pesticide.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "pesticide",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "35nsZ7vn_ASi",
        "outputId": "2243bdac-e80e-4170-f903-52dc63cde2ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "!pip install category_encoders"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting category_encoders\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/44/57/fcef41c248701ee62e8325026b90c432adea35555cbc870aff9cfba23727/category_encoders-2.2.2-py2.py3-none-any.whl (80kB)\n",
            "\r\u001b[K     |████                            | 10kB 13.3MB/s eta 0:00:01\r\u001b[K     |████████▏                       | 20kB 1.7MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 30kB 2.0MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 40kB 2.4MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 51kB 2.0MB/s eta 0:00:01\r\u001b[K     |████████████████████████▍       | 61kB 2.2MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▍   | 71kB 2.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 81kB 2.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.5.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.22.2.post1)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.1.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.18.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20.0->category_encoders) (0.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
            "Installing collected packages: category-encoders\n",
            "Successfully installed category-encoders-2.2.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a--pY0IG2t_",
        "outputId": "a6ca3c2c-6995-4f1c-e479-35f47a7e916b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "!pip install imblearn"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.6/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.6/dist-packages (from imblearn) (0.4.3)\n",
            "Requirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn->imblearn) (1.18.5)\n",
            "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn->imblearn) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20->imbalanced-learn->imblearn) (0.16.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yxXhJ4pjZ2O",
        "outputId": "e3cdac35-b0fd-47ad-e1eb-832678af4d8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.ensemble import RUSBoostClassifier\n",
        "from lightgbm import plot_importance\n",
        "from lightgbm.sklearn import LGBMClassifier\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold, train_test_split\n",
        "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier \n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.feature_selection import SelectFromModel, RFECV\n",
        "from sklearn.inspection import permutation_importance\n",
        "from sklearn.metrics import (plot_confusion_matrix, \n",
        "                             accuracy_score, \n",
        "                             balanced_accuracy_score, \n",
        "                             log_loss, \n",
        "                             precision_score,\n",
        "                             recall_score, \n",
        "                             f1_score)\n",
        "from hyperopt import fmin, hp, Trials, tpe\n",
        "from functools import partial\n",
        "from category_encoders import LeaveOneOutEncoder, TargetEncoder\n",
        "from category_encoders.wrapper import PolynomialWrapper\n",
        "from collections import Counter\n",
        "import seaborn as sns"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.bagging module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbLvJiFrj2To",
        "outputId": "8dec9262-c500-4d50-f7d6-53ab9da2fd77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "df_trn = pd.read_csv('/content/train_yaOffsB.csv')\n",
        "df_tst = pd.read_csv('/content/test_pFkWwen.csv')\n",
        "df_sub = pd.read_csv('/content/sample_submission_O1oDc4H.csv')\n",
        "\n",
        "df_trn.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>88858.000000</td>\n",
              "      <td>88858.000000</td>\n",
              "      <td>88858.000000</td>\n",
              "      <td>88858.000000</td>\n",
              "      <td>88858.000000</td>\n",
              "      <td>79858.000000</td>\n",
              "      <td>88858.000000</td>\n",
              "      <td>88858.000000</td>\n",
              "      <td>88858.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1399.012210</td>\n",
              "      <td>0.284375</td>\n",
              "      <td>0.458417</td>\n",
              "      <td>2.264186</td>\n",
              "      <td>25.849952</td>\n",
              "      <td>28.623970</td>\n",
              "      <td>9.589986</td>\n",
              "      <td>1.896959</td>\n",
              "      <td>0.190562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>849.048781</td>\n",
              "      <td>0.451119</td>\n",
              "      <td>0.498271</td>\n",
              "      <td>0.461772</td>\n",
              "      <td>15.554428</td>\n",
              "      <td>12.391881</td>\n",
              "      <td>9.900631</td>\n",
              "      <td>0.701322</td>\n",
              "      <td>0.454215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1212.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1898.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4097.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Estimated_Insects_Count     Crop_Type  ...        Season   Crop_Damage\n",
              "count             88858.000000  88858.000000  ...  88858.000000  88858.000000\n",
              "mean               1399.012210      0.284375  ...      1.896959      0.190562\n",
              "std                 849.048781      0.451119  ...      0.701322      0.454215\n",
              "min                 150.000000      0.000000  ...      1.000000      0.000000\n",
              "25%                 731.000000      0.000000  ...      1.000000      0.000000\n",
              "50%                1212.000000      0.000000  ...      2.000000      0.000000\n",
              "75%                1898.000000      1.000000  ...      2.000000      0.000000\n",
              "max                4097.000000      1.000000  ...      3.000000      2.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYRPVMFwrfVS"
      },
      "source": [
        "df_trn = df_trn[~((df_trn['Crop_Damage'] == 2) & (df_trn['Pesticide_Use_Category'] == 1))]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-qoKzuQctK3",
        "outputId": "6f0e2b38-9339-4540-8a09-d345b1f4ce9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "plt.figure(figsize=(13,10))\n",
        "pd.plotting.radviz(df_trn.select_dtypes('number'), 'Crop_Damage', color=('yellow', 'gray', 'red'))\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltjC-qATi_pX",
        "outputId": "a036c27c-2f93-4392-cd59-ca73a222004e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "df_trn['Test'] = 0\n",
        "df_tst['Test'] = 1\n",
        "\n",
        "df_trn.head()"
      ],
      "execution_count": 7,
      "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>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>Test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F00000001</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000003</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F00000004</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000005</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F00000006</td>\n",
