AgriField_Modelling.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vKt6utEFK03v"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import log_loss\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import random\n",
        "pd.set_option('display.max_columns', None)\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import os\n",
        "import gc\n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = \"all\"\n",
        "from tqdm import tqdm_notebook"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the seed value all over the place to make this reproducible.\n",
        "def seed_all(SEED=42):\n",
        "  random.seed(SEED)\n",
        "  np.random.seed(SEED)\n",
        "  os.environ['PYTHONHASHSEED'] = str(SEED)\n",
        "seed_all()"
      ],
      "metadata": {
        "id": "fkluhdaCK7Mu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Joining Data"
      ],
      "metadata": {
        "id": "jIOCRIOqLBbc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"/content/train_mean_fe.csv\")\n",
        "test  = pd.read_csv(\"/content/test_mean_fe.csv\")"
      ],
      "metadata": {
        "id": "06PvBnU-K_Nq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chips_map_train=pd.read_csv(\"/content/chips_mapping_train.csv\")"
      ],
      "metadata": {
        "id": "PhvLoezvLFQZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train=pd.merge(train,chips_map_train,on='field_id')"
      ],
      "metadata": {
        "id": "J9fsQIDgLHlt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_geo=pd.read_csv(\"/content/train_geo.csv\")"
      ],
      "metadata": {
        "id": "TE3rSGMwLJHc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x in train.chip.unique():\n",
        "  if(x not in train_geo.chip.unique()):\n",
        "    train_geo.loc[len(train_geo.index)] = [x, np.nan] \n"
      ],
      "metadata": {
        "id": "pY8mdHToLLh_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train=pd.merge(train,train_geo,on='chip')"
      ],
      "metadata": {
        "id": "hZMJz4frLNj-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "field_geo=pd.read_csv(\"/content/fields_geo_train.csv\")"
      ],
      "metadata": {
        "id": "TGV_W1WhLPFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train=pd.merge(train,field_geo,on='field_id')"
      ],
      "metadata": {
        "id": "2bLrgOn1LQvN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pixels=pd.read_csv(\"/content/train_pixels.csv\")"
      ],
      "metadata": {
        "id": "iwod0THELSow"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train=pd.merge(train,pixels,on='field_id')"
      ],
      "metadata": {
        "id": "NFrioAbmLUvH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "components=pd.read_csv(\"/content/train_components.csv\")"
      ],
      "metadata": {
        "id": "g1vHWvxELXAC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train=pd.merge(train,components,on='field_id')"
      ],
      "metadata": {
        "id": "Ml3xrFIKLZah"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "components=pd.read_csv(\"/content/test_components.csv\")"
      ],
      "metadata": {
        "id": "6NamrQaMLbtp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=pd.merge(test,components,on='field_id')"
      ],
      "metadata": {
        "id": "T_LfE4hILdzC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pixels=pd.read_csv(\"/content/test_pixels.csv\")"
      ],
      "metadata": {
        "id": "X8cYzpcrLfvc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=pd.merge(test,pixels,on='field_id')"
      ],
      "metadata": {
        "id": "tP3_oJ3pLhYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chips_map_test=pd.read_csv(\"/content/chips_mapping_test.csv\")"
      ],
      "metadata": {
        "id": "QU0TgkgkLjQQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=pd.merge(test,chips_map_test,on='field_id')"
      ],
      "metadata": {
        "id": "_AQZH3bsLk8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_geo=pd.read_csv(\"/content/test_geo.csv\")"
      ],
      "metadata": {
        "id": "7UitpxJMLmqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x in test.chip.unique():\n",
        "  if(x not in test_geo.chip.unique()):\n",
        "    test_geo.loc[len(test_geo.index)] = [x, np.nan] "
      ],
      "metadata": {
        "id": "pMqCOIWcLoT9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=pd.merge(test,test_geo,on='chip')"
      ],
      "metadata": {
        "id": "ILHh2pcRLqCr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "field_geo=pd.read_csv(\"/content/fields_geo_test.csv\")"
      ],
      "metadata": {
        "id": "LPVy7Jm0Lr_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=pd.merge(test,field_geo,on='field_id')"
      ],
      "metadata": {
        "id": "C1u_Ok3iLuYi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([train, test]).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "g9faCd64LwYC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0DKc-9LLyvD",
        "outputId": "65a720ad-ba64-4a8e-ef3b-71714a4a7319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7081, 109)"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "VTAKWQ3dFTVx",
        "outputId": "d7161faf-e2b5-4e5d-8402-6acfab3ab163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      field_id        B01        B02        B03        B04        B05  \\\n",
              "0            1  45.000000  42.444444  42.722222  48.000000  49.666667   \n",
              "1            2  45.000000  42.000000  42.166667  47.666667  49.250000   \n",
              "2            3  45.000000  42.687500  43.500000  49.187500  51.437500   \n",
              "3           91  43.000000  38.545455  37.545455  36.272727  40.045455   \n",
              "4            4  45.866667  42.466667  43.800000  47.733333  49.466667   \n",
              "...        ...        ...        ...        ...        ...        ...   \n",
              "7076      7330  46.562500  40.000000  34.750000  29.125000  31.562500   \n",
              "7077      7301  44.000000  35.679245  30.547170  24.037736  27.679245   \n",
              "7078      7307  46.000000  40.187500  34.979167  29.833333  31.833333   \n",
              "7079      7313  47.196078  40.627451  35.450980  30.431373  32.470588   \n",
              "7080      7317  46.090909  38.818182  34.818182  30.636364  33.818182   \n",
              "\n",
              "            B06        B07        B08        B8A        B09        B11  \\\n",
              "0     58.000000  65.222222  60.277778  71.944444  12.000000  80.277778   \n",
              "1     59.916667  69.000000  63.916667  76.333333  12.833333  79.916667   \n",
              "2     62.875000  71.625000  66.625000  79.312500  13.000000  82.125000   \n",
              "3     59.000000  69.636364  65.136364  75.727273  12.000000  61.954545   \n",
              "4     59.733333  68.133333  62.600000  73.466667  11.266667  77.600000   \n",
              "...         ...        ...        ...        ...        ...        ...   \n",
              "7076  52.250000  67.125000  63.500000  72.875000  11.000000  37.937500   \n",
              "7077  54.716981  71.094340  68.396226  77.169811  13.452830  39.377358   \n",
              "7078  46.187500  54.395833  49.729167  56.979167   8.854167  35.833333   \n",
              "7079  51.549020  64.411765  59.490196  69.294118  11.000000  38.784314   \n",
              "7080  56.090909  69.454545  65.545455  75.000000  13.454545  55.181818   \n",
              "\n",
              "            B12    SARVI2     SAVI3      SAVI         SBL        SC     SLAVI  \\\n",
              "0     61.333333  1.024722  0.169292  0.150340 -103.200000  0.142394  0.551333   \n",
              "1     56.750000  1.268056  0.217923  0.193519 -101.566667  0.111142  0.613069   \n",
              "2     58.062500  1.325858  0.226337  0.200982 -105.050000  0.103965  0.623316   \n",
              "3     39.045455       NaN  0.424342  0.376878  -75.054545  1.616986  0.866321   \n",
              "4     55.000000  1.405349  0.202035  0.179414 -103.293333  0.106948  0.610871   \n",
              "...         ...       ...       ...       ...         ...       ...       ...   \n",
              "7076  19.125000 -1.418742  0.553195  0.491396  -58.900000  2.273411  1.322396   \n",
              "7077  18.943396 -2.073138  0.715643  0.635699  -44.237736  2.108728  1.592049   \n",
              "7078  20.270833 -0.701620  0.370955  0.329611  -62.745833  2.853719  1.000174   \n",
              "7079  19.392157 -1.186741  0.480907  0.427206  -62.035294  2.401198  1.198191   \n",
              "7080  36.454545 -2.270807  0.540891  0.480435  -60.072727  1.854081  0.997139   \n",
              "\n",
              "        ATSAVI   AFRI1600   AFRI1200  Alteration       ARI        AVI  \\\n",
              "0     0.016904  59.809916  59.953634    1.309101  0.003274 -24.000000   \n",
              "1     0.055910  63.464582  63.620252    1.409631  0.003409 -22.000000   \n",
              "2     0.062548  66.176502  66.332392    1.416588  0.003543 -23.187500   \n",
              "3     0.218999  64.750489  64.911744    1.588142  0.001667 -12.272727   \n",
              "4     0.043136  62.150023  62.305681    1.413025  0.002618 -25.200000   \n",
              "...        ...        ...        ...         ...       ...        ...   \n",
              "7076  0.318852  63.217158  63.371298    2.016124 -0.002913  -7.125000   \n",
              "7077  0.442209  68.121070  68.276330    2.080039 -0.003404   2.867925   \n",
              "7078  0.177420  49.408724  49.564683    1.794877 -0.002829 -12.125000   \n",
              "7079  0.263080  59.189533  59.352443    2.014008 -0.002596  -8.431373   \n",
              "7080  0.309002  65.188128  65.333581    1.536914 -0.001002  -3.727273   \n",
              "\n",
              "          ARVI     ARVI2   BDWDRVI       BRI      CCCI        CARI      CARI2  \\\n",
              "0     0.117834 -0.047342 -0.751306  0.000054  0.850027   99.624472  66.518980   \n",
              "1     0.153590 -0.009256 -0.735783  0.000053  0.894888   98.698509  65.890098   \n",
              "2     0.147666 -0.002689 -0.729875  0.000053  0.867416  103.643654  69.096936   \n",
              "3     0.377032  0.152617 -0.710851  0.000026  0.840363   83.759669  55.206420   \n",
              "4     0.136773 -0.021696 -0.742957  0.000042  0.872480   99.567443  66.493820   \n",
              "...        ...       ...       ...       ...       ...         ...        ...   \n",
              "7076  0.529044  0.253820 -0.726034 -0.000046  0.904990  286.798290  92.937573   \n",
              "7077  0.612979  0.381221 -0.678253 -0.000050  0.883075  294.430252  82.529045   \n",
              "7078  0.444393  0.111157 -0.779782 -0.000057  0.876242  283.528389  93.450132   \n",
              "7079  0.492493  0.197190 -0.744518 -0.000044  0.909298  284.738961  95.239035   \n",
              "7080  0.494972  0.244088 -0.711068 -0.000014  0.883928  188.916578  73.921129   \n",
              "\n",
              "      Chlgreen   Chgreen  Clrededge  Chlrededge       CVI         CI  \\\n",
              "0     0.655131  0.410934   0.213792    0.761600  0.066731   0.115442   \n",
              "1     0.611218  0.516696   0.298916    0.713945  0.058953   0.117921   \n",
              "2     0.607275  0.533434   0.297279    0.717986  0.059188   0.129228   \n",
              "3     0.539941  0.735866   0.627494    0.576018  0.081085   6.416518   \n",
              "4     0.642853  0.432149   0.268239    0.726002  0.045638   0.108752   \n",
              "...        ...       ...        ...         ...       ...        ...   \n",
              "7076  0.518402  0.827731   1.012901    0.470933  0.087478   8.417241   \n",
              "7077  0.431676  1.239927   1.472655    0.391042  0.121171  10.166797   \n",
              "7078  0.644862  0.422333   0.563189    0.586899  0.091263   8.239206   \n",
              "7079  0.551369  0.679698   0.834348    0.505054  0.064663   8.082516   \n",
              "7080  0.503192  0.887601   0.957151    0.489551  0.115943   8.150245   \n",
              "\n",
              "           CTI     Datt1     Datt4     Datt6       GDVI     DVIMSS       EVI  \\\n",
              "0     0.747047  0.862374  1.916141  2.908613  17.555556 -19.200000  1.024722   \n",
              "1     0.748936  0.906587  0.448729  0.718754  21.750000 -16.866667  1.268056   \n",
              "2     0.748501  0.882754  0.346054  0.559439  23.125000 -17.987500  1.325858   \n",
              "3     1.761624  0.870983  0.356766  0.716834  27.590909  -7.472727       NaN   \n",
              "4     0.736669  0.885597  1.686105  2.915930  18.800000 -20.693333  1.405349   \n",
              "...        ...       ...       ...       ...        ...        ...       ...   \n",
              "7076  2.102389  0.928719  0.507645  1.278030  28.750000  -2.725000 -1.418742   \n",
              "7077  2.251929  0.917723  0.359062  1.164500  37.849057   8.249057 -2.073138   \n",
              "7078  2.090774  0.897978  0.387580  0.755299  14.750000  -8.583333 -0.701620   \n",
              "7079  2.076255  0.929730  0.341404  0.763924  24.039216  -4.031373 -1.186741   \n",
              "7080  2.087742  0.910733  0.513552  1.219885  30.727273   1.654545 -2.270807   \n",
              "\n",
              "          EVI2     EVI22      Fe2+      Fe3+        FO        FI        FS  \\\n",
              "0     0.269627  0.173940  1.908305  1.123463  1.332427  1.908305  0.764035   \n",
              "1     0.347125  0.227369  1.773349  1.130073  1.250473  1.773349  0.709872   \n",
              "2     0.360578  0.237255  1.757889  1.129451  1.232650  1.757889  0.706441   \n",
              "3     0.675633  0.471323  1.637049  0.965857  0.952673  1.637049  0.630208   \n",
              "4     0.321797  0.210118  1.797006  1.089429  1.239751  1.797006  0.708477   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "7076  0.880387  0.639010  1.495207  0.838235  0.598243  1.495207  0.501892   \n",
              "7077  1.138901  0.872286  1.548005  0.787076  0.575574  1.548005  0.481899   \n",
              "7078  0.589857  0.405574  1.579672  0.852865  0.719267  1.579672  0.560244   \n",
              "7079  0.765228  0.543575  1.492105  0.858354  0.652298  1.492105  0.499110   \n",
