machathon_filteration.ipynb

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      "name": "machathon filteration.ipynb",
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    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NaderJS/hacknotebooks/blob/master/machathon_filteration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXTCXrfaKw3X"
      },
      "source": [
        "#Machathon Filteration\n",
        "https://www.kaggle.com/c/machathon-10-filteration-test \n",
        "\n",
        "team name: Data Wizard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2ziL8sBLg-q"
      },
      "source": [
        "\"\"\"\n",
        "I this is the download address of the data file\n",
        "\n",
        "\"\"\"\n",
        "!wget \"https://storage.googleapis.com/kaggle-competitions-data/kaggle-v2/18668/951082/bundle/archive.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1620270180&Signature=N6TLZp%2FTrxUxWpG4wbt8WXkdpyFk5W5xX22%2BzgZF9Dffe3oXcM4HAbPzh%2Fe7ksXTkhMK5MXvJa9AtOil1Ziul865PdGQaPJC%2B0m3dNKocFX8r8S7rAbBgXbmG3NzqXpZdVaW35PaJYFc2VBw45FTCdJiRNLvoUJ0jE%2FdAzGsKNQpLQQYOFgIYSzpXRAjQdj3ORcG1Tr5AYIWRC8dnXZjnoGmr76wkXcoOEiNvJMStoisqKtpHpkQ4zlIqiuoQe8KY2cdiklWcIY7Omu2srvu7RnwrwhOdCupraJa5q6ny0niABtM%2BPUHMnGsUs2j2bjatjKPwojC%2BekciNvuM0%2B%2BCg%3D%3D&response-content-disposition=attachment%3B+filename%3Dmachathon-10-filteration-test.zip\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErbbgmtWLjsn"
      },
      "source": [
        "\"\"\"\n",
        "unzipping the file\n",
        "\"\"\"\n",
        "!unzip  \"/content/archive.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1620270180&Signature=N6TLZp%2FTrxUxWpG4wbt8WXkdpyFk5W5xX22+zgZF9Dffe3oXcM4HAbPzh%2Fe7ksXTkhMK5MXvJa9AtOil1Ziul865PdGQaPJC+0m3dNKocFX8r8S7rAbBgXbmG3NzqXpZd\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LG1JqhiIMGkN"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcPx1XByUzx7"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from catboost import CatBoostRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from catboost import Pool\n",
        "\n",
        "# from sklearn.preprocessing import StandardScaler\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14gpxzuxh-42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbc2675a-3e59-4f49-edc8-299b2bf4db92"
      },
      "source": [
        "!pip install bayesian-optimization"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting bayesian-optimization\n",
            "  Downloading https://files.pythonhosted.org/packages/bb/7a/fd8059a3881d3ab37ac8f72f56b73937a14e8bb14a9733e68cc8b17dbe3c/bayesian-optimization-1.2.0.tar.gz\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from bayesian-optimization) (1.19.5)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from bayesian-optimization) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.18.0 in /usr/local/lib/python3.7/dist-packages (from bayesian-optimization) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18.0->bayesian-optimization) (1.0.1)\n",
            "Building wheels for collected packages: bayesian-optimization\n",
            "  Building wheel for bayesian-optimization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for bayesian-optimization: filename=bayesian_optimization-1.2.0-cp37-none-any.whl size=11687 sha256=2f080b6c119a06cd8b9bc51d48fcb406636e5cad2be45138947ca28030944456\n",
            "  Stored in directory: /root/.cache/pip/wheels/5a/56/ae/e0e3c1fc1954dc3ec712e2df547235ed072b448094d8f94aec\n",
            "Successfully built bayesian-optimization\n",
            "Installing collected packages: bayesian-optimization\n",
            "Successfully installed bayesian-optimization-1.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QU4HEgxUzyK"
      },
      "source": [
        "traindata = pd.read_csv(\"./train.csv\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkB_HakFUzyR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "49d36be8-c2ed-4753-e9f3-ab6f2ae8b07e"
      },
      "source": [
        "traindata.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>WellID</th>\n",
              "      <th>FirstMonthOil</th>\n",
              "      <th>FirstMonthGas</th>\n",
              "      <th>FirstMonthRatioGasOil</th>\n",
              "      <th>FirstMonthWater</th>\n",
              "      <th>firstMonthPercentWater</th>\n",
              "      <th>FirstMonthDaysOn</th>\n",
              "      <th>SecondMonthOil</th>\n",
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              "      <th>SecondMonthRatioGasOil</th>\n",
              "      <th>SecondMonthWater</th>\n",
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              "      <th>SecondMonthDaysOn</th>\n",
              "      <th>ThirdMonthOil</th>\n",
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              "      <th>ThirdMonthWater</th>\n",
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              "      <th>ThirdMonthDaysOn</th>\n",
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              "      <th>FourthMonthGas</th>\n",
              "      <th>FourthMonthRatioGasOil</th>\n",
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              "      <th>FourthMonthPercentWater</th>\n",
              "      <th>FourthMonthDaysOn</th>\n",
              "      <th>FifthMonthOil</th>\n",
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              "      <th>FifthMonthRatioGasOil</th>\n",
              "      <th>FifthMonthWater</th>\n",
              "      <th>FifthMonthPercentWater</th>\n",
              "      <th>FifthMonthDaysOn</th>\n",
              "      <th>SixthMonthOil</th>\n",
