1256.25      9.35  ...     314.17    2388.37   \n",
              "41213    556.40   1378.58   1140.70      5.48  ...     181.88    2388.59   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8048.98     9.2229       0.02        362       2324     100.00   \n",
              "1        7863.46    10.8632       0.02        306       1915      84.93   \n",
              "2        8071.13     9.3960       0.02        328       2212     100.00   \n",
              "3        8078.89     9.3594       0.02        328       2212     100.00   \n",
              "4        8057.83     9.3030       0.02        333       2223     100.00   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "41209    8112.61     9.4427       0.02        331       2212     100.00   \n",
              "41210    8086.83     9.2772       0.02        366       2324     100.00   \n",
              "41211    8100.84     9.3982       0.02        336       2223     100.00   \n",
              "41212    8085.24     9.2727       0.03        366       2324     100.00   \n",
              "41213    8098.17     9.3964       0.02        335       2223     100.00   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          24.31    14.7007  \n",
              "1          14.36     8.5748  \n",
              "2          10.39     6.4365  \n",
              "3          10.56     6.2367  \n",
              "4          14.85     8.9326  \n",
              "...          ...        ...  \n",
              "41209      10.53     6.2620  \n",
              "41210      24.33    14.6486  \n",
              "41211      14.69     8.8389  \n",
              "41212      24.44    14.6887  \n",
              "41213      14.72     8.8502  \n",
              "\n",
              "[41214 rows x 26 columns]"
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              "      <td>1340.13</td>\n",
              "      <td>1105.88</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>129.98</td>\n",
              "      <td>2387.95</td>\n",
              "      <td>8071.13</td>\n",
              "      <td>9.3960</td>\n",
              "      <td>0.02</td>\n",
              "      <td>328</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.39</td>\n",
              "      <td>6.4365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0035</td>\n",
              "      <td>0.8402</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.19</td>\n",
              "      <td>1339.70</td>\n",
              "      <td>1107.26</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>129.48</td>\n",
              "      <td>2387.90</td>\n",
              "      <td>8078.89</td>\n",
              "      <td>9.3594</td>\n",
              "      <td>0.02</td>\n",
              "      <td>328</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.56</td>\n",
              "      <td>6.2367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>35.0079</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.10</td>\n",
              "      <td>1353.04</td>\n",
              "      <td>1117.80</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>181.82</td>\n",
              "      <td>2387.87</td>\n",
              "      <td>8057.83</td>\n",
              "      <td>9.3030</td>\n",
              "      <td>0.02</td>\n",
              "      <td>333</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.85</td>\n",
              "      <td>8.9326</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>41209</th>\n",
              "      <td>248</td>\n",
              "      <td>277</td>\n",
              "      <td>41.9991</td>\n",
              "      <td>0.8401</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>550.30</td>\n",
              "      <td>1364.40</td>\n",
              "      <td>1129.17</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.87</td>\n",
              "      <td>2388.50</td>\n",
              "      <td>8112.61</td>\n",
              "      <td>9.4427</td>\n",
              "      <td>0.02</td>\n",
              "      <td>331</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.53</td>\n",
              "      <td>6.2620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41210</th>\n",
              "      <td>248</td>\n",
              "      <td>278</td>\n",
              "      <td>20.0026</td>\n",
              "      <td>0.7005</td>\n",
              "      <td>100.0</td>\n",
              "      <td>491.19</td>\n",
              "      <td>608.00</td>\n",
              "      <td>1494.75</td>\n",
              "      <td>1260.88</td>\n",
              "      <td>9.35</td>\n",
              "      <td>...</td>\n",
              "      <td>314.51</td>\n",
              "      <td>2388.33</td>\n",
              "      <td>8086.83</td>\n",
              "      <td>9.2772</td>\n",
              "      <td>0.02</td>\n",
              "      <td>366</td>\n",
              "      <td>2324</td>\n",
              "      <td>100.00</td>\n",
              "      <td>24.33</td>\n",
              "      <td>14.6486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41211</th>\n",
              "      <td>248</td>\n",
              "      <td>279</td>\n",
              "      <td>34.9988</td>\n",
              "      <td>0.8413</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.92</td>\n",
              "      <td>1370.65</td>\n",
              "      <td>1130.97</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>182.76</td>\n",
