{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import rioxarray\n",
    "import json, os\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n",
    "from sklearn.metrics import f1_score, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "verbose = False\n",
    "details = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>Neutral grassland</th>\n",
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       "      <th>Metamitron_10km</th>\n",
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       "      <th>Sulphur_10km</th>\n",
       "      <th>Tri-allate_10km</th>\n",
       "      <th>Occurrence</th>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "5000.0    185000.0                   0                    0       0   \n",
       "155000.0  595000.0                  17                    0      44   \n",
       "345000.0  515000.0                   2                    0      94   \n",
       "615000.0  345000.0                  11                    1       1   \n",
       "1265000.0 295000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "5000.0    185000.0                  0                  0   \n",
       "155000.0  595000.0                 18                  0   \n",
       "345000.0  515000.0                  3                  0   \n",
       "615000.0  345000.0                 70                  0   \n",
       "1265000.0 295000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "5000.0    185000.0                     0               0    0        0   \n",
       "155000.0  595000.0                    21               0    0        0   \n",
       "345000.0  515000.0                     0               0    0        0   \n",
       "615000.0  345000.0                     0              16    0        0   \n",
       "1265000.0 295000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
       "5000.0    185000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
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       "1265000.0 295000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "5000.0    185000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
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       "615000.0  345000.0     6.460490e+00     8.218880e+00        1.587924e+01   \n",
       "1265000.0 295000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
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       "1265000.0 295000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "5000.0    185000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "155000.0  595000.0 -3.400000e+38    -3.400000e+38           0  \n",
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       "615000.0  345000.0  4.151691e-01     1.232334e-04           0  \n",
       "1265000.0 295000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1235000.0 315000.0                   0                    0       0   \n",
       "1275000.0 605000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
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       "y         x                                                              \n",
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       "y         x                                                                \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1165000.0 265000.0                   0                    0       0   \n",
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       "995000.0  75000.0                    0                    0       0   \n",
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       "\n",
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       "y         x                                                \n",
       "675000.0  305000.0                 15                  0   \n",
       "1165000.0 265000.0                  0                  0   \n",
       "1225000.0 95000.0                   0                  0   \n",
       "995000.0  75000.0                   0                  0   \n",
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       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "1165000.0 265000.0                     0               0    0        0   \n",
       "1225000.0 95000.0                      0               0    0        0   \n",
       "995000.0  75000.0                      0               0    0        0   \n",
       "645000.0  75000.0                      0               0    0        0   \n",
       "\n",
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       "y         x                            ...                                   \n",
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       "645000.0  75000.0                   0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
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       "645000.0  75000.0     -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
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       "645000.0  75000.0                   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
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       "645000.0 35000.0                    0                    0       0   \n",
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       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
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       "95000.0  95000.0                   0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
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       "775000.0 315000.0               1    0        8                  0  ...   \n",
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       "y        x                                                           \n",
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       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
       "515000.0 575000.0    -3.400000e+38       -3.400000e+38   \n",
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       "95000.0  95000.0     -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
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       "y        x                                                    \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "y         x                                                                \n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55000.0</th>\n",
       "      <th>415000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125000.0</th>\n",
       "      <th>335000.0</th>\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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525000.0</th>\n",
       "      <th>25000.0</th>\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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545000.0</th>\n",
       "      <th>305000.0</th>\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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055000.0</th>\n",
       "      <th>375000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "55000.0   415000.0                   0                    0       0   \n",
       "1125000.0 335000.0                   0                    0       0   \n",
       "525000.0  25000.0                    0                    0       0   \n",
       "545000.0  305000.0                   0                    0       0   \n",
       "1055000.0 375000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "55000.0   415000.0                  0                  0   \n",
       "1125000.0 335000.0                  0                  0   \n",
       "525000.0  25000.0                   0                  0   \n",
       "545000.0  305000.0                  0                  0   \n",
       "1055000.0 375000.0                 33                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "55000.0   415000.0                     0               0    0        0   \n",
       "1125000.0 335000.0                     0               0    0        0   \n",
       "525000.0  25000.0                      0               0    0        0   \n",
       "545000.0  305000.0                     0               0    0        0   \n",
       "1055000.0 375000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
       "55000.0   415000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "1125000.0 335000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "525000.0  25000.0                   0  ...    -3.400000e+38  -3.400000e+38   \n",
       "545000.0  305000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "1055000.0 375000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "55000.0   415000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "1125000.0 335000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "525000.0  25000.0     -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "545000.0  305000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "1055000.0 375000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
       "55000.0   415000.0                  -3.400000e+38      -3.400000e+38   \n",
       "1125000.0 335000.0                  -3.400000e+38      -3.400000e+38   \n",
       "525000.0  25000.0                   -3.400000e+38      -3.400000e+38   \n",
       "545000.0  305000.0                  -3.400000e+38      -3.400000e+38   \n",
       "1055000.0 375000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "55000.0   415000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "1125000.0 335000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "525000.0  25000.0  -3.400000e+38    -3.400000e+38           0  \n",
       "545000.0  305000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "1055000.0 375000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
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       "      <th>Pendimethalin_10km</th>\n",
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       "      <th>1225000.0</th>\n",
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       "      <th>295000.0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>215000.0</th>\n",
       "      <th>215000.0</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.098964e-02</td>\n",
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       "      <td>1.327652e-02</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>575000.0</th>\n",
       "      <th>455000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "1225000.0 575000.0                   0                    0       0   \n",
       "295000.0  655000.0                   0                    0       0   \n",
       "215000.0  215000.0                   4                    0       0   \n",
       "575000.0  455000.0                   0                    0       0   \n",
       "625000.0  515000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1225000.0 575000.0                  0                  0   \n",
       "295000.0  655000.0                  9                  0   \n",
       "215000.0  215000.0                 85                  0   \n",
       "575000.0  455000.0                  0                  0   \n",
       "625000.0  515000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "1225000.0 575000.0                     0               0    0        0   \n",
       "295000.0  655000.0                     0               0    0        0   \n",
       "215000.0  215000.0                     0               0    0        0   \n",
       "575000.0  455000.0                     0               0    0        0   \n",
       "625000.0  515000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
       "1225000.0 575000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "295000.0  655000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "215000.0  215000.0                  0  ...     2.098964e-02   4.535805e-03   \n",
       "575000.0  455000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "625000.0  515000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "1225000.0 575000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "295000.0  655000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "215000.0  215000.0     2.770425e-02    -3.400000e+38        1.327652e-02   \n",
       "575000.0  455000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "625000.0  515000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
       "1225000.0 575000.0                  -3.400000e+38      -3.400000e+38   \n",
       "295000.0  655000.0                  -3.400000e+38      -3.400000e+38   \n",
       "215000.0  215000.0                  -3.400000e+38      -3.400000e+38   \n",
       "575000.0  455000.0                  -3.400000e+38      -3.400000e+38   \n",
       "625000.0  515000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "1225000.0 575000.0 -3.400000e+38    -3.400000e+38           0  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "y         x                                                              \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y        x                                                           \n",
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       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "\n",
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       "y         x                                                \n",
       "1225000.0 525000.0                  0                  0   \n",
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       "\n",
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       "y         x                                                              \n",
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       "475000.0  75000.0                      0               0    0        0   \n",
       "955000.0  155000.0                     0               0    0        5   \n",
       "\n",
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       "y         x                            ...                                   \n",
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       "955000.0  155000.0                  1  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
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       "955000.0  155000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
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       "y         x                                                    \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "      <th>365000.0</th>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "985000.0  345000.0                   0                    0       0   \n",
       "1085000.0 365000.0                   0                    0       0   \n",
       "325000.0  645000.0                   2                    0      19   \n",
       "195000.0  125000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1095000.0 185000.0                  0                  0   \n",
       "985000.0  345000.0                 98                  0   \n",
       "1085000.0 365000.0                  0                  0   \n",
       "325000.0  645000.0                 67                  0   \n",
       "195000.0  125000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "985000.0  345000.0                     0               0    0        0   \n",
       "1085000.0 365000.0                     0               0    0        0   \n",
       "325000.0  645000.0                     0               0    1        0   \n",
       "195000.0  125000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
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       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "1095000.0 185000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "985000.0  345000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
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       "195000.0  125000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
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       "985000.0  345000.0                  -3.400000e+38      -3.400000e+38   \n",
       "1085000.0 365000.0                  -3.400000e+38      -3.400000e+38   \n",
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       "195000.0  125000.0                  -3.400000e+38       4.487841e-02   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
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       "195000.0  125000.0  3.156656e-02     2.022360e+00           0  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "y         x                                                              \n",
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       "y         x                                                                \n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>685000.0</th>\n",
       "      <th>165000.0</th>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>705000.0</th>\n",
       "      <th>325000.0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "    <tr>\n",
       "      <th>465000.0</th>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "685000.0 165000.0                   0                    0       0   \n",
       "705000.0 325000.0                   6                   20       0   \n",
       "325000.0 685000.0                   0                    0       0   \n",
       "465000.0 55000.0                    0                    0       0   \n",
       "865000.0 175000.0                   0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "685000.0 165000.0                  0                  0                     0   \n",
       "705000.0 325000.0                 72                  0                     0   \n",
       "325000.0 685000.0                  0                  0                     0   \n",
       "465000.0 55000.0                   0                  0                     0   \n",
       "865000.0 175000.0                  0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "685000.0 165000.0               0    0        0                  0  ...   \n",
       "705000.0 325000.0               2    0        0                  0  ...   \n",
       "325000.0 685000.0               0    0        0                  0  ...   \n",
       "465000.0 55000.0                0    0        0                  0  ...   \n",
       "865000.0 175000.0               0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_10km  Mancozeb_10km  Mecoprop-P_10km  \\\n",
       "y        x                                                           \n",
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       "865000.0 175000.0    -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
       "685000.0 165000.0    -3.400000e+38       -3.400000e+38   \n",
       "705000.0 325000.0    -3.400000e+38       -3.400000e+38   \n",
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       "865000.0 175000.0    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
       "685000.0 165000.0                  -3.400000e+38      -3.400000e+38   \n",
       "705000.0 325000.0                  -3.400000e+38      -3.400000e+38   \n",
       "325000.0 685000.0                  -3.400000e+38      -3.400000e+38   \n",
       "465000.0 55000.0                   -3.400000e+38      -3.400000e+38   \n",
       "865000.0 175000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y        x                                                    \n",
       "685000.0 165000.0 -3.400000e+38    -3.400000e+38           1  \n",
       "705000.0 325000.0 -3.400000e+38    -3.400000e+38           1  \n",
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       "865000.0 175000.0 -3.400000e+38    -3.400000e+38           0  \n",
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       "      <th>Deciduous woodland</th>\n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "845000.0 685000.0                   0                    0       0   \n",
       "545000.0 425000.0                   5                    0      72   \n",
       "15000.0  645000.0                   0                    0       0   \n",
       "975000.0 475000.0                   0                    0       0   \n",
       "135000.0 605000.0                   0                    0      87   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "845000.0 685000.0                  0                  0                     0   \n",
       "545000.0 425000.0                 18                  0                     0   \n",
       "15000.0  645000.0                  0                  0                     0   \n",
       "975000.0 475000.0                  0                  0                     0   \n",
       "135000.0 605000.0                 11                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "845000.0 685000.0               0    0        0                  0  ...   \n",
       "545000.0 425000.0               0    0        0                  0  ...   \n",
       "15000.0  645000.0               0    0        0                  0  ...   \n",
       "975000.0 475000.0               0    0        0                  0  ...   \n",
       "135000.0 605000.0               0    2        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_10km  Mancozeb_10km  Mecoprop-P_10km  \\\n",
       "y        x                                                           \n",
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       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
       "845000.0 685000.0    -3.400000e+38       -3.400000e+38   \n",
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       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
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       "\n",
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       "y        x                                                    \n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
       "      <th>Improve grassland</th>\n",
       "      <th>Neutral grassland</th>\n",
       "      <th>Calcareous grassland</th>\n",
       "      <th>Acid grassland</th>\n",
       "      <th>Fen</th>\n",
       "      <th>Heather</th>\n",
       "      <th>Heather grassland</th>\n",
       "      <th>...</th>\n",
       "      <th>Glyphosate_10km</th>\n",
       "      <th>Mancozeb_10km</th>\n",
       "      <th>Mecoprop-P_10km</th>\n",
       "      <th>Metamitron_10km</th>\n",
       "      <th>Pendimethalin_10km</th>\n",
       "      <th>PropamocarbHydrochloride_10km</th>\n",
       "      <th>Prosulfocarb_10km</th>\n",
       "      <th>Sulphur_10km</th>\n",
       "      <th>Tri-allate_10km</th>\n",
       "      <th>Occurrence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>875000.0</th>\n",
       "      <th>625000.0</th>\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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15000.0</th>\n",
       "      <th>635000.0</th>\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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845000.0</th>\n",
       "      <th>125000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055000.0</th>\n",
       "      <th>195000.0</th>\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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>525000.0</th>\n",
       "      <th>565000.0</th>\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>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "875000.0  625000.0                   0                    0       0   \n",
       "15000.0   635000.0                   0                    0       0   \n",
       "845000.0  125000.0                   0                    0       0   \n",
       "1055000.0 195000.0                   0                    0       0   \n",
       "525000.0  565000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "875000.0  625000.0                  0                  0   \n",
       "15000.0   635000.0                  0                  0   \n",
       "845000.0  125000.0                 11                  0   \n",
       "1055000.0 195000.0                  0                  0   \n",
       "525000.0  565000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "875000.0  625000.0                     0               0    0        0   \n",
       "15000.0   635000.0                     0               0    0        0   \n",
       "845000.0  125000.0                     0               0    0        0   \n",
       "1055000.0 195000.0                     0               0    0        0   \n",
       "525000.0  565000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
       "875000.0  625000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "15000.0   635000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "845000.0  125000.0                 88  ...    -3.400000e+38  -3.400000e+38   \n",
       "1055000.0 195000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "525000.0  565000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "875000.0  625000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "15000.0   635000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "845000.0  125000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "1055000.0 195000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "525000.0  565000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
       "875000.0  625000.0                  -3.400000e+38      -3.400000e+38   \n",
       "15000.0   635000.0                  -3.400000e+38      -3.400000e+38   \n",
       "845000.0  125000.0                  -3.400000e+38      -3.400000e+38   \n",
       "1055000.0 195000.0                  -3.400000e+38      -3.400000e+38   \n",
       "525000.0  565000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "875000.0  625000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "15000.0   635000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "845000.0  125000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "1055000.0 195000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "525000.0  565000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "      <th>Deciduous woodland</th>\n",
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       "      <th>75000.0</th>\n",
       "      <th>375000.0</th>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "      <th>985000.0</th>\n",
       "      <th>15000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>335000.0</th>\n",
       "      <th>585000.0</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>9</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "75000.0  375000.0                   0                    0       0   \n",
       "15000.0  155000.0                   0                    0       0   \n",
       "985000.0 15000.0                    0                    0       0   \n",
       "335000.0 585000.0                   3                    2      86   \n",
       "75000.0  655000.0                   0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "75000.0  375000.0                  0                  0                     0   \n",
       "15000.0  155000.0                  0                  0                     0   \n",
       "985000.0 15000.0                   0                  0                     0   \n",
       "335000.0 585000.0                  9                  0                     0   \n",
       "75000.0  655000.0                  0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "75000.0  375000.0               0    0        0                  0  ...   \n",
       "15000.0  155000.0               0    0        0                  0  ...   \n",