              "7080  0.860973  0.625443  1.703166  0.878324  0.847219  1.703166  0.655861   \n",
              "\n",
              "             GEMI      GVMI      GARI       GLI    GBNDVI    GRNDVI       Hue  \\\n",
              "0     -136.511459 -0.008096  0.521027 -0.028416  1.589345  1.494350  0.102323   \n",
              "1     -248.997094  0.060381  0.587865 -0.030592  1.592323  1.497322  0.224903   \n",
              "2     -294.202318  0.069989  0.566211 -0.026975  1.548327  1.446119  0.328710   \n",
              "3     -811.839226  0.250544  1.178647  0.001914  1.735574  1.780446 -1.191005   \n",
              "4     -211.160009  0.065697  0.560120 -0.014726  1.561461  1.475022  0.637288   \n",
              "...           ...       ...       ...       ...       ...       ...       ...   \n",
              "7076 -1144.031436  0.538597  2.235917  0.002672  1.770531  0.888896 -1.563410   \n",
              "7077 -1914.535914  0.566411  1.898004  0.011356  0.163521  0.112116 -1.564082   \n",
              "7078  -384.596508  0.427530       NaN -0.000435  1.846432  2.104056 -1.562895   \n",
              "7079  -821.589119  0.509436  2.591479 -0.001113  1.766134  1.991040 -1.562764   \n",
              "7080 -1213.125437  0.288475  1.731474  0.001508  1.619297  1.066354 -1.559335   \n",
              "\n",
              "             I  Latirite       LCI      logR         r  Maccioni     McRIG  \\\n",
              "0     4.366120  1.309101  0.097825  0.227753  1.255874  0.905103  0.009178   \n",
              "1     4.322404  1.409631  0.131570  0.294108  1.343173  0.928386  0.005561   \n",
              "2     4.438525  1.416588  0.131678  0.305855  1.361214  0.907339  0.028253   \n",
              "3     3.684054  1.588142  0.246988  0.585387  1.800144  0.888032 -0.045191   \n",
              "4     4.393443  1.413025  0.119480  0.272526  1.315592  0.916905  0.042732   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "7076  3.405738  2.016124  0.344449  0.778940  2.180747  0.935496 -0.240231   \n",
              "7077  2.959480  2.080039  0.440246  1.045366  2.845846  0.921889 -0.322422   \n",
              "7078  3.442623  1.794877  0.223681  0.509159  1.669154  0.918088 -0.184316   \n",
              "7079  3.492125  2.014008  0.299735  0.669435  1.958161  0.939907 -0.214616   \n",
              "7080  3.418778  1.536914  0.328986  0.762782  2.164104  0.920039 -0.197769   \n",
              "\n",
              "           MVI       MGVI      MNSI       MSBI       MYVI      mARI  \\\n",
              "0     0.149491  -4.516778 -4.758222  86.471222  10.844167  0.197142   \n",
              "1     0.160657  -2.656917 -5.302583  87.484417  10.804667  0.217780   \n",
              "2     0.158490  -2.306250 -6.190188  90.886875  11.423812  0.236155   \n",
              "3     0.193760   4.231909 -6.897273  77.978091  14.308545  0.108372   \n",
              "4     0.145188  -4.254267 -5.746533  87.688600  12.343533  0.163910   \n",
              "...        ...        ...       ...        ...        ...       ...   \n",
              "7076  0.290633   4.956000 -5.101125  67.957750  15.442125 -0.185169   \n",
              "7077  0.342105  11.899283 -5.977434  65.386283  15.266887 -0.232728   \n",
              "7078  0.252016   0.107104 -3.609729  64.044042  14.532604 -0.140856   \n",
              "7079  0.285337   3.618020 -4.514882  68.574039  15.024627 -0.154651   \n",
              "7080  0.246649   7.489364 -5.201818  71.966091  14.698000 -0.069550   \n",
              "\n",
              "          mCARI      mNDVI       NLI    NORM_G  NORM_NIR    NORM_R       PPR  \\\n",
              "0     14.244313   0.681253  0.973905  0.282966  0.399171  0.317863  2.892441   \n",
              "1      0.214106   0.767494  0.976926  0.274236  0.415853  0.309911  2.904644   \n",
              "2     16.460528   0.713032  0.978076  0.273030  0.418586  0.308383  1.811658   \n",
              "3      3.673449   1.904975  0.982934  0.270249  0.468676  0.261076  3.110809   \n",
              "4     17.350276   0.839486  0.975917  0.284052  0.406470  0.309479  2.453442   \n",
              "...         ...        ...       ...       ...       ...       ...       ...   \n",
              "7076 -52.145216        NaN  0.985606  0.272883  0.498398  0.228719  3.003295   \n",
              "7077 -54.090632  10.450225  0.989750  0.248443  0.556058  0.195499  3.253707   \n",
              "7078 -51.835450  -1.759870  0.975822  0.305596  0.433758  0.260646  3.025283   \n",
              "7079 -51.821707  -9.425303  0.982776  0.282868  0.474293  0.242839  2.955787   \n",
              "7080  18.941014        NaN  0.985588  0.265847  0.500367  0.233786  3.025993   \n",
              "\n",
              "           PVR     SIWSI      NDVI     BNDVI         REP        Rre        IF  \\\n",
              "0     2.764679  1.627160  0.357557  0.173493  733.339947  56.611111       NaN   \n",
              "1     2.791695  1.615816  2.062536  0.206949  734.030174  58.333333       NaN   \n",
              "2     2.710238  1.161629  1.986158  0.219050  731.633117  60.406250       NaN   \n",
              "3     0.315974  0.099964  2.208521  0.255993  727.197270  52.954545       NaN   \n",
              "4     2.760623  1.667358  2.112613  0.191685  733.241026  57.933333  5.402614   \n",
              "...        ...       ...       ...       ...         ...        ...       ...   \n",
              "7076  0.088050  0.315290  2.564345  0.226724  731.991954  48.125000 -0.065801   \n",
              "7077  0.119210  0.323900  2.366936  0.314140  729.347910  47.566038 -0.072353   \n",
              "7078  0.079516  0.232727  3.265676  0.104951  728.580929  42.114583 -0.061806   \n",
              "7079  0.076275  0.282807  2.744472  0.187712  731.323670  47.421569 -0.060668   \n",
              "7080  0.065493  0.152811  2.229187  0.254984  729.134297  50.045455 -0.049103   \n",
              "\n",
              "           BGI    RENDVI      FIDET    RATIO2      Ratio3    Ratio4  \\\n",
              "0     1.053559  0.077409   0.655534  0.906242   28.000000  3.750000   \n",
              "1     1.067790  0.097859   0.276726  0.914402   65.500000  3.509615   \n",
              "2     1.036020  0.100466  14.089844  0.876579   73.000000  3.461538   \n",
              "3     1.145777  0.191303   0.285513  1.074114    4.000000  3.583333   \n",
              "4     1.049248  0.094436   1.107987  0.929147  192.400000  4.077778   \n",
              "...        ...       ...        ...       ...         ...       ...   \n",
              "7076  1.340126  0.246866   0.832499  1.475734  112.187500  4.232955   \n",
              "7077  1.440779  0.327729   0.693516  1.590551  128.226415  3.300629   \n",
              "7078  1.315315  0.183783   1.743017  1.445463  151.291667  5.220255   \n",
              "7079  1.331606  0.227006   2.272429  1.454080    7.156863  4.290553   \n",
              "7080  1.325773  0.248387   0.227566  1.371948  107.727273  3.438561   \n",
              "\n",
              "          Ratio5    Ratio6    Ratio7    Ratio8    Ratio9        S2REP  \\\n",
              "0     179.777778  1.359298  1.053559  5.995370  1.124509  2680.361772   \n",
              "1     181.000000  1.449993  1.067790  5.952991  1.151660  2741.995183   \n",
              "2     157.812500  1.463396  1.036020  6.100962  1.139261  2814.622971   \n",
              "3      46.909091  1.924144  1.145777  6.310606  1.179989  2556.275477   \n",
              "4      98.933333  1.431448  1.049248  6.531313  1.140762  2727.701595   \n",
              "...          ...       ...       ...       ...       ...          ...   \n",
              "7076  102.687500  2.305460  1.340126  6.625000  1.284516  2387.845127   \n",
              "7077  133.886792  2.958419  1.440779  5.769860  1.298400  2368.779405   \n",
              "7078  123.875000  1.825303  1.315315  6.443519  1.177070  2176.760576   \n",
              "7079  110.254902  2.119369  1.331606  6.299465  1.249228  2363.036103   \n",
              "7080   98.818182  2.293856  1.325773  5.585181  1.238061  2454.990227   \n",
              "\n",
              "          RESR        MI  crop_id         chip  \\\n",
              "0     0.125559  1.627160      1.0        92039   \n",
              "1     0.141470  1.615816      1.0        7e392   \n",
              "2     0.164884  1.161629      1.0        7e392   \n",
              "3     0.155996  0.099964      2.0        7e392   \n",
              "4     0.130666  1.667358      2.0        b2de1   \n",
              "...        ...       ...      ...          ...   \n",
              "7076  0.057279  0.315290      NaN        0a729   \n",
              "7077  0.107534  0.323900      NaN        545fb   \n",
              "7078  0.880016  0.232727      NaN        fbf24   \n",
              "7079  0.043744  0.282807      NaN  6ac8d+79a1b   \n",
              "7080  0.097077  0.152811      NaN        7ee61   \n",
              "\n",
              "                                                   bbox  top_left_x  \\\n",
              "0     [82.29275858265439, 27.234713779563087, 82.318...   82.295105   \n",
              "1     [82.31860826863934, 27.234467994552023, 82.344...   82.323507   \n",
              "2     [82.31860826863934, 27.234467994552023, 82.344...   82.319425   \n",
              "3     [82.31860826863934, 27.234467994552023, 82.344...   82.341470   \n",
              "4     [82.55608772500814, 27.57858710081893, 82.5823...   82.566448   \n",
              "...                                                 ...         ...   \n",
              "7076  [87.56086529081658, 25.598575317837987, 87.586...   87.575769   \n",
              "7077  [87.96891709578303, 25.6194853359099, 87.99460...   87.994202   \n",
              "7078  [87.66207405481401, 25.45943330118038, 87.6876...   87.676271   \n",
              "7079                                                NaN   87.386907   \n",
              "7080  [87.8686141319019, 25.85128831069902, 87.89433...   87.875747   \n",
              "\n",
              "      top_left_y  down_right_x  down_right_y  nbr_pixels  component  \n",
              "0      27.255234     82.294901     27.255508          18       9329  \n",
              "1      27.234833     82.323507     27.234924          12      12306  \n",
              "2      27.243316     82.319425     27.243499          16      12307  \n",
              "3      27.249337     82.341367     27.249519          22      12305  \n",
              "4      27.595677     82.566448     27.595951          15       9606  \n",
              "...          ...           ...           ...         ...        ...  \n",
              "7076   25.601115     87.575569     25.601387          16       8058  \n",
              "7077   25.625852     87.993500     25.626580          53       9515  \n",
              "7078   25.466966     87.676071     25.467874          48       8957  \n",
              "7079   25.622277     87.386807     25.622277          51       9045  \n",
              "7080   25.871376     87.875747     25.871467          11       9994  \n",
              "\n",
              "[7081 rows x 109 columns]"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>field_id</th>\n",
              "      <th>B01</th>\n",
              "      <th>B02</th>\n",
              "      <th>B03</th>\n",
              "      <th>B04</th>\n",
              "      <th>B05</th>\n",
              "      <th>B06</th>\n",
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              "      <th>B8A</th>\n",
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              "      <th>B11</th>\n",
              "      <th>B12</th>\n",
              "      <th>SARVI2</th>\n",
              "      <th>SAVI3</th>\n",
              "      <th>SAVI</th>\n",
              "      <th>SBL</th>\n",
              "      <th>SC</th>\n",
              "      <th>SLAVI</th>\n",
              "      <th>ATSAVI</th>\n",
              "      <th>AFRI1600</th>\n",
              "      <th>AFRI1200</th>\n",
              "      <th>Alteration</th>\n",
              "      <th>ARI</th>\n",
              "      <th>AVI</th>\n",
              "      <th>ARVI</th>\n",
              "      <th>ARVI2</th>\n",
              "      <th>BDWDRVI</th>\n",
              "      <th>BRI</th>\n",
              "      <th>CCCI</th>\n",
              "      <th>CARI</th>\n",
              "      <th>CARI2</th>\n",
              "      <th>Chlgreen</th>\n",
              "      <th>Chgreen</th>\n",
              "      <th>Clrededge</th>\n",
              "      <th>Chlrededge</th>\n",
              "      <th>CVI</th>\n",
              "      <th>CI</th>\n",
              "      <th>CTI</th>\n",
              "      <th>Datt1</th>\n",
              "      <th>Datt4</th>\n",
              "      <th>Datt6</th>\n",
              "      <th>GDVI</th>\n",
              "      <th>DVIMSS</th>\n",
              "      <th>EVI</th>\n",
              "      <th>EVI2</th>\n",
              "      <th>EVI22</th>\n",
              "      <th>Fe2+</th>\n",
              "      <th>Fe3+</th>\n",
              "      <th>FO</th>\n",
              "      <th>FI</th>\n",
              "      <th>FS</th>\n",
              "      <th>GEMI</th>\n",
              "      <th>GVMI</th>\n",
              "      <th>GARI</th>\n",
              "      <th>GLI</th>\n",
              "      <th>GBNDVI</th>\n",
              "      <th>GRNDVI</th>\n",
              "      <th>Hue</th>\n",
              "      <th>I</th>\n",
              "      <th>Latirite</th>\n",
              "      <th>LCI</th>\n",
              "      <th>logR</th>\n",
              "      <th>r</th>\n",
              "      <th>Maccioni</th>\n",
              "      <th>McRIG</th>\n",
              "      <th>MVI</th>\n",
              "      <th>MGVI</th>\n",
              "      <th>MNSI</th>\n",
              "      <th>MSBI</th>\n",
              "      <th>MYVI</th>\n",
              "      <th>mARI</th>\n",
              "      <th>mCARI</th>\n",
              "      <th>mNDVI</th>\n",
              "      <th>NLI</th>\n",
              "      <th>NORM_G</th>\n",
              "      <th>NORM_NIR</th>\n",
              "      <th>NORM_R</th>\n",
              "      <th>PPR</th>\n",
              "      <th>PVR</th>\n",