              "      <td>342</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ID  Estimated_Insects_Count  Crop_Type  ...  Season  Crop_Damage  Test\n",
              "0  F00000001                      188          1  ...       1            0     0\n",
              "1  F00000003                      209          1  ...       2            1     0\n",
              "2  F00000004                      257          1  ...       2            1     0\n",
              "3  F00000005                      257          1  ...       2            1     0\n",
              "4  F00000006                      342          1  ...       2            1     0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6qWfrCJjSpF",
        "outputId": "b6b6d553-e61d-4e00-ddfb-41531119578f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "df_tst.head()"
      ],
      "execution_count": 8,
      "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>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F00000002</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000007</td>\n",
              "      <td>410</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F00000011</td>\n",
              "      <td>626</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000013</td>\n",
              "      <td>731</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F00000014</td>\n",
              "      <td>789</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ID  Estimated_Insects_Count  ...  Season  Test\n",
              "0  F00000002                      188  ...       2     1\n",
              "1  F00000007                      410  ...       2     1\n",
              "2  F00000011                      626  ...       2     1\n",
              "3  F00000013                      731  ...       2     1\n",
              "4  F00000014                      789  ...       1     1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeXb3fBliSVc"
      },
      "source": [
        "#oversample = RandomOverSampler(random_state=3)\n",
        "#df_trn = oversample.fit_resample(df_trn, df_trn['Crop_Damage'])\n",
        "#df_trn.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3RcZxHvi1Bx"
      },
      "source": [
        "df = pd.concat([df_trn, df_tst], axis=0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4neOijo_dQXa"
      },
      "source": [
        "#df.sort_values('Estimated_Insects_Count', inplace=True, ignore_index=True)\n",
        "#\n",
        "#for row in df[['Estimated_Insects_Count']].itertuples():\n",
        "#    if row[0] == 0:\n",
        "#        df.loc[row[0], 'Insect_Group'] = row[1]\n",
        "#        pre = row[1]\n",
        "#    \n",
        "#    else:\n",
        "#        if row[1]-pre == 1:\n",
        "#            df.loc[row[0], 'Insect_Group'] = pre\n",
        "#            \n",
        "#        \n",
        "#        else:\n",
        "#            df.loc[row[0], 'Insect_Group'] = row[1]\n",
        "#            pre = row[1]\n",
        "#"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkiWRD9evre8",
        "outputId": "e2ca7a30-f545-4d23-ac8c-3e3eb7ba731c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#df['Insect_Group'] = df['Insect_Group'].astype(int)\n",
        "#df['Insect_Group'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dT89nLm-kUpj"
      },
      "source": [
        "df.sort_values('ID', ignore_index=True, inplace=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hYI5Tr57OsP"
      },
      "source": [
        "#group = 0\n",
        "#\n",
        "#for i in df.index:\n",
        "#    if i == 0:\n",
        "#        df.loc[i,'Group'] = 0\n",
        "#\n",
        "#    else:\n",
        "#        if df.loc[i,'Estimated_Insects_Count'] - df.loc[i-1,'Estimated_Insects_Count'] >= -1\n",
        "#            df.loc[i,'Group'] = group\n",
        "#            \n",
        "#        else:\n",
        "#            group += 1\n",
        "#            df.loc[i,'Group'] = group"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfdGKC6KwLjL",
        "outputId": "ad492c75-6352-43c6-aba6-8f5d2dd39b1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 11,
      "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>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>Test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>148003.000000</td>\n",
              "      <td>148003.000000</td>\n",
              "      <td>148003.000000</td>\n",
              "      <td>148003.000000</td>\n",
              "      <td>148003.000000</td>\n",
              "      <td>133137.000000</td>\n",
              "      <td>148003.000000</td>\n",
              "      <td>148003.000000</td>\n",
              "      <td>88693.000000</td>\n",
              "      <td>148003.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1397.498017</td>\n",
              "      <td>0.285021</td>\n",
              "      <td>0.457072</td>\n",
              "      <td>2.265211</td>\n",
              "      <td>25.879543</td>\n",
              "      <td>28.686188</td>\n",
              "      <td>9.575988</td>\n",
              "      <td>1.898502</td>\n",
              "      <td>0.187196</td>\n",
              "      <td>0.400735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>848.769816</td>\n",
              "      <td>0.451426</td>\n",
              "      <td>0.498155</td>\n",
              "      <td>0.460079</td>\n",
              "      <td>15.510034</td>\n",
              "      <td>12.375712</td>\n",
              "      <td>9.882882</td>\n",
              "      <td>0.701583</td>\n",
              "      <td>0.447876</td>\n",
              "      <td>0.490049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1212.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1898.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4097.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Estimated_Insects_Count      Crop_Type  ...   Crop_Damage           Test\n",
              "count            148003.000000  148003.000000  ...  88693.000000  148003.000000\n",
              "mean               1397.498017       0.285021  ...      0.187196       0.400735\n",
              "std                 848.769816       0.451426  ...      0.447876       0.490049\n",
              "min                 150.000000       0.000000  ...      0.000000       0.000000\n",
              "25%                 731.000000       0.000000  ...      0.000000       0.000000\n",
              "50%                1212.000000       0.000000  ...      0.000000       0.000000\n",
              "75%                1898.000000       1.000000  ...      0.000000       1.000000\n",
              "max                4097.000000       1.000000  ...      2.000000       1.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-sGuhdOXa8T"
      },
      "source": [
        "cat_feat = ['Crop_Type', \n",
        "            'Soil_Type', \n",
        "            'Pesticide_Use_Category', \n",
        "            'Season',\n",
        "            ]\n",
        "\n",
        "df[cat_feat] = df[cat_feat].astype('str')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9S8PmspAWHF6"
      },
      "source": [
        "df['CSPS'] = (df['Crop_Type']+\n",
        "              df['Soil_Type']+\n",
        "              df['Pesticide_Use_Category']+\n",
        "              df['Season'])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-icjP7Dcmd7"
      },
      "source": [
