ABC4.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPIzDe0aP/YCZk9aeq8cA8f",
      "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/ABC4.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": "_dF92h6Pf_FT",
        "outputId": "2e290205-f25c-4f5d-83af-514d3a5b5051"
      },
      "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[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m92.2/98.4 kB\u001b[0m \u001b[31m2.8 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[31m2.2 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": "Iu2di95sgOC0"
      },
      "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": "ut2uWNh0gSUR",
        "outputId": "4223fd81-33ec-4d5d-c944-6cd7c789052a"
      },
      "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": "02jLhn3zgOLZ",
        "outputId": "f469cd7b-0fb7-4f6b-e6fd-02b15f90f035"
      },
      "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": "GivjM_ULgdNu"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/CMAPSS/'\n",
        "df_train = pd.read_csv(path+'train_FD004.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_FD004.txt',sep='\\s+',header=None,index_col=False,names=col_names)\n",
        "y_test = pd.read_csv(path+'RUL_FD004.txt',sep='\\s+',header=None,index_col=False,names=['RUL'])\n",
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5nWgh4jgdRd",
        "outputId": "16e14cb3-b7f5-4851-d8ef-0acd54eb4f63"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(248, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "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": "PbS1mz2kgOQW",
        "outputId": "5173ce48-e132-4d62-f159-6403dc6c14e2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    42.0049     0.8400      100.0    445.00   \n",
              "1                1            2    20.0020     0.7002      100.0    491.19   \n",
              "2                1            3    42.0038     0.8409      100.0    445.00   \n",
              "3                1            4    42.0000     0.8400      100.0    445.00   \n",
              "4                1            5    25.0063     0.6207       60.0    462.54   \n",
              "...            ...          ...        ...        ...        ...       ...   \n",
              "61244          249          251     9.9998     0.2500      100.0    489.05   \n",
              "61245          249          252     0.0028     0.0015      100.0    518.67   \n",
              "61246          249          253     0.0029     0.0000      100.0    518.67   \n",
              "61247          249          254    35.0046     0.8400      100.0    449.44   \n",
              "61248          249          255    42.0030     0.8400      100.0    445.00   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        549.68   1343.43   1112.93      3.91  ...     129.78    2387.99   \n",
              "1        606.07   1477.61   1237.50      9.35  ...     312.59    2387.73   \n",
              "2        548.95   1343.12   1117.05      3.91  ...     129.62    2387.97   \n",
              "3        548.70   1341.24   1118.03      3.91  ...     129.80    2388.02   \n",
              "4        536.10   1255.23   1033.59      7.05  ...     164.11    2028.08   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "61244    605.33   1516.36   1315.28     10.52  ...     380.16    2388.73   \n",
              "61245    643.42   1598.92   1426.77     14.62  ...     535.02    2388.46   \n",
              "61246    643.68   1607.72   1430.56     14.62  ...     535.41    2388.48   \n",
              "61247    555.77   1381.29   1148.18      5.48  ...     187.92    2388.83   \n",
              "61248    549.85   1369.75   1147.45      3.91  ...     134.32    2388.66   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8074.83     9.3335       0.02        330       2212     100.00   \n",
              "1        8046.13     9.1913       0.02        361       2324     100.00   \n",
              "2        8066.62     9.4007       0.02        329       2212     100.00   \n",
              "3        8076.05     9.3369       0.02        328       2212     100.00   \n",
              "4        7865.80    10.8366       0.02        305       1915      84.93   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "61244    8185.69     8.4541       0.03        372       2319     100.00   \n",
              "61245    8185.47     8.2221       0.03        396       2388     100.00   \n",
              "61246    8193.94     8.2525       0.03        395       2388     100.00   \n",
              "61247    8125.64     9.0515       0.02        337       2223     100.00   \n",
              "61248    8144.33     9.1207       0.02        333       2212     100.00   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          10.62     6.3670  \n",
              "1          24.37    14.6552  \n",
              "2          10.48     6.4213  \n",
              "3          10.54     6.4176  \n",
              "4          14.03     8.6754  \n",
              "...          ...        ...  \n",
              "61244      29.11    17.5234  \n",
              "61245      39.38    23.7151  \n",
              "61246      39.78    23.8270  \n",
              "61247      15.26     9.0774  \n",
              "61248      10.66     6.4341  \n",
              "\n",
              "[61249 rows x 26 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit_number</th>\n",
              "      <th>time_cycles</th>\n",
              "      <th>setting_1</th>\n",
              "      <th>setting_2</th>\n",
              "      <th>setting_3</th>\n",
              "      <th>sensor_1</th>\n",
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              "      <th>sensor_4</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>42.0049</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.68</td>\n",
              "      <td>1343.43</td>\n",
              "      <td>1112.93</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>129.78</td>\n",
              "      <td>2387.99</td>\n",
              "      <td>8074.83</td>\n",
