Classification ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16d2ea05",
   "metadata": {},
   "source": [
    "# Info\n",
    "\n",
    "3 models are tested on bands and vegedation indices data. \n",
    "Model training are customised to the dataset structure, please, explore the ``utils.py`` for helper functions before to start.\n",
    "\n",
    "The function ``get_predictions`` is organised to address common problems of multiclass satellite image classification:\n",
    "\n",
    "1) leakage of train data to test sample is addressed using GroupShuffleSplit, where groups that must not overlap in train and test data is represented by label data unique plot \\\n",
    "2) class imbalance is addressed by SMOTE option \\\n",
    "3) presence of all target classes is checked at the stage of the train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a654401f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c13f3808",
   "metadata": {},
   "outputs": [],
   "source": [
    "#main libraries\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GroupShuffleSplit\n",
    "\n",
    "\n",
    "#ML\n",
    "from sklearn import preprocessing\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, confusion_matrix, ConfusionMatrixDisplay, precision_score, recall_score, classification_report, cohen_kappa_score \n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "#stats\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6910db9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries and helpers for visuals\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scienceplots\n",
    "\n",
    "plt.style.use(['science', 'no-latex']) \n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "plt.rcParams.update({\n",
    "    \"font.family\": \"Palatino\",   \n",
    "    \"font.serif\": [\"Times\"],  \n",
    "    \"font.size\":9,\n",
    "    \"axes.labelsize\":9,\n",
    "    \"xtick.labelsize\" : 9,\n",
    "    \"ytick.labelsize\" : 9})   \n",
    "\n",
    "colors =[\n",
    "    '#117733', '#9e2a90', '#88ccee', '#ca7878', '#dbd73e', '#c7c7c9', '#60d52a','#418c84' #, '#92462d'\n",
    "]\n",
    "\n",
    "dict_normal_names = {1: 'trees', \n",
    "2: 'flooded vegetation',\n",
    "3: 'open water',\n",
    "4: 'settlements', \n",
    "5: 'bare soil',\n",
    "6: 'agriculture and grass', \n",
    "7: 'shrubs',\n",
    "#8: 'open rocks',\n",
    "9:'sparse vegetation'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "869a2a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model saving\n",
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6643934",
   "metadata": {},
   "outputs": [],
   "source": [
    "#classification customised helpers\n",
    "from utils import get_predictions, metrics_description, get_classes_metrics, get_metrics_average, get_best_model, get_worst_model\n",
    "\n",
    "def get_scaled_dataset(path):\n",
    "    df = pd.read_csv(path)\n",
    "    x = df.loc[:, df.columns!='id'].values #returns values as a numpy array\n",
    "    min_max_scaler = preprocessing.MinMaxScaler() #minmax scaling\n",
    "    x_scaled = min_max_scaler.fit_transform(x)\n",
    "    df_scaled = pd.DataFrame(x_scaled, columns = list(df.loc[:, df.columns!='id'].columns))\n",
    "    df_scaled = pd.concat([df_scaled, df['id']], axis=1)\n",
    "    return df_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0d3752",
   "metadata": {},
   "source": [
    "# Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4355d06",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.702748</td>\n",
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       "      <td>0.361821</td>\n",
       "      <td>0.355605</td>\n",
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       "      <td>0.182077</td>\n",
       "      <td>0.307519</td>\n",
       "      <td>0.360268</td>\n",
       "      <td>0.337192</td>\n",
       "      <td>0.346057</td>\n",
       "      <td>0.361821</td>\n",
       "      <td>0.355605</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751159</td>\n",
       "      <td>0.429454</td>\n",
       "      <td>0.711765</td>\n",
       "      <td>0.645572</td>\n",
       "      <td>0.163900</td>\n",
       "      <td>0.551282</td>\n",
       "      <td>0.735143</td>\n",
       "      <td>0.920111</td>\n",
       "      <td>0.137507</td>\n",
       "      <td>9</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.255574</td>\n",
       "      <td>0.293745</td>\n",
       "      <td>0.321273</td>\n",
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       "      <td>0.347455</td>\n",
       "      <td>0.318090</td>\n",
       "      <td>0.315799</td>\n",
       "      <td>0.333791</td>\n",
       "      <td>0.355605</td>\n",
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       "      <td>0.577217</td>\n",
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       "      <td>0.823826</td>\n",
       "      <td>0.752672</td>\n",
       "      <td>0.344032</td>\n",
       "      <td>0.663531</td>\n",
       "      <td>0.769656</td>\n",
       "      <td>0.864593</td>\n",
       "      <td>0.224927</td>\n",
       "      <td>9</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.223154</td>\n",
       "      <td>0.255574</td>\n",
       "      <td>0.293745</td>\n",
       "      <td>0.321273</td>\n",
       "      <td>0.376107</td>\n",
       "      <td>0.347455</td>\n",
       "      <td>0.318090</td>\n",
       "      <td>0.315799</td>\n",
       "      <td>0.333791</td>\n",
       "      <td>0.355605</td>\n",
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       "      <td>0.577217</td>\n",
       "      <td>0.557051</td>\n",
       "      <td>0.823826</td>\n",
       "      <td>0.752672</td>\n",
       "      <td>0.344032</td>\n",
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       "      <td>0.769656</td>\n",
       "      <td>0.864593</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>0.250442</td>\n",
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       "      <td>0.300580</td>\n",
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       "      <td>5</td>\n",
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       "      <td>0.689668</td>\n",
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       "      <td>0.345953</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>0.294123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500465</td>\n",
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       "      <td>0.866000</td>\n",
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       "      <td>0.711260</td>\n",
       "      <td>0.766810</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>0.293776</td>\n",
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       "      <td>0.383082</td>\n",
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       "      <td>0.293641</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.624983</td>\n",
       "      <td>0.866000</td>\n",
       "      <td>0.801304</td>\n",
       "      <td>0.446393</td>\n",
       "      <td>0.711260</td>\n",
       "      <td>0.766810</td>\n",
       "      <td>0.849635</td>\n",
       "      <td>0.267449</td>\n",
       "      <td>5</td>\n",
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      "text/plain": [
       "            B1        B2        B3        B4        B5        B6        B7  \\\n",
       "0     0.199613  0.167017  0.208243  0.210385  0.316374  0.375967  0.358707   \n",
       "1     0.223154  0.146403  0.188178  0.182077  0.307519  0.360268  0.337192   \n",
       "2     0.223154  0.146403  0.188178  0.182077  0.307519  0.360268  0.337192   \n",
       "3     0.223154  0.255574  0.293745  0.321273  0.376107  0.347455  0.318090   \n",
       "4     0.223154  0.255574  0.293745  0.321273  0.376107  0.347455  0.318090   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "3933  0.390519  0.267985  0.287780  0.294137  0.336661  0.266074  0.230005   \n",
       "3934  0.390519  0.332772  0.368402  0.387605  0.408469  0.306476  0.263193   \n",
       "3935  0.390519  0.332772  0.368402  0.387605  0.408469  0.306476  0.263193   \n",
       "3936  0.293776  0.304796  0.341829  0.383082  0.413943  0.324945  0.286541   \n",
       "3937  0.293776  0.304796  0.341829  0.383082  0.413943  0.324945  0.286541   \n",
       "\n",
       "            B8       B8A        B9  ...      savi       msi       bsi  \\\n",