              "      <th>SixthMonthGas</th>\n",
              "      <th>SixthMonthRatioGasOil</th>\n",
              "      <th>SixthMonthWater</th>\n",
              "      <th>SixthMonthPercentWater</th>\n",
              "      <th>SixthMonthDaysOn</th>\n",
              "      <th>TotalOilInNext6Months</th>\n",
              "      <th>TotalGasInNext6Months</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>5343</td>\n",
              "      <td>22672.0</td>\n",
              "      <td>3818.0</td>\n",
              "      <td>0.168402</td>\n",
              "      <td>14759.0</td>\n",
              "      <td>0.650979</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12766.0</td>\n",
              "      <td>2308.0</td>\n",
              "      <td>0.180793</td>\n",
              "      <td>7921.0</td>\n",
              "      <td>0.620476</td>\n",
              "      <td>28.0</td>\n",
              "      <td>4990.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>0.121643</td>\n",
              "      <td>2330.0</td>\n",
              "      <td>0.466934</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11002.0</td>\n",
              "      <td>1419.0</td>\n",
              "      <td>0.128977</td>\n",
              "      <td>6576.0</td>\n",
              "      <td>0.597710</td>\n",
              "      <td>31.0</td>\n",
              "      <td>7141.0</td>\n",
              "      <td>506.0</td>\n",
              "      <td>0.070858</td>\n",
              "      <td>4451.0</td>\n",
              "      <td>0.623302</td>\n",
              "      <td>22.0</td>\n",
              "      <td>6516.0</td>\n",
              "      <td>479.0</td>\n",
              "      <td>0.073511</td>\n",
              "      <td>4075.0</td>\n",
              "      <td>0.625384</td>\n",
              "      <td>27.0</td>\n",
              "      <td>61152.0</td>\n",
              "      <td>5893.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5663</td>\n",
              "      <td>7919.0</td>\n",
              "      <td>4328.0</td>\n",
              "      <td>0.546534</td>\n",
              "      <td>5278.0</td>\n",
              "      <td>0.666498</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5082.0</td>\n",
              "      <td>2779.0</td>\n",
              "      <td>0.546832</td>\n",
              "      <td>2402.0</td>\n",
              "      <td>0.472649</td>\n",
              "      <td>28.0</td>\n",
              "      <td>3168.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>0.170455</td>\n",
              "      <td>1683.0</td>\n",
              "      <td>0.531250</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2774.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>0.102019</td>\n",
              "      <td>1172.0</td>\n",
              "      <td>0.422495</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>378.0</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>782.0</td>\n",
              "      <td>0.406234</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>1195.0</td>\n",
              "      <td>0.593052</td>\n",
              "      <td>796.0</td>\n",
              "      <td>0.395037</td>\n",
              "      <td>23.0</td>\n",
              "      <td>13172.0</td>\n",
              "      <td>7529.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>7697</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.513858</td>\n",
              "      <td>30.0</td>\n",
              "      <td>13372.0</td>\n",
              "      <td>23154.0</td>\n",
              "      <td>1.731529</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.0</td>\n",
              "      <td>12189.0</td>\n",
              "      <td>19935.0</td>\n",
              "      <td>1.635491</td>\n",
              "      <td>3665.0</td>\n",
              "      <td>0.300681</td>\n",
              "      <td>30.0</td>\n",
              "      <td>10034.0</td>\n",
              "      <td>16316.0</td>\n",
              "      <td>1.626071</td>\n",
              "      <td>2913.0</td>\n",
              "      <td>0.290313</td>\n",
              "      <td>31.0</td>\n",
              "      <td>11982.0</td>\n",
              "      <td>23337.0</td>\n",
              "      <td>1.947672</td>\n",
              "      <td>4137.0</td>\n",
              "      <td>0.345268</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9070.0</td>\n",
              "      <td>18020.0</td>\n",
              "      <td>1.986770</td>\n",
              "      <td>3102.0</td>\n",
              "      <td>0.342007</td>\n",
              "      <td>31.0</td>\n",
              "      <td>32858.0</td>\n",
              "      <td>57060.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>7394</td>\n",
              "      <td>19784.0</td>\n",
              "      <td>33837.0</td>\n",
              "      <td>1.710321</td>\n",
              "      <td>9433.0</td>\n",
              "      <td>0.476799</td>\n",
              "      <td>30.0</td>\n",
              "      <td>17166.0</td>\n",
              "      <td>27167.0</td>\n",
              "      <td>1.582605</td>\n",
              "      <td>6641.0</td>\n",
              "      <td>0.386869</td>\n",
              "      <td>31.0</td>\n",
              "      <td>16525.0</td>\n",
              "      <td>22777.0</td>\n",
              "      <td>1.378336</td>\n",
              "      <td>5927.0</td>\n",
              "      <td>0.358669</td>\n",
              "      <td>30.0</td>\n",
              "      <td>13113.0</td>\n",
              "      <td>18209.0</td>\n",
