GRU2.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMY8eDyrexriSzrjW+qNZHz",
      "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/GRU2.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": "shwiE2-vW0uK"
      },
      "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')"
      ]
    },
    {
      "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": "5gEjRu4FXHaR"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5kTTCOpXJ--",
        "outputId": "220328f9-58eb-4a46-b8dd-b2057b00d482"
      },
      "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": "75DF-xfGXKAu",
        "outputId": "3ebc091b-d5f8-4236-ab43-4f465cc3fce8"
      },
      "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 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": "_c5T0EvxXKEL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index_names = ['unit_number', 'time_cycles']\n",
        "setting_names = ['setting_1', 'setting_2', 'setting_3']\n",
        "sensor_names = ['sensor_{}'.format(i) for i in range(1,22)]\n",
        "col_names = index_names + setting_names + sensor_names"
      ],
      "metadata": {
        "id": "JWTkfuoqXZ5a"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/CMAPSS/'\n",
        "df_train = pd.read_csv(path+'train_FD002.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_FD002.txt',sep='\\s+',header=None,index_col=False,names=col_names)\n",
        "y_test = pd.read_csv(path+'RUL_FD002.txt',sep='\\s+',header=None,index_col=False,names=['RUL'])\n",
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcRA_HVLXZ8T",
        "outputId": "323d96c7-79dd-4e22-c128-2cc2a635cde0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(259, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "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": "RP51pXMmXKGr",
        "outputId": "8c2de12e-fc57-4831-f896-e46602129af9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    34.9983     0.8400      100.0    449.44   \n",
              "1                1            2    41.9982     0.8408      100.0    445.00   \n",
              "2                1            3    24.9988     0.6218       60.0    462.54   \n",
              "3                1            4    42.0077     0.8416      100.0    445.00   \n",
              "4                1            5    25.0005     0.6203       60.0    462.54   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "53754          260          312    20.0037     0.7000      100.0    491.19   \n",
              "53755          260          313    10.0022     0.2510      100.0    489.05   \n",
              "53756          260          314    25.0041     0.6200       60.0    462.54   \n",
              "53757          260          315    25.0033     0.6220       60.0    462.54   \n",
              "53758          260          316    35.0036     0.8400      100.0    449.44   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        555.32   1358.61   1137.23      5.48  ...     183.06    2387.72   \n",
              "1        549.90   1353.22   1125.78      3.91  ...     130.42    2387.66   \n",
              "2        537.31   1256.76   1047.45      7.05  ...     164.22    2028.03   \n",
              "3        549.51   1354.03   1126.38      3.91  ...     130.72    2387.61   \n",
              "4        537.07   1257.71   1047.93      7.05  ...     164.31    2028.00   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "53754    608.79   1495.60   1269.51      9.35  ...     314.05    2389.02   \n",
              "53755    605.81   1514.32   1324.12     10.52  ...     371.22    2388.42   \n",
              "53756    537.48   1276.24   1057.92      7.05  ...     163.74    2030.33   \n",
              "53757    537.84   1272.95   1066.30      7.05  ...     164.37    2030.35   \n",
              "53758    556.64   1374.61   1145.52      5.48  ...     183.09    2390.38   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8048.56     9.3461       0.02        334       2223     100.00   \n",
              "1        8072.30     9.3774       0.02        330       2212     100.00   \n",
              "2        7864.87    10.8941       0.02        309       1915      84.93   \n",
              "3        8068.66     9.3528       0.02        329       2212     100.00   \n",
              "4        7861.23    10.8963       0.02        309       1915      84.93   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "53754    8169.64     9.3035       0.03        369       2324     100.00   \n",
              "53755    8245.36     8.7586       0.03        374       2319     100.00   \n",
              "53756    7971.25    11.0657       0.02        310       1915      84.93   \n",
              "53757    7972.47    11.0537       0.02        311       1915      84.93   \n",
              "53758    8185.35     9.3998       0.02        338       2223     100.00   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          14.73     8.8071  \n",
              "1          10.41     6.2665  \n",
              "2          14.08     8.6723  \n",
              "3          10.59     6.4701  \n",
              "4          14.13     8.5286  \n",
              "...          ...        ...  \n",
              "53754      24.36    14.5189  \n",
              "53755      28.10    16.9454  \n",
              "53756      14.19     8.5503  \n",
              "53757      14.05     8.3729  \n",
              "53758      14.75     8.8446  \n",
              "\n",
              "[53759 rows x 26 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit_number</th>\n",
              "      <th>time_cycles</th>\n",
              "      <th>setting_1</th>\n",
              "      <th>setting_2</th>\n",
              "      <th>setting_3</th>\n",
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              "      <th>sensor_3</th>\n",
              "      <th>sensor_4</th>\n",
              "      <th>sensor_5</th>\n",
              "      <th>...</th>\n",
              "      <th>sensor_12</th>\n",
              "      <th>sensor_13</th>\n",
              "      <th>sensor_14</th>\n",