       "0     0.356950  0.381057  0.383305  ...  0.725588  0.401503  0.693089   \n",
       "1     0.346057  0.361821  0.355605  ...  0.751159  0.429454  0.711765   \n",
       "2     0.346057  0.361821  0.355605  ...  0.751159  0.429454  0.711765   \n",
       "3     0.315799  0.333791  0.355605  ...  0.577217  0.557051  0.823826   \n",
       "4     0.315799  0.333791  0.355605  ...  0.577217  0.557051  0.823826   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "3933  0.214133  0.221673  0.300580  ...  0.480378  0.586221  0.810590   \n",
       "3934  0.250442  0.237611  0.300580  ...  0.444722  0.548478  0.806810   \n",
       "3935  0.250442  0.237611  0.300580  ...  0.444722  0.548478  0.806810   \n",
       "3936  0.293641  0.299166  0.294123  ...  0.500465  0.624983  0.866000   \n",
       "3937  0.293641  0.299166  0.294123  ...  0.500465  0.624983  0.866000   \n",
       "\n",
       "          ndbi       nbi       bal       mbi    ndsoil     blfei  id  \n",
       "0     0.618811  0.179211  0.550028  0.702748  0.894440  0.162250   9  \n",
       "1     0.645572  0.163900  0.551282  0.735143  0.920111  0.137507   9  \n",
       "2     0.645572  0.163900  0.551282  0.735143  0.920111  0.137507   9  \n",
       "3     0.752672  0.344032  0.663531  0.769656  0.864593  0.224927   9  \n",
       "4     0.752672  0.344032  0.663531  0.769656  0.864593  0.224927   9  \n",
       "...        ...       ...       ...       ...       ...       ...  ..  \n",
       "3933  0.774182  0.328683  0.630435  0.725392  0.795146  0.308131   5  \n",
       "3934  0.746160  0.407919  0.668200  0.689668  0.762618  0.345953   5  \n",
       "3935  0.746160  0.407919  0.668200  0.689668  0.762618  0.345953   5  \n",
       "3936  0.801304  0.446393  0.711260  0.766810  0.849635  0.267449   5  \n",
       "3937  0.801304  0.446393  0.711260  0.766810  0.849635  0.267449   5  \n",
       "\n",
       "[3938 rows x 25 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled = get_scaled_dataset('data/general_osm_2021.csv')\n",
    "df_scaled['id'] = df_scaled['id'].apply(lambda x: 5 if x==8 else x) #works better\n",
    "df_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045942a7",
   "metadata": {},
   "source": [
    "# Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2ccd76c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df_scaled.loc[:, ~df_scaled.columns.isin(['id', 'plot'])].corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corr, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3994b419",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In case the cleaning based on the correlation analysis is required\n",
    "\n",
    "# upper = corr.abs().where(np.triu(np.ones(corr.abs().shape), k=1).astype(bool))\n",
    "\n",
    "# Find features with correlation greater than correlation threshold\n",
    "# #to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]\n",
    "\n",
    "# Drop features \n",
    "\n",
    "# df_scaled = df_scaled.drop(to_drop, axis=1)\n",
    "# to_drop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f4c1ad",
   "metadata": {},
   "source": [
    "# Model training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf434d61",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f9ba8868",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/Users/polinagogol/opt/anaconda3/envs/geo/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "#for random forest\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "# vector of 10 model iterations to evaluate average performance\n",
    "\n",
    "datavector_rf_wf = []\n",
    "for i in range(10):\n",
    "    rf_model = get_predictions(data_scaled = df_scaled,\n",
    "                model = RandomForestClassifier(),\n",
    "                param_grid = random_grid,\n",
    "                target_column = 'id', \n",
    "                stratify_column = 'plot',\n",
    "                smote_balance=False,\n",
    "                cv=5, \n",
    "                n_iter_search=3)\n",
    "    datavector_rf_wf.append(rf_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "809b3a02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 84.21%\n",
      "Precision score: 83.27%\n",
      "Recall score: 84.21%\n",
      "F1-Score: 69.20%\n",
      "Kappa score: 79.88%\n"
     ]
    },
    {
     "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>names</th>\n",
       "      <th>f1_scores</th>\n",
       "      <th>precision_list</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trees</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>flooded vegetation</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open water</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>settlements</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bare soil</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>agriculture and grass</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>shrubs</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sparse vegetation</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   names  f1_scores  precision_list  recall\n",
       "0                  trees       0.85            0.84    0.87\n",
       "1     flooded vegetation       0.58            0.61    0.55\n",
       "2             open water       1.00            1.00    1.00\n",
       "3            settlements       0.79            0.81    0.78\n",
       "4              bare soil       0.86            0.84    0.89\n",
       "5  agriculture and grass       0.71            0.63    0.81\n",
       "6                 shrubs       0.05            0.07    0.05\n",
       "7      sparse vegetation       0.68            0.72    0.65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_description(datavector_rf_wf[0]['y test data'], datavector_rf_wf[0]['y predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5204a871",
   "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>iteration</th>\n",
       "      <th>f1_scores</th>\n",
       "      <th>precision_list</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iteration  f1_scores  precision_list  recall\n",
       "0          1       0.69            0.83    0.84\n",
       "1          2       0.76            0.88    0.89\n",
       "2          3       0.66            0.83    0.84\n",
       "3          4       0.71            0.89    0.89\n",
       "4          5       0.66            0.89    0.84\n",
       "5          6       0.62            0.75    0.74\n",
       "6          7       0.64            0.81    0.76\n",
       "7          8       0.67            0.81    0.81\n",
       "8          9       0.65            0.87    0.86\n",
       "9         10       0.69            0.85    0.82"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_class_metrics = get_classes_metrics(datavector_rf_wf)\n",
    "display(get_metrics_average(datavector_rf_wf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f7e3ab63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "# plot a bar chart\n",
    "ax = sns.barplot(x = 'names', y = 'f1_scores', palette=colors, data=rf_class_metrics, estimator=np.mean, ci=95, capsize=.3)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('RF multiclass F1 score')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0, 1)\n",
    "#plt.savefig('RF_multiclass_F1_score_scale_2.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "491d0e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_best_model = get_best_model(datavector_rf_wf)\n",
    "rf_worst_model = get_worst_model(datavector_rf_wf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7baa9144",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "22095704",
   "metadata": {},
   "outputs": [],
   "source": [
    "# svc random grid\n",
    "\n",
    "svc_grid = {'C': [0.1, 1, 10, 100, 1000], \n",
    "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "              'kernel': ['rbf', 'linear', 'poly']\n",
    "             }\n",
    "\n",
    "# vector of 10 model iterations to evaluate average performance\n",
    "\n",
    "datavector_svc_wf = []\n",
    "for i in range(10):\n",
    "    svc_model = get_predictions(data_scaled = df_scaled,\n",
    "                model = SVC(),\n",
    "                param_grid = svc_grid,\n",
    "                target_column = 'id', \n",
    "                smote_balance=False,\n",
    "                stratify_column = 'plot',\n",
    "                cv=5, \n",