              "      <td>9.3335</td>\n",
              "      <td>0.02</td>\n",
              "      <td>330</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.62</td>\n",
              "      <td>6.3670</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0020</td>\n",
              "      <td>0.7002</td>\n",
              "      <td>100.0</td>\n",
              "      <td>491.19</td>\n",
              "      <td>606.07</td>\n",
              "      <td>1477.61</td>\n",
              "      <td>1237.50</td>\n",
              "      <td>9.35</td>\n",
              "      <td>...</td>\n",
              "      <td>312.59</td>\n",
              "      <td>2387.73</td>\n",
              "      <td>8046.13</td>\n",
              "      <td>9.1913</td>\n",
              "      <td>0.02</td>\n",
              "      <td>361</td>\n",
              "      <td>2324</td>\n",
              "      <td>100.00</td>\n",
              "      <td>24.37</td>\n",
              "      <td>14.6552</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>42.0038</td>\n",
              "      <td>0.8409</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>548.95</td>\n",
              "      <td>1343.12</td>\n",
              "      <td>1117.05</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>129.62</td>\n",
              "      <td>2387.97</td>\n",
              "      <td>8066.62</td>\n",
              "      <td>9.4007</td>\n",
              "      <td>0.02</td>\n",
              "      <td>329</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.48</td>\n",
              "      <td>6.4213</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>548.70</td>\n",
              "      <td>1341.24</td>\n",
              "      <td>1118.03</td>\n",
              "      <td>3.91</td>\n",
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              "      <td>129.80</td>\n",
              "      <td>2388.02</td>\n",
              "      <td>8076.05</td>\n",
              "      <td>9.3369</td>\n",
              "      <td>0.02</td>\n",
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              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.54</td>\n",
              "      <td>6.4176</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.6207</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>536.10</td>\n",
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              "      <td>1033.59</td>\n",
              "      <td>7.05</td>\n",
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              "      <td>10.8366</td>\n",
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              "      <td>518.67</td>\n",
              "      <td>643.68</td>\n",
              "      <td>1607.72</td>\n",
              "      <td>1430.56</td>\n",
              "      <td>14.62</td>\n",
              "      <td>...</td>\n",
              "      <td>535.41</td>\n",
              "      <td>2388.48</td>\n",
              "      <td>8193.94</td>\n",
              "      <td>8.2525</td>\n",
              "      <td>0.03</td>\n",
              "      <td>395</td>\n",
              "      <td>2388</td>\n",
              "      <td>100.00</td>\n",
              "      <td>39.78</td>\n",
              "      <td>23.8270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61247</th>\n",
              "      <td>249</td>\n",
              "      <td>254</td>\n",
              "      <td>35.0046</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.77</td>\n",
              "      <td>1381.29</td>\n",
              "      <td>1148.18</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>187.92</td>\n",
              "      <td>2388.83</td>\n",
              "      <td>8125.64</td>\n",
              "      <td>9.0515</td>\n",
              "      <td>0.02</td>\n",
              "      <td>337</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>15.26</td>\n",
              "      <td>9.0774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61248</th>\n",
              "      <td>249</td>\n",
              "      <td>255</td>\n",
              "      <td>42.0030</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.85</td>\n",
              "      <td>1369.75</td>\n",
              "      <td>1147.45</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>134.32</td>\n",
              "      <td>2388.66</td>\n",
              "      <td>8144.33</td>\n",
              "      <td>9.1207</td>\n",
              "      <td>0.02</td>\n",
              "      <td>333</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.66</td>\n",
              "      <td>6.4341</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>61249 rows × 26 columns</p>\n",
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              "       unit_number  time_cycles  setting_1  setting_2  setting_3  sensor_1  \\\n",
              "0                1            1    20.0072     0.7000      100.0    491.19   \n",
              "1                1            2    24.9984     0.6200       60.0    462.54   \n",
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              "...            ...          ...        ...        ...        ...       ...   \n",
              "41209          248          277    41.9991     0.8401      100.0    445.00   \n",
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              "41213          248          281    35.0075     0.8402      100.0    449.44   \n",
              "\n",
              "       sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  sensor_13  \\\n",
              "0        606.67   1481.04   1227.81      9.35  ...     313.03    2387.78   \n",
              "1        536.22   1256.17   1031.48      7.05  ...     163.61    2028.09   \n",
              "2        549.23   1340.13   1105.88      3.91  ...     129.98    2387.95   \n",
              "3        549.19   1339.70   1107.26      3.91  ...     129.48    2387.90   \n",
              "4        555.10   1353.04   1117.80      5.48  ...     181.82    2387.87   \n",
              "...         ...       ...       ...       ...  ...        ...        ...   \n",