              "      <th>sensor_15</th>\n",
              "      <th>sensor_16</th>\n",
              "      <th>sensor_17</th>\n",
              "      <th>sensor_18</th>\n",
              "      <th>sensor_19</th>\n",
              "      <th>sensor_20</th>\n",
              "      <th>sensor_21</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>34.9983</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.32</td>\n",
              "      <td>1358.61</td>\n",
              "      <td>1137.23</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>183.06</td>\n",
              "      <td>2387.72</td>\n",
              "      <td>8048.56</td>\n",
              "      <td>9.3461</td>\n",
              "      <td>0.02</td>\n",
              "      <td>334</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.73</td>\n",
              "      <td>8.8071</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>41.9982</td>\n",
              "      <td>0.8408</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.90</td>\n",
              "      <td>1353.22</td>\n",
              "      <td>1125.78</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.42</td>\n",
              "      <td>2387.66</td>\n",
              "      <td>8072.30</td>\n",
              "      <td>9.3774</td>\n",
              "      <td>0.02</td>\n",
              "      <td>330</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.41</td>\n",
              "      <td>6.2665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>24.9988</td>\n",
              "      <td>0.6218</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>537.31</td>\n",
              "      <td>1256.76</td>\n",
              "      <td>1047.45</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>164.22</td>\n",
              "      <td>2028.03</td>\n",
              "      <td>7864.87</td>\n",
              "      <td>10.8941</td>\n",
              "      <td>0.02</td>\n",
              "      <td>309</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.08</td>\n",
              "      <td>8.6723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0077</td>\n",
              "      <td>0.8416</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.51</td>\n",
              "      <td>1354.03</td>\n",
              "      <td>1126.38</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.72</td>\n",
              "      <td>2387.61</td>\n",
              "      <td>8068.66</td>\n",
              "      <td>9.3528</td>\n",
              "      <td>0.02</td>\n",
              "      <td>329</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.59</td>\n",
              "      <td>6.4701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>25.0005</td>\n",
              "      <td>0.6203</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>537.07</td>\n",
              "      <td>1257.71</td>\n",
              "      <td>1047.93</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>164.31</td>\n",
              "      <td>2028.00</td>\n",
              "      <td>7861.23</td>\n",
              "      <td>10.8963</td>\n",
              "      <td>0.02</td>\n",
              "      <td>309</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.13</td>\n",
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              "      <th>53754</th>\n",
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              "      <td>491.19</td>\n",
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              "      <td>9.3035</td>\n",
              "      <td>0.03</td>\n",
              "      <td>369</td>\n",
              "      <td>2324</td>\n",
              "      <td>100.00</td>\n",
              "      <td>24.36</td>\n",
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              "      <td>100.0</td>\n",
              "      <td>489.05</td>\n",
              "      <td>605.81</td>\n",
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              "      <td>1324.12</td>\n",
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              "      <td>84.93</td>\n",
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              "    <tr>\n",
              "      <th>53758</th>\n",
              "      <td>260</td>\n",
              "      <td>316</td>\n",
              "      <td>35.0036</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>556.64</td>\n",
              "      <td>1374.61</td>\n",
              "      <td>1145.52</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>183.09</td>\n",
              "      <td>2390.38</td>\n",
              "      <td>8185.35</td>\n",
              "      <td>9.3998</td>\n",
              "      <td>0.02</td>\n",
              "      <td>338</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.75</td>\n",
              "      <td>8.8446</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>53759 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     9.9987     0.2502      100.0    489.05   \n",
              "1                1            2    20.0026     0.7000      100.0    491.19   \n",
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              "4                1            5    24.9985     0.6213       60.0    462.54   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "33986          259          119    35.0015     0.8403      100.0    449.44   \n",
              "33987          259          120    42.0066     0.8405      100.0    445.00   \n",
              "33988          259          121    42.0061     0.8400      100.0    445.00   \n",
              "33989          259          122     0.0024     0.0003      100.0    518.67   \n",
              "33990          259          123    42.0033     0.8400      100.0    445.00   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        605.03   1497.17   1304.99     10.52  ...     371.69    2388.18   \n",
              "1        607.82   1481.20   1246.11      9.35  ...     315.32    2388.12   \n",
              "2        556.00   1359.08   1128.36      5.48  ...     183.04    2387.75   \n",
              "3        550.17   1349.69   1127.89      3.91  ...     130.40    2387.72   \n",