              "      <th>SIWSI</th>\n",
              "      <th>NDVI</th>\n",
              "      <th>BNDVI</th>\n",
              "      <th>REP</th>\n",
              "      <th>Rre</th>\n",
              "      <th>IF</th>\n",
              "      <th>BGI</th>\n",
              "      <th>RENDVI</th>\n",
              "      <th>FIDET</th>\n",
              "      <th>RATIO2</th>\n",
              "      <th>Ratio3</th>\n",
              "      <th>Ratio4</th>\n",
              "      <th>Ratio5</th>\n",
              "      <th>Ratio6</th>\n",
              "      <th>Ratio7</th>\n",
              "      <th>Ratio8</th>\n",
              "      <th>Ratio9</th>\n",
              "      <th>S2REP</th>\n",
              "      <th>RESR</th>\n",
              "      <th>MI</th>\n",
              "      <th>crop_id</th>\n",
              "      <th>chip</th>\n",
              "      <th>bbox</th>\n",
              "      <th>top_left_x</th>\n",
              "      <th>top_left_y</th>\n",
              "      <th>down_right_x</th>\n",
              "      <th>down_right_y</th>\n",
              "      <th>nbr_pixels</th>\n",
              "      <th>component</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>42.444444</td>\n",
              "      <td>42.722222</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>49.666667</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>65.222222</td>\n",
              "      <td>60.277778</td>\n",
              "      <td>71.944444</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>80.277778</td>\n",
              "      <td>61.333333</td>\n",
              "      <td>1.024722</td>\n",
              "      <td>0.169292</td>\n",
              "      <td>0.150340</td>\n",
              "      <td>-103.200000</td>\n",
              "      <td>0.142394</td>\n",
              "      <td>0.551333</td>\n",
              "      <td>0.016904</td>\n",
              "      <td>59.809916</td>\n",
              "      <td>59.953634</td>\n",
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              "      <td>0.117834</td>\n",
              "      <td>-0.047342</td>\n",
              "      <td>-0.751306</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.850027</td>\n",
              "      <td>99.624472</td>\n",
              "      <td>66.518980</td>\n",
              "      <td>0.655131</td>\n",
              "      <td>0.410934</td>\n",
              "      <td>0.213792</td>\n",
              "      <td>0.761600</td>\n",
              "      <td>0.066731</td>\n",
              "      <td>0.115442</td>\n",
              "      <td>0.747047</td>\n",
              "      <td>0.862374</td>\n",
              "      <td>1.916141</td>\n",
              "      <td>2.908613</td>\n",
              "      <td>17.555556</td>\n",
              "      <td>-19.200000</td>\n",
              "      <td>1.024722</td>\n",
              "      <td>0.269627</td>\n",
              "      <td>0.173940</td>\n",
              "      <td>1.908305</td>\n",
              "      <td>1.123463</td>\n",
              "      <td>1.332427</td>\n",
              "      <td>1.908305</td>\n",
              "      <td>0.764035</td>\n",
              "      <td>-136.511459</td>\n",
              "      <td>-0.008096</td>\n",
              "      <td>0.521027</td>\n",
              "      <td>-0.028416</td>\n",
              "      <td>1.589345</td>\n",
              "      <td>1.494350</td>\n",
              "      <td>0.102323</td>\n",
              "      <td>4.366120</td>\n",
              "      <td>1.309101</td>\n",
              "      <td>0.097825</td>\n",
              "      <td>0.227753</td>\n",
              "      <td>1.255874</td>\n",
              "      <td>0.905103</td>\n",
              "      <td>0.009178</td>\n",
              "      <td>0.149491</td>\n",
              "      <td>-4.516778</td>\n",
              "      <td>-4.758222</td>\n",
              "      <td>86.471222</td>\n",
              "      <td>10.844167</td>\n",
              "      <td>0.197142</td>\n",
              "      <td>14.244313</td>\n",
              "      <td>0.681253</td>\n",
              "      <td>0.973905</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.399171</td>\n",
              "      <td>0.317863</td>\n",
              "      <td>2.892441</td>\n",
              "      <td>2.764679</td>\n",
              "      <td>1.627160</td>\n",
              "      <td>0.357557</td>\n",
              "      <td>0.173493</td>\n",
              "      <td>733.339947</td>\n",
              "      <td>56.611111</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.053559</td>\n",
              "      <td>0.077409</td>\n",
              "      <td>0.655534</td>\n",
              "      <td>0.906242</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>179.777778</td>\n",
              "      <td>1.359298</td>\n",
              "      <td>1.053559</td>\n",
              "      <td>5.995370</td>\n",
              "      <td>1.124509</td>\n",
              "      <td>2680.361772</td>\n",
              "      <td>0.125559</td>\n",
              "      <td>1.627160</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92039</td>\n",
              "      <td>[82.29275858265439, 27.234713779563087, 82.318...</td>\n",
              "      <td>82.295105</td>\n",
              "      <td>27.255234</td>\n",
              "      <td>82.294901</td>\n",
              "      <td>27.255508</td>\n",
              "      <td>18</td>\n",
              "      <td>9329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>42.166667</td>\n",
              "      <td>47.666667</td>\n",
              "      <td>49.250000</td>\n",
              "      <td>59.916667</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>63.916667</td>\n",
              "      <td>76.333333</td>\n",
              "      <td>12.833333</td>\n",
              "      <td>79.916667</td>\n",
              "      <td>56.750000</td>\n",
              "      <td>1.268056</td>\n",
              "      <td>0.217923</td>\n",
              "      <td>0.193519</td>\n",
              "      <td>-101.566667</td>\n",
              "      <td>0.111142</td>\n",
              "      <td>0.613069</td>\n",
              "      <td>0.055910</td>\n",
              "      <td>63.464582</td>\n",
              "      <td>63.620252</td>\n",
              "      <td>1.409631</td>\n",
              "      <td>0.003409</td>\n",
              "      <td>-22.000000</td>\n",
              "      <td>0.153590</td>\n",
              "      <td>-0.009256</td>\n",
              "      <td>-0.735783</td>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.894888</td>\n",
              "      <td>98.698509</td>\n",
              "      <td>65.890098</td>\n",
              "      <td>0.611218</td>\n",
              "      <td>0.516696</td>\n",
              "      <td>0.298916</td>\n",
              "      <td>0.713945</td>\n",
              "      <td>0.058953</td>\n",
              "      <td>0.117921</td>\n",
              "      <td>0.748936</td>\n",
              "      <td>0.906587</td>\n",
              "      <td>0.448729</td>\n",
              "      <td>0.718754</td>\n",
              "      <td>21.750000</td>\n",
              "      <td>-16.866667</td>\n",
              "      <td>1.268056</td>\n",
              "      <td>0.347125</td>\n",
              "      <td>0.227369</td>\n",
              "      <td>1.773349</td>\n",
              "      <td>1.130073</td>\n",
              "      <td>1.250473</td>\n",
              "      <td>1.773349</td>\n",
              "      <td>0.709872</td>\n",
              "      <td>-248.997094</td>\n",
              "      <td>0.060381</td>\n",
              "      <td>0.587865</td>\n",
              "      <td>-0.030592</td>\n",
              "      <td>1.592323</td>\n",
              "      <td>1.497322</td>\n",
              "      <td>0.224903</td>\n",
              "      <td>4.322404</td>\n",
              "      <td>1.409631</td>\n",
              "      <td>0.131570</td>\n",
              "      <td>0.294108</td>\n",
              "      <td>1.343173</td>\n",
              "      <td>0.928386</td>\n",
              "      <td>0.005561</td>\n",
              "      <td>0.160657</td>\n",
              "      <td>-2.656917</td>\n",
              "      <td>-5.302583</td>\n",
              "      <td>87.484417</td>\n",
              "      <td>10.804667</td>\n",
              "      <td>0.217780</td>\n",
              "      <td>0.214106</td>\n",
              "      <td>0.767494</td>\n",
              "      <td>0.976926</td>\n",
              "      <td>0.274236</td>\n",
              "      <td>0.415853</td>\n",
              "      <td>0.309911</td>\n",
              "      <td>2.904644</td>\n",
              "      <td>2.791695</td>\n",
              "      <td>1.615816</td>\n",
              "      <td>2.062536</td>\n",
              "      <td>0.206949</td>\n",
              "      <td>734.030174</td>\n",
              "      <td>58.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.067790</td>\n",
              "      <td>0.097859</td>\n",
              "      <td>0.276726</td>\n",
              "      <td>0.914402</td>\n",
              "      <td>65.500000</td>\n",
              "      <td>3.509615</td>\n",
              "      <td>181.000000</td>\n",
              "      <td>1.449993</td>\n",
              "      <td>1.067790</td>\n",
              "      <td>5.952991</td>\n",
              "      <td>1.151660</td>\n",
              "      <td>2741.995183</td>\n",
              "      <td>0.141470</td>\n",
              "      <td>1.615816</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7e392</td>\n",
              "      <td>[82.31860826863934, 27.234467994552023, 82.344...</td>\n",
              "      <td>82.323507</td>\n",
              "      <td>27.234833</td>\n",
              "      <td>82.323507</td>\n",
              "      <td>27.234924</td>\n",
              "      <td>12</td>\n",
              "      <td>12306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>42.687500</td>\n",
              "      <td>43.500000</td>\n",
              "      <td>49.187500</td>\n",
              "      <td>51.437500</td>\n",
              "      <td>62.875000</td>\n",
              "      <td>71.625000</td>\n",
              "      <td>66.625000</td>\n",
              "      <td>79.312500</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>82.125000</td>\n",
              "      <td>58.062500</td>\n",
              "      <td>1.325858</td>\n",
              "      <td>0.226337</td>\n",
              "      <td>0.200982</td>\n",
              "      <td>-105.050000</td>\n",
              "      <td>0.103965</td>\n",
              "      <td>0.623316</td>\n",
              "      <td>0.062548</td>\n",
              "      <td>66.176502</td>\n",
              "      <td>66.332392</td>\n",
              "      <td>1.416588</td>\n",
              "      <td>0.003543</td>\n",
              "      <td>-23.187500</td>\n",
              "      <td>0.147666</td>\n",
              "      <td>-0.002689</td>\n",
              "      <td>-0.729875</td>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.867416</td>\n",
              "      <td>103.643654</td>\n",
              "      <td>69.096936</td>\n",
              "      <td>0.607275</td>\n",
              "      <td>0.533434</td>\n",
              "      <td>0.297279</td>\n",
              "      <td>0.717986</td>\n",
              "      <td>0.059188</td>\n",
              "      <td>0.129228</td>\n",
              "      <td>0.748501</td>\n",
              "      <td>0.882754</td>\n",
              "      <td>0.346054</td>\n",
              "      <td>0.559439</td>\n",
              "      <td>23.125000</td>\n",
              "      <td>-17.987500</td>\n",
              "      <td>1.325858</td>\n",
              "      <td>0.360578</td>\n",
              "      <td>0.237255</td>\n",
              "      <td>1.757889</td>\n",
              "      <td>1.129451</td>\n",
              "      <td>1.232650</td>\n",
              "      <td>1.757889</td>\n",
              "      <td>0.706441</td>\n",
              "      <td>-294.202318</td>\n",
              "      <td>0.069989</td>\n",
              "      <td>0.566211</td>\n",
              "      <td>-0.026975</td>\n",
              "      <td>1.548327</td>\n",
              "      <td>1.446119</td>\n",
              "      <td>0.328710</td>\n",
              "      <td>4.438525</td>\n",
              "      <td>1.416588</td>\n",
              "      <td>0.131678</td>\n",
              "      <td>0.305855</td>\n",
              "      <td>1.361214</td>\n",
              "      <td>0.907339</td>\n",
              "      <td>0.028253</td>\n",
              "      <td>0.158490</td>\n",
              "      <td>-2.306250</td>\n",
              "      <td>-6.190188</td>\n",
              "      <td>90.886875</td>\n",
              "      <td>11.423812</td>\n",
              "      <td>0.236155</td>\n",
              "      <td>16.460528</td>\n",
              "      <td>0.713032</td>\n",
              "      <td>0.978076</td>\n",
              "      <td>0.273030</td>\n",
              "      <td>0.418586</td>\n",
              "      <td>0.308383</td>\n",
              "      <td>1.811658</td>\n",
              "      <td>2.710238</td>\n",
              "      <td>1.161629</td>\n",
              "      <td>1.986158</td>\n",
              "      <td>0.219050</td>\n",
              "      <td>731.633117</td>\n",
              "      <td>60.406250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.036020</td>\n",
              "      <td>0.100466</td>\n",
              "      <td>14.089844</td>\n",
              "      <td>0.876579</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>3.461538</td>\n",
              "      <td>157.812500</td>\n",
              "      <td>1.463396</td>\n",
              "      <td>1.036020</td>\n",
              "      <td>6.100962</td>\n",
              "      <td>1.139261</td>\n",
              "      <td>2814.622971</td>\n",
              "      <td>0.164884</td>\n",
              "      <td>1.161629</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7e392</td>\n",
              "      <td>[82.31860826863934, 27.234467994552023, 82.344...</td>\n",