              "      <td>1.388622</td>\n",
              "      <td>4568.0</td>\n",
              "      <td>0.348357</td>\n",
              "      <td>31.0</td>\n",
              "      <td>9202.0</td>\n",
              "      <td>11550.0</td>\n",
              "      <td>1.255162</td>\n",
              "      <td>3504.0</td>\n",
              "      <td>0.380787</td>\n",
              "      <td>31.0</td>\n",
              "      <td>6102.0</td>\n",
              "      <td>8675.0</td>\n",
              "      <td>1.421665</td>\n",
              "      <td>2145.0</td>\n",
              "      <td>0.351524</td>\n",
              "      <td>18.0</td>\n",
              "      <td>31784.0</td>\n",
              "      <td>45579.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>7318</td>\n",
              "      <td>16157.0</td>\n",
              "      <td>21461.0</td>\n",
              "      <td>1.328279</td>\n",
              "      <td>7529.0</td>\n",
              "      <td>0.465990</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12263.0</td>\n",
              "      <td>14664.0</td>\n",
              "      <td>1.195792</td>\n",
              "      <td>5728.0</td>\n",
              "      <td>0.467096</td>\n",
              "      <td>31.0</td>\n",
              "      <td>11019.0</td>\n",
              "      <td>14039.0</td>\n",
              "      <td>1.274072</td>\n",
              "      <td>4518.0</td>\n",
              "      <td>0.410019</td>\n",
              "      <td>30.0</td>\n",
              "      <td>14154.0</td>\n",
              "      <td>19746.0</td>\n",
              "      <td>1.395083</td>\n",
              "      <td>5486.0</td>\n",
              "      <td>0.387594</td>\n",
              "      <td>31.0</td>\n",
              "      <td>4046.0</td>\n",
              "      <td>4337.0</td>\n",
              "      <td>1.071923</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>0.402126</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2941.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40715.0</td>\n",
              "      <td>59066.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0  WellID  ...  TotalOilInNext6Months  TotalGasInNext6Months\n",
              "0           3    5343  ...                61152.0                 5893.0\n",
              "1           4    5663  ...                13172.0                 7529.0\n",
              "2           8    7697  ...                32858.0                57060.0\n",
              "3           9    7394  ...                31784.0                45579.0\n",
              "4          10    7318  ...                40715.0                59066.0\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MWhtSwRUzyc"
      },
      "source": [
        "traindata.drop([\"Unnamed: 0\",\"WellID\"],axis=1,inplace=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbIQ5f7jUzyi"
      },
      "source": [
        "y = traindata[\"TotalOilInNext6Months\"]\n",
        "X = traindata.drop(\"TotalOilInNext6Months\",axis=1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_-xE3tfUzyn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "06079952-5380-4970-c17b-72a4ba8d43f1"
      },
      "source": [
        "traindata.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>SecondMonthGas</th>\n",
              "      <th>SecondMonthRatioGasOil</th>\n",
              "      <th>SecondMonthWater</th>\n",
              "      <th>SecondMonthPercentWater</th>\n",
              "      <th>SecondMonthDaysOn</th>\n",
              "      <th>ThirdMonthOil</th>\n",
              "      <th>ThirdMonthGas</th>\n",
              "      <th>ThirdMonthRatioGasOil</th>\n",
              "      <th>ThirdMonthWater</th>\n",
              "      <th>ThirdMonthPercentWater</th>\n",
              "      <th>ThirdMonthDaysOn</th>\n",
              "      <th>FourthMonthOil</th>\n",
              "      <th>FourthMonthGas</th>\n",
              "      <th>FourthMonthRatioGasOil</th>\n",
              "      <th>FourthMonthWater</th>\n",
              "      <th>FourthMonthPercentWater</th>\n",
              "      <th>FourthMonthDaysOn</th>\n",
              "      <th>FifthMonthOil</th>\n",
              "      <th>FifthMonthGas</th>\n",
              "      <th>FifthMonthRatioGasOil</th>\n",
              "      <th>FifthMonthWater</th>\n",
              "      <th>FifthMonthPercentWater</th>\n",
              "      <th>FifthMonthDaysOn</th>\n",
              "      <th>SixthMonthOil</th>\n",
              "      <th>SixthMonthGas</th>\n",
              "      <th>SixthMonthRatioGasOil</th>\n",
              "      <th>SixthMonthWater</th>\n",
              "      <th>SixthMonthPercentWater</th>\n",
              "      <th>SixthMonthDaysOn</th>\n",
              "      <th>TotalOilInNext6Months</th>\n",
              "      <th>TotalGasInNext6Months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9.324000e+03</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9.324000e+03</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9.324000e+03</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9324.000000</td>\n",
              "      <td>9.324000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10975.998391</td>\n",
              "      <td>23654.428142</td>\n",
              "      <td>36.483768</td>\n",
              "      <td>7.050517e+03</td>\n",
              "      <td>0.793089</td>\n",
              "      <td>19.624303</td>\n",
              "      <td>7361.601459</td>\n",
              "      <td>19751.486808</td>\n",
              "      <td>71.088354</td>\n",
              "      <td>3.985776e+03</td>\n",
              "      <td>0.719347</td>\n",
              "      <td>19.085800</td>\n",
              "      <td>5917.555019</td>\n",
              "      <td>17147.918168</td>\n",
              "      <td>22.511987</td>\n",
              "      <td>3.263830e+03</td>\n",
              "      <td>0.719102</td>\n",
              "      <td>18.774346</td>\n",
              "      <td>5206.465251</td>\n",
              "      <td>15410.326362</td>\n",
              "      <td>11.778866</td>\n",
              "      <td>2982.964715</td>\n",
              "      <td>0.733901</td>\n",
              "      <td>18.914200</td>\n",
              "      <td>4615.480266</td>\n",
              "      <td>14014.701952</td>\n",
              "      <td>25.640350</td>\n",
              "      <td>2715.161090</td>\n",
              "      <td>0.739190</td>\n",
              "      <td>18.994316</td>\n",
              "      <td>4239.232947</td>\n",
              "      <td>12985.894466</td>\n",
              "      <td>21.405811</td>\n",
              "      <td>2551.490562</td>\n",
              "      <td>0.763946</td>\n",
              "      <td>19.130309</td>\n",
              "      <td>19742.820249</td>\n",
              "      <td>6.355971e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8091.976505</td>\n",
              "      <td>35067.286099</td>\n",
              "      <td>891.599302</td>\n",
              "      <td>1.800020e+04</td>\n",
              "      <td>5.943600</td>\n",
              "      <td>13.369777</td>\n",
              "      <td>5960.634208</td>\n",
              "      <td>30780.151750</td>\n",
              "      <td>1865.962676</td>\n",
              "      <td>1.440518e+04</td>\n",
              "      <td>3.212954</td>\n",
              "      <td>13.493335</td>\n",
              "      <td>4895.693768</td>\n",
              "      <td>27350.194074</td>\n",
              "      <td>507.117402</td>\n",
              "      <td>1.270589e+04</td>\n",
              "      <td>2.923105</td>\n",
              "      <td>13.504617</td>\n",
              "      <td>4342.544054</td>\n",
              "      <td>23895.626766</td>\n",
              "      <td>198.644846</td>\n",
              "      <td>11494.973825</td>\n",
              "      <td>3.796721</td>\n",
              "      <td>13.641073</td>\n",
              "      <td>3889.654536</td>\n",
              "      <td>21561.736081</td>\n",
              "      <td>734.585937</td>\n",
              "      <td>9831.307311</td>\n",
              "      <td>3.002129</td>\n",
              "      <td>13.670981</td>\n",
              "      <td>3618.018873</td>\n",
              "      <td>19470.328468</td>\n",
              "      <td>601.380057</td>\n",
              "      <td>8158.773532</td>\n",
              "      <td>3.272239</td>\n",
              "      <td>13.737993</td>\n",
              "      <td>15678.751814</td>\n",
              "      <td>9.197275e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5140.500000</td>\n",
              "      <td>5928.000000</td>\n",
              "      <td>0.664069</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3057.750000</td>\n",
              "      <td>4207.750000</td>\n",
              "      <td>0.671820</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2415.250000</td>\n",
              "      <td>3606.750000</td>\n",
              "      <td>0.691011</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2065.750000</td>\n",
              "      <td>3385.750000</td>\n",
              "      <td>0.685409</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1834.750000</td>\n",
              "      <td>3081.500000</td>\n",
              "      <td>0.689508</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "      <td>2930.500000</td>\n",
              "      <td>0.702584</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8073.250000</td>\n",
              "      <td>1.690325e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9281.000000</td>\n",
              "      <td>13158.000000</td>\n",
              "      <td>1.151556</td>\n",
              "      <td>4.115000e+03</td>\n",
              "      <td>0.345983</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>6039.000000</td>\n",
              "      <td>10200.000000</td>\n",
              "      <td>1.257137</td>\n",
              "      <td>2.158500e+03</td>\n",
              "      <td>0.282594</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>4860.000000</td>\n",
              "      <td>8482.000000</td>\n",
              "      <td>1.304684</td>\n",
              "      <td>1.554000e+03</td>\n",
              "      <td>0.258139</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>4277.500000</td>\n",
              "      <td>7683.000000</td>\n",
              "      <td>1.325213</td>\n",
              "      <td>1297.000000</td>\n",
              "      <td>0.248840</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>3768.500000</td>\n",
              "      <td>7011.500000</td>\n",
              "      <td>1.355228</td>\n",
              "      <td>1173.500000</td>\n",
              "      <td>0.250708</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>3433.500000</td>\n",
              "      <td>6542.000000</td>\n",
              "      <td>1.412128</td>\n",
              "      <td>1077.000000</td>\n",
              "      <td>0.250451</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>16437.000000</td>\n",
              "      <td>3.321850e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15075.500000</td>\n",
