Bi_LSTM_upgrad2_6.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPcrjLGS/f75a0G0awDHUzs",
      "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_LSTM_upgrad2_6.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": "5OdJvveRv354"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gjPc1fYWwPU-",
        "outputId": "98e9e9cc-3190-4821-a4cb-1be573dfaaf2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp \"/content/drive/MyDrive/CMAPSS.zip\" \"/content\"\n",
        "!unzip CMAPSS.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzIl-oykwPW8",
        "outputId": "f3bb6101-7c07-43f7-ec7b-108586778182"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  CMAPSS.zip\n",
            "  inflating: CMAPSS/readme.txt       \n",
            "  inflating: CMAPSS/RUL_FD001.txt    \n",
            "  inflating: CMAPSS/RUL_FD002.txt    \n",
            "  inflating: CMAPSS/RUL_FD003.txt    \n",
            "  inflating: CMAPSS/RUL_FD004.txt    \n",
            "  inflating: CMAPSS/test_FD001.txt   \n",
            "  inflating: CMAPSS/test_FD002.txt   \n",
            "  inflating: CMAPSS/test_FD003.txt   \n",
            "  inflating: CMAPSS/test_FD004.txt   \n",
            "  inflating: CMAPSS/train_FD001.txt  \n",
            "  inflating: CMAPSS/train_FD002.txt  \n",
            "  inflating: CMAPSS/train_FD003.txt  \n",
            "  inflating: CMAPSS/train_FD004.txt  \n",
            "  inflating: CMAPSS/x.txt            \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import time\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler"
      ],
      "metadata": {
        "id": "zw-gN0GJwezE"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Conv1D\n",
        "from tensorflow.keras.layers import BatchNormalization, Dropout\n",
        "from tensorflow.keras.layers import TimeDistributed, Flatten\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau,EarlyStopping"
      ],
      "metadata": {
        "id": "HEmC46UdwhkF"
      },
      "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": "5DJ02cmBwkcP"
      },
      "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": "lT2l35QdwnHe",
        "outputId": "8c9b8ffb-ca23-4a43-986b-edeec135dddd"
      },
      "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": "hCV0KPKPwPZX",
        "outputId": "c85dc87f-a5bf-4513-af19-174b0a49e2ad"
      },
      "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",
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              "      <th>sensor_4</th>\n",
              "      <th>sensor_5</th>\n",
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              "      <th>sensor_14</th>\n",
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              "      <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",
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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",
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              "    <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",
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              "    <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",
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              "      <td>309</td>\n",
              "      <td>1915</td>\n",
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              "      <td>14.05</td>\n",
              "      <td>8.3729</td>\n",
              "    </tr>\n",
              "    <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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              "3                1            4    42.0066     0.8410      100.0    445.00   \n",
              "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>2387.75</td>\n",
              "      <td>8053.04</td>\n",
              "      <td>9.3472</td>\n",
              "      <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",
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              "      <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",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>24.9985</td>\n",
              "      <td>0.6213</td>\n",
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              "      <td>462.54</td>\n",
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              "      <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",
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              "      <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",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\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": "-NwBTeE0ww8L",
        "outputId": "f2000126-fdfb-4e56-b264-a5b8ef447e80"
      },
      "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": [
        "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": "UZWAB6ZWwx3W"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def process_input_data_with_targets(input_data, target_data , window_length , shift ):\n",
        "  num_batches = int(np.floor((len(input_data) - window_length)/shift)) + 1 # 共有多少組小資料\n",
        "  num_features = input_data.shape[1] # 每筆資料的特徵數\n",
        "  output = np.repeat(np.nan, repeats = num_batches * window_length * num_features)\n",
        "  output_data = output.reshape(num_batches, window_length , num_features)\n",
        "\n",
        "  if target_data is None: #測試資料無target data\n",
        "    for batch in range(num_batches):\n",
        "      output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
        "    return output_data\n",
        "  else : #處理訓練資料的target data\n",
        "    output_targets = np.repeat(np.nan, repeats = num_batches) #target rul\n",
        "    for batch in range(num_batches):\n",
        "      window_start = shift * batch\n",
        "      window_end = window_start + window_length\n",
        "\n",
        "      output_data[batch,:,:] = input_data[window_start:window_end,:]\n",
        "      output_targets[batch] = target_data[window_end-1]\n",
        "    return output_data, output_targets"
      ],
      "metadata": {
        "id": "8Qp4zjJwwx6O"
      },
      "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": "E_jk-YQMwPbo"
      },
      "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": "3oT1Bonjw8Jl"
      },
      "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": "knOkbTnMw8Ls"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "processed_train_data = []\n",
        "processed_train_targets = []\n",
        "\n",
        "for i in np.arange(1, num_train_machines + 1):\n",
        "    temp_train_data = train_data[train_data[0] == i].drop(columns = [0]).values #每台機器的資料\n",
        "\n",
        "    temp_train_targets = process_targets(data_length = temp_train_data.shape[0], early_rul = early_rul)\n",
        "    data_for_a_machine, targets_for_a_machine = process_input_data_with_targets(temp_train_data, temp_train_targets,window_length= window_length, shift = shift)\n",
        "\n",
        "    processed_train_data.append(data_for_a_machine)\n",
        "    processed_train_targets.append(targets_for_a_machine)\n",
        "\n",
        "processed_train_data = np.concatenate(processed_train_data)\n",
        "processed_train_targets = np.concatenate(processed_train_targets)"
      ],
      "metadata": {
        "id": "jvsaK9aAw8Ov"
      },
      "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": "5uDMhGwAxFKi",
        "outputId": "cdc06cde-af05-4738-df5e-109d378d478b"
      },
      "execution_count": 18,
      "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": "J4zbUDsLxWv8",
        "outputId": "3fdc4e5c-bf30-447e-83b0-ed4bbd960768"
      },
      "execution_count": 19,
      "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": "RJwFk7ddxZZ7",
        "outputId": "87cbdce7-27cd-4b29-92a5-78e9fa03039c"
      },
      "execution_count": 20,
      "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 Bi_LSTM_model():\n",
        "    input_shape = (window_length, 15)\n",
        "    model = Sequential([\n",
        "        layers.Bidirectional(LSTM(32, return_sequences=True, activation=\"tanh\")),\n",
        "        layers.Bidirectional(LSTM(16, activation=\"tanh\")),\n",
        "        layers.Dense(16, activation=\"relu\"),\n",
        "        layers.Dense(32, activation=\"relu\"),\n",
        "        layers.Dense(1)\n",
        "    ])\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
        "    model.compile(loss=\"mse\", optimizer=optimizer)\n",
        "    return model\n"
      ],
      "metadata": {
        "id": "vfWMbHP1xfE_"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def scheduler(epoch):\n",
        "  if epoch < 10:\n",
        "    return 0.001\n",
        "  else:\n",
        "    return 0.0001"
      ],
      "metadata": {
        "id": "bq9kpjV_wPd1"
      },
      "execution_count": 81,
      "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": "VWVatC0swPfw"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bi_lstm_model = Bi_LSTM_model()\n",
        "models = { 'Bi-LSTM': bi_lstm_model}"
      ],
      "metadata": {
        "id": "au2iw0z5wPix"
      },
      "execution_count": 83,
      "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": "lyyjDX2_wPlH"
      },
      "execution_count": 84,
      "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, len(ruls_for_each_engine)), axis=0)\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": "qhEo4_3Uxv4p",
        "outputId": "a8e821ba-df7f-4c3e-a477-445d0e56e6cc"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================ Bi-LSTM ================\n",
            "Training : loss =  306.8785705566406\n",
            "Validation : loss =  318.7059020996094\n",
            "Testing : R-square =  -0.3728891058233703 MAE =  49.58441308279753 RMSE =  62.9460324484082\n",
            "Run Time : 693 sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JF22SGiTwPod"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}