              "      <td>82.319425</td>\n",
              "      <td>27.243316</td>\n",
              "      <td>82.319425</td>\n",
              "      <td>27.243499</td>\n",
              "      <td>16</td>\n",
              "      <td>12307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>91</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>38.545455</td>\n",
              "      <td>37.545455</td>\n",
              "      <td>36.272727</td>\n",
              "      <td>40.045455</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>69.636364</td>\n",
              "      <td>65.136364</td>\n",
              "      <td>75.727273</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>61.954545</td>\n",
              "      <td>39.045455</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.424342</td>\n",
              "      <td>0.376878</td>\n",
              "      <td>-75.054545</td>\n",
              "      <td>1.616986</td>\n",
              "      <td>0.866321</td>\n",
              "      <td>0.218999</td>\n",
              "      <td>64.750489</td>\n",
              "      <td>64.911744</td>\n",
              "      <td>1.588142</td>\n",
              "      <td>0.001667</td>\n",
              "      <td>-12.272727</td>\n",
              "      <td>0.377032</td>\n",
              "      <td>0.152617</td>\n",
              "      <td>-0.710851</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.840363</td>\n",
              "      <td>83.759669</td>\n",
              "      <td>55.206420</td>\n",
              "      <td>0.539941</td>\n",
              "      <td>0.735866</td>\n",
              "      <td>0.627494</td>\n",
              "      <td>0.576018</td>\n",
              "      <td>0.081085</td>\n",
              "      <td>6.416518</td>\n",
              "      <td>1.761624</td>\n",
              "      <td>0.870983</td>\n",
              "      <td>0.356766</td>\n",
              "      <td>0.716834</td>\n",
              "      <td>27.590909</td>\n",
              "      <td>-7.472727</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.675633</td>\n",
              "      <td>0.471323</td>\n",
              "      <td>1.637049</td>\n",
              "      <td>0.965857</td>\n",
              "      <td>0.952673</td>\n",
              "      <td>1.637049</td>\n",
              "      <td>0.630208</td>\n",
              "      <td>-811.839226</td>\n",
              "      <td>0.250544</td>\n",
              "      <td>1.178647</td>\n",
              "      <td>0.001914</td>\n",
              "      <td>1.735574</td>\n",
              "      <td>1.780446</td>\n",
              "      <td>-1.191005</td>\n",
              "      <td>3.684054</td>\n",
              "      <td>1.588142</td>\n",
              "      <td>0.246988</td>\n",
              "      <td>0.585387</td>\n",
              "      <td>1.800144</td>\n",
              "      <td>0.888032</td>\n",
              "      <td>-0.045191</td>\n",
              "      <td>0.193760</td>\n",
              "      <td>4.231909</td>\n",
              "      <td>-6.897273</td>\n",
              "      <td>77.978091</td>\n",
              "      <td>14.308545</td>\n",
              "      <td>0.108372</td>\n",
              "      <td>3.673449</td>\n",
              "      <td>1.904975</td>\n",
              "      <td>0.982934</td>\n",
              "      <td>0.270249</td>\n",
              "      <td>0.468676</td>\n",
              "      <td>0.261076</td>\n",
              "      <td>3.110809</td>\n",
              "      <td>0.315974</td>\n",
              "      <td>0.099964</td>\n",
              "      <td>2.208521</td>\n",
              "      <td>0.255993</td>\n",
              "      <td>727.197270</td>\n",
              "      <td>52.954545</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.145777</td>\n",
              "      <td>0.191303</td>\n",
              "      <td>0.285513</td>\n",
              "      <td>1.074114</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.583333</td>\n",
              "      <td>46.909091</td>\n",
              "      <td>1.924144</td>\n",
              "      <td>1.145777</td>\n",
              "      <td>6.310606</td>\n",
              "      <td>1.179989</td>\n",
              "      <td>2556.275477</td>\n",
              "      <td>0.155996</td>\n",
              "      <td>0.099964</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7e392</td>\n",
              "      <td>[82.31860826863934, 27.234467994552023, 82.344...</td>\n",
              "      <td>82.341470</td>\n",
              "      <td>27.249337</td>\n",
              "      <td>82.341367</td>\n",
              "      <td>27.249519</td>\n",
              "      <td>22</td>\n",
              "      <td>12305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>45.866667</td>\n",
              "      <td>42.466667</td>\n",
              "      <td>43.800000</td>\n",
              "      <td>47.733333</td>\n",
              "      <td>49.466667</td>\n",
              "      <td>59.733333</td>\n",
              "      <td>68.133333</td>\n",
              "      <td>62.600000</td>\n",
              "      <td>73.466667</td>\n",
              "      <td>11.266667</td>\n",
              "      <td>77.600000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.405349</td>\n",
              "      <td>0.202035</td>\n",
              "      <td>0.179414</td>\n",
              "      <td>-103.293333</td>\n",
              "      <td>0.106948</td>\n",
              "      <td>0.610871</td>\n",
              "      <td>0.043136</td>\n",
              "      <td>62.150023</td>\n",
              "      <td>62.305681</td>\n",
              "      <td>1.413025</td>\n",
              "      <td>0.002618</td>\n",
              "      <td>-25.200000</td>\n",
              "      <td>0.136773</td>\n",
              "      <td>-0.021696</td>\n",
              "      <td>-0.742957</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.872480</td>\n",
              "      <td>99.567443</td>\n",
              "      <td>66.493820</td>\n",
              "      <td>0.642853</td>\n",
              "      <td>0.432149</td>\n",
              "      <td>0.268239</td>\n",
              "      <td>0.726002</td>\n",
              "      <td>0.045638</td>\n",
              "      <td>0.108752</td>\n",
              "      <td>0.736669</td>\n",
              "      <td>0.885597</td>\n",
              "      <td>1.686105</td>\n",
              "      <td>2.915930</td>\n",
              "      <td>18.800000</td>\n",
              "      <td>-20.693333</td>\n",
              "      <td>1.405349</td>\n",
              "      <td>0.321797</td>\n",
              "      <td>0.210118</td>\n",
              "      <td>1.797006</td>\n",
              "      <td>1.089429</td>\n",
              "      <td>1.239751</td>\n",
              "      <td>1.797006</td>\n",
              "      <td>0.708477</td>\n",
              "      <td>-211.160009</td>\n",
              "      <td>0.065697</td>\n",
              "      <td>0.560120</td>\n",
              "      <td>-0.014726</td>\n",
              "      <td>1.561461</td>\n",
              "      <td>1.475022</td>\n",
              "      <td>0.637288</td>\n",
              "      <td>4.393443</td>\n",
              "      <td>1.413025</td>\n",
              "      <td>0.119480</td>\n",
              "      <td>0.272526</td>\n",
              "      <td>1.315592</td>\n",
              "      <td>0.916905</td>\n",
              "      <td>0.042732</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>-4.254267</td>\n",
              "      <td>-5.746533</td>\n",
              "      <td>87.688600</td>\n",
              "      <td>12.343533</td>\n",
              "      <td>0.163910</td>\n",
              "      <td>17.350276</td>\n",
              "      <td>0.839486</td>\n",
              "      <td>0.975917</td>\n",
              "      <td>0.284052</td>\n",
              "      <td>0.406470</td>\n",
              "      <td>0.309479</td>\n",
              "      <td>2.453442</td>\n",
              "      <td>2.760623</td>\n",
              "      <td>1.667358</td>\n",
              "      <td>2.112613</td>\n",
              "      <td>0.191685</td>\n",
              "      <td>733.241026</td>\n",
              "      <td>57.933333</td>\n",
              "      <td>5.402614</td>\n",
              "      <td>1.049248</td>\n",
              "      <td>0.094436</td>\n",
              "      <td>1.107987</td>\n",
              "      <td>0.929147</td>\n",
              "      <td>192.400000</td>\n",
              "      <td>4.077778</td>\n",
              "      <td>98.933333</td>\n",
              "      <td>1.431448</td>\n",
              "      <td>1.049248</td>\n",
              "      <td>6.531313</td>\n",
              "      <td>1.140762</td>\n",
              "      <td>2727.701595</td>\n",
              "      <td>0.130666</td>\n",
              "      <td>1.667358</td>\n",
              "      <td>2.0</td>\n",
              "      <td>b2de1</td>\n",
              "      <td>[82.55608772500814, 27.57858710081893, 82.5823...</td>\n",
              "      <td>82.566448</td>\n",
              "      <td>27.595677</td>\n",
              "      <td>82.566448</td>\n",
              "      <td>27.595951</td>\n",
              "      <td>15</td>\n",
              "      <td>9606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7076</th>\n",
              "      <td>7330</td>\n",
              "      <td>46.562500</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>34.750000</td>\n",
              "      <td>29.125000</td>\n",
              "      <td>31.562500</td>\n",
              "      <td>52.250000</td>\n",
              "      <td>67.125000</td>\n",
              "      <td>63.500000</td>\n",
              "      <td>72.875000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>37.937500</td>\n",
              "      <td>19.125000</td>\n",
              "      <td>-1.418742</td>\n",
              "      <td>0.553195</td>\n",
              "      <td>0.491396</td>\n",
              "      <td>-58.900000</td>\n",
              "      <td>2.273411</td>\n",
              "      <td>1.322396</td>\n",
              "      <td>0.318852</td>\n",
              "      <td>63.217158</td>\n",
              "      <td>63.371298</td>\n",
              "      <td>2.016124</td>\n",
              "      <td>-0.002913</td>\n",
              "      <td>-7.125000</td>\n",
              "      <td>0.529044</td>\n",
              "      <td>0.253820</td>\n",
              "      <td>-0.726034</td>\n",
              "      <td>-0.000046</td>\n",
              "      <td>0.904990</td>\n",
              "      <td>286.798290</td>\n",
              "      <td>92.937573</td>\n",
              "      <td>0.518402</td>\n",
              "      <td>0.827731</td>\n",
              "      <td>1.012901</td>\n",
              "      <td>0.470933</td>\n",
              "      <td>0.087478</td>\n",
              "      <td>8.417241</td>\n",
              "      <td>2.102389</td>\n",
              "      <td>0.928719</td>\n",
              "      <td>0.507645</td>\n",
              "      <td>1.278030</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>-2.725000</td>\n",
              "      <td>-1.418742</td>\n",
              "      <td>0.880387</td>\n",
              "      <td>0.639010</td>\n",
              "      <td>1.495207</td>\n",
              "      <td>0.838235</td>\n",
              "      <td>0.598243</td>\n",
              "      <td>1.495207</td>\n",
              "      <td>0.501892</td>\n",
              "      <td>-1144.031436</td>\n",
              "      <td>0.538597</td>\n",
              "      <td>2.235917</td>\n",
              "      <td>0.002672</td>\n",
              "      <td>1.770531</td>\n",
              "      <td>0.888896</td>\n",
              "      <td>-1.563410</td>\n",
              "      <td>3.405738</td>\n",
              "      <td>2.016124</td>\n",
              "      <td>0.344449</td>\n",
              "      <td>0.778940</td>\n",
              "      <td>2.180747</td>\n",
              "      <td>0.935496</td>\n",
              "      <td>-0.240231</td>\n",
              "      <td>0.290633</td>\n",
              "      <td>4.956000</td>\n",
              "      <td>-5.101125</td>\n",
              "      <td>67.957750</td>\n",
              "      <td>15.442125</td>\n",
              "      <td>-0.185169</td>\n",
              "      <td>-52.145216</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.985606</td>\n",
              "      <td>0.272883</td>\n",
              "      <td>0.498398</td>\n",
              "      <td>0.228719</td>\n",
              "      <td>3.003295</td>\n",
              "      <td>0.088050</td>\n",
              "      <td>0.315290</td>\n",
              "      <td>2.564345</td>\n",
              "      <td>0.226724</td>\n",
              "      <td>731.991954</td>\n",
              "      <td>48.125000</td>\n",
              "      <td>-0.065801</td>\n",
              "      <td>1.340126</td>\n",
              "      <td>0.246866</td>\n",
              "      <td>0.832499</td>\n",
              "      <td>1.475734</td>\n",
              "      <td>112.187500</td>\n",
              "      <td>4.232955</td>\n",
              "      <td>102.687500</td>\n",
              "      <td>2.305460</td>\n",
              "      <td>1.340126</td>\n",
              "      <td>6.625000</td>\n",
              "      <td>1.284516</td>\n",
              "      <td>2387.845127</td>\n",
              "      <td>0.057279</td>\n",
              "      <td>0.315290</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0a729</td>\n",
              "      <td>[87.56086529081658, 25.598575317837987, 87.586...</td>\n",
              "      <td>87.575769</td>\n",
              "      <td>25.601115</td>\n",
              "      <td>87.575569</td>\n",
              "      <td>25.601387</td>\n",
              "      <td>16</td>\n",
              "      <td>8058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7077</th>\n",
              "      <td>7301</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>35.679245</td>\n",
              "      <td>30.547170</td>\n",
              "      <td>24.037736</td>\n",
              "      <td>27.679245</td>\n",