              "      <td>26262.500000</td>\n",
              "      <td>2.745121</td>\n",
              "      <td>1.019175e+04</td>\n",
              "      <td>0.935886</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>10172.250000</td>\n",
              "      <td>22444.250000</td>\n",
              "      <td>3.433525</td>\n",
              "      <td>5.748500e+03</td>\n",
              "      <td>0.789590</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>8097.250000</td>\n",
              "      <td>19500.000000</td>\n",
              "      <td>3.734243</td>\n",
              "      <td>4.682250e+03</td>\n",
              "      <td>0.768735</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>7175.250000</td>\n",
              "      <td>17762.750000</td>\n",
              "      <td>4.072924</td>\n",
              "      <td>4229.250000</td>\n",
              "      <td>0.754927</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>6379.000000</td>\n",
              "      <td>16241.250000</td>\n",
              "      <td>4.288935</td>\n",
              "      <td>3912.500000</td>\n",
              "      <td>0.775836</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>5938.250000</td>\n",
              "      <td>15021.750000</td>\n",
              "      <td>4.593442</td>\n",
              "      <td>3674.250000</td>\n",
              "      <td>0.797680</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>27737.500000</td>\n",
              "      <td>7.100925e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64499.000000</td>\n",
              "      <td>476177.000000</td>\n",
              "      <td>55789.500000</td>\n",
              "      <td>1.451478e+06</td>\n",
              "      <td>527.666667</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>54790.000000</td>\n",
              "      <td>481633.000000</td>\n",
              "      <td>115698.000000</td>\n",
              "      <td>1.226836e+06</td>\n",
              "      <td>169.292402</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>52654.000000</td>\n",
              "      <td>395168.000000</td>\n",
              "      <td>36268.000000</td>\n",
              "      <td>1.100392e+06</td>\n",
              "      <td>141.005923</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>46940.000000</td>\n",
              "      <td>384468.000000</td>\n",
              "      <td>15176.266667</td>\n",
              "      <td>970001.000000</td>\n",
              "      <td>215.562500</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>36534.000000</td>\n",
              "      <td>361151.000000</td>\n",
              "      <td>53048.000000</td>\n",
              "      <td>825349.000000</td>\n",
              "      <td>129.594937</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>39832.000000</td>\n",
              "      <td>329776.000000</td>\n",
              "      <td>45804.250000</td>\n",
              "      <td>649898.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>203364.000000</td>\n",
              "      <td>1.169180e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       FirstMonthOil  ...  TotalGasInNext6Months\n",
              "count    9324.000000  ...           9.324000e+03\n",
              "mean    10975.998391  ...           6.355971e+04\n",
              "std      8091.976505  ...           9.197275e+04\n",
              "min         0.000000  ...           0.000000e+00\n",
              "25%      5140.500000  ...           1.690325e+04\n",
              "50%      9281.000000  ...           3.321850e+04\n",
              "75%     15075.500000  ...           7.100925e+04\n",
              "max     64499.000000  ...           1.169180e+06\n",
              "\n",
              "[8 rows x 38 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgat9HJoUzy2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33a07610-1fce-4580-cdf3-e513172f2af2"
      },
      "source": [
        "traindata.eq(0).sum()/len(traindata)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FirstMonthOil              0.003325\n",
              "FirstMonthGas              0.036358\n",
              "FirstMonthRatioGasOil      0.039575\n",
              "FirstMonthWater            0.339768\n",
              "firstMonthPercentWater     0.340519\n",
              "FirstMonthDaysOn           0.302982\n",
              "SecondMonthOil             0.030459\n",
              "SecondMonthGas             0.036251\n",
              "SecondMonthRatioGasOil     0.057701\n",
              "SecondMonthWater           0.334728\n",
              "SecondMonthPercentWater    0.336444\n",
              "SecondMonthDaysOn          0.309095\n",
              "ThirdMonthOil              0.030245\n",
              "ThirdMonthGas              0.032604\n",
              "ThirdMonthRatioGasOil      0.054376\n",
              "ThirdMonthWater            0.325504\n",
              "ThirdMonthPercentWater     0.326791\n",
              "ThirdMonthDaysOn           0.308988\n",
              "FourthMonthOil             0.035178\n",
              "FourthMonthGas             0.033462\n",
              "FourthMonthRatioGasOil     0.057164\n",
              "FourthMonthWater           0.321214\n",
              "FourthMonthPercentWater    0.323252\n",
              "FourthMonthDaysOn          0.311776\n",
              "FifthMonthOil              0.036894\n",
              "FifthMonthGas              0.034535\n",
              "FifthMonthRatioGasOil      0.059524\n",
              "FifthMonthWater            0.321858\n",
              "FifthMonthPercentWater     0.324003\n",
