Geocif_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Geocif_Project.ipynb",
      "provenance": [],
      "mount_file_id": "1CCfXQrWlG2HiF15wmPlxM9vRwQO7YRvf",
      "authorship_tag": "ABX9TyMODzzsmkrhok/ixjZIDOmV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Transega/Geocif-Crop-Yield-Prediction-/blob/main/Geocif_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgtZnvPqrJA0"
      },
      "source": [
        "# ** Geocif Training project attempt: Collins Asega** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRADxqlfrPtK"
      },
      "source": [
        "# Install the Libraries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFxESepVW3YK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1d645c02-eb62-495e-9b90-1b13fa7c8b95"
      },
      "source": [
        "!pip install catboost\n",
        "!pip install merf\n",
        "!pip install shap\n",
        "!pip install shapash\n",
        "!pip install tsfresh\n",
        "# !pip install scipy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading locket-0.2.1-py2.py3-none-any.whl (4.1 kB)\n",
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            "Collecting scipy>=1.2.0\n",
            "  Downloading scipy-1.7.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (28.5 MB)\n",
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            "Requirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed>=2.11.0->tsfresh) (1.0.1)\n",
            "Installing collected packages: locket, scipy, protobuf, partd, fsspec, cloudpickle, stumpy, matrixprofile, distributed, tsfresh\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.4.1\n",
            "    Uninstalling scipy-1.4.1:\n",
            "      Successfully uninstalled scipy-1.4.1\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.17.3\n",
            "    Uninstalling protobuf-3.17.3:\n",
            "      Successfully uninstalled protobuf-3.17.3\n",
            "  Attempting uninstall: cloudpickle\n",
            "    Found existing installation: cloudpickle 1.3.0\n",
            "    Uninstalling cloudpickle-1.3.0:\n",
            "      Successfully uninstalled cloudpickle-1.3.0\n",
            "  Attempting uninstall: distributed\n",
            "    Found existing installation: distributed 1.25.3\n",
            "    Uninstalling distributed-1.25.3:\n",
            "      Successfully uninstalled distributed-1.25.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-metadata 1.2.0 requires protobuf<4,>=3.13, but you have protobuf 3.11.2 which is incompatible.\n",
            "gym 0.17.3 requires cloudpickle<1.7.0,>=1.2.0, but you have cloudpickle 2.0.0 which is incompatible.\n",
            "googleapis-common-protos 1.53.0 requires protobuf>=3.12.0, but you have protobuf 3.11.2 which is incompatible.\n",
            "google-api-core 1.26.3 requires protobuf>=3.12.0, but you have protobuf 3.11.2 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed cloudpickle-2.0.0 distributed-2.30.1 fsspec-2021.10.1 locket-0.2.1 matrixprofile-1.1.10 partd-1.2.0 protobuf-3.11.2 scipy-1.7.1 stumpy-1.9.2 tsfresh-0.18.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mxgs1gqkXpYY"
      },
      "source": [
        "import pandas as pd\n",
        "import shap\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHgPeZAbUapM"
      },
      "source": [
        "# !pip install scipy>=1.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yqXtM5KXeku",
        "outputId": "f1edad68-82fd-410e-9b5c-794681500399"
      },
      "source": [
        "from tsfresh.feature_extraction import ComprehensiveFCParameters"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "qJSZ3aiwXjpm",
        "outputId": "c6f0fa0b-0b74-4d6b-8acc-f989d61fb905"
      },
      "source": [
        "# Read in data, compute features, train model\n",
        "data = pd.read_csv('/content/drive/MyDrive/data_training/kenya_maize_s1.csv')\n",
        "\n",
        "# subset to when crop is actively growing i.e. crop_cal != 0\n",
        "data = data[data['crop_cal'] > 0]\n",
        "#  inspect to see the various features available in the data \n",
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>str_ID</th>\n",
              "      <th>Year</th>\n",
              "      <th>JD</th>\n",
              "      <th>crop_cal</th>\n",
              "      <th>yield</th>\n",
              "      <th>production</th>\n",
              "      <th>tot_pix</th>\n",
              "      <th>mean_crop</th>\n",
              "      <th>med_crop</th>\n",
              "      <th>min_crop</th>\n",
              "      <th>max_crop</th>\n",
              "      <th>ndvi</th>\n",
              "      <th>ndvi_tot_pix</th>\n",
              "      <th>ndvi_wa_crop</th>\n",
              "      <th>ndvi_wmed_crop</th>\n",
              "      <th>ndvi_min_crop</th>\n",
              "      <th>ndvi_max_crop</th>\n",
              "      <th>cpc_tmin</th>\n",
              "      <th>cpc_tmin_tot_pix</th>\n",
              "      <th>cpc_tmin_wa_crop</th>\n",
              "      <th>cpc_tmin_wmed_crop</th>\n",
              "      <th>cpc_tmin_min_crop</th>\n",
              "      <th>cpc_tmin_max_crop</th>\n",
              "      <th>cpc_tmax</th>\n",
              "      <th>cpc_tmax_tot_pix</th>\n",
              "      <th>cpc_tmax_wa_crop</th>\n",
              "      <th>cpc_tmax_wmed_crop</th>\n",
              "      <th>cpc_tmax_min_crop</th>\n",
              "      <th>cpc_tmax_max_crop</th>\n",
              "      <th>chirps</th>\n",
              "      <th>chirps_tot_pix</th>\n",
              "      <th>chirps_wa_crop</th>\n",
              "      <th>chirps_wmed_crop</th>\n",
              "      <th>chirps_min_crop</th>\n",
              "      <th>chirps_max_crop</th>\n",
              "      <th>esi_4wk</th>\n",
              "      <th>esi_4wk_tot_pix</th>\n",
              "      <th>esi_4wk_wa_crop</th>\n",
              "      <th>esi_4wk_wmed_crop</th>\n",
              "      <th>esi_4wk_min_crop</th>\n",
              "      <th>esi_4wk_max_crop</th>\n",
              "      <th>soil_moisture_as1</th>\n",
              "      <th>soil_moisture_as1_tot_pix</th>\n",
              "      <th>soil_moisture_as1_wa_crop</th>\n",
              "      <th>soil_moisture_as1_wmed_crop</th>\n",
              "      <th>soil_moisture_as1_min_crop</th>\n",
              "      <th>soil_moisture_as1_max_crop</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Season</th>\n",
              "      <th>perc_season</th>\n",
              "      <th>GDD</th>\n",
              "      <th>cGDD</th>\n",
              "      <th>cNDVI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>36570.000000</td>\n",
              "      <td>3.657000e+04</td>\n",
              "      <td>36570.00000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>21249.000000</td>\n",
              "      <td>21249.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>34553.000000</td>\n",
              "      <td>4172.000000</td>\n",
              "      <td>4172.000000</td>\n",
              "      <td>4172.000000</td>\n",
              "      <td>4172.000000</td>\n",
              "      <td>4172.000000</td>\n",
              "      <td>34590.000000</td>\n",
              "      <td>34590.000000</td>\n",
              "      <td>34590.000000</td>\n",
              "      <td>34590.000000</td>\n",
              "      <td>34590.000000</td>\n",
              "      <td>34590.000000</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34602.000000</td>\n",
              "      <td>25142.000000</td>\n",
              "      <td>25142.000000</td>\n",
              "      <td>25142.000000</td>\n",
              "      <td>25142.000000</td>\n",
              "      <td>25142.000000</td>\n",
              "      <td>32748.000000</td>\n",
              "      <td>4689.00000</td>\n",
              "      <td>4689.000000</td>\n",
              "      <td>4689.000000</td>\n",
              "      <td>4689.000000</td>\n",
              "      <td>4689.000000</td>\n",
              "      <td>18789.000000</td>\n",
              "      <td>6228.000000</td>\n",
              "      <td>6228.000000</td>\n",
              "      <td>6228.000000</td>\n",
              "      <td>6228.000000</td>\n",
              "      <td>6228.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>36570.000000</td>\n",
              "      <td>36570.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34582.000000</td>\n",
              "      <td>34553.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38411.320755</td>\n",
              "      <td>8.900508e+07</td>\n",
              "      <td>2011.00000</td>\n",
              "      <td>183.735849</td>\n",
              "      <td>1.810063</td>\n",
              "      <td>1.618647</td>\n",
              "      <td>1.618647</td>\n",
              "      <td>561.842767</td>\n",
              "      <td>7798.181333</td>\n",
              "      <td>8456.996855</td>\n",
              "      <td>2241.478616</td>\n",
              "      <td>9488.267925</td>\n",
              "      <td>164.580267</td>\n",
              "      <td>458.204219</td>\n",
              "      <td>7867.373699</td>\n",
              "      <td>8452.171860</td>\n",
              "      <td>2571.812320</td>\n",
              "      <td>9371.240412</td>\n",
              "      <td>16.449000</td>\n",
              "      <td>559.414166</td>\n",
              "      <td>7787.347320</td>\n",
              "      <td>8445.029199</td>\n",
              "      <td>2240.296386</td>\n",
              "      <td>9482.498179</td>\n",
              "      <td>26.851615</td>\n",
              "      <td>559.535076</td>\n",
              "      <td>7787.417670</td>\n",
              "      <td>8445.108467</td>\n",
              "      <td>2240.303366</td>\n",
              "      <td>9482.532242</td>\n",
              "      <td>3.696819</td>\n",
              "      <td>192.533450</td>\n",
              "      <td>8233.388611</td>\n",
              "      <td>8956.108862</td>\n",
              "      <td>2248.963169</td>\n",
              "      <td>9752.140005</td>\n",
              "      <td>0.078153</td>\n",
              "      <td>542.29132</td>\n",
              "      <td>7863.354480</td>\n",
              "      <td>8433.099168</td>\n",
              "      <td>2460.332054</td>\n",
              "      <td>9470.822563</td>\n",
              "      <td>12.235332</td>\n",
              "      <td>549.806198</td>\n",
              "      <td>7802.685242</td>\n",
              "      <td>8433.215157</td>\n",
              "      <td>2239.256262</td>\n",
              "      <td>9477.421965</td>\n",
              "      <td>6.560377</td>\n",
              "      <td>15.792453</td>\n",
              "      <td>2011.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.413135</td>\n",
              "      <td>10030.212124</td>\n",
              "      <td>126002.533627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>19522.265370</td>\n",
              "      <td>2.302743e+03</td>\n",
              "      <td>6.63334</td>\n",