              "      <td>54.716981</td>\n",
              "      <td>71.094340</td>\n",
              "      <td>68.396226</td>\n",
              "      <td>77.169811</td>\n",
              "      <td>13.452830</td>\n",
              "      <td>39.377358</td>\n",
              "      <td>18.943396</td>\n",
              "      <td>-2.073138</td>\n",
              "      <td>0.715643</td>\n",
              "      <td>0.635699</td>\n",
              "      <td>-44.237736</td>\n",
              "      <td>2.108728</td>\n",
              "      <td>1.592049</td>\n",
              "      <td>0.442209</td>\n",
              "      <td>68.121070</td>\n",
              "      <td>68.276330</td>\n",
              "      <td>2.080039</td>\n",
              "      <td>-0.003404</td>\n",
              "      <td>2.867925</td>\n",
              "      <td>0.612979</td>\n",
              "      <td>0.381221</td>\n",
              "      <td>-0.678253</td>\n",
              "      <td>-0.000050</td>\n",
              "      <td>0.883075</td>\n",
              "      <td>294.430252</td>\n",
              "      <td>82.529045</td>\n",
              "      <td>0.431676</td>\n",
              "      <td>1.239927</td>\n",
              "      <td>1.472655</td>\n",
              "      <td>0.391042</td>\n",
              "      <td>0.121171</td>\n",
              "      <td>10.166797</td>\n",
              "      <td>2.251929</td>\n",
              "      <td>0.917723</td>\n",
              "      <td>0.359062</td>\n",
              "      <td>1.164500</td>\n",
              "      <td>37.849057</td>\n",
              "      <td>8.249057</td>\n",
              "      <td>-2.073138</td>\n",
              "      <td>1.138901</td>\n",
              "      <td>0.872286</td>\n",
              "      <td>1.548005</td>\n",
              "      <td>0.787076</td>\n",
              "      <td>0.575574</td>\n",
              "      <td>1.548005</td>\n",
              "      <td>0.481899</td>\n",
              "      <td>-1914.535914</td>\n",
              "      <td>0.566411</td>\n",
              "      <td>1.898004</td>\n",
              "      <td>0.011356</td>\n",
              "      <td>0.163521</td>\n",
              "      <td>0.112116</td>\n",
              "      <td>-1.564082</td>\n",
              "      <td>2.959480</td>\n",
              "      <td>2.080039</td>\n",
              "      <td>0.440246</td>\n",
              "      <td>1.045366</td>\n",
              "      <td>2.845846</td>\n",
              "      <td>0.921889</td>\n",
              "      <td>-0.322422</td>\n",
              "      <td>0.342105</td>\n",
              "      <td>11.899283</td>\n",
              "      <td>-5.977434</td>\n",
              "      <td>65.386283</td>\n",
              "      <td>15.266887</td>\n",
              "      <td>-0.232728</td>\n",
              "      <td>-54.090632</td>\n",
              "      <td>10.450225</td>\n",
              "      <td>0.989750</td>\n",
              "      <td>0.248443</td>\n",
              "      <td>0.556058</td>\n",
              "      <td>0.195499</td>\n",
              "      <td>3.253707</td>\n",
              "      <td>0.119210</td>\n",
              "      <td>0.323900</td>\n",
              "      <td>2.366936</td>\n",
              "      <td>0.314140</td>\n",
              "      <td>729.347910</td>\n",
              "      <td>47.566038</td>\n",
              "      <td>-0.072353</td>\n",
              "      <td>1.440779</td>\n",
              "      <td>0.327729</td>\n",
              "      <td>0.693516</td>\n",
              "      <td>1.590551</td>\n",
              "      <td>128.226415</td>\n",
              "      <td>3.300629</td>\n",
              "      <td>133.886792</td>\n",
              "      <td>2.958419</td>\n",
              "      <td>1.440779</td>\n",
              "      <td>5.769860</td>\n",
              "      <td>1.298400</td>\n",
              "      <td>2368.779405</td>\n",
              "      <td>0.107534</td>\n",
              "      <td>0.323900</td>\n",
              "      <td>NaN</td>\n",
              "      <td>545fb</td>\n",
              "      <td>[87.96891709578303, 25.6194853359099, 87.99460...</td>\n",
              "      <td>87.994202</td>\n",
              "      <td>25.625852</td>\n",
              "      <td>87.993500</td>\n",
              "      <td>25.626580</td>\n",
              "      <td>53</td>\n",
              "      <td>9515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7078</th>\n",
              "      <td>7307</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>40.187500</td>\n",
              "      <td>34.979167</td>\n",
              "      <td>29.833333</td>\n",
              "      <td>31.833333</td>\n",
              "      <td>46.187500</td>\n",
              "      <td>54.395833</td>\n",
              "      <td>49.729167</td>\n",
              "      <td>56.979167</td>\n",
              "      <td>8.854167</td>\n",
              "      <td>35.833333</td>\n",
              "      <td>20.270833</td>\n",
              "      <td>-0.701620</td>\n",
              "      <td>0.370955</td>\n",
              "      <td>0.329611</td>\n",
              "      <td>-62.745833</td>\n",
              "      <td>2.853719</td>\n",
              "      <td>1.000174</td>\n",
              "      <td>0.177420</td>\n",
              "      <td>49.408724</td>\n",
              "      <td>49.564683</td>\n",
              "      <td>1.794877</td>\n",
              "      <td>-0.002829</td>\n",
              "      <td>-12.125000</td>\n",
              "      <td>0.444393</td>\n",
              "      <td>0.111157</td>\n",
              "      <td>-0.779782</td>\n",
              "      <td>-0.000057</td>\n",
              "      <td>0.876242</td>\n",
              "      <td>283.528389</td>\n",
              "      <td>93.450132</td>\n",
              "      <td>0.644862</td>\n",
              "      <td>0.422333</td>\n",
              "      <td>0.563189</td>\n",
              "      <td>0.586899</td>\n",
              "      <td>0.091263</td>\n",
              "      <td>8.239206</td>\n",
              "      <td>2.090774</td>\n",
              "      <td>0.897978</td>\n",
              "      <td>0.387580</td>\n",
              "      <td>0.755299</td>\n",
              "      <td>14.750000</td>\n",
              "      <td>-8.583333</td>\n",
              "      <td>-0.701620</td>\n",
              "      <td>0.589857</td>\n",
              "      <td>0.405574</td>\n",
              "      <td>1.579672</td>\n",
              "      <td>0.852865</td>\n",
              "      <td>0.719267</td>\n",
              "      <td>1.579672</td>\n",
              "      <td>0.560244</td>\n",
              "      <td>-384.596508</td>\n",
              "      <td>0.427530</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.000435</td>\n",
              "      <td>1.846432</td>\n",
              "      <td>2.104056</td>\n",
              "      <td>-1.562895</td>\n",
              "      <td>3.442623</td>\n",
              "      <td>1.794877</td>\n",
              "      <td>0.223681</td>\n",
              "      <td>0.509159</td>\n",
              "      <td>1.669154</td>\n",
              "      <td>0.918088</td>\n",
              "      <td>-0.184316</td>\n",
              "      <td>0.252016</td>\n",
              "      <td>0.107104</td>\n",
              "      <td>-3.609729</td>\n",
              "      <td>64.044042</td>\n",
              "      <td>14.532604</td>\n",
              "      <td>-0.140856</td>\n",
              "      <td>-51.835450</td>\n",
              "      <td>-1.759870</td>\n",
              "      <td>0.975822</td>\n",
              "      <td>0.305596</td>\n",
              "      <td>0.433758</td>\n",
              "      <td>0.260646</td>\n",
              "      <td>3.025283</td>\n",
              "      <td>0.079516</td>\n",
              "      <td>0.232727</td>\n",
              "      <td>3.265676</td>\n",
              "      <td>0.104951</td>\n",
              "      <td>728.580929</td>\n",
              "      <td>42.114583</td>\n",
              "      <td>-0.061806</td>\n",
              "      <td>1.315315</td>\n",
              "      <td>0.183783</td>\n",
              "      <td>1.743017</td>\n",
              "      <td>1.445463</td>\n",
              "      <td>151.291667</td>\n",
              "      <td>5.220255</td>\n",
              "      <td>123.875000</td>\n",
              "      <td>1.825303</td>\n",
              "      <td>1.315315</td>\n",
              "      <td>6.443519</td>\n",
              "      <td>1.177070</td>\n",
              "      <td>2176.760576</td>\n",
              "      <td>0.880016</td>\n",
              "      <td>0.232727</td>\n",
              "      <td>NaN</td>\n",
              "      <td>fbf24</td>\n",
              "      <td>[87.66207405481401, 25.45943330118038, 87.6876...</td>\n",
              "      <td>87.676271</td>\n",
              "      <td>25.466966</td>\n",
              "      <td>87.676071</td>\n",
              "      <td>25.467874</td>\n",
              "      <td>48</td>\n",
              "      <td>8957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7079</th>\n",
              "      <td>7313</td>\n",
              "      <td>47.196078</td>\n",
              "      <td>40.627451</td>\n",
              "      <td>35.450980</td>\n",
              "      <td>30.431373</td>\n",
              "      <td>32.470588</td>\n",
              "      <td>51.549020</td>\n",
              "      <td>64.411765</td>\n",
              "      <td>59.490196</td>\n",
              "      <td>69.294118</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>38.784314</td>\n",
              "      <td>19.392157</td>\n",
              "      <td>-1.186741</td>\n",
              "      <td>0.480907</td>\n",
              "      <td>0.427206</td>\n",
              "      <td>-62.035294</td>\n",
              "      <td>2.401198</td>\n",
              "      <td>1.198191</td>\n",
              "      <td>0.263080</td>\n",
              "      <td>59.189533</td>\n",
              "      <td>59.352443</td>\n",
              "      <td>2.014008</td>\n",
              "      <td>-0.002596</td>\n",
              "      <td>-8.431373</td>\n",
              "      <td>0.492493</td>\n",
              "      <td>0.197190</td>\n",
              "      <td>-0.744518</td>\n",
              "      <td>-0.000044</td>\n",
              "      <td>0.909298</td>\n",
              "      <td>284.738961</td>\n",
              "      <td>95.239035</td>\n",
              "      <td>0.551369</td>\n",
              "      <td>0.679698</td>\n",
              "      <td>0.834348</td>\n",
              "      <td>0.505054</td>\n",
              "      <td>0.064663</td>\n",
              "      <td>8.082516</td>\n",
              "      <td>2.076255</td>\n",
              "      <td>0.929730</td>\n",
              "      <td>0.341404</td>\n",
              "      <td>0.763924</td>\n",
              "      <td>24.039216</td>\n",
              "      <td>-4.031373</td>\n",
              "      <td>-1.186741</td>\n",
              "      <td>0.765228</td>\n",
              "      <td>0.543575</td>\n",
              "      <td>1.492105</td>\n",
              "      <td>0.858354</td>\n",
              "      <td>0.652298</td>\n",
              "      <td>1.492105</td>\n",
              "      <td>0.499110</td>\n",
              "      <td>-821.589119</td>\n",
              "      <td>0.509436</td>\n",
              "      <td>2.591479</td>\n",
              "      <td>-0.001113</td>\n",
              "      <td>1.766134</td>\n",
              "      <td>1.991040</td>\n",
              "      <td>-1.562764</td>\n",
              "      <td>3.492125</td>\n",
              "      <td>2.014008</td>\n",
              "      <td>0.299735</td>\n",
              "      <td>0.669435</td>\n",
              "      <td>1.958161</td>\n",
              "      <td>0.939907</td>\n",
              "      <td>-0.214616</td>\n",
              "      <td>0.285337</td>\n",
              "      <td>3.618020</td>\n",
              "      <td>-4.514882</td>\n",
              "      <td>68.574039</td>\n",
              "      <td>15.024627</td>\n",
              "      <td>-0.154651</td>\n",
              "      <td>-51.821707</td>\n",
              "      <td>-9.425303</td>\n",
              "      <td>0.982776</td>\n",
              "      <td>0.282868</td>\n",
              "      <td>0.474293</td>\n",
              "      <td>0.242839</td>\n",
              "      <td>2.955787</td>\n",
              "      <td>0.076275</td>\n",
              "      <td>0.282807</td>\n",
              "      <td>2.744472</td>\n",
              "      <td>0.187712</td>\n",
              "      <td>731.323670</td>\n",
              "      <td>47.421569</td>\n",
              "      <td>-0.060668</td>\n",
              "      <td>1.331606</td>\n",
              "      <td>0.227006</td>\n",
              "      <td>2.272429</td>\n",
              "      <td>1.454080</td>\n",
              "      <td>7.156863</td>\n",
              "      <td>4.290553</td>\n",
              "      <td>110.254902</td>\n",
              "      <td>2.119369</td>\n",
              "      <td>1.331606</td>\n",
              "      <td>6.299465</td>\n",
              "      <td>1.249228</td>\n",
              "      <td>2363.036103</td>\n",
              "      <td>0.043744</td>\n",
              "      <td>0.282807</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6ac8d+79a1b</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87.386907</td>\n",
              "      <td>25.622277</td>\n",
              "      <td>87.386807</td>\n",
              "      <td>25.622277</td>\n",
              "      <td>51</td>\n",
              "      <td>9045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7080</th>\n",
              "      <td>7317</td>\n",
              "      <td>46.090909</td>\n",
              "      <td>38.818182</td>\n",
              "      <td>34.818182</td>\n",
              "      <td>30.636364</td>\n",
              "      <td>33.818182</td>\n",
              "      <td>56.090909</td>\n",
              "      <td>69.454545</td>\n",
              "      <td>65.545455</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>13.454545</td>\n",