              "      <td>2388.64</td>\n",
              "      <td>8100.84</td>\n",
              "      <td>9.3982</td>\n",
              "      <td>0.02</td>\n",
              "      <td>336</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.69</td>\n",
              "      <td>8.8389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41212</th>\n",
              "      <td>248</td>\n",
              "      <td>280</td>\n",
              "      <td>20.0027</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>491.19</td>\n",
              "      <td>608.19</td>\n",
              "      <td>1489.11</td>\n",
              "      <td>1256.25</td>\n",
              "      <td>9.35</td>\n",
              "      <td>...</td>\n",
              "      <td>314.17</td>\n",
              "      <td>2388.37</td>\n",
              "      <td>8085.24</td>\n",
              "      <td>9.2727</td>\n",
              "      <td>0.03</td>\n",
              "      <td>366</td>\n",
              "      <td>2324</td>\n",
              "      <td>100.00</td>\n",
              "      <td>24.44</td>\n",
              "      <td>14.6887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41213</th>\n",
              "      <td>248</td>\n",
              "      <td>281</td>\n",
              "      <td>35.0075</td>\n",
              "      <td>0.8402</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>556.40</td>\n",
              "      <td>1378.58</td>\n",
              "      <td>1140.70</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>181.88</td>\n",
              "      <td>2388.59</td>\n",
              "      <td>8098.17</td>\n",
              "      <td>9.3964</td>\n",
              "      <td>0.02</td>\n",
              "      <td>335</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.72</td>\n",
              "      <td>8.8502</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41214 rows × 26 columns</p>\n",
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              "  }\n",
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              "\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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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\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": "61dbea02-24bc-482d-d99a-e76f1f50bcdf"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the train dataset :  (61249, 26)\n",
            "Shape of the test dataset :  (41214, 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": 47,
      "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": 48,
      "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": 49,
      "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": 50,
      "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": 51,
      "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": 52,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySS7fM5h2kEL",
        "outputId": "d9b4e5f5-77c9-4efc-faef-27ef84c32e79"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skipping engine 10 due to insufficient data for windowing.\n",
            "Skipping engine 19 due to insufficient data for windowing.\n",
            "Skipping engine 28 due to insufficient data for windowing.\n",
            "Skipping engine 125 due to insufficient data for windowing.\n",
            "Skipping engine 141 due to insufficient data for windowing.\n",
            "Skipping engine 156 due to insufficient data for windowing.\n",
            "Skipping engine 164 due to insufficient data for windowing.\n",
            "Skipping engine 204 due to insufficient data for windowing.\n",
            "Skipping engine 229 due to insufficient data for windowing.\n",
            "Skipping engine 239 due to insufficient data for windowing.\n",
            "Skipping engine 246 due to insufficient data for windowing.\n"
          ]
        }
      ]
    },
    {
      "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": "9a931f77-6010-4dfe-bae2-9e87ef51e921"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed trianing data shape:  (54028, 30, 15)\n",
            "Processed training ruls shape:  (54028,)\n",
            "Processed test data shape:  (1185, 30, 15)\n",
            "True RUL shape:  (248, 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": "4821eeeb-50dd-4821-c5fa-096627a441d0"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed train data shape:  (43222, 30, 15)\n",
            "Processed validation data shape:  (10806, 30, 15)\n",
            "Processed train targets shape:  (43222,)\n",
            "Processed validation targets shape:  (10806,)\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": 56,
      "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": 57,
      "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": 58,
      "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": 59,
      "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": 60,
      "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": "e5424571-1ea3-47ef-ba2b-10dd45825048"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================ Bi-LSTM ================\n",
            "Training : loss =  321.0929260253906\n",
            "Validation : loss =  313.28594970703125\n",
            "Testing : R-square =  -0.509966244408051 MAE =  53.71173348125023 RMSE =  66.59849155211636\n",
            "Run Time : 3940 sec\n"
          ]
        }
      ]
    }
  ]
}