              "41209    550.30   1364.40   1129.17      3.91  ...     130.87    2388.50   \n",
              "41210    608.00   1494.75   1260.88      9.35  ...     314.51    2388.33   \n",
              "41211    555.92   1370.65   1130.97      5.48  ...     182.76    2388.64   \n",
              "41212    608.19   1489.11   1256.25      9.35  ...     314.17    2388.37   \n",
              "41213    556.40   1378.58   1140.70      5.48  ...     181.88    2388.59   \n",
              "\n",
              "       sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  sensor_19  \\\n",
              "0        8048.98     9.2229       0.02        362       2324     100.00   \n",
              "1        7863.46    10.8632       0.02        306       1915      84.93   \n",
              "2        8071.13     9.3960       0.02        328       2212     100.00   \n",
              "3        8078.89     9.3594       0.02        328       2212     100.00   \n",
              "4        8057.83     9.3030       0.02        333       2223     100.00   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
              "41209    8112.61     9.4427       0.02        331       2212     100.00   \n",
              "41210    8086.83     9.2772       0.02        366       2324     100.00   \n",
              "41211    8100.84     9.3982       0.02        336       2223     100.00   \n",
              "41212    8085.24     9.2727       0.03        366       2324     100.00   \n",
              "41213    8098.17     9.3964       0.02        335       2223     100.00   \n",
              "\n",
              "       sensor_20  sensor_21  \n",
              "0          24.31    14.7007  \n",
              "1          14.36     8.5748  \n",
              "2          10.39     6.4365  \n",
              "3          10.56     6.2367  \n",
              "4          14.85     8.9326  \n",
              "...          ...        ...  \n",
              "41209      10.53     6.2620  \n",
              "41210      24.33    14.6486  \n",
              "41211      14.69     8.8389  \n",
              "41212      24.44    14.6887  \n",
              "41213      14.72     8.8502  \n",
              "\n",
              "[41214 rows x 26 columns]"
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              "      <td>14.7007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>24.9984</td>\n",
              "      <td>0.6200</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>536.22</td>\n",
              "      <td>1256.17</td>\n",
              "      <td>1031.48</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>163.61</td>\n",
              "      <td>2028.09</td>\n",
              "      <td>7863.46</td>\n",
              "      <td>10.8632</td>\n",
              "      <td>0.02</td>\n",
              "      <td>306</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.36</td>\n",
              "      <td>8.5748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>42.0000</td>\n",
              "      <td>0.8420</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.23</td>\n",
              "      <td>1340.13</td>\n",
              "      <td>1105.88</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>129.98</td>\n",
              "      <td>2387.95</td>\n",
              "      <td>8071.13</td>\n",
              "      <td>9.3960</td>\n",
              "      <td>0.02</td>\n",
              "      <td>328</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.39</td>\n",
              "      <td>6.4365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0035</td>\n",
              "      <td>0.8402</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.19</td>\n",
              "      <td>1339.70</td>\n",
              "      <td>1107.26</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>129.48</td>\n",
              "      <td>2387.90</td>\n",
              "      <td>8078.89</td>\n",
              "      <td>9.3594</td>\n",
              "      <td>0.02</td>\n",
              "      <td>328</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.56</td>\n",
              "      <td>6.2367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>35.0079</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.10</td>\n",
              "      <td>1353.04</td>\n",
              "      <td>1117.80</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>181.82</td>\n",
              "      <td>2387.87</td>\n",
              "      <td>8057.83</td>\n",
              "      <td>9.3030</td>\n",
              "      <td>0.02</td>\n",
              "      <td>333</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.85</td>\n",
              "      <td>8.9326</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>41209</th>\n",
              "      <td>248</td>\n",
              "      <td>277</td>\n",
              "      <td>41.9991</td>\n",
              "      <td>0.8401</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>550.30</td>\n",
              "      <td>1364.40</td>\n",
              "      <td>1129.17</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.87</td>\n",
              "      <td>2388.50</td>\n",
              "      <td>8112.61</td>\n",
              "      <td>9.4427</td>\n",
              "      <td>0.02</td>\n",
              "      <td>331</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.53</td>\n",
              "      <td>6.2620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41210</th>\n",
              "      <td>248</td>\n",
              "      <td>278</td>\n",
              "      <td>20.0026</td>\n",
              "      <td>0.7005</td>\n",
              "      <td>100.0</td>\n",
              "      <td>491.19</td>\n",
              "      <td>608.00</td>\n",
              "      <td>1494.75</td>\n",
              "      <td>1260.88</td>\n",
              "      <td>9.35</td>\n",
              "      <td>...</td>\n",
              "      <td>314.51</td>\n",
              "      <td>2388.33</td>\n",
              "      <td>8086.83</td>\n",
              "      <td>9.2772</td>\n",