              "      <td>55.181818</td>\n",
              "      <td>36.454545</td>\n",
              "      <td>-2.270807</td>\n",
              "      <td>0.540891</td>\n",
              "      <td>0.480435</td>\n",
              "      <td>-60.072727</td>\n",
              "      <td>1.854081</td>\n",
              "      <td>0.997139</td>\n",
              "      <td>0.309002</td>\n",
              "      <td>65.188128</td>\n",
              "      <td>65.333581</td>\n",
              "      <td>1.536914</td>\n",
              "      <td>-0.001002</td>\n",
              "      <td>-3.727273</td>\n",
              "      <td>0.494972</td>\n",
              "      <td>0.244088</td>\n",
              "      <td>-0.711068</td>\n",
              "      <td>-0.000014</td>\n",
              "      <td>0.883928</td>\n",
              "      <td>188.916578</td>\n",
              "      <td>73.921129</td>\n",
              "      <td>0.503192</td>\n",
              "      <td>0.887601</td>\n",
              "      <td>0.957151</td>\n",
              "      <td>0.489551</td>\n",
              "      <td>0.115943</td>\n",
              "      <td>8.150245</td>\n",
              "      <td>2.087742</td>\n",
              "      <td>0.910733</td>\n",
              "      <td>0.513552</td>\n",
              "      <td>1.219885</td>\n",
              "      <td>30.727273</td>\n",
              "      <td>1.654545</td>\n",
              "      <td>-2.270807</td>\n",
              "      <td>0.860973</td>\n",
              "      <td>0.625443</td>\n",
              "      <td>1.703166</td>\n",
              "      <td>0.878324</td>\n",
              "      <td>0.847219</td>\n",
              "      <td>1.703166</td>\n",
              "      <td>0.655861</td>\n",
              "      <td>-1213.125437</td>\n",
              "      <td>0.288475</td>\n",
              "      <td>1.731474</td>\n",
              "      <td>0.001508</td>\n",
              "      <td>1.619297</td>\n",
              "      <td>1.066354</td>\n",
              "      <td>-1.559335</td>\n",
              "      <td>3.418778</td>\n",
              "      <td>1.536914</td>\n",
              "      <td>0.328986</td>\n",
              "      <td>0.762782</td>\n",
              "      <td>2.164104</td>\n",
              "      <td>0.920039</td>\n",
              "      <td>-0.197769</td>\n",
              "      <td>0.246649</td>\n",
              "      <td>7.489364</td>\n",
              "      <td>-5.201818</td>\n",
              "      <td>71.966091</td>\n",
              "      <td>14.698000</td>\n",
              "      <td>-0.069550</td>\n",
              "      <td>18.941014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.985588</td>\n",
              "      <td>0.265847</td>\n",
              "      <td>0.500367</td>\n",
              "      <td>0.233786</td>\n",
              "      <td>3.025993</td>\n",
              "      <td>0.065493</td>\n",
              "      <td>0.152811</td>\n",
              "      <td>2.229187</td>\n",
              "      <td>0.254984</td>\n",
              "      <td>729.134297</td>\n",
              "      <td>50.045455</td>\n",
              "      <td>-0.049103</td>\n",
              "      <td>1.325773</td>\n",
              "      <td>0.248387</td>\n",
              "      <td>0.227566</td>\n",
              "      <td>1.371948</td>\n",
              "      <td>107.727273</td>\n",
              "      <td>3.438561</td>\n",
              "      <td>98.818182</td>\n",
              "      <td>2.293856</td>\n",
              "      <td>1.325773</td>\n",
              "      <td>5.585181</td>\n",
              "      <td>1.238061</td>\n",
              "      <td>2454.990227</td>\n",
              "      <td>0.097077</td>\n",
              "      <td>0.152811</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7ee61</td>\n",
              "      <td>[87.8686141319019, 25.85128831069902, 87.89433...</td>\n",
              "      <td>87.875747</td>\n",
              "      <td>25.871376</td>\n",
              "      <td>87.875747</td>\n",
              "      <td>25.871467</td>\n",
              "      <td>11</td>\n",
              "      <td>9994</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7081 rows × 109 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edc9c053-aaec-4775-ad08-175351726eb9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-edc9c053-aaec-4775-ad08-175351726eb9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Feature Engineering"
      ],
      "metadata": {
        "id": "3DHU4jtKL1v5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['x1']=(df['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[0][1:])).astype(float)\n",
        "df['y1']=(df['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[1])).astype(float)\n",
        "df['x2']=(df['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[2])).astype(float)\n",
        "df['y2']=(df['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[-1][:-1])).astype(float)"
      ],
      "metadata": {
        "id": "te1ZNBm3L1_Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the field that fall across multiple chips,i will create a new chip the union of the corresponding chips"
      ],
      "metadata": {
        "id": "eyp3cdSUL9fj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "l=set()\n",
        "for x in df.chip.values:\n",
        "  if('+' in x):\n",
        "    l.add(x)"
      ],
      "metadata": {
        "id": "L7b2hQjlL6ID"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D=dict(zip(df.chip.values,tuple(x for x in zip(df.x2.values,df.y1.values,df.x1.values,df.y2.values))))"
      ],
      "metadata": {
        "id": "Z8fOpMHsMRtP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d=dict()\n",
        "for chip in l:\n",
        "  chips=chip.split('+')\n",
        "  start=False\n",
        "  for idx in chips:\n",
        "    try:\n",
        "      x,y,z,a=D[idx]\n",
        "    except:\n",
        "      continue\n",
        "    if(start):\n",
        "      x2,y1,x1,y2=d[chip]\n",
        "      d[chip]=(max(x2,x),min(y1,y),min(x1,z),max(a,y2))\n",
        "    else:\n",
        "      d[chip]=(x,y,z,a)"
      ],
      "metadata": {
        "id": "MTrq_rlmMTcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['x2']=df['chip'].apply(lambda x: d[x][0] if x in d.keys() else D[x][0])\n",
        "df['y1']=df['chip'].apply(lambda x: d[x][1] if x in d.keys() else D[x][1])\n",
        "df['x1']=df['chip'].apply(lambda x: d[x][2] if x in d.keys() else D[x][2])\n",
        "df['y2']=df['chip'].apply(lambda x: d[x][-1] if x in d.keys() else D[x][-1])"
      ],
      "metadata": {
        "id": "b-FDQMYZMVte"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "clustering"
      ],
      "metadata": {
        "id": "ja_d_cUNMYHW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features=df.columns.difference(['crop_id','field_id','chip','bbox']).tolist()\n",
        "df[features]=df[features].fillna(-9999).replace([np.inf, -np.inf], -9999)"
      ],
      "metadata": {
        "id": "CL6nMgSUMZkL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "kmeans=KMeans(n_clusters=125,random_state=11)\n",
        "kmeans.fit(df[features])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6I0gy4ogMbtO",
        "outputId": "8dbcd963-86ca-42ea-bf9c-f3982ea049c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=125, random_state=11)"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster']=kmeans.labels_"
      ],
      "metadata": {
        "id": "GvIK2OQIMdVQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['reacher1']=df['B09'] * df['B02'] / np.power(df['B01'], 2.0)\n",
        "df['reacher2']=df['B11'] * df['B05'] / np.power(df['B04'], 2.0)\n",
        "df['reacher3']=df['B12'] * df['B08'] / np.power(df['B03'], 2.0)"
      ],
      "metadata": {
        "id": "R9F8Up_4MjVX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Polar(X,y, a = 0, b = 0): \n",
        "    r = np.sqrt((X-a)**2 + (y-b)**2)\n",
        "    phi = np.arctan2((y-a), (X-b))\n",
        "    return r, phi"
      ],
      "metadata": {
        "id": "vShrpVD5Mn_K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def AggChip(feature):\n",
        "  deviceMean =dict(df.groupby('chip')[feature].mean())\n",
        "  deviceStd = dict(df.groupby('chip')[feature].std())\n",
        "  deviceMin = dict(df.groupby('chip')[feature].min())\n",
        "  deviceMax = dict(df.groupby('chip')[feature].max())\n",
        "  deviceMedian = dict(df.groupby('chip')[feature].median())\n",
        "  deviceUnique=dict(df.groupby('chip')[feature].nunique())\n",
        "\n",
        "  df[f'{feature}_chipUnique'] =df['chip'].map(deviceUnique)\n",
        "\n",
        "  df[f'{feature}_chipMean'] =df['chip'].map(deviceMean)\n",
        "  df[f'{feature}_chipMean_diff'] =df[f'{feature}']-df[f'{feature}_chipMean']\n",
        "\n",
        "  df[f'{feature}_chipMedian'] =df['chip'].map(deviceMedian)\n",
        "  df[f'{feature}_chipMedian_diff'] =df[f'{feature}']-df[f'{feature}_chipMedian']\n",
        "\n",
        "  df[f'{feature}_chipStd'] = df['chip'].map(deviceStd)\n",
        "  df[f'{feature}_chipStd_diff'] =df[f'{feature}']-df[f'{feature}_chipStd']\n",
        "\n",
        "  df[f'{feature}_chipMin'] = df['chip'].map(deviceMin)\n",
        "  df[f'{feature}_chipMin_diff'] =df[f'{feature}']-df[f'{feature}_chipMin']\n",
        "\n",
        "  df[f'{feature}_chipMax'] = df['chip'].map(deviceMax)\n",
        "  df[f'{feature}_chipMax_diff'] =df[f'{feature}']-df[f'{feature}_chipMax']\n",
        "\n"
      ],
      "metadata": {
        "id": "_ftIo5YUM6EZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features=df.columns.difference(['field_id','crop_id','chip','cluster','y','bbox','top_left_x',\t'top_left_y',\t'down_right_x',\t'down_right_y','istest','top_class_in_chip','nbr_pixels','x1','x2','y1','y2']).tolist()"
      ],
      "metadata": {
        "id": "LSJYp65UM8A4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "to_remove=['AFRI1200',\n",
        " 'NORM_NIR',\n",
        " 'GDVI_max',\n",
        " 'GARI_max',\n",
        " 'FS_min',\n",
        " 'MSBI',\n",
        " 'EVI22',\n",
        " 'B07',\n",
        " 'B01_min',\n",
        " 'MGVI_max',\n",
        " 'B04_min',\n",
        " 'B03_max',\n",
        " 'LCI_median',\n",
        " 'SLAVI_max',\n",
        " 'B04_max',\n",
        " 'S2REP_median',\n",
        " 'AFRI1600',\n",
        " 'AVI_median',\n",
        " 'B05_max',\n",
        " 'B09_max',\n",
        " 'AVI_max',\n",
        " 'B09_min',\n",
        " 'B04_median',\n",
        " 'BNDVI_min',\n",
        " 'B08_max',\n",
        " 'Rre_max',\n",
        " 'SBL',\n",
        " 'SAVI_std',\n",
        " 'AFRI1600_std',\n",
        " 'Hue_min',\n",
        " 'B8A_median',\n",
        " 'B03_min',\n",
        " 'LCI',\n",
        " 'ATSAVI_min',\n",
        " 'SLAVI_median',\n",
        " 'ARVI2',\n",
        " 'B02_median',\n",
        " 'Rre_min',\n",
        " 'B05_median',\n",
        " 'EVI22_min',\n",
        " 'B01_median',\n",
        " 'EVI22_median',\n",
        " 'EVI2_min',\n",
        " 'ATSAVI_median',\n",
        " 'IF_max',\n",
        " 'B07_median',\n",
        " 'ATSAVI',\n",
        " 'B03_median',\n",
        " 'logR_median',\n",
        " 'EVI2_max',\n",
        " 'SLAVI',\n",
        " 'r',\n",
        " 'GDVI_min',\n",
        " 'SAVI3_std',\n",
        " 'EVI22_max',\n",
        " 'SAVI_min',\n",
        " 'Ratio7_max',\n",
        " 'EVI2_median',\n",
        " 'Fe2+',\n",
        " 'Fe2+_max',\n",
        " 'Fe2+_median',\n",
        " 'Fe2+_min',\n",
        " 'Fe2+_std',\n",
        " 'Ratio7_std',\n",
        " 'Ratio7_min',\n",
        " 'Ratio7_median',\n",
        " 'Ratio7',\n",
        " 'ATSAVI_max',\n",
        " 'Latirite',\n",
        " 'Latirite_max',\n",
        " 'Latirite_median',\n",
        " 'Latirite_min',\n",
        " 'Latirite_std',\n",
        " 'r_max',\n",
        " 'r_median',\n",
        " 'r_min',\n",
        " 'logR_min',\n",
        " 'logR_max',\n",
        " 'SAVI_max',\n",
        " 'logR',\n",
        " 'B09_median',\n",
        " 'SAVI3_min',\n",
        " 'B08_min',\n",
        " 'B08_median',\n",
        " 'SAVI3_median',\n",
        " 'SAVI3_max',\n",
        " 'SAVI3',\n",
        " 'SIWSI',\n",
        " 'SIWSI_max',\n",
        " 'SIWSI_median',\n",
        " 'SIWSI_min',\n",
        " 'SIWSI_std',\n",
        " 'SAVI',\n",
        " 'SARVI2_std',\n",
        " 'SARVI2_min',\n",
        " 'SARVI2_median',\n",
        " 'SARVI2_max',\n",
        " 'SARVI2',\n",
        " 'EVI2',\n",
        " 'SAVI_median']"
      ],
      "metadata": {
        "id": "PQFgcpauM9eo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feats=[c for c in features if c not in to_remove]"
      ],
      "metadata": {
        "id": "hWmGy826M_TV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['R1'], df['Phi1'] = Polar(df[\"x1\"],df[\"y2\"])\n",
        "df['R2'], df['Phi2'] = Polar(df[\"x2\"],df[\"y1\"])\n",
        "df['filed_R1'], df['field_Phi1'] = Polar(df[\"top_left_x\"],df[\"top_left_y\"])\n",
        "df['field_R2'], df['field_Phi2'] = Polar(df[\"down_right_x\"],df[\"down_right_y\"])\n",
        "df['field_Rdiff']=df['filed_R1']-df['field_R2']\n",
        "df['field_Phidiff']=df['field_Phi1']-df['field_Phi2']"
      ],
      "metadata": {
        "id": "XVP785hQNGu0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['chip_height']=df['x2']-df['x1']\n",
        "df['chip_widht']=df['y1']-df['y2']\n",
        "df['chip_area']=df['chip_height']*df['chip_widht']"
      ],
      "metadata": {
        "id": "hzLo0nspNLo3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['chip_height']=df['x2']-df['x1']\n",
        "df['chip_widht']=df['y1']-df['y2']\n",
        "df['chip_area']=df['chip_height']*df['chip_widht']"
      ],
      "metadata": {
        "id": "DRaJtkoZNNfm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['xdiv']=df['x1']/df['x2']\n",
        "df['ydiv']=df['y1']/df['y2']\n",
        "df['xydiv']=df['xdiv']*df['ydiv']"
      ],
      "metadata": {
        "id": "QUDf_INrNPj4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['field_height']=df['top_left_x']-df['down_right_x']\n",
        "df['field_widht']=df['down_right_y']-df['top_left_x']\n",
        "df['field_area']=(df['field_height'].apply(lambda x:max(x,1)))*(df['field_widht'].apply(lambda x:max(x,1)))"