       "985000.0 15000.0                0    0        0                  0  ...   \n",
       "335000.0 585000.0               0    0        0                  0  ...   \n",
       "75000.0  655000.0               0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_10km  Mancozeb_10km  Mecoprop-P_10km  \\\n",
       "y        x                                                           \n",
       "75000.0  375000.0    -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
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       "75000.0  655000.0    -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
       "75000.0  375000.0     1.790037e-01       -3.400000e+38   \n",
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       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
       "75000.0  375000.0                   9.841958e-02       1.722172e+01   \n",
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       "75000.0  655000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y        x                                                    \n",
       "75000.0  375000.0  3.735671e+00     2.387910e+01           1  \n",
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   ],
   "source": [
    "INVASIVE_BIRDS_PATH = 'Datasets/Machine Learning/10km Rasters/Birds'\n",
    "# Use this if using coordinates as separate columns\n",
    "# df_10km = pd.read_csv('Datasets/Machine Learning/Dataframes/10km_All_Birds_DF.csv')\n",
    "\n",
    "# Use this if using coordinates as indices\n",
    "df_10km = pd.read_csv('Datasets/Machine Learning/Dataframes/10km_All_Birds_DF.csv', index_col=[0,1])\n",
    "\n",
    "total_birds = (df_10km['Occurrence']==1).sum()\n",
    "df_dicts = []\n",
    "\n",
    "for file in os.listdir(INVASIVE_BIRDS_PATH):\n",
    "    filename = os.fsdecode(file)\n",
    "    if not filename.endswith('.tif') or filename.endswith('All_Invasive_Birds_10km.tif') :\n",
    "         continue\n",
    "\n",
    "\n",
    "\n",
    "    bird_name = filename[:-4].replace('_', ' ')\n",
    "\n",
    "    bird_dataset = rioxarray.open_rasterio(f'{INVASIVE_BIRDS_PATH}/{file}')\n",
    "    bird_dataset.name = 'data'\n",
    "    bird_df = bird_dataset.squeeze().drop(\"spatial_ref\").drop(\"band\").to_dataframe()\n",
    "\n",
    "    # Check if index matches\n",
    "    if not df_10km.index.equals(bird_df.index):\n",
    "        print('Warning: Index does not match')\n",
    "        continue\n",
    "\n",
    "    bird_df['Occurrence'] = [0 if x == -1 else 1 for x in bird_df['data']]\n",
    "    bird_df = df_10km.drop(columns='Occurrence').join(bird_df.drop(columns='data'))\n",
    "    \n",
    "    bird_dict = {'name' : bird_name, 'dataframe' : bird_df }\n",
    "    df_dicts.append(bird_dict)\n",
    "    display(bird_df.sample(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "y         x                                                           \n",
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       "y         x                                                              \n",
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       "y         x                                                                 \n",
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       "445000.0  305000.0            -1                             -1   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "y         x                                                           \n",
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       "1185000.0 95000.0             -1                             -1   \n",
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       "775000.0  305000.0                             8           0  \n",
       "955000.0  295000.0                            16           0  \n",
       "1185000.0 95000.0                            255           0  \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
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       "y        x                                                                      \n",
       "515000.0 465000.0                 31                  0                     0   \n",
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       "                   Cumulative catchment area  Surface type  \\\n",
       "y        x                                                   \n",
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       "205000.0 445000.0                              8   \n",
       "865000.0 375000.0                             -2   \n",
       "955000.0 105000.0                             -1   \n",
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       "                   Inflowing drainage direction  Occurrence  \n",
       "y        x                                                   \n",
       "515000.0 465000.0                             7           1  \n",
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       "865000.0 375000.0                             0           0  \n",
       "955000.0 105000.0                           255           0  \n",
       "345000.0 555000.0                             0           0  \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
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       "y        x                                                                      \n",
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       "955000.0 675000.0                      -9999            -1   \n",
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       "                   Outflowing drainage direction  \\\n",
       "y        x                                         \n",
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       "955000.0 675000.0                             -1   \n",
       "245000.0 605000.0                              7   \n",
       "795000.0 25000.0                              -1   \n",
       "375000.0 135000.0                             -1   \n",
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       "                   Inflowing drainage direction  Occurrence  \n",
       "y        x                                                   \n",
       "605000.0 485000.0                           255           0  \n",
       "955000.0 675000.0                           255           0  \n",
       "245000.0 605000.0                            16           0  \n",
       "795000.0 25000.0                            255           0  \n",
       "375000.0 135000.0                           255           0  \n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345000.0</th>\n",
       "      <th>515000.0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>3</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "335000.0  25000.0                    0                    0       0   \n",
       "1135000.0 205000.0                   0                    0       0   \n",
       "545000.0  195000.0                   0                    0       0   \n",
       "245000.0  215000.0                   2                    0      28   \n",
       "345000.0  515000.0                   2                    0      94   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "335000.0  25000.0                   0                  0   \n",
       "1135000.0 205000.0                  0                  0   \n",
       "545000.0  195000.0                  0                  0   \n",
       "245000.0  215000.0                 69                  0   \n",
       "345000.0  515000.0                  3                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "335000.0  25000.0                      0               0    0        0   \n",
       "1135000.0 205000.0                     0               0    0        0   \n",
       "545000.0  195000.0                     0               0    0        0   \n",
       "245000.0  215000.0                     0               0    0        0   \n",
       "345000.0  515000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Littoral sediment  Saltmarsh  \\\n",
       "y         x                            ...                                 \n",
       "335000.0  25000.0                   0  ...                  0          0   \n",
       "1135000.0 205000.0                  0  ...                  0          0   \n",
       "545000.0  195000.0                  0  ...                  0          0   \n",
       "245000.0  215000.0                  0  ...                  0          0   \n",
       "345000.0  515000.0                  0  ...                  0          0   \n",
       "\n",
       "                    Urban  Suburban  Elevation  Cumulative catchment area  \\\n",
       "y         x                                                                 \n",
       "335000.0  25000.0       0         0      -9999                      -9999   \n",
       "1135000.0 205000.0      0         0      -9999                      -9999   \n",
       "545000.0  195000.0      0         0      -9999                      -9999   \n",
       "245000.0  215000.0      0         1       1606                          1   \n",
       "345000.0  515000.0      0         1         85                          3   \n",
       "\n",
       "                    Surface type  Outflowing drainage direction  \\\n",
       "y         x                                                       \n",
       "335000.0  25000.0             -1                             -1   \n",
       "1135000.0 205000.0            -1                             -1   \n",
       "545000.0  195000.0            -1                             -1   \n",
       "245000.0  215000.0             2                              2   \n",
       "345000.0  515000.0             2                              8   \n",
       "\n",
       "                    Inflowing drainage direction  Occurrence  \n",
       "y         x                                                   \n",
       "335000.0  25000.0                            255           0  \n",
       "1135000.0 205000.0                           255           0  \n",
       "545000.0  195000.0                           255           0  \n",
       "245000.0  215000.0                             0           0  \n",
       "345000.0  515000.0                             3           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
       "      <th>Improve grassland</th>\n",
       "      <th>Neutral grassland</th>\n",
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       "      <th>Acid grassland</th>\n",
       "      <th>Fen</th>\n",
       "      <th>Heather</th>\n",
       "      <th>Heather grassland</th>\n",
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       "      <th>Saltmarsh</th>\n",
       "      <th>Urban</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Cumulative catchment area</th>\n",
       "      <th>Surface type</th>\n",
       "      <th>Outflowing drainage direction</th>\n",
       "      <th>Inflowing drainage direction</th>\n",
       "      <th>Occurrence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>235000.0</th>\n",
       "      <th>255000.0</th>\n",
       "      <td>59</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <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>2962</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465000.0</th>\n",
       "      <th>115000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895000.0</th>\n",
       "      <th>175000.0</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1000</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35000.0</th>\n",
       "      <th>165000.0</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1643</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115000.0</th>\n",
       "      <th>235000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "235000.0  255000.0                  59                   25       0   \n",
       "465000.0  115000.0                   0                    0       0   \n",
       "895000.0  175000.0                   0                    0       0   \n",
       "35000.0   165000.0                   9                    0       2   \n",
       "1115000.0 235000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "235000.0  255000.0                 16                  0   \n",
       "465000.0  115000.0                  0                  0   \n",
       "895000.0  175000.0                  0                  0   \n",
       "35000.0   165000.0                 74                  0   \n",
       "1115000.0 235000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "235000.0  255000.0                     0               0    0        0   \n",
       "465000.0  115000.0                     0               0    0        0   \n",
       "895000.0  175000.0                     0               0    0        0   \n",
       "35000.0   165000.0                     0               0    0        6   \n",
       "1115000.0 235000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Littoral sediment  Saltmarsh  \\\n",
       "y         x                            ...                                 \n",
       "235000.0  255000.0                  0  ...                  0          0   \n",
       "465000.0  115000.0                  0  ...                  0          0   \n",
       "895000.0  175000.0                  0  ...                  0          0   \n",
       "35000.0   165000.0                  0  ...                  0          0   \n",
       "1115000.0 235000.0                  0  ...                  0          0   \n",
       "\n",
       "                    Urban  Suburban  Elevation  Cumulative catchment area  \\\n",
       "y         x                                                                 \n",
       "235000.0  255000.0      0         0       2962                          2   \n",
       "465000.0  115000.0      0         0      -9999                      -9999   \n",
       "895000.0  175000.0      0         0      -1000                      -9999   \n",
       "35000.0   165000.0      0         1       1643                          1   \n",
       "1115000.0 235000.0      0         0      -9999                      -9999   \n",
       "\n",
       "                    Surface type  Outflowing drainage direction  \\\n",
       "y         x                                                       \n",
       "235000.0  255000.0             2                              6   \n",
       "465000.0  115000.0            -1                             -1   \n",
       "895000.0  175000.0             0                             -2   \n",
       "35000.0   165000.0             2                              4   \n",
       "1115000.0 235000.0            -1                             -1   \n",
       "\n",
       "                    Inflowing drainage direction  Occurrence  \n",
       "y         x                                                   \n",
       "235000.0  255000.0                            32           0  \n",
       "465000.0  115000.0                           255           0  \n",
       "895000.0  175000.0                             0           0  \n",
       "35000.0   165000.0                             0           0  \n",
       "1115000.0 235000.0                           255           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop Fertiliser and Pesticide\n",
    "for dict in df_dicts:\n",
    "    cur_df = dict[\"dataframe\"]\n",
    "    dict[\"dataframe\"].drop(cur_df.iloc[:, 26:-1], inplace=True, axis=1)\n",
    "    display(cur_df.sample(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 10km data before drop: \n",
      " Occurrence\n",
      "0    8634\n",
      "1     466\n",
      "dtype: int64 \n",
      "\n",
      "Barnacle Goose 10km data after drop: \n",
      " Occurrence\n",
      "0    2206\n",
      "1     466\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 10km data before drop: \n",
      " Occurrence\n",
      "0    7311\n",
      "1    1789\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 10km data after drop: \n",
      " Occurrence\n",
      "1    1789\n",
      "0     883\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 10km data before drop: \n",
      " Occurrence\n",
      "0    8800\n",
      "1     300\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 10km data after drop: \n",
      " Occurrence\n",
      "0    2372\n",
      "1     300\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 10km data before drop: \n",
      " Occurrence\n",
      "0    8270\n",
      "1     830\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 10km data after drop: \n",
      " Occurrence\n",
      "0    1842\n",
      "1     830\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 10km data before drop: \n",
      " Occurrence\n",
      "0    8654\n",
      "1     446\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 10km data after drop: \n",
      " Occurrence\n",
      "0    2226\n",
      "1     446\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 10km data before drop: \n",
      " Occurrence\n",
      "0    8098\n",
      "1    1002\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 10km data after drop: \n",
      " Occurrence\n",
      "0    1670\n",
      "1    1002\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 10km data before drop: \n",
      " Occurrence\n",
      "0    8853\n",
      "1     247\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 10km data after drop: \n",
      " Occurrence\n",
      "0    2425\n",
      "1     247\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 10km data before drop: \n",
      " Occurrence\n",
      "0    8023\n",
      "1    1077\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 10km data after drop: \n",
      " Occurrence\n",
      "0    1595\n",
      "1    1077\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 10km data before drop: \n",
      " Occurrence\n",
      "0    8622\n",
      "1     478\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 10km data after drop: \n",
      " Occurrence\n",
      "0    2194\n",
      "1     478\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 10km data before drop: \n",
      " Occurrence\n",
      "0    7093\n",
      "1    2007\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 10km data after drop: \n",
      " Occurrence\n",
      "1    2007\n",
      "0     665\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 10km data before drop: \n",
      " Occurrence\n",
      "0    7182\n",
      "1    1918\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 10km data after drop: \n",
      " Occurrence\n",
      "1    1918\n",
      "0     754\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 10km data before drop: \n",
      " Occurrence\n",
      "0    8370\n",
      "1     730\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 10km data after drop: \n",
      " Occurrence\n",
      "0    1942\n",
      "1     730\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 10km data before drop: \n",
      " Occurrence\n",
      "0    8587\n",
      "1     513\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 10km data after drop: \n",
      " Occurrence\n",
      "0    2159\n",
      "1     513\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 10km data before drop: \n",
      " Occurrence\n",
      "0    8362\n",
      "1     738\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 10km data after drop: \n",
      " Occurrence\n",
      "0    1934\n",
      "1     738\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 10km data before drop: \n",
      " Occurrence\n",
      "0    7892\n",
      "1    1208\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 10km data after drop: \n",
      " Occurrence\n",
      "0    1464\n",
      "1    1208\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 10km data before drop: \n",
      " Occurrence\n",
      "0    9004\n",
      "1      96\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 10km data after drop: \n",
      " Occurrence\n",
      "0    2576\n",
      "1      96\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 10km data before drop: \n",
      " Occurrence\n",
      "0    7480\n",
      "1    1620\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 10km data after drop: \n",
      " Occurrence\n",
      "1    1620\n",
      "0    1052\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 10km data before drop: \n",
      " Occurrence\n",
      "0    9003\n",
      "1      97\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 10km data after drop: \n",
      " Occurrence\n",
      "0    2575\n",
      "1      97\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 10km data before drop: \n",
      " Occurrence\n",
      "0    8441\n",
      "1     659\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 10km data after drop: \n",
      " Occurrence\n",
      "0    2013\n",
      "1     659\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 10km data before drop: \n",
      " Occurrence\n",
      "0    7833\n",
      "1    1267\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 10km data after drop: \n",
      " Occurrence\n",
      "0    1405\n",
      "1    1267\n",
      "dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data Cleaning\n",
    "np.random.seed(seed=seed)\n",
    "\n",
    "for dict in df_dicts:\n",
    "    cur_df = dict[\"dataframe\"]\n",
    "    cur_df_name = dict[\"name\"]\n",
    "\n",
    "    print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "    no_occurences = cur_df[cur_df['Occurrence']==0].index \n",
    "    sample_size = sum(cur_df['Occurrence']==0) - total_birds + sum(cur_df['Occurrence']==1)\n",
    "    random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "    dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "    print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')\n",
    "\n",
    "\n",
    "# for dict in df_dicts:\n",
    "#     cur_df = dict[\"dataframe\"]\n",
    "#     cur_df_name = dict[\"name\"]\n",
    "\n",
    "#     print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "#     no_occurences = cur_df[cur_df['Occurrence']==0].index\n",
    "#     sample_size = sum(cur_df['Occurrence']==0) - sum(cur_df['Occurrence']==1)\n",
    "#     random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "#     dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "#     print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardisation\n",
    "def standardise(X):\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    # Add headers back\n",
    "    X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "\n",
    "    # Revert 'Surface type' back to non-standardised column as it is a categorical feature\n",
    "    X_scaled_df['Surface type'] = X['Surface type'].values\n",
    "    return X_scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Selection\n",
    "\n",
    "# Check if any columns have NaN in them\n",
    "# nan_columns = []\n",
    "# for column in X_scaled_df:\n",
    "#     if X_scaled_df[column].isnull().values.any():\n",
    "#         nan_columns.append(column)\n",
    "# print(nan_columns if len(nan_columns)!= 0 else 'None')\n",
    "\n",
    "\n",
    "# Using ANOVA F-Score as a feature selection method\n",
    "def feature_select(X, y):\n",
    "    k_nums = [5, 10, 15, 20]\n",
    "    kbest_dict = {}\n",
    "    for num in k_nums:\n",
    "        # Needs to be 1d array, y.values.ravel() converts y into a 1d array\n",
    "        best_X = SelectKBest(f_classif, k=num).fit(X, y.values.ravel())\n",
    "        # kbest_dict[str(num)] = best_X.get_feature_names_out().tolist()\n",
    "        kbest_dict[str(num)] = best_X\n",
    "    # kbest_dict['40'] = list(X.columns)\n",
    "\n",
    "    best_X = SelectKBest(f_classif, k='all').fit(X, y.values.ravel())\n",
    "\n",
    "    feat_scores = pd.DataFrame()\n",
    "    feat_scores[\"F Score\"] = best_X.scores_\n",
    "    feat_scores[\"P Value\"] = best_X.pvalues_\n",
    "    feat_scores[\"Attribute\"] = X.columns\n",
    "    kbest_dict['Dataframe'] = feat_scores.sort_values([\"F Score\", \"P Value\"], ascending=[False, False])\n",
    "\n",
    "\n",
    "    if details:\n",
    "        print(f'K-Best Features Dataframe: \\n{kbest_dict[\"Dataframe\"]} \\n')\n",
    "    # print(json.dumps(kbest_dict, indent=4))\n",
    "    return kbest_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample (upsample) minority data\n",
    "# for dict in df_dicts:\n",
    "#     if sum(dict['dataframe']['Occurence']==1) > sum(dict['dataframe']['Occurence']==0):\n",
    "#         continue\n",
    "\n",
    "# from sklearn.utils import resample\n",
    "\n",
    "# def upsample(X, y):\n",
    "#     X_1 = X[y['Occurrence'] == 1] # Getting positive occurrences (minority)\n",
    "#     X_0 = X[y['Occurrence'] == 0] # Getting negative occurrences (majority)\n",
    "    \n",
    "#     X_1_upsampled = resample(X_1 ,random_state=seed,n_samples=total_birds/2,replace=True)\n",
    "\n",
    "\n",
    "#     print(f'Resampling: \\n {y.value_counts()} \\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oversample(X_train, y_train):\n",
    "    over = RandomOverSampler(sampling_strategy='minority', random_state=seed)\n",
    "    smote = SMOTE(random_state=seed, sampling_strategy='minority')\n",
    "    X_smote, y_smote = smote.fit_resample(X_train, y_train)\n",
    "    \n",
    "    if details:\n",
    "        print(f'Resampled Value Counts: \\n {y_smote.value_counts()} \\n')\n",
    "\n",
    "    return X_smote, y_smote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Occurrence Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mute Swan 10km</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.751123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 10km</td>\n",
       "      <td>1918</td>\n",
       "      <td>0.717814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 10km</td>\n",
       "      <td>1789</td>\n",
       "      <td>0.669536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 10km</td>\n",
       "      <td>1620</td>\n",
       "      <td>0.606287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 10km</td>\n",
       "      <td>1267</td>\n",
       "      <td>0.474177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 10km</td>\n",
       "      <td>1208</td>\n",
       "      <td>0.452096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 10km</td>\n",
       "      <td>1077</td>\n",
       "      <td>0.403069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 10km</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 10km</td>\n",
       "      <td>830</td>\n",
       "      <td>0.310629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 10km</td>\n",
       "      <td>738</td>\n",
       "      <td>0.276198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 10km</td>\n",
       "      <td>730</td>\n",
       "      <td>0.273204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 10km</td>\n",
       "      <td>659</td>\n",
       "      <td>0.246632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 10km</td>\n",
       "      <td>513</td>\n",
       "      <td>0.191991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 10km</td>\n",
       "      <td>478</td>\n",
       "      <td>0.178892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 10km</td>\n",