    "                n_iter_search=3)\n",
    "    datavector_svc_wf.append(svc_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "19ab3823",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 85.48%\n",
      "Precision score: 86.48%\n",
      "Recall score: 85.48%\n",
      "F1-Score: 74.65%\n",
      "Kappa score: 81.60%\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>names</th>\n",
       "      <th>f1_scores</th>\n",
       "      <th>precision_list</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trees</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>flooded vegetation</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open water</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>settlements</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bare soil</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>agriculture and grass</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>shrubs</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sparse vegetation</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   names  f1_scores  precision_list  recall\n",
       "0                  trees       0.98            0.97    0.99\n",
       "1     flooded vegetation       0.78            0.74    0.83\n",
       "2             open water       1.00            1.00    1.00\n",
       "3            settlements       0.74            0.75    0.73\n",
       "4              bare soil       0.85            0.81    0.89\n",
       "5  agriculture and grass       0.53            0.57    0.50\n",
       "6                 shrubs       0.40            0.30    0.58\n",
       "7      sparse vegetation       0.69            0.91    0.56"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_description(datavector_svc_wf[3]['y test data'], datavector_svc_wf[3]['y predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "465c4cf7",
   "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>iteration</th>\n",
       "      <th>f1_scores</th>\n",
       "      <th>precision_list</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iteration  f1_scores  precision_list  recall\n",
       "0          1       0.72            0.84    0.83\n",
       "1          2       0.70            0.86    0.82\n",
       "2          3       0.72            0.89    0.89\n",
       "3          4       0.75            0.86    0.85\n",
       "4          5       0.71            0.85    0.84\n",
       "5          6       0.65            0.82    0.79\n",
       "6          7       0.75            0.89    0.87\n",
       "7          8       0.71            0.89    0.85\n",
       "8          9       0.75            0.89    0.88\n",
       "9         10       0.78            0.86    0.86"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc_class_metrics = get_classes_metrics(datavector_svc_wf)\n",
    "display(get_metrics_average(datavector_svc_wf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8252af9a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "# plot a bar chart\n",
    "ax = sns.barplot(x = 'names', y = 'f1_scores', palette=colors, data=svc_class_metrics, estimator=np.mean, ci=95, capsize=.3)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('SVC multiclass F1 score')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0, 1)\n",
    "#plt.savefig('SVC_multiclass_F1_score_scale_2.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a965398e",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_best_model = get_best_model(datavector_svc_wf)\n",
    "svc_worst_model = get_worst_model(datavector_svc_wf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38830b91",
   "metadata": {},
   "source": [
    "## kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3fc66cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#knn random grid\n",
    "metric = ['euclidean','manhattan','chebyshev','minkowski']\n",
    "n_neighbors = [int(x) for x in np.linspace(start = 1, stop = 100, num = 50)] \n",
    "weights = ['uniform','distance']\n",
    "random_grid_knn = {\n",
    "    'n_neighbors': n_neighbors,\n",
    "    'weights': weights,\n",
    "    'metric': metric\n",
    "}\n",
    "\n",
    "# vector of 10 model iterations to evaluate average performance on non-filtered data\n",
    "datavector_knn_wf = []\n",
    "\n",
    "for i in range(10):\n",
    "    knn_model_wf = get_predictions(data_scaled=df_scaled,\n",
    "                model = KNeighborsClassifier(),\n",
    "                param_grid = random_grid_knn,\n",
    "                target_column = 'id',\n",
    "                stratify_column = 'plot', \n",
    "                smote_balance=False,\n",
    "                cv=5, \n",
    "                n_iter_search=3)\n",
    "    datavector_knn_wf.append(knn_model_wf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "346cd388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 71.32%\n",
      "Precision score: 70.21%\n",
      "Recall score: 71.32%\n",
      "F1-Score: 48.76%\n",
      "Kappa score: 63.86%\n"
     ]
    },
    {
     "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>names</th>\n",
       "      <th>f1_scores</th>\n",
       "      <th>precision_list</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trees</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>flooded vegetation</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open water</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>settlements</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bare soil</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>agriculture and grass</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>shrubs</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sparse vegetation</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   names  f1_scores  precision_list  recall\n",
       "0                  trees       0.68            0.53    0.93\n",
       "1     flooded vegetation       0.43            0.67    0.32\n",
       "2             open water       1.00            1.00    1.00\n",
       "3            settlements       0.61            0.52    0.73\n",
       "4              bare soil       0.58            0.60    0.56\n",
       "5  agriculture and grass       0.00            0.00    0.00\n",
       "6                 shrubs       0.00            0.00    0.00\n",
       "7      sparse vegetation       0.60            0.64    0.57"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VCqt++B5Tpk6D//4DqO3sjIUL54sdS+9SUlJga2urM2ZtbY2U/zXfxoJ1ss7CiHWKVycbNSPRrFkz2NnZYeTIkXBzc8OqVatw/vx5LFq0CJGRkZg3bx46dOgALy8vdOrUCW5ubvDy8sKGDRveOP7LL79g5MiRcHFxgYuLC9Rqtdgl6shVq6FWa/KNm5qY4GTIRbRs3LDgQxmIRqPBpbAw7Ni+Fc+eP8eCBYswaPAQ7Ni+zai2oObm5ub7nMlkMpgawVbR17FO1lkYsU7x6mSjZqTkcjlcXV1Ro0YN1KhRA4sXL0alSpWQmpoKAOjQoQMEQcC5c+feOF6zZk2MHj0aXl5eaNGiBby8vMQr5g1KFreGMitLZ+xFTg5K25TEj9t2IubhQwCAILy8bfjseejV3hvjBvYr6Kj/2T5/f1gUKwYHBwc4ODhgx/at8GrXHhcvXkLr1oV/Dt4rNjY2SE9P1xnLzs5G2bJlRUpkGKyTdRZGrFO8OtmoGbHXvwFkZmaiSZMmcHd3BwD07NnzneOOjo5o164dQkND8f333yMmJgajRo0q4ArernqlSjj+V7DOWHJ6OmpWrowN82drxxKePkPvib44vXF9QUfUm/DwCNSsUUO7bGVlhQrly8Pc3FzEVPrn6OiIgwcP6ow9e/YMzs7OIiUyDNbJOgsj1ilenTyYwIjI5XJkZGS88bYWLVpg+/btyMvLA/CyQVOr1W8d37ZtG2rXro2hQ4di4sSJ0Gjy72YsSJr/5XvFs0kj3I6L057INjz6LprXrwcrS91dgdk5OQAguV23/4aLiwvOnjsP4X+bB+/ffwATUxM0a9ZU5GT65e3tjYiICCQlJQEArl69Ck9PT6M63QzAOlln4cQ6xatTJrz67U+F3oYNG/D777+jbt260Gg0kMlkGD58ONzd3fH8+XPMnj0bCQkJcHFxQcWKFTF69Oi3js+dOxfVqlWDra0tHj9+jM8+++y950M9u3RBr3WF3gxHWPgtbD98BJ5NGqF10yb42LMFLoVHYOvBALjUqA5NngafdesKq9cyngq9hJDrN3D47Hn0//gjeDZpjIbOtfSWy7ZxM7091rvk5eXhhx9WITEpCVWrVkVGegY+/3wYypYtUyDPL1cU3NyMCxcuYN26dWjQoAHUajVGjx5tdH8IANZpbFincZFanWzUSO/03ahJVUE1amIryEaNiIh0cdcnERERkUSxUSMiIiKSKDZqRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkSiYIgiB2CDIuOalJYkcoEJrsbLEjFAjL8lXFjkB6lP00XuwIBcKibEWxI5Ae5WakiB2hwJgWL6WzzC1qRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1MgoqFQqqNVqsWPozct6NGLHIPoggiBg55/+ePrs+XuNE0mRRqOBRiP+72ETsQO8j7S0NKSlpaFKlSp6e0ylUon09HRUqFDhX93vwYMHsLe3h7m5ud6y0PvbsGkLdu71Q3qGEo0b1MP8WdNhV7YMPuk7GI8TErTrWVhYIDTouIhJ/5senw7H4yeJ2mWLYsVw4egB3L0Xi70HA2CikOP+o3hMGDUCNRyqiRdUz+7cuYNdu3ZBoVAgLi4Ovr6+cHR0FDuW3hl7nfsPH8WS71ejaaMGKFum9D+OF3bG/n6+Yqx1qtVqfL/mZxw8fARZWVlo3Kgh5s6YiothlzFz3sJ86zdqUB9bfltXYPkk26ht3rwZ27Ztw6effopt27ahU6dO+PLLL/Xy2CEhIZgzZw66dOnyrx5z7dq1+OGHH3Dy5ElUqlRJL1k+RGZmJiwsLCCX//MGUaVSCWtrawDA5MmT0aBBAwwaNMjQEQ3i7r1YPHj4CH+sX4ucnBxMmTUPq37+FYvmzEDD+nWxYvF87brv89pIWYO6Llg2d5Z2WaGQQ6VSYfaS5fht1XJYWFjg9Lm/4DtnAf