ABC1_2.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN2ogEbm85xFyUW+hEoUO6T",
      "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/ABC1_2.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": "mxVLXKNR1OBX",
        "outputId": "abf929ac-734d-4514-d15f-35002c3eff5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyMetaheuristic\n",
            "  Downloading pyMetaheuristic-1.9.5-py3-none-any.whl (98 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/98.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/98.4 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.4/98.4 kB\u001b[0m \u001b[31m1.8 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.linear_model import LinearRegression\n",
        "from pyMetaheuristic.algorithm import artificial_bee_colony_optimization\n",
        "from pyMetaheuristic.utils import graphs"
      ],
      "metadata": {
        "id": "IW2T18ps2fFF"
      },
      "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": "43Vy36Lc2jm9",
        "outputId": "3c4d54b4-b9a6-43af-be24-2764f5bf8dd9"
      },
      "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": "0k7rMZnh2rfN",
        "outputId": "04e79efe-c97e-49b0-fa4d-fcfe4a9568f7"
      },
      "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": "UroCYBJv2rhM"
      },
      "execution_count": 5,
      "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": "e_LPdUie2x-W",
        "outputId": "838846cf-2444-4b54-e463-10029793e9c5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(259, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "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": "A1OGOWaU22sU",
        "outputId": "f11dc301-dd98-4c0e-c526-e89dbcd6f806"
      },
      "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    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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              "  <div id=\"df-d0d37cad-0136-427a-a97a-6132698a3cdf\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit_number</th>\n",
              "      <th>time_cycles</th>\n",
              "      <th>setting_1</th>\n",
              "      <th>setting_2</th>\n",
              "      <th>setting_3</th>\n",
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              "      <th>sensor_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",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>34.9983</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.32</td>\n",
              "      <td>1358.61</td>\n",
              "      <td>1137.23</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>183.06</td>\n",
              "      <td>2387.72</td>\n",
              "      <td>8048.56</td>\n",
              "      <td>9.3461</td>\n",
              "      <td>0.02</td>\n",
              "      <td>334</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.73</td>\n",
              "      <td>8.8071</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>41.9982</td>\n",
              "      <td>0.8408</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.90</td>\n",
              "      <td>1353.22</td>\n",
              "      <td>1125.78</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.42</td>\n",
              "      <td>2387.66</td>\n",
              "      <td>8072.30</td>\n",
              "      <td>9.3774</td>\n",
              "      <td>0.02</td>\n",
              "      <td>330</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.41</td>\n",
              "      <td>6.2665</td>\n",
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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",
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              "    <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",
              "      <td>10.8963</td>\n",
              "      <td>0.02</td>\n",
              "      <td>309</td>\n",
              "      <td>1915</td>\n",
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              "      <td>1269.51</td>\n",
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              "      <td>25.0041</td>\n",
              "      <td>0.6200</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>537.48</td>\n",
              "      <td>1276.24</td>\n",
              "      <td>1057.92</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>163.74</td>\n",
              "      <td>2030.33</td>\n",
              "      <td>7971.25</td>\n",
              "      <td>11.0657</td>\n",
              "      <td>0.02</td>\n",
              "      <td>310</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.19</td>\n",
              "      <td>8.5503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53757</th>\n",
              "      <td>260</td>\n",
              "      <td>315</td>\n",
              "      <td>25.0033</td>\n",
              "      <td>0.6220</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>537.84</td>\n",
              "      <td>1272.95</td>\n",
              "      <td>1066.30</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>164.37</td>\n",
              "      <td>2030.35</td>\n",
              "      <td>7972.47</td>\n",
              "      <td>11.0537</td>\n",
              "      <td>0.02</td>\n",
              "      <td>311</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <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>28.42</td>\n",
              "      <td>17.1551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0026</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>491.19</td>\n",
              "      <td>607.82</td>\n",
              "      <td>1481.20</td>\n",
              "      <td>1246.11</td>\n",
              "      <td>9.35</td>\n",
              "      <td>...</td>\n",
              "      <td>315.32</td>\n",
              "      <td>2388.12</td>\n",
              "      <td>8053.06</td>\n",
              "      <td>9.2405</td>\n",
              "      <td>0.02</td>\n",
              "      <td>364</td>\n",
              "      <td>2324</td>\n",
              "      <td>100.00</td>\n",
              "      <td>24.29</td>\n",
              "      <td>14.8039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>35.0045</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>556.00</td>\n",
              "      <td>1359.08</td>\n",
              "      <td>1128.36</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>183.04</td>\n",
              "      <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",
              "      <td>445.00</td>\n",
              "      <td>550.17</td>\n",
              "      <td>1349.69</td>\n",
              "      <td>1127.89</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.40</td>\n",
              "      <td>2387.72</td>\n",
              "      <td>8066.90</td>\n",
              "      <td>9.3961</td>\n",
              "      <td>0.02</td>\n",
              "      <td>332</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.35</td>\n",
              "      <td>6.4181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>24.9985</td>\n",
              "      <td>0.6213</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>536.72</td>\n",