       "      <td>466</td>\n",
       "      <td>0.174401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 10km</td>\n",
       "      <td>446</td>\n",
       "      <td>0.166916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 10km</td>\n",
       "      <td>300</td>\n",
       "      <td>0.112275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 10km</td>\n",
       "      <td>247</td>\n",
       "      <td>0.092440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 10km</td>\n",
       "      <td>97</td>\n",
       "      <td>0.036302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 10km</td>\n",
       "      <td>96</td>\n",
       "      <td>0.035928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  Occurrence Count  Percentage\n",
       "9              Mute Swan 10km              2007    0.751123\n",
       "10              Pheasant 10km              1918    0.717814\n",
       "1           Canada Goose 10km              1789    0.669536\n",
       "16             Rock Dove 10km              1620    0.606287\n",
       "19                Wigeon 10km              1267    0.474177\n",
       "14  Red-legged Partridge 10km              1208    0.452096\n",
       "7             Little Owl 10km              1077    0.403069\n",
       "5         Grey Partridge 10km              1002    0.375000\n",
       "3                Gadwall 10km               830    0.310629\n",
       "13               Pochard 10km               738    0.276198\n",
       "11     Pink-footed Goose 10km               730    0.273204\n",
       "18          Whooper Swan 10km               659    0.246632\n",
       "12               Pintail 10km               513    0.191991\n",
       "8          Mandarin Duck 10km               478    0.178892\n",
       "0         Barnacle Goose 10km               466    0.174401\n",
       "4                Goshawk 10km               446    0.166916\n",
       "2         Egyptian Goose 10km               300    0.112275\n",
       "6         Indian Peafowl 10km               247    0.092440\n",
       "17            Ruddy Duck 10km                97    0.036302\n",
       "15  Ring-necked Parakeet 10km                96    0.035928"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_bird_occurrences = pd.DataFrame([(dict['name'],sum(dict['dataframe']['Occurrence'] == 1)) for dict in df_dicts], columns=['Name', 'Occurrence Count'])\n",
    "All_bird_occurrences['Percentage'] = All_bird_occurrences['Occurrence Count']/total_birds\n",
    "\n",
    "All_bird_occurrences.sort_values('Occurrence Count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training with Barnacle Goose 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8850174216027874,\n",
      "        \"recall\": 0.9136690647482014,\n",
      "        \"f1-score\": 0.8991150442477875,\n",
      "        \"support\": 556\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.48936170212765956,\n",
      "        \"recall\": 0.4107142857142857,\n",
      "        \"f1-score\": 0.4466019417475728,\n",
      "        \"support\": 112\n",
      "    },\n",
      "    \"accuracy\": 0.8293413173652695,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6871895618652235,\n",
      "        \"recall\": 0.6621916752312436,\n",
      "        \"f1-score\": 0.6728584929976802,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8186799357027659,\n",
      "        \"recall\": 0.8293413173652695,\n",
      "        \"f1-score\": 0.8232445839483503,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Barnacle Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8957169459962756,\n",
      "        \"recall\": 0.8651079136690647,\n",
      "        \"f1-score\": 0.8801463860933211,\n",
      "        \"support\": 556\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.42748091603053434,\n",
      "        \"recall\": 0.5,\n",
      "        \"f1-score\": 0.46090534979423864,\n",
      "        \"support\": 112\n",
      "    },\n",
      "    \"accuracy\": 0.8038922155688623,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.661598931013405,\n",
      "        \"recall\": 0.6825539568345323,\n",
      "        \"f1-score\": 0.6705258679437799,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8172103062415405,\n",
      "        \"recall\": 0.8038922155688623,\n",
      "        \"f1-score\": 0.8098544758156306,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Canada Goose 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada Goose 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9739583333333334,\n",
      "        \"recall\": 0.8862559241706162,\n",
      "        \"f1-score\": 0.9280397022332506,\n",
      "        \"support\": 211\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9495798319327731,\n",
      "        \"recall\": 0.9890590809628009,\n",
      "        \"f1-score\": 0.9689174705251876,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.9565868263473054,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9617690826330532,\n",
      "        \"recall\": 0.9376575025667085,\n",
      "        \"f1-score\": 0.9484785863792191,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9572802268362435,\n",
      "        \"recall\": 0.9565868263473054,\n",
      "        \"f1-score\": 0.9560054808401597,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Canada Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9178743961352657,\n",
      "        \"recall\": 0.9004739336492891,\n",
      "        \"f1-score\": 0.9090909090909091,\n",
      "        \"support\": 211\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9544468546637744,\n",
      "        \"recall\": 0.962800875273523,\n",
      "        \"f1-score\": 0.9586056644880175,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.9431137724550899,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.93616062539952,\n",
      "        \"recall\": 0.931637404461406,\n",
      "        \"f1-score\": 0.9338482867894633,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9428947756974342,\n",
      "        \"recall\": 0.9431137724550899,\n",
      "        \"f1-score\": 0.9429655246844397,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Egyptian Goose 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9357495881383855,\n",
      "        \"recall\": 0.9594594594594594,\n",
      "        \"f1-score\": 0.9474562135112593,\n",
      "        \"support\": 592\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6065573770491803,\n",
      "        \"recall\": 0.4868421052631579,\n",
      "        \"f1-score\": 0.5401459854014599,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.905688622754491,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7711534825937829,\n",
      "        \"recall\": 0.7231507823613087,\n",
      "        \"f1-score\": 0.7438010994563595,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8982965820863202,\n",
      "        \"recall\": 0.905688622754491,\n",
      "        \"f1-score\": 0.9011155288760128,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Egyptian Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9380234505862647,\n",
      "        \"recall\": 0.9459459459459459,\n",
      "        \"f1-score\": 0.9419680403700589,\n",
      "        \"support\": 592\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5492957746478874,\n",
      "        \"recall\": 0.5131578947368421,\n",
      "        \"f1-score\": 0.5306122448979592,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.8967065868263473,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.743659612617076,\n",
      "        \"recall\": 0.729551920341394,\n",
      "        \"f1-score\": 0.736290142634009,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8937969485333954,\n",
      "        \"recall\": 0.8967065868263473,\n",
      "        \"f1-score\": 0.8951670816037721,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Gadwall 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gadwall 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9131403118040089,\n",
      "        \"recall\": 0.8951965065502183,\n",
      "        \"f1-score\": 0.9040793825799339,\n",
      "        \"support\": 458\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7808219178082192,\n",
      "        \"recall\": 0.8142857142857143,\n",
      "        \"f1-score\": 0.7972027972027972,\n",
      "        \"support\": 210\n",
      "    },\n",
      "    \"accuracy\": 0.8697604790419161,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.846981114806114,\n",
      "        \"recall\": 0.8547411104179663,\n",
      "        \"f1-score\": 0.8506410898913656,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8715432118951528,\n",
      "        \"recall\": 0.8697604790419161,\n",
      "        \"f1-score\": 0.8704804560392173,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Gadwall 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.91725768321513,\n",
      "        \"recall\": 0.8471615720524017,\n",
      "        \"f1-score\": 0.8808172531214529,\n",
      "        \"support\": 458\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7142857142857143,\n",
      "        \"recall\": 0.8333333333333334,\n",
      "        \"f1-score\": 0.7692307692307692,\n",
      "        \"support\": 210\n",
      "    },\n",
      "    \"accuracy\": 0.842814371257485,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8157716987504222,\n",
      "        \"recall\": 0.8402474526928676,\n",
      "        \"f1-score\": 0.825024011176111,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8534491301085771,\n",
      "        \"recall\": 0.842814371257485,\n",
      "        \"f1-score\": 0.8457376698624055,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Goshawk 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goshawk 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9281867145421903,\n",
      "        \"recall\": 0.9150442477876106,\n",
      "        \"f1-score\": 0.9215686274509804,\n",
      "        \"support\": 565\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5675675675675675,\n",
      "        \"recall\": 0.6116504854368932,\n",
      "        \"f1-score\": 0.5887850467289719,\n",
      "        \"support\": 103\n",
      "    },\n",
      "    \"accuracy\": 0.8682634730538922,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7478771410548789,\n",
      "        \"recall\": 0.7633473666122519,\n",
      "        \"f1-score\": 0.7551768370899762,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8725822652332291,\n",
      "        \"recall\": 0.8682634730538922,\n",
      "        \"f1-score\": 0.87025618910612,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Goshawk 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9074733096085409,\n",
      "        \"recall\": 0.9026548672566371,\n",
      "        \"f1-score\": 0.9050576752440107,\n",
      "        \"support\": 565\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4811320754716981,\n",
      "        \"recall\": 0.49514563106796117,\n",
      "        \"f1-score\": 0.4880382775119617,\n",
      "        \"support\": 103\n",
      "    },\n",
      "    \"accuracy\": 0.8398203592814372,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6943026925401194,\n",
      "        \"recall\": 0.6989002491622991,\n",
      "        \"f1-score\": 0.6965479763779863,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8417350654227703,\n",
      "        \"recall\": 0.8398203592814372,\n",
      "        \"f1-score\": 0.8407567800847275,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Grey Partridge 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grey Partridge 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9417721518987342,\n",
      "        \"recall\": 0.8794326241134752,\n",
      "        \"f1-score\": 0.9095354523227385,\n",
      "        \"support\": 423\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8131868131868132,\n",
      "        \"recall\": 0.9061224489795918,\n",
      "        \"f1-score\": 0.8571428571428572,\n",
      "        \"support\": 245\n",
      "    },\n",
      "    \"accuracy\": 0.8892215568862275,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8774794825427736,\n",
      "        \"recall\": 0.8927775365465336,\n",
      "        \"f1-score\": 0.8833391547327978,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8946113615028949,\n",
      "        \"recall\": 0.8892215568862275,\n",
      "        \"f1-score\": 0.8903196052882012,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Grey Partridge 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9493670886075949,\n",
      "        \"recall\": 0.8865248226950354,\n",
      "        \"f1-score\": 0.9168704156479217,\n",
      "        \"support\": 423\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8241758241758241,\n",
      "        \"recall\": 0.9183673469387755,\n",
      "        \"f1-score\": 0.8687258687258687,\n",
      "        \"support\": 245\n",
      "    },\n",
      "    \"accuracy\": 0.8982035928143712,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8867714563917095,\n",
      "        \"recall\": 0.9024460848169055,\n",
      "        \"f1-score\": 0.8927981421868951,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9034511308444454,\n",
      "        \"recall\": 0.8982035928143712,\n",
      "        \"f1-score\": 0.8992126102648335,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Indian Peafowl 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indian Peafowl 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9095163806552262,\n",
      "        \"recall\": 0.9749163879598662,\n",
      "        \"f1-score\": 0.941081517352704,\n",
      "        \"support\": 598\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4444444444444444,\n",
      "        \"recall\": 0.17142857142857143,\n",
      "        \"f1-score\": 0.2474226804123711,\n",
      "        \"support\": 70\n",
      "    },\n",
      "    \"accuracy\": 0.8907185628742516,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6769804125498353,\n",
      "        \"recall\": 0.5731724796942188,\n",
      "        \"f1-score\": 0.5942520988825375,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8607812975193658,\n",
      "        \"recall\": 0.8907185628742516,\n",
      "        \"f1-score\": 0.8683927170745255,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Indian Peafowl 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9257425742574258,\n",
      "        \"recall\": 0.9381270903010034,\n",
      "        \"f1-score\": 0.9318936877076412,\n",
      "        \"support\": 598\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4032258064516129,\n",
      "        \"recall\": 0.35714285714285715,\n",
      "        \"f1-score\": 0.3787878787878788,\n",
      "        \"support\": 70\n",
      "    },\n",
      "    \"accuracy\": 0.8772455089820359,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6644841903545193,\n",
      "        \"recall\": 0.6476349737219302,\n",
      "        \"f1-score\": 0.65534078324776,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8709878231400502,\n",
      "        \"recall\": 0.8772455089820359,\n",
      "        \"f1-score\": 0.8739334981501811,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Little Owl 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Little Owl 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9414893617021277,\n",
      "        \"recall\": 0.8962025316455696,\n",
      "        \"f1-score\": 0.9182879377431907,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8595890410958904,\n",
      "        \"recall\": 0.9194139194139194,\n",
      "        \"f1-score\": 0.8884955752212389,\n",
      "        \"support\": 273\n",
      "    },\n",
      "    \"accuracy\": 0.905688622754491,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.900539201399009,\n",
      "        \"recall\": 0.9078082255297445,\n",
      "        \"f1-score\": 0.9033917564822148,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9080181228914949,\n",
      "        \"recall\": 0.905688622754491,\n",
      "        \"f1-score\": 0.906112316532872,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Little Owl 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9510869565217391,\n",
      "        \"recall\": 0.8860759493670886,\n",
      "        \"f1-score\": 0.9174311926605504,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.85,\n",
      "        \"recall\": 0.9340659340659341,\n",
      "        \"f1-score\": 0.8900523560209425,\n",
      "        \"support\": 273\n",
      "    },\n",
      "    \"accuracy\": 0.905688622754491,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9005434782608696,\n",
      "        \"recall\": 0.9100709417165114,\n",
      "        \"f1-score\": 0.9037417743407464,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9097744727935433,\n",
      "        \"recall\": 0.905688622754491,\n",
      "        \"f1-score\": 0.9062419375668183,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mandarin Duck 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9386973180076629,\n",
      "        \"recall\": 0.9040590405904059,\n",
      "        \"f1-score\": 0.9210526315789475,\n",
      "        \"support\": 542\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6438356164383562,\n",
      "        \"recall\": 0.746031746031746,\n",
      "        \"f1-score\": 0.6911764705882354,\n",
      "        \"support\": 126\n",
      "    },\n",
      "    \"accuracy\": 0.874251497005988,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7912664672230095,\n",
      "        \"recall\": 0.825045393311076,\n",
      "        \"f1-score\": 0.8061145510835914,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8830796916637517,\n",
      "        \"recall\": 0.874251497005988,\n",
      "        \"f1-score\": 0.8776927569010586,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Mandarin Duck 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9019963702359347,\n",
      "        \"recall\": 0.9169741697416974,\n",
      "        \"f1-score\": 0.9094236047575481,\n",
      "        \"support\": 542\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6153846153846154,\n",
      "        \"recall\": 0.5714285714285714,\n",
      "        \"f1-score\": 0.5925925925925927,\n",
      "        \"support\": 126\n",
      "    },\n",
      "    \"accuracy\": 0.8517964071856288,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7586904928102751,\n",
      "        \"recall\": 0.7442013705851345,\n",
      "        \"f1-score\": 0.7510080986750705,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8479348715663745,\n",
      "        \"recall\": 0.8517964071856288,\n",
      "        \"f1-score\": 0.849662066534817,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mute Swan 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8452380952380952,\n",
      "        \"recall\": 0.8452380952380952,\n",
      "        \"f1-score\": 0.8452380952380952,\n",
      "        \"support\": 168\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.948,\n",
      "        \"recall\": 0.948,\n",
      "        \"f1-score\": 0.948,\n",
      "        \"support\": 500\n",
      "    },\n",
      "    \"accuracy\": 0.9221556886227545,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8966190476190476,\n",
      "        \"recall\": 0.8966190476190476,\n",
      "        \"f1-score\": 0.8966190476190476,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9221556886227545,\n",
      "        \"recall\": 0.9221556886227545,\n",
      "        \"f1-score\": 0.9221556886227545,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Mute Swan 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8275862068965517,\n",
      "        \"recall\": 0.8571428571428571,\n",
      "        \"f1-score\": 0.8421052631578947,\n",
      "        \"support\": 168\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.951417004048583,\n",
      "        \"recall\": 0.94,\n",
      "        \"f1-score\": 0.9456740442655935,\n",
      "        \"support\": 500\n",
      "    },\n",
      "    \"accuracy\": 0.9191616766467066,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8895016054725673,\n",
      "        \"recall\": 0.8985714285714286,\n",
      "        \"f1-score\": 0.8938896537117441,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9202739293157367,\n",
      "        \"recall\": 0.9191616766467066,\n",
      "        \"f1-score\": 0.9196268059031782,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pheasant 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pheasant 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.825,\n",
      "        \"recall\": 0.8823529411764706,\n",
      "        \"f1-score\": 0.8527131782945736,\n",
      "        \"support\": 187\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9529914529914529,\n",
      "        \"recall\": 0.9272349272349273,\n",
      "        \"f1-score\": 0.9399367755532139,\n",
      "        \"support\": 481\n",
      "    },\n",
      "    \"accuracy\": 0.9146706586826348,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8889957264957264,\n",
      "        \"recall\": 0.904793934205699,\n",
      "        \"f1-score\": 0.8963249769238937,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9171615103127079,\n",
      "        \"recall\": 0.9146706586826348,\n",
      "        \"f1-score\": 0.9155193912906904,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pheasant 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.7899543378995434,\n",
      "        \"recall\": 0.9251336898395722,\n",
      "        \"f1-score\": 0.852216748768473,\n",
      "        \"support\": 187\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9688195991091314,\n",
      "        \"recall\": 0.9043659043659044,\n",
      "        \"f1-score\": 0.9354838709677419,\n",
      "        \"support\": 481\n",
      "    },\n",
      "    \"accuracy\": 0.9101796407185628,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8793869685043374,\n",
      "        \"recall\": 0.9147497971027383,\n",
      "        \"f1-score\": 0.8938503098681074,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9187480364651299,\n",
      "        \"recall\": 0.9101796407185628,\n",
      "        \"f1-score\": 0.9121740628071681,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pink-footed Goose 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pink-footed Goose 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.87527352297593,\n",
      "        \"recall\": 0.8368200836820083,\n",
      "        \"f1-score\": 0.855614973262032,\n",
      "        \"support\": 478\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6303317535545023,\n",
      "        \"recall\": 0.7,\n",
      "        \"f1-score\": 0.6633416458852868,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.7979041916167665,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7528026382652162,\n",
      "        \"recall\": 0.7684100418410041,\n",
      "        \"f1-score\": 0.7594783095736594,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8056044568231286,\n",
      "        \"recall\": 0.7979041916167665,\n",
      "        \"f1-score\": 0.8009264520021793,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pink-footed Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8937644341801386,\n",
      "        \"recall\": 0.8096234309623431,\n",
      "        \"f1-score\": 0.849615806805708,\n",
      "        \"support\": 478\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6127659574468085,\n",
      "        \"recall\": 0.7578947368421053,\n",
      "        \"f1-score\": 0.6776470588235294,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.7949101796407185,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7532651958134735,\n",
      "        \"recall\": 0.7837590839022242,\n",
      "        \"f1-score\": 0.7636314328146188,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8138397177440116,\n",
      "        \"recall\": 0.7949101796407185,\n",
      "        \"f1-score\": 0.8007025401640705,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pintail 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8866906474820144,\n",
      "        \"recall\": 0.9354838709677419,\n",
      "        \"f1-score\": 0.9104339796860572,\n",
      "        \"support\": 527\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6964285714285714,\n",
      "        \"recall\": 0.5531914893617021,\n",
      "        \"f1-score\": 0.6166007905138341,\n",
      "        \"support\": 141\n",
      "    },\n",
      "    \"accuracy\": 0.8547904191616766,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7915596094552929,\n",
      "        \"recall\": 0.744337680164722,\n",
      "        \"f1-score\": 0.7635173850999457,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8465305386144464,\n",
      "        \"recall\": 0.8547904191616766,\n",
      "        \"f1-score\": 0.8484123035284473,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pintail 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9094488188976378,\n",
      "        \"recall\": 0.8766603415559773,\n",
      "        \"f1-score\": 0.8927536231884059,\n",
      "        \"support\": 527\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.59375,\n",
      "        \"recall\": 0.6737588652482269,\n",
      "        \"f1-score\": 0.6312292358803987,\n",
      "        \"support\": 141\n",
      "    },\n",
      "    \"accuracy\": 0.8338323353293413,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.751599409448819,\n",
      "        \"recall\": 0.7752096034021021,\n",
      "        \"f1-score\": 0.7619914295344024,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8428117927530766,\n",
      "        \"recall\": 0.8338323353293413,\n",
      "        \"f1-score\": 0.8375516192805781,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pochard 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9307359307359307,\n",
      "        \"recall\": 0.8921161825726142,\n",
      "        \"f1-score\": 0.9110169491525425,\n",
      "        \"support\": 482\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7475728155339806,\n",
      "        \"recall\": 0.8279569892473119,\n",
      "        \"f1-score\": 0.7857142857142857,\n",
      "        \"support\": 186\n",
      "    },\n",
      "    \"accuracy\": 0.874251497005988,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8391543731349557,\n",
      "        \"recall\": 0.860036585909963,\n",
      "        \"f1-score\": 0.8483656174334141,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8797354226108368,\n",
      "        \"recall\": 0.874251497005988,\n",
      "        \"f1-score\": 0.8761272853808123,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pochard 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9054621848739496,\n",
      "        \"recall\": 0.8941908713692946,\n",
      "        \"f1-score\": 0.8997912317327766,\n",
      "        \"support\": 482\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.734375,\n",
      "        \"recall\": 0.7580645161290323,\n",
      "        \"f1-score\": 0.7460317460317459,\n",
      "        \"support\": 186\n",
      "    },\n",