7cvAEymUzEtPqhUqkwdepUbN26FZaWlggMDMT48eMREBBgFPW9Yux1pqWn4979++89XtgZ+/v5ijHXeT44BM+ePcPPq1YiLT0d8xYtxtRZc9GzW1fMmjoZzk61tOsGHDuONp4tCzSfJP+abdiwAfb29jh+/DgGDhyIBg0a6PXxmzdvjoYNG/7r+/n4+Og1x4fIycmBl5cXsrOz/3HduXPnwt/fX7v82WefwcvLy4DpDEulUmHqpK9QoXw5OFSrih6fdIaQlwcAKG9vhzq1nbX/1X7tB6swKmdnhzrOtbT/OTvWxLmQi6hSqSIsLCwAAK08miMhKQkxsXHihtWToKAgVKtWDZaWlgCAtm3bIiEhAdHR0SIn0y9jr9PvwGH06NLpvccLO2N/P18x5jqfPX+Or8ePQ12X2mjR3A1jR43E1es3oNZo0L5dWzSoVxcN6tWFk2NNKJVKuLu5Fmg+STZq586dQ6lSpaDRaAy2ZeRDvgFI4VuDqakpUlNTtT8s75KVlaX9ow4AderUgZWVlSHjGZSLs5O2brVajajb0Rj9xecAgITEJHw++ku4tm6P3oOG4mLYFTGj/mdPkpIw4qtv4N6xK/oO98Glq9dw+24M7MuW0a6jUChQqXx5JBnJfJ/IyEjY29trlxUKBSpXrozExMR33KvwMeY6I6Juo0rlijAzM3uvcWNgzO/n64y5zt7du8Hezk67bG1tBVvbUujepTNsS5XSjm/ZsQu9e3Qv8HyS2/UZGRmJuLg4rFu3DidOnMDMmTN1bs/Ly8Mff/wBMzMzpKam4vbt25g+fTrs7e2Rnp6OTZs2oVy5crh79y4EQYCvry/MzMyQlZWFvXv34tmzZ7h58ybi4uLQs2dPAC8nDO7cuRPW1ta4ceMG5HI5pk+fDplMhjt37uDkyZN48uQJ4uLi3pg5PT0ds2bNQlhYGJYvXw53d3fs3r0bmzdvxqpVq1CxYkXs2rULNjY2CA0NhYODA0aOHImcnBxs3rwZ1tbWOHnyJBISEtC0aVMMGDAA9vb22L9/P0qUKIHz58/D09MT3bp1w8aNG2Fqaoo5c+agfv366NixI7Zu3Yro6GiEh4dj7ty5cHd3x6VLl3D9+nU8f/4cN27cQOfOnbFkyRK4ublhypQpb30dVSoVfv31V5iamqJkyZIIDAyETCbD4sWLUbduXUO//e9lwZLleJ6cjMSkp4iOiUHlShVhamKCGb5fw7yYOZYs/wETJk/HUf89KFGiuNhxP4ipiSmmTfwSxczNsHTVz/h65lx0bOcF21I2OutZW1khNS1NnJB6lpKSAltbW50xa2trpKSkiJTIMIy5zqMnTmHSl6MRn/DkvcaNgTG/n68rKnUCwPm/gjGgT2+YmPx/i/Q8ORlRt+/gi2GfFXgeyTVqtWvXRpUqVTBy5Ei4ubnlu33r1q1IS0vDV199BQDYuHEjvv76a2zbtg3Tp0/HoEGD4O7uDuDlrsq1a9fiq6++wqhRozB9+nTUrl0bGo0Gw4cP1z7mzz//DBsbG3Tr1g3dunXDRx99hJYtW6JSpUqYPXs2tm/fDrlcjkePHqFdu3b5MpUoUQLTp09H+/btUavWy11uZcuWxddff42aNWti6tSp6NChA7y8vNCpUye4ubnBy8sLR48ehbW1NQYOHIiePXvC3d0dkyZNQsmSJTFkyBDMnDkTTk5OaNmyJdq1awdvb2907NgR27dvx/z5L+dj3b9/H15eXvDx8cHp06exePFiHDhwAM2aNYOdnZ3O61i9evX3eh0bNmyIzZs3Y8eOHZgwYQJmz56NH3/8Eb/++qs+3uL/bMbkryGXy3H85GlMmjYbfjv+wILZ07W3L5wzA60/6oqLl6/Au21rEZN+uHlTv9H+e/40X3h174PgS2H4yKuNznoymQympqYFnM4wcnNz8x25a0z1vWKsdR47GYTWLT3ee9xYGOv7+XdFpc5bUVF4GB+P6VO+0Rn/Yc1aDB7QT5RMkmvU/snhw4cxbNgw7bK3tzeWLFmCjIwMnDx5UtvAAED79u2xZcsW9OjRA5GRkahduzaAl5tsy5Urp13v3LlzqFevHvz8/AAAbm5uKFasGA4ePIh69eppd7++6wACe3t7tGrVCgcOHMCwYcNw8+ZN7YEK586dQ6VKlZCamgoA6NChAwRBgEqlQmxsLICXPwSurq4oWbIkXrx4gUuXLiEsLAwRERHaOnNycvI9b9WqVREVFYWtW7dCqVRCqVS+NePr3w7e9TrK5XLUrl1bexBCvXr1EBkZ+dbHLWiv3o/2Xm0wY95CRN+NgUPV/z8i2KZkSdiWKmU0R+balCwBWxsblLIpiYy/vb/ZL16gzN++5RZWNjY2SE9P1xnLzs5G2bJlRUpkGMZa5x87dyE65h4AQBBejg0c4YOa1avj7r384/179cDXY8Wf9/tfGev7+XdFoc6U1FSs+20TVi79Fqav/b28GXELjxMS0KhBfVFyFbpGLSsrC0+e/P/mcysrKygUCmRlZSEvLw9PnjzRbp61srKCubk5UlNToVarkZeXp/0j/3rTk5mZCWdnZ+2u0Ff/P3LkCPL+N1n97/d5k4EDB2LhwoXo0qULqlSpop3TlpmZiSZNmmi39L16/JEjR2LKlClYvXo1KlasiOXLl2trFAQBnp6e2lOSvLrPo0ePdJ5z9+7dOHv2LJYtW4bk5GTs3r37P72OCoUCgO5RkwqFQud1EMvJoLNo16aVdvnVt7vKlSrqrJebmwsAaFCvTsGF06Oo6LtwdqypXX5VT78e3XDkxCmddZ+npMCxhkOB5jMUR0dHHDx4UGfs2bNncHZ2FimRYRhrndvWr9X+Oz7hCTr3GYiLp47prPO28cLMWN/PvzP2OrOys7Hqp7WYO2MaShT//ykzgiBg8XcrMM5npGjZJHkwwbu0bt0ap0+f1i6Hh4ejc+fOsLe3R4MGDfLd1rt3b9SqVQumpqbYunUrACAxMRFhYWHa9Vq0aIG9e/dqGzGVSoUXL16gWbNmOHbsmLY5OnTo0Duzubu7Q6PRYNGiRfjoo490Hn/79u3aZiczMxNqtRqpqalQKpUYP348evXqheL/+3DY2tqidu3a2rwAtN9kFAoFMjMztY918uRJ1KtXD5aWljo1AS+brYyMjH/1Or7tIAXh1VdhEd2Kuo2bEbe0y/sPHcFnAwcg7MpVPHwUrx3f9ac/xnzxuc4PW2ESdu06HsY/1i7v2X8IPsOGoG1LD9y6E42nz18ePHA94hY8XJvCuhAfIPI6b29vREREICkpCQBw9epVeHp6arfsGouiUOero9Jz/7ar7G3jhVlReD8B464zNzcX02fPQ+uWLXD/4UNcvxmO0EtheBT/GH/uP4Dc3Fx4NM8/FaugyAQp/AV+TXBwMKZPnw4vLy/06tULADB79myULl0akydPRvny5bFgwQLI5XK4uLggIyMDn332GSwsLPDgwQPMmzcPderUgZ2dHUxNTdGv38t9yufOncP8+fNRvHhxdOrUCZGRkUhISMD06dNRvXp1LFiwAOHh4ahbty7s7OwwZswYmJmZYcmSJTh06BBq166NMWPGYMCAARg+fDjGjh37xiMof/vtNzx+/BizZv3/ObCeP3+O2bNnIyEhAS4uLqhYsSJGjx6NmJgYDBkyBKampjAzM4NCoUCbNm0wdepU3L9/H7NmzYJKpYKTkxNq1aqFQYMGQa1Wo2/fvhAEAZMnT0ZSUhKWLFmCdu3aoUOHDhg/fjxWrlwJLy8vbNiwAb///jv69OkDb29vzJ07F9bW1pg2bRqqVKnyxtcxJSUFq1atQkxMDGbOnImGDRvCz88PK1aswLJly9CiRYt/fA9zUpP09GnQdfnqdSxctgLVqlRG5UoVUatmDXT5+CPEP07AV77TUM7eDuXLlUPjhvXxcQdvg2R4neY9TpHyIR4nPMGEmXNRrmxZlC9nh0b16qJju7YAgJCwK9i4fRfquThDo9FgxJCBsHqPI4D/C8vyVQ36+K+7cOEC1q1bhwYNGkCtVmP06NFG8Yfg78SsM/tp/D+v9B8cPxWEv0IuYn/AUQzp1wdtW7VA4wb13zpuKBZlK/7zSnrCz63h5WYY7qCFeYuWYM8+/3zj30z4Ehs2/oGJX45Fr+6fGOz5/860eCmdZck1akXJunXrYGpqiiFDhsDExASJiYno2LEjTp8+DRsbG7Hj5SMIwnudosRQjZrUGKpRk5qCbNTI8AzdqElFQTZqZHiGbNTeRBAE7X9yubxAT8/190at0O36NCaHDx9GkyZNtJP85XI5nJycULJkSZGTvZkUziNHRERkaDKZDHK5HAqFQvS/fYXuYAJjMnPmTGzevBmNGjVCXl4eUlNT8eOPP4r+oSAiIiJpYKMmombNmqFZs2ZixyAiIiKJ4q5PIiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkio0aERERkUSxUSMiIiKSKDZqRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikiiZIAiC2CGISLrqV20tdoQCceP+GbEjEBHlwy1qRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IioQJmamUKhUIgdg4ioUDAROwDRh7pz5w527doFhUKBuLg4+Pr6wtHRUexYBmFMtR44tQUVK5fXLmdlZqG5y8eoWcsBvQd9Ao1ajarVK+P7b39BTHQcAGD4mEHo/1kPlChhjSuXbmL2N0vwNOm5SBX8d8b0fr4L6zQurFMcMkEQBNGenUSjVCphbW0tdowPplKp0L9/f2zduhWWlpYIDAzEihUrEBAQAJlMJnY8vRK71vpVW+v18RavmoktG/Zol/Py8hATHYc//lyDz/t+hezsF2jboSW+mjIS3dt9ihqO1fDpyH745YdNMC9mjqU/zkZ0ZAxmTlqs11w37p/R6+O9jdjvZ0FhnayzMJJindz1WQRdvnwZI0aMEDvGfxIUFIRq1arB0tISANC2bVskJCQgOjpa5GT6Z2y1JsQn4tbN29r/oiKi0cqrOR7ExiM7+wUA4EzgBZSvYIeatRxgZm6KJXNWIyE+EXExD7Bv12HI5IX3V5exvZ9vwzpZZ2EkxToL7287+mAqlQomJoV7r3dkZCTs7e21ywqFApUrV0ZiYqKIqQzD2GotX9Eev+38AaGRR7HnyG9o5t4ITi6OSHySpF0nLy8Pjx4kwK5cGUSGRyM7KxvAy9qd6zhi7fcbxYr/nxnb+/k2rJN1FkZSrJON2gc6deoU1q1bh82bN2Ps2LG4cuUK1Go11q1bhy5duuDGjRvw8fGBq6srfHx8kJaWBgAICQnBrl27sGnTJowaNQoPHjxAaGgofHx8sGnTJkybNg2tW7fG8OHDkZKSovOcKpUKS5cuRZ06dXDixAkAwLVr17BhwwYAQFRUFHr16oVNmzYBAAICAvDtt9+ia9eu+PbbbwEAz549g7+/Px4/fozZs2cjLCwMDx8+xKZNm7B7926MGTMGZ86cQXZ2Nr7//nsMHjwYu3btQpMmTfD48eMCenX/WUpKCiwsLHTGrK2t871mxsDYas1VqbFo1vfo2WEoniQk4Yd1C2Fra4MX2Tk662VkKGFjW1K7PHPR11j+8zzUcq4BR+fqBR1bb4zt/Xwb1sk6CyMp1slG7QNERERg06ZNGDlyJD777DOMGTMGPj4+SE9Px+eff47o6Gikpqbil19+walTp5CYmIiff/4ZcXFxWLFiBfr164ehQ4eiYcOGWLFiBdzc3FCiRAlERUXh22+/xYkTJ5CUlAQ/Pz+d5zUzM8PEiRNhYWEBJycnAMDRo0fh7+8PAHB2dkadOnUwdOhQAICpqSmmT5+OPXv24Pjx47h16xbKlCkDNzc3uLq6Yv78+WjUqBGGDx+Onj17om/fvhg+fDimTp0KCwsL9O7dGxEREbCzs8O8efNQokSJgnyZ3yk3NxdqtVpnTCaTwdTUVKREhmNstc72XYJ70fcR//AJZn69GFbWlvBo3Sz/Vl7hZVP3yqKZ32PiqJnY+Mt2rFg7D9WqVy7g5PphbO/n27BO1lkYSbHOwr3/SyRHjx5FzZo1tct16tSBlZUVrly5Am9vbwBAq1atALzsxD/++GNcvHgRwcHBUKvV2gZMo9FojySRy+Vo2rQpZDIZzMzM4OTkhOTk5HzPbWZmBk9PT5w7dw7t27eHo6Mjzp07h2vXrqFEiRJo2rSpdt2mTZvC398fmZmZMDc3R0ZGRr7Hi4uLQ2JiIk6cOAGZTIbs7Gx4eHhArVZDJpPBysoKbdu21d+Lpyc2NjZIT0/XGcvOzkbZsmVFSmQ4xlxrWmo6kp+lIOV5KoqX1D24xcKyGJ69dmTnq+OeTgScwaLvNXB0ro64ew8LNK8+GPP7+TrWyToLIynWyUbtA2RlZSEhIUFnzMrKCubm5m9cX6FQoFatWsjMzISFhQV69uz5xvXkr02QNjExQV5e3hvXa9u2LQICApCTk4MBAwYgNTUVu3btgpOTEwYMGAAAePr0Kfr3749169ahRo0aOHr06BsfKzMzE2q1Gl27doWZmRkAYNCgQdrbX41JjaOjIw4ePKgz9uzZMzg7O4uUyHCMqVbnOo6Iivj/Sbkmpi9/Be3YvA+du3vrrGtbphTuRMXA6yNPnDp2Lt99bt28XQCJ9c+Y3s93YZ2sszCSYp3c9fkBWrdujcuXL2u3UKWmpkImk8HNzU27zquJh1lZWQgODsawYcPg4eGBGzdu4ObNm9r1/t65v4+WLVsiNDQUGo0GFhYW6NatG06cOAGVSqVtFi9fvgwTExPUqFEDjx8/1plfJpfLtc/r7OwMGxsb7Nix4z9lKmje3t6IiIhAUtLLCehXr16Fp6dnoT7lyNsYU61NmzdEpSoVtMv9BnfDz99vxKnj51C7nhPK2NkCAOo3roO/zlxEpjILLvVqoW6D//8l2a33x9i8bifiHz4p8Pz6YEzv57uwTtZZGEmxTp5H7QOtX78eV69ehZubGzIyMtCtWzdUrvxyzoyTkxPq1asHe3t7VKtWDZ9++qn2KBJ/f3+sX78eDg4OKFeuHLp16wYAmDt3LhwdHTF27FhUrlwZU6dORXx8PGbMmPHGTr5///5YtmwZqlSpAgAYM2YMPv/8c+2uz6SkJAwbNgwlS5bEZ599ho0bN6JGjRqYM2cO4uPjMXjwYNSrVw8zZ85EcnIy5s+fD2trazg4OKBp06bw9PTEjh07sG7dOsyaNQsdO3ZEsWLFCuKlfW8XLlzAunXr0KBBA6jVaowePdrofmm8Imat+jyPWoVK5bBq/SI8SUhCQnwirly8gaMHTwEA3Fo0wfAxA3HzWiQUCgXW/fgHsjKz0bhZfcxcNBFxsY/wMC4ed6JicHjfCb1leqWgzqMGFJ3PLus0LqxTHGzUDMDJyQm3b+tnt4wgCEZ1MkEqfPR9wlupKshGjYjofXHXp8SxSSMiIiq62Kjp2atTZezduxe5ubnihiEiIqJCjbs+9Yy7KsnYcNcnEZF4uEVNz9ikERERkb6wUSMiIiKSKDZqRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSZRMEARB7BBERGKL3btf7AgFonybZmJHKBDFylQQOwLpkSYnS+wIBUZhbqmzzC1qRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIDCgr5wXSszK1yyq1GhqNRsREhicIAnb+uQ9Pnz0XOwrRv5KZmYnU1FQAgEajkcTPqonYAYo6jUaD27dvw8XFRewohc6dO3ewa9cuKBQKxMXFwdfXF46OjmLHMoiiUqsx1fkkORlHL1/EoZBg+PbpBzfnlz/jX/zwHRJTUrTrFTMzg/+chQCAqIcPcPbmdZiamCA6/hH6tGqDRjUKX/3+h49g8crVaNKoIcqWKY3oe7HY638QJgoF7j98iAljRqFmdQexY+qNMX1u38WY63z0KB5/7vPHjp27sOTbhWjTuhUOHDqMGbPm5Fu3caOG2Lp5Y4FlY6NWAJRKJaytrfMtJyYmYuXKlbh06RJOnTolYsKXMjMzYWFhAblc+htaVSoVpk6diq1bt8LS0hKBgYEYP348AgICIJPJxI6nV0WlVmOqMyvnBfZdOIc+nm2wM0j3Z9ulSlXM6D9Yu/zq5+2FSoWfDuzDDz7joFAoEPXwARZu34KtU2YUaPb/Ki09HbFxD7TLKpUKsxcuwW8//QBLCwucOnse38yci33bNhW69/VNjOlz+y7GXGdmZib+2LoNw4cNxa/rN+jcNnvmdNR2ctIuHz5yFG3atCrQfNL/i1zIBQQEYMaM//9Fe/nyZYwYMQIAYG9vj549e4oVTUdOTg68vLyQnZ0tdpT3EhQUhGrVqsHS0hIA0LZtWyQkJCA6OlrkZPpXVGo1pjotzYthdJduKFOyZL7bypYshVqVKmv/q1mhIgDgflIicjUaKBQKAEA1+3JAIfz79+eBw+jRtZN2+eyFEFSpXAmWFhYAgNYt3JGQmIS7sXEiJdQvY/rcvosx12llZYXpUyfD3t4u320dvNuhQYP6aNCgPpycakGpVMKjefMCzcdGzcCysrJQrFgx7bJKpYKJifQ2ZJqamiI1NVX7Qyh1kZGRsLe31y4rFApUrlwZiYmJIqYyjKJSa1Gp82laCnw3/IJuc2dg9I/f4/q9uwCAcqVs8fj5M+w4fRKavDycC78Bn86fiJz234mIvI0qlSrCzMxMO3Y7+i7sy5bVLisUClSqUB5JT5+KEVHvisrntqjU+bqunTvB1tZWu/zH1m3o07vgN65Ir2MoBDIyMrB+/Xo4OTkhPDwczZs3h7m5Oe7fv4/s7GwEBwdrt6IdP34cCQkJmD17Nrp06QJ/f388fvwYs2fPxief5P8lfOLECaSnp+Pp06cIDw/H/PnzcffuXfzxxx+oWrUqnj9/jnPnzqFdu3bo3r071q9fjytXruCrr77CwIEDAQAhISH5siQkJGDjxo1o3rw5bt++jQsXLqBmzZpYvnw5SpQogY0bN8LU1BRz5sxB/fr1UbduXZw+fRrly5dHaGgovvrqK5QrV65AX+d3SUlJ0fkBAgBra2ukvDb3x1gUlVqLSp0mChOM+6QHzE1M8fMhf8zb+gc2+05FSSsrzBw4BN/t2YXAq5dRvXwFTOzZW+y4/8qRwJP45ssxiE94oh1LTUtDKRsbnfWsra2QmppWwOkMo6h8botKna97faPK8+fJiIy6jZEjhhd4Dm5R+wAHDx6Era0tOnfuDF9fX8TFxWHFihXo168fhg4dioYNG2LFihWoUqUK6tWrhw4dOmD+/PlwdXWFm5sbXF1dMX/+fDRt2lTncUNCQnDgwAH06tULPj4+sLGxwYYNG+Dq6gpLS0vcunUL3377LXbv3o0///wTV69exdq1azFt2jQsWbIEeXl5b83i5uaGEiVKICoqCt9++y1OnDiBpKQk+Pn5QaFQoGPHjrCzs8P8+fPRu3dv/PLLL2jdujW6d++OYcOGQa1Wi/Rqv1lubm6+TDKZDKampiIlMpyiUmtRqXNSr76oamePcra2+KZ3P2SrcnD9Xgw0eXm4GRuLlaPGYEKP3nj4NAmT1q3FC5VK7Mjv5djJ02jT0iPfeG6uOv/7CuN5X4vK57ao1Pk2369ejU8HDxTlublF7QM4ODhg/PjxCA8Ph5ubG+RyOdRqNfz8/AC8PJLzQ46EOX/+PJRKpfZxSpUqhfLlywN4OeG4fv36kMvlqFixIsqUKaM9UrRhw4bIyclBTk4OgoOD35pFLpejadOmkMlkMDMzg5OTE5KTk9+YpWbNmhg9ejS8vLzQokULeHl5/et6DMnGxgbp6ek6Y9nZ2Sj72i4WY1FUai0qdb6uhKUVbKysYG5iihNXwmBuaorKZe1QuawdVo4ag0+XLcaN2Bi4OtUWO+o/2rxjN6JjYgAAgvBybODwURjQqycylEqddbNfvECZ0qULOqJBFJXPbVGp801uhkcgPj4BjRo2FOX52ah9AHd3dxw4cADBwcEICAjAX3/9hUaNGv3nAwMyMzNRpkyZtz7O65thFQqF9mixV+N5eXnaIzff9hivH9FpYmKCvLy8N643btw4tGvXDqGhofj+++8RExODUaNGfVBdhuDo6IiDBw/qjD179gzOzs4iJTKcolJrUajz7uN47cEDAJD7vy0UtatUxcbjR1DF7v/nAFmaF4OdjQ3MTArHFovtG9Zq/x2f8ASdeg/ApdPHcfDocQQcD9RZ93lyCmrVrF7QEQ2iKHxugaJT598JgoBFi5di/LgxomXgrs8PsGPHDu0Rm99//z1cXV1x48YN3Lx5U7vOq28ecrlc51vI35df16JFCwQFBSE+Pl47lpGR8a+yeXh4vDXLuygUCmRmZmobt23btqF27doYOnQoJk6cKImT/r3O29sbERERSEpKAgBcvXoVnp6eOqdBMRZFpVZjrFPzty9CN2Jj8Pj5/58E9vDFEAxu1wHWFhaoWaEiwu5EQfjf5qjHz59BoVCgnkPha2heHT2eq1bDy7MlIm/f0Z789np4BFo0bwZrKysxI+qNMX5u36Qo1Pmmv3N/+u1Dbm4uPNwL9kjP13GL2gcwMTHB8uXL4eTkhKdPn2Lu3Lm4fv06pk6dCgcHB5QrVw7dunVDvXr14OrqinHjxiE5ORmzZs1CgwYN8N1338HHxwdffvklzp49i5SUFJw5cwbe3t6IjY3FiBEj4OTkBDs7OwwePBhxcXGIjo5GcnIy7t27h4cPHyI1NRVBQUGoVauWNtf+/fvRv39/LFy4MF8WANrDqJs1a4bKlSsDAMLDwxEVFYWaNWuiYsWK6NWrFyZPnozo6Ghs2rQJtra2ePz4MYYNG1bwL/Q7WFtbY+XKlZg8eTIaNGgAtVqN6dOnix3LIIpKrcZW5+XoO7ga8/Jn7mjYJWRkZ8PDpS7mbd2MsiVLws6mFOpWc0Cb+g0BAB81aYaE5GQs37sLFUqXQeaLbMwbMgyKQnBew9cdPxWE8yGhAIBVa9fBy7MllsydhRkLvkU9l9pQazSYPH6cyCn1x9g+t29j7HX+dSEYwf/73O7124e0tDS0adMaK1f9iEkTxouaTSa8+vpGRZYgCIX+hIVE/1Xs3v1iRygQ5ds0EztCgShWpoLYEUiPNDlZ4jyvRgO5XF6gfyMV5rqnyeIWNWKTRkRE9AavTkAtpsK1TZ2IiIioCGGjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKI+qFG7deuWvnMQERER0d/kO+FtTEwMNmzY8M47xcTEYPfu3QYLRURERERvaNQqVKiAuLg4tGjR4q13SklJMWgoIiIiInpDo2ZhYYHp06ejXr16b71TbGysQUMRERER0VvmqP29SUtPT8fjx4+1/x08eLBAwhEREREVZf94Ufbff/8dq1evRrFixWBpaQmlUgkLCwuMHz++IPIRERERFVn/2Kjt3bsXp06dwrFjxzBgwADcvn0bqampBRCNiIiIqGj7x9NzuLm5wdbWFiYmJnj69CmcnJzw+++/F0Q2IiIioiLtH7eo1a5dG4GBgWjVqhWGDRuGcuXK8WACIiIiogIgEwRBeN+Vo6KicOHCBbRs2RK1atUyZC4iogIVu3e/2BEKRPk2zcSOUCCKlakgdgTSI01OltgRCozC3FJn+R+3qL3O2dkZzs7OuHTpkl5DEREREVF+/9iozZo1C/fv39cZe/z4MQIDAw0WioiIiIjeo1ErU6YMqlevDjMzMwDAlStX0LJlS4MHIyIiIirq/rFR6969O6pWrapd7t+/P5YuXWrQUERERET0HqfneL1JA4C8vDycOXPGYIGIiIiI6KV/3KI2ZcoUxMTEaJeTkpJQrlw5g4YiIiIiovdo1CpWrIhGjRrB3NwcgiBAoVCgdevWBZGNiKjAKMz+1UHwhdY9/3NiRygQLiP6iR2B9Ojvp6woSv7xN5OHhweaNm2qM/b48WPY2NgYKhMRERER4T3mqB06dCjf2IIFCwwShoiIiIj+3xu3qCmVSowYMQKmpqZISEjAtGnTtLelp6fj5s2bBRaQiIiIqKh6Y6NmbW2NyZMnQ6VSwd/fH66urtrb5HI5fH19CywgERERUVH11jlqjRs3BvDy9Bzly5fXuU2j0Rg2FRERERH98xy1UaNGYceOHTpjq1evNlggIiIiInrpHxu1atWqYcCAATpjiYmJBgtERERERC/9Y6NWsWJFneXk5GRERkYaLBARERERvfSP51Fr1aoVfHx80KxZM6SkpODgwYPo3LlzQWQjIiIiKtL+sVFzd3eHWq3G/v37kZ2djREjRsDd3b0gshEREREVae91zRRPT094enoCAG7cuIGZM2di586dBg1GREREVNS9V6OmUqkQEBCAHTt2ICoqCiVKlDB0LiIiIqIi752N2sOHD7F9+3b4+fnBysoK48ePh7e3N86ePVtQ+YiIiIiKrDce9RkYGIjhw4eje/fuyMrKwtatW9G5c2d0794d1tbW6NSpU0HnJCIiIipy3rhF7c6dO0hKSsL333+PVq1aAQBkMlmBBiMiIiIq6t64RW3MmDHYv38/VCoVVqxYgUOHDiE3N1d7e15eXoEFJCIiIiqq3nrCW7lcDm9vb0yaNAnOzs4AgJUrV+LGjRvYvHlzgQUkIiIiKqpkgiAI77tyWloaduzYgc2bNyM4ONiQuYiICtSDA4fFjlAglElKsSMUCJcR/cSOQKQX73V6jldKliwJHx8fVK9eHXl5eZDL//EKVERERET0gf5Vo/ZKhw4d9J2DiIiIiP6Gm8SIiIiIJIqNGhGRAWW9eIH0zMx844IgYP9f5/E8PR0AoMnLg6aQH1EvNzWBwtxM7BhERuWDdn3SfycIAm7duoU6deqIHeWdNBoNbt++DRcXF7Gj5HPnzh3s2rULCoUCcXFx8PX1haOjo9ixDKKo1GpMdSYkP8fRi6E4cOEvTOk/EM1ddH/Wj126iDX+fqhfowZKlyiBwMthWL47/zWU61RzwA9jvyyo2B/E1NoKNk4OsHVxRHxQCJQPEwAAVhXtYV25AgS1Gua2Nnh6ORwvnqcAAMxLlUQp5xoQ8vJgVrI4ki5eR05quphlfDBj+ty+C+sUBxs1ESiVSvz444/YtGkTbt++LXYcHYIgoGPHjli5ciXKlCmDlStX4tKlSzh16pTY0XSoVCpMnToVW7duhaWlJQIDAzF+/HgEBAQY3cmZi0qtxlRn1osX8Dt3Fv3atMX2k4H5bk/PysSDpMR84+N79kaNChW0y6evXYV7bWl/mZObmsC2bi08vxGFso3+P6uZTXGUbVQHcYde/u6wsC+Dyh1a4u7uAEAQUKGVK+IOn4Kg1qB41Yqo5N0CMXuPiFXGBzOmz+27sE7x6uSuTxFYW1tjyJAhYsd4I5lMhlmzZqFGjRqwt7dHz549xY70RkFBQahWrRosLS0BAG3btkVCQgKio6NFTqZ/RaVWY6rTslgxjO3WA2VK2rzx9oDQEHR0dcs37lmvPlyqVoNL1WqoUaECMrNfoHGtWgZO+9/k5aqRGHIV6qxsnfGS1asgK+mZdjk78RnkCgWsytvBukp5qNIzIKg1AICMB49hamUJ81IlCzS7PhjT5/ZdWKd4dbJRo3zc3Nwkf/WJyMhI2Nvba5cVCgUqV66MxMT8WykKu6JSa1Gp8/bDh6hYpizMTEx1xr0bN4GNtbV22e/cWXRu3ryg4+mNwtwcJsWK6Q7K5TCxskAx21LIzXytsRMEqDKUMLG0KNiQelBUPresU7w62aiJ7Ny5c/j888/RrFkz/PTTT1Cr1di0aROmTZuGjz76CAcPHgQAREREYNKkSVizZg2++OILDB8+HNnZ2di0aRP8/f0xbdo0rFu3Lt/jJyQkYNWqVTh48CDmzZuH8PBwAMCVK1fw008/Ydu2bRg/fjxOnjwJANi7dy9atWqF0NDQgnsRPkBKSgosLHR/qVtbWyMlJUWkRIZTVGotKnWevnYFnvXq5xtXKBTaf6coM3A3Ph51qjkUZDS9ykp8ihLVK8PCrjQgk6GUS03ITRTQqFRQFDOHoFbrrJ+nyoWimLlIaT9cUfncsk7x6uQcNZEpFAr8/vvvuHz5MoYMGYJmzZrByckJQ4cOxZ07dzBgwAB07doVderUQZUqVXD+/HksWrQIkZGRmDdvHjp06AAvLy906tQJbm5u8PLyQs2aNbWPv3XrVjRs2BDt27dHixYt8PDhQzx58gRz587Fvn37oFAo4O3tjY8//hh79uxB7969sXr1ahFfkfeTm5sL9d9+0ctkMpiamr7lHoVXUam1KNR55vo1uLv885yz3wMOo6dnqwJIZDjp9x6imK0NKnl5QP3iBZ5eDodMLkdOchqsK5WH7A0nTBfyNCIk/W+KwucWYJ1i1slGTWQeHh4AgCZNmqBatWp49uwZ6tSpgx07dgB4eeDBK3K5HK6urqhRowZq1KiBxYsXo1KlSkhNTQXw8kTEf78iWI0aNTBr1iwEBQXB3d0dHTp0gJ+fH8qXL6/9Bm9vbw8XFxecP38eNWrUgImJ9D8WNjY2SE/XPUIsOzsbZcuWFSmR4RSVWotCnXvOnMa9hJdHROJ/P6tjV32P7i1aYmSXTwAAtx8+wJOUlEK9Ne2VpLCbSAq7CQAoWbMqspKeQ5WWAc2LHMjNdE/jITMxgTrrhRgx/5Oi8LkFWKeYdUr/L3IRYmJiguTkZEydOhVr166FjY0N5s6dq7PO6119ZmYmmjRpAnd3dwB448T/nj17olGjRggJCcGWLVtw8eJFVKtWDQmv/lj8j5WVFczNC89uB0dHR+1u4VeePXsGZ2dnkRIZTlGptSjUuWb8RO2/nyQnY8jihQhYvEw7JggC1vj7YehHH4sRz2BkCjlKN6iNx0EhAICclDSUrFFVZx0TC3PkJKeKkO6/KQqfW4B1ilkn56iJ7Pnz5wBeTmCsXbs27t27B0dHR9jY2CAsLOyd923RogW2b9+unfifmZmZb5Pttm3b4ODggAEDBmDevHnQaDTw9PREbGwsHj58CODlpt4nT54UqkuDeXt7IyIiAklJSQCAq1evwtPTE9avTcY2FkWlVmOs810nsH2hUgEA1Jr/39135GIo1BoNmtRyMng2vXvLqQsU5mao5OWBpEs38OJ5KgAg4348ipUpBROLlwcbWNiVhvLRE+Tlqt/4GFJmjJ/bN2Gd4tXJLWoiUalUaN++PQYPHgxnZ2c0b94cS5YsQUhICHx9fZGRkYGhQ4cCAPbs2YM6derg2rVrkMlkCA4Ohru7O+bPn4/Zs2ejd+/ecHFxQcWKFTF69Gid50lJScHatWtRsWJFPH78GBMnTkSZMmWwYMECzJ07Fy1atMCLFy+wZMkS2Nra4vTp00hJScH58+fh4uKCs2fPIiUlBWfOnEHr1q1FeKXezNraGitXrsTkyZPRoEEDqNVqTJ8+XexYBlFUajW2OsNu38bV6DsAXjZgGVlZaN+0GYCXc9Uu3Y4CAGwIOIQWdeqhWrly+C3gMEZ07ixa5g9lVdEeVhVfHiln41QdCnNzpN17gDINnGFqZYnES9ehSs3Qrp+Xq0b86RBUaOOG7KfJkMlkSAy5Klb8/8TYPrdvwzrFIxP+PqmJiKgIenDgsNgRoMnLg1wmM+iJNZVJyn9eyQi4jOgndgQiveAWNSIiiVC84UhIIira+FuBiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkio0aERERkUSxUSMiIiKSKDZqRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikiiZIAiC2CHIuORpcsWOUCDkClOxIxARkZHjFjUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkio0aERERkUSxUSMiIiKSKDZqRERERBJlInaAghQTE4MKFSrAwsJC7Cj/Sk5ODhITE1GlShWxo5BI7ty5g127dkGhUCAuLg6+vr5wdHQUO5besU7jwjqNC+sUiVBIDB48WAgMDPyg++bl5Qlr164VatWqJTx8+FA7npGRoa94BnPz5k2hT58+wpQpU8SO8t40alWB/Hf2zGlhwYL5wvLvlglffDFCuHHjWoE9t0atKrDXMycnR+jRo4eQmZkpCIIgnDhxQujYsaOQl5dXYBkKAutknYUR62SdhlZodn1+9dVXaNKkyQfdVyaTwcfHR2fs8uXLGDFihD6iGVTdunXRsmVLsWNITkxMDNau/RXTpk7BxIkTMPKLERgzZhxevHghdjS9CwoKQrVq1WBpaQkAaNu2LRISEhAdHS1yMv1inayzMGKdrNPQCk2j1rhxY5iY6G9PrUql0uvjGZJMJhM7guQEBBxBg4YNtK9N48aNkfPiBUJDL4qcTP8iIyNhb2+vXVYoFKhcuTISExNFTKV/rJN1Fkask3UamsE7lV27diEyMhIhISHo06cPhg8fDgC4ePEiIiIioFQq4e/vD0dHR9SvXx+tW7fG77//DgcHB1y/fh15eXkYMGAAli5dis8++wyDBw8GABw9ehTXrl1DbGws0tLS8PXXX+P48eOws7PDyJEjERISghMnTqBKlSr47LPPdDI9e/YM/v7+ePz4MWbPno0uXbogODgY8fHxWLZsGeLi4uDv749nz55h4cKFCAkJwaZNm9CiRQvs378fLi4umDBhAvbv348SJUrg/Pnz8PT0RM+ePXWeR6lUYuvWrYiOjkZ4eDjmzp0Ld3d37N27F9u3b4ePjw/8/Pxw48YNfPzxx5g1axYAQK1Ww8/PD0+ePMHNmzdx//59NG7