BAT3.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPxt6D4ZQwg1wC6g5h0/JUB",
      "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/BAT3.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": "j8A3h6YSb4Fs",
        "outputId": "f46cbbed-9cc2-4e06-8c0b-d71bedfc77e6"
      },
      "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[31m2.7 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 sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "MvWcGTKVcPos"
      },
      "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": "xPrbRbnecSup",
        "outputId": "fa1105aa-4383-466c-fd3a-019f075204d1"
      },
      "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": "Ly7CnsYkcbJi",
        "outputId": "9d9f2233-d942-4993-a97d-dc8b5e4e2007"
      },
      "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": "0ITQUy3fckgS"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/CMAPSS/'\n",
        "df_train = pd.read_csv(path+'train_FD003.txt',sep='\\s+',header=None,index_col=False,names=col_names) #\\s表示以空白為間隔\n",
        "df_test = pd.read_csv(path+'test_FD003.txt',sep='\\s+',header=None,index_col=False,names=col_names)\n",
        "y_test = pd.read_csv(path+'RUL_FD003.txt',sep='\\s+',header=None,index_col=False,names=['RUL'])\n",
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MI22yOZdcbMo",
        "outputId": "7e43c999-357a-4bf4-c118-fece32f30b78"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "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": "6yBJ5laxcbP5",
        "outputId": "44d7bc29-38cd-4e9b-9831-4a3b6c98870a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    -0.0005     0.0004      100.0    518.67   \n",
              "1                1            2     0.0008    -0.0003      100.0    518.67   \n",
              "2                1            3    -0.0014    -0.0002      100.0    518.67   \n",
              "3                1            4    -0.0020     0.0001      100.0    518.67   \n",
              "4                1            5     0.0016     0.0000      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "24715          100          148    -0.0016    -0.0003      100.0    518.67   \n",
              "24716          100          149     0.0034    -0.0003      100.0    518.67   \n",
              "24717          100          150    -0.0016     0.0004      100.0    518.67   \n",
              "24718          100          151    -0.0023     0.0004      100.0    518.67   \n",
              "24719          100          152     0.0000     0.0003      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        642.36   1583.23   1396.84     14.62  ...     522.31    2388.01   \n",
              "1        642.50   1584.69   1396.89     14.62  ...     522.42    2388.03   \n",
              "2        642.18   1582.35   1405.61     14.62  ...     522.03    2388.00   \n",
              "3        642.92   1585.61   1392.27     14.62  ...     522.49    2388.08   \n",
              "4        641.68   1588.63   1397.65     14.62  ...     522.58    2388.03   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "24715    643.78   1596.01   1424.11     14.62  ...     519.66    2388.30   \n",
              "24716    643.29   1596.38   1429.14     14.62  ...     519.91    2388.28   \n",
              "24717    643.84   1604.53   1431.41     14.62  ...     519.44    2388.24   \n",
              "24718    643.94   1597.56   1426.57     14.62  ...     520.01    2388.26   \n",
              "24719    643.64   1599.04   1436.06     14.62  ...     519.48    2388.24   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8145.32     8.4246       0.03        391       2388      100.0   \n",
              "1        8152.85     8.4403       0.03        392       2388      100.0   \n",
              "2        8150.17     8.3901       0.03        391       2388      100.0   \n",
              "3        8146.56     8.3878       0.03        392       2388      100.0   \n",
              "4        8147.80     8.3869       0.03        392       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "24715    8138.08     8.5036       0.03        394       2388      100.0   \n",
              "24716    8144.36     8.5174       0.03        395       2388      100.0   \n",
              "24717    8135.95     8.5223       0.03        396       2388      100.0   \n",
              "24718    8141.24     8.5148       0.03        395       2388      100.0   \n",
              "24719    8136.98     8.5150       0.03        396       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          39.11    23.3537  \n",
              "1          38.99    23.4491  \n",
              "2          38.85    23.3669  \n",
              "3          38.96    23.2951  \n",
              "4          39.14    23.4583  \n",
              "...          ...        ...  \n",
              "24715      38.44    22.9631  \n",
              "24716      38.50    22.9746  \n",
              "24717      38.39    23.0682  \n",
              "24718      38.31    23.0753  \n",
              "24719      38.56    23.0847  \n",
              "\n",
              "[24720 rows x 26 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit_number</th>\n",
              "      <th>time_cycles</th>\n",