      "    \"accuracy\": 0.8562874251497006,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8199185924369747,\n",
      "        \"recall\": 0.8261276937491635,\n",
      "        \"f1-score\": 0.8229114888822613,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8578241363910835,\n",
      "        \"recall\": 0.8562874251497006,\n",
      "        \"f1-score\": 0.8569779617621303,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Red-legged Partridge 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red-legged Partridge 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9393939393939394,\n",
      "        \"recall\": 0.8757062146892656,\n",
      "        \"f1-score\": 0.9064327485380117,\n",
      "        \"support\": 354\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8698224852071006,\n",
      "        \"recall\": 0.9363057324840764,\n",
      "        \"f1-score\": 0.901840490797546,\n",
      "        \"support\": 314\n",
      "    },\n",
      "    \"accuracy\": 0.9041916167664671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.90460821230052,\n",
      "        \"recall\": 0.906005973586671,\n",
      "        \"f1-score\": 0.9041366196677789,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9066911899707848,\n",
      "        \"recall\": 0.9041916167664671,\n",
      "        \"f1-score\": 0.9042741124144995,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Red-legged Partridge 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9296636085626911,\n",
      "        \"recall\": 0.8587570621468926,\n",
      "        \"f1-score\": 0.8928046989720998,\n",
      "        \"support\": 354\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8533724340175953,\n",
      "        \"recall\": 0.9267515923566879,\n",
      "        \"f1-score\": 0.8885496183206107,\n",
      "        \"support\": 314\n",
      "    },\n",
      "    \"accuracy\": 0.8907185628742516,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8915180212901432,\n",
      "        \"recall\": 0.8927543272517903,\n",
      "        \"f1-score\": 0.8906771586463553,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8938021881926909,\n",
      "        \"recall\": 0.8907185628742516,\n",
      "        \"f1-score\": 0.8908045562706515,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ring-necked Parakeet 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring-necked Parakeet 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9698340874811463,\n",
      "        \"recall\": 0.9984472049689441,\n",
      "        \"f1-score\": 0.9839326702371843,\n",
      "        \"support\": 644\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8,\n",
      "        \"recall\": 0.16666666666666666,\n",
      "        \"f1-score\": 0.27586206896551724,\n",
      "        \"support\": 24\n",
      "    },\n",
      "    \"accuracy\": 0.968562874251497,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8849170437405731,\n",
      "        \"recall\": 0.5825569358178054,\n",
      "        \"f1-score\": 0.6298973696013508,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9637322639788297,\n",
      "        \"recall\": 0.968562874251497,\n",
      "        \"f1-score\": 0.9584930079160466,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Ring-necked Parakeet 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9721362229102167,\n",
      "        \"recall\": 0.9751552795031055,\n",
      "        \"f1-score\": 0.9736434108527131,\n",
      "        \"support\": 644\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2727272727272727,\n",
      "        \"recall\": 0.25,\n",
      "        \"f1-score\": 0.2608695652173913,\n",
      "        \"support\": 24\n",
      "    },\n",
      "    \"accuracy\": 0.9491017964071856,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6224317478187447,\n",
      "        \"recall\": 0.6125776397515528,\n",
      "        \"f1-score\": 0.6172564880350522,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9470077576341828,\n",
      "        \"recall\": 0.9491017964071856,\n",
      "        \"f1-score\": 0.9480347696921626,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Rock Dove 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock Dove 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.888,\n",
      "        \"recall\": 0.8473282442748091,\n",
      "        \"f1-score\": 0.8671874999999999,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9043062200956937,\n",
      "        \"recall\": 0.9310344827586207,\n",
      "        \"f1-score\": 0.9174757281553398,\n",
      "        \"support\": 406\n",
      "    },\n",
      "    \"accuracy\": 0.8982035928143712,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8961531100478468,\n",
      "        \"recall\": 0.889181363516715,\n",
      "        \"f1-score\": 0.8923316140776698,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8979106667048677,\n",
      "        \"recall\": 0.8982035928143712,\n",
      "        \"f1-score\": 0.8977519021423174,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Rock Dove 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8692307692307693,\n",
      "        \"recall\": 0.8625954198473282,\n",
      "        \"f1-score\": 0.8659003831417624,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9117647058823529,\n",
      "        \"recall\": 0.916256157635468,\n",
      "        \"f1-score\": 0.9140049140049141,\n",
      "        \"support\": 406\n",
      "    },\n",
      "    \"accuracy\": 0.8952095808383234,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.890497737556561,\n",
      "        \"recall\": 0.8894257887413981,\n",
      "        \"f1-score\": 0.8899526485733382,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8950822337226001,\n",
      "        \"recall\": 0.8952095808383234,\n",
      "        \"f1-score\": 0.8951375680675702,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ruddy Duck 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ruddy Duck 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9818181818181818,\n",
      "        \"recall\": 0.9893129770992366,\n",
      "        \"f1-score\": 0.985551330798479,\n",
      "        \"support\": 655\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.125,\n",
      "        \"recall\": 0.07692307692307693,\n",
      "        \"f1-score\": 0.09523809523809525,\n",
      "        \"support\": 13\n",
      "    },\n",
      "    \"accuracy\": 0.9715568862275449,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.553409090909091,\n",
      "        \"recall\": 0.5331180270111567,\n",
      "        \"f1-score\": 0.5403947130182871,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9651435764833969,\n",
      "        \"recall\": 0.9715568862275449,\n",
      "        \"f1-score\": 0.9682248756154176,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Ruddy Duck 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9818181818181818,\n",
      "        \"recall\": 0.9893129770992366,\n",
      "        \"f1-score\": 0.985551330798479,\n",
      "        \"support\": 655\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.125,\n",
      "        \"recall\": 0.07692307692307693,\n",
      "        \"f1-score\": 0.09523809523809525,\n",
      "        \"support\": 13\n",
      "    },\n",
      "    \"accuracy\": 0.9715568862275449,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.553409090909091,\n",
      "        \"recall\": 0.5331180270111567,\n",
      "        \"f1-score\": 0.5403947130182871,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9651435764833969,\n",
      "        \"recall\": 0.9715568862275449,\n",
      "        \"f1-score\": 0.9682248756154176,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Whooper Swan 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whooper Swan 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8260869565217391,\n",
      "        \"recall\": 0.8918367346938776,\n",
      "        \"f1-score\": 0.857703631010795,\n",
      "        \"support\": 490\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6187050359712231,\n",
      "        \"recall\": 0.48314606741573035,\n",
      "        \"f1-score\": 0.5425867507886436,\n",
      "        \"support\": 178\n",
      "    },\n",
      "    \"accuracy\": 0.7829341317365269,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7223959962464811,\n",
      "        \"recall\": 0.687491401054804,\n",
      "        \"f1-score\": 0.7001451908997194,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.7708265046385178,\n",
      "        \"recall\": 0.7829341317365269,\n",
      "        \"f1-score\": 0.7737353605324373,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Whooper Swan 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8509316770186336,\n",
      "        \"recall\": 0.8387755102040816,\n",
      "        \"f1-score\": 0.8448098663926001,\n",
      "        \"support\": 490\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.572972972972973,\n",
      "        \"recall\": 0.5955056179775281,\n",
      "        \"f1-score\": 0.5840220385674931,\n",
      "        \"support\": 178\n",
      "    },\n",
      "    \"accuracy\": 0.7739520958083832,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7119523249958033,\n",
      "        \"recall\": 0.7171405640908048,\n",
      "        \"f1-score\": 0.7144159524800466,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.7768648367190414,\n",
      "        \"recall\": 0.7739520958083832,\n",
      "        \"f1-score\": 0.775318499097886,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Wigeon 10km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wigeon 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9477124183006536,\n",
      "        \"recall\": 0.7816711590296496,\n",
      "        \"f1-score\": 0.8567208271787297,\n",
      "        \"support\": 371\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7762430939226519,\n",
      "        \"recall\": 0.9461279461279462,\n",
      "        \"f1-score\": 0.8528072837632777,\n",
      "        \"support\": 297\n",
      "    },\n",
      "    \"accuracy\": 0.8547904191616766,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8619777561116527,\n",
      "        \"recall\": 0.8638995525787978,\n",
      "        \"f1-score\": 0.8547640554710036,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8714753085098355,\n",
      "        \"recall\": 0.8547904191616766,\n",
      "        \"f1-score\": 0.8549808235943147,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Wigeon 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9478827361563518,\n",
      "        \"recall\": 0.784366576819407,\n",
      "        \"f1-score\": 0.8584070796460177,\n",
      "        \"support\": 371\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.778393351800554,\n",
      "        \"recall\": 0.9461279461279462,\n",
      "        \"f1-score\": 0.8541033434650456,\n",
      "        \"support\": 297\n",
      "    },\n",
      "    \"accuracy\": 0.8562874251497006,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8631380439784528,\n",
      "        \"recall\": 0.8652472614736766,\n",
      "        \"f1-score\": 0.8562552115555316,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8725259290400764,\n",
      "        \"recall\": 0.8562874251497006,\n",
      "        \"f1-score\": 0.8564935921523819,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add model pipeline\n",
    "estimators = [\n",
    "    ('lr', LogisticRegression(max_iter=10000, solver='saga', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('knn', KNeighborsClassifier(n_neighbors=5, weights='distance')),\n",
    "    ('sgd', SGDClassifier( max_iter=10000, loss='modified_huber', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('rf', RandomForestClassifier(n_estimators=20,max_features=None, random_state=seed, verbose=verbose))\n",
    "]\n",
    "\n",
    "\n",
    "for dict in df_dicts:\n",
    "    print(f'Training with {dict[\"name\"]} cells... \\n')\n",
    "    # Use this if using coordinates as separate columns\n",
    "    # coords, X, y = data['dataframe'].iloc[:, :2], data['dataframe'].iloc[:, 2:-1], data['dataframe'].iloc[:, [-1]]\n",
    "    # data['coords'] = coords\n",
    "    \n",
    "    # Use this if using coordinates as indices\n",
    "    X, y = dict['dataframe'].iloc[:, 0:-1], dict['dataframe'].iloc[:, [-1]], \n",
    "\n",
    "    dict['X'] = standardise(X)\n",
    "    dict['y'] = y\n",
    "    dict['kbest'] = feature_select(dict['X'], dict['y'])\n",
    "\n",
    "    # dict['X'] = dict['kbest']['10'].transform(dict['X'])\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(dict['X'], dict['y'], random_state=seed)\n",
    "    dict['X_train'], dict['X_test'], dict['y_train'], dict['y_test'] = X_train, X_test, y_train, y_test # for debugging purposes\n",
    "\n",
    "    dict['X_smote'], dict['y_smote'] = oversample(X_train, y_train)\n",
    "\n",
    "    stack_clf = StackingClassifier(\n",
    "        estimators=estimators, \n",
    "        final_estimator=GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=None, max_depth=2, random_state=seed)\n",
    "    )\n",
    "\n",
    "    # Classifier without SMOTE\n",
    "    stack_clf.fit(dict['X_train'], dict['y_train'])\n",
    "    y_pred = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions'] = y_pred\n",
    "    dict['report'] = classification_report(y_test, y_pred, output_dict=True)\n",
    "    \n",
    "\n",
    "    # Classifier with SMOTE\n",
    "    stack_clf.fit(dict['X_smote'], dict['y_smote'])\n",
    "    y_pred_smote = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions_smote'] = y_pred_smote\n",
    "    dict['report_smote'] = classification_report(y_test, y_pred_smote, output_dict=True)\n",
    "    \n",
    "    print(f'{dict[\"name\"]} Classification Report: \\n {json.dumps(dict[\"report\"], indent=4)} \\n')\n",
    "    print(f'{dict[\"name\"]} SMOTE Classification Report: \\n {json.dumps(dict[\"report_smote\"], indent=4)} \\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8850174216027874,\n",
      "        \"recall\": 0.9136690647482014,\n",
      "        \"f1-score\": 0.8991150442477875,\n",
      "        \"support\": 556\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.48936170212765956,\n",
      "        \"recall\": 0.4107142857142857,\n",
      "        \"f1-score\": 0.4466019417475728,\n",
      "        \"support\": 112\n",
      "    },\n",
      "    \"accuracy\": 0.8293413173652695,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6871895618652235,\n",
      "        \"recall\": 0.6621916752312436,\n",
      "        \"f1-score\": 0.6728584929976802,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8186799357027659,\n",
      "        \"recall\": 0.8293413173652695,\n",
      "        \"f1-score\": 0.8232445839483503,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Canada Goose 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9739583333333334,\n",
      "        \"recall\": 0.8862559241706162,\n",
      "        \"f1-score\": 0.9280397022332506,\n",
      "        \"support\": 211\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9495798319327731,\n",
      "        \"recall\": 0.9890590809628009,\n",
      "        \"f1-score\": 0.9689174705251876,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.9565868263473054,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9617690826330532,\n",
      "        \"recall\": 0.9376575025667085,\n",
      "        \"f1-score\": 0.9484785863792191,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9572802268362435,\n",
      "        \"recall\": 0.9565868263473054,\n",
      "        \"f1-score\": 0.9560054808401597,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Egyptian Goose 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9357495881383855,\n",
      "        \"recall\": 0.9594594594594594,\n",
      "        \"f1-score\": 0.9474562135112593,\n",
      "        \"support\": 592\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6065573770491803,\n",
      "        \"recall\": 0.4868421052631579,\n",
      "        \"f1-score\": 0.5401459854014599,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.905688622754491,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7711534825937829,\n",
      "        \"recall\": 0.7231507823613087,\n",
      "        \"f1-score\": 0.7438010994563595,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8982965820863202,\n",
      "        \"recall\": 0.905688622754491,\n",
      "        \"f1-score\": 0.9011155288760128,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Gadwall 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9131403118040089,\n",
      "        \"recall\": 0.8951965065502183,\n",
      "        \"f1-score\": 0.9040793825799339,\n",
      "        \"support\": 458\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7808219178082192,\n",
      "        \"recall\": 0.8142857142857143,\n",
      "        \"f1-score\": 0.7972027972027972,\n",
      "        \"support\": 210\n",
      "    },\n",
      "    \"accuracy\": 0.8697604790419161,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.846981114806114,\n",
      "        \"recall\": 0.8547411104179663,\n",
      "        \"f1-score\": 0.8506410898913656,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8715432118951528,\n",
      "        \"recall\": 0.8697604790419161,\n",
      "        \"f1-score\": 0.8704804560392173,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Goshawk 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9281867145421903,\n",
      "        \"recall\": 0.9150442477876106,\n",
      "        \"f1-score\": 0.9215686274509804,\n",
      "        \"support\": 565\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5675675675675675,\n",
      "        \"recall\": 0.6116504854368932,\n",
      "        \"f1-score\": 0.5887850467289719,\n",
      "        \"support\": 103\n",
      "    },\n",
      "    \"accuracy\": 0.8682634730538922,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7478771410548789,\n",
      "        \"recall\": 0.7633473666122519,\n",
      "        \"f1-score\": 0.7551768370899762,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8725822652332291,\n",
      "        \"recall\": 0.8682634730538922,\n",
      "        \"f1-score\": 0.87025618910612,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Grey Partridge 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9417721518987342,\n",
      "        \"recall\": 0.8794326241134752,\n",
      "        \"f1-score\": 0.9095354523227385,\n",
      "        \"support\": 423\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8131868131868132,\n",
      "        \"recall\": 0.9061224489795918,\n",
      "        \"f1-score\": 0.8571428571428572,\n",
      "        \"support\": 245\n",
      "    },\n",
      "    \"accuracy\": 0.8892215568862275,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8774794825427736,\n",
      "        \"recall\": 0.8927775365465336,\n",
      "        \"f1-score\": 0.8833391547327978,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8946113615028949,\n",
      "        \"recall\": 0.8892215568862275,\n",
      "        \"f1-score\": 0.8903196052882012,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Indian Peafowl 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9095163806552262,\n",
      "        \"recall\": 0.9749163879598662,\n",
      "        \"f1-score\": 0.941081517352704,\n",
      "        \"support\": 598\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4444444444444444,\n",
      "        \"recall\": 0.17142857142857143,\n",
      "        \"f1-score\": 0.2474226804123711,\n",
      "        \"support\": 70\n",
      "    },\n",
      "    \"accuracy\": 0.8907185628742516,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6769804125498353,\n",
      "        \"recall\": 0.5731724796942188,\n",
      "        \"f1-score\": 0.5942520988825375,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8607812975193658,\n",
      "        \"recall\": 0.8907185628742516,\n",
      "        \"f1-score\": 0.8683927170745255,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Little Owl 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9414893617021277,\n",
      "        \"recall\": 0.8962025316455696,\n",
      "        \"f1-score\": 0.9182879377431907,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8595890410958904,\n",
      "        \"recall\": 0.9194139194139194,\n",
      "        \"f1-score\": 0.8884955752212389,\n",
      "        \"support\": 273\n",
      "    },\n",
      "    \"accuracy\": 0.905688622754491,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.900539201399009,\n",
      "        \"recall\": 0.9078082255297445,\n",
      "        \"f1-score\": 0.9033917564822148,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9080181228914949,\n",
      "        \"recall\": 0.905688622754491,\n",
      "        \"f1-score\": 0.906112316532872,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Mandarin Duck 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9386973180076629,\n",
      "        \"recall\": 0.9040590405904059,\n",
      "        \"f1-score\": 0.9210526315789475,\n",
      "        \"support\": 542\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6438356164383562,\n",
      "        \"recall\": 0.746031746031746,\n",
      "        \"f1-score\": 0.6911764705882354,\n",
      "        \"support\": 126\n",
      "    },\n",
      "    \"accuracy\": 0.874251497005988,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7912664672230095,\n",
      "        \"recall\": 0.825045393311076,\n",
      "        \"f1-score\": 0.8061145510835914,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8830796916637517,\n",
      "        \"recall\": 0.874251497005988,\n",
      "        \"f1-score\": 0.8776927569010586,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Mute Swan 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8452380952380952,\n",
      "        \"recall\": 0.8452380952380952,\n",
      "        \"f1-score\": 0.8452380952380952,\n",
      "        \"support\": 168\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.948,\n",
      "        \"recall\": 0.948,\n",
      "        \"f1-score\": 0.948,\n",
      "        \"support\": 500\n",
      "    },\n",
      "    \"accuracy\": 0.9221556886227545,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8966190476190476,\n",
      "        \"recall\": 0.8966190476190476,\n",
      "        \"f1-score\": 0.8966190476190476,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9221556886227545,\n",
      "        \"recall\": 0.9221556886227545,\n",
      "        \"f1-score\": 0.9221556886227545,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pheasant 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.825,\n",
      "        \"recall\": 0.8823529411764706,\n",
      "        \"f1-score\": 0.8527131782945736,\n",
      "        \"support\": 187\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9529914529914529,\n",
      "        \"recall\": 0.9272349272349273,\n",
      "        \"f1-score\": 0.9399367755532139,\n",
      "        \"support\": 481\n",
      "    },\n",
      "    \"accuracy\": 0.9146706586826348,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8889957264957264,\n",
      "        \"recall\": 0.904793934205699,\n",
      "        \"f1-score\": 0.8963249769238937,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9171615103127079,\n",
      "        \"recall\": 0.9146706586826348,\n",
      "        \"f1-score\": 0.9155193912906904,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pink-footed Goose 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.87527352297593,\n",
      "        \"recall\": 0.8368200836820083,\n",
      "        \"f1-score\": 0.855614973262032,\n",
      "        \"support\": 478\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6303317535545023,\n",
      "        \"recall\": 0.7,\n",
      "        \"f1-score\": 0.6633416458852868,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.7979041916167665,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7528026382652162,\n",
      "        \"recall\": 0.7684100418410041,\n",
      "        \"f1-score\": 0.7594783095736594,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8056044568231286,\n",
      "        \"recall\": 0.7979041916167665,\n",
      "        \"f1-score\": 0.8009264520021793,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pintail 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8866906474820144,\n",
      "        \"recall\": 0.9354838709677419,\n",
      "        \"f1-score\": 0.9104339796860572,\n",
      "        \"support\": 527\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6964285714285714,\n",
      "        \"recall\": 0.5531914893617021,\n",
      "        \"f1-score\": 0.6166007905138341,\n",
      "        \"support\": 141\n",
      "    },\n",
      "    \"accuracy\": 0.8547904191616766,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7915596094552929,\n",
      "        \"recall\": 0.744337680164722,\n",
      "        \"f1-score\": 0.7635173850999457,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8465305386144464,\n",
      "        \"recall\": 0.8547904191616766,\n",
      "        \"f1-score\": 0.8484123035284473,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pochard 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9307359307359307,\n",
      "        \"recall\": 0.8921161825726142,\n",
      "        \"f1-score\": 0.9110169491525425,\n",
      "        \"support\": 482\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7475728155339806,\n",
      "        \"recall\": 0.8279569892473119,\n",
      "        \"f1-score\": 0.7857142857142857,\n",
      "        \"support\": 186\n",
      "    },\n",
      "    \"accuracy\": 0.874251497005988,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8391543731349557,\n",
      "        \"recall\": 0.860036585909963,\n",
      "        \"f1-score\": 0.8483656174334141,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8797354226108368,\n",
      "        \"recall\": 0.874251497005988,\n",
      "        \"f1-score\": 0.8761272853808123,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Red-legged Partridge 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9393939393939394,\n",
      "        \"recall\": 0.8757062146892656,\n",
      "        \"f1-score\": 0.9064327485380117,\n",
      "        \"support\": 354\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8698224852071006,\n",
      "        \"recall\": 0.9363057324840764,\n",
      "        \"f1-score\": 0.901840490797546,\n",
      "        \"support\": 314\n",
      "    },\n",
      "    \"accuracy\": 0.9041916167664671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.90460821230052,\n",
      "        \"recall\": 0.906005973586671,\n",
      "        \"f1-score\": 0.9041366196677789,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9066911899707848,\n",
      "        \"recall\": 0.9041916167664671,\n",
      "        \"f1-score\": 0.9042741124144995,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Ring-necked Parakeet 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9698340874811463,\n",
      "        \"recall\": 0.9984472049689441,\n",