              "      <td>79.508450</td>\n",
              "      <td>0.834277</td>\n",
              "      <td>0.825780</td>\n",
              "      <td>0.825780</td>\n",
              "      <td>481.068583</td>\n",
              "      <td>1972.886412</td>\n",
              "      <td>2272.640070</td>\n",
              "      <td>222.504559</td>\n",
              "      <td>1396.432173</td>\n",
              "      <td>23.654452</td>\n",
              "      <td>422.531350</td>\n",
              "      <td>2049.960006</td>\n",
              "      <td>2308.456571</td>\n",
              "      <td>789.487733</td>\n",
              "      <td>1682.135370</td>\n",
              "      <td>3.764438</td>\n",
              "      <td>481.229670</td>\n",
              "      <td>1977.846650</td>\n",
              "      <td>2279.812559</td>\n",
              "      <td>222.046882</td>\n",
              "      <td>1403.219990</td>\n",
              "      <td>3.431242</td>\n",
              "      <td>481.262093</td>\n",
              "      <td>1977.813047</td>\n",
              "      <td>2279.762787</td>\n",
              "      <td>222.049607</td>\n",
              "      <td>1403.180093</td>\n",
              "      <td>6.363449</td>\n",
              "      <td>226.910101</td>\n",
              "      <td>1610.087246</td>\n",
              "      <td>1820.204536</td>\n",
              "      <td>234.649617</td>\n",
              "      <td>1018.409267</td>\n",
              "      <td>0.895980</td>\n",
              "      <td>480.14995</td>\n",
              "      <td>2051.518973</td>\n",
              "      <td>2286.601342</td>\n",
              "      <td>597.435203</td>\n",
              "      <td>1426.262931</td>\n",
              "      <td>7.398609</td>\n",
              "      <td>482.749550</td>\n",
              "      <td>2003.728668</td>\n",
              "      <td>2287.339931</td>\n",
              "      <td>221.652660</td>\n",
              "      <td>1409.237148</td>\n",
              "      <td>2.584423</td>\n",
              "      <td>8.829086</td>\n",
              "      <td>6.63334</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.220256</td>\n",
              "      <td>5763.529286</td>\n",
              "      <td>74056.291624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>59.000000</td>\n",
              "      <td>8.900100e+07</td>\n",
              "      <td>2000.00000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042000</td>\n",
              "      <td>0.042000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>2777.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>5238.000000</td>\n",
              "      <td>89.291179</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2380.000000</td>\n",
              "      <td>2244.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>2380.000000</td>\n",
              "      <td>-18.839827</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>2777.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>5238.000000</td>\n",
              "      <td>13.009275</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>2777.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>5238.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>2777.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>5238.000000</td>\n",
              "      <td>-2.699216</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>2618.571429</td>\n",
              "      <td>2533.500000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "      <td>1.849637</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>2777.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>5238.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2000.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.024504</td>\n",
              "      <td>9.511460</td>\n",
              "      <td>142.496170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21709.250000</td>\n",
              "      <td>8.900300e+07</td>\n",
              "      <td>2005.00000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.839000</td>\n",
              "      <td>0.839000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>6635.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>151.878239</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>6595.058344</td>\n",
              "      <td>6938.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>13.701684</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>6635.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>24.410697</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>6635.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>7700.477169</td>\n",
              "      <td>8979.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>-0.551281</td>\n",
              "      <td>245.00000</td>\n",
              "      <td>6395.849020</td>\n",
              "      <td>6635.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>4.961773</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>6635.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2005.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.813747</td>\n",
              "      <td>5151.383720</td>\n",
              "      <td>63940.568991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>42934.500000</td>\n",
              "      <td>8.900600e+07</td>\n",
              "      <td>2011.00000</td>\n",
              "      <td>170.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>441.000000</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2142.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>170.863342</td>\n",
              "      <td>313.500000</td>\n",
              "      <td>8702.738825</td>\n",
              "      <td>9761.000000</td>\n",
              "      <td>2142.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>15.811767</td>\n",
              "      <td>441.000000</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2142.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>26.838997</td>\n",
              "      <td>441.000000</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2142.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>0.952311</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2142.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>0.037622</td>\n",
              "      <td>385.00000</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2142.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>11.941954</td>\n",
              "      <td>398.000000</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2142.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>2011.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.324626</td>\n",
              "      <td>10384.663439</td>\n",
              "      <td>119548.116543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>55046.750000</td>\n",
              "      <td>8.900700e+07</td>\n",
              "      <td>2017.00000</td>\n",
              "      <td>233.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.359000</td>\n",
              "      <td>2.359000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2380.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>182.056914</td>\n",
              "      <td>603.500000</td>\n",
              "      <td>9457.023113</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2619.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>18.843400</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2380.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>28.972841</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2380.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>5.072102</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2380.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>0.651787</td>\n",
              "      <td>1280.00000</td>\n",
              "      <td>9652.140260</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2619.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>18.712975</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>9346.072115</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2380.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>2017.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.912611</td>\n",
              "      <td>14997.891200</td>\n",
              "      <td>188387.356073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>67159.000000</td>\n",
              "      <td>8.900800e+07</td>\n",
              "      <td>2022.00000</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.374000</td>\n",
              "      <td>3.374000</td>\n",
              "      <td>1314.000000</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2619.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>209.220418</td>\n",
              "      <td>1314.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>28.847228</td>\n",
              "      <td>1314.000000</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2619.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>38.595823</td>\n",
              "      <td>1314.000000</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2619.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>119.038377</td>\n",
              "      <td>1314.000000</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2619.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>3.286994</td>\n",
              "      <td>1310.00000</td>\n",
              "      <td>9909.083333</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>9523.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>25.372271</td>\n",
              "      <td>1313.000000</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>2619.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2022.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>20260.369766</td>\n",
              "      <td>278797.388259</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Unnamed: 0        str_ID  ...          cGDD          cNDVI\n",
              "count  36570.000000  3.657000e+04  ...  34582.000000   34553.000000\n",
              "mean   38411.320755  8.900508e+07  ...  10030.212124  126002.533627\n",
              "std    19522.265370  2.302743e+03  ...   5763.529286   74056.291624\n",
              "min       59.000000  8.900100e+07  ...      9.511460     142.496170\n",
              "25%    21709.250000  8.900300e+07  ...   5151.383720   63940.568991\n",
              "50%    42934.500000  8.900600e+07  ...  10384.663439  119548.116543\n",
              "75%    55046.750000  8.900700e+07  ...  14997.891200  188387.356073\n",
              "max    67159.000000  8.900800e+07  ...  20260.369766  278797.388259\n",
              "\n",
              "[8 rows x 55 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzjZWFfAYwnS"
      },
      "source": [
        "# drop null values \n",
        "# subset to when crop is actively growing i.e. crop_cal != 0\n",
        "data = data[data['crop_cal'] > 0]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gvpSe-0ncC69",
        "outputId": "6c468689-6bb3-4dd9-9db3-145253cdf06c"
      },
      "source": [