c+I2v7/Hjx5GYmIjHjx/j2LFjqFu3Lpo3b47q1avnyzx79mxs3boVt2/fxpUrVzBu3Dh07doVubm5WLNmDZycnHDv3j3Y29ujT58+2Lp1K4oXL460tDQkJCRgypQp+v54fLC0tHQoM5XaZUEQoNZokJiUJGIqw0hJSYGtra3OmLW1NVJSUkRKZBisk3UWRqyTdRqawRs1W1tbzJ07F8nJyWjbti169eoFc3NzTJ48GadOnYJcLodcLkdKSgrGjBkDAKhSpQrOnz+PRYsWITIyEt7e3vjtt9+0j7lz505ERUVh7ty5AIBt27bB1dUVO3bsgKmpKQCgefPm2LRpE2xsbPJlKlOmDNzc3CCTyTB//nwAL3elnTlzBgBQrVo1lChRApcvX9Y+lp+fH0JCQvDdd98hOjoaX331FWbOnAknJye0bNkS7dq1g7e3N0qUKKF9nufPn8PLyws+Pj44ffo0Fi9ejAMHDqB3795Ys2YNMjIy8Msvv+Dx48fo0KEDunfvjnr16mHmzJnw9vZG3759AQDTpk2DIAj56nj06BHWrl2Lffv2AQDS0tJQq1YtDBw4EADyZc7OzoaTkxOGDh2KO3fuYMCAAejatSsuXLiAzMxMdOrUCQBw6tQpKJVK+Pn5wc/PTzsmJY0aN8TMmbPRr19f1K9XDzt37kJ2djZKFC8udjS9y83NhVqt1hmTyWTaz7qxYJ2sszBinazT0AzeqNWvXx979uxBbm4uihUrBqVSCVNTU6SkpODp06ewt7eHWq1Gu3bttPeRy+VwdXVFjRo1UKNGjZdBX9tN6e/vj08//VS7PGjQIACAubm5znObmZm9d85/uq9cLkerVq3g4OCA8uXLY/z48QgLC0NERAQAwNvbGzk5OTr3qVq1KqKiorB161YolUoolUqdx3N1dQUAVKhQAba2tkhOToZKpcKBAwcwffp07boVKlRAfHx8vsy5ublITEyEUqmEtbU1FAqFzny21zM7ODhoH2vHjh0AoM1TqVIl7N+/H4mJiXBzc0OnTp1QrFgxZGZmYtCgQfDw8MBHH3303q9lQej08ce4c/sOvv56EmxtS+PLL8ciLy8PTk61xI6mdzY2NkhPT9cZy87ORtmyZUVKZBisk3UWRqyTdRqaQeeoRUVF4fPPP0erVq0wcOBAWFlZAQCsrKywatUqfPPNN9izZw88PDzg7u6uc993da/Z2dlv3Az5pq1O7+t97vsqU1ZWFgRB0O7u7NmzJ3744Yd8b+Tu3buxZs0a9OzZE126dMn3eK/PPTMxMYEgCMjIyIBGo9Fp+v7eAL7i4OCAiRMnYsKECdizZw8GDBiAmjVrvjEzAJw9exZTp07Fxx9/jAEDBmjHa9SogWPHjuGjjz7ClStXMHDgQJiYmODAgQMYOXIklEol+vfv/8ZmUSwymQwTJ07A6VMn8efe3UhPS0f9+vW1DakxcXR0xMOHD3XGnj17BmdnZ5ESGQbrZJ2FEetknYZm0Ebtr7/+Qrly5WBvb4/bt28jIyND57Zhw4ahT58+aNas2b963ObNm2P37t3Izs7WGVcoFHj06BEAIDk5+Z1Hacjlcp2uWaFQICkpCSqVCmq1GleuXHnrfW1tbVG7dm1s3bpVO/b3DhwATp48iXr16sHS0hJhYWH/WJcgCChdujQcHBy0u3ozMjJw9uzZt97n9OnT8PX1RZ8+feDi4vLOxw8KCoKjoyNsbGx08pw4cQK5ubno0qULVq5cCUtLS6SmpuLEiRNo3bo1pkyZkq+RlpKcnBysW78Bc2bPEjuKQXh7eyMiIgJJ/5t/d/XqVXh6esLa2lrkZPrFOllnYcQ6WaehGXTXZ8uWLbFt2zZ88cUXGD58OIoVK4a9e/diwoQJyMvLw6RJk1C6dGnI5XLY2Nhg/vz5yMvLw7Vr1yCTyRAcHAx3d3dcvHgR8fHxuHz5Mlq1aoUvv/wS6enpGDhwIJydnVG1alX4+PigT58+mDhxIvr27YuOHTvC0dERN2/exN27d3Hr1i0AwLFjxzBo0CA0aNAA3333HXx8fDBz5ky0bt0aW7duRZcuXdCqVSvUqlUL165dQ2hoKBQKBe7cuQONRoOaNWuifv36WLVqFWbNmoX+/fvDyckJtWrV0u6CfaVTp05YsmQJHj58iA4dOiA5ORmnTp2CTCZDSkoKjhw5orOl8ezZs6hbty5WrFiBadOmoUuXLmjbti08PT0RFBSEkJAQNG/eXOc58vLyMGjQIJQqVQoymQz29vZYtmwZ4uPj82Vu3749fH19kZGRgaFDhwIA9uzZAzs7O/z0009o3Lgx0tLSMGfOHOTm5uLIkSPIycmBIAho3749KlasaMiPywdJSU3FjBkzMenriXBxqS12HIOwtrbGypUrMXnyZDRo0ABqtVpn17ixYJ3GhXUaF9YpHpnwX/YXfqAnT55g/vz5mD59OipVqoS8vDwsW7YMarUaM2fOLOg4kiEIwr86FUdkZCTWr1+PmTNnwtbWFmq1GpMmTULDhg0xbNgwAyZ9tzxNrsGfQ6XKxfoNG/DkyRMM/exT7VzGgiRXGNckWiIikh5RTiQWHh4OtVqNSpUqAXi5G1IQhHxbi4qaf3u+tNDQUJQsWVJ7KPGrAy7+7a7kwsjMzBRjRvvwHHNERGTURNmilpubi0WLFqFMmTIoVaoUMjMzUaFChTdOuKe3UyqVmD9/PmrWrAkrKyukpqaiQYMGol/JoCC2qEkBt6gREZGhidKokXFjo0ZERKQfheYSUkRERERFDRs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkio0aERERkUSxUSMiIiKSKDZqRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEmYgdgIxPzvMnYkcoEMXKVhQ7QoGQyfh9johILPwNTERERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkio0aERERkUSxUSMiIiKSKDZqRERERBLFRo2IiIhIotioFSJHjhzBrFmzcPr0ab08nkqlwu3bt/XyWAVNEATs9NuPp8+eQ6PRQKPRiB3J4ARBwLZt25CUlCR2FCIiKiAmYgeg99exY0ds2rQJjRo1+s+Pde/ePSxduhRZWVnYsmWLHtIVrP0BR7Hk+x/RtGF9XLgYhjmLv8u3TsN6dbDp51UipDMMPz8/LFiwEM2aNYOdnZ3YcQzizp072LVrFxQKBeLi4uDr6wtHR0exY+kd6zQurNO4SK1ONmqFiEwmg5mZmV4eq3r16vjoo4+wb98+vTxeQUpLT8e9uAc6YzMmfQUnxxra5SOBp9G6RfOCjmYwqampiIm5J3YMg1KpVJg6dSq2bt0KS0tLBAYGYvz48QgICIBMJhM7nt6wTtZZGLFO8erkrk8qdPwOBqBHl491xtq19kT9Oi6oX8cFtWrWgFKZieZNm4iUUP/27NmD3r17iR3DoIKCglCtWjVYWloCANq2bYuEhARER0eLnEy/WCfrLIxYp3h1slGTsIyMDKxcuRKHDx/G0qVLcebMGQBAeno6ZsyYAXd3d4wcORJZWVnIzs7G999/j8GDB2PXrl1o0qQJLl68iIULF2LdunUAgJCQECxYsACbN2/WeZ7Nmzeje/fuaN26tXb+W3BwMDZu3Ig///wTkyZNgkqlKtji3yIi6jaqVKqos2Wxcwdv2Jay0S5v2/0nen3SWYR0hnHzZjiqVq0KMzNzsaMYVGRkJOzt7bXLCoUClStXRmJiooip9I91ss7CiHWKVycbNQk7ePAgbG1t0blzZ/j6+mrHIyIisGDBAhw7dgw3b97E6dOnYWFhgd69eyMiIgJ2dnaYN28eXFxc8Pz5c6SkpAAAmjdvjvj4eKSnp2sfKzY2Fu3bt4e/vz8GDRqEadOmQa1WY+XKlejZsyd69eqFXr16QRCEAq//TY4Gnka71p46YyYmCu2/k1NSEBV9Fw3r1SnoaAZz+PBhdOjQQewYBpeSkgILCwudMWtra+3n11iwTtZZGLFO8epkoyZhDg4O+Omnn/DNN9/gzz//RJMmL3flNW7cGHK5HCVKlED16tWRmpoK4OUcNisrK7Rt2xZdunSBtbU1zM11t8L8fY6bg4MDKlSoAADo1asXUlJSkJqaipo1a6Jv375YsmQJZDJZvscRw7FTQWjdwv2d66z+9TcM6mM8uwiPHDkCL6+2YscoELm5uVCr1TpjMpkMpqamIiUyDNbJOgsj1ilenTyYQMLc3d1x4MABBAcHIyAgAEePHs23jlwu19na9fdG7N9sCTMxMYGtrS1Kly6Nb7/9FteuXcOlS5cwbdo0fPPNN+jSpcuHF6MHf+zcg+j/Tah/VdbAL8agf8/u+HrsKIRHRuFxQqJRbU37/feN2lOovHove/fug8GDB2PyZN933bXQsbGx0dnaCwDZ2dkoW7asSIkMg3WyzsKIdYpXJxs1CduxYwf69euHnj17on379vjyyy/zrfNP5w9TKBR49OgRACA5ORnR0dE6hxmnpqZCo9FAoVDgwIEDmDhxImQyGbZt24ZBgwahUaNGMDc3l8R5yrat+0n77/iEJ+jcdzAunjwC4GUTs/SHNRgzYphY8Qxiz57d2n8/ehQPb29v3LhxXcREhuPo6IiDBw/qjD179gzOzs4iJTIM1sk6CyPWKV6d3PUpYSYmJli+fDn279+PXbt2YcyYMYiPj0dYWBji4+MBvOz0Xy0fPnwYaWlp8Pf3x4sXLwAAffr0wc2bN9G3b1/4+/vD0dERN2/exN27dyGXy1GtWjX06NEDs2bNQvXq1dG3b18AQFhYGLZv3479+/cjNzcXXbt2Fe11eJPs/9WX+79N1PsOHUFurhruzYznSM+/y87OAvBy07wx8vb2RkREhPaEvlevXoWnpyesra1FTqZfrJN1FkasU7w6ZYJUZomT0chOemjQxz9++gz+CrmI/QHHMKRfb7T1bIGvZ8zBVz5f5DtthyEVK1uxwJ7r6NGjOHv2HPz8/DB06FB4e7dD06ZNC+S5ZbKC+z534cIFrFu3Dg0aNIBarcbo0aON7g8BwDqNDes0LlKrk40a6Z2hG7U30Wg0kMvlBXpCwoJs1MRUkI0aERHp4hw1MgoKheKfVyIiIipk+FWZiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkio0aERERkUSxUSMiIiKSKDZqRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEyQRBEMQOQVQYaVQvxI5QIBRmxcSOQHr04tljsSMUiGJlKogdgfQoLeqG2BEKTEnn+jrL3KJGREREJFFs1IiIiIgkio0aERERkUSxUSMiIiKSKDZqRERERBLFRo2IiIhIotioEREREUkUGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KiRUVAqlUhJSRE7hkFkZmYiNTVV7BhE700QBOz8cx+ePnsudhSif6TRaKDRaN57vKCZiB2A/j2lUglra+t/XC8zMxMWFhaQy+VIS0tD165d4efnhzJlyhRAyoLx8OFD7N27F9u3b8eyZcvQtm1bsSPpzaNHj/Cnnz927NyJJYsXoU3r1gCAdRt+w/YdO5CRkYHGjRph0YL5sLOzEzmtfty5cwe7du2CQqFAXFwcfH194ejoKHYsvTP2Ov0PH8HilavRpFFDlC1TGr/9sQ07/vRHhlKJRvXrYv70KbArazy/h4z9/XzFWOs8EnQW81f/nG/crrQtkp4n5xtvUNsJ65csLIhoANioFToBAQE4duwYVq1a9c71cnJy4OXlhVOnTsHKygolS5bE3LlzYWtrW0BJDU+pVGLz5s344osv8Msvv4gdR68yMzPxx9ZtGD5sKH5dv147Hn33Lu7fv49tW/5AzoscfDNlCr5ftRqLFxXcLw1DUalUmDp1KrZu3QpLS0sEBgZi/PjxCAgIgEwmEzue3hh7nWnp6YiNe6BdvnsvFvcfPcIfv/6InBwVpsxZgNW/rMfCWdNETKk/xv5+vmLsdU7x+QK1qlfTLh8/ex41qlaFRqPJN+7ZrGmBZuOuz0ImKysLxYoV+8f1TE1NkZqaCktLS+2Yu7s71Gq1IeMVKGtra8ycORP29vZiR9E7KysrTJ86JV9tuapcTJ82FRUrVED16g7o1aMH8vLyREqpX0FBQahWrZr2M9u2bVskJCQgOjpa5GT6Zex1/nngMHp07aRdzs3NxdQJ41GhXDk4VK2CHl06IU8QREyoX8b+fr5i7HV6ebihnlMt1HOqBcdqVaHMyoKJQv7GcdeG9Qs0W6HaopaRkYH169fDyckJ4eHhaN68OUqWLInt27ejevXqyM7ORmBgIDIzM/H111/jk08+gVKpxNatWxEdHY3w8HDMnTsX7u7uCAkJwaZNm9CiRQvs378fLi4uGD16NHbv3o3q1avjypUr6NWrF+rWrYsTJ04gPT0dT58+RXh4OObPn6+zZerGjRsYO3YsGjZsiOnTp0Mul2PRokUoUaIE5s6di+joaFy+fBkymQxnzpzBuHHjUL9+fURFReHcuXMQBAF+fn4oX748nJycMHnyZFy8eBH3799HdnY2goODMWPGDADA8ePHkZCQgNmzZ+Ozzz5DZmYmjh07hhs3bkAul+Onn36ChYUFNm7cCFNTU8yZMwf169fHs2fPsG7dOuzcuRO1atXCkydPsHPnTpQrVw4REREoX748xowZg8DAQGzevBk9evTA2bNnERYWBldXVyxevBjm5uZivfX0Py4utbX/VqvViIyKwtgxo0VMpD+RkZE6jalCoUDlypWRmJiIWrVqiZhMv4y5zojI26hSqSLMzMy0Y7Wd/r8mtVqDqOhojP78MzHiGYQxv5+vM+Y6O7ZpBROFQru882AAenRoD5daNd84XtAK1Ra1gwcPwtbWFp07d4avry8AoGHDhihfvjxCQ0PxxRdf4PDhw5gwYQKmT5+O5ORkPH/+HF5eXlixYgWmTp2KxYsXAwCaN2+OEiVKICQkBN999x1atmyJrVu3wsXFBV27dsWXX34JjUaDkJAQHDhwAL169YKPjw9sbGywYcMGnVz169fHkCFDkJubi/Lly8Pe3h52dnaYPXs2srKyMGHCBPTv3x+DBg1Cly5dMH/+fADA2LFj0a9fP4wcORI+Pj4oVqwYpk6digcPHmDFihXo168fhg4dioYNG2LFihWoUqUK6tWrhw4dOmD+/PmoUaMGkpKS4Ovriy1btqBkyZLYu3cvFAoFOnbsCDs7O8yfPx+9e/fGiBEjkJmZCQDIy8vDmDFj0KtXL/Tv3x/z5s3DsWPHsHfvXnh7eyMvLw+PHj3CDz/8gCNHjiAkJASnT58uwHea/snc+QswcdI3uHMnGnfuGMc32pSUFFhYWOiMWVtbG91BIsZc55HAk/Bu0+qNty1YthLfzJqL6Lv3EH0vtoCTGY4xv5+vM+Y6X2/GklPTcPteLOrXdnrreIHnK/Bn/A8cHBwwfvx4hIeHw83NDR9//DEAwMTEBI0bN9ZOsO/atSvmzZuHhw8fokGDBoiKisLWrVuhVCqhVCq1jyeXy9GqVSs4ODjAwcEBSqUSs2bNQlBQENzd3dGhQwesXr0aSqUSfn5+AIBSpUqhfPny+bL17t0ba9euxbNnz7TfNMzMzBASEoKcnBwcOHAAwMsPe7169QC8nEcWGxuLBg0aIDc3Fx06dAAABAcHQ61Wa59To9G8dcJmq1atcOrUKTx69AgqlQoZGRlvXM/E5P/f6ri4OMTGxqJy5cra16Fdu3Y4ffo0evfuDblcjmbNmgEAihcvjqpVqyI5Of+EShLP7JkzIJfLcez4CUyc9A0O7PODg0M1sWP9J7m5ufl2zctkMpiamoqUyDCMtc5jJ0+jTUuPt94+45sJkMvlOHH6DL6ZOQd+WzaiWtUqBZjQMIz1/fy7olLnT1u2YUDXzu89XhAKVaPm7u6OAwcOIDg4GAEBATh69Ch+++23fOvJZDKYm5vDwcEBu3fvxtmzZ7Fs2TIkJydj9+7dOuu+/iHr2bMnGjVqhJCQEGzZsgUXL16EQqFAmTJl0LNnz3dms7W1Rbt27bBv3z6UKFFCu35mZiYEQXjj/X/88UcsWbIE3bt3h729Pdq0aaO9j4WFxT8+JwB8+umnGDx4MD799FOkpaX94/rAy3luWVlZSEtLQ8mSJQG8nBP1+q5Nufz/N7aamJhAMKI5Jcbg1fvTob03TExMcCc6utA3ajY2NkhPT9cZy87ORtmyZUVKZBjGWufmHbsRHRMDAHj162Lg8FEY0Ksnvh7no/3MerdpBROFCaLvxRpFo2as7+ffFYU6b0XfRULS03xbzd42XlAK1a7PHTt2wN7eHj179sT333+vc36TpKQk7b