      "        \"f1-score\": 0.9839326702371843,\n",
      "        \"support\": 644\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8,\n",
      "        \"recall\": 0.16666666666666666,\n",
      "        \"f1-score\": 0.27586206896551724,\n",
      "        \"support\": 24\n",
      "    },\n",
      "    \"accuracy\": 0.968562874251497,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8849170437405731,\n",
      "        \"recall\": 0.5825569358178054,\n",
      "        \"f1-score\": 0.6298973696013508,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9637322639788297,\n",
      "        \"recall\": 0.968562874251497,\n",
      "        \"f1-score\": 0.9584930079160466,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Rock Dove 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.888,\n",
      "        \"recall\": 0.8473282442748091,\n",
      "        \"f1-score\": 0.8671874999999999,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9043062200956937,\n",
      "        \"recall\": 0.9310344827586207,\n",
      "        \"f1-score\": 0.9174757281553398,\n",
      "        \"support\": 406\n",
      "    },\n",
      "    \"accuracy\": 0.8982035928143712,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8961531100478468,\n",
      "        \"recall\": 0.889181363516715,\n",
      "        \"f1-score\": 0.8923316140776698,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8979106667048677,\n",
      "        \"recall\": 0.8982035928143712,\n",
      "        \"f1-score\": 0.8977519021423174,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Ruddy Duck 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9818181818181818,\n",
      "        \"recall\": 0.9893129770992366,\n",
      "        \"f1-score\": 0.985551330798479,\n",
      "        \"support\": 655\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.125,\n",
      "        \"recall\": 0.07692307692307693,\n",
      "        \"f1-score\": 0.09523809523809525,\n",
      "        \"support\": 13\n",
      "    },\n",
      "    \"accuracy\": 0.9715568862275449,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.553409090909091,\n",
      "        \"recall\": 0.5331180270111567,\n",
      "        \"f1-score\": 0.5403947130182871,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9651435764833969,\n",
      "        \"recall\": 0.9715568862275449,\n",
      "        \"f1-score\": 0.9682248756154176,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Whooper Swan 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8260869565217391,\n",
      "        \"recall\": 0.8918367346938776,\n",
      "        \"f1-score\": 0.857703631010795,\n",
      "        \"support\": 490\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6187050359712231,\n",
      "        \"recall\": 0.48314606741573035,\n",
      "        \"f1-score\": 0.5425867507886436,\n",
      "        \"support\": 178\n",
      "    },\n",
      "    \"accuracy\": 0.7829341317365269,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7223959962464811,\n",
      "        \"recall\": 0.687491401054804,\n",
      "        \"f1-score\": 0.7001451908997194,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.7708265046385178,\n",
      "        \"recall\": 0.7829341317365269,\n",
      "        \"f1-score\": 0.7737353605324373,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Wigeon 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9477124183006536,\n",
      "        \"recall\": 0.7816711590296496,\n",
      "        \"f1-score\": 0.8567208271787297,\n",
      "        \"support\": 371\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7762430939226519,\n",
      "        \"recall\": 0.9461279461279462,\n",
      "        \"f1-score\": 0.8528072837632777,\n",
      "        \"support\": 297\n",
      "    },\n",
      "    \"accuracy\": 0.8547904191616766,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8619777561116527,\n",
      "        \"recall\": 0.8638995525787978,\n",
      "        \"f1-score\": 0.8547640554710036,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8714753085098355,\n",
      "        \"recall\": 0.8547904191616766,\n",
      "        \"f1-score\": 0.8549808235943147,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'], '\\n', json.dumps(dict['report'], indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Labels</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Precision (Smote)</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Recall (Smote)</th>\n",
       "      <th>F1</th>\n",
       "      <th>F1 (Smote)</th>\n",
       "      <th>Occurrence Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mute Swan 10km</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>0.951417</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>0.945674</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.751123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 10km</td>\n",
       "      <td>0.952991</td>\n",
       "      <td>0.968820</td>\n",
       "      <td>0.927235</td>\n",
       "      <td>0.904366</td>\n",
       "      <td>0.939937</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>1918</td>\n",
       "      <td>0.717814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 10km</td>\n",
       "      <td>0.949580</td>\n",
       "      <td>0.954447</td>\n",
       "      <td>0.989059</td>\n",
       "      <td>0.962801</td>\n",
       "      <td>0.968917</td>\n",
       "      <td>0.958606</td>\n",
       "      <td>1789</td>\n",
       "      <td>0.669536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 10km</td>\n",
       "      <td>0.904306</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>0.916256</td>\n",
       "      <td>0.917476</td>\n",
       "      <td>0.914005</td>\n",
       "      <td>1620</td>\n",
       "      <td>0.606287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 10km</td>\n",
       "      <td>0.776243</td>\n",
       "      <td>0.778393</td>\n",
       "      <td>0.946128</td>\n",
       "      <td>0.946128</td>\n",
       "      <td>0.852807</td>\n",
       "      <td>0.854103</td>\n",
       "      <td>1267</td>\n",
       "      <td>0.474177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 10km</td>\n",
       "      <td>0.869822</td>\n",
       "      <td>0.853372</td>\n",
       "      <td>0.936306</td>\n",
       "      <td>0.926752</td>\n",
       "      <td>0.901840</td>\n",
       "      <td>0.888550</td>\n",
       "      <td>1208</td>\n",
       "      <td>0.452096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 10km</td>\n",
       "      <td>0.859589</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.919414</td>\n",
       "      <td>0.934066</td>\n",
       "      <td>0.888496</td>\n",
       "      <td>0.890052</td>\n",
       "      <td>1077</td>\n",
       "      <td>0.403069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 10km</td>\n",
       "      <td>0.813187</td>\n",
       "      <td>0.824176</td>\n",
       "      <td>0.906122</td>\n",
       "      <td>0.918367</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.868726</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 10km</td>\n",
       "      <td>0.780822</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.797203</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>830</td>\n",
       "      <td>0.310629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 10km</td>\n",
       "      <td>0.747573</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>0.827957</td>\n",
       "      <td>0.758065</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.746032</td>\n",
       "      <td>738</td>\n",
       "      <td>0.276198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 10km</td>\n",
       "      <td>0.630332</td>\n",
       "      <td>0.612766</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.757895</td>\n",
       "      <td>0.663342</td>\n",
       "      <td>0.677647</td>\n",
       "      <td>730</td>\n",
       "      <td>0.273204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 10km</td>\n",
       "      <td>0.618705</td>\n",
       "      <td>0.572973</td>\n",
       "      <td>0.483146</td>\n",
       "      <td>0.595506</td>\n",
       "      <td>0.542587</td>\n",
       "      <td>0.584022</td>\n",
       "      <td>659</td>\n",
       "      <td>0.246632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 10km</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.673759</td>\n",
       "      <td>0.616601</td>\n",
       "      <td>0.631229</td>\n",
       "      <td>513</td>\n",
       "      <td>0.191991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 10km</td>\n",
       "      <td>0.643836</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.746032</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.691176</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>478</td>\n",
       "      <td>0.178892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 10km</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.427481</td>\n",
       "      <td>0.410714</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.446602</td>\n",
       "      <td>0.460905</td>\n",
       "      <td>466</td>\n",
       "      <td>0.174401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 10km</td>\n",
       "      <td>0.567568</td>\n",
       "      <td>0.481132</td>\n",
       "      <td>0.611650</td>\n",
       "      <td>0.495146</td>\n",
       "      <td>0.588785</td>\n",
       "      <td>0.488038</td>\n",
       "      <td>446</td>\n",
       "      <td>0.166916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 10km</td>\n",
       "      <td>0.606557</td>\n",
       "      <td>0.549296</td>\n",
       "      <td>0.486842</td>\n",
       "      <td>0.513158</td>\n",
       "      <td>0.540146</td>\n",
       "      <td>0.530612</td>\n",
       "      <td>300</td>\n",
       "      <td>0.112275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 10km</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>0.171429</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.247423</td>\n",
       "      <td>0.378788</td>\n",
       "      <td>247</td>\n",
       "      <td>0.092440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 10km</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>97</td>\n",
       "      <td>0.036302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 10km</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>96</td>\n",
       "      <td>0.035928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Labels  Precision  Precision (Smote)    Recall  \\\n",
       "9              Mute Swan 10km   0.948000           0.951417  0.948000   \n",
       "10              Pheasant 10km   0.952991           0.968820  0.927235   \n",
       "1           Canada Goose 10km   0.949580           0.954447  0.989059   \n",
       "16             Rock Dove 10km   0.904306           0.911765  0.931034   \n",
       "19                Wigeon 10km   0.776243           0.778393  0.946128   \n",
       "14  Red-legged Partridge 10km   0.869822           0.853372  0.936306   \n",
       "7             Little Owl 10km   0.859589           0.850000  0.919414   \n",
       "5         Grey Partridge 10km   0.813187           0.824176  0.906122   \n",
       "3                Gadwall 10km   0.780822           0.714286  0.814286   \n",
       "13               Pochard 10km   0.747573           0.734375  0.827957   \n",
       "11     Pink-footed Goose 10km   0.630332           0.612766  0.700000   \n",
       "18          Whooper Swan 10km   0.618705           0.572973  0.483146   \n",
       "12               Pintail 10km   0.696429           0.593750  0.553191   \n",
       "8          Mandarin Duck 10km   0.643836           0.615385  0.746032   \n",
       "0         Barnacle Goose 10km   0.489362           0.427481  0.410714   \n",
       "4                Goshawk 10km   0.567568           0.481132  0.611650   \n",
       "2         Egyptian Goose 10km   0.606557           0.549296  0.486842   \n",
       "6         Indian Peafowl 10km   0.444444           0.403226  0.171429   \n",
       "17            Ruddy Duck 10km   0.125000           0.125000  0.076923   \n",
       "15  Ring-necked Parakeet 10km   0.800000           0.272727  0.166667   \n",
       "\n",
       "    Recall (Smote)        F1  F1 (Smote)  Occurrence Count  Percentage  \n",
       "9         0.940000  0.948000    0.945674              2007    0.751123  \n",
       "10        0.904366  0.939937    0.935484              1918    0.717814  \n",
       "1         0.962801  0.968917    0.958606              1789    0.669536  \n",
       "16        0.916256  0.917476    0.914005              1620    0.606287  \n",
       "19        0.946128  0.852807    0.854103              1267    0.474177  \n",
       "14        0.926752  0.901840    0.888550              1208    0.452096  \n",
       "7         0.934066  0.888496    0.890052              1077    0.403069  \n",
       "5         0.918367  0.857143    0.868726              1002    0.375000  \n",
       "3         0.833333  0.797203    0.769231               830    0.310629  \n",
       "13        0.758065  0.785714    0.746032               738    0.276198  \n",
       "11        0.757895  0.663342    0.677647               730    0.273204  \n",
       "18        0.595506  0.542587    0.584022               659    0.246632  \n",
       "12        0.673759  0.616601    0.631229               513    0.191991  \n",
       "8         0.571429  0.691176    0.592593               478    0.178892  \n",
       "0         0.500000  0.446602    0.460905               466    0.174401  \n",
       "4         0.495146  0.588785    0.488038               446    0.166916  \n",
       "2         0.513158  0.540146    0.530612               300    0.112275  \n",
       "6         0.357143  0.247423    0.378788               247    0.092440  \n",
       "17        0.076923  0.095238    0.095238                97    0.036302  \n",
       "15        0.250000  0.275862    0.260870                96    0.035928  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create graphs to show off data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [9, 12]\n",
    "\n",
    "occurrence_count, occurrence_percentage = All_bird_occurrences['Occurrence Count'], All_bird_occurrences['Percentage']\n",
    "precision = []\n",
    "precision_smote = []\n",
    "recall = []\n",
    "recall_smote = []\n",
    "f1 = []\n",
    "f1_smote = []\n",
    "labels = []\n",
    "for dict in df_dicts:\n",
    "    precision.append(dict['report']['1']['precision'])\n",
    "    precision_smote.append(dict['report_smote']['1']['precision'])\n",
    "    recall.append(dict['report']['1']['recall'])\n",
    "    recall_smote.append(dict['report_smote']['1']['recall'])\n",
    "    f1.append(dict['report']['1']['f1-score'])\n",
    "    f1_smote.append(dict['report_smote']['1']['f1-score'])\n",
    "    labels.append(dict['name'])\n",
    "\n",
    "\n",
    "\n",
    "scores = pd.DataFrame({'Labels' : labels, \n",
    "                        'Precision': precision, 'Precision (Smote)': precision_smote, \n",
    "                        'Recall': recall, 'Recall (Smote)': recall_smote, \n",
    "                        'F1': f1, 'F1 (Smote)': f1_smote,\n",
    "                        'Occurrence Count' : occurrence_count, 'Percentage' : occurrence_percentage} )\n",
    "                        \n",
    "scores.sort_values('Occurrence Count', inplace=True)\n",
    "\n",
    "n=20\n",
    "r = np.arange(n)\n",
    "height = 0.25\n",
    "\n",
    "plt.barh(r, 'Percentage', data=scores, label='Occurrence Percentage', height = height, color='g')\n",
    "plt.barh(r+height, 'F1', data=scores, label='F1-Score',  height= height, color='b')\n",
    "plt.barh(r+height*2, 'F1 (Smote)', data=scores, label='F1-Score (Smote)',  height = height, color='r')\n",
    "plt.legend(framealpha=1, frameon=True)\n",
    "plt.yticks(r+height*2, scores['Labels'])\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "scores.sort_values('Occurrence Count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'df_dicts_10km_no_fp' (list)\n"
     ]
    }
   ],
   "source": [
    "# Store dictionaries for later use\n",
    "df_dicts_10km_no_fp = df_dicts\n",
    "%store df_dicts_10km_no_fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Export predictions to CSV for QGIS\n",
    "RESULTS_PATH = 'Datasets/Machine Learning/Results/10km/'\n",
    "for dict in df_dicts:\n",
    "    # Join with y_test datafram\n",
    "    result_df = dict['y_test'] \n",
    "    result_df['Predictions'] = dict['predictions_smote']\n",
    "    display(result_df)\n",
    "    result_df.to_csv(RESULTS_PATH + dict['name'] + '(without Fertiliser+Pesticides).csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 10km\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
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       "  <tbody>\n",
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       "      <th>25</th>\n",
       "      <td>1009.098268</td>\n",
       "      <td>3.952755e-188</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
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       "      <th>21</th>\n",
       "      <td>822.960158</td>\n",
       "      <td>5.383451e-158</td>\n",
       "      <td>Elevation</td>\n",
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       "      <th>23</th>\n",
       "      <td>773.642924</td>\n",
       "      <td>9.675580e-150</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>286.074265</td>\n",
       "      <td>4.806316e-61</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>268.765539</td>\n",
       "      <td>1.255395e-57</td>\n",
       "      <td>Arable</td>\n",
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       "      <th>22</th>\n",
       "      <td>235.656713</td>\n",
       "      <td>4.939605e-51</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>199.364053</td>\n",
       "      <td>1.033356e-43</td>\n",
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       "      <th>0</th>\n",
       "      <td>161.344102</td>\n",
       "      <td>6.172420e-36</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>92.362595</td>\n",
       "      <td>1.602427e-21</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>79.830321</td>\n",
       "      <td>7.431887e-19</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>65.850294</td>\n",
       "      <td>7.342235e-16</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>54.991282</td>\n",
       "      <td>1.617037e-13</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>47.707380</td>\n",
       "      <td>6.162071e-12</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35.077761</td>\n",
       "      <td>3.574253e-09</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32.408605</td>\n",
       "      <td>1.385572e-08</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27.999637</td>\n",
       "      <td>1.311868e-07</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.715444</td>\n",
       "      <td>2.531488e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.814489</td>\n",
       "      <td>1.020153e-03</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.406941</td>\n",
       "      <td>2.183328e-03</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.853502</td>\n",
       "      <td>5.108829e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.092810</td>\n",
       "      <td>2.410577e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.030409</td>\n",
       "      <td>4.478769e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.305533</td>\n",
       "      <td>2.533074e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.503498</td>\n",
       "      <td>4.780290e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.304783</td>\n",
       "      <td>5.809453e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.030913</td>\n",
       "      <td>8.604477e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1009.098268  3.952755e-188   Inflowing drainage direction\n",
       "21   822.960158  5.383451e-158                      Elevation\n",
       "23   773.642924  9.675580e-150                   Surface type\n",
       "3    286.074265   4.806316e-61              Improve grassland\n",
       "2    268.765539   1.255395e-57                         Arable\n",
       "22   235.656713   4.939605e-51      Cumulative catchment area\n",
       "24   199.364053   1.033356e-43  Outflowing drainage direction\n",
       "0    161.344102   6.172420e-36             Deciduous woodland\n",
       "20    92.362595   1.602427e-21                       Suburban\n",
       "19    79.830321   7.431887e-19                          Urban\n",
       "17    65.850294   7.342235e-16              Littoral sediment\n",
       "18    54.991282   1.617037e-13                      Saltmarsh\n",
       "15    47.707380   6.162071e-12         Supralittoral sediment\n",
       "9     35.077761   3.574253e-09              Heather grassland\n",
       "14    32.408605   1.385572e-08             Supralittoral rock\n",
       "16    27.999637   1.311868e-07                  Littoral rock\n",
       "7     26.715444   2.531488e-07                            Fen\n",
       "13    10.814489   1.020153e-03                     Freshwater\n",
       "4      9.406941   2.183328e-03              Neutral grassland\n",
       "1      7.853502   5.108829e-03            Coniferous woodland\n",
       "5      5.092810   2.410577e-02           Calcareous grassland\n",
       "12     4.030409   4.478769e-02                      Saltwater\n",
       "10     1.305533   2.533074e-01                            Bog\n",
       "6      0.503498   4.780290e-01                 Acid grassland\n",
       "11     0.304783   5.809453e-01                    Inland rock\n",
       "8      0.030913   8.604477e-01                        Heather"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada Goose 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7343.666630</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5748.855110</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5520.138505</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>732.084357</td>\n",
       "      <td>1.082679e-142</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572.662758</td>\n",
       "      <td>7.999142e-115</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>560.712879</td>\n",
       "      <td>1.115248e-112</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>324.290358</td>\n",
       "      <td>1.622397e-68</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>180.176262</td>\n",
       "      <td>8.407061e-40</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>50.311028</td>\n",
       "      <td>1.673556e-12</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.107127</td>\n",
       "      <td>1.615132e-08</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>28.330681</td>\n",
       "      <td>1.107605e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24.550281</td>\n",
       "      <td>7.691838e-07</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.588824</td>\n",
       "      <td>3.542001e-06</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.731579</td>\n",
       "      <td>9.274335e-06</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.901638</td>\n",
       "      <td>5.695458e-04</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.468525</td>\n",
       "      <td>7.180931e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.225843</td>\n",
       "      <td>1.401059e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.771196</td>\n",
       "      <td>3.087237e-03</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.234798</td>\n",
       "      <td>7.194880e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.603089</td>\n",
       "      <td>1.799978e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.319150</td>\n",
       "      <td>6.858907e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.498448</td>\n",
       "      <td>2.210184e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.364810</td>\n",
       "      <td>2.428099e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.087899</td>\n",
       "      <td>2.970316e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.651580</td>\n",
       "      <td>4.196203e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.149181</td>\n",
       "      <td>6.993498e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  7343.666630   0.000000e+00                      Elevation\n",
       "23  5748.855110   0.000000e+00                   Surface type\n",
       "25  5520.138505   0.000000e+00   Inflowing drainage direction\n",
       "3    732.084357  1.082679e-142              Improve grassland\n",
       "2    572.662758  7.999142e-115                         Arable\n",
       "24   560.712879  1.115248e-112  Outflowing drainage direction\n",
       "0    324.290358   1.622397e-68             Deciduous woodland\n",
       "20   180.176262   8.407061e-40                       Suburban\n",
       "19    50.311028   1.673556e-12                          Urban\n",
       "1     32.107127   1.615132e-08            Coniferous woodland\n",
       "22    28.330681   1.107605e-07      Cumulative catchment area\n",
       "6     24.550281   7.691838e-07                 Acid grassland\n",
       "4     21.588824   3.542001e-06              Neutral grassland\n",
       "5     19.731579   9.274335e-06           Calcareous grassland\n",
       "18    11.901638   5.695458e-04                      Saltmarsh\n",
       "13    11.468525   7.180931e-04                     Freshwater\n",
       "17    10.225843   1.401059e-03              Littoral sediment\n",
       "8      8.771196   3.087237e-03                        Heather\n",
       "7      7.234798   7.194880e-03                            Fen\n",
       "12     5.603089   1.799978e-02                      Saltwater\n",
       "15     3.319150   6.858907e-02         Supralittoral sediment\n",
       "9      1.498448   2.210184e-01              Heather grassland\n",
       "14     1.364810   2.428099e-01             Supralittoral rock\n",
       "10     1.087899   2.970316e-01                            Bog\n",
       "11     0.651580   4.196203e-01                    Inland rock\n",
       "16     0.149181   6.993498e-01                  Littoral rock"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 10km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>827.599456</td>\n",
       "      <td>9.132354e-159</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>641.921266</td>\n",
       "      <td>4.265779e-127</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>641.772899</td>\n",
       "      <td>4.529104e-127</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>589.698428</td>\n",
       "      <td>7.242005e-118</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>432.216501</td>\n",
       "      <td>4.225777e-89</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>314.637554</td>\n",
       "      <td>1.224799e-66</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305.393057</td>\n",
       "      <td>7.805663e-65</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>279.121118</td>\n",
       "      <td>1.127077e-59</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>163.844471</td>\n",