        "\n",
        "# Select Features from data frame \n",
        "var = data[['mean_crop','yield','ndvi_wmed_crop' , 'cpc_tmin','chirps', 'cGDD']]\n",
        "\n",
        "\n",
        "# var = var.dropna(subset=['mean_crop','yield','ndvi_wmed_crop' , 'cpc_tmin','chirps'])\n",
        "# var.describe()\n",
        "\n",
        "# var = var.dropna(axis=1, how='any', thresh=None, subset=None, inplace=False)\n",
        "var = var.dropna(subset=['mean_crop','ndvi_wmed_crop' , 'cpc_tmin','chirps', 'cGDD','yield'])\n",
        "\n",
        "var.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2630 entries, 794 to 64964\n",
            "Data columns (total 6 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   mean_crop       2630 non-null   float64\n",
            " 1   yield           2630 non-null   float64\n",
            " 2   ndvi_wmed_crop  2630 non-null   float64\n",
            " 3   cpc_tmin        2630 non-null   float64\n",
            " 4   chirps          2630 non-null   float64\n",
            " 5   cGDD            2630 non-null   float64\n",
            "dtypes: float64(6)\n",
            "memory usage: 143.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQxE2vsry-uR"
      },
      "source": [
        "Feature Extraction "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UXPSQqFpYQt"
      },
      "source": [
        "#  Create a copy of the data \n",
        "df1 = var.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkCwF0sgdjtI"
      },
      "source": [
        "from tsfresh.feature_extraction import feature_calculators\n",
        "\n",
        "# variation_coefficient\n",
        "\n",
        "var.loc[:, 'variation_coef'] = feature_calculators.variation_coefficient(var.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ca7D-Ks9grBS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3005ffd1-85b0-445a-9541-391ef55fd792"
      },
      "source": [
        "# Look at autocorrrelation\n",
        "auto_co = var.loc[:, 'autocorrelation'] = feature_calculators.autocorrelation(var.values, 5)\n",
        "\n",
        "auto_co"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.000000013840175"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4IK9Gy8nRc2",
        "outputId": "4f696e18-b8e1-45eb-dd88-69ae4bc8e4c6"
      },
      "source": [
        "# symmetry \n",
        "assymmetry = var.loc[:, 'assymetry'] = feature_calculators.time_reversal_asymmetry_statistic(var.values, 1)\n",
        "assymmetry"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "460231821.76059246"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Qk6H9dioDil",
        "outputId": "65b001a0-7fe4-451f-87a9-f4fe3a7f2f65"
      },
      "source": [
        "from tsfresh.feature_extraction import ComprehensiveFCParameters\n",
        "settings = ComprehensiveFCParameters()\n",
        "# Set here the options of the settings object as shown in the paragraphs below\n",
        "# ...\n",
        "from tsfresh.feature_extraction import extract_features\n",
        "features = extract_features(df1, column_id='yield',default_fc_parameters=settings)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Feature Extraction: 100%|██████████| 5/5 [00:44<00:00,  8.88s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "yIHtRKx6qmsx",
        "outputId": "7555064f-4e21-4a7e-c5d5-1f98a9644f21"
      },
      "source": [
        "features.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "invalid value encountered in multiply\n"
          ]
        },
        {
          "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>mean_crop__variance_larger_than_standard_deviation</th>\n",
              "      <th>mean_crop__has_duplicate_max</th>\n",
              "      <th>mean_crop__has_duplicate_min</th>\n",
              "      <th>mean_crop__has_duplicate</th>\n",
              "      <th>mean_crop__sum_values</th>\n",
              "      <th>mean_crop__abs_energy</th>\n",
              "      <th>mean_crop__mean_abs_change</th>\n",
              "      <th>mean_crop__mean_change</th>\n",
              "      <th>mean_crop__mean_second_derivative_central</th>\n",
              "      <th>mean_crop__median</th>\n",
              "      <th>mean_crop__mean</th>\n",
              "      <th>mean_crop__length</th>\n",
              "      <th>mean_crop__standard_deviation</th>\n",
              "      <th>mean_crop__variation_coefficient</th>\n",
              "      <th>mean_crop__variance</th>\n",
              "      <th>mean_crop__skewness</th>\n",
              "      <th>mean_crop__kurtosis</th>\n",
              "      <th>mean_crop__root_mean_square</th>\n",
              "      <th>mean_crop__absolute_sum_of_changes</th>\n",
              "      <th>mean_crop__longest_strike_below_mean</th>\n",
              "      <th>mean_crop__longest_strike_above_mean</th>\n",
              "      <th>mean_crop__count_above_mean</th>\n",
              "      <th>mean_crop__count_below_mean</th>\n",
              "      <th>mean_crop__last_location_of_maximum</th>\n",
              "      <th>mean_crop__first_location_of_maximum</th>\n",
              "      <th>mean_crop__last_location_of_minimum</th>\n",
              "      <th>mean_crop__first_location_of_minimum</th>\n",
              "      <th>mean_crop__percentage_of_reoccurring_values_to_all_values</th>\n",
              "      <th>mean_crop__percentage_of_reoccurring_datapoints_to_all_datapoints</th>\n",
              "      <th>mean_crop__sum_of_reoccurring_values</th>\n",
              "      <th>mean_crop__sum_of_reoccurring_data_points</th>\n",
              "      <th>mean_crop__ratio_value_number_to_time_series_length</th>\n",
              "      <th>mean_crop__sample_entropy</th>\n",
              "      <th>mean_crop__maximum</th>\n",
              "      <th>mean_crop__minimum</th>\n",
              "      <th>mean_crop__benford_correlation</th>\n",
              "      <th>mean_crop__time_reversal_asymmetry_statistic__lag_1</th>\n",
              "      <th>mean_crop__time_reversal_asymmetry_statistic__lag_2</th>\n",
              "      <th>mean_crop__time_reversal_asymmetry_statistic__lag_3</th>\n",
              "      <th>mean_crop__c3__lag_1</th>\n",
              "      <th>...</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_4</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_5</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_6</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_7</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_8</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_9</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_0.5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_1</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_1.5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_2</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_2.5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_3</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_6</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_7</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_10</th>\n",
              "      <th>cGDD__count_above__t_0</th>\n",
              "      <th>cGDD__count_below__t_0</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_2</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_3</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_5</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_10</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_100</th>\n",
              "      <th>cGDD__fourier_entropy__bins_2</th>\n",
              "      <th>cGDD__fourier_entropy__bins_3</th>\n",
              "      <th>cGDD__fourier_entropy__bins_5</th>\n",
              "      <th>cGDD__fourier_entropy__bins_10</th>\n",
              "      <th>cGDD__fourier_entropy__bins_100</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_3__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_4__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_5__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_6__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_7__tau_1</th>\n",
              "      <th>cGDD__query_similarity_count__query_None__threshold_0.0</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"min\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"max\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"mean\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"median\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"25\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"75\"__threshold_0.98</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.020000e+02</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.020000e+02</td>\n",
              "      <td>1.020000e+02</td>\n",
              "      <td>1.020000e+02</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.020000e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>207720.849938</td>\n",
              "      <td>1.767198e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7758.167378</td>\n",
              "      <td>7758.167378</td>\n",
              "      <td>25.784314</td>\n",
              "      <td>1.163618e-12</td>\n",
              "      <td>1.492743e-16</td>\n",
              "      <td>1.859129e-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7758.167378</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.196078</td>\n",
              "      <td>11.254902</td>\n",
              "      <td>11.254902</td>\n",
              "      <td>10.196078</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7758.167378</td>\n",
              "      <td>207720.849938</td>\n",
              "      <td>0.042528</td>\n",
              "      <td>0.093773</td>\n",
              "      <td>7758.167378</td>\n",
              "      <td>7758.167378</td>\n",
              "      <td>-0.221855</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.568294e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.092595</td>\n",
              "      <td>0.103638</td>\n",
              "      <td>0.116005</td>\n",
              "      <td>0.128829</td>\n",
              "      <td>0.140552</td>\n",
              "      <td>0.099313</td>\n",
              "      <td>0.721108</td>\n",
              "      <td>0.417104</td>\n",
              "      <td>0.120074</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.280689</td>\n",
              "      <td>0.392669</td>\n",
              "      <td>0.531537</td>\n",
              "      <td>0.696784</td>\n",
              "      <td>0.998414</td>\n",
              "      <td>0.277450</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>0.279952</td>\n",
              "      <td>0.282029</td>\n",
              "      <td>0.730857</td>\n",
              "      <td>0.001537</td>\n",
              "      <td>0.002331</td>\n",
              "      <td>0.003142</td>\n",
              "      <td>0.003970</td>\n",
              "      <td>0.004817</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.063402</td>\n",
              "      <td>9.092702</td>\n",
              "      <td>9.086433</td>\n",
              "      <td>9.091427</td>\n",