        "df['Doses_Ratio'] = df['Number_Doses_Week'] / df['Estimated_Insects_Count']\n",
        "df['ToT_Doses'] = df['Number_Doses_Week'] * df['Number_Weeks_Used']"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vPsyYg_r78k"
      },
      "source": [
        "df.drop(cat_feat, axis=1, inplace=True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PDReVQmcmWe",
        "outputId": "bd70ac57-e9d0-405d-ebbe-9ec7212758fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        }
      },
      "source": [
        "features = list(df.columns)\n",
        "features.remove('ID')\n",
        "features.remove('Test')\n",
        "features"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Estimated_Insects_Count',\n",
              " 'Number_Doses_Week',\n",
              " 'Number_Weeks_Used',\n",
              " 'Number_Weeks_Quit',\n",
              " 'Crop_Damage',\n",
              " 'CSPS',\n",
              " 'Doses_Ratio',\n",
              " 'ToT_Doses']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IwMuijwcmT6"
      },
      "source": [
        "for col in features:\n",
        "    for i in [-2, -1, 1, 2]:\n",
        "        df[col+'('+str(-i)+')'] = df[col].shift(periods=i)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIY9IIvM6xjz",
        "outputId": "980f4ecd-d1bd-4b88-992e-b8f22d774b29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Estimated_Insects_Count', 'Number_Doses_Week',\n",
              "       'Number_Weeks_Used', 'Number_Weeks_Quit', 'Crop_Damage', 'Test', 'CSPS',\n",
              "       'Doses_Ratio', 'ToT_Doses', 'Estimated_Insects_Count(2)',\n",
              "       'Estimated_Insects_Count(1)', 'Estimated_Insects_Count(-1)',\n",
              "       'Estimated_Insects_Count(-2)', 'Number_Doses_Week(2)',\n",
              "       'Number_Doses_Week(1)', 'Number_Doses_Week(-1)',\n",
              "       'Number_Doses_Week(-2)', 'Number_Weeks_Used(2)', 'Number_Weeks_Used(1)',\n",
              "       'Number_Weeks_Used(-1)', 'Number_Weeks_Used(-2)',\n",
              "       'Number_Weeks_Quit(2)', 'Number_Weeks_Quit(1)', 'Number_Weeks_Quit(-1)',\n",
              "       'Number_Weeks_Quit(-2)', 'Crop_Damage(2)', 'Crop_Damage(1)',\n",
              "       'Crop_Damage(-1)', 'Crop_Damage(-2)', 'CSPS(2)', 'CSPS(1)', 'CSPS(-1)',\n",
              "       'CSPS(-2)', 'Doses_Ratio(2)', 'Doses_Ratio(1)', 'Doses_Ratio(-1)',\n",
              "       'Doses_Ratio(-2)', 'ToT_Doses(2)', 'ToT_Doses(1)', 'ToT_Doses(-1)',\n",
              "       'ToT_Doses(-2)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Pr01rZX0wph"
      },
      "source": [
        "df['Diff_Bck_Estimated_Insects_Count'] = df['Estimated_Insects_Count'] - df['Estimated_Insects_Count(-1)']\n",
        "df['Diff_Fwd_Estimated_Insect_Counts'] = df['Estimated_Insects_Count'] - df['Estimated_Insects_Count(1)']\n",
        "df['Diff_1_2_Estimated_Insect_Counts'] = df['Estimated_Insects_Count(-1)'] - df['Estimated_Insects_Count(-2)']\n",
        "df['Diff_2_1_Estimated_Insect_Counts'] = df['Estimated_Insects_Count(1)'] - df['Estimated_Insects_Count(2)']\n",
        "\n",
        "df['Diff_Bck_Doses_Ratio'] = df['Doses_Ratio'] - df['Doses_Ratio(-1)']\n",
        "df['Diff_Fwd_Doses_Ratio'] = df['Doses_Ratio'] - df['Doses_Ratio(1)']\n",
        "df['Diff_1_2_Doses_Ratio'] = df['Doses_Ratio(-1)'] - df['Doses_Ratio(-2)']\n",
        "df['Diff_2_1_Doses_Ratio'] = df['Doses_Ratio(1)'] - df['Doses_Ratio(2)']\n",
        "\n",
        "df['Diff_Bck_ToT_Doses'] = df['ToT_Doses'] - df['ToT_Doses(-1)']\n",
        "df['Diff_Fwd_ToT_Doses'] = df['ToT_Doses'] - df['ToT_Doses(1)']\n",
        "df['Diff_1_2_ToT_Doses'] = df['ToT_Doses(-1)'] - df['ToT_Doses(-2)']\n",
        "df['Diff_2_1_ToT_Doses'] = df['ToT_Doses(1)'] - df['ToT_Doses(2)']\n",
        "\n",
        "df['Diff_Bck_Number_Doses_Week'] = df['Number_Doses_Week'] - df['Number_Doses_Week(-1)']\n",
        "df['Diff_Fwd_Number_Doses_Week'] = df['Number_Doses_Week'] - df['Number_Doses_Week(1)']\n",
        "df['Diff_1_2_Number_Doses_Week'] = df['Number_Doses_Week(-1)'] - df['Number_Doses_Week(-2)']\n",
        "df['Diff_2_1_Number_Doses_Week'] = df['Number_Doses_Week(1)'] - df['Number_Doses_Week(2)']"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0e7F8Rh-40qh",
        "outputId": "53d3b9ed-d2ad-4efc-fdef-0ff5c07eaea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 148003 entries, 0 to 148002\n",
            "Data columns (total 58 columns):\n",
            " #   Column                            Non-Null Count   Dtype  \n",
            "---  ------                            --------------   -----  \n",
            " 0   ID                                148003 non-null  object \n",
            " 1   Estimated_Insects_Count           148003 non-null  int64  \n",
            " 2   Number_Doses_Week                 148003 non-null  int64  \n",
            " 3   Number_Weeks_Used                 133137 non-null  float64\n",
            " 4   Number_Weeks_Quit                 148003 non-null  int64  \n",
            " 5   Crop_Damage                       88693 non-null   float64\n",
            " 6   Test                              148003 non-null  int64  \n",
            " 7   CSPS                              148003 non-null  object \n",
            " 8   Doses_Ratio                       148003 non-null  float64\n",
            " 9   ToT_Doses                         133137 non-null  float64\n",
            " 10  Estimated_Insects_Count(2)        148001 non-null  float64\n",
            " 11  Estimated_Insects_Count(1)        148002 non-null  float64\n",
            " 12  Estimated_Insects_Count(-1)       148002 non-null  float64\n",
            " 13  Estimated_Insects_Count(-2)       148001 non-null  float64\n",
            " 14  Number_Doses_Week(2)              148001 non-null  float64\n",
            " 15  Number_Doses_Week(1)              148002 non-null  float64\n",
            " 16  Number_Doses_Week(-1)             148002 non-null  float64\n",
            " 17  Number_Doses_Week(-2)             148001 non-null  float64\n",
            " 18  Number_Weeks_Used(2)              133136 non-null  float64\n",
            " 19  Number_Weeks_Used(1)              133136 non-null  float64\n",
            " 20  Number_Weeks_Used(-1)             133136 non-null  float64\n",
            " 21  Number_Weeks_Used(-2)             133135 non-null  float64\n",
            " 22  Number_Weeks_Quit(2)              148001 non-null  float64\n",
            " 23  Number_Weeks_Quit(1)              148002 non-null  float64\n",
            " 24  Number_Weeks_Quit(-1)             148002 non-null  float64\n",
            " 25  Number_Weeks_Quit(-2)             148001 non-null  float64\n",
            " 26  Crop_Damage(2)                    88692 non-null   float64\n",
            " 27  Crop_Damage(1)                    88692 non-null   float64\n",
            " 28  Crop_Damage(-1)                   88692 non-null   float64\n",
            " 29  Crop_Damage(-2)                   88692 non-null   float64\n",
            " 30  CSPS(2)                           148001 non-null  object \n",
            " 31  CSPS(1)                           148002 non-null  object \n",
            " 32  CSPS(-1)                          148002 non-null  object \n",
            " 33  CSPS(-2)                          148001 non-null  object \n",
            " 34  Doses_Ratio(2)                    148001 non-null  float64\n",
            " 35  Doses_Ratio(1)                    148002 non-null  float64\n",