              "      <td>0.02</td>\n",
              "      <td>366</td>\n",
              "      <td>2324</td>\n",
              "      <td>100.00</td>\n",
              "      <td>24.33</td>\n",
              "      <td>14.6486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41211</th>\n",
              "      <td>248</td>\n",
              "      <td>279</td>\n",
              "      <td>34.9988</td>\n",
              "      <td>0.8413</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.92</td>\n",
              "      <td>1370.65</td>\n",
              "      <td>1130.97</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>182.76</td>\n",
              "      <td>2388.64</td>\n",
              "      <td>8100.84</td>\n",
              "      <td>9.3982</td>\n",
              "      <td>0.02</td>\n",
              "      <td>336</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.69</td>\n",
              "      <td>8.8389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41212</th>\n",
              "      <td>248</td>\n",
              "      <td>280</td>\n",
              "      <td>20.0027</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>491.19</td>\n",
              "      <td>608.19</td>\n",
              "      <td>1489.11</td>\n",
              "      <td>1256.25</td>\n",
              "      <td>9.35</td>\n",
              "      <td>...</td>\n",
              "      <td>314.17</td>\n",
              "      <td>2388.37</td>\n",
              "      <td>8085.24</td>\n",
              "      <td>9.2727</td>\n",
              "      <td>0.03</td>\n",
              "      <td>366</td>\n",
              "      <td>2324</td>\n",
              "      <td>100.00</td>\n",
              "      <td>24.44</td>\n",
              "      <td>14.6887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41213</th>\n",
              "      <td>248</td>\n",
              "      <td>281</td>\n",
              "      <td>35.0075</td>\n",
              "      <td>0.8402</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>556.40</td>\n",
              "      <td>1378.58</td>\n",
              "      <td>1140.70</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>181.88</td>\n",
              "      <td>2388.59</td>\n",
              "      <td>8098.17</td>\n",
              "      <td>9.3964</td>\n",
              "      <td>0.02</td>\n",
              "      <td>335</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.72</td>\n",
              "      <td>8.8502</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41214 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\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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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
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              "      .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test')\"\n",
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              "        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 = artificial_bee_colony_optimization()\n",
        "    model.fit(X_train, y_train)\n",
        "    return model.predict(X)"
      ],
      "metadata": {
        "id": "7rD9yJnTg1Cv"
      },
      "execution_count": 36,
      "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": "KDZ4qp2hg1Fd"
      },
      "execution_count": 37,
      "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 = df_test['RUL']"
      ],
      "metadata": {
        "id": "7rsfcF_ag1I1"
      },
      "execution_count": 38,
      "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": "4vvxmDrChELv"
      },
      "execution_count": 39,
      "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": "ZKq94RQihEOV"
      },
      "execution_count": 40,
      "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": "WQVVTytZhESZ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def target_function(variables_values):\n",
        "    # Train the regression model\n",
        "    model = LinearRegression()\n",
        "    model.fit(X_train, y_train)\n",
        "    # Predict using the trained model\n",
        "    predictions = model.predict(X_train)\n",
        "    # Calculate RMSE\n",
        "    rmse = np.sqrt(mean_squared_error(y_train, predictions))\n",
        "    return rmse"
      ],
      "metadata": {
        "id": "jes7GEXhhOb-"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "abco_result = artificial_bee_colony_optimization(target_function=target_function, **parameters)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z3amGjb7hOe6",
        "outputId": "c03b4ce2-cf1d-426c-fc5c-e9b8b0b29a9f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration =  1  f(x) =  55.76886181227007\n",
            "Iteration =  2  f(x) =  55.76886181227007\n",
            "Iteration =  3  f(x) =  55.76886181227007\n",
            "Iteration =  4  f(x) =  55.76886181227007\n",
            "Iteration =  5  f(x) =  55.76886181227007\n",
            "Iteration =  6  f(x) =  55.76886181227007\n",
            "Iteration =  7  f(x) =  55.76886181227007\n",
            "Iteration =  8  f(x) =  55.76886181227007\n",
            "Iteration =  9  f(x) =  55.76886181227007\n",
            "Iteration =  10  f(x) =  55.76886181227007\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": "dmlzIabogOU4"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimized_model = LinearRegression().fit(X_test_scaled_subset, y_test_subset)"
      ],
      "metadata": {
        "id": "-VVt2Ppfh13s"
      },
      "execution_count": 45,
      "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": "q1oUsrgHh4Fr"
      },
      "execution_count": 46,
      "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": "9C94LVpIh4K4",
        "outputId": "32713bf0-0c5c-4095-b0df-9d2e9a3995aa"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE (Test): 3.979039320256561e-14\n",
            "R2 Score (Test): 1.0\n",
            "MAE (Test): 3.069544618483633e-14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gv5FFz7Uh4PE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}