              "4        536.72   1253.18   1050.69      7.05  ...     164.56    2028.05   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "33986    555.56   1366.01   1129.47      5.48  ...     183.05    2388.39   \n",
              "33987    549.42   1351.13   1123.86      3.91  ...     130.74    2388.31   \n",
              "33988    549.65   1349.14   1118.91      3.91  ...     130.96    2388.34   \n",
              "33989    642.58   1589.61   1408.16     14.62  ...     522.25    2388.00   \n",
              "33990    549.77   1342.50   1126.96      3.91  ...     130.93    2388.40   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8114.10     8.6476       0.03        369       2319     100.00   \n",
              "1        8053.06     9.2405       0.02        364       2324     100.00   \n",
              "2        8053.04     9.3472       0.02        333       2223     100.00   \n",
              "3        8066.90     9.3961       0.02        332       2212     100.00   \n",
              "4        7865.66    10.8682       0.02        305       1915      84.93   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "33986    8088.36     9.3215       0.02        334       2223     100.00   \n",
              "33987    8108.48     9.3542       0.02        332       2212     100.00   \n",
              "33988    8098.77     9.3836       0.02        331       2212     100.00   \n",
              "33989    8161.85     8.4279       0.03        393       2388     100.00   \n",
              "33990    8105.28     9.3458       0.02        331       2212     100.00   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          28.42    17.1551  \n",
              "1          24.29    14.8039  \n",
              "2          14.98     8.9125  \n",
              "3          10.35     6.4181  \n",
              "4          14.31     8.5740  \n",
              "...          ...        ...  \n",
              "33986      14.94     8.9065  \n",
              "33987      10.57     6.4075  \n",
              "33988      10.57     6.4805  \n",
              "33989      39.08    23.3589  \n",
              "33990      10.63     6.3480  \n",
              "\n",
              "[33991 rows x 26 columns]"
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              "      <td>9.35</td>\n",
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              "      <td>5.48</td>\n",
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              "      <td>0.02</td>\n",
              "      <td>333</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.98</td>\n",
              "      <td>8.9125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0066</td>\n",
              "      <td>0.8410</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>550.17</td>\n",
              "      <td>1349.69</td>\n",
              "      <td>1127.89</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.40</td>\n",
              "      <td>2387.72</td>\n",
              "      <td>8066.90</td>\n",
              "      <td>9.3961</td>\n",
              "      <td>0.02</td>\n",
              "      <td>332</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.35</td>\n",
              "      <td>6.4181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>24.9985</td>\n",
              "      <td>0.6213</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>536.72</td>\n",
              "      <td>1253.18</td>\n",
              "      <td>1050.69</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>164.56</td>\n",
              "      <td>2028.05</td>\n",
              "      <td>7865.66</td>\n",
              "      <td>10.8682</td>\n",
              "      <td>0.02</td>\n",
              "      <td>305</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.31</td>\n",
              "      <td>8.5740</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>33986</th>\n",
              "      <td>259</td>\n",
              "      <td>119</td>\n",
              "      <td>35.0015</td>\n",
              "      <td>0.8403</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.56</td>\n",
              "      <td>1366.01</td>\n",
              "      <td>1129.47</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>183.05</td>\n",
              "      <td>2388.39</td>\n",
              "      <td>8088.36</td>\n",
              "      <td>9.3215</td>\n",
              "      <td>0.02</td>\n",
              "      <td>334</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.94</td>\n",
              "      <td>8.9065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33987</th>\n",
              "      <td>259</td>\n",
              "      <td>120</td>\n",
              "      <td>42.0066</td>\n",
              "      <td>0.8405</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.42</td>\n",
              "      <td>1351.13</td>\n",
              "      <td>1123.86</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.74</td>\n",
              "      <td>2388.31</td>\n",
              "      <td>8108.48</td>\n",
              "      <td>9.3542</td>\n",
              "      <td>0.02</td>\n",
              "      <td>332</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.57</td>\n",
              "      <td>6.4075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33988</th>\n",
              "      <td>259</td>\n",
              "      <td>121</td>\n",
              "      <td>42.0061</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.65</td>\n",
              "      <td>1349.14</td>\n",
              "      <td>1118.91</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.96</td>\n",
              "      <td>2388.34</td>\n",
              "      <td>8098.77</td>\n",
              "      <td>9.3836</td>\n",
              "      <td>0.02</td>\n",
              "      <td>331</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.57</td>\n",
              "      <td>6.4805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33989</th>\n",