            " 36  Doses_Ratio(-1)                   148002 non-null  float64\n",
            " 37  Doses_Ratio(-2)                   148001 non-null  float64\n",
            " 38  ToT_Doses(2)                      133136 non-null  float64\n",
            " 39  ToT_Doses(1)                      133136 non-null  float64\n",
            " 40  ToT_Doses(-1)                     133136 non-null  float64\n",
            " 41  ToT_Doses(-2)                     133135 non-null  float64\n",
            " 42  Diff_Bck_Estimated_Insects_Count  148002 non-null  float64\n",
            " 43  Diff_Fwd_Estimated_Insect_Counts  148002 non-null  float64\n",
            " 44  Diff_1_2_Estimated_Insect_Counts  148001 non-null  float64\n",
            " 45  Diff_2_1_Estimated_Insect_Counts  148001 non-null  float64\n",
            " 46  Diff_Bck_Doses_Ratio              148002 non-null  float64\n",
            " 47  Diff_Fwd_Doses_Ratio              148002 non-null  float64\n",
            " 48  Diff_1_2_Doses_Ratio              148001 non-null  float64\n",
            " 49  Diff_2_1_Doses_Ratio              148001 non-null  float64\n",
            " 50  Diff_Bck_ToT_Doses                119747 non-null  float64\n",
            " 51  Diff_Fwd_ToT_Doses                119747 non-null  float64\n",
            " 52  Diff_1_2_ToT_Doses                119746 non-null  float64\n",
            " 53  Diff_2_1_ToT_Doses                119747 non-null  float64\n",
            " 54  Diff_Bck_Number_Doses_Week        148002 non-null  float64\n",
            " 55  Diff_Fwd_Number_Doses_Week        148002 non-null  float64\n",
            " 56  Diff_1_2_Number_Doses_Week        148001 non-null  float64\n",
            " 57  Diff_2_1_Number_Doses_Week        148001 non-null  float64\n",
            "dtypes: float64(48), int64(4), object(6)\n",
            "memory usage: 65.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8txRFevTWHAJ"
      },
      "source": [
        "train = df[df['Test'] == 0]\n",
        "test = df[df['Test'] == 1]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEqHCuyNWG8x",
        "outputId": "a0bd4b9b-38dc-4c5b-c2c4-f0200f580afe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "cat_feat = ['CSPS',\n",
        "            'CSPS(2)', \n",
        "            'CSPS(1)', \n",
        "            'CSPS(-2)', \n",
        "            'CSPS(-1)',\n",
        "            'Crop_Damage(-1)',\n",
        "            'Crop_Damage(-2)',\n",
        "            'Crop_Damage(1)',\n",
        "            'Crop_Damage(2)'\n",
        "            ]\n",
        "\n",
        "cat_en = LeaveOneOutEncoder(cols=cat_feat, sigma=0.05, handle_missing='value')\n",
        "cat_poly = PolynomialWrapper(cat_en)\n",
        "\n",
        "train_en = cat_poly.fit_transform(train, train['Crop_Damage'])\n",
        "test_en  = cat_poly.transform(test)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n",
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYCMGi1WcF85",
        "outputId": "9ceb94d0-b0db-4a47-d7d6-330b90081f20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train_en.shape, test_en.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((88693, 67), (59310, 67))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuPN1b3DcmhC"
      },
      "source": [
        "df = pd.concat([train_en, test_en], axis=0)\n",
        "df.sort_values('ID', ignore_index=True, inplace=True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJ9QpETanEvj",
        "outputId": "70fffbd9-d9c2-4b8c-c44f-c7ab13f13025",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 148003 entries, 0 to 148002\n",
            "Data columns (total 67 columns):\n",
            " #   Column                            Non-Null Count   Dtype  \n",
            "---  ------                            --------------   -----  \n",
            " 0   ID                                148003 non-null  object \n",
            " 1   Estimated_Insects_Count           148003 non-null  int64  \n",
            " 2   Number_Doses_Week                 148003 non-null  int64  \n",
            " 3   Number_Weeks_Used                 133137 non-null  float64\n",
            " 4   Number_Weeks_Quit                 148003 non-null  int64  \n",
            " 5   Crop_Damage                       88693 non-null   float64\n",
            " 6   Test                              148003 non-null  int64  \n",
            " 7   Doses_Ratio                       148003 non-null  float64\n",
            " 8   ToT_Doses                         133137 non-null  float64\n",
            " 9   Estimated_Insects_Count(2)        148001 non-null  float64\n",
            " 10  Estimated_Insects_Count(1)        148002 non-null  float64\n",
            " 11  Estimated_Insects_Count(-1)       148002 non-null  float64\n",
            " 12  Estimated_Insects_Count(-2)       148001 non-null  float64\n",
            " 13  Number_Doses_Week(2)              148001 non-null  float64\n",
            " 14  Number_Doses_Week(1)              148002 non-null  float64\n",
            " 15  Number_Doses_Week(-1)             148002 non-null  float64\n",
            " 16  Number_Doses_Week(-2)             148001 non-null  float64\n",
            " 17  Number_Weeks_Used(2)              133136 non-null  float64\n",
            " 18  Number_Weeks_Used(1)              133136 non-null  float64\n",
            " 19  Number_Weeks_Used(-1)             133136 non-null  float64\n",
            " 20  Number_Weeks_Used(-2)             133135 non-null  float64\n",
            " 21  Number_Weeks_Quit(2)              148001 non-null  float64\n",
            " 22  Number_Weeks_Quit(1)              148002 non-null  float64\n",
            " 23  Number_Weeks_Quit(-1)             148002 non-null  float64\n",
            " 24  Number_Weeks_Quit(-2)             148001 non-null  float64\n",
            " 25  Doses_Ratio(2)                    148001 non-null  float64\n",
            " 26  Doses_Ratio(1)                    148002 non-null  float64\n",
            " 27  Doses_Ratio(-1)                   148002 non-null  float64\n",
            " 28  Doses_Ratio(-2)                   148001 non-null  float64\n",
            " 29  ToT_Doses(2)                      133136 non-null  float64\n",
            " 30  ToT_Doses(1)                      133136 non-null  float64\n",
            " 31  ToT_Doses(-1)                     133136 non-null  float64\n",
            " 32  ToT_Doses(-2)                     133135 non-null  float64\n",
            " 33  Diff_Bck_Estimated_Insects_Count  148002 non-null  float64\n",
            " 34  Diff_Fwd_Estimated_Insect_Counts  148002 non-null  float64\n",
            " 35  Diff_1_2_Estimated_Insect_Counts  148001 non-null  float64\n",
            " 36  Diff_2_1_Estimated_Insect_Counts  148001 non-null  float64\n",
            " 37  Diff_Bck_Doses_Ratio              148002 non-null  float64\n",
            " 38  Diff_Fwd_Doses_Ratio              148002 non-null  float64\n",
            " 39  Diff_1_2_Doses_Ratio              148001 non-null  float64\n",
            " 40  Diff_2_1_Doses_Ratio              148001 non-null  float64\n",
            " 41  Diff_Bck_ToT_Doses                119747 non-null  float64\n",
            " 42  Diff_Fwd_ToT_Doses                119747 non-null  float64\n",
            " 43  Diff_1_2_ToT_Doses                119746 non-null  float64\n",
            " 44  Diff_2_1_ToT_Doses                119747 non-null  float64\n",
            " 45  Diff_Bck_Number_Doses_Week        148002 non-null  float64\n",
            " 46  Diff_Fwd_Number_Doses_Week        148002 non-null  float64\n",