      ],
      "metadata": {
        "id": "PMcMb8dEFkO4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['field/chip_area']=df['field_area']/df['chip_area']"
      ],
      "metadata": {
        "id": "QquK5Im2NRm7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['pixel_ratio']=df['nbr_pixels']/256"
      ],
      "metadata": {
        "id": "oQIbL9ONNT17"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feats.extend(['field_area'])"
      ],
      "metadata": {
        "id": "hhEIhC02NZyd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feats.extend(['field_Phi1','field_Phi2','filed_R1','field_R2','field_Phidiff'])"
      ],
      "metadata": {
        "id": "zPMIjUAqNbW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##new\n",
        "feats.extend(['field_height','field_widht'])"
      ],
      "metadata": {
        "id": "Vh73j3YfNc3-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x in feats:\n",
        "  AggChip(x)"
      ],
      "metadata": {
        "id": "igQ4m2JJNeVB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fq = df.groupby('chip').size()/len(df)"
      ],
      "metadata": {
        "id": "J54XfTSLNgoW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['chipFreq']=df['chip'].map(fq)"
      ],
      "metadata": {
        "id": "kqSquMlkNiBo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['is_overlapping']=df['chip'].apply(lambda x :x.count('+'))"
      ],
      "metadata": {
        "id": "xw6V1w8oNjwl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Rdiff']=df['R1']-df['R2']\n",
        "df['Phidiff']=df['Phi1']-df['Phi2']"
      ],
      "metadata": {
        "id": "jy-XTUUpNl9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEcK6LkENnpD",
        "outputId": "e28c8f13-2868-418b-b318-f47e922c7188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7081, 1132)"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.get_dummies(df, columns = ['chip'])"
      ],
      "metadata": {
        "id": "g6fAeTGcNpZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9NJj7rZNqVh",
        "outputId": "23164012-a903-44cc-fac7-01e526e70101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7081, 2520)"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train=df[df['crop_id'].isnull()==False].copy()\n",
        "test=df[df['crop_id'].isnull()==True].copy()"
      ],
      "metadata": {
        "id": "nN2RG0PhNs_O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features=train.columns.difference(['crop_id','field_id','bbox','istest']).tolist()"
      ],
      "metadata": {
        "id": "i7tXB7l_NuXq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kewLbj6dNwDZ",
        "outputId": "97b9c692-9a14-4646-f3b9-d6bd78184f43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2517"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d={1.: 0,\n",
        " 2.: 1,\n",
        " 3.: 2,\n",
        " 4.: 3,\n",
        " 6.: 4,\n",
        " 8.: 5,\n",
        " 15.: 6,\n",
        " 5.: 7,\n",
        " 16.: 8,\n",
        " 14.: 9,\n",
        " 13.: 10,\n",
        " 9.: 11,\n",
        " 36.: 12}"
      ],
      "metadata": {
        "id": "GiKkF4ifNxih"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['crop_id']=train['crop_id'].map(d)"
      ],
      "metadata": {
        "id": "fiDx2dMVNzKw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.fillna(-9999).replace([np.inf, -np.inf], -9999)\n",
        "test = test.fillna(-9999).replace([np.inf, -np.inf], -9999)"
      ],
      "metadata": {
        "id": "_uy3pzTZN0n_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Modelling"
      ],
      "metadata": {
        "id": "nB_JO6i4N2un"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To deal with the high dimension we will use low feature fraction(percentage of feature used when building each tree) and for the imbalance problem we will use balanced cross entropy as loss function (setting class weights in lgbm)"
      ],
      "metadata": {
        "id": "BnUUYpY0N7W-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "lgbm_params = {\n",
        "    'n_estimators': 3000,\n",
        "    'learning_rate': 0.07,\n",
        "   # 'colsample_bytree': 0.6,\n",
        "    'subsample': 0.35,\n",
        "    'reg_alpha': 10.5,\n",
        "    'max_depth':18,\n",
        "    'feature_fraction':0.15,\n",
        "    'reg_lambda': 8.5,\n",
        "    'num_leaves': 80,\n",
        "    'min_data_in_leaf':120,\n",
        "    'boosting': 'gbdt',\n",
        "    'num_class':13,\n",
        "    'class_weight':{0: 0.2102412604628262,\n",
        " 1: 0.4313131313131313,\n",
        " 2: 4.145631067961165,\n",
        " 3: 0.26020719073735527,\n",
        " 4: 2.6196319018404908,\n",
        " 5: 8.895833333333334,\n",
        " 6: 10.414634146341463,\n",
        " 7: 18.565217391304348,\n",
        " 8: 26.6875,\n",
        " 9: 30.5,\n",
        " 10: 7.237288135593221,\n",
        " 11: 1.4573378839590443,\n",
        " 12: 3.310077519379845}\n",
        "  \n",
        "}"
      ],
      "metadata": {
        "id": "x6u2Nji0N3vK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "# Select X and y features\n",
        "X =train[features]\n",
        "y = train['crop_id']\n",
        "tess =test[features]\n",
        "\n",
        "# Stratified Validation\n",
        "folds = StratifiedKFold(n_splits = 5,shuffle=True,random_state=222)\n",
        "\n",
        "# Dataframe to store feature importance\n",
        "feature_importance_df = pd.DataFrame()\n",
        "\n",
        "# Lists to store predictions and losses\n",
        "predictions = []\n",
        "losses = []\n",
        "for i,( train_index, test_index) in enumerate(folds.split(X, y)):\n",
        "  X_train, X_test, y_train, y_test = X.iloc[train_index], X.iloc[test_index], y.iloc[train_index], y.iloc[test_index]\n",
        "  # Instantiate model\n",
        "  model =LGBMClassifier(**lgbm_params)\n",
        "  model.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test, y_test)],verbose=200, early_stopping_rounds = 150)\n",
        "  # Make predictions\n",
        "  preds = model.predict_proba(tess,num_iteration=model.best_iteration_)\n",
        " \n",
        "  y_pred = model.predict_proba(X_test, num_iteration=model.best_iteration_)\n",
        "\n",
        "  # Append predictions and losses\n",
        "  predictions.append(preds)\n",
        "  loss = log_loss(y_test, y_pred)\n",
        "\n",
        "  # Print loss\n",
        "  print(f'\\n {i+1}:  {loss}\\n')\n",
        "  losses.append(loss)\n",
        "\n",
        "print(f'Mean Loss: {np.mean(losses)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pC1N5s7OOQyo",
        "outputId": "22b8fafa-ae25-4503-8acb-d47bf40d8305"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 150 rounds.\n",
            "[200]\ttraining's multi_logloss: 0.391053\tvalid_1's multi_logloss: 1.16268\n",
            "[400]\ttraining's multi_logloss: 0.377561\tvalid_1's multi_logloss: 1.14565\n",
            "[600]\ttraining's multi_logloss: 0.377435\tvalid_1's multi_logloss: 1.1455\n",
            "[800]\ttraining's multi_logloss: 0.377422\tvalid_1's multi_logloss: 1.14543\n",
            "[1000]\ttraining's multi_logloss: 0.37742\tvalid_1's multi_logloss: 1.14542\n",
            "[1200]\ttraining's multi_logloss: 0.377418\tvalid_1's multi_logloss: 1.1454\n",
            "[1400]\ttraining's multi_logloss: 0.377418\tvalid_1's multi_logloss: 1.1454\n",
            "[1600]\ttraining's multi_logloss: 0.377418\tvalid_1's multi_logloss: 1.1454\n",
            "[1800]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n",
            "[2000]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n",
            "[2200]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n",
            "[2400]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n",
            "[2600]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n",
            "[2800]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n",
            "[3000]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[2999]\ttraining's multi_logloss: 0.377417\tvalid_1's multi_logloss: 1.1454\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting='gbdt',\n",
              "               class_weight={0: 0.2102412604628262, 1: 0.4313131313131313,\n",
              "                             2: 4.145631067961165, 3: 0.26020719073735527,\n",
              "                             4: 2.6196319018404908, 5: 8.895833333333334,\n",
              "                             6: 10.414634146341463, 7: 18.565217391304348,\n",
              "                             8: 26.6875, 9: 30.5, 10: 7.237288135593221,\n",
              "                             11: 1.4573378839590443, 12: 3.310077519379845},\n",
              "               feature_fraction=0.15, learning_rate=0.07, max_depth=18,\n",
              "               min_data_in_leaf=120, n_estimators=3000, num_class=13,\n",
              "               num_leaves=80, reg_alpha=10.5, reg_lambda=8.5, subsample=0.35)"
            ]
          },
          "metadata": {},
          "execution_count": 215
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 1:  1.145399616125296\n",
            "\n",
            "Training until validation scores don't improve for 150 rounds.\n",
            "[200]\ttraining's multi_logloss: 0.385288\tvalid_1's multi_logloss: 1.16629\n",
            "[400]\ttraining's multi_logloss: 0.374184\tvalid_1's multi_logloss: 1.15373\n",
            "[600]\ttraining's multi_logloss: 0.37397\tvalid_1's multi_logloss: 1.15348\n",
            "[800]\ttraining's multi_logloss: 0.373895\tvalid_1's multi_logloss: 1.15322\n",
            "[1000]\ttraining's multi_logloss: 0.373887\tvalid_1's multi_logloss: 1.15318\n",
            "[1200]\ttraining's multi_logloss: 0.373874\tvalid_1's multi_logloss: 1.15314\n",
            "[1400]\ttraining's multi_logloss: 0.373872\tvalid_1's multi_logloss: 1.15312\n",
            "[1600]\ttraining's multi_logloss: 0.373872\tvalid_1's multi_logloss: 1.15312\n",
            "[1800]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n",
            "[2000]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n",
            "[2200]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n",
            "[2400]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n",
            "[2600]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n",
            "[2800]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n",
            "[3000]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[3000]\ttraining's multi_logloss: 0.373871\tvalid_1's multi_logloss: 1.15312\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting='gbdt',\n",
              "               class_weight={0: 0.2102412604628262, 1: 0.4313131313131313,\n",
              "                             2: 4.145631067961165, 3: 0.26020719073735527,\n",
              "                             4: 2.6196319018404908, 5: 8.895833333333334,\n",
              "                             6: 10.414634146341463, 7: 18.565217391304348,\n",
              "                             8: 26.6875, 9: 30.5, 10: 7.237288135593221,\n",
              "                             11: 1.4573378839590443, 12: 3.310077519379845},\n",
              "               feature_fraction=0.15, learning_rate=0.07, max_depth=18,\n",
              "               min_data_in_leaf=120, n_estimators=3000, num_class=13,\n",
              "               num_leaves=80, reg_alpha=10.5, reg_lambda=8.5, subsample=0.35)"
            ]
          },
          "metadata": {},
          "execution_count": 215
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 2:  1.1531151468148788\n",
            "\n",
            "Training until validation scores don't improve for 150 rounds.\n",
            "[200]\ttraining's multi_logloss: 0.386256\tvalid_1's multi_logloss: 1.17182\n",
            "[400]\ttraining's multi_logloss: 0.373077\tvalid_1's multi_logloss: 1.15514\n",
            "[600]\ttraining's multi_logloss: 0.372971\tvalid_1's multi_logloss: 1.15488\n",
            "[800]\ttraining's multi_logloss: 0.372895\tvalid_1's multi_logloss: 1.15483\n",
            "Early stopping, best iteration is:\n",
            "[746]\ttraining's multi_logloss: 0.372922\tvalid_1's multi_logloss: 1.15479\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting='gbdt',\n",
              "               class_weight={0: 0.2102412604628262, 1: 0.4313131313131313,\n",
              "                             2: 4.145631067961165, 3: 0.26020719073735527,\n",
              "                             4: 2.6196319018404908, 5: 8.895833333333334,\n",
              "                             6: 10.414634146341463, 7: 18.565217391304348,\n",
              "                             8: 26.6875, 9: 30.5, 10: 7.237288135593221,\n",
              "                             11: 1.4573378839590443, 12: 3.310077519379845},\n",
              "               feature_fraction=0.15, learning_rate=0.07, max_depth=18,\n",
              "               min_data_in_leaf=120, n_estimators=3000, num_class=13,\n",
              "               num_leaves=80, reg_alpha=10.5, reg_lambda=8.5, subsample=0.35)"
            ]
          },
          "metadata": {},
          "execution_count": 215
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 3:  1.1548078854664532\n",
            "\n",
            "Training until validation scores don't improve for 150 rounds.\n",
            "[200]\ttraining's multi_logloss: 0.382627\tvalid_1's multi_logloss: 1.19534\n",
            "[400]\ttraining's multi_logloss: 0.37142\tvalid_1's multi_logloss: 1.18229\n",
            "[600]\ttraining's multi_logloss: 0.371199\tvalid_1's multi_logloss: 1.1822\n",
            "[800]\ttraining's multi_logloss: 0.371143\tvalid_1's multi_logloss: 1.18211\n",
            "Early stopping, best iteration is:\n",
            "[794]\ttraining's multi_logloss: 0.371147\tvalid_1's multi_logloss: 1.18209\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting='gbdt',\n",
              "               class_weight={0: 0.2102412604628262, 1: 0.4313131313131313,\n",
              "                             2: 4.145631067961165, 3: 0.26020719073735527,\n",
              "                             4: 2.6196319018404908, 5: 8.895833333333334,\n",