       "      <td>1.886102e-36</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>37.622328</td>\n",
       "      <td>9.858221e-10</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33.079310</td>\n",
       "      <td>9.853602e-09</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.003073</td>\n",
       "      <td>4.715957e-08</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.229728</td>\n",
       "      <td>8.161764e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.043311</td>\n",
       "      <td>1.546316e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.307928</td>\n",
       "      <td>2.130504e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.193269</td>\n",
       "      <td>2.275315e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.900666</td>\n",
       "      <td>2.693057e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.648895</td>\n",
       "      <td>5.621345e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.501216</td>\n",
       "      <td>6.143306e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.434389</td>\n",
       "      <td>1.188187e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.371591</td>\n",
       "      <td>2.416432e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.094713</td>\n",
       "      <td>2.955240e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.977077</td>\n",
       "      <td>3.230110e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.938625</td>\n",
       "      <td>3.327196e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.283602</td>\n",
       "      <td>5.943946e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.136474</td>\n",
       "      <td>7.118410e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       F Score        P Value                      Attribute\n",
       "23  827.599456  9.132354e-159                   Surface type\n",
       "2   641.921266  4.265779e-127                         Arable\n",
       "25  641.772899  4.529104e-127   Inflowing drainage direction\n",
       "21  589.698428  7.242005e-118                      Elevation\n",
       "20  432.216501   4.225777e-89                       Suburban\n",
       "24  314.637554   1.224799e-66  Outflowing drainage direction\n",
       "3   305.393057   7.805663e-65              Improve grassland\n",
       "0   279.121118   1.127077e-59             Deciduous woodland\n",
       "19  163.844471   1.886102e-36                          Urban\n",
       "22   37.622328   9.858221e-10      Cumulative catchment area\n",
       "18   33.079310   9.853602e-09                      Saltmarsh\n",
       "4    30.003073   4.715957e-08              Neutral grassland\n",
       "13   11.229728   8.161764e-04                     Freshwater\n",
       "6    10.043311   1.546316e-03                 Acid grassland\n",
       "9     5.307928   2.130504e-02              Heather grassland\n",
       "7     5.193269   2.275315e-02                            Fen\n",
       "5     4.900666   2.693057e-02           Calcareous grassland\n",
       "10    3.648895   5.621345e-02                            Bog\n",
       "8     3.501216   6.143306e-02                        Heather\n",
       "1     2.434389   1.188187e-01            Coniferous woodland\n",
       "16    1.371591   2.416432e-01                  Littoral rock\n",
       "12    1.094713   2.955240e-01                      Saltwater\n",
       "14    0.977077   3.230110e-01             Supralittoral rock\n",
       "11    0.938625   3.327196e-01                    Inland rock\n",
       "17    0.283602   5.943946e-01              Littoral sediment\n",
       "15    0.136474   7.118410e-01         Supralittoral sediment"
      ]
     },
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     "text": [
      "Gadwall 10km\n"
     ]
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1903.437525</td>\n",
       "      <td>2.224486e-314</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1645.682577</td>\n",
       "      <td>9.981393e-281</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1515.568547</td>\n",
       "      <td>5.744852e-263</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>949.689658</td>\n",
       "      <td>1.109256e-178</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>594.080640</td>\n",
       "      <td>1.201251e-118</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>466.821680</td>\n",
       "      <td>1.513555e-95</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>367.505198</td>\n",
       "      <td>7.557822e-77</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>344.808292</td>\n",
       "      <td>1.734076e-72</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>127.613146</td>\n",
       "      <td>6.120135e-29</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.409704</td>\n",
       "      <td>8.812937e-11</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38.573928</td>\n",
       "      <td>6.094998e-10</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29.514784</td>\n",
       "      <td>6.049909e-08</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.969658</td>\n",
       "      <td>1.735944e-06</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.396323</td>\n",
       "      <td>3.130899e-05</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.556296</td>\n",
       "      <td>2.361016e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.254536</td>\n",
       "      <td>2.770855e-04</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.567615</td>\n",
       "      <td>1.165165e-03</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.284403</td>\n",
       "      <td>1.357450e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.050744</td>\n",
       "      <td>2.650406e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.110622</td>\n",
       "      <td>4.434319e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.085671</td>\n",
       "      <td>1.369060e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.201548</td>\n",
       "      <td>4.048459e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.766321</td>\n",
       "      <td>3.814364e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.296763</td>\n",
       "      <td>5.859643e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.285778</td>\n",
       "      <td>5.929833e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.088228</td>\n",
       "      <td>7.664655e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  1903.437525  2.224486e-314                   Surface type\n",
       "25  1645.682577  9.981393e-281   Inflowing drainage direction\n",
       "21  1515.568547  5.744852e-263                      Elevation\n",
       "2    949.689658  1.109256e-178                         Arable\n",
       "3    594.080640  1.201251e-118              Improve grassland\n",
       "24   466.821680   1.513555e-95  Outflowing drainage direction\n",
       "20   367.505198   7.557822e-77                       Suburban\n",
       "0    344.808292   1.734076e-72             Deciduous woodland\n",
       "19   127.613146   6.120135e-29                          Urban\n",
       "4     42.409704   8.812937e-11              Neutral grassland\n",
       "18    38.573928   6.094998e-10                      Saltmarsh\n",
       "22    29.514784   6.049909e-08      Cumulative catchment area\n",
       "7     22.969658   1.735944e-06                            Fen\n",
       "17    17.396323   3.130899e-05              Littoral sediment\n",
       "13    13.556296   2.361016e-04                     Freshwater\n",
       "12    13.254536   2.770855e-04                      Saltwater\n",
       "8     10.567615   1.165165e-03                        Heather\n",
       "6     10.284403   1.357450e-03                 Acid grassland\n",
       "5      9.050744   2.650406e-03           Calcareous grassland\n",
       "10     8.110622   4.434319e-03                            Bog\n",
       "15     6.085671   1.369060e-02         Supralittoral sediment\n",
       "9      4.201548   4.048459e-02              Heather grassland\n",
       "11     0.766321   3.814364e-01                    Inland rock\n",
       "1      0.296763   5.859643e-01            Coniferous woodland\n",
       "16     0.285778   5.929833e-01                  Littoral rock\n",
       "14     0.088228   7.664655e-01             Supralittoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goshawk 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1516.675979</td>\n",
       "      <td>4.034569e-263</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1440.341773</td>\n",
       "      <td>1.912231e-252</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1051.448282</td>\n",
       "      <td>9.009989e-195</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615.251091</td>\n",
       "      <td>2.114437e-122</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>480.598054</td>\n",
       "      <td>4.304565e-98</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>412.738950</td>\n",
       "      <td>1.932868e-85</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>257.265143</td>\n",
       "      <td>2.402678e-55</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>240.765684</td>\n",
       "      <td>4.687337e-52</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.612918</td>\n",
       "      <td>8.702609e-22</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>47.786916</td>\n",
       "      <td>5.921305e-12</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>41.501374</td>\n",
       "      <td>1.392317e-10</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.665672</td>\n",
       "      <td>9.597340e-06</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.908913</td>\n",
       "      <td>1.958784e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.905596</td>\n",
       "      <td>2.868761e-03</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.891045</td>\n",
       "      <td>8.918962e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.474858</td>\n",
       "      <td>1.157984e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.866841</td>\n",
       "      <td>1.719524e-01</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.799764</td>\n",
       "      <td>1.798551e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.525152</td>\n",
       "      <td>2.169502e-01</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.414285</td>\n",
       "      <td>2.344522e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.880416</td>\n",
       "      <td>3.481726e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.595848</td>\n",
       "      <td>4.402348e-01</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.430001</td>\n",
       "      <td>5.120451e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.143413</td>\n",
       "      <td>7.049410e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.042886</td>\n",
       "      <td>8.359561e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.035680</td>\n",
       "      <td>8.501928e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  1516.675979  4.034569e-263                   Surface type\n",
       "21  1440.341773  1.912231e-252                      Elevation\n",
       "25  1051.448282  9.009989e-195   Inflowing drainage direction\n",
       "3    615.251091  2.114437e-122              Improve grassland\n",
       "24   480.598054   4.304565e-98  Outflowing drainage direction\n",
       "0    412.738950   1.932868e-85             Deciduous woodland\n",
       "1    257.265143   2.402678e-55            Coniferous woodland\n",
       "6    240.765684   4.687337e-52                 Acid grassland\n",
       "2     93.612918   8.702609e-22                         Arable\n",
       "20    47.786916   5.921305e-12                       Suburban\n",
       "8     41.501374   1.392317e-10                        Heather\n",
       "5     19.665672   9.597340e-06           Calcareous grassland\n",
       "13    13.908913   1.958784e-04                     Freshwater\n",
       "22     8.905596   2.868761e-03      Cumulative catchment area\n",
       "10     2.891045   8.918962e-02                            Bog\n",
       "18     2.474858   1.157984e-01                      Saltmarsh\n",
       "19     1.866841   1.719524e-01                          Urban\n",
       "9      1.799764   1.798551e-01              Heather grassland\n",
       "7      1.525152   2.169502e-01                            Fen\n",
       "17     1.414285   2.344522e-01              Littoral sediment\n",
       "14     0.880416   3.481726e-01             Supralittoral rock\n",
       "4      0.595848   4.402348e-01              Neutral grassland\n",
       "16     0.430001   5.120451e-01                  Littoral rock\n",
       "15     0.143413   7.049410e-01         Supralittoral sediment\n",
       "11     0.042886   8.359561e-01                    Inland rock\n",
       "12     0.035680   8.501928e-01                      Saltwater"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grey Partridge 10km\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3400.634758</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2971.831794</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2704.779889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1571.720673</td>\n",
       "      <td>1.066172e-270</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>772.281638</td>\n",
       "      <td>1.641378e-149</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>584.987101</td>\n",
       "      <td>5.010287e-117</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>327.534974</td>\n",
       "      <td>3.804785e-69</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>298.002024</td>\n",
       "      <td>2.183527e-63</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>77.634624</td>\n",
       "      <td>2.187610e-18</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.253781</td>\n",
       "      <td>3.486330e-11</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.060397</td>\n",
       "      <td>9.948735e-09</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.530191</td>\n",
       "      <td>7.771715e-07</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.459902</td>\n",
       "      <td>2.484816e-04</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.699635</td>\n",
       "      <td>3.721351e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.344633</td>\n",
       "      <td>7.673946e-04</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9.944567</td>\n",
       "      <td>1.631166e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.961874</td>\n",
       "      <td>4.664365e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.643353</td>\n",
       "      <td>5.640058e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.993600</td>\n",
       "      <td>8.370969e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.048774</td>\n",
       "      <td>1.524459e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.786211</td>\n",
       "      <td>1.815023e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.212361</td>\n",
       "      <td>2.709643e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.784977</td>\n",
       "      <td>3.757031e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.422222</td>\n",
       "      <td>5.158862e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.219961</td>\n",
       "      <td>6.391078e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.189243</td>\n",
       "      <td>6.635828e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  3400.634758   0.000000e+00                   Surface type\n",
       "21  2971.831794   0.000000e+00                      Elevation\n",
       "25  2704.779889   0.000000e+00   Inflowing drainage direction\n",
       "2   1571.720673  1.066172e-270                         Arable\n",
       "3    772.281638  1.641378e-149              Improve grassland\n",
       "24   584.987101  5.010287e-117  Outflowing drainage direction\n",
       "0    327.534974   3.804785e-69             Deciduous woodland\n",
       "20   298.002024   2.183527e-63                       Suburban\n",
       "19    77.634624   2.187610e-18                          Urban\n",
       "4     44.253781   3.486330e-11              Neutral grassland\n",
       "5     33.060397   9.948735e-09           Calcareous grassland\n",
       "18    24.530191   7.771715e-07                      Saltmarsh\n",
       "22    13.459902   2.484816e-04      Cumulative catchment area\n",
       "13    12.699635   3.721351e-04                     Freshwater\n",
       "7     11.344633   7.673946e-04                            Fen\n",
       "17     9.944567   1.631166e-03              Littoral sediment\n",
       "11     3.961874   4.664365e-02                    Inland rock\n",
       "15     3.643353   5.640058e-02         Supralittoral sediment\n",
       "10     2.993600   8.370969e-02                            Bog\n",
       "6      2.048774   1.524459e-01                 Acid grassland\n",
       "14     1.786211   1.815023e-01             Supralittoral rock\n",
       "9      1.212361   2.709643e-01              Heather grassland\n",
       "16     0.784977   3.757031e-01                  Littoral rock\n",
       "8      0.422222   5.158862e-01                        Heather\n",
       "12     0.219961   6.391078e-01                      Saltwater\n",
       "1      0.189243   6.635828e-01            Coniferous woodland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indian Peafowl 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>754.797758</td>\n",
       "      <td>1.483797e-146</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>562.392260</td>\n",
       "      <td>5.565883e-113</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>554.303821</td>\n",
       "      <td>1.587591e-111</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494.199893</td>\n",
       "      <td>1.352579e-100</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>362.537756</td>\n",
       "      <td>6.759522e-76</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>355.699518</td>\n",
       "      <td>1.388033e-74</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>296.541227</td>\n",
       "      <td>4.222218e-63</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>246.800525</td>\n",
       "      <td>2.918994e-53</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>35.737943</td>\n",
       "      <td>2.558060e-09</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30.997451</td>\n",
       "      <td>2.841086e-08</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.689072</td>\n",
       "      <td>9.222468e-08</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.928487</td>\n",
       "      <td>5.614321e-04</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.214172</td>\n",
       "      <td>4.188976e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.512478</td>\n",
       "      <td>1.895389e-02</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.709709</td>\n",
       "      <td>5.420288e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.099160</td>\n",
       "      <td>7.844719e-02</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.110375</td>\n",
       "      <td>1.464209e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.749114</td>\n",
       "      <td>1.861015e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.030285</td>\n",
       "      <td>3.101837e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.433261</td>\n",
       "      <td>5.104501e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.349987</td>\n",
       "      <td>5.541705e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.275544</td>\n",
       "      <td>5.996806e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.131766</td>\n",
       "      <td>7.166368e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.107604</td>\n",
       "      <td>7.429141e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.025027</td>\n",
       "      <td>8.743110e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.007901</td>\n",
       "      <td>9.291788e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       F Score        P Value                      Attribute\n",
       "23  754.797758  1.483797e-146                   Surface type\n",
       "25  562.392260  5.565883e-113   Inflowing drainage direction\n",
       "21  554.303821  1.587591e-111                      Elevation\n",
       "2   494.199893  1.352579e-100                         Arable\n",
       "0   362.537756   6.759522e-76             Deciduous woodland\n",
       "3   355.699518   1.388033e-74              Improve grassland\n",
       "20  296.541227   4.222218e-63                       Suburban\n",
       "24  246.800525   2.918994e-53  Outflowing drainage direction\n",
       "19   35.737943   2.558060e-09                          Urban\n",
       "22   30.997451   2.841086e-08      Cumulative catchment area\n",
       "5    28.689072   9.222468e-08           Calcareous grassland\n",
       "4    11.928487   5.614321e-04              Neutral grassland\n",
       "1     8.214172   4.188976e-03            Coniferous woodland\n",
       "13    5.512478   1.895389e-02                     Freshwater\n",
       "7     3.709709   5.420288e-02                            Fen\n",
       "18    3.099160   7.844719e-02                      Saltmarsh\n",
       "16    2.110375   1.464209e-01                  Littoral rock\n",
       "10    1.749114   1.861015e-01                            Bog\n",
       "14    1.030285   3.101837e-01             Supralittoral rock\n",
       "11    0.433261   5.104501e-01                    Inland rock\n",
       "6     0.349987   5.541705e-01                 Acid grassland\n",
       "15    0.275544   5.996806e-01         Supralittoral sediment\n",
       "12    0.131766   7.166368e-01                      Saltwater\n",
       "9     0.107604   7.429141e-01              Heather grassland\n",
       "17    0.025027   8.743110e-01              Littoral sediment\n",
       "8     0.007901   9.291788e-01                        Heather"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Little Owl 10km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3559.278760</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3136.796514</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3021.897153</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1533.729395</td>\n",
       "      <td>1.767848e-265</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>805.594552</td>\n",
       "      <td>4.209572e-155</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>578.181222</td>\n",
       "      <td>8.231576e-116</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>413.099124</td>\n",
       "      <td>1.653117e-85</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>334.433765</td>\n",
       "      <td>1.751859e-70</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>78.360343</td>\n",
       "      <td>1.530878e-18</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.334893</td>\n",
       "      <td>4.499781e-12</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.548857</td>\n",
       "      <td>2.250039e-10</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>32.640589</td>\n",
       "      <td>1.231441e-08</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21.008830</td>\n",
       "      <td>4.781964e-06</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.572271</td>\n",
       "      <td>3.442080e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.147986</td>\n",
       "      <td>4.178254e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.747195</td>\n",
       "      <td>5.300166e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.077734</td>\n",
       "      <td>7.948576e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.992167</td>\n",
       "      <td>8.378370e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.808580</td>\n",
       "      <td>9.387843e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.785733</td>\n",
       "      <td>9.522440e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.798244</td>\n",
       "      <td>1.800390e-01</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.897553</td>\n",
       "      <td>3.435244e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.723618</td>\n",
       "      <td>3.950360e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.238446</td>\n",
       "      <td>6.253705e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.090069</td>\n",
       "      <td>7.641124e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.003703</td>\n",
       "      <td>9.514821e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  3559.278760   0.000000e+00                   Surface type\n",
       "21  3136.796514   0.000000e+00                      Elevation\n",
       "25  3021.897153   0.000000e+00   Inflowing drainage direction\n",
       "2   1533.729395  1.767848e-265                         Arable\n",
       "3    805.594552  4.209572e-155              Improve grassland\n",
       "24   578.181222  8.231576e-116  Outflowing drainage direction\n",
       "0    413.099124   1.653117e-85             Deciduous woodland\n",
       "20   334.433765   1.751859e-70                       Suburban\n",
       "19    78.360343   1.530878e-18                          Urban\n",
       "4     48.334893   4.499781e-12              Neutral grassland\n",
       "5     40.548857   2.250039e-10           Calcareous grassland\n",
       "22    32.640589   1.231441e-08      Cumulative catchment area\n",
       "18    21.008830   4.781964e-06                      Saltmarsh\n",
       "7      8.572271   3.442080e-03                            Fen\n",
       "11     4.147986   4.178254e-02                    Inland rock\n",
       "8      3.747195   5.300166e-02                        Heather\n",
       "16     3.077734   7.948576e-02                  Littoral rock\n",
       "17     2.992167   8.378370e-02              Littoral sediment\n",
       "15     2.808580   9.387843e-02         Supralittoral sediment\n",
       "9      2.785733   9.522440e-02              Heather grassland\n",
       "13     1.798244   1.800390e-01                     Freshwater\n",
       "1      0.897553   3.435244e-01            Coniferous woodland\n",
       "12     0.723618   3.950360e-01                      Saltwater\n",
       "6      0.238446   6.253705e-01                 Acid grassland\n",
       "14     0.090069   7.641124e-01             Supralittoral rock\n",
       "10     0.003703   9.514821e-01                            Bog"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1570.251863</td>\n",
       "      <td>1.693384e-270</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1262.402052</td>\n",
       "      <td>9.104361e-227</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1125.652741</td>\n",
       "      <td>3.142630e-206</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>719.444452</td>\n",
       "      <td>1.568797e-140</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>542.531902</td>\n",
       "      <td>2.114696e-109</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>465.257609</td>\n",