            " 47  Diff_1_2_Number_Doses_Week        148001 non-null  float64\n",
            " 48  Diff_2_1_Number_Doses_Week        148001 non-null  float64\n",
            " 49  CSPS_1.0                          148003 non-null  float64\n",
            " 50  CSPS(2)_1.0                       148003 non-null  float64\n",
            " 51  CSPS(1)_1.0                       148003 non-null  float64\n",
            " 52  CSPS(-2)_1.0                      148003 non-null  float64\n",
            " 53  CSPS(-1)_1.0                      148003 non-null  float64\n",
            " 54  Crop_Damage(-1)_1.0               148003 non-null  float64\n",
            " 55  Crop_Damage(-2)_1.0               148003 non-null  float64\n",
            " 56  Crop_Damage(1)_1.0                148003 non-null  float64\n",
            " 57  Crop_Damage(2)_1.0                148003 non-null  float64\n",
            " 58  CSPS_2.0                          148003 non-null  float64\n",
            " 59  CSPS(2)_2.0                       148003 non-null  float64\n",
            " 60  CSPS(1)_2.0                       148003 non-null  float64\n",
            " 61  CSPS(-2)_2.0                      148003 non-null  float64\n",
            " 62  CSPS(-1)_2.0                      148003 non-null  float64\n",
            " 63  Crop_Damage(-1)_2.0               148003 non-null  float64\n",
            " 64  Crop_Damage(-2)_2.0               148003 non-null  float64\n",
            " 65  Crop_Damage(1)_2.0                148003 non-null  float64\n",
            " 66  Crop_Damage(2)_2.0                148003 non-null  float64\n",
            "dtypes: float64(62), int64(4), object(1)\n",
            "memory usage: 75.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVIDZfjf1ObZ",
        "outputId": "f0c966d9-ff09-4d65-eff7-1b85c2d46caa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "features = list(df.columns)\n",
        "features.remove('Crop_Damage')\n",
        "features.remove('ID')\n",
        "features.remove('Test')\n",
        "features"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Estimated_Insects_Count',\n",
              " 'Number_Doses_Week',\n",
              " 'Number_Weeks_Used',\n",
              " 'Number_Weeks_Quit',\n",
              " 'Doses_Ratio',\n",
              " 'ToT_Doses',\n",
              " 'Estimated_Insects_Count(2)',\n",
              " 'Estimated_Insects_Count(1)',\n",
              " 'Estimated_Insects_Count(-1)',\n",
              " 'Estimated_Insects_Count(-2)',\n",
              " 'Number_Doses_Week(2)',\n",
              " 'Number_Doses_Week(1)',\n",
              " 'Number_Doses_Week(-1)',\n",
              " 'Number_Doses_Week(-2)',\n",
              " 'Number_Weeks_Used(2)',\n",
              " 'Number_Weeks_Used(1)',\n",
              " 'Number_Weeks_Used(-1)',\n",
              " 'Number_Weeks_Used(-2)',\n",
              " 'Number_Weeks_Quit(2)',\n",
              " 'Number_Weeks_Quit(1)',\n",
              " 'Number_Weeks_Quit(-1)',\n",
              " 'Number_Weeks_Quit(-2)',\n",
              " 'Doses_Ratio(2)',\n",
              " 'Doses_Ratio(1)',\n",
              " 'Doses_Ratio(-1)',\n",
              " 'Doses_Ratio(-2)',\n",
              " 'ToT_Doses(2)',\n",
              " 'ToT_Doses(1)',\n",
              " 'ToT_Doses(-1)',\n",
              " 'ToT_Doses(-2)',\n",
              " 'Diff_Bck_Estimated_Insects_Count',\n",
              " 'Diff_Fwd_Estimated_Insect_Counts',\n",
              " 'Diff_1_2_Estimated_Insect_Counts',\n",
              " 'Diff_2_1_Estimated_Insect_Counts',\n",
              " 'Diff_Bck_Doses_Ratio',\n",
              " 'Diff_Fwd_Doses_Ratio',\n",
              " 'Diff_1_2_Doses_Ratio',\n",
              " 'Diff_2_1_Doses_Ratio',\n",
              " 'Diff_Bck_ToT_Doses',\n",
              " 'Diff_Fwd_ToT_Doses',\n",
              " 'Diff_1_2_ToT_Doses',\n",
              " 'Diff_2_1_ToT_Doses',\n",
              " 'Diff_Bck_Number_Doses_Week',\n",
              " 'Diff_Fwd_Number_Doses_Week',\n",
              " 'Diff_1_2_Number_Doses_Week',\n",
              " 'Diff_2_1_Number_Doses_Week',\n",
              " 'CSPS_1.0',\n",
              " 'CSPS(2)_1.0',\n",
              " 'CSPS(1)_1.0',\n",
              " 'CSPS(-2)_1.0',\n",
              " 'CSPS(-1)_1.0',\n",
              " 'Crop_Damage(-1)_1.0',\n",
              " 'Crop_Damage(-2)_1.0',\n",
              " 'Crop_Damage(1)_1.0',\n",
              " 'Crop_Damage(2)_1.0',\n",
              " 'CSPS_2.0',\n",
              " 'CSPS(2)_2.0',\n",
              " 'CSPS(1)_2.0',\n",
              " 'CSPS(-2)_2.0',\n",
              " 'CSPS(-1)_2.0',\n",
              " 'Crop_Damage(-1)_2.0',\n",
              " 'Crop_Damage(-2)_2.0',\n",
              " 'Crop_Damage(1)_2.0',\n",
              " 'Crop_Damage(2)_2.0']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULXvZCrfEDt9"
      },
      "source": [
        "trn = df[df['Test'] == 0][features]\n",
        "lbl = df[df['Test'] == 0]['Crop_Damage']\n",
        "tst = df[df['Test'] == 1][features]"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yxZ4z-fTZB_"
      },
      "source": [
        "trn.fillna(-999, inplace=True)\n",
        "tst.fillna(-999, inplace=True)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEbzXUXzOunO"
      },
      "source": [
        "model = DecisionTreeClassifier().fit(trn, lbl)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkT4yq4tS8Rf",
        "outputId": "3465001f-9b69-472f-ff07-5a133108a771",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "permuter = permutation_importance(model, trn, lbl, n_jobs=-1, random_state=3)\n",
        "\n",
        "feat_importance = pd.DataFrame({'features':trn.columns, 'importance': permuter.importances_mean})\n",
        "feat_importance.sort_values(by='importance', ascending=False, inplace=True, ignore_index=True)\n",
        "feat_importance"
      ],
      "execution_count": 35,
      "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>features</th>\n",
              "      <th>importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Crop_Damage(1)_1.0</td>\n",
              "      <td>0.084036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Crop_Damage(-1)_1.0</td>\n",
              "      <td>0.077972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Diff_Bck_Number_Doses_Week</td>\n",
              "      <td>0.074996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Diff_Fwd_Number_Doses_Week</td>\n",
              "      <td>0.069446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Diff_Fwd_Estimated_Insect_Counts</td>\n",
              "      <td>0.044409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Estimated_Insects_Count(-2)</td>\n",
              "      <td>0.001434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Estimated_Insects_Count(-1)</td>\n",
              "      <td>0.001130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Number_Weeks_Quit(2)</td>\n",
              "      <td>0.001067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Number_Weeks_Quit(-1)</td>\n",
              "      <td>0.000965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Number_Doses_Week(2)</td>\n",
              "      <td>0.000503</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                            features  importance\n",
              "0                 Crop_Damage(1)_1.0    0.084036\n",
              "1                Crop_Damage(-1)_1.0    0.077972\n",
              "2         Diff_Bck_Number_Doses_Week    0.074996\n",