              "      <td>9.083939</td>\n",
              "      <td>9.092261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>101438.685814</td>\n",
              "      <td>1.060034e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2104.389706</td>\n",
              "      <td>2104.389706</td>\n",
              "      <td>8.549943</td>\n",
              "      <td>7.142288e-13</td>\n",
              "      <td>6.882549e-17</td>\n",
              "      <td>1.824857e-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2104.389706</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.885666</td>\n",
              "      <td>14.027333</td>\n",
              "      <td>14.027333</td>\n",
              "      <td>12.885666</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2104.389706</td>\n",
              "      <td>101438.685814</td>\n",
              "      <td>0.011869</td>\n",
              "      <td>0.028193</td>\n",
              "      <td>2104.389706</td>\n",
              "      <td>2104.389706</td>\n",
              "      <td>0.113882</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.874705e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.008725</td>\n",
              "      <td>0.010891</td>\n",
              "      <td>0.021326</td>\n",
              "      <td>0.033570</td>\n",
              "      <td>0.047990</td>\n",
              "      <td>0.021206</td>\n",
              "      <td>0.017341</td>\n",
              "      <td>0.006077</td>\n",
              "      <td>0.018749</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041897</td>\n",
              "      <td>0.039297</td>\n",
              "      <td>0.057647</td>\n",
              "      <td>0.049564</td>\n",
              "      <td>0.016017</td>\n",
              "      <td>0.054960</td>\n",
              "      <td>0.054683</td>\n",
              "      <td>0.057999</td>\n",
              "      <td>0.067963</td>\n",
              "      <td>0.287046</td>\n",
              "      <td>0.015525</td>\n",
              "      <td>0.023539</td>\n",
              "      <td>0.031728</td>\n",
              "      <td>0.040096</td>\n",
              "      <td>0.048649</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.314594</td>\n",
              "      <td>1.288388</td>\n",
              "      <td>1.288336</td>\n",
              "      <td>1.288125</td>\n",
              "      <td>1.289181</td>\n",
              "      <td>1.288283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>54072.782609</td>\n",
              "      <td>1.624370e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>54072.782609</td>\n",
              "      <td>0.014706</td>\n",
              "      <td>0.030305</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>-0.297356</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.710932e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.077209</td>\n",
              "      <td>0.081027</td>\n",
              "      <td>0.092377</td>\n",
              "      <td>0.096806</td>\n",
              "      <td>0.067422</td>\n",
              "      <td>0.066081</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.388889</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.161765</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.338235</td>\n",
              "      <td>0.485294</td>\n",
              "      <td>0.838235</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.425848</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.431111</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>120944.243243</td>\n",
              "      <td>7.698689e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>5.684342e-13</td>\n",
              "      <td>1.181089e-16</td>\n",
              "      <td>3.618915e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>120944.243243</td>\n",
              "      <td>0.029412</td>\n",
              "      <td>0.062520</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>-0.297356</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.579258e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.088265</td>\n",
              "      <td>0.091272</td>\n",
              "      <td>0.094183</td>\n",
              "      <td>0.097242</td>\n",
              "      <td>0.100300</td>\n",
              "      <td>0.074605</td>\n",
              "      <td>0.705882</td>\n",
              "      <td>0.411765</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>0.352941</td>\n",
              "      <td>0.470588</td>\n",
              "      <td>0.647059</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.214559</td>\n",
              "      <td>0.425848</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>169574.158136</td>\n",
              "      <td>1.513442e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>9.094947e-13</td>\n",
              "      <td>1.513784e-16</td>\n",
              "      <td>8.271806e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>169574.158136</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>8884.533693</td>\n",
              "      <td>-0.272809</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.013001e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.088492</td>\n",
              "      <td>0.109497</td>\n",
              "      <td>0.115602</td>\n",
              "      <td>0.121697</td>\n",
              "      <td>0.127789</td>\n",
              "      <td>0.103118</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>313368.362812</td>\n",
              "      <td>2.888227e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.818989e-12</td>\n",
              "      <td>2.038093e-16</td>\n",
              "      <td>3.308722e-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>313368.362812</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>9216.716553</td>\n",
              "      <td>-0.200946</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.829404e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.101973</td>\n",
              "      <td>0.114057</td>\n",
              "      <td>0.133786</td>\n",
              "      <td>0.154454</td>\n",
              "      <td>0.176211</td>\n",
              "      <td>0.111891</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>626736.725624</td>\n",
              "      <td>5.776455e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>3.637979e-12</td>\n",
              "      <td>3.947153e-16</td>\n",
              "      <td>1.323489e-23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>626736.725624</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.125163</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>9674.312000</td>\n",
              "      <td>0.062915</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.054412e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.121597</td>\n",
              "      <td>0.117543</td>\n",
              "      <td>0.155721</td>\n",
              "      <td>0.195882</td>\n",
              "      <td>0.240012</td>\n",
              "      <td>0.139054</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.473684</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.474208</td>\n",
              "      <td>0.686086</td>\n",
              "      <td>2.105810</td>\n",
              "      <td>0.156798</td>\n",
              "      <td>0.237736</td>\n",
              "      <td>0.320435</td>\n",
              "      <td>0.404949</td>\n",
              "      <td>0.491333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "      <td>10.583005</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 3935 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       mean_crop__variance_larger_than_standard_deviation  ...  cGDD__matrix_profile__feature_\"75\"__threshold_0.98\n",
              "count                                              102.0   ...                                         102.000000 \n",
              "mean                                                 0.0   ...                                           9.092261 \n",
              "std                                                  0.0   ...                                           1.288283 \n",
              "min                                                  0.0   ...                                           7.483315 \n",
              "25%                                                  0.0   ...                                           8.000000 \n",
              "50%                                                  0.0   ...                                           8.000000 \n",
              "75%                                                  0.0   ...                                          10.583005 \n",
              "max                                                  0.0   ...                                          10.583005 \n",
              "\n",
              "[8 rows x 3935 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "E9bf3tbrxYtP",
        "outputId": "c53f969f-848f-4255-bd77-b69a302085df"
      },
      "source": [
        "features.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_crop__variance_larger_than_standard_deviation</th>\n",
              "      <th>mean_crop__has_duplicate_max</th>\n",
              "      <th>mean_crop__has_duplicate_min</th>\n",
              "      <th>mean_crop__has_duplicate</th>\n",
              "      <th>mean_crop__sum_values</th>\n",
              "      <th>mean_crop__abs_energy</th>\n",
              "      <th>mean_crop__mean_abs_change</th>\n",
              "      <th>mean_crop__mean_change</th>\n",
              "      <th>mean_crop__mean_second_derivative_central</th>\n",
              "      <th>mean_crop__median</th>\n",
              "      <th>mean_crop__mean</th>\n",
              "      <th>mean_crop__length</th>\n",
              "      <th>mean_crop__standard_deviation</th>\n",
              "      <th>mean_crop__variation_coefficient</th>\n",
              "      <th>mean_crop__variance</th>\n",
              "      <th>mean_crop__skewness</th>\n",
              "      <th>mean_crop__kurtosis</th>\n",
              "      <th>mean_crop__root_mean_square</th>\n",
              "      <th>mean_crop__absolute_sum_of_changes</th>\n",
              "      <th>mean_crop__longest_strike_below_mean</th>\n",
              "      <th>mean_crop__longest_strike_above_mean</th>\n",
              "      <th>mean_crop__count_above_mean</th>\n",
              "      <th>mean_crop__count_below_mean</th>\n",
              "      <th>mean_crop__last_location_of_maximum</th>\n",
              "      <th>mean_crop__first_location_of_maximum</th>\n",
              "      <th>mean_crop__last_location_of_minimum</th>\n",
              "      <th>mean_crop__first_location_of_minimum</th>\n",
              "      <th>mean_crop__percentage_of_reoccurring_values_to_all_values</th>\n",
              "      <th>mean_crop__percentage_of_reoccurring_datapoints_to_all_datapoints</th>\n",
              "      <th>mean_crop__sum_of_reoccurring_values</th>\n",
              "      <th>mean_crop__sum_of_reoccurring_data_points</th>\n",