              "                             6: 10.414634146341463, 7: 18.565217391304348,\n",
              "                             8: 26.6875, 9: 30.5, 10: 7.237288135593221,\n",
              "                             11: 1.4573378839590443, 12: 3.310077519379845},\n",
              "               feature_fraction=0.15, learning_rate=0.07, max_depth=18,\n",
              "               min_data_in_leaf=120, n_estimators=3000, num_class=13,\n",
              "               num_leaves=80, reg_alpha=10.5, reg_lambda=8.5, subsample=0.35)"
            ]
          },
          "metadata": {},
          "execution_count": 215
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 4:  1.182132567608199\n",
            "\n",
            "Training until validation scores don't improve for 150 rounds.\n",
            "[200]\ttraining's multi_logloss: 0.382792\tvalid_1's multi_logloss: 1.17025\n",
            "[400]\ttraining's multi_logloss: 0.369906\tvalid_1's multi_logloss: 1.15311\n",
            "[600]\ttraining's multi_logloss: 0.369812\tvalid_1's multi_logloss: 1.15295\n",
            "[800]\ttraining's multi_logloss: 0.3698\tvalid_1's multi_logloss: 1.15286\n",
            "[1000]\ttraining's multi_logloss: 0.369763\tvalid_1's multi_logloss: 1.15275\n",
            "[1200]\ttraining's multi_logloss: 0.369715\tvalid_1's multi_logloss: 1.1527\n",
            "[1400]\ttraining's multi_logloss: 0.369713\tvalid_1's multi_logloss: 1.15268\n",
            "[1600]\ttraining's multi_logloss: 0.369712\tvalid_1's multi_logloss: 1.15267\n",
            "[1800]\ttraining's multi_logloss: 0.369712\tvalid_1's multi_logloss: 1.15266\n",
            "[2000]\ttraining's multi_logloss: 0.369711\tvalid_1's multi_logloss: 1.15266\n",
            "[2200]\ttraining's multi_logloss: 0.369711\tvalid_1's multi_logloss: 1.15266\n",
            "[2400]\ttraining's multi_logloss: 0.369711\tvalid_1's multi_logloss: 1.15266\n",
            "[2600]\ttraining's multi_logloss: 0.369711\tvalid_1's multi_logloss: 1.15266\n",
            "[2800]\ttraining's multi_logloss: 0.369711\tvalid_1's multi_logloss: 1.15266\n",
            "[3000]\ttraining's multi_logloss: 0.369711\tvalid_1's multi_logloss: 1.15266\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[3000]\ttraining's multi_logloss: 0.369711\tvalid_1's multi_logloss: 1.15266\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting='gbdt',\n",
              "               class_weight={0: 0.2102412604628262, 1: 0.4313131313131313,\n",
              "                             2: 4.145631067961165, 3: 0.26020719073735527,\n",
              "                             4: 2.6196319018404908, 5: 8.895833333333334,\n",
              "                             6: 10.414634146341463, 7: 18.565217391304348,\n",
              "                             8: 26.6875, 9: 30.5, 10: 7.237288135593221,\n",
              "                             11: 1.4573378839590443, 12: 3.310077519379845},\n",
              "               feature_fraction=0.15, learning_rate=0.07, max_depth=18,\n",
              "               min_data_in_leaf=120, n_estimators=3000, num_class=13,\n",
              "               num_leaves=80, reg_alpha=10.5, reg_lambda=8.5, subsample=0.35)"
            ]
          },
          "metadata": {},
          "execution_count": 215
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 5:  1.1526596290903595\n",
            "\n",
            "Mean Loss: 1.1576229690210371\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feats = pd.DataFrame({'features': X.columns}) \n",
        "feats['Importances']=model.feature_importances_\n",
        "plt.figure(figsize=(32, 32))\n",
        "sns.barplot(data=feats.sort_values(by='Importances', ascending=False).head(100), x='Importances', y='features');\n",
        "plt.title('70 TOP feature importance');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eL8w0irTOnUe",
        "outputId": "c2467017-db85-4a90-8cef-ff73b0535230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2304x2304 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d={0: 'Wheat',\n",
        " 1: 'Mustard',\n",
        " 2: 'Lentil',\n",
        " 3: 'No Crop',\n",
        " 4: 'Sugarcane',\n",
        " 5: 'Garlic',\n",
        " 6: 'Potato',\n",
        " 7: 'Green pea',\n",
        " 8: 'Bersem',\n",
        " 9: 'Coriander',\n",
        " 10: 'Gram',\n",
        " 11: 'Maize',\n",
        " 12: 'Rice'}"
      ],
      "metadata": {
        "id": "4h7l8LOIOpeN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.read_csv('SampleSubmission.csv')\n",
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "v7Vz5njYOrbx",
        "outputId": "460ad26a-ac24-4532-99f6-aacbee32eb94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Field ID  Wheat  Mustard  Lentil  No Crop  Green pea  Sugarcane  Garlic  \\\n",
              "0           11      0        0       0        0          0          0       0   \n",
              "1           13      0        0       0        0          0          0       0   \n",
              "2           19      0        0       0        0          0          0       0   \n",
              "3           21      0        0       0        0          0          0       0   \n",
              "4           25      0        0       0        0          0          0       0   \n",
              "...        ...    ...      ...     ...      ...        ...        ...     ...   \n",
              "1525      7319      0        0       0        0          0          0       0   \n",
              "1526      7325      0        0       0        0          0          0       0   \n",
              "1527      7329      0        0       0        0          0          0       0   \n",
              "1528      7330      0        0       0        0          0          0       0   \n",
              "1529      7333      0        0       0        0          0          0       0   \n",
              "\n",
              "      Maize  Gram  Coriander  Potato  Bersem  Rice  \n",
              "0         0     0          0       0       0     0  \n",
              "1         0     0          0       0       0     0  \n",
              "2         0     0          0       0       0     0  \n",
              "3         0     0          0       0       0     0  \n",
              "4         0     0          0       0       0     0  \n",
              "...     ...   ...        ...     ...     ...   ...  \n",
              "1525      0     0          0       0       0     0  \n",
              "1526      0     0          0       0       0     0  \n",
              "1527      0     0          0       0       0     0  \n",
              "1528      0     0          0       0       0     0  \n",
              "1529      0     0          0       0       0     0  \n",
              "\n",
              "[1530 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93d4aaca-cd87-4a39-85c5-3c510f2d92a5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "        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>Field ID</th>\n",
              "      <th>Wheat</th>\n",
              "      <th>Mustard</th>\n",
              "      <th>Lentil</th>\n",
              "      <th>No Crop</th>\n",
              "      <th>Green pea</th>\n",
              "      <th>Sugarcane</th>\n",
              "      <th>Garlic</th>\n",
              "      <th>Maize</th>\n",
              "      <th>Gram</th>\n",
              "      <th>Coriander</th>\n",
              "      <th>Potato</th>\n",
              "      <th>Bersem</th>\n",
              "      <th>Rice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1525</th>\n",
              "      <td>7319</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1526</th>\n",
              "      <td>7325</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1527</th>\n",
              "      <td>7329</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1528</th>\n",
              "      <td>7330</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1529</th>\n",
              "      <td>7333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1530 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93d4aaca-cd87-4a39-85c5-3c510f2d92a5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-93d4aaca-cd87-4a39-85c5-3c510f2d92a5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-93d4aaca-cd87-4a39-85c5-3c510f2d92a5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction =np.mean(predictions,0) #model.predict_proba(test[cols])\n",
        "\n",
        "sub = pd.DataFrame(test_prediction)\n",
        "sub = sub.rename(columns=d)\n",
        "sub['Field ID'] =  test['field_id'].values\n",
        "submission = pd.merge(sub[['Field ID']],submission,on='Field ID',how='left')\n",
        "\n",
        "for col in submission.columns[1:] :\n",
        "  submission[col] = sub[col]\n",
        "  \n",
        "submission.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FDWGc8gDOt3D",
        "outputId": "b21bd155-c34f-4c64-e9c7-14e4eb20f336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Field ID     Wheat   Mustard    Lentil   No Crop  Green pea  Sugarcane  \\\n",
              "0        11  0.121670  0.103810  0.019056  0.057620   0.014405   0.620043   \n",
              "1      1434  0.110078  0.078949  0.016648  0.122103   0.012568   0.582992   \n",
              "2      1438  0.031057  0.044871  0.013978  0.065667   0.007429   0.694575   \n",
              "3        13  0.610395  0.150524  0.013583  0.108671   0.022071   0.035045   \n",
              "4        19  0.215034  0.211102  0.325839  0.055690   0.024343   0.107488   \n",
              "\n",
              "     Garlic     Maize      Gram  Coriander    Potato    Bersem      Rice  \n",
              "0  0.007191  0.009241  0.004276   0.005785  0.023097  0.008038  0.005770  \n",
              "1  0.006407  0.015228  0.004142   0.008186  0.020769  0.015281  0.006650  \n",
              "2  0.005280  0.081282  0.003421   0.003634  0.018615  0.024951  0.005241  \n",
              "3  0.009621  0.009938  0.007502   0.009864  0.006755  0.010810  0.005222  \n",
              "4  0.010835  0.013381  0.007148   0.007609  0.007330  0.007908  0.006293  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96de88a0-187e-4c8f-9c89-65c6fd6d788a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Field ID</th>\n",
              "      <th>Wheat</th>\n",
              "      <th>Mustard</th>\n",
              "      <th>Lentil</th>\n",
              "      <th>No Crop</th>\n",
              "      <th>Green pea</th>\n",
              "      <th>Sugarcane</th>\n",
              "      <th>Garlic</th>\n",
              "      <th>Maize</th>\n",
              "      <th>Gram</th>\n",
              "      <th>Coriander</th>\n",
              "      <th>Potato</th>\n",
              "      <th>Bersem</th>\n",
              "      <th>Rice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>0.121670</td>\n",
              "      <td>0.103810</td>\n",
              "      <td>0.019056</td>\n",
              "      <td>0.057620</td>\n",
              "      <td>0.014405</td>\n",
              "      <td>0.620043</td>\n",
              "      <td>0.007191</td>\n",
              "      <td>0.009241</td>\n",
              "      <td>0.004276</td>\n",
              "      <td>0.005785</td>\n",
              "      <td>0.023097</td>\n",
              "      <td>0.008038</td>\n",
              "      <td>0.005770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1434</td>\n",
              "      <td>0.110078</td>\n",
              "      <td>0.078949</td>\n",
              "      <td>0.016648</td>\n",
              "      <td>0.122103</td>\n",
              "      <td>0.012568</td>\n",
              "      <td>0.582992</td>\n",
              "      <td>0.006407</td>\n",
              "      <td>0.015228</td>\n",
              "      <td>0.004142</td>\n",
              "      <td>0.008186</td>\n",
              "      <td>0.020769</td>\n",
              "      <td>0.015281</td>\n",
              "      <td>0.006650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1438</td>\n",
              "      <td>0.031057</td>\n",
              "      <td>0.044871</td>\n",
              "      <td>0.013978</td>\n",
              "      <td>0.065667</td>\n",
              "      <td>0.007429</td>\n",
              "      <td>0.694575</td>\n",
              "      <td>0.005280</td>\n",
              "      <td>0.081282</td>\n",
              "      <td>0.003421</td>\n",
              "      <td>0.003634</td>\n",
              "      <td>0.018615</td>\n",
              "      <td>0.024951</td>\n",
              "      <td>0.005241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13</td>\n",
              "      <td>0.610395</td>\n",
              "      <td>0.150524</td>\n",
              "      <td>0.013583</td>\n",
              "      <td>0.108671</td>\n",
              "      <td>0.022071</td>\n",
              "      <td>0.035045</td>\n",
              "      <td>0.009621</td>\n",
              "      <td>0.009938</td>\n",
              "      <td>0.007502</td>\n",
              "      <td>0.009864</td>\n",
              "      <td>0.006755</td>\n",
              "      <td>0.010810</td>\n",
              "      <td>0.005222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>0.215034</td>\n",
              "      <td>0.211102</td>\n",
              "      <td>0.325839</td>\n",
              "      <td>0.055690</td>\n",
              "      <td>0.024343</td>\n",
              "      <td>0.107488</td>\n",
              "      <td>0.010835</td>\n",
              "      <td>0.013381</td>\n",
              "      <td>0.007148</td>\n",
              "      <td>0.007609</td>\n",
              "      <td>0.007330</td>\n",
              "      <td>0.007908</td>\n",
              "      <td>0.006293</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96de88a0-187e-4c8f-9c89-65c6fd6d788a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-96de88a0-187e-4c8f-9c89-65c6fd6d788a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-96de88a0-187e-4c8f-9c89-65c6fd6d788a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.to_csv('Top.csv',index=False)"
      ],
      "metadata": {
        "id": "ouHq-xxuOwrl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8IyKQE2DbImZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}