              "      <td>259</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0024</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.58</td>\n",
              "      <td>1589.61</td>\n",
              "      <td>1408.16</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.25</td>\n",
              "      <td>2388.00</td>\n",
              "      <td>8161.85</td>\n",
              "      <td>8.4279</td>\n",
              "      <td>0.03</td>\n",
              "      <td>393</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.00</td>\n",
              "      <td>39.08</td>\n",
              "      <td>23.3589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33990</th>\n",
              "      <td>259</td>\n",
              "      <td>123</td>\n",
              "      <td>42.0033</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.77</td>\n",
              "      <td>1342.50</td>\n",
              "      <td>1126.96</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.93</td>\n",
              "      <td>2388.40</td>\n",
              "      <td>8105.28</td>\n",
              "      <td>9.3458</td>\n",
              "      <td>0.02</td>\n",
              "      <td>331</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.63</td>\n",
              "      <td>6.3480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>33991 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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              "    </style>\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\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": "CkyDSv0pXjSt",
        "outputId": "85a92483-34ae-4632-cb08-886dc4c0fdb5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the train dataset :  (53759, 26)\n",
            "Shape of the test dataset :  (33991, 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": "pgqvTwa7XjVj"
      },
      "execution_count": 10,
      "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": "lWvHuocHXjZp"
      },
      "execution_count": 11,
      "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": "5yP_8UtiXsBE"
      },
      "execution_count": 12,
      "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": "2oRULcPJXsC-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "window_length = 30\n",
        "shift = 1\n",
        "early_rul = 125\n",
        "num_test_windows = 5"
      ],
      "metadata": {
        "id": "CNO9BTGoXsGg"
      },
      "execution_count": 14,
      "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": "NkDUo0_0XsJa"
      },
      "execution_count": 15,
      "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": "3TNwkIfiXjcI",
        "outputId": "2f816b32-aba9-4393-e6e9-a2479fc6e42f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skipping engine 28 due to insufficient data for windowing.\n",
            "Skipping engine 59 due to insufficient data for windowing.\n",
            "Skipping engine 83 due to insufficient data for windowing.\n",
            "Skipping engine 122 due to insufficient data for windowing.\n",
            "Skipping engine 201 due to insufficient data for windowing.\n",
            "Skipping engine 223 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": "W8xktYz5X79N",
        "outputId": "e92a9303-e7f4-445c-adf0-133cefb67196"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed trianing data shape:  (46219, 30, 15)\n",
            "Processed training ruls shape:  (46219,)\n",
            "Processed test data shape:  (1261, 30, 15)\n",
            "True RUL shape:  (259, 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": "y-mXUSJYX8Y-",
        "outputId": "994008f0-56ca-46ce-bee4-506c69b01d1e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed train data shape:  (36975, 30, 15)\n",
            "Processed validation data shape:  (9244, 30, 15)\n",
            "Processed train targets shape:  (36975,)\n",
            "Processed validation targets shape:  (9244,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def GRU_model():\n",
        "  input_shape = (window_length, 15)\n",
        "  model = Sequential([\n",
        "    layers.GRU(128, input_shape = input_shape , return_sequences=True, activation = \"tanh\"),\n",
        "    layers.GRU(64, activation = \"tanh\", return_sequences = True),\n",
        "    layers.GRU(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",
        "GRU_model().summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNfczH79X8b6",
        "outputId": "c15c5d1a-7c17-4753-bc5f-ae124122aec9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gru (GRU)                   (None, 30, 128)           55680     \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, 30, 64)            37248     \n",
            "                                                                 \n",
            " gru_2 (GRU)                 (None, 32)                9408      \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: 118049 (461.13 KB)\n",
            "Trainable params: 118049 (461.13 KB)\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": "tdz0YnzVYI6X"
      },
      "execution_count": 20,
      "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": "hX9EP5WqYI9Z"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gru_model = GRU_model()"
      ],
      "metadata": {
        "id": "m5eRvQSYYJCE"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\"GRU\":gru_model}"
      ],
      "metadata": {
        "id": "3OEkvBVAXjeV"
      },
      "execution_count": 23,
      "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": "ejzMoBSLYWGa"
      },
      "execution_count": 24,
      "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": "zcxzZO1kXKKG",
        "outputId": "49e50128-4e4c-46ec-b541-1c5e7a6b955a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================ GRU ================\n",
            "Training : loss =  200.71142578125\n",
            "Validation : loss =  201.2578887939453\n",
            "Testing : R-square =  -0.3828207710825262 MAE =  49.23749120725474 RMSE =  63.17330221518365\n",
            "Run Time : 1289 sec\n"
          ]
        }
      ]
    }
  ]
}