              "      <th>mean_crop__ratio_value_number_to_time_series_length</th>\n",
              "      <th>mean_crop__sample_entropy</th>\n",
              "      <th>mean_crop__maximum</th>\n",
              "      <th>mean_crop__minimum</th>\n",
              "      <th>mean_crop__benford_correlation</th>\n",
              "      <th>mean_crop__time_reversal_asymmetry_statistic__lag_1</th>\n",
              "      <th>mean_crop__time_reversal_asymmetry_statistic__lag_2</th>\n",
              "      <th>mean_crop__time_reversal_asymmetry_statistic__lag_3</th>\n",
              "      <th>mean_crop__c3__lag_1</th>\n",
              "      <th>...</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_4</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_5</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_6</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_7</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_8</th>\n",
              "      <th>cGDD__energy_ratio_by_chunks__num_segments_10__segment_focus_9</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_0.5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_1</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_1.5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_2</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_2.5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_3</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_5</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_6</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_7</th>\n",
              "      <th>cGDD__ratio_beyond_r_sigma__r_10</th>\n",
              "      <th>cGDD__count_above__t_0</th>\n",
              "      <th>cGDD__count_below__t_0</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_2</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_3</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_5</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_10</th>\n",
              "      <th>cGDD__lempel_ziv_complexity__bins_100</th>\n",
              "      <th>cGDD__fourier_entropy__bins_2</th>\n",
              "      <th>cGDD__fourier_entropy__bins_3</th>\n",
              "      <th>cGDD__fourier_entropy__bins_5</th>\n",
              "      <th>cGDD__fourier_entropy__bins_10</th>\n",
              "      <th>cGDD__fourier_entropy__bins_100</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_3__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_4__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_5__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_6__tau_1</th>\n",
              "      <th>cGDD__permutation_entropy__dimension_7__tau_1</th>\n",
              "      <th>cGDD__query_similarity_count__query_None__threshold_0.0</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"min\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"max\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"mean\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"median\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"25\"__threshold_0.98</th>\n",
              "      <th>cGDD__matrix_profile__feature_\"75\"__threshold_0.98</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.042</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>1.714613e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.547474e-13</td>\n",
              "      <td>1.513784e-16</td>\n",
              "      <td>2.067952e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.062915</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.710932e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.103491</td>\n",
              "      <td>0.109409</td>\n",
              "      <td>0.115398</td>\n",
              "      <td>0.121503</td>\n",
              "      <td>0.127540</td>\n",
              "      <td>0.066081</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.186</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>54072.782609</td>\n",
              "      <td>1.624370e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>18.0</td>\n",
              "      <td>4.547474e-13</td>\n",
              "      <td>1.513784e-16</td>\n",
              "      <td>2.067952e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>54072.782609</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.125163</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.062915</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.710932e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.109899</td>\n",
              "      <td>0.116371</td>\n",
              "      <td>0.124511</td>\n",
              "      <td>0.132631</td>\n",
              "      <td>0.068807</td>\n",
              "      <td>0.070494</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "      <td>7.483315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.212</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>1.714613e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.547474e-13</td>\n",
              "      <td>1.513784e-16</td>\n",
              "      <td>2.067952e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.062915</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.710932e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.103430</td>\n",
              "      <td>0.109500</td>\n",
              "      <td>0.115630</td>\n",
              "      <td>0.121793</td>\n",
              "      <td>0.128076</td>\n",
              "      <td>0.066439</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.215</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>1.714613e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.547474e-13</td>\n",
              "      <td>1.513784e-16</td>\n",
              "      <td>2.067952e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.062915</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.710932e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.103411</td>\n",
              "      <td>0.109494</td>\n",
              "      <td>0.115575</td>\n",
              "      <td>0.121670</td>\n",
              "      <td>0.127643</td>\n",
              "      <td>0.066170</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.259</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>1.714613e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.547474e-13</td>\n",
              "      <td>1.513784e-16</td>\n",
              "      <td>2.067952e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.062915</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.710932e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.103481</td>\n",
              "      <td>0.109484</td>\n",
              "      <td>0.115531</td>\n",
              "      <td>0.121638</td>\n",
              "      <td>0.127777</td>\n",
              "      <td>0.066263</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.428</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>169574.158136</td>\n",
              "      <td>1.513442e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.818989e-12</td>\n",
              "      <td>2.038093e-16</td>\n",
              "      <td>3.308722e-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>169574.158136</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>-0.272809</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.109159e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.102458</td>\n",
              "      <td>0.111912</td>\n",
              "      <td>0.121351</td>\n",
              "      <td>0.130932</td>\n",
              "      <td>0.140313</td>\n",
              "      <td>0.073951</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.157895</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.450</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>169574.158136</td>\n",
              "      <td>1.513442e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.818989e-12</td>\n",
              "      <td>2.038093e-16</td>\n",
              "      <td>3.308722e-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>169574.158136</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>-0.272809</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.109159e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.102090</td>\n",
              "      <td>0.111691</td>\n",
              "      <td>0.121412</td>\n",
              "      <td>0.131177</td>\n",
              "      <td>0.140643</td>\n",
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              "      <td>0.736842</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.421053</td>\n",
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              "      <td>0.736842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.466</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>1.714613e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.547474e-13</td>\n",
              "      <td>1.513784e-16</td>\n",
              "      <td>2.067952e-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>57076.826087</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>0.117783</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>3004.043478</td>\n",
              "      <td>0.062915</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.710932e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.103516</td>\n",
              "      <td>0.109645</td>\n",
              "      <td>0.115639</td>\n",
              "      <td>0.121736</td>\n",
              "      <td>0.127802</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.315789</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.488</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>169574.158136</td>\n",
              "      <td>1.513442e+09</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.818989e-12</td>\n",
              "      <td>2.038093e-16</td>\n",
              "      <td>3.308722e-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8924.955691</td>\n",
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              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8924.955691</td>\n",
              "      <td>169574.158136</td>\n",
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              "      <td>8924.955691</td>\n",
              "      <td>8924.955691</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>7.109159e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.101972</td>\n",
              "      <td>0.111512</td>\n",
              "      <td>0.121239</td>\n",
              "      <td>0.131038</td>\n",
              "      <td>0.141182</td>\n",
              "      <td>0.074518</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.421053</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.325083</td>\n",
              "      <td>0.940448</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.502</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>120944.243243</td>\n",
              "      <td>7.698689e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>19.0</td>\n",
              "      <td>9.094947e-13</td>\n",
              "      <td>1.428791e-16</td>\n",
              "      <td>8.271806e-25</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>19.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6365.486486</td>\n",
              "      <td>120944.243243</td>\n",
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              "      <td>6365.486486</td>\n",
              "      <td>6365.486486</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>2.579258e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.095581</td>\n",
              "      <td>0.114531</td>\n",
              "      <td>0.134512</td>\n",
              "      <td>0.155925</td>\n",
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              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 3935 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       mean_crop__variance_larger_than_standard_deviation  ...  cGDD__matrix_profile__feature_\"75\"__threshold_0.98\n",
              "0.042                                                0.0   ...                                           8.000000 \n",
              "0.186                                                0.0   ...                                           7.483315 \n",
              "0.212                                                0.0   ...                                           8.000000 \n",
              "0.215                                                0.0   ...                                           8.000000 \n",
              "0.259                                                0.0   ...                                           8.000000 \n",
              "0.428                                                0.0   ...                                           8.000000 \n",
              "0.450                                                0.0   ...                                           8.000000 \n",
              "0.466                                                0.0   ...                                           8.000000 \n",
              "0.488                                                0.0   ...                                           8.000000 \n",
              "0.502                                                0.0   ...                                           8.000000 \n",
              "\n",
              "[10 rows x 3935 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlJU22vnrBFN"
      },
      "source": [
        "Run Random Forest Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jah9dWkxW4MK"
      },
      "source": [
        "# Random forest\n",
        "model = RandomForestRegressor(n_estimators=200)  # 100 is default value for n_estimators"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhcFFMa-XDxy"
      },
      "source": [
        "# Split the dataframe such that we use 70% of the data for training and predict on the remaining 20%\n",
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(df1, test_size=0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PTijk6j7XS_4",
        "outputId": "9dda4c8d-88f1-41a2-d850-454227c4c920"
      },
      "source": [
        "df1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>mean_crop</th>\n",
              "      <th>yield</th>\n",
              "      <th>ndvi_wmed_crop</th>\n",
              "      <th>cpc_tmin</th>\n",
              "      <th>chirps</th>\n",
              "      <th>cGDD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>794</th>\n",
              "      <td>8884.533693</td>\n",
              "      <td>1.212</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>13.911002</td>\n",
              "      <td>1.006067</td>\n",
              "      <td>66.676902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>802</th>\n",
              "      <td>8884.533693</td>\n",
              "      <td>1.212</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>12.485395</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>149.038975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>810</th>\n",
              "      <td>8884.533693</td>\n",
              "      <td>1.212</td>\n",
              "      <td>9174.5</td>\n",
              "      <td>12.152578</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>237.178575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>818</th>\n",
              "      <td>8884.533693</td>\n",
              "      <td>1.212</td>\n",
              "      <td>8611.0</td>\n",
              "      <td>13.988485</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>326.543045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>826</th>\n",
              "      <td>8884.533693</td>\n",
              "      <td>1.212</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>14.918971</td>\n",
              "      <td>0.985184</td>\n",
              "      <td>420.998313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64932</th>\n",
              "      <td>9674.312000</td>\n",
              "      <td>2.425</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>14.884531</td>\n",
              "      <td>4.714965</td>\n",
              "      <td>19288.390052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64940</th>\n",
              "      <td>9674.312000</td>\n",
              "      <td>2.425</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>15.205432</td>\n",
              "      <td>2.280402</td>\n",
              "      <td>19384.417555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64948</th>\n",
              "      <td>9674.312000</td>\n",
              "      <td>2.425</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>14.415150</td>\n",
              "      <td>0.388167</td>\n",
              "      <td>19476.530033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64956</th>\n",
              "      <td>9674.312000</td>\n",
              "      <td>2.425</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>13.767635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19570.881748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64964</th>\n",
              "      <td>9674.312000</td>\n",
              "      <td>2.425</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>15.262173</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19663.839494</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2630 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         mean_crop  yield  ndvi_wmed_crop   cpc_tmin    chirps          cGDD\n",
              "794    8884.533693  1.212         10000.0  13.911002  1.006067     66.676902\n",
              "802    8884.533693  1.212         10000.0  12.485395  0.000000    149.038975\n",
              "810    8884.533693  1.212          9174.5  12.152578  0.000000    237.178575\n",
              "818    8884.533693  1.212          8611.0  13.988485  0.000000    326.543045\n",
              "826    8884.533693  1.212         10000.0  14.918971  0.985184    420.998313\n",
              "...            ...    ...             ...        ...       ...           ...\n",
              "64932  9674.312000  2.425         10000.0  14.884531  4.714965  19288.390052\n",
              "64940  9674.312000  2.425         10000.0  15.205432  2.280402  19384.417555\n",
              "64948  9674.312000  2.425         10000.0  14.415150  0.388167  19476.530033\n",
              "64956  9674.312000  2.425         10000.0  13.767635  0.000000  19570.881748\n",
              "64964  9674.312000  2.425         10000.0  15.262173  0.000000  19663.839494\n",
              "\n",
              "[2630 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRt-pf9_X2VZ"
      },
      "source": [
        "Define the columns for training and testing and fit the data to the model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "bTj8tMK0XN0N",
        "outputId": "f81acb44-13be-40b3-fcb1-14e989a38d94"
      },
      "source": [
        "feature_names = ['mean_crop', 'ndvi_wmed_crop', 'cpc_tmin', 'chirps','cGDD']\n",
        "X = train[feature_names].values\n",
        "y = train['yield'].ravel()\n",
        "X_test = test[feature_names].values\n",
        "y_test = test['yield'].ravel()\n",
        "\n",
        "# Fit the model\n",
        "model.fit(X, y)\n",
        "y_pred = pd.DataFrame(model.predict(X_test),columns=['yield'],index=test.index)\n",
        "\n",
        "# Inspect the prediction \n",
        "y_pred.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>789.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.598268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.773317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.183260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.885535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.572360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.371210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.968250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            yield\n",
              "count  789.000000\n",
              "mean     1.598268\n",
              "std      0.773317\n",
              "min      0.183260\n",
              "25%      0.885535\n",
              "50%      1.572360\n",
              "75%      2.371210\n",
              "max      2.968250"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "UJRZ4XeeiZm7",
        "outputId": "5698a70e-d329-47a2-dbe6-247b80d410bb"
      },
      "source": [
        "#  inspect the testing data as well for the yield\n",
        "y_n = pd.DataFrame(y_test, columns=['yield'])\n",
        "y_n.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>789.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.596158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.851232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.042000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.826000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.580000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.425000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.374000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            yield\n",
              "count  789.000000\n",
              "mean     1.596158\n",
              "std      0.851232\n",
              "min      0.042000\n",
              "25%      0.826000\n",
              "50%      1.580000\n",
              "75%      2.425000\n",
              "max      3.374000"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOLV0jfsYMep"
      },
      "source": [
        "Looking into the Model Results "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qajFEheMYHZs"
      },
      "source": [
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "result = permutation_importance(model, X_test, y_test, n_repeats=10, random_state=42, n_jobs=5)\n",
        "forest_importances = pd.Series(result.importances_mean, index=feature_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "w53-SA07YaAs",
        "outputId": "464328d9-d5a4-40af-c804-af81d0d0a244"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots()\n",
        "forest_importances.plot.bar(yerr=result.importances_std, ax=ax)\n",
        "ax.set_title(\"Feature importances using permutation on full model\")\n",
        "ax.set_ylabel(\"Mean accuracy decrease\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Mean accuracy decrease')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VvRS7ARYhyB",
        "outputId": "84e7cdd5-3619-4062-839d-bf3d71d190f5"
      },
      "source": [
        "from shapash.explainer.smart_explainer import SmartExplainer\n",
        "xpl = SmartExplainer()\n",
        "\n",
        "xpl.compile(\n",
        "    x=test[feature_names],\n",
        "    model=model,\n",
        "    y_pred=y_pred\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Backend: Shap TreeExplainer\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "qtwCEp7vY0hH",
        "outputId": "4e63c455-c587-4de7-cf5b-da664d71f52d"
      },
      "source": [
        "xpl.plot.features_importance()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "4jepwThQY4Cx",
        "outputId": "126e6cc6-2435-4216-97af-3c2975e6d558"
      },
      "source": [
        "# Checking for feature contribution\n",
        "# A feature contribution plot answers questions about how a feature value influences model outcomes \n",
        "\n",
        "xpl.plot.contribution_plot(\"cGDD\")"
      ],
      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-ky_XCueanbI",
        "outputId": "b365fe87-2914-411d-d34c-51a7d39c10f0"
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      "source": [
        "# The plot shows features each contributing to push the model output from the base value \n",
        "# (the average model output over the training dataset we passed) to the model output. \n",
        "summary_df = xpl.to_pandas()\n",
        "summary_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>yield</th>\n",
              "      <th>feature_1</th>\n",
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              "      <th>52680</th>\n",
              "      <td>2.495530</td>\n",
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              "      <td>13754.7</td>\n",
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              "      <th>60910</th>\n",
              "      <td>2.410030</td>\n",
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              "      <td>10000</td>\n",
              "      <td>-0.0412514</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>15.6462</td>\n",
              "      <td>0.0104495</td>\n",
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              "      <td>0.00421735</td>\n",
              "      <td>-0.00753365</td>\n",
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              "      <th>37795</th>\n",
              "      <td>0.528795</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>10142.3</td>\n",
              "      <td>-0.82996</td>\n",
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              "      <td>3004.04</td>\n",
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              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>2777</td>\n",
              "      <td>-0.091328</td>\n",
              "      <td>chirps</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.0392503</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>21.6282</td>\n",
              "      <td>-0.00389778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63758</th>\n",
              "      <td>2.361935</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>18214.8</td>\n",
              "      <td>0.672076</td>\n",
              "      <td>mean_crop</td>\n",
              "      <td>9674.31</td>\n",
              "      <td>0.0580026</td>\n",
              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>10000</td>\n",
              "      <td>-0.0251518</td>\n",
              "      <td>chirps</td>\n",
              "      <td>9.35597</td>\n",
              "      <td>0.0239026</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>15.5699</td>\n",
              "      <td>0.00508019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38898</th>\n",
              "      <td>0.517840</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>10070.1</td>\n",
              "      <td>-0.875998</td>\n",
              "      <td>mean_crop</td>\n",
              "      <td>3004.04</td>\n",
              "      <td>-0.126653</td>\n",
              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>3061</td>\n",
              "      <td>-0.0848657</td>\n",
              "      <td>chirps</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.0317908</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>22.3212</td>\n",
              "      <td>0.00912101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53911</th>\n",
              "      <td>2.624740</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>15509.7</td>\n",
              "      <td>0.76156</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>10.9467</td>\n",
              "      <td>0.153485</td>\n",
              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>8979</td>\n",
              "      <td>0.116983</td>\n",
              "      <td>chirps</td>\n",
              "      <td>1.9687</td>\n",
              "      <td>-0.0711576</td>\n",
              "      <td>mean_crop</td>\n",
              "      <td>7700.48</td>\n",
              "      <td>0.0358442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>906</th>\n",
              "      <td>1.104490</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>1215.84</td>\n",
              "      <td>-0.571504</td>\n",
              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>9166</td>\n",
              "      <td>0.0533777</td>\n",
              "      <td>mean_crop</td>\n",
              "      <td>8884.53</td>\n",
              "      <td>-0.0389306</td>\n",
              "      <td>chirps</td>\n",
              "      <td>4.08349</td>\n",
              "      <td>0.025869</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>11.4125</td>\n",
              "      <td>0.00765157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14014</th>\n",
              "      <td>0.838300</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>2907.59</td>\n",
              "      <td>-0.702964</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>22.518</td>\n",
              "      <td>-0.0813984</td>\n",
              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>6530</td>\n",
              "      <td>0.0529437</td>\n",
              "      <td>chirps</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.0372266</td>\n",
              "      <td>mean_crop</td>\n",
              "      <td>6365.49</td>\n",
              "      <td>-0.0210809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17640</th>\n",
              "      <td>0.964640</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>4801.37</td>\n",
              "      <td>-0.705707</td>\n",
              "      <td>mean_crop</td>\n",
              "      <td>8924.96</td>\n",
              "      <td>-0.0552666</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>19.791</td>\n",
              "      <td>0.0424959</td>\n",
              "      <td>chirps</td>\n",
              "      <td>5.35693</td>\n",
              "      <td>0.042452</td>\n",
              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>10000</td>\n",
              "      <td>0.0126395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36667</th>\n",
              "      <td>0.550795</td>\n",
              "      <td>cGDD</td>\n",
              "      <td>9189.77</td>\n",
              "      <td>-0.92423</td>\n",
              "      <td>mean_crop</td>\n",
              "      <td>3004.04</td>\n",
              "      <td>-0.0963219</td>\n",
              "      <td>ndvi_wmed_crop</td>\n",
              "      <td>2777</td>\n",
              "      <td>-0.0634322</td>\n",
              "      <td>cpc_tmin</td>\n",
              "      <td>20.1568</td>\n",
              "      <td>0.0260918</td>\n",
              "      <td>chirps</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.0193383</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>789 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          yield feature_1  value_1  ...       feature_5     value_5 contribution_5\n",
              "52680  2.495530      cGDD  13754.7  ...          chirps    0.447505    -0.00897012\n",
              "60910  2.410030      cGDD    19257  ...          chirps  0.00421735    -0.00753365\n",
              "37795  0.528795      cGDD  10142.3  ...        cpc_tmin     21.6282    -0.00389778\n",
              "63758  2.361935      cGDD  18214.8  ...        cpc_tmin     15.5699     0.00508019\n",
              "38898  0.517840      cGDD  10070.1  ...        cpc_tmin     22.3212     0.00912101\n",
              "...         ...       ...      ...  ...             ...         ...            ...\n",
              "53911  2.624740      cGDD  15509.7  ...       mean_crop     7700.48      0.0358442\n",
              "906    1.104490      cGDD  1215.84  ...        cpc_tmin     11.4125     0.00765157\n",
              "14014  0.838300      cGDD  2907.59  ...       mean_crop     6365.49     -0.0210809\n",
              "17640  0.964640      cGDD  4801.37  ...  ndvi_wmed_crop       10000      0.0126395\n",
              "36667  0.550795      cGDD  9189.77  ...          chirps           0     -0.0193383\n",
              "\n",
              "[789 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 566
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        "id": "C7swmPL9aygE",
        "outputId": "a03cb8d1-2364-453c-ea06-37430a52423f"
      },
      "source": [
        "# The plot shows features each contributing to push the model output from the base value \n",
        "# (the average model output over the training dataset we passed) to the model output. \n",
        "xpl.plot.local_plot(row_num=12)"
      ],
      "execution_count": null,
      "outputs": [
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    {
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      "execution_count": null,
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJCNw5pMcKMj"
      },
      "source": [
        "## **Mixed Effects Random Forest **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "28Yf4Rhpml7g",
        "outputId": "acc295b7-3fde-401c-a8f6-984d8760b8c8"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/data_training/kenya_maize_s1.csv')\n",
        "\n",
        "df = df[['adm1_name', 'Season','mean_crop','ndvi_wmed_crop' , 'cpc_tmin','chirps', 'cGDD','yield']]\n",
        "\n",
        "df = df.dropna(subset=['adm1_name', 'Season','mean_crop','ndvi_wmed_crop' , 'cpc_tmin','chirps', 'cGDD','yield'])\n",
        "\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-f064225d2aa5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/data_training/kenya_maize_s1.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'adm1_name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Season'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mean_crop'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'ndvi_wmed_crop'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m'cpc_tmin'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'chirps'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cGDD'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'yield'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'adm1_name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Season'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mean_crop'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'ndvi_wmed_crop'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m'cpc_tmin'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'chirps'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cGDD'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'yield'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpT459dzcQXR"
      },
      "source": [
        "# Creat e features \n",
        "def create_features(key, group):\n",
        "    percentiles = ['p10', 'p20', 'p30', 'p40', 'p50', 'p60', 'p70', 'p80', 'p90', 'p100']\n",
        "    vars = ['mean_crop', 'ndvi_wmed_crop', 'cpc_tmin', 'chirps','cGDD']\n",
        "    \n",
        "    df_ml = pd.DataFrame()\n",
        "\n",
        "    group.loc[:, 'Fraction Season'] = range(1, len(group) + 1)\n",
        "    group.loc[:, 'Fraction Season'] = group.loc[:, 'Fraction Season'] * 100 / len(group)\n",
        "\n",
        "    for var in vars:\n",
        "      if var in group:\n",
        "        for f in percentiles:\n",
        "          perc = int(f[1:])\n",
        "          closest = group['Fraction Season'].sub(perc).abs().idxmin()\n",
        "          df_ml[f'{f}_{var}'] = [group.loc[closest][var]]\n",
        "    \n",
        "    df_ml['region'] = key[0]\n",
        "    df_ml['season'] = key[1]\n",
        "    df_ml['yield'] = group['yield'].unique()[0]\n",
        "    df_ml['Season'] = group['Season'].unique()[0]\n",
        "\n",
        "\n",
        "    return df_ml"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PUcXq-1mjDH"
      },
      "source": [
        "# For each Admin 1 create a column with information on fraction of season\n",
        "groups = df.groupby(['adm1_name', 'Season'])\n",
        "\n",
        "frames = []\n",
        "for key, vals in groups:\n",
        "    df_group = create_features(key, vals)\n",
        "    frames.append(df_group)\n",
        "\n",
        "df_updated = pd.concat(frames)\n",
        "\n",
        "df_updated.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYfyJaS1q4yp"
      },
      "source": [
        "df_updated.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5VNvNztnI-c"
      },
      "source": [
        "\n",
        "df_updated['Season'].unique()\n",
        "df_updated.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgP_QAzTplCO"
      },
      "source": [
        "print(len(list_colums))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1baGEywnNu9"
      },
      "source": [
        "# create a list of features from the data \n",
        "list_colums = df_updated.columns.tolist()\n",
        "\n",
        "# print(len(list_colums))\n",
        "# Generate a list of features \n",
        "feature_names_mixed = list_colums[0:50]\n",
        "feature_names_mixed "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Dp8GD7uoqWu"
      },
      "source": [
        "import numpy as np\n",
        "from merf.merf import MERF\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "df_train_mx, df_test_mx = train_test_split(df_updated, test_size=0.5)\n",
        "\n",
        "merf_cluster = 'region'\n",
        "\n",
        "#  use the feature names generated to subset the training and testing data \n",
        "X_mx = df_train_mx[feature_names_mixed].values\n",
        "y_mx = df_train_mx['yield'].ravel()\n",
        "X_test_mx = df_test_mx[feature_names_mixed].values\n",
        "y_test_mx = df_test_mx['yield'].ravel()\n",
        "\n",
        "model_mx = MERF()\n",
        "\n",
        "# Mixed Effects Random Forest Training\n",
        "Z_train = np.ones((len(X_mx), 1))\n",
        "clusters_train = df_train_mx[merf_cluster]\n",
        "model_mx.fit(X_mx, Z_train, clusters_train, y_mx)\n",
        "\n",
        "# Mixed Effects Random Forest Test\n",
        "Z_test = np.ones((len(X_test_mx), 1))\n",
        "clusters_test = df_test_mx[merf_cluster]\n",
        "\n",
        "y_pred_mx = pd.DataFrame(model_mx.predict(X_test_mx, Z_test, clusters_test),\n",
        "                      columns=['yield'],\n",
        "                      index=df_test_mx.index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48VgWVYms8NS"
      },
      "source": [
        "result_mx = permutation_importance(model_mx.trained_fe_model, X_test_mx, y_test_mx, n_repeats=10, random_state=42, n_jobs=2)\n",
        "forest_importances_mx = pd.Series(result_mx.importances_mean, index=feature_names_mixed)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lv_JlHDau76r"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots()\n",
        "forest_importances_mx.plot.bar(yerr=result_mx.importances_std, ax=ax)\n",
        "ax.set_title(\"Feature importances using permutation on full model\")\n",
        "ax.set_ylabel(\"Mean accuracy decrease\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDv_vqX-x0lq"
      },
      "source": [
        "Usin the Shapash to understand the results "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V92I8xxA1TYz"
      },
      "source": [
        "## **Compare the results using MAE and MAPE **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sB6wQ1pW1O0Z"
      },
      "source": [
        "\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_squared_log_error, median_absolute_error, r2_score\n",
        "\n",
        "#\n",
        "def compare_results(y_true, y_pred):\n",
        "  abs_e = mean_absolute_error(y_true, y_pred)\n",
        "  m_sq_e = mean_squared_error(y_true, y_pred)\n",
        "  m_sq_lg_e = mean_squared_log_error(y_true, y_pred)\n",
        "  # m_ab_per_E = mean_absolute_percentage_error(y_true, y_pred)\n",
        "  median_ab_e = median_absolute_error(y_true, y_pred)\n",
        "  R_score = r2_score(y_true, y_pred)\n",
        "  \n",
        "  print('Mean Absolute Error: {}\\nMean squared Error :{}\\nMean Squared Log Error: {} \\nMedian Absolute Error :{} '.format(abs_e, m_sq_e, m_sq_lg_e, median_ab_e ))\n",
        "  print(\"R square score \", R_score)\n",
        "\n",
        "  return [abs_e,m_sq_e,m_sq_lg_e,median_ab_e,R_score]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtRSWsSu9qwL"
      },
      "source": [
        "Compare for Mixed Random Forest "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_57G8mh08V_",
        "outputId": "fbea6d3b-32a1-4970-a585-30dc81115923"
      },
      "source": [
        "compare_mx = compare_results(y_test_mx, y_pred_mx)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 0.26058026868121686\n",
            "Mean square Error :0.10849911018005566\n",
            "Mean Square Log Error: 0.023824221547679138 \n",
            "Median Absolute Error :0.22967508559256666 \n",
            "R square score  0.7935153372221017\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzcGBSrNp9AS"
      },
      "source": [
        "Compare for the Random forest Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r9cVDBHxmfX0",
        "outputId": "61824d34-5a99-41a1-ee49-f5a9c145e87f"
      },
      "source": [
        "compare_Random_forest = compare_results(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 0.26082180833685414\n",
            "Mean square Error :0.11635690926673947\n",
            "Mean Square Log Error: 0.02049908220494964 \n",
            "Median Absolute Error :0.2034000000000009 \n",
            "R square score  0.8392142804597716\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9tpbKpp--t2"
      },
      "source": [
        "#  Make a datafame for easier comparison\n",
        "#  create a list of Error metric \n",
        "colum = [\"Error Metric\", \"Random Forest\", \"Mixed Random Forest\"]\n",
        "error_metrics = [\"MAE\", \"MSE\", \"MSLE\", \"MEdAE\", \"R2\"]\n",
        "\n",
        "data_dictionary = {'Error Metric':error_metrics, 'Random Forest':compare_Random_forest, 'Mixed Random Forest':compare_mx}\n",
        "dataframe = pd.DataFrame(data_dictionary)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veYZ63SmqVmi"
      },
      "source": [
        "***Description of Results*** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sDJZKxsVJAKj",
        "outputId": "f2b8e005-6aa2-4774-f450-1fe365a27c7e"
      },
      "source": [
        "dataframe"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Error Metric</th>\n",
              "      <th>Random Forest</th>\n",
              "      <th>Mixed Random Forest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MAE</td>\n",
              "      <td>0.260822</td>\n",
              "      <td>0.260580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MSE</td>\n",
              "      <td>0.116357</td>\n",
              "      <td>0.108499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MSLE</td>\n",
              "      <td>0.020499</td>\n",
              "      <td>0.023824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MEdAE</td>\n",
              "      <td>0.203400</td>\n",
              "      <td>0.229675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>R2</td>\n",
              "      <td>0.839214</td>\n",
              "      <td>0.793515</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Error Metric  Random Forest  Mixed Random Forest\n",
              "0          MAE       0.260822             0.260580\n",
              "1          MSE       0.116357             0.108499\n",
              "2         MSLE       0.020499             0.023824\n",
              "3        MEdAE       0.203400             0.229675\n",
              "4           R2       0.839214             0.793515"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFFqZKkyJC4Y"
      },
      "source": [
        " \n",
        "\n",
        "1.   From the above dataframe it is Random forest seems to have a higher R squared score compared to the Mixed Random forest model \n",
        "2.   in terms of the Median absolute error random forest has a lower value compred to the mixed random Forest \n",
        "3. For the Mean Squared Logarithmic error Random forest also seem to have better values \n",
        "4. for the Mean Squared Error and the Mean absolute Error, the Mixed Random forest seem to have better value.\n",
        "\n",
        "In my own assesment I think the Random Forest model performs better, but this could be different maybe if different features were to be chosen. \n",
        "\n",
        " *The results make intiutive sense, since am able to do a yield predictions, and this can be further enhanced by fine tuning the model and using more data to get insights and have it crosschecked with actual produce.*\n",
        "\n",
        "!!! I wish i could do more but time was not that patient on me but I would reach out on some small aspects kind regards. \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}