              "3         Diff_Fwd_Number_Doses_Week    0.069446\n",
              "4   Diff_Fwd_Estimated_Insect_Counts    0.044409\n",
              "..                               ...         ...\n",
              "59       Estimated_Insects_Count(-2)    0.001434\n",
              "60       Estimated_Insects_Count(-1)    0.001130\n",
              "61              Number_Weeks_Quit(2)    0.001067\n",
              "62             Number_Weeks_Quit(-1)    0.000965\n",
              "63              Number_Doses_Week(2)    0.000503\n",
              "\n",
              "[64 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O-uGufjUUJe",
        "outputId": "019d7661-4fe4-4c16-df78-62931714db75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        }
      },
      "source": [
        "sel = feat_importance[feat_importance['importance'] >= 0.7*feat_importance['importance'].median()]\n",
        "selected = sel['features'].to_list()\n",
        "selected"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Crop_Damage(1)_1.0',\n",
              " 'Crop_Damage(-1)_1.0',\n",
              " 'Diff_Bck_Number_Doses_Week',\n",
              " 'Diff_Fwd_Number_Doses_Week',\n",
              " 'Diff_Fwd_Estimated_Insect_Counts',\n",
              " 'Diff_Bck_Estimated_Insects_Count',\n",
              " 'Diff_Bck_Doses_Ratio',\n",
              " 'CSPS_1.0',\n",
              " 'Crop_Damage(-2)_2.0',\n",
              " 'Number_Doses_Week',\n",
              " 'Doses_Ratio',\n",
              " 'Crop_Damage(-1)_2.0',\n",
              " 'Diff_1_2_ToT_Doses',\n",
              " 'CSPS_2.0',\n",
              " 'CSPS(-2)_1.0',\n",
              " 'Diff_2_1_Number_Doses_Week',\n",
              " 'Diff_1_2_Doses_Ratio',\n",
              " 'Doses_Ratio(1)',\n",
              " 'Diff_Bck_ToT_Doses',\n",
              " 'Doses_Ratio(2)',\n",
              " 'Diff_Fwd_Doses_Ratio',\n",
              " 'Crop_Damage(2)_1.0',\n",
              " 'Doses_Ratio(-1)',\n",
              " 'CSPS(-1)_2.0',\n",
              " 'CSPS(1)_1.0',\n",
              " 'Crop_Damage(-2)_1.0',\n",
              " 'Doses_Ratio(-2)',\n",
              " 'Crop_Damage(1)_2.0',\n",
              " 'CSPS(-1)_1.0',\n",
              " 'CSPS(2)_2.0',\n",
              " 'ToT_Doses',\n",
              " 'Number_Doses_Week(-2)',\n",
              " 'CSPS(1)_2.0',\n",
              " 'Diff_2_1_ToT_Doses',\n",
              " 'Diff_1_2_Number_Doses_Week',\n",
              " 'Diff_2_1_Estimated_Insect_Counts',\n",
              " 'Number_Doses_Week(-1)',\n",
              " 'CSPS(2)_1.0']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdKePznGlcTs",
        "outputId": "694f990a-e3d0-4df2-c3bf-9d39caa1563c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        }
      },
      "source": [
        "#model = LGBMClassifier(subsample=0.5, subsample_freq=1, colsample_by_node=0.5, random_state=3)\n",
        "model = DecisionTreeClassifier(random_state=3)\n",
        "rfecv = RFECV(estimator=model, step=1, cv=StratifiedKFold(5), scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "rfecv.fit(trn, lbl)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RFECV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
              "      estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
              "                                       criterion='gini', max_depth=None,\n",
              "                                       max_features=None, max_leaf_nodes=None,\n",
              "                                       min_impurity_decrease=0.0,\n",
              "                                       min_impurity_split=None,\n",
              "                                       min_samples_leaf=1, min_samples_split=2,\n",
              "                                       min_weight_fraction_leaf=0.0,\n",
              "                                       presort='deprecated', random_state=3,\n",
              "                                       splitter='best'),\n",
              "      min_features_to_select=1, n_jobs=-1, scoring='accuracy', step=1,\n",
              "      verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iobGcqo_ZOLe",
        "outputId": "2a8f4e69-01c1-409a-ceb5-bb08260612c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "selected = rfecv.support_\n",
        "selected = np.array(features)[selected]\n",
        "selected"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Estimated_Insects_Count', 'Number_Doses_Week', 'Doses_Ratio',\n",
              "       'ToT_Doses', 'Number_Weeks_Used(1)', 'Number_Weeks_Used(-1)',\n",
              "       'Doses_Ratio(2)', 'Doses_Ratio(1)', 'Doses_Ratio(-1)',\n",
              "       'Doses_Ratio(-2)', 'Diff_Bck_Estimated_Insects_Count',\n",
              "       'Diff_Fwd_Estimated_Insect_Counts',\n",
              "       'Diff_1_2_Estimated_Insect_Counts',\n",
              "       'Diff_2_1_Estimated_Insect_Counts', 'Diff_Bck_Doses_Ratio',\n",
              "       'Diff_Fwd_Doses_Ratio', 'Diff_1_2_Doses_Ratio',\n",
              "       'Diff_Bck_ToT_Doses', 'Diff_1_2_ToT_Doses', 'Diff_2_1_ToT_Doses',\n",
              "       'Diff_Bck_Number_Doses_Week', 'Diff_Fwd_Number_Doses_Week',\n",
              "       'Diff_2_1_Number_Doses_Week', 'CSPS_1.0', 'CSPS(1)_1.0',\n",
              "       'CSPS(-2)_1.0', 'CSPS(-1)_1.0', 'Crop_Damage(-1)_1.0',\n",
              "       'Crop_Damage(-2)_1.0', 'Crop_Damage(1)_1.0', 'Crop_Damage(2)_1.0',\n",
              "       'CSPS_2.0', 'CSPS(2)_2.0', 'CSPS(1)_2.0', 'CSPS(-2)_2.0',\n",
              "       'CSPS(-1)_2.0', 'Crop_Damage(-1)_2.0', 'Crop_Damage(-2)_2.0',\n",
              "       'Crop_Damage(1)_2.0', 'Crop_Damage(2)_2.0'], dtype='<U32')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ul7P9TxNcmNJ",
        "outputId": "267288e0-af5a-4cda-e80e-5f6988ff698b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_trn, y_trn = trn[selected].to_numpy(), lbl.to_numpy()\n",
        "X_tst = tst[selected].to_numpy()\n",
        "\n",
        "X_trn.shape, y_trn.shape, X_tst.shape"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((88693, 40), (88693,), (59310, 40))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTOFqkz4XGJY"
      },
      "source": [
        "est = LGBMClassifier(boosting_type='gbdt',\n",
        "                     random_state=3,\n",
        "                     subsample_freq=1, \n",
        "                     max_depth=-1, \n",
        "                     min_child_samples=1,\n",
        "                     #class_weight='balanced',\n",
        "                     #device='cpu',\n",
        "                     n_estimators=2500,\n",
        "                     colsample_by_node=0.5,\n",
        "                     subsample=0.5,\n",
        "                     #colsample_by_tree=1/np.sqrt(len(features)),\n",
        "                     #subsample=0.6,\n",
        "                     #class_weight={0: 1, 1: 1, 2: 1}\n",
        "                     )"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfmWfN0bXrLw"
      },
      "source": [
        "space = {'learning_rate': hp.qloguniform('learning_rate', np.log(0.01), np.log(0.3), 0.01),\n",
        "         #'max_depth': hp.quniform('max_depth', 1, 16, 1),\n",
        "         'min_child_weight': hp.quniform('min_child_weight', 0.1, 10, 0.01),\n",
        "         #'min_child_samples': hp.quniform('min_child_samples', 1, 50, 1),\n",
        "         #'n_estimators': hp.quniform('n_estimators', 100, 1000, 10),\n",