              "      <td>1253.18</td>\n",
              "      <td>1050.69</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>164.56</td>\n",
              "      <td>2028.05</td>\n",
              "      <td>7865.66</td>\n",
              "      <td>10.8682</td>\n",
              "      <td>0.02</td>\n",
              "      <td>305</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.31</td>\n",
              "      <td>8.5740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33986</th>\n",
              "      <td>259</td>\n",
              "      <td>119</td>\n",
              "      <td>35.0015</td>\n",
              "      <td>0.8403</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.56</td>\n",
              "      <td>1366.01</td>\n",
              "      <td>1129.47</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>183.05</td>\n",
              "      <td>2388.39</td>\n",
              "      <td>8088.36</td>\n",
              "      <td>9.3215</td>\n",
              "      <td>0.02</td>\n",
              "      <td>334</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.94</td>\n",
              "      <td>8.9065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33987</th>\n",
              "      <td>259</td>\n",
              "      <td>120</td>\n",
              "      <td>42.0066</td>\n",
              "      <td>0.8405</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.42</td>\n",
              "      <td>1351.13</td>\n",
              "      <td>1123.86</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.74</td>\n",
              "      <td>2388.31</td>\n",
              "      <td>8108.48</td>\n",
              "      <td>9.3542</td>\n",
              "      <td>0.02</td>\n",
              "      <td>332</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.57</td>\n",
              "      <td>6.4075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33988</th>\n",
              "      <td>259</td>\n",
              "      <td>121</td>\n",
              "      <td>42.0061</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.65</td>\n",
              "      <td>1349.14</td>\n",
              "      <td>1118.91</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.96</td>\n",
              "      <td>2388.34</td>\n",
              "      <td>8098.77</td>\n",
              "      <td>9.3836</td>\n",
              "      <td>0.02</td>\n",
              "      <td>331</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.57</td>\n",
              "      <td>6.4805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33989</th>\n",
              "      <td>259</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0024</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>100.0</td>\n",
              "      <td>518.67</td>\n",
              "      <td>642.58</td>\n",
              "      <td>1589.61</td>\n",
              "      <td>1408.16</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>522.25</td>\n",
              "      <td>2388.00</td>\n",
              "      <td>8161.85</td>\n",
              "      <td>8.4279</td>\n",
              "      <td>0.03</td>\n",
              "      <td>393</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.00</td>\n",
              "      <td>39.08</td>\n",
              "      <td>23.3589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33990</th>\n",
              "      <td>259</td>\n",
              "      <td>123</td>\n",
              "      <td>42.0033</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.77</td>\n",
              "      <td>1342.50</td>\n",
              "      <td>1126.96</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.93</td>\n",
              "      <td>2388.40</td>\n",
              "      <td>8105.28</td>\n",
              "      <td>9.3458</td>\n",
              "      <td>0.02</td>\n",
              "      <td>331</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.63</td>\n",
              "      <td>6.3480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>33991 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    .colab-df-convert:hover {\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\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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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test')\"\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_2baa1450-931c-4d89-a922-d7f37f240cfe button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "pqZnpmmn2rjw"
      },
      "execution_count": 8,
      "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": "wqUlzTT22rlo"
      },
      "execution_count": 9,
      "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": "24VftnLd2rn7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "y8HRL6a42rpz"
      },
      "execution_count": 11,
      "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": "I5WQNCWQ2rsE"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = {\n",
        "    'food_sources': 20,\n",
        "    'min_values': [-5] * len(X_train.columns),  # Adjust according to your feature space\n",
        "    'max_values': [5] * len(X_train.columns),  # Adjust according to your feature space\n",
        "    'iterations': 10,\n",
        "    'employed_bees': 20,\n",
        "    'outlookers_bees': 20,\n",
        "    'limit': 20,\n",
        "    'verbose': True,\n",
        "    'start_init': None,\n",
        "    'target_value': None\n",
        "}\n"
      ],
      "metadata": {
        "id": "MrMfV32m3W5m"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "abco_result = artificial_bee_colony_optimization(target_function=target_function, **parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJqskLsm3bXO",
        "outputId": "fcc7a61a-93ce-4980-a395-be21dc374cc0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration =  1  f(x) =  40.79328879009199\n",
            "Iteration =  2  f(x) =  40.79328879009199\n",
            "Iteration =  3  f(x) =  40.79328879009199\n",
            "Iteration =  4  f(x) =  40.79328879009199\n",
            "Iteration =  5  f(x) =  40.79328879009199\n",
            "Iteration =  6  f(x) =  40.79328879009199\n",
            "Iteration =  7  f(x) =  40.79328879009199\n",
            "Iteration =  8  f(x) =  40.79328879009199\n",
            "Iteration =  9  f(x) =  40.79328879009199\n",
            "Iteration =  10  f(x) =  40.79328879009199\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": "w-I-9Hgo3W8g"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimized_model = LinearRegression().fit(X_test_scaled_subset, y_test_subset)"
      ],
      "metadata": {
        "id": "YrO2pyaC2rvA"
      },
      "execution_count": 16,
      "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": "3S4jwKhH49mz"
      },
      "execution_count": 17,
      "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": "Z9rg052649rz",
        "outputId": "acc911b8-852d-4625-f260-9643e926bb70"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE (Test): 43.957950794189806\n",
            "R2 Score (Test): 0.20961802158559184\n",
            "MAE (Test): 35.95326157522187\n"
          ]
        }
      ]
    }
  ]
}