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              "      <td>1396.84</td>\n",
              "      <td>14.62</td>\n",
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              "      <td>8.4246</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
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              "      <td>39.11</td>\n",
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              "      <th>1</th>\n",
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              "      <td>-0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.50</td>\n",
              "      <td>1584.69</td>\n",
              "      <td>1396.89</td>\n",
              "      <td>14.62</td>\n",
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              "      <td>522.42</td>\n",
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              "      <td>8.4403</td>\n",
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              "      <th>2</th>\n",
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              "      <td>-0.0014</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.18</td>\n",
              "      <td>1582.35</td>\n",
              "      <td>1405.61</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.03</td>\n",
              "      <td>2388.00</td>\n",
              "      <td>8150.17</td>\n",
              "      <td>8.3901</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.85</td>\n",
              "      <td>23.3669</td>\n",
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              "      <td>642.92</td>\n",
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              "      <td>392</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.96</td>\n",
              "      <td>23.2951</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.0000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>641.68</td>\n",
              "      <td>1588.63</td>\n",
              "      <td>1397.65</td>\n",
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              "      <td>518.67</td>\n",
              "      <td>643.94</td>\n",
              "      <td>1597.56</td>\n",
              "      <td>1426.57</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>520.01</td>\n",
              "      <td>2388.26</td>\n",
              "      <td>8141.24</td>\n",
              "      <td>8.5148</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.31</td>\n",
              "      <td>23.0753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24719</th>\n",
              "      <td>100</td>\n",
              "      <td>152</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.64</td>\n",
              "      <td>1599.04</td>\n",
              "      <td>1436.06</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>519.48</td>\n",
              "      <td>2388.24</td>\n",
              "      <td>8136.98</td>\n",
              "      <td>8.5150</td>\n",
              "      <td>0.03</td>\n",
              "      <td>396</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.56</td>\n",
              "      <td>23.0847</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24720 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    -0.0017    -0.0004      100.0    518.67   \n",
              "1                1            2     0.0006    -0.0002      100.0    518.67   \n",
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              "4                1            5    -0.0001     0.0001      100.0    518.67   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "16591          100          243     0.0011    -0.0003      100.0    518.67   \n",
              "16592          100          244    -0.0024     0.0002      100.0    518.67   \n",
              "16593          100          245    -0.0053     0.0002      100.0    518.67   \n",
              "16594          100          246    -0.0006     0.0001      100.0    518.67   \n",
              "16595          100          247     0.0023     0.0004      100.0    518.67   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        641.94   1581.93   1396.93     14.62  ...     521.89    2387.94   \n",
              "1        642.02   1584.86   1398.90     14.62  ...     521.85    2388.01   \n",
              "2        641.68   1581.78   1391.92     14.62  ...     522.10    2387.94   \n",
              "3        642.20   1584.53   1395.34     14.62  ...     522.45    2387.96   \n",
              "4        642.46   1589.03   1395.86     14.62  ...     521.91    2387.97   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "16591    643.04   1594.99   1411.28     14.62  ...     529.23    2388.24   \n",
              "16592    642.47   1591.27   1417.64     14.62  ...     529.27    2388.26   \n",
              "16593    642.70   1593.81   1412.70     14.62  ...     529.48    2388.28   \n",
              "16594    642.19   1595.63   1406.58     14.62  ...     529.39    2388.33   \n",
              "16595    643.19   1592.54   1407.20     14.62  ...     529.59    2388.29   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8133.48     8.3760       0.03        391       2388      100.0   \n",
              "1        8137.44     8.4062       0.03        391       2388      100.0   \n",