              "FifthMonthDaysOn           0.311562\n",
              "SixthMonthOil              0.036572\n",
              "SixthMonthGas              0.033998\n",
              "SixthMonthRatioGasOil      0.057808\n",
              "SixthMonthWater            0.317782\n",
              "SixthMonthPercentWater     0.319927\n",
              "SixthMonthDaysOn           0.312312\n",
              "TotalOilInNext6Months      0.009009\n",
              "TotalGasInNext6Months      0.008580\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tk03EX-2Uzy_"
      },
      "source": [
        "xt,xp,yt,yp = train_test_split(X,y,shuffle=True)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XO3V1BaUzzH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40e40dc3-d7d7-4946-c2c4-db3360a1274f"
      },
      "source": [
        "rf= RandomForestRegressor(max_features=0.6,n_estimators=125,min_samples_leaf=7)\n",
        "rf.fit(xt,yt)\n",
        "vals = rf.predict(xp)\n",
        "mean_absolute_error(yp,vals)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3198.466764568371"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJKzxVYRUzzn"
      },
      "source": [
        "from bayes_opt import BayesianOptimization as opt  \n",
        "def traincat(learning_rate,max_depth,l2_leaf_reg,colsample_bylevel,subsample):\n",
        "    cat =  CatBoostRegressor(random_state=42,learning_rate=learning_rate,subsample = subsample,iterations=2500,max_depth=int(round(max_depth)),l2_leaf_reg =l2_leaf_reg,colsample_bylevel=colsample_bylevel,eval_metric=\"MAE\",early_stopping_rounds=500)\n",
        "    valset = Pool(xp,yp)\n",
        "    cat.fit(xt,yt,eval_set=valset,verbose=500)\n",
        "    return -cat.best_score_[\"validation\"][\"MAE\"]\n",
        "\n",
        "bounds = {\n",
        "    'max_depth':(5,10),\n",
        "#     'min_rows':(10,30),\n",
        "    'l2_leaf_reg':(0.1,0.8),\n",
        "    'learning_rate':(0.001, 0.06),\n",
        "#     'sample_rate':(0.5,0.8),\n",
        "     'colsample_bylevel': (0.3,1),\n",
        "    'subsample' : (0.7,1)\n",
        "}\n",
        "\n",
        "optimizer = opt(\n",
        "    f=traincat,\n",
        "    pbounds=bounds,\n",
        "    random_state=1,\n",
        ")\n",
        "optimizer.maximize(init_points=10, n_iter=55)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAPDGyo7RvP_",
        "outputId": "0808c949-73e3-49b5-9517-67e3217690ec"
      },
      "source": [
        "optimizer.max"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'params': {'colsample_bylevel': 0.3646370163381584,\n",
              "  'l2_leaf_reg': 0.23038214796436965,\n",
              "  'learning_rate': 0.021388082895539815,\n",
              "  'max_depth': 6.983837371153349,\n",
              "  'subsample': 0.8616450202010071},\n",
              " 'target': -2682.2087811496117}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGaQ_E-HUzzd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64e659cd-0831-4497-bd38-334e5f5eda2f"
      },
      "source": [
        "cat = CatBoostRegressor(random_state=56,learning_rate=0.03078496171814678,subsample =  0.8616450202010071,iterations=4000,max_depth=int(round(6.983837371153349)),l2_leaf_reg =0.23038214796436965,colsample_bylevel=0.3646370163381584,eval_metric=\"MAE\",early_stopping_rounds=900)\n",
        "val_set=Pool(xp,yp)\n",
        "cat.fit(xt,yt,eval_set=val_set,early_stopping_rounds=900,verbose=500)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0:\tlearn: 11680.7836381\ttest: 11347.8907190\tbest: 11347.8907190 (0)\ttotal: 8.02ms\tremaining: 32.1s\n",
            "500:\tlearn: 2133.1251937\ttest: 2799.8697024\tbest: 2799.8697024 (500)\ttotal: 3.34s\tremaining: 23.4s\n",
            "1000:\tlearn: 1542.9577456\ttest: 2705.9961910\tbest: 2705.9961910 (1000)\ttotal: 6.63s\tremaining: 19.9s\n",
            "1500:\tlearn: 1202.7461084\ttest: 2680.8212086\tbest: 2680.7295194 (1470)\ttotal: 9.95s\tremaining: 16.6s\n",
            "2000:\tlearn: 969.7259947\ttest: 2671.8558238\tbest: 2671.1240926 (1972)\ttotal: 13.3s\tremaining: 13.2s\n",
            "2500:\tlearn: 794.6149316\ttest: 2668.7309136\tbest: 2668.7137919 (2498)\ttotal: 16.6s\tremaining: 9.95s\n",
            "3000:\tlearn: 662.5306933\ttest: 2668.4135599\tbest: 2667.7389376 (2930)\ttotal: 19.9s\tremaining: 6.62s\n",
            "3500:\tlearn: 557.9347907\ttest: 2668.1715013\tbest: 2667.2994495 (3398)\ttotal: 23.1s\tremaining: 3.3s\n",
            "3999:\tlearn: 473.2737189\ttest: 2668.0480583\tbest: 2667.2994495 (3398)\ttotal: 26.4s\tremaining: 0us\n",
            "\n",
            "bestTest = 2667.29945\n",
            "bestIteration = 3398\n",
            "\n",
            "Shrink model to first 3399 iterations.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostRegressor at 0x7fc982181050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNN_MiZ-kvLw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "984b61d0-4805-4764-aae9-43f862f7f78b"
      },
      "source": [
        "cat.best_score_[\"validation\"][\"MAE\"]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2667.299449521499"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19HAL61zsPIB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "768faebc-2ce8-4b4c-9ed6-172847448776"
      },
      "source": [
        "test = pd.read_csv(\"./test.csv\")\n",
        "test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>WellID</th>\n",
              "      <th>FirstMonthOil</th>\n",
              "      <th>FirstMonthGas</th>\n",
              "      <th>FirstMonthRatioGasOil</th>\n",
              "      <th>FirstMonthWater</th>\n",
              "      <th>firstMonthPercentWater</th>\n",
              "      <th>FirstMonthDaysOn</th>\n",
              "      <th>SecondMonthOil</th>\n",
              "      <th>SecondMonthGas</th>\n",
              "      <th>SecondMonthRatioGasOil</th>\n",
              "      <th>SecondMonthWater</th>\n",
              "      <th>SecondMonthPercentWater</th>\n",
              "      <th>SecondMonthDaysOn</th>\n",
              "      <th>ThirdMonthOil</th>\n",
              "      <th>ThirdMonthGas</th>\n",
              "      <th>ThirdMonthRatioGasOil</th>\n",
              "      <th>ThirdMonthWater</th>\n",
              "      <th>ThirdMonthPercentWater</th>\n",
              "      <th>ThirdMonthDaysOn</th>\n",
              "      <th>FourthMonthOil</th>\n",
              "      <th>FourthMonthGas</th>\n",
              "      <th>FourthMonthRatioGasOil</th>\n",
              "      <th>FourthMonthWater</th>\n",
              "      <th>FourthMonthPercentWater</th>\n",
              "      <th>FourthMonthDaysOn</th>\n",
              "      <th>FifthMonthOil</th>\n",
              "      <th>FifthMonthGas</th>\n",
              "      <th>FifthMonthRatioGasOil</th>\n",
              "      <th>FifthMonthWater</th>\n",
              "      <th>FifthMonthPercentWater</th>\n",
              "      <th>FifthMonthDaysOn</th>\n",
              "      <th>SixthMonthOil</th>\n",
              "      <th>SixthMonthGas</th>\n",
              "      <th>SixthMonthRatioGasOil</th>\n",
              "      <th>SixthMonthWater</th>\n",
              "      <th>SixthMonthPercentWater</th>\n",
              "      <th>SixthMonthDaysOn</th>\n",
              "      <th>TotalGasInNext6Months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>562</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>15031.0</td>\n",
              "      <td>3.304969</td>\n",
              "      <td>7923.0</td>\n",
              "      <td>1.742084</td>\n",
              "      <td>31.0</td>\n",
              "      <td>4361.0</td>\n",
              "      <td>12504.0</td>\n",
              "      <td>2.867232</td>\n",
              "      <td>4257.0</td>\n",
              "      <td>0.976152</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2911.0</td>\n",
              "      <td>8559.0</td>\n",
              "      <td>2.940227</td>\n",
              "      <td>2582.0</td>\n",
              "      <td>0.886980</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2404.0</td>\n",
              "      <td>6081.0</td>\n",
              "      <td>2.529534</td>\n",
              "      <td>1822.0</td>\n",
              "      <td>0.757903</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>4608.0</td>\n",
              "      <td>2.393766</td>\n",
              "      <td>1359.0</td>\n",
              "      <td>0.705974</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1734.0</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>2.306805</td>\n",
              "      <td>1282.0</td>\n",
              "      <td>0.739331</td>\n",
              "      <td>31.0</td>\n",
              "      <td>15321.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>15199.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11067.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>1.188312</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.077922</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3604.0</td>\n",
              "      <td>2493.0</td>\n",
              "      <td>0.691731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10441.0</td>\n",
              "      <td>13457.0</td>\n",
              "      <td>1.288861</td>\n",
              "      <td>1197.0</td>\n",
              "      <td>0.114644</td>\n",
              "      <td>31.0</td>\n",
              "      <td>7212.0</td>\n",
              "      <td>20610.0</td>\n",
              "      <td>2.857737</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.0</td>\n",
              "      <td>61855.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>5695</td>\n",
              "      <td>8664.0</td>\n",
              "      <td>6527.0</td>\n",
              "      <td>0.753347</td>\n",
              "      <td>2748.0</td>\n",
              "      <td>0.317175</td>\n",
              "      <td>17.0</td>\n",
              "      <td>6947.0</td>\n",
              "      <td>5068.0</td>\n",
              "      <td>0.729524</td>\n",
              "      <td>6802.0</td>\n",
              "      <td>0.979128</td>\n",
              "      <td>29.0</td>\n",
              "      <td>3470.0</td>\n",
              "      <td>2525.0</td>\n",
              "      <td>0.727666</td>\n",
              "      <td>2813.0</td>\n",
              "      <td>0.810663</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1177.0</td>\n",
              "      <td>889.0</td>\n",
              "      <td>0.755310</td>\n",
              "      <td>963.0</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>12.0</td>\n",
              "      <td>3216.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>0.461443</td>\n",
              "      <td>2382.0</td>\n",
              "      <td>0.740672</td>\n",
              "      <td>17.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>392.0</td>\n",
              "      <td>0.773176</td>\n",
              "      <td>930.0</td>\n",
              "      <td>1.834320</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12371.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>6185</td>\n",
              "      <td>14408.0</td>\n",
              "      <td>9223.0</td>\n",