        "         'num_leaves': hp.quniform('num_leaves', 2, 256, 2),\n",
        "         'class_weight': hp.choice('class_weight',[None, 'balanced']),\n",
        "         #'subsample': hp.quniform('subsample', 0.3, 1, 0.1),\n",
        "         #'colsample_by_tree': hp.quniform('colsample_by_tree', 0.3, 1, 0.05),\n",
        "         #'colsample_by_node': hp.quniform('colsample_by_node', 0.3, 1, 0.1),\n",
        "         'reg_alpha': hp.quniform('reg_alpha', 0.1, 5, 0.1),\n",
        "         'reg_lambda': hp.quniform('reg_lambda', 0.1, 5, 0.1),\n",
        "         #'min_split_gain': hp.quniform('min_split_gain', 0.01, 2, 0.001)\n",
        "         }"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEC6a3vTXGJx"
      },
      "source": [
        "def evaluate(estimator, params, data, label, cv=True):\n",
        "\n",
        "    params['num_leaves'] = int(params['num_leaves'])\n",
        "    #params['n_estimators'] = int(params['n_estimators'])\n",
        "    #params['min_child_samples'] = int(params['min_child_samples'])\n",
        "    #params['max_depth'] = int(params['max_depth'])\n",
        "\n",
        "    estimator.set_params(**params)\n",
        "\n",
        "    if cv:\n",
        "        cv = StratifiedKFold(n_splits=5)\n",
        "        cv_scores = []\n",
        "\n",
        "        for in_idx, out_idx in cv.split(data, label):\n",
        "            X_in, X_out = data[in_idx], data[out_idx]\n",
        "            y_in, y_out = label[in_idx], label[out_idx]\n",
        "\n",
        "            estimator.fit(X_in, \n",
        "                          y_in, \n",
        "                          eval_set=[(X_out, y_out)],\n",
        "                          early_stopping_rounds=50,\n",
        "                          verbose=False)\n",
        "            \n",
        "            \n",
        "            y_pred = estimator.predict(X_out)\n",
        "            \n",
        "            cv_scores.append(accuracy_score(y_out, y_pred))\n",
        "\n",
        "\n",
        "        \n",
        "        scores = -np.mean(cv_scores)\n",
        "        #scores = (1-np.amin(scores))/np.amax(scores)\n",
        "        #scores = (1-np.mean(scores))/np.amax(scores)\n",
        "\n",
        "    return scores"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6rzFbsuXGJ-"
      },
      "source": [
        "objective = lambda params: evaluate(est, \n",
        "                                    params,\n",
        "                                    X_trn,\n",
        "                                    y_trn,\n",
        "                                    cv=True)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwmqWxb2XGKH"
      },
      "source": [
        "def optimize(n_evals, space, objective):\n",
        "\n",
        "    algo = partial(tpe.suggest, n_startup_jobs=25, n_EI_candidates=24, gamma=0.25)\n",
        "                   \n",
        "    trials = Trials()\n",
        "\n",
        "    best_params = fmin(fn=objective, \n",
        "                       space=space, \n",
        "                       algo=algo, \n",
        "                       max_evals=n_evals, \n",
        "                       trials=trials)\n",
        "    \n",
        "    return best_params"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHWyZUqJZWJ7",
        "outputId": "0ae9830c-7599-451d-97fe-ae0044a4ca5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "best = optimize(100, space, objective)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [10:15:45<00:00, 369.45s/it, best loss: -0.9668519480526413]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6LbtASEZWKS",
        "outputId": "425a79fe-905c-4153-fd6b-20b4dec2361e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "#best['max_depth'] = int(best['max_depth'])\n",
        "#best['n_estimators'] = int(best['n_estimators'])\n",
        "#best['min_child_samples'] = int(best['min_child_samples'])\n",
        "best['class_weight'] = None if best['class_weight'] == 0 else 'balanced'\n",
        "best['num_leaves'] = int(best['num_leaves'])\n",
        "\n",
        "best"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'class_weight': None,\n",
              " 'learning_rate': 0.01,\n",
              " 'min_child_weight': 4.74,\n",
              " 'num_leaves': 20,\n",
              " 'reg_alpha': 1.74,\n",
              " 'reg_lambda': 2.06}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvZaGT7H_9pN"
      },
      "source": [
        "best = {'class_weight': None,\n",
        "        'learning_rate': 0.01,\n",
        "        'min_child_weight': 4.74,\n",
        "        'num_leaves': 20,\n",
        "        'reg_alpha': 1.74,\n",
        "        'reg_lambda': 2.06}"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUquGaiDiKnC"
      },
      "source": [
        "X_train, X_dev, y_train, y_dev = train_test_split(X_trn, \n",
        "                                                  y_trn, \n",
        "                                                  train_size=0.80, \n",
        "                                                  stratify=y_trn, \n",
        "                                                  random_state=3,\n",
        "                                                  )"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWOlZ_I0h8Tq",
        "outputId": "5cbaa8de-5a47-4572-d64d-9422d4af7d31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "est.set_params(**best)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_by_node=0.5,\n",
              "               colsample_bytree=1.0, importance_type='split',\n",
              "               learning_rate=0.01, max_depth=-1, min_child_samples=1,\n",
              "               min_child_weight=4.74, min_split_gain=0.0, n_estimators=2500,\n",
              "               n_jobs=-1, num_leaves=20, objective=None, random_state=3,\n",
              "               reg_alpha=1.74, reg_lambda=2.06, silent=True, subsample=0.5,\n",
              "               subsample_for_bin=200000, subsample_freq=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3VHYHutY3jP"
      },
      "source": [
        "cv = StratifiedKFold(n_splits=5)\n",
        "cv_estimators = []\n",
        "\n",
        "for in_idx, out_idx in cv.split(X_trn, y_trn):\n",
        "    X_in, X_out = X_trn[in_idx], X_trn[out_idx]\n",
        "    y_in, y_out = y_trn[in_idx], y_trn[out_idx]\n",
        "\n",
        "    est.fit(X_in, \n",
        "            y_in, \n",
        "            eval_set=[(X_out, y_out)],\n",
        "            early_stopping_rounds=100,\n",
        "            verbose=False)\n",
        "            \n",
        "    cv_estimators.append(est.best_iteration_)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rgz2iGEtqpU",
        "outputId": "6f6216c3-d588-4aaa-901d-afbf759f837a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "cv_estimators"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2499, 2327, 2500, 2499, 2500]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHNJillrtX-b",
        "outputId": "47bf061d-2c49-46e0-ac3a-f19e3bab836e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "best['n_estimators'] = np.mean(cv_estimators, dtype=np.int16)\n",
        "\n",
        "est.set_params(**best)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_by_node=0.5,\n",
              "               colsample_bytree=1.0, importance_type='split',\n",