              "2        8138.25     8.3553       0.03        391       2388      100.0   \n",
              "3        8137.07     8.3709       0.03        392       2388      100.0   \n",
              "4        8134.20     8.4146       0.03        391       2388      100.0   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "16591    8162.24     8.2939       0.03        395       2388      100.0   \n",
              "16592    8163.24     8.3247       0.03        394       2388      100.0   \n",
              "16593    8162.12     8.3264       0.03        394       2388      100.0   \n",
              "16594    8164.20     8.2908       0.03        395       2388      100.0   \n",
              "16595    8169.24     8.3191       0.03        392       2388      100.0   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          39.07    23.4468  \n",
              "1          39.04    23.4807  \n",
              "2          39.10    23.4244  \n",
              "3          38.97    23.4782  \n",
              "4          39.09    23.3950  \n",
              "...          ...        ...  \n",
              "16591      39.40    23.4949  \n",
              "16592      39.42    23.6011  \n",
              "16593      39.43    23.5482  \n",
              "16594      39.40    23.6687  \n",
              "16595      39.41    23.6536  \n",
              "\n",
              "[16596 rows x 26 columns]"
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              "      <td>1</td>\n",
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              "      <td>0.0014</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>641.68</td>\n",
              "      <td>1581.78</td>\n",
              "      <td>1391.92</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.10</td>\n",
              "      <td>2387.94</td>\n",
              "      <td>8138.25</td>\n",
              "      <td>8.3553</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.10</td>\n",
              "      <td>23.4244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0027</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.20</td>\n",
              "      <td>1584.53</td>\n",
              "      <td>1395.34</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.45</td>\n",
              "      <td>2387.96</td>\n",
              "      <td>8137.07</td>\n",
              "      <td>8.3709</td>\n",
              "      <td>0.03</td>\n",
              "      <td>392</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.97</td>\n",
              "      <td>23.4782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.46</td>\n",
              "      <td>1589.03</td>\n",
              "      <td>1395.86</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>521.91</td>\n",
              "      <td>2387.97</td>\n",
              "      <td>8134.20</td>\n",
              "      <td>8.4146</td>\n",
              "      <td>0.03</td>\n",
              "      <td>391</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.09</td>\n",
              "      <td>23.3950</td>\n",
              "    </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>16591</th>\n",
              "      <td>100</td>\n",
              "      <td>243</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.04</td>\n",
              "      <td>1594.99</td>\n",
              "      <td>1411.28</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.23</td>\n",
              "      <td>2388.24</td>\n",
              "      <td>8162.24</td>\n",
              "      <td>8.2939</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.40</td>\n",
              "      <td>23.4949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16592</th>\n",
              "      <td>100</td>\n",
              "      <td>244</td>\n",
              "      <td>-0.0024</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.47</td>\n",
              "      <td>1591.27</td>\n",
              "      <td>1417.64</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.27</td>\n",
              "      <td>2388.26</td>\n",
              "      <td>8163.24</td>\n",
              "      <td>8.3247</td>\n",
              "      <td>0.03</td>\n",
              "      <td>394</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.42</td>\n",
              "      <td>23.6011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16593</th>\n",
              "      <td>100</td>\n",
              "      <td>245</td>\n",
              "      <td>-0.0053</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.70</td>\n",
              "      <td>1593.81</td>\n",
              "      <td>1412.70</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.48</td>\n",
              "      <td>2388.28</td>\n",
              "      <td>8162.12</td>\n",
              "      <td>8.3264</td>\n",
              "      <td>0.03</td>\n",
              "      <td>394</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.43</td>\n",
              "      <td>23.5482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16594</th>\n",
              "      <td>100</td>\n",
              "      <td>246</td>\n",
              "      <td>-0.0006</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.19</td>\n",
              "      <td>1595.63</td>\n",
              "      <td>1406.58</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.39</td>\n",
              "      <td>2388.33</td>\n",
              "      <td>8164.20</td>\n",
              "      <td>8.2908</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.40</td>\n",
              "      <td>23.6687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16595</th>\n",
              "      <td>100</td>\n",
              "      <td>247</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>0.0004</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>643.19</td>\n",
              "      <td>1592.54</td>\n",
              "      <td>1407.20</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>529.59</td>\n",
              "      <td>2388.29</td>\n",
              "      <td>8169.24</td>\n",
              "      <td>8.3191</td>\n",
              "      <td>0.03</td>\n",
              "      <td>392</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.41</td>\n",
              "      <td>23.6536</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16596 rows × 26 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\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": [
        "def regression_model(X):\n",
        "    # Define your regression model here\n",
        "    model = LinearRegression()  # Example: Linear Regression\n",
        "    model.fit(X_train, y_train)\n",
        "    return model.predict(X)"
      ],
      "metadata": {
        "id": "KY2ypX1jctsc"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate 'RUL' column based on 'time_cycles' and 'unit_number' columns\n",
        "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)\n",
        "# Calculate 'RUL' column based on 'time_cycles' and 'unit_number' columns\n",
        "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)\n"
      ],
      "metadata": {
        "id": "vD27nMIdcbS1"
      },
      "execution_count": 23,
      "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": "BgJy56dBcba_"
      },
      "execution_count": 24,
      "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": "Q4A5a1X0c5Dc"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def target_function(variables_values):\n",
        "    # Train the regression model\n",
        "    model = LinearRegression()\n",
        "    model.fit(X_train_scaled, y_train)\n",
        "    # Predict using the trained model\n",
        "    predictions = model.predict(X_train_scaled)\n",
        "    # Calculate RMSE\n",
        "    rmse = np.sqrt(mean_squared_error(y_train, predictions))\n",
        "    return rmse"
      ],
      "metadata": {
        "id": "kqqktvwyc5GX"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = {\n",
        "    'swarm_size': 25,\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": "INWTdurEc_uq"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyMetaheuristic.algorithm import bat_algorithm\n"
      ],
      "metadata": {
        "id": "AzyR4Jzdc_2l"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bat_result = bat_algorithm(target_function=target_function, **parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Usa9pp0c_8o",
        "outputId": "f1487f8f-c28a-4b1a-b350-407b2a492364"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration =  0  f(x) =  60.900424760090814\n",
            "Iteration =  1  f(x) =  60.900424760090814\n",
            "Iteration =  2  f(x) =  60.900424760090814\n",
            "Iteration =  3  f(x) =  60.900424760090814\n",
            "Iteration =  4  f(x) =  60.900424760090814\n",
            "Iteration =  5  f(x) =  60.900424760090814\n",
            "Iteration =  6  f(x) =  60.900424760090814\n",
            "Iteration =  7  f(x) =  60.900424760090814\n",
            "Iteration =  8  f(x) =  60.900424760090814\n",
            "Iteration =  9  f(x) =  60.900424760090814\n",
            "Iteration =  10  f(x) =  60.900424760090814\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the first 100 samples from X_test_scaled and their corresponding target values\n",
        "X_test_scaled_subset = X_test_scaled[:100]\n",
        "y_test_subset = y_test[:100]\n"
      ],
      "metadata": {
        "id": "UbzpWHdYdPKp"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimized_model = LinearRegression().fit(X_test_scaled_subset, y_test_subset)"
      ],
      "metadata": {
        "id": "KvwUNcgzcbd5"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_test = np.sqrt(mean_squared_error(y_test_subset, optimized_model.predict(X_test_scaled_subset)))\n",
        "r2_test = r2_score(y_test_subset, optimized_model.predict(X_test_scaled_subset))\n",
        "mae_test = mean_absolute_error(y_test_subset, optimized_model.predict(X_test_scaled_subset))"
      ],
      "metadata": {
        "id": "dq6ZT8VCdmTJ"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('RMSE (Test):', rmse_test)\n",
        "print('R2 Score (Test):', r2_test)\n",
        "print('MAE (Test):', mae_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tb5VXJJ8dmWI",
        "outputId": "2c7df614-c2e6-4a65-bc95-a7a70c731482"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE (Test): 38.78620159346096\n",
            "R2 Score (Test): 0.12209877391950519\n",
            "MAE (Test): 33.02829583470959\n"
          ]
        }
      ]
    }
  ]
}