       "      <td>2.949695e-95</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>449.870098</td>\n",
       "      <td>2.130590e-92</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>407.303250</td>\n",
       "      <td>2.050692e-84</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>128.616823</td>\n",
       "      <td>3.777432e-29</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42.361723</td>\n",
       "      <td>9.028347e-11</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.814586</td>\n",
       "      <td>8.945254e-10</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35.248440</td>\n",
       "      <td>3.278061e-09</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25.421537</td>\n",
       "      <td>4.915887e-07</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.882851</td>\n",
       "      <td>6.483057e-07</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.949163</td>\n",
       "      <td>2.801367e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.116605</td>\n",
       "      <td>4.419752e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.180315</td>\n",
       "      <td>2.292305e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.543835</td>\n",
       "      <td>3.312856e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.696160</td>\n",
       "      <td>1.007080e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.941605</td>\n",
       "      <td>1.636104e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.695076</td>\n",
       "      <td>1.930455e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.982589</td>\n",
       "      <td>3.216502e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.912160</td>\n",
       "      <td>3.396284e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.529182</td>\n",
       "      <td>4.670147e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.054095</td>\n",
       "      <td>8.161030e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000975</td>\n",
       "      <td>9.750981e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  1570.251863  1.693384e-270                   Surface type\n",
       "21  1262.402052  9.104361e-227                      Elevation\n",
       "25  1125.652741  3.142630e-206   Inflowing drainage direction\n",
       "3    719.444452  1.568797e-140              Improve grassland\n",
       "0    542.531902  2.114696e-109             Deciduous woodland\n",
       "2    465.257609   2.949695e-95                         Arable\n",
       "24   449.870098   2.130590e-92  Outflowing drainage direction\n",
       "20   407.303250   2.050692e-84                       Suburban\n",
       "19   128.616823   3.777432e-29                          Urban\n",
       "5     42.361723   9.028347e-11           Calcareous grassland\n",
       "4     37.814586   8.945254e-10              Neutral grassland\n",
       "22    35.248440   3.278061e-09      Cumulative catchment area\n",
       "13    25.421537   4.915887e-07                     Freshwater\n",
       "1     24.882851   6.483057e-07            Coniferous woodland\n",
       "7      8.949163   2.801367e-03                            Fen\n",
       "18     8.116605   4.419752e-03                      Saltmarsh\n",
       "10     5.180315   2.292305e-02                            Bog\n",
       "6      4.543835   3.312856e-02                 Acid grassland\n",
       "16     2.696160   1.007080e-01                  Littoral rock\n",
       "12     1.941605   1.636104e-01                      Saltwater\n",
       "14     1.695076   1.930455e-01             Supralittoral rock\n",
       "15     0.982589   3.216502e-01         Supralittoral sediment\n",
       "17     0.912160   3.396284e-01              Littoral sediment\n",
       "9      0.529182   4.670147e-01              Heather grassland\n",
       "8      0.054095   8.161030e-01                        Heather\n",
       "11     0.000975   9.750981e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 10km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4285.029849</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3424.622932</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2829.482084</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>446.956456</td>\n",
       "      <td>7.437265e-92</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380.563329</td>\n",
       "      <td>2.424716e-79</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>322.537009</td>\n",
       "      <td>3.554675e-68</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>220.990258</td>\n",
       "      <td>4.368413e-48</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>128.128309</td>\n",
       "      <td>4.777336e-29</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.403502</td>\n",
       "      <td>1.826230e-09</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.029498</td>\n",
       "      <td>2.155301e-07</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.473657</td>\n",
       "      <td>1.060429e-05</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.927381</td>\n",
       "      <td>2.372524e-05</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.878184</td>\n",
       "      <td>5.767304e-04</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.624678</td>\n",
       "      <td>6.604854e-04</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.096155</td>\n",
       "      <td>2.585619e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.262555</td>\n",
       "      <td>4.079125e-03</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.944114</td>\n",
       "      <td>4.859938e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.085884</td>\n",
       "      <td>7.815924e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.004938</td>\n",
       "      <td>8.176260e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.793136</td>\n",
       "      <td>1.615635e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.064779</td>\n",
       "      <td>2.449789e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.574230</td>\n",
       "      <td>3.254681e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.521071</td>\n",
       "      <td>3.357137e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.430012</td>\n",
       "      <td>6.413195e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.735838</td>\n",
       "      <td>1.877801e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.082067</td>\n",
       "      <td>7.745383e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        F Score       P Value                      Attribute\n",
       "21  4285.029849  0.000000e+00                      Elevation\n",
       "25  3424.622932  0.000000e+00   Inflowing drainage direction\n",
       "23  2829.482084  0.000000e+00                   Surface type\n",
       "3    446.956456  7.437265e-92              Improve grassland\n",
       "2    380.563329  2.424716e-79                         Arable\n",
       "24   322.537009  3.554675e-68  Outflowing drainage direction\n",
       "0    220.990258  4.368413e-48             Deciduous woodland\n",
       "20   128.128309  4.777336e-29                       Suburban\n",
       "19    36.403502  1.826230e-09                          Urban\n",
       "1     27.029498  2.155301e-07            Coniferous woodland\n",
       "22    19.473657  1.060429e-05      Cumulative catchment area\n",
       "6     17.927381  2.372524e-05                 Acid grassland\n",
       "4     11.878184  5.767304e-04              Neutral grassland\n",
       "17    11.624678  6.604854e-04              Littoral sediment\n",
       "18     9.096155  2.585619e-03                      Saltmarsh\n",
       "13     8.262555  4.079125e-03                     Freshwater\n",
       "5      7.944114  4.859938e-03           Calcareous grassland\n",
       "12     7.085884  7.815924e-03                      Saltwater\n",
       "10     7.004938  8.176260e-03                            Bog\n",
       "8      5.793136  1.615635e-02                        Heather\n",
       "15     5.064779  2.449789e-02         Supralittoral sediment\n",
       "7      4.574230  3.254681e-02                            Fen\n",
       "16     4.521071  3.357137e-02                  Littoral rock\n",
       "9      3.430012  6.413195e-02              Heather grassland\n",
       "14     1.735838  1.877801e-01             Supralittoral rock\n",
       "11     0.082067  7.745383e-01                    Inland rock"
      ]
     },
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    },
    {
     "name": "stdout",
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     "text": [
      "Pheasant 10km\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3918.088889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3761.808452</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3082.737272</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>588.096290</td>\n",
       "      <td>1.397570e-117</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>489.630631</td>\n",
       "      <td>9.347569e-100</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>456.120338</td>\n",
       "      <td>1.464185e-93</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>242.808633</td>\n",
       "      <td>1.830101e-52</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>133.391825</td>\n",
       "      <td>3.814028e-30</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.297078</td>\n",
       "      <td>3.198252e-09</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31.560526</td>\n",
       "      <td>2.132963e-08</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29.348583</td>\n",
       "      <td>6.585463e-08</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.940059</td>\n",
       "      <td>1.052892e-06</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.710172</td>\n",
       "      <td>4.483242e-05</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.309706</td>\n",
       "      <td>9.352465e-05</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.331895</td>\n",
       "      <td>2.659400e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.343273</td>\n",
       "      <td>6.774440e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.407960</td>\n",
       "      <td>1.141792e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.214079</td>\n",
       "      <td>2.248293e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.454565</td>\n",
       "      <td>3.490084e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.143647</td>\n",
       "      <td>4.188959e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.258077</td>\n",
       "      <td>1.330373e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.621027</td>\n",
       "      <td>2.030596e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.122800</td>\n",
       "      <td>2.894122e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.946162</td>\n",
       "      <td>3.307864e-01</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.687315</td>\n",
       "      <td>4.071534e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.235982</td>\n",
       "      <td>6.271634e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  3918.088889   0.000000e+00                      Elevation\n",
       "23  3761.808452   0.000000e+00                   Surface type\n",
       "25  3082.737272   0.000000e+00   Inflowing drainage direction\n",
       "3    588.096290  1.397570e-117              Improve grassland\n",
       "24   489.630631  9.347569e-100  Outflowing drainage direction\n",
       "2    456.120338   1.464185e-93                         Arable\n",
       "0    242.808633   1.830101e-52             Deciduous woodland\n",
       "20   133.391825   3.814028e-30                       Suburban\n",
       "1     35.297078   3.198252e-09            Coniferous woodland\n",
       "19    31.560526   2.132963e-08                          Urban\n",
       "6     29.348583   6.585463e-08                 Acid grassland\n",
       "22    23.940059   1.052892e-06      Cumulative catchment area\n",
       "4     16.710172   4.483242e-05              Neutral grassland\n",
       "5     15.309706   9.352465e-05           Calcareous grassland\n",
       "8     13.331895   2.659400e-04                        Heather\n",
       "18     7.343273   6.774440e-03                      Saltmarsh\n",
       "9      6.407960   1.141792e-02              Heather grassland\n",
       "17     5.214079   2.248293e-02              Littoral sediment\n",
       "11     4.454565   3.490084e-02                    Inland rock\n",
       "7      4.143647   4.188959e-02                            Fen\n",
       "10     2.258077   1.330373e-01                            Bog\n",
       "14     1.621027   2.030596e-01             Supralittoral rock\n",
       "16     1.122800   2.894122e-01                  Littoral rock\n",
       "13     0.946162   3.307864e-01                     Freshwater\n",
       "15     0.687315   4.071534e-01         Supralittoral sediment\n",
       "12     0.235982   6.271634e-01                      Saltwater"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pink-footed Goose 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1682.218122</td>\n",
       "      <td>1.284141e-285</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1663.088957</td>\n",
       "      <td>4.613765e-283</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1582.046768</td>\n",
       "      <td>4.142091e-272</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563.718068</td>\n",
       "      <td>3.216301e-113</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>397.048799</td>\n",
       "      <td>1.786134e-82</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>354.864185</td>\n",
       "      <td>2.008795e-74</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166.093714</td>\n",
       "      <td>6.498543e-37</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>89.444154</td>\n",
       "      <td>6.671769e-21</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>61.599086</td>\n",
       "      <td>6.041977e-15</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>47.686527</td>\n",
       "      <td>6.226801e-12</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.574103</td>\n",
       "      <td>8.113151e-11</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28.459930</td>\n",
       "      <td>1.036802e-07</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22.349456</td>\n",
       "      <td>2.390759e-06</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.292642</td>\n",
       "      <td>4.128544e-06</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.624488</td>\n",
       "      <td>5.835555e-06</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.433700</td>\n",
       "      <td>1.082683e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19.232874</td>\n",
       "      <td>1.201842e-05</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.744420</td>\n",
       "      <td>1.549662e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.768206</td>\n",
       "      <td>3.588010e-04</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.405852</td>\n",
       "      <td>1.271339e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.323252</td>\n",
       "      <td>1.329281e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.309235</td>\n",
       "      <td>6.903595e-03</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.060852</td>\n",
       "      <td>7.925588e-03</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.804120</td>\n",
       "      <td>1.605596e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.561093</td>\n",
       "      <td>3.279693e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.003258</td>\n",
       "      <td>9.544873e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1682.218122  1.284141e-285   Inflowing drainage direction\n",
       "21  1663.088957  4.613765e-283                      Elevation\n",
       "23  1582.046768  4.142091e-272                   Surface type\n",
       "2    563.718068  3.216301e-113                         Arable\n",
       "24   397.048799   1.786134e-82  Outflowing drainage direction\n",
       "3    354.864185   2.008795e-74              Improve grassland\n",
       "0    166.093714   6.498543e-37             Deciduous woodland\n",
       "20    89.444154   6.671769e-21                       Suburban\n",
       "17    61.599086   6.041977e-15              Littoral sediment\n",
       "18    47.686527   6.226801e-12                      Saltmarsh\n",
       "4     42.574103   8.113151e-11              Neutral grassland\n",
       "19    28.459930   1.036802e-07                          Urban\n",
       "13    22.349456   2.390759e-06                     Freshwater\n",
       "1     21.292642   4.128544e-06            Coniferous woodland\n",
       "6     20.624488   5.835555e-06                 Acid grassland\n",
       "7     19.433700   1.082683e-05                            Fen\n",
       "15    19.232874   1.201842e-05         Supralittoral sediment\n",
       "8     18.744420   1.549662e-05                        Heather\n",
       "10    12.768206   3.588010e-04                            Bog\n",
       "12    10.405852   1.271339e-03                      Saltwater\n",
       "9     10.323252   1.329281e-03              Heather grassland\n",
       "22     7.309235   6.903595e-03      Cumulative catchment area\n",
       "16     7.060852   7.925588e-03                  Littoral rock\n",
       "14     5.804120   1.605596e-02             Supralittoral rock\n",
       "5      4.561093   3.279693e-02           Calcareous grassland\n",
       "11     0.003258   9.544873e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 10km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1063.762969</td>\n",
       "      <td>1.090230e-196</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>858.206427</td>\n",
       "      <td>7.999859e-164</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>827.358145</td>\n",
       "      <td>1.001460e-158</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>467.480402</td>\n",
       "      <td>1.142876e-95</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>323.738474</td>\n",
       "      <td>2.076614e-68</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>253.487931</td>\n",
       "      <td>1.355850e-54</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>181.209514</td>\n",
       "      <td>5.168309e-40</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144.311367</td>\n",
       "      <td>2.048534e-32</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>112.352423</td>\n",
       "      <td>9.640675e-26</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>92.811561</td>\n",
       "      <td>1.286938e-21</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>72.835214</td>\n",
       "      <td>2.326583e-17</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.173412</td>\n",
       "      <td>8.971952e-14</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>43.439880</td>\n",
       "      <td>5.248672e-11</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29.234581</td>\n",
       "      <td>6.980062e-08</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>28.134624</td>\n",
       "      <td>1.224372e-07</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.136192</td>\n",
       "      <td>1.024709e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.288320</td>\n",
       "      <td>3.847095e-02</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.232586</td>\n",
       "      <td>2.670047e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.131802</td>\n",
       "      <td>2.874875e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.024465</td>\n",
       "      <td>3.115539e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.743897</td>\n",
       "      <td>3.884928e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.298707</td>\n",
       "      <td>5.847395e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.226972</td>\n",
       "      <td>6.338170e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.212749</td>\n",
       "      <td>6.446585e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.105490</td>\n",
       "      <td>7.453641e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.061888</td>\n",
       "      <td>8.035558e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1063.762969  1.090230e-196   Inflowing drainage direction\n",
       "23   858.206427  7.999859e-164                   Surface type\n",
       "21   827.358145  1.001460e-158                      Elevation\n",
       "2    467.480402   1.142876e-95                         Arable\n",
       "3    323.738474   2.076614e-68              Improve grassland\n",
       "24   253.487931   1.355850e-54  Outflowing drainage direction\n",
       "20   181.209514   5.168309e-40                       Suburban\n",
       "0    144.311367   2.048534e-32             Deciduous woodland\n",
       "17   112.352423   9.640675e-26              Littoral sediment\n",
       "18    92.811561   1.286938e-21                      Saltmarsh\n",
       "19    72.835214   2.326583e-17                          Urban\n",
       "4     56.173412   8.971952e-14              Neutral grassland\n",
       "12    43.439880   5.248672e-11                      Saltwater\n",
       "7     29.234581   6.980062e-08                            Fen\n",
       "15    28.134624   1.224372e-07         Supralittoral sediment\n",
       "13    15.136192   1.024709e-04                     Freshwater\n",
       "22     4.288320   3.847095e-02      Cumulative catchment area\n",
       "10     1.232586   2.670047e-01                            Bog\n",
       "8      1.131802   2.874875e-01                        Heather\n",
       "9      1.024465   3.115539e-01              Heather grassland\n",
       "16     0.743897   3.884928e-01                  Littoral rock\n",
       "6      0.298707   5.847395e-01                 Acid grassland\n",
       "11     0.226972   6.338170e-01                    Inland rock\n",
       "14     0.212749   6.446585e-01             Supralittoral rock\n",
       "5      0.105490   7.453641e-01           Calcareous grassland\n",
       "1      0.061888   8.035558e-01            Coniferous woodland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1829.302778</td>\n",
       "      <td>6.722059e-305</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1683.265511</td>\n",
       "      <td>9.311467e-286</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1558.054709</td>\n",
       "      <td>7.942916e-269</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>787.153513</td>\n",
       "      <td>5.158704e-152</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>562.763291</td>\n",
       "      <td>4.773860e-113</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>530.095858</td>\n",
       "      <td>3.785982e-107</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>412.911486</td>\n",
       "      <td>1.793393e-85</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>317.068854</td>\n",
       "      <td>4.116031e-67</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>152.812725</td>\n",
       "      <td>3.557010e-34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.072834</td>\n",
       "      <td>1.885312e-12</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31.644878</td>\n",
       "      <td>2.043336e-08</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27.531121</td>\n",
       "      <td>1.667173e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25.794352</td>\n",
       "      <td>4.059689e-07</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.028471</td>\n",
       "      <td>1.684083e-06</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.547624</td>\n",
       "      <td>4.881792e-05</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.922847</td>\n",
       "      <td>6.775311e-05</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.692650</td>\n",
       "      <td>3.222677e-03</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.001764</td>\n",
       "      <td>4.708055e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.821431</td>\n",
       "      <td>9.057488e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.737654</td>\n",
       "      <td>1.667345e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.180887</td>\n",
       "      <td>1.398519e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.110568</td>\n",
       "      <td>1.464025e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.764399</td>\n",
       "      <td>1.841902e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.765235</td>\n",
       "      <td>3.817738e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.208957</td>\n",
       "      <td>6.476236e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.133778</td>\n",
       "      <td>7.145760e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  1829.302778  6.722059e-305                   Surface type\n",
       "25  1683.265511  9.311467e-286   Inflowing drainage direction\n",
       "21  1558.054709  7.942916e-269                      Elevation\n",
       "2    787.153513  5.158704e-152                         Arable\n",
       "3    562.763291  4.773860e-113              Improve grassland\n",
       "24   530.095858  3.785982e-107  Outflowing drainage direction\n",
       "20   412.911486   1.793393e-85                       Suburban\n",
       "0    317.068854   4.116031e-67             Deciduous woodland\n",
       "19   152.812725   3.557010e-34                          Urban\n",
       "4     50.072834   1.885312e-12              Neutral grassland\n",
       "18    31.644878   2.043336e-08                      Saltmarsh\n",
       "22    27.531121   1.667173e-07      Cumulative catchment area\n",
       "13    25.794352   4.059689e-07                     Freshwater\n",
       "7     23.028471   1.684083e-06                            Fen\n",
       "17    16.547624   4.881792e-05              Littoral sediment\n",
       "12    15.922847   6.775311e-05                      Saltwater\n",
       "16     8.692650   3.222677e-03                  Littoral rock\n",
       "10     8.001764   4.708055e-03                            Bog\n",
       "1      6.821431   9.057488e-03            Coniferous woodland\n",
       "15     5.737654   1.667345e-02         Supralittoral sediment\n",
       "14     2.180887   1.398519e-01             Supralittoral rock\n",
       "6      2.110568   1.464025e-01                 Acid grassland\n",
       "5      1.764399   1.841902e-01           Calcareous grassland\n",
       "11     0.765235   3.817738e-01                    Inland rock\n",
       "8      0.208957   6.476236e-01                        Heather\n",
       "9      0.133778   7.145760e-01              Heather grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red-legged Partridge 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4665.558943</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3938.262278</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3326.396445</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1357.041556</td>\n",
       "      <td>1.447426e-240</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>877.316210</td>\n",
       "      <td>5.857134e-167</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>800.790440</td>\n",
       "      <td>2.674164e-154</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>333.873027</td>\n",
       "      <td>2.249262e-70</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>215.130108</td>\n",
       "      <td>6.638999e-47</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>41.288957</td>\n",
       "      <td>1.549560e-10</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35.695864</td>\n",
       "      <td>2.613167e-09</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.346891</td>\n",
       "      <td>5.177697e-09</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31.037194</td>\n",
       "      <td>2.784158e-08</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.036564</td>\n",
       "      <td>3.779031e-05</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.320668</td>\n",
       "      <td>9.298663e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.692745</td>\n",
       "      <td>1.294536e-04</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12.966514</td>\n",
       "      <td>3.228901e-04</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.630734</td>\n",
       "      <td>1.007677e-02</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.922399</td>\n",
       "      <td>1.501478e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.265842</td>\n",
       "      <td>3.898227e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.963782</td>\n",
       "      <td>4.659090e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.159555</td>\n",
       "      <td>7.559756e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.079135</td>\n",
       "      <td>1.494420e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.751981</td>\n",
       "      <td>3.859278e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.685745</td>\n",
       "      <td>4.076894e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.684070</td>\n",
       "      <td>4.082628e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.068934</td>\n",
       "      <td>7.929161e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  4665.558943   0.000000e+00                   Surface type\n",
       "21  3938.262278   0.000000e+00                      Elevation\n",
       "25  3326.396445   0.000000e+00   Inflowing drainage direction\n",
       "2   1357.041556  1.447426e-240                         Arable\n",
       "3    877.316210  5.857134e-167              Improve grassland\n",
       "24   800.790440  2.674164e-154  Outflowing drainage direction\n",
       "0    333.873027   2.249262e-70             Deciduous woodland\n",
       "20   215.130108   6.638999e-47                       Suburban\n",
       "5     41.288957   1.549560e-10           Calcareous grassland\n",
       "22    35.695864   2.613167e-09      Cumulative catchment area\n",
       "4     34.346891   5.177697e-09              Neutral grassland\n",
       "19    31.037194   2.784158e-08                          Urban\n",
       "1     17.036564   3.779031e-05            Coniferous woodland\n",
       "8     15.320668   9.298663e-05                        Heather\n",
       "7     14.692745   1.294536e-04                            Fen\n",
       "18    12.966514   3.228901e-04                      Saltmarsh\n",
       "13     6.630734   1.007677e-02                     Freshwater\n",
       "10     5.922399   1.501478e-02                            Bog\n",
       "12     4.265842   3.898227e-02                      Saltwater\n",
       "11     3.963782   4.659090e-02                    Inland rock\n",
       "6      3.159555   7.559756e-02                 Acid grassland\n",
       "17     2.079135   1.494420e-01              Littoral sediment\n",
       "16     0.751981   3.859278e-01                  Littoral rock\n",
       "14     0.685745   4.076894e-01             Supralittoral rock\n",
       "9      0.684070   4.082628e-01              Heather grassland\n",
       "15     0.068934   7.929161e-01         Supralittoral sediment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring-necked Parakeet 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>423.338015</td>\n",
       "      <td>1.956491e-87</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>263.539369</td>\n",
       "      <td>1.363343e-56</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>223.881090</td>\n",
       "      <td>1.143620e-48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>198.446382</td>\n",
       "      <td>1.587181e-43</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>191.636187</td>\n",
       "      <td>3.852663e-42</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>178.348178</td>\n",
       "      <td>1.989223e-39</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>150.535710</td>\n",
       "      <td>1.051957e-33</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139.678605</td>\n",
       "      <td>1.876150e-31</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>54.149221</td>\n",
       "      <td>2.460819e-13</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.990900</td>\n",
       "      <td>3.978728e-11</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.485837</td>\n",
       "      <td>8.524703e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.050661</td>\n",
       "      <td>2.650526e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.728565</td>\n",
       "      <td>2.975315e-02</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.543688</td>\n",
       "      <td>5.988106e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.422349</td>\n",
       "      <td>6.442985e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.669694</td>\n",
       "      <td>1.964114e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.383683</td>\n",
       "      <td>2.395797e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.916534</td>\n",
       "      <td>3.384733e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.632534</td>\n",
       "      <td>4.264981e-01</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.426454</td>\n",
       "      <td>5.137902e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.367288</td>\n",
       "      <td>5.445378e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.326919</td>\n",
       "      <td>5.675272e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.301721</td>\n",
       "      <td>5.828517e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.181267</td>\n",
       "      <td>6.703211e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.060003</td>\n",
       "      <td>8.065096e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.056731</td>\n",
       "      <td>8.117571e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       F Score       P Value                      Attribute\n",
       "20  423.338015  1.956491e-87                       Suburban\n",
       "23  263.539369  1.363343e-56                   Surface type\n",
       "19  223.881090  1.143620e-48                          Urban\n",
       "25  198.446382  1.587181e-43   Inflowing drainage direction\n",
       "0   191.636187  3.852663e-42             Deciduous woodland\n",
       "21  178.348178  1.989223e-39                      Elevation\n",
       "24  150.535710  1.051957e-33  Outflowing drainage direction\n",
       "3   139.678605  1.876150e-31              Improve grassland\n",
       "22   54.149221  2.460819e-13      Cumulative catchment area\n",
       "2    43.990900  3.978728e-11                         Arable\n",
       "7    15.485837  8.524703e-05                            Fen\n",
       "12    9.050661  2.650526e-03                      Saltwater\n",
       "4     4.728565  2.975315e-02              Neutral grassland\n",
       "1     3.543688  5.988106e-02            Coniferous woodland\n",
       "6     3.422349  6.442985e-02                 Acid grassland\n",
       "9     1.669694  1.964114e-01              Heather grassland\n",
       "10    1.383683  2.395797e-01                            Bog\n",
       "8     0.916534  3.384733e-01                        Heather\n",
       "13    0.632534  4.264981e-01                     Freshwater\n",
       "11    0.426454  5.137902e-01                    Inland rock\n",
       "17    0.367288  5.445378e-01              Littoral sediment\n",
       "14    0.326919  5.675272e-01             Supralittoral rock\n",
       "16    0.301721  5.828517e-01                  Littoral rock\n",
       "18    0.181267  6.703211e-01                      Saltmarsh\n",
       "5     0.060003  8.065096e-01           Calcareous grassland\n",
       "15    0.056731  8.117571e-01         Supralittoral sediment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock Dove 10km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3705.998132</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3523.491473</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3167.612036</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>657.131199</td>\n",
       "      <td>9.319155e-130</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>574.453772</td>\n",
       "      <td>3.822481e-115</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>551.770288</td>\n",
       "      <td>4.542649e-111</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>327.864746</td>\n",
       "      <td>3.283643e-69</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>215.806810</td>\n",
       "      <td>4.847374e-47</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>63.875294</td>\n",
       "      <td>1.953389e-15</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26.401491</td>\n",
       "      <td>2.973417e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.443583</td>\n",
       "      <td>4.860554e-07</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.105638</td>\n",
       "      <td>6.155325e-05</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.987362</td>\n",
       "      <td>3.193321e-04</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.549173</td>\n",
       "      <td>4.031728e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.818544</td>\n",
       "      <td>1.746353e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.049166</td>\n",
       "      <td>2.652688e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.408340</td>\n",
       "      <td>3.765535e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.278622</td>\n",
       "      <td>7.021903e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.951936</td>\n",
       "      <td>8.421371e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.807778</td>\n",
       "      <td>9.126817e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.410824</td>\n",
       "      <td>1.139956e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.211470</td>\n",
       "      <td>1.275272e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.769668</td>\n",
       "      <td>5.229508e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.376299</td>\n",
       "      <td>1.233076e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.048863</td>\n",
       "      <td>3.058619e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.076750</td>\n",
       "      <td>7.817728e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  3705.998132   0.000000e+00                      Elevation\n",
       "23  3523.491473   0.000000e+00                   Surface type\n",
       "25  3167.612036   0.000000e+00   Inflowing drainage direction\n",
       "3    657.131199  9.319155e-130              Improve grassland\n",
       "2    574.453772  3.822481e-115                         Arable\n",
       "24   551.770288  4.542649e-111  Outflowing drainage direction\n",
       "0    327.864746   3.283643e-69             Deciduous woodland\n",
       "20   215.806810   4.847374e-47                       Suburban\n",
       "19    63.875294   1.953389e-15                          Urban\n",
       "22    26.401491   2.973417e-07      Cumulative catchment area\n",
       "4     25.443583   4.860554e-07              Neutral grassland\n",
       "1     16.105638   6.155325e-05            Coniferous woodland\n",
       "5     12.987362   3.193321e-04           Calcareous grassland\n",
       "13    12.549173   4.031728e-04                     Freshwater\n",
       "18     9.818544   1.746353e-03                      Saltmarsh\n",
       "6      9.049166   2.652688e-03                 Acid grassland\n",
       "7      8.408340   3.765535e-03                            Fen\n",
       "9      7.278622   7.021903e-03              Heather grassland\n",
       "10     6.951936   8.421371e-03                            Bog\n",
       "17     6.807778   9.126817e-03              Littoral sediment\n",
       "16     6.410824   1.139956e-02                  Littoral rock\n",
       "15     6.211470   1.275272e-02         Supralittoral sediment\n",
       "14     3.769668   5.229508e-02             Supralittoral rock\n",
       "12     2.376299   1.233076e-01                      Saltwater\n",
       "8      1.048863   3.058619e-01                        Heather\n",
       "11     0.076750   7.817728e-01                    Inland rock"
      ]
     },
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    },
    {
     "name": "stdout",
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     "text": [
      "Ruddy Duck 10km\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>222.092265</td>\n",
       "      <td>2.620429e-48</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>201.385984</td>\n",
       "      <td>4.016297e-44</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>172.842486</td>\n",
       "      <td>2.671584e-38</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165.229518</td>\n",
       "      <td>9.785071e-37</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>161.746426</td>\n",
       "      <td>5.100030e-36</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>132.834071</td>\n",
       "      <td>4.984261e-30</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>85.793209</td>\n",
       "      <td>3.984836e-20</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>71.341242</td>\n",
       "      <td>4.862584e-17</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66.548473</td>\n",
       "      <td>5.196588e-16</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.792935</td>\n",
       "      <td>7.978585e-13</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>42.712031</td>\n",
       "      <td>7.569164e-11</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.652417</td>\n",
       "      <td>1.113138e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.738880</td>\n",
       "      <td>9.485083e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.537197</td>\n",
       "      <td>6.011553e-02</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.864963</td>\n",
       "      <td>1.721681e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.561720</td>\n",
       "      <td>2.115231e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.540714</td>\n",
       "      <td>2.146205e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.471490</td>\n",
       "      <td>2.252181e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.412058</td>\n",
       "      <td>2.348207e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.686957</td>\n",
       "      <td>4.072755e-01</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.581560</td>\n",
       "      <td>4.457690e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.540364</td>\n",
       "      <td>4.623466e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.278701</td>\n",
       "      <td>5.975977e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.059568</td>\n",
       "      <td>8.071982e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.055293</td>\n",
       "      <td>8.141138e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.054017</td>\n",
       "      <td>8.162336e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       F Score       P Value                      Attribute\n",
       "20  222.092265  2.620429e-48                       Suburban\n",
       "23  201.385984  4.016297e-44                   Surface type\n",
       "25  172.842486  2.671584e-38   Inflowing drainage direction\n",
       "2   165.229518  9.785071e-37                         Arable\n",
       "21  161.746426  5.100030e-36                      Elevation\n",
       "19  132.834071  4.984261e-30                          Urban\n",
       "24   85.793209  3.984836e-20  Outflowing drainage direction\n",
       "4    71.341242  4.862584e-17              Neutral grassland\n",
       "3    66.548473  5.196588e-16              Improve grassland\n",
       "0    51.792935  7.978585e-13             Deciduous woodland\n",
       "22   42.712031  7.569164e-11      Cumulative catchment area\n",
       "12   10.652417  1.113138e-03                      Saltwater\n",
       "17    6.738880  9.485083e-03              Littoral sediment\n",
       "13    3.537197  6.011553e-02                     Freshwater\n",
       "18    1.864963  1.721681e-01                      Saltmarsh\n",
       "9     1.561720  2.115231e-01              Heather grassland\n",
       "8     1.540714  2.146205e-01                        Heather\n",
       "10    1.471490  2.252181e-01                            Bog\n",
       "6     1.412058  2.348207e-01                 Acid grassland\n",
       "7     0.686957  4.072755e-01                            Fen\n",
       "11    0.581560  4.457690e-01                    Inland rock\n",
       "14    0.540364  4.623466e-01             Supralittoral rock\n",
       "16    0.278701  5.975977e-01                  Littoral rock\n",
       "15    0.059568  8.071982e-01         Supralittoral sediment\n",
       "1     0.055293  8.141138e-01            Coniferous woodland\n",
       "5     0.054017  8.162336e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whooper Swan 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1078.343958</td>\n",
       "      <td>5.965819e-199</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1035.304027</td>\n",
       "      <td>3.004691e-192</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>961.388454</td>\n",
       "      <td>1.486786e-180</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>318.424551</td>\n",
       "      <td>2.241705e-67</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>265.819805</td>\n",
       "      <td>4.812689e-57</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>203.103471</td>\n",
       "      <td>1.800704e-44</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158.129913</td>\n",
       "      <td>2.838432e-35</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>66.652066</td>\n",
       "      <td>4.936860e-16</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55.135835</td>\n",
       "      <td>1.504615e-13</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>48.778897</td>\n",
       "      <td>3.602661e-12</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38.969209</td>\n",
       "      <td>4.992149e-10</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.589008</td>\n",
       "      <td>1.618513e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.078754</td>\n",
       "      <td>1.640978e-06</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21.307649</td>\n",
       "      <td>4.096605e-06</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.965698</td>\n",
       "      <td>4.889980e-06</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.823870</td>\n",
       "      <td>5.262746e-06</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19.643447</td>\n",
       "      <td>9.708795e-06</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.278206</td>\n",
       "      <td>1.173841e-05</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.340362</td>\n",
       "      <td>9.202786e-05</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.220989</td>\n",
       "      <td>9.799603e-05</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.947603</td>\n",
       "      <td>1.919085e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.718829</td>\n",
       "      <td>3.683531e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.465741</td>\n",
       "      <td>4.214970e-04</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.357531</td>\n",
       "      <td>2.440699e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.941124</td>\n",
       "      <td>3.320768e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.003208</td>\n",
       "      <td>9.548383e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1078.343958  5.965819e-199   Inflowing drainage direction\n",
       "21  1035.304027  3.004691e-192                      Elevation\n",
       "23   961.388454  1.486786e-180                   Surface type\n",
       "3    318.424551   2.241705e-67              Improve grassland\n",
       "24   265.819805   4.812689e-57  Outflowing drainage direction\n",
       "2    203.103471   1.800704e-44                         Arable\n",
       "0    158.129913   2.838432e-35             Deciduous woodland\n",
       "20    66.652066   4.936860e-16                       Suburban\n",
       "9     55.135835   1.504615e-13              Heather grassland\n",
       "17    48.778897   3.602661e-12              Littoral sediment\n",
       "16    38.969209   4.992149e-10                  Littoral rock\n",
       "7     27.589008   1.618513e-07                            Fen\n",
       "1     23.078754   1.640978e-06            Coniferous woodland\n",
       "14    21.307649   4.096605e-06             Supralittoral rock\n",
       "19    20.965698   4.889980e-06                          Urban\n",
       "18    20.823870   5.262746e-06                      Saltmarsh\n",
       "15    19.643447   9.708795e-06         Supralittoral sediment\n",
       "22    19.278206   1.173841e-05      Cumulative catchment area\n",
       "6     15.340362   9.202786e-05                 Acid grassland\n",
       "10    15.220989   9.799603e-05                            Bog\n",
       "13    13.947603   1.919085e-04                     Freshwater\n",
       "8     12.718829   3.683531e-04                        Heather\n",
       "4     12.465741   4.214970e-04              Neutral grassland\n",
       "11     1.357531   2.440699e-01                    Inland rock\n",
       "12     0.941124   3.320768e-01                      Saltwater\n",
       "5      0.003208   9.548383e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wigeon 10km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2620.080822</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2480.090800</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2181.122669</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>510.895940</td>\n",
       "      <td>1.185521e-103</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>465.092494</td>\n",
       "      <td>3.165023e-95</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>411.757352</td>\n",
       "      <td>2.959990e-85</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>204.705588</td>\n",
       "      <td>8.524358e-45</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>193.263684</td>\n",
       "      <td>1.796472e-42</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>77.447594</td>\n",
       "      <td>2.398464e-18</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>31.117309</td>\n",
       "      <td>2.672853e-08</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.396261</td>\n",
       "      <td>3.859326e-08</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28.206138</td>\n",
       "      <td>1.180414e-07</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24.248589</td>\n",
       "      <td>8.983058e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.876736</td>\n",
       "      <td>3.052102e-06</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20.162283</td>\n",
       "      <td>7.416147e-06</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.487899</td>\n",
       "      <td>2.984583e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.260356</td>\n",
       "      <td>5.675365e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.823912</td>\n",
       "      <td>7.136713e-05</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.044356</td>\n",
       "      <td>1.823323e-04</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.813446</td>\n",
       "      <td>3.502691e-04</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.336977</td>\n",
       "      <td>3.915851e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.098755</td>\n",
       "      <td>4.463355e-03</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.360418</td>\n",
       "      <td>3.687781e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.656884</td>\n",
       "      <td>4.177344e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.171550</td>\n",
       "      <td>6.787714e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.002713</td>\n",
       "      <td>9.584656e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  2620.080822   0.000000e+00   Inflowing drainage direction\n",
       "21  2480.090800   0.000000e+00                      Elevation\n",
       "23  2181.122669   0.000000e+00                   Surface type\n",
       "3    510.895940  1.185521e-103              Improve grassland\n",
       "2    465.092494   3.165023e-95                         Arable\n",
       "24   411.757352   2.959990e-85  Outflowing drainage direction\n",
       "0    204.705588   8.524358e-45             Deciduous woodland\n",
       "20   193.263684   1.796472e-42                       Suburban\n",
       "19    77.447594   2.398464e-18                          Urban\n",
       "17    31.117309   2.672853e-08              Littoral sediment\n",
       "4     30.396261   3.859326e-08              Neutral grassland\n",
       "18    28.206138   1.180414e-07                      Saltmarsh\n",
       "22    24.248589   8.983058e-07      Cumulative catchment area\n",
       "12    21.876736   3.052102e-06                      Saltwater\n",
       "13    20.162283   7.416147e-06                     Freshwater\n",
       "7     17.487899   2.984583e-05                            Fen\n",
       "8     16.260356   5.675365e-05                        Heather\n",
       "1     15.823912   7.136713e-05            Coniferous woodland\n",
       "9     14.044356   1.823323e-04              Heather grassland\n",
       "15    12.813446   3.502691e-04         Supralittoral sediment\n",
       "5      8.336977   3.915851e-03           Calcareous grassland\n",
       "16     8.098755   4.463355e-03                  Littoral rock\n",
       "6      4.360418   3.687781e-02                 Acid grassland\n",
       "14     0.656884   4.177344e-01             Supralittoral rock\n",
       "10     0.171550   6.787714e-01                            Bog\n",
       "11     0.002713   9.584656e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'])\n",
    "    display(dict['kbest']['Dataframe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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