              "               learning_rate=0.01, max_depth=-1, min_child_samples=1,\n",
              "               min_child_weight=4.74, min_split_gain=0.0, n_estimators=2465,\n",
              "               n_jobs=-1, num_leaves=20, objective=None, random_state=3,\n",
              "               reg_alpha=1.74, reg_lambda=2.06, silent=True, subsample=0.5,\n",
              "               subsample_for_bin=200000, subsample_freq=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mcrf8C4f1wVm",
        "outputId": "c34ccddb-addb-4f9a-86df-a71b752d03cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "est.fit(X_trn, \n",
        "        y_trn,\n",
        "        feature_name=list(selected)\n",
        "        )"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_by_node=0.5,\n",
              "               colsample_bytree=1.0, importance_type='split',\n",
              "               learning_rate=0.01, max_depth=-1, min_child_samples=1,\n",
              "               min_child_weight=4.74, min_split_gain=0.0, n_estimators=2465,\n",
              "               n_jobs=-1, num_leaves=20, objective=None, random_state=3,\n",
              "               reg_alpha=1.74, reg_lambda=2.06, silent=True, subsample=0.5,\n",
              "               subsample_for_bin=200000, subsample_freq=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbX1BFr_gFXe",
        "outputId": "71d73b8e-7b16-4ae0-bea5-33bed6eebcd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "est.score(X_trn, y_trn)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9833357762168379"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0b5Mzu-Oh8UU",
        "outputId": "2cb40779-d73f-4a9d-ff26-1ea75fb5745e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plot_confusion_matrix(est, X_trn, y_trn, normalize=None)\n",
        "plt.show()"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ymWba07Oq_X",
        "outputId": "901e1661-9d54-4455-d19e-b43891ab54e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "source": [
        "plot_importance(est, figsize=(13,10))\n",
        "plt.show()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjh-PfBYC5QV"
      },
      "source": [
        "predicted = est.predict(X_tst)"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-n5guFfEVO-",
        "outputId": "1649ffbb-798d-413d-d144-31ad6f7c6494",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "Counter(predicted)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0.0: 49878, 1.0: 8426, 2.0: 1006})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zstvZHktcmHc"
      },
      "source": [
        "df_sub['ID'] = df_tst['ID']\n",
        "df_sub['Crop_Damage'] = predicted"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FBBbOwkhms3"
      },
      "source": [
        "df_sub.to_csv('submission_sel.csv', index=False, )"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkDkTfTEC4bW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6RLNbMVqTJS"
      },
      "source": [
        "bag = BaggingClassifier(base_estimator=est, \n",
        "                        max_samples=1.0, \n",
        "                        max_features=1.0,\n",
        "                        n_estimators=10, \n",
        "                        oob_score=False,\n",
        "                        warm_start=True, \n",
        "                        n_jobs=-1,\n",
        "                        bootstrap=True)"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wakRPOKiqTB0",
        "outputId": "051a069c-2ab9-447d-b241-baeea81c2312",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "bag.fit(X_trn, y_trn)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaggingClassifier(base_estimator=LGBMClassifier(boosting_type='gbdt',\n",
              "                                                class_weight=None,\n",
              "                                                colsample_by_node=0.5,\n",
              "                                                colsample_bytree=1.0,\n",
              "                                                importance_type='split',\n",
              "                                                learning_rate=0.01,\n",
              "                                                max_depth=-1,\n",
              "                                                min_child_samples=1,\n",
              "                                                min_child_weight=4.74,\n",
              "                                                min_split_gain=0.0,\n",
              "                                                n_estimators=2000, n_jobs=-1,\n",
              "                                                num_leaves=20, objective=None,\n",
              "                                                random_state=3, reg_alpha=1.74,\n",
              "                                                reg_lambda=2.06, silent=True,\n",
              "                                                subsample=0.5,\n",
              "                                                subsample_for_bin=200000,\n",
              "                                                subsample_freq=1),\n",
              "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
              "                  max_samples=1.0, n_estimators=10, n_jobs=-1, oob_score=False,\n",
              "                  random_state=None, verbose=0, warm_start=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UE3XshWxqS5P",
        "outputId": "fd0f9a8a-c756-4d34-c461-5289f78f6b0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "bag.oob_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9634923135789687"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8ZEh2JlqSv9"
      },
      "source": [
        "bag_prediction = bag.predict(X_tst)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q98FMn0Ahmmp",
        "outputId": "0a66cc6e-5720-4648-a2ee-bf6167e5281c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "Counter(bag_prediction)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0.0: 49905, 1.0: 8470, 2.0: 935})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_5iXO-dyjpO"
      },
      "source": [
        "df_sub['ID'] = df_tst['ID']\n",
        "df_sub['Crop_Damage'] = bag_prediction"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1CW-04jyjpr"
      },
      "source": [
        "df_sub.to_csv('submission_sel_bag.csv', index=False)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6UUoFj_hmiy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxxmY0w8a7qT"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators=200, oob_score=True, n_jobs=-1, random_state=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjxmRN-5a7lm",
        "outputId": "c46e9e25-7941-4197-a50b-5545eca5b6c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        }
      },
      "source": [
        "rf.fit(X_trn, y_trn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=-1, oob_score=True, random_state=3, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JN4zGDcXa7i6",
        "outputId": "65c40b0e-e2ec-419a-f530-6d8074d91920",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "rf.oob_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9607688671813455"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taBl0YSRa7gY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQARFVqGa7dN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vo6XEWp5cl_P"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9EOKBuo1xvS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}