8PHDiATz/9FCVKlMDJkydRr149WFpaIiws7J2Pv23bNjg4OGDAgAGYN28eNBoNWrRogaCgIMTHx2vXe9tWqwEDBmDXrl1IT09H8eLFAQBNmzZFamoqAgMDteu9+rBfv34dnTp1Qr9+/bRNGgB4eHjgxo0buHnzZr77yOVy7b+Tk5Nx9epVuLq6QqVS6ayvUCiQmZn5xonmTk5OKFeunM7uzIiICPTu3futrw0bNWkIPHlSZzn3f99w67w2d62wcnR0xMOHD3XGnj17BmdnZ5ESGYax1rl9w1pcOn0cl04fx/4dfwAALp0+jgb16uis92qrzOtz1wozY30//87Y6xQEAcvX/Y7PenV/r/GCVKi2qJmYmGD58uVwcnLC06dPMWfOHO1t586dQ//+/VGrVi24urpizJgxAIBOnTphyZIlePjwITp06IDk5GScOnUKJUqUwJ07d6DRaFCzZk3Ur18fKSkpWLt2LSpWrIjHjx9j4sSJKFOmDGJjYzFixAg4OTnBzs4OgwcP1jZir2vSpAksLCzg7u6uHStbtix+/PFHLF++HHv27EGlSpXQtm1btGzZEhqNBitXrsTmzZshk8lgbW0NX19feHh4YOHChZg6dSocHBxQrlw5dOvWDfXq1YOrqyvGjRuH5ORkzJw5E15eXhgyZAj69u2LmjVrIjQ0FN27d0f58uVRsWJF9OrVC5MnT8aTJ08AvDwYYejQoVizZg1WrFiBR48ewdraGu3bt4eHhwcuXryI+Ph4BAUFoUaNGtpzrl26dAmtWrVClSrS+wYshRMSGsrfa4uIiISdnR3q/2/3ub//fgz77FNUqlRJjHh65e3tjSVLliApKQl2dna4evUqPD093+ucgYVJUagzOzsbwMsvEpG378CubBnU+9+Xif0BR/HpgH6oVCH/FJLCqCi8n4Dx17n/xCnkqtVwa9jgvcYLkkwwgk0lP/74IwDgyy+/FDnJ+3t1ZOqkSZO088+2bNmCCxcuSPacYIIgSO58OefPn8eFCxfw22+/oV27dujQoQO6d+9eIM+tUb0w6OP/deECgkNC8fvGTfBq2xbtvduhYsWKmL9wIapVrYoqVaqglqMjPunaxaA5FGb/fDoYfblw4QLWrVuHBg0aQK1WY/To0Ubzh+B1Ytb54tljgz7+8VNBOB8Siv2Hj2JI/z7w8myJhcu/R9XKlVGlUgU41qyBLh8Z/si5YmUqGPw5XuHn1vDSom4Y7LHTlUr08hmPcZ8NQrf27f5x3NBKOuue/oONmkgiIyPh6+uLQ4cOacc2b94MAPjsM+M5dN2YGbpRk4qCbNTI8AzdqElFQTZqZHiGbNSAl3sv5HJ5vo0Rbxs3pL83aoVq1+ebxMTE4ObNmxAEAbdu3YKLi4vYkd5L7dq10b59eyxbtgwVK1ZETk4OzMzMMHjwYLGjERERFSmK107F8T7jBanQb1GT4u44Khq4RY0KI25Ro8LI0FvUpOTvW9QK1VGfb8ImjYiIiIxVoW/UiIiIiIwVGzUiIiIiiWKjRkRERCRRbNSIiIiIJIqNGhEREZFEsVEjIiIikig2akREREQSxUaNiIiISKLYqBERERFJFBs1IiIiIolio0ZEREQkUWzUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkSiYIgiB2CCIiIiLKj1vUiIiIiCSKjRoRERGRRLFRIyIiIpIoNmpEREREEsVGjYiIiEii2KgRERERSRQbNSIiIiKJYqNGREREJFFs1IiIiIgkio0aERERkUSxUSMqZIKDg8WOoHdPnjxBVlaW2DHIgM6dOyd2BINKSEhAQkKC2DHIAB4+fCjq8/Nan1QoJSYmQqPRoHz58li/fj3y8vIwbNgwmJubix1Nr27duoXVq1cjJSUFeXl5EAQBT548wfnz58WOpldNmzbF/Pnz0alTJ+2YSqWCmZmZiKkMwxg/u5GRkRg3btw710lNTcXly5cLKFHBWLZsGVxdXVG5cmX0798f7u7u6NChA7p06SJ2NL3Lzc2FIAgwMzNDQEAANBoNunbtKnYsvVOpVAgKCkJycrL2d25YWBi+//570TKZiPbMRP/BqlWr8Pnnn+PUqVM4ePAgJkyYgM2bN2PkyJFiR9OrqVOnolGjRmjXrh1MTIz3x3Xy5MmwsrLSGTty5Ai6desmUiLDMcbPrpOTE5o1a4aePXu+8XZBEIxyi1r58uXRpk0b/Pzzz+jduzemTJmCP//8U+xYBjFt2jT06tULMpkM3333HYYNG4bff/8dn3/+udjR9GratGkICQlB1apVoVAoxI4DgI0aFVJubm6oWbMmtm/fjj59+qBdu3ZGuetMEATMmzdP7BgGd/DgQYSHh6N48eIwNTWFIAjIyMgwykbNGD+7crkcX3/9Nezs7N66TvXq1QswUcGwsbEB8HI6wqtGWy43zhlFHh4ecHd3x9KlSzFw4EB8+umnOHDggNix9C40NBTHjh2DtbW12FG02KhRofTkyRNERUXh1KlT+OWXXwAAKSkpIqfSP29vb2RlZcHS0lI7duPGDdSvX1/EVPrXuHFjfPvtt9o/coIgICAgQORUhpGQkGCUn93nz5/DxsYGGo0GFy9ezHf7uXPnMHPmTBGSGU56ejp69uyJrKwsuLm54fDhwzh16hR69OghdjS9S09PR0ZGBk6fPo2lS5cCQKH/gvEmzZo1y9ekJScnw9bWVqREnKNGhdTt27fxyy+/oEaNGhg3bhy2bduGu3fvYs6cOWJH06v58+cjLCwMLi4ukMlkAICYmBjs3r1b5GT69eLFCxQrVgx5eXnaZi0jIwPFixcXOZn+vemzGxMTg9mzZ4sd7T/56KOPMGfOHLi6uqJVq1YwNTXV2XWUlpZmdHPUACAzMxPFihWDQqFAeno6VCoVypQpI3YsvQsJCcHKlSvh6OiIRYsWYdu2bbhy5QpWrFghdjS92rdvH+Lj49GsWTPt76KgoCD4+vqKlomNGhVaMTExAIAaNWpAo9FIZj6BPs2aNQsNGzbU2Z0SGhqKJUuWiJhK/86ePYupU6fCw8MDy5cvR0ZGBs6dO6dzcIGx8PPzyzeXKyQkBM2bNxcpkf79+eef6NWrl85YUFAQ2rRpI04gA/nzzz9RunRpNGjQAAMHDoSVlRVmz55tdFu8X9FoNEhLSxN165KhDRkyBIIgaL8YAy+3ggcGBooXSiAqhH799VehYcOGwtSpUwVBEITMzEzhl19+ETmV/j148EAQBEHIyMgQsrOzBUEQhMePH4sZySDGjBkjXL58Wdi2bZt2bP78+SIm0q+8vDwhKytLEARBmDNnTr7be/ToUcCJDO/evXvCH3/8IezatUtISUkRO45BrFmzRhAEQdi6davw9ddfC4IgCJs3bxYzksEEBQUJ7u7ugrOzs+Dh4SHs3r1b7EgGceLEiXxjFy9eFCHJ/+McNSqUYmNjcfHiRRw6dAgAYGlpifj4eJFT6Z+ZmRkGDx6My5cvQ6FQoHPnzpg/f77YsfSuTZs2aNy4Me7fv68di42NFTGRfmVnZ6Nt27ZIT0+HqampzhGQmZmZqFixoojp9O/UqVPw9fWFi4sLrKyssGPHDixevBjOzs5iR9OrVwdIhIaGomPHjgD+/wADYzNnzhwMHToUvXv3hkKhwMGDBxEcHAx3d3exo+mVt7c3/Pz8cPr0aZiZmaFPnz6ib+1mo0aFkru7O0xNTbWbpwVBQEREhMip9G/x4sXw9vbGkiVLYGVlhfDw8EJ/Koc3ycrKglKpBPDyfE07d+7ULhsDS0tLnDt3DiYmJti4caP2jzoAKBQKlC1bVsR0+ufv748jR45ojwLNzc3Fb7/9ZnSN2u3bt3HlyhWEhIRg3rx5SEpKwpUrV/DJJ5+IHU3vSpUqpfN7Z/DgwZg9e7bRNWpr167FsWPH0K5dO1hZWWHPnj1IS0vDRx99JFomNmpUKKWkpCAoKAjx8fEICQnB9u3bUaFCBbFj6Z2TkxOGDh2qXfb09MSjR4/EC2Qg7dq1Q69evZCYmIhZs2bBxsZGe0SksXh18t5hw4blO4XDnTt3UKtWLTFiGYSbm5vOqTpMTU1RrFgxERMZxrhx43DhwgX0798fpUqVQmBgINq2bSt2LIPo0aMHEhMTYW9vDwDIy8vDjRs3RE6lfwkJCdi3b5/OHLXNmzeLmIiNGhVS/fr1w7x583Du3Dls2LABLVu2NMrzjZUqVSrf2K1bt0RIYlhJSUk4cuQI4uLikJmZCScnJzx79kzsWAaRkZEBPz8/nTOfR0REiP7HQJ/S0tJ0JmTHx8fjr7/+0vnSYQxMTEzQqlUr7bK3t7fRnPD2zp07WLhwoXZZEARs2LAB1apVA/DyPa5atapI6QynTp06Ok0a8HJ6gpjYqFGhVKxYMYwYMQLDhw9HzZo1jfaoz5ycHPz4449wcnJCQkICgoKCUL58ebFj6d2BAwfQuHFjnZOiLliwAGvXrhUxlWFMmjQJSUlJcHJy0l5twti2Bru7u6N79+4oX748njx5gtjYWKOcW3no0CHMnTs33x/yvx/xWhhVq1YNlpaW79zl9/eGxhg8efIEjx49QqVKlbTnjQsLCxM1E0/PQYXSunXrsHbtWnTs2BGLFy9GVlYWtmzZglGjRokdTa8EQcDWrVtx+PBhZGZmok2bNhgzZgwsLCzEjvafKZVKjBgxAqampkhISECzZs20t6Wnp+P69etGd01T4OWBE4GBgUZ9STDg5TVNg4KCkJWVBU9PT9SsWVPsSHrXsWNH/Pjjj7h8+TL69++PsLAwmJmZGc3pOWJjY+Hg4PDW22NiYlCjRo0CTGR4iYmJGDNmDGJjY5GdnY1y5cphzZo1qFOnjmiZjPs3BRmtonLUp0wmw5AhQzBkyBDtmLHMZ7K2tsbkyZOhUqng7+8PV1dX7W1yuVzUE0wakoeHR74m7dU3eGNx69YtqNVq9OjRAwsXLsS9e/cwceJEozv/VuPGjeHo6IjLly8jIyMDTZs2xaRJk4zmJLCvN2lbtmxBWlqazu3h4eFGN5fU3t4ee/fuRVxcHLKysuDk5ASVSiVqJjZqVCgZ81Gf27dvR7t27WBpafnGXX+RkZHYuHGjCMn0r3HjxgAAR0dHlC5dWuc2tVotRiSDc3JywrRp03TOfB4aGorFixeLnEx//vzzT4wbNw4nTpzAnTt3MHfuXOzdu9fojlauVasWQkND4eHhgVGjRsHR0fGNl88yBvfv30d8fDxMTU0BAHfv3oWjo6PIqfTj1SWicnNzdU4RZG5ujri4OBw/fhxjxowRLR8bNSqUjPmoz+vXr6NRo0awt7dHSEgIatSoobMFJiMjQ8R0hlG6dGlERUXp1HbgwAEsWLBAxFSGERwcjJIlS+LSpUvasbt374qYSP8aN26MUqVKITg4GD169ICzszOio6PFjqV3n376KbKysmBtbQ0fHx+cOXMGy5YtEzuWQbRv3x5ubm7a5eTkZKO5KHuPHj0wb948NG/eHP369cs351Amk4naqHGOGhVKL1680B71mZmZqT3q09h2rVy6dEln7hbwcndD3bp1RUpkGEuWLMGxY8dQrFgxlClTBgkJCbCzs8P27dvFjqZ3Fy9e1NnNCxjfe7p69Wq4uLhg1qxZ2LlzJ6pWrYpff/3V6OaQzpkzB56envD29hY7iij69++PnTt3ih3jP0tPT0eJEiUAAL/++itGjBihc3Cav78/unfvLlI6blGjQurLL79Ejx49jGp30ZsEBATka9QCAwON6o86AFy4cAFHjx7F3r17MWjQIDx+/NhodmX/Xfny5TFx4kTUrFkTY8eORUZGhtEdPde3b19s3boVkydPRtWqVbFt2zaj3JV969YtfP755zpjN2/eRL169URKZDgbNmxAcnKydvnRo0dISkoSMZH+vGrSAKBs2bL5ziBQrly5go6kg40aFUoymQy1a9fWGQsNDdXZNF9YKZVK7N+/H5aWlnj06JHOLrK0tDTs3bsXEyZMEC+gAbi7u8Pc3BzW1taIi4tDtWrVMHnyZLRv317saHq3aNEiNGzYEHl5eQCA4sWLw9/fX9SjyvStXLly+Oabb7TLgwYNEjGN4XTr1g3Lly9Hq1attHO3goODsXTpUpGT6V9iYiKeP38Oc3NzCIKA4sWLY+XKlWLH0gtBEPDixQtYWFjgxo0b6Nmzp85ty5Ytg5+fn2j52KhRodSgQQNMnz4drq6u2l+QN27cMIpGzdraGnfv3kVWVhYePHiA1atXa29TKBQYNmyYiOkMo0GDBggMDETr1q3Ru3dvmJqaGu3pK5o3b46hQ4di37592rHExEQRE9GHOn/+PEqWLIkrV65ox2JiYkRMZDgfffQRateuDSsrK7Gj6N0/XYtX7COyjfM3IRm9e/fuwd3dXedSPMY03XLOnDkAgLCwMDRt2lTkNIbXqVMn7b937tyJy5cva48INTav/tC92t0ZHh5uVBegL0qGDRuW78uh2CdHNRQfHx/Mnz9f52dVpVJpL41WmEn9Wrxs1KhQysjIwPjx43XGfHx8REpjOG9q0oKCgtCmTZuCD2NAmzdvxmeffQYAKFOmjKgXQDa0KlWqoG/fvlCr1di7dy9u3LhhlEe3vtqyVKNGDeTl5eW7vqkxiI2NzdeoVa5cGZs2bYKrqytcXFxESqZ/kydPzrc17ciRI+jWrZtIifTrXdfijY+PR8WKFcWIBYCNGhUir8/dEgRBZ+5Weno6wsPDRUxnGKGhofj222+hVCq1Y8nJybh69aqIqfTv4MGDiI2NRdOmTdGhQwej+Jb+Nm5ubvjuu+8QHByMzMxMzJgxI998y8Lu71cOefHihVFeOeTChQvIyclBy5YttWfonzx5MkqVKoXr16+jT58+8PDwEDmlfhw8eBDh4eEoXrw4TE1NIQgCMjIyjKZRe+XFixfYs2ePzqmCQkNDsWXLFtEysVGjQqMozt2aO3cupkyZgtu3b6NLly4ICwszulOQAMDChQvh7OyM2NhY7Ny5EyqVCo0bNzba3Z9Vq1ZFuXLltAcU7Ny5E/379xc5lf4UlSuHvGpcYmJiMHDgQDg7O+PJkyfYuHEj5HI51q5dazSNWuPGjfHtt99qtzYJgoCAgACRU+nf2LFjUbx4cajVari4uODmzZto0KCBqJnYqFGhUtTmbrm4uKBNmzaIj49H6dKl0a1bN8yePRuenp5iR9MrZ2dnAC93aUdFReHEiRNwc3MzykZtx44dWLJkifayNIIgQCaTGVWjZsxXDnldx44dMXnyZAAvr8bw6nP8qpkpWbKkaNn0bfTo0ShWrJjOWNeuXUVKYzgajQarV6/Gli1bMGTIEKhUKmzbtk3UTGzUqFAqCk0a8PISNXfv3kXTpk0xefJk1K1bFydPnsT8+fPFjqZXv/32G/z9/QEAPXv2xKRJk/JdUspYrFq1CvPnz0fDhg2N9shWY75yyOte1ZSVlYVjx46hfv36SE5ORmpqKmxsbPKd4b6wCwgIQGpqqvbArcuXLxvNKTpeqV+/PgCgUqVKCAgIgKurK3bv3i3qHhtemYBIwjIzM/H8+XNUqVIFu3fvxqlTp9CpUyd88sknYkfTq+vXr+Po0aNwdHRE586dYW5uLnYkgxkzZgx+/vlnnbG0tDSj2vpSVK4ccuLECaxduxZJSUmYMGECTp8+DY1GAysrK22jtmTJErFj6sWoUaNw8+ZNVK9eHTKZDHl5ecjOzhb1/GKGcOPGDTx9+hStWrXCp59+imvXrsHLyws//fSTaJnYqBFJmJ+fn87JFwEgJCQEzZs3FymRYT19+hSHDx+GRqNBhw4dULlyZbEj6d3Vq1dx7do1uLi4aHeRBQUFwdfXV+Rk+vPFF1+gR48eOqdyMFbx8fHIzc1FtWrVtGNRUVEIDQ1Fr169YG1tLV44PfLw8MCxY8dQvHhxsaMY1L1791C9enXtckpKCuRyuahfpIzveGmiQk4QBGRnZwN4+e3u74zxos+BgYF4+PAhAgMDERISgp9++kk798fY+Pv7Y8WKFZgyZYr2v127dokdS6/eduUQY1SxYkWdJg14Oefys88+M5omDQC8vLzyNWkJCQkipTGcP/74Q2e5VKlSWLhwoUhpXuIWNSKJycrK0jlL9usnW1QqlahcuTL27t0rYkL9q1u3LiwtLeHp6Yk2bdqgZcuWKFWqlNixDKJly5bYvXu3zpytM2fOoHXr1iKm0q+ffvoJ58+fz3flkHXr1omcjN7XmTNnkJKSol1OTU1FZGSkzgE+ly5dwvLly8WIp1dKpRIzZ86EhYUFbt26pXPpuvT0dBw8eBDBwcGi5TPOmaxEhZjUz5JtCEOGDMHXX3+t/aNuzDw9PfNNrK9atapIaQzjTVcOeXUqEiocTp48iTt37uT7mXz8+DFkMhkEQTCaLWrW1tbo0KEDsrOzdS48DwA2Njb48ccfRUr2EreoEUlYXl4esrKykJOTY7RHQQJAbm5ukWjSAGD58uWIjIxEvXr1tEd9hoeH45dffhE5mf7cunUr31n5IyMjje7Evm9y8eJFuLq6ih3jPzt69KjOl8QzZ84AeHnE/fjx45GXl4exY8ca3RH4Dx8+lNzcWG5RI5Kw/fv3Y9asWejQoQNWrlyJjIwMHDp0CAMGDBA7ml4VlSYNAO7fv48yZcroXIj9+fPnIibSvzddOunWrVuFvlF7+PBhviN2/y48PBwHDx4soESG83qTBrxsQH19fbF3714oFAqsWrUKu3fvNrpGDQAmTpyImjVrYuzYscjIyMCDBw9Qp04d0fKwUSOSsHPnzuHkyZO4cOECAKB48eK4c+eOyKnov/j000/RrFkznTFju/zZoUOHMHfu3HznEevVq5dIifTD3t4e9+7dQ8uWLSGTyXDw4EE0btwYFStWRE5ODs6ePWt01+F95VWjcvHiRXTq1AnW1tYoU6aMyKn0b9GiRWjYsKF2V33x4sXh7+/PRo2I3qxly5awt7fXGbt9+7ZIaQrWnTt3UKtWLbFj6J2joyOWL1+OSpUqoX///lAqlUZ1DjUAWLNmDXbs2IHLly+jf//+CAsLM4rrt5qZmWH27NnaP9o3btzA4sWLtbePHDkSGzZsECueQd2+fRtJSUkICgrCpEmTIAiCUX5pbN68OYYOHYp9+/Zpx17f+i0Gnp6DSMLS09MRHR2N7OxsPHz4EEuWLDGKP3h/l5aWho0bN2LFihX47rvvsGzZMixatEjsWAYxa9YspKWlaSdiW1tbi36JGn1r3LgxHB0dAby8LFjTpk2xefNmkVPpx+tbVurVq6dzW/HixXHq1KmCjlQgvvjiC5ibm2PVqlWwt7fHrl27jO4gGACwsrICAO3lz8LDwxEbGytmJG5RI5Kybt26Yfz48QgLCwMA1K5dGz/88IO4oQzgm2++QWJiIpycnLQT7I3xkkPAy1ORjBo1Sucbe1JSkoiJ9K9WrVoIDQ2Fh4cHRo0aBUdHR1y8eFHsWHpXrlw5nDx5Em5ubsjJycGGDRt0jnQ1JtbW1jpzY43p2rSvq1KlCvr27Qu1Wo29e/fixo0bWLBggaiZeNQnkYRFRUXB2dkZOTk5yMzM1F6CJz09HSVKlBA5nf60adMGgYGBRnvty9ft27cPPXr0gL+/P7p3747Hjx/jiy++wOHDh8WOpjcajQZXr17F06dPkZycjNjYWLRr1w7u7u5iR9MrjUaDmTNn4sCBA8jLy4ODgwOWL1/+xoMpqPC4f/8+goODkZmZCQ8PD9EPgjH+34pEhdjOnTvxzTffwNraWnv9y6VLl2Lnzp2oXbs2fvzxR6M4bYeHh0e+Ju3Ro0eoVKmSSIkMp3jx4hg/fjwyMzMRHByMoKAgjBw5UuxYerVu3TqsWbMG1apVg4mJCVxcXIyuSQNentdw8eLFmD9/PjIyMmBra2t0R/AWNUuXLsWUKVN0dusGBwdjy5YtcHd3x5AhQwo8E7eoEUlY586dkZCQAFdXVyxfvhzW1tbo1KkTNm3aBJlMhl27dmHcuHFix/zPNm/ejKioKDRr1ky76yg0NFRnorYxCQ0Nxfnz55GZmYkWLVqgXbt2YkfSK1dXV/zyyy/as9hHRUXh5s2b6NOnj8jJ9C8iIgLJycnaowTPnj2LWbNmiZyKPtTnn38OCwsLtG/fHt27dwcA9OzZEz169IAgCFAoFBg0aFCBZuIWNSIJK1++PPz8/KBSqXDixAn06NEDarUadnZ2AGA0RwsGBwejZMmSuHTpknbs7t27IiYyLDc3N7i5uWmXz507B09PTxET6Ve9evV0LjXk7OyM9evXG12jtnDhQuzcuROlS5eGQqEA8HISOhu1wishIQE+Pj5QqVTaS7splUrtlrSNGzcWeCY2akQS1q5dO5ibm8Pc3Bzx8fEAXp7FPy8vD3K53GgmLn/++ef5zuZuLOcWi4yM/Metnqmpqbh8+XIBJTK8zz//HMHBwdrdncnJyYiKihI5lf4dOXIEJ06cQPny5cWOQnrSo0cPdOvWDQCwZ8+efLcXK1asoCOxUSOSMqVSiT/++AOxsbF4+vQphg4dCpVKhT/++ANNmzbFtWvXCnwzvCGUL18+39nAXx0eX9g5OTmhWbNm6Nmz51vXOXv2bAEm0r9bt26hZ8+eOu+ZIAg6y69f6NpY1KtXL1+Tlp2dDQsLC5ES0X8ll8uhVCpx79497Nu3DyqVComJiYiJiUGNGjW0X5gLEueoEUmYSqXC+vXroVKp8MUXXyAtLQ0vXrzAyZMnceTIEcyYMcMoLuHi4+OjPRv4mDFjALw8Q/iMGTNETqYfSUlJ2t3VV65c0dktCBjHdTCXLFnyzonWKpUKDg4OBZjI8E6cOAGlUqkzt/LIkSMYPny4yMnoQ92+fRtjx46FSqXC6tWrcf36dWg0Gly8eBHx8fHo0KEDvvzyywLNxEaNSOJiYmIAADVq1NDu8jQ2mzZt0p4NvEePHgCA8ePHY/Xq1SIn07+5c+di7ty5OmOjR4/G2rVrxQmkJ5mZmdqThRYVvXr1QnJyss6Ww7S0NKPajU0vZWVl4cGDB3B2di7w5+auTyIJW7duHdauXYuOHTti8eLFePHiBbZs2YJRo0aJHU2vpHg2cH1SKpUYMWIETE1NkZCQgGnTpmlvS09Px82bN0VMpx9FrUkDgIEDB+a7fmlQUJA4YUgvbt68iQULFqB+/fqYOXMmMjIycPPmTXh4eIjSpAFs1IgkLTY2FhcvXsShQ4cAAJaWlqLMkTA0KZ4NXJ+sra0xefJkqFQq+Pv76xw4IZfL4evrK2I6+lCF/SLzlN+aNWswZMgQPH36FMD/XxbMw8NDtExs1IgkzN3dHaamptotTYIgICIiQuRU+ufm5obvvvtOezbwGTNmFPo5W3/3al6aQqFAs2bNdG5LT08XIxL9R6Ghofj222+hVCq1Y8nJybh69aqIqei/aN26Nbp27apzibdHjx6JmIiNGpGkpaSkICgoCPHx8QgJCcH27duN8hqYGRkZ2LlzJ2JiYlCrVi3txHtjdPjw4XyN2oIFC/Ddd9+JlIg+1Ny5czFlyhTcvn0bXbp0QVhYmPYyb1Q4CYKAvLw87ZfjU6dO4cmTJ6JmYqNGJGH9+vXDvHnzcO7cuf9r797Doizz/4G/ZzifNIWUMsFS46CuaJxMPAXlhuuqeBbt8tClblsGyybJStIukm2iZRHqqnu5opZga6Jc6gpqwuIQ67pg6GimhICgAg0z4DAw/P4gRyfS769h5H4Y3q+/eO4Z5nn7h3N9eO77/tzYtm0bQkJC8O6774qOZXaxsbG4ceMGXn75ZVhZWWHVqlVYu3YtHn/8cdHRzEKtVmP16tVwcHBASUkJPvnkE8NrKpUKubm5AtORqXx9fTF+/HiUl5fD1dUVU6ZMwTvvvGNRzYu7m+eeew4vv/wydDodPvroI9TV1eHjjz8Wmom7Pokk7G7vHksXFBSEnJwcw4L0hoYGpKamIiYmRnAy88nKykJjYyOOHTuGYcOGGcblcjkCAwMtos1Kd7NlyxaEhoaipaUFKSkpGDp0KHbu3Im8vDzR0chEer0eGo0G586dg0ajgb+/P9zc3IRm4hM1Igl78803ERMTgwkTJoiO8kiNGDHCaNego6Mjrl27ZrhuaWkxHNHTVYWHhwNoOwezf//+gtOQOcyfPx+3b9+Gh4cHQkJCkJOTg9jYWNGxqAMSEhIQHx8vqaeifKJGJGHp6el4/PHHUVxcDE9PT0ycOBF2dnaiY5ldUVERVCoVQkJCAAA3b97E73//e+zbtw8A8MUXXzy0s39XUlZWhg0bNhidwvD9999jyJAhoqMRdXtvvvkm+vTpAw8PD0ybNg3Ozs6iI7FQI+oqbt26haSkJPTu3RurV68WHcesJk+ejMuXL0Mmk+H+r6S71zKZDBcuXBCY0Hws/RQGoq7s7ikidXV1OHbsGOrr6zFq1Cj4+voKy8SpTyIJUygUGDx4MA4ePIj09HQ0Nzdjzpw5omOZ3eTJk7FkyZIHTm8eOHCgcwM9QsHBwYZTGO6qqqoSmIiI7rr7h6Jer4dKpcLnn3+OL7/8EgcPHhSWiYUakYTFxMRArVZj7NixWL16NUaNGiU60iOxZMkS1NfX4+zZs7C3t0dgYCCsre99PU2dOlVcODOz9FMYuhu1Wg2tVgtXV1fRUcgM1q5di+bmZpw5cwZhYWF4//33253N29lYqBFJmL+/P1atWoW+ffuKjvJIFRUVYfny5XBycoKTkxNkMhlSU1PRr18/0dHMzsPDA7Nnz4ZOp7PIUxi6k3/+85+Ij4/HSy+9hA0bNqC+vh6HDh3C3LlzRUcjE507dw4LFixAYmKiZHrisVAjkrC//OUvcHFxER3jkdu+fTv+/ve/G9aB1NXVYc+ePYY1XJbkzp07mDRpEuRyOZKSkhAYGAg/Pz/RscgEp0+fRnZ2Nv79738DaDtu6NKlS4JTUUekpKQYtc+RArnoAET0YN2hSAPaWlbcv1j3scces9h/e0FBARYsWAB7e3uMHj0an3zyCbKzs0XHIhOEhIS0e9qtVCoFpSFzkFqRBrBQIyIJaGpqMrpWq9VQKBSC0jxaQ4YMgUwmQ0FBASZNmgRnZ2fhDTXJNCqVCpcvX0ZjYyPKysqwbt062Nraio5FFoZTn0RdTGNjIxwcHETHMKuBAwdi4cKFGDRoECorK1FYWIilS5eKjvVIKJVKVFdX4+TJk4iJiUFrayuny7qoKVOmYMWKFSgsLAQA+Pj44MMPPxQbiiwO+6gRSUhTUxNu3br10Pfs2bMHf/zjHzspUecpKSnBkSNH0NDQgHHjxkmqM7g5qdVqZGZmYsCAARg1ahQ+++wzWFlZYebMmaKj0S+0Zs0ahISEYOzYsdBoNJJZfE6mq6urw7Zt2/DUU09hzpw5UKvVqK2tFXqaCAs1IgkpKyvDiy++aNToFbjX2+futaU0fyXqymbOnIn169fD09PTMFZcXCzJdU70/+eNN97AY489ht69eyM6OhoAsG7dOrz99tvCMnHqk0hC+vfvj6ioKCxfvhwAsHDhQqxduxb9+vWDVqvF4cOH8fTTTwtOSURA29Tn+vXrMXbsWNjY2AAA8vPz8f777wtORqYaOnQoli1bZtSQurq6WmAiFmpEknO3SAOAAQMGGHqJ2dnZISIiAkuXLsXWrVtFxSOiH+Xm5qJnz544e/asYezKlSsCE1FH9enTB8C92YuKigrhO3lZqBFJmLu7u9F1eXk5SkpKBKUhovstWrQIQUFBRmN3NxZQ1+Ti4oIVK1ZAo9EgPz8fJ0+eFL6xiYUakYSFhoZizpw5GD58OLRaLY4cOYJp06aJjkVEQLsiDbi3npS6prCwMLi4uCA3NxcajQbvvfceXnjhBaGZuJmASOK+++47ZGZmoq6uDgEBAQgPDxcdiYgAxMfHo7S01GisoqICx48fF5SIOqqkpATNzc3w9vZGYmIiWltbER0dLXRHL5+oEUnc9evX8fzzzyMgIABarRZNTU1sqkkkAW5ubnjmmWcM/x/Pnj2LkJAQwamoI/bv34/XX38d//rXv3Dp0iUkJCQgIyND6PQnCzUiCUtKSsKpU6cQHByMgIAAyOVypKSkICoqSnQ0om5v6tSpRq055syZwx2fXdzIkSPRq1cv5OfnY9q0afD29sbly5eFZuIRUkQSptfrcfToUcOh3TY2NsK3ihNRm/uLNKDt/+upU6cEpSFzuHLlCo4fP47s7GwEBwcDaJvOFolP1IgkbPjw4QDubRVvamrCN998IzISEf0oNjbWqB1HdXV1u53a1LXMmjULaWlpWLlyJTw9PbF7927odDqhmVioEUlYfX09du/ejatXr6KlpQXp6ekYOnSo6FhEBKBfv34YMWIE7Ozs0NraCisrK4wbN050LOqA1NRUjBkzBmFhYQCAyMhIwYm465NI0vR6PT799FOcPn0aGo0Go0ePRnR0NOzt7UVHI+r2CgsL4e/vbzRWUVGBJ598UlAi6igpHgvGQo2oiykpKYGvr6/oGETdXkJCAhISEozGfve73yE1NVVMIOqwtLQ0KBQKSR0LxqlPIgmprKzE/v37H/qeM2fOIC0trZMSEdH91Go1Xn31VdjY2KCyshKrVq0yvKZSqVBcXCwwHXWUFI8FY6FGJCE9evTAoUOH4OfnB5lMBoVCAU9PT7i7u0Or1aKwsPBnu6ETUedwdnbGypUr0dTUhAMHDiAwMNDwmlwux1tvvSUwHXWUFI8FY6FGJCFOTk549913DV8Uixcvxvbt2w27PquqqnD48GGREYm6vZEjRwIABg8eDFdXV6PXmpqaREQiM/m5P4TVarWAJPewUCOSmPu/KIYNG2Yo0gCgb9++OHz4MBYvXiwiGlG3ptVqcfv2baOxn/bYOnr0KBYtWtSZsciMFAoFkpKSjIqzmpoa/Pe//xWWiYUakYS5ubnh4sWL8Pb2BtC20LW+vl5wKqLu6fr165g0aRJkMtkDD1+XyWQs1LqwhIQExMbGQqlU4je/+Q0KCwuFnvMJsFAjkrQZM2Zg6dKlUCqV0Ol0sLW1xYYNG0THIuqWBg4ciOjoaCxbtuyB78nMzOzERGRuvr6+GD9+PMrLy+Hq6oopU6bgnXfewZgxY4RlYnsOoi6grKwMtbW18PLygp2dneg4RPQAV65cwcCBA0XHIBNt2bIFoaGhaGlpQUpKCoYOHYqdO3ciLy9PWCYWakQSptPpsHnzZpw4cQK2traYNWsWIiIiRMciIgC7du3CDz/8YDR2/vx5bN68WVAi6iiNRoPbt2/Dw8MD+/btQ05ODsLDw/Hb3/5WWCZOfRJJ2AcffICysjLMnz8fTk5OOH/+PNLT0zFz5kzR0Yi6vdLSUpSXlxsao3777bcYPHiw4FTUEU5OTtDpdCgqKkJ4eDhmzZolOhILNSIpk8lkRl3OJ06ciN27dwtMRER3vfjii0a7tGtqanDw4EGBiaijMjMzERcXB2trazg4OOCVV17B8uXLhWaSC707ET3UoEGD2o1VV1cLSEJEP/XTnlu9e/fGkSNHBKUhc9i4cSMSExPxn//8B7m5uXB3d0dWVpbQTHyiRiRhN27cgEKhgJeXF27cuIETJ06gpKREdCwiArBt2zbU1NQYrq9fv84/pLo4R0dHTJkyxXA9depUrFmzBuHh4cIysVAjkrCFCxciLi4O2dnZ0Ov18PPzwwcffCA6FhGh7aSQ27dvw87ODq2trXBxcWH7nC4uLCwMDQ0NcHR0NIxdunTJ8PP9fS07C3d9EnUBarUaGo0Gffv2FR2FiH5UWFgIf39/0THIjLZs2YKCggKMGDECQNtSk9OnT2P69OkAgOLiYmzZsqVTM3GNGpGEVVVVYf78+QgICEBoaChiY2Oh1WpFxyIiAMnJydizZ4/R2NatWwWlIXP47rvvMGzYMMN1nz59DEWaKJz6JJKw9957D2FhYVi3bp2hPcfOnTuxdOlS0dGIur2ePXti3rx5RmOlpaWC0pA5zJs3D8OHD3/g61euXOnENG34RI1Iwry8vLBw4UI89dRT6NWrF8aMGQMXFxfRsYgIwNNPP210fefOHVy4cEFQGjKHH374AV999RUaGhrw6quvYvHixUbFt4hTJ1ioEUlYr1692o1x1yeRNPj5+eHPf/4zjhw5gr1792Lu3Lnw8fERHYs6QKFQYMyYMcjKyoJcLsemTZuQnZ0tNBOnPokkTKvV4uOPP4aXlxcqKytx8uRJPPHEE6JjERHaGlDX1dVh586daGxsRHBwMN544w3RsagDhgwZAplMhoKCAoSHh8PZ2Rlubm5CM7FQI5KwV155BWlpadixYwc0Gg3Gjx+P1157TXQsIvrR7NmzMXv2bMN1WVkZnJycBCaijlAqlaiursbJkycRExOD1tZWo/YcIrA9B1EXc/Xq1XZrY4ioc5w4cQLPPfcc5HI5vvzyy3avnz17FsnJyQKSkTmo1WpkZmZiwIABGDVqFD777DNYWVkJPV+ZhRqRhJSVlf3sl//9zp8/j82bN3dSIiK634IFCxAVFYWRI0di4sSJcHNzg5WVleH1iooK4WuayLJw6pNIQnr27ImsrKyHbg+//8gaIupcu3btMvy8cuVKhIWFGb3+9ddfd3YksnAs1IgkpEePHoiMjMS8efMgk8l+9j3c/k8kDZ9//jmampqMzoEMCAgQmIgsEdtzEEnMP/7xD8hkMuj1eiQlJbV7/UEFHBF1LplM1q4dh0KhEJSGLBWfqBFJTGRkJI4dO4ZevXrh6tWr7aZSjh49itWrVwtKR0R3DR8+HHFxcQgMDISNjQ0AoKioCEFBQYKTkSXhZgIiCdq+fTtOnz6Na9euoX///kavlZeXIycnR1AyIrorJiYGnp6ekMvvTU6dO3cO27ZtE5iKLA0LNSIJu3jxIry9vY3Gzpw5g+DgYEGJiOