BAT_LSTM1.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOniw6EZWuYhWg5gCgseX3w",
      "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/BAT_LSTM1.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CsLeMuZWTXp-",
        "outputId": "5c7865e2-e284-4d66-b071-c33930168f4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyMetaheuristic\n",
            "  Downloading pyMetaheuristic-1.9.5-py3-none-any.whl (98 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.4/98.4 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pyMetaheuristic) (1.25.2)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pyMetaheuristic) (5.15.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pyMetaheuristic) (1.11.4)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pyMetaheuristic) (8.2.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly->pyMetaheuristic) (24.0)\n",
            "Installing collected packages: pyMetaheuristic\n",
            "Successfully installed pyMetaheuristic-1.9.5\n"
          ]
        }
      ],
      "source": [
        "!pip install pyMetaheuristic"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense\n",
        "from pyMetaheuristic.algorithm import bat_algorithm"
      ],
      "metadata": {
        "id": "raegjAo4Tfda"
      },
      "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": "CWAJzyTITieE",
        "outputId": "8c5a747d-5b4d-42e9-c2e7-91270e4ab27d"
      },
      "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\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AT73HiGHTrdL",
        "outputId": "62cf0c89-5741-49b4-f37b-b60633e20b79"
      },
      "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": [
        "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": "R32SUOZ3T0v4"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/CMAPSS/'\n",
        "df_train = pd.read_csv(path+'train_FD001.txt', sep='\\s+', header=None, index_col=False, names=col_names)\n",
        "df_test = pd.read_csv(path+'test_FD001.txt', sep='\\s+', header=None, index_col=False, names=col_names)\n",
        "y_test = pd.read_csv(path+'RUL_FD001.txt', sep='\\s+', header=None, index_col=False, names=['RUL'])"
      ],
      "metadata": {
        "id": "wdy3OiW-T0xx"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "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": "C6ImSf1OT9Jz",
        "outputId": "117db4fe-3e8d-4e42-f9f2-5c88dbf6cc69"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    -0.0007    -0.0004      100.0    518.67   \n",
              "1                1            2     0.0019    -0.0003      100.0    518.67   \n",
              "2                1            3    -0.0043     0.0003      100.0    518.67   \n",
              "3                1            4     0.0007     0.0000      100.0    518.67   \n",
              "4                1            5    -0.0019    -0.0002      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "20626          100          196    -0.0004    -0.0003      100.0    518.67   \n",
              "20627          100          197    -0.0016    -0.0005      100.0    518.67   \n",
              "20628          100          198     0.0004     0.0000      100.0    518.67   \n",
              "20629          100          199    -0.0011     0.0003      100.0    518.67   \n",
              "20630          100          200    -0.0032    -0.0005      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        641.82   1589.70   1400.60     14.62  ...     521.66    2388.02   \n",
              "1        642.15   1591.82   1403.14     14.62  ...     522.28    2388.07   \n",
              "2        642.35   1587.99   1404.20     14.62  ...     522.42    2388.03   \n",
              "3        642.35   1582.79   1401.87     14.62  ...     522.86    2388.08   \n",
              "4        642.37   1582.85   1406.22     14.62  ...     522.19    2388.04   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "20626    643.49   1597.98   1428.63     14.62  ...     519.49    2388.26   \n",
              "20627    643.54   1604.50   1433.58     14.62  ...     519.68    2388.22   \n",
              "20628    643.42   1602.46   1428.18     14.62  ...     520.01    2388.24   \n",
              "20629    643.23   1605.26   1426.53     14.62  ...     519.67    2388.23   \n",
              "20630    643.85   1600.38   1432.14     14.62  ...     519.30    2388.26   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8138.62     8.4195       0.03        392       2388      100.0   \n",
              "1        8131.49     8.4318       0.03        392       2388      100.0   \n",
              "2        8133.23     8.4178       0.03        390       2388      100.0   \n",
              "3        8133.83     8.3682       0.03        392       2388      100.0   \n",
              "4        8133.80     8.4294       0.03        393       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "20626    8137.60     8.4956       0.03        397       2388      100.0   \n",
              "20627    8136.50     8.5139       0.03        395       2388      100.0   \n",
              "20628    8141.05     8.5646       0.03        398       2388      100.0   \n",
              "20629    8139.29     8.5389       0.03        395       2388      100.0   \n",
              "20630    8137.33     8.5036       0.03        396       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          39.06    23.4190  \n",
              "1          39.00    23.4236  \n",
              "2          38.95    23.3442  \n",
              "3          38.88    23.3739  \n",
              "4          38.90    23.4044  \n",
              "...          ...        ...  \n",
              "20626      38.49    22.9735  \n",
              "20627      38.30    23.1594  \n",
              "20628      38.44    22.9333  \n",
              "20629      38.29    23.0640  \n",
              "20630      38.37    23.0522  \n",
              "\n",
              "[20631 rows x 26 columns]"
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              "      <td>642.35</td>\n",
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              "      <td>522.42</td>\n",
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              "      <td>8139.29</td>\n",
              "      <td>8.5389</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.29</td>\n",
              "      <td>23.0640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20630</th>\n",
              "      <td>100</td>\n",
              "      <td>200</td>\n",
              "      <td>-0.0032</td>\n",
              "      <td>-0.0005</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.85</td>\n",
              "      <td>1600.38</td>\n",
              "      <td>1432.14</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>519.30</td>\n",
              "      <td>2388.26</td>\n",
              "      <td>8137.33</td>\n",
              "      <td>8.5036</td>\n",
              "      <td>0.03</td>\n",
              "      <td>396</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.37</td>\n",
              "      <td>23.0522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20631 rows × 26 columns</p>\n",
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            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1     0.0023     0.0003      100.0    518.67   \n",
              "1                1            2    -0.0027    -0.0003      100.0    518.67   \n",
              "2                1            3     0.0003     0.0001      100.0    518.67   \n",
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              "4                1            5     0.0014     0.0000      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "13091          100          194     0.0049     0.0000      100.0    518.67   \n",
              "13092          100          195    -0.0011    -0.0001      100.0    518.67   \n",
              "13093          100          196    -0.0006    -0.0003      100.0    518.67   \n",
              "13094          100          197    -0.0038     0.0001      100.0    518.67   \n",
              "13095          100          198     0.0013     0.0003      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        643.02   1585.29   1398.21     14.62  ...     521.72    2388.03   \n",
              "1        641.71   1588.45   1395.42     14.62  ...     522.16    2388.06   \n",
              "2        642.46   1586.94   1401.34     14.62  ...     521.97    2388.03   \n",
              "3        642.44   1584.12   1406.42     14.62  ...     521.38    2388.05   \n",
              "4        642.51   1587.19   1401.92     14.62  ...     522.15    2388.03   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "13091    643.24   1599.45   1415.79     14.62  ...     520.69    2388.00   \n",
              "13092    643.22   1595.69   1422.05     14.62  ...     521.05    2388.09   \n",
              "13093    643.44   1593.15   1406.82     14.62  ...     521.18    2388.04   \n",
              "13094    643.26   1594.99   1419.36     14.62  ...     521.33    2388.08   \n",
              "13095    642.95   1601.62   1424.99     14.62  ...     521.07    2388.05   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8125.55     8.4052       0.03        392       2388      100.0   \n",
              "1        8139.62     8.3803       0.03        393       2388      100.0   \n",
              "2        8130.10     8.4441       0.03        393       2388      100.0   \n",
              "3        8132.90     8.3917       0.03        391       2388      100.0   \n",
              "4        8129.54     8.4031       0.03        390       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "13091    8213.28     8.4715       0.03        394       2388      100.0   \n",
              "13092    8210.85     8.4512       0.03        395       2388      100.0   \n",
              "13093    8217.24     8.4569       0.03        395       2388      100.0   \n",
              "13094    8220.48     8.4711       0.03        395       2388      100.0   \n",
              "13095    8214.64     8.4903       0.03        396       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          38.86    23.3735  \n",
              "1          39.02    23.3916  \n",
              "2          39.08    23.4166  \n",
              "3          39.00    23.3737  \n",
              "4          38.99    23.4130  \n",
              "...          ...        ...  \n",
              "13091      38.65    23.1974  \n",
              "13092      38.57    23.2771  \n",
              "13093      38.62    23.2051  \n",
              "13094      38.66    23.2699  \n",
              "13095      38.70    23.1855  \n",
              "\n",
              "[13096 rows x 26 columns]"
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              "      <td>642.46</td>\n",
              "      <td>1586.94</td>\n",
              "      <td>1401.34</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.97</td>\n",
              "      <td>2388.03</td>\n",
              "      <td>8130.10</td>\n",
              "      <td>8.4441</td>\n",
              "      <td>0.03</td>\n",
              "      <td>393</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.08</td>\n",
              "      <td>23.4166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.44</td>\n",
              "      <td>1584.12</td>\n",
              "      <td>1406.42</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.38</td>\n",
              "      <td>2388.05</td>\n",
              "      <td>8132.90</td>\n",
              "      <td>8.3917</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.00</td>\n",
              "      <td>23.3737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.51</td>\n",
              "      <td>1587.19</td>\n",
              "      <td>1401.92</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.15</td>\n",
              "      <td>2388.03</td>\n",
              "      <td>8129.54</td>\n",
              "      <td>8.4031</td>\n",
              "      <td>0.03</td>\n",
              "      <td>390</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.99</td>\n",
              "      <td>23.4130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13091</th>\n",
              "      <td>100</td>\n",
              "      <td>194</td>\n",
              "      <td>0.0049</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.24</td>\n",
              "      <td>1599.45</td>\n",
              "      <td>1415.79</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>520.69</td>\n",
              "      <td>2388.00</td>\n",
              "      <td>8213.28</td>\n",
              "      <td>8.4715</td>\n",
              "      <td>0.03</td>\n",
              "      <td>394</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.65</td>\n",
              "      <td>23.1974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13092</th>\n",
              "      <td>100</td>\n",
              "      <td>195</td>\n",
              "      <td>-0.0011</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.22</td>\n",
              "      <td>1595.69</td>\n",
              "      <td>1422.05</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.05</td>\n",
              "      <td>2388.09</td>\n",
              "      <td>8210.85</td>\n",
              "      <td>8.4512</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.57</td>\n",
              "      <td>23.2771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13093</th>\n",
              "      <td>100</td>\n",
              "      <td>196</td>\n",
              "      <td>-0.0006</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.44</td>\n",
              "      <td>1593.15</td>\n",
              "      <td>1406.82</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.18</td>\n",
              "      <td>2388.04</td>\n",
              "      <td>8217.24</td>\n",
              "      <td>8.4569</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.62</td>\n",
              "      <td>23.2051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13094</th>\n",
              "      <td>100</td>\n",
              "      <td>197</td>\n",
              "      <td>-0.0038</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.26</td>\n",
              "      <td>1594.99</td>\n",
              "      <td>1419.36</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.33</td>\n",
              "      <td>2388.08</td>\n",
              "      <td>8220.48</td>\n",
              "      <td>8.4711</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.66</td>\n",
              "      <td>23.2699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13095</th>\n",
              "      <td>100</td>\n",
              "      <td>198</td>\n",
              "      <td>0.0013</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.95</td>\n",
              "      <td>1601.62</td>\n",
              "      <td>1424.99</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.07</td>\n",
              "      <td>2388.05</td>\n",
              "      <td>8214.64</td>\n",
              "      <td>8.4903</td>\n",
              "      <td>0.03</td>\n",
              "      <td>396</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.70</td>\n",
              "      <td>23.1855</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13096 rows × 26 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        google.colab.notebook.generateWithVariable('test');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_cycles = df_train.groupby('unit_number')['time_cycles'].max()\n",
        "df_train['RUL'] = df_train.apply(lambda row: max_cycles[row['unit_number']] - row['time_cycles'], axis=1)"
      ],
      "metadata": {
        "id": "FFmzKpumUAND"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_cycles = df_test.groupby('unit_number')['time_cycles'].max()\n",
        "df_test['RUL'] = df_test.apply(lambda row: max_cycles[row['unit_number']] - row['time_cycles'], axis=1)"
      ],
      "metadata": {
        "id": "a5IW0NYWUKr5"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train.drop(columns=['RUL'])\n",
        "y_train = df_train['RUL']\n",
        "X_test = df_test.drop(columns=['RUL'])\n",
        "y_test = y_test['RUL']"
      ],
      "metadata": {
        "id": "A_8uw8WaUOeF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "HEjqzsQ-URWQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.optimizers import Optimizer"
      ],
      "metadata": {
        "id": "nwVNKmdtVEUc"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=50, input_shape=(X_train_scaled.shape[1], 1)))\n",
        "model.add(Dense(units=1))\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')"
      ],
      "metadata": {
        "id": "ODVjGETcUVYw"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MMklYQJSWVqQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def target_function(variables_values):\n",
        "    X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], X_train_scaled.shape[1], 1))\n",
        "    model.fit(X_train_reshaped, y_train, epochs=10, batch_size=32, verbose=0)\n",
        "    X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1))\n",
        "    predictions = model.predict(X_test_reshaped)\n",
        "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
        "    return rmse"
      ],
      "metadata": {
        "id": "zUemPFqzUAOz"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = {\n",
        "    'swarm_size': 10,\n",
        "    'min_values': [0] * X_train_scaled.shape[1],  # Adjust according to your feature space\n",
        "    'max_values': [1] * X_train_scaled.shape[1],  # Adjust according to your feature space\n",
        "    'iterations': 10,\n",
        "    'alpha': 0.8,\n",
        "    'gama': 0.8,\n",
        "    'fmin': 0,\n",
        "    'fmax': 2,\n",
        "    'verbose': True,\n",
        "    'start_init': None,\n",
        "    'target_value': None\n",
        "}"
      ],
      "metadata": {
        "id": "fd_wtgIwVZlW"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_resized = y_train[:len(X_train_scaled)]\n",
        "y_test_resized = y_test[:len(X_test_scaled)]"
      ],
      "metadata": {
        "id": "S7LN8zGcW4HH"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def target_function_resized(variables_values):\n",
        "    X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], X_train_scaled.shape[1], 1))\n",
        "    model.fit(X_train_reshaped, y_train_resized, epochs=10, batch_size=32, verbose=0)\n",
        "    X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1))\n",
        "    predictions = model.predict(X_test_reshaped)\n",
        "    rmse = np.sqrt(mean_squared_error(y_test_resized, predictions))\n",
        "    return rmse"
      ],
      "metadata": {
        "id": "V3mJkh77VdDs"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resize y_test_resized to match the size of X_test_scaled\n",
        "y_test_resized = np.repeat(y_test_resized, len(X_test_scaled) // len(y_test_resized))\n",
        "\n",
        "# Check the shape of y_test_resized after resizing\n",
        "print(\"Resized y_test_resized shape:\", y_test_resized.shape)\n",
        "X_test_scaled = X_test_scaled[:13000, :]\n",
        "\n",
        "print(\"Resized X_test_scaled shape:\", X_test_scaled.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m75PKrHoZGWj",
        "outputId": "06e77067-6b2a-4245-af1a-97b99ce0e7ad"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resized y_test_resized shape: (13000,)\n",
            "Resized X_test_scaled shape: (13000, 26)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bat_result_resized = bat_algorithm(target_function=target_function_resized, **parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7c16bobaW6p",
        "outputId": "1e6533ae-4f24-44c1-9bc9-dd1c53fee18a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  0  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  1  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  2  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  3  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  4  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  5  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  6  f(x) =  84.56351545733291\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  7  f(x) =  84.56351545733291\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  8  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  9  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "Iteration =  10  f(x) =  84.56351545733291\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
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            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n",
            "407/407 [==============================] - 1s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1))\n",
        "optimized_model_resized = Sequential()\n",
        "optimized_model_resized.add(LSTM(units=50, input_shape=(X_test_scaled.shape[1], 1)))\n",
        "optimized_model_resized.add(Dense(units=1))\n",
        "optimized_model_resized.compile(optimizer='adam', loss='mean_squared_error')\n",
        "optimized_model_resized.fit(X_train_scaled, y_train_resized, epochs=10, batch_size=32, verbose=0)\n",
        "predictions_resized = optimized_model_resized.predict(X_test_scaled_reshaped)\n",
        "rmse_test_resized = np.sqrt(mean_squared_error(y_test_resized, predictions_resized))\n",
        "r2_test_resized = r2_score(y_test_resized, predictions_resized)\n",
        "mae_test_resized = mean_absolute_error(y_test_resized, predictions_resized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PitiuQDtWZ2L",
        "outputId": "b8fd929c-e89f-4ebd-b300-aae3b566903f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "407/407 [==============================] - 1s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('RMSE (Test) - Resized:', rmse_test_resized)\n",
        "print('R2 Score (Test) - Resized:', r2_test_resized)\n",
        "print('MAE (Test) - Resized:', mae_test_resized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zShLXKXWZ5L",
        "outputId": "31ead221-7cb6-4979-9c47-7ad80589e8e7"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE (Test) - Resized: 82.76982667355699\n",
            "R2 Score (Test) - Resized: -2.9672041291193425\n",
            "MAE (Test) - Resized: 71.24832096085181\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1FBCU62jUAR2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w5yYUWKEUAU2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sp7V703dUAXU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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