              "      <td>0.640130</td>\n",
              "      <td>21573.0</td>\n",
              "      <td>1.497293</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13753.0</td>\n",
              "      <td>8659.0</td>\n",
              "      <td>0.629608</td>\n",
              "      <td>15812.0</td>\n",
              "      <td>1.149713</td>\n",
              "      <td>31.0</td>\n",
              "      <td>7103.0</td>\n",
              "      <td>4863.0</td>\n",
              "      <td>0.684640</td>\n",
              "      <td>8190.0</td>\n",
              "      <td>1.153034</td>\n",
              "      <td>25.0</td>\n",
              "      <td>4873.0</td>\n",
              "      <td>3176.0</td>\n",
              "      <td>0.651755</td>\n",
              "      <td>5546.0</td>\n",
              "      <td>1.138108</td>\n",
              "      <td>20.0</td>\n",
              "      <td>6307.0</td>\n",
              "      <td>4311.0</td>\n",
              "      <td>0.683526</td>\n",
              "      <td>7129.0</td>\n",
              "      <td>1.130331</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6849.0</td>\n",
              "      <td>4467.0</td>\n",
              "      <td>0.652212</td>\n",
              "      <td>7475.0</td>\n",
              "      <td>1.091400</td>\n",
              "      <td>31.0</td>\n",
              "      <td>23574.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>7919</td>\n",
              "      <td>10830.0</td>\n",
              "      <td>5868.0</td>\n",
              "      <td>0.541828</td>\n",
              "      <td>3749.0</td>\n",
              "      <td>0.346168</td>\n",
              "      <td>22.0</td>\n",
              "      <td>3712.0</td>\n",
              "      <td>1424.0</td>\n",
              "      <td>0.383621</td>\n",
              "      <td>5509.0</td>\n",
              "      <td>1.484106</td>\n",
              "      <td>24.0</td>\n",
              "      <td>4807.0</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>0.414188</td>\n",
              "      <td>10489.0</td>\n",
              "      <td>2.182026</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3703.0</td>\n",
              "      <td>2625.0</td>\n",
              "      <td>0.708885</td>\n",
              "      <td>7594.0</td>\n",
              "      <td>2.050770</td>\n",
              "      <td>30.0</td>\n",
              "      <td>6287.0</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>0.184508</td>\n",
              "      <td>3562.0</td>\n",
              "      <td>0.566566</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1648.0</td>\n",
              "      <td>473.0</td>\n",
              "      <td>0.287015</td>\n",
              "      <td>3207.0</td>\n",
              "      <td>1.945995</td>\n",
              "      <td>28.0</td>\n",
              "      <td>3220.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  WellID  ...  SixthMonthDaysOn  TotalGasInNext6Months\n",
              "0           1     562  ...              31.0                15321.0\n",
              "1           2      16  ...              30.0                61855.0\n",
              "2           5    5695  ...               5.0                12371.0\n",
              "3           6    6185  ...              31.0                23574.0\n",
              "4           7    7919  ...              28.0                 3220.0\n",
              "\n",
              "[5 rows x 39 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PjGp4dasars"
      },
      "source": [
        "# # ids = test[\"WellID\"]\n",
        "# # test.drop([\"WellID\",\"Unnamed: 0\"],axis=1,inplace=True)\n",
        "# # preds = catress.predict(test)\n",
        "# sub = {\"WellID\" : ids , \"TotalOilInNext6Months\" : (preds1-y.mean()*0.05)}\n",
        "# sub = pd.DataFrame(sub)\n",
        "# sub.to_csv(\"basecatboost.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5SuxLvVDPih"
      },
      "source": [
        "# cat4 =  CatBoostRegressor(random_state=63,learning_rate=0.03078496171814678,subsample = 0.5066067900567058,iterations=2500,max_depth=int(round(5.941162788606626)),l2_leaf_reg =0.3240391456470025,colsample_bylevel=0.5063514233486307,eval_metric=\"MAE\",early_stopping_rounds=500)\n",
        "# # cat.fit(xt,yt,eval_set=Pool(xp,yp),verbose=500)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTd-wctvEs0v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "03de3de3-df0e-4fc0-b8cd-49b0a4830790"
      },
      "source": [
        "def Sort_Tuple(tup):      \n",
        "    lst = len(tup)  \n",
        "    for i in range(0, lst):  \n",
        "          \n",
        "        for j in range(0, lst-i-1):  \n",
        "            if (tup[j][1] < tup[j + 1][1]):  \n",
        "                temp = tup[j]  \n",
        "                tup[j]= tup[j + 1]  \n",
        "                tup[j + 1]= temp  \n",
        "    return tup \n",
        "    \n",
        "feature_importances =cat.get_feature_importance()\n",
        "feature_importances = [(xt.columns[i],feature_importances[i])  for  i in range(len(feature_importances)) ]\n",
        "# importances = cat.get_feature_importance()\n",
        " \n",
        "feature_importances = Sort_Tuple(feature_importances)\n",
        "df = pd.DataFrame(feature_importances, columns=['feature', 'importance'])\n",
        "df.plot(kind='bar', x='feature',figsize=(10,10))\n",
        "targetted= []\n",
        "for i in range(25):\n",
        "    targetted.append(feature_importances[i][0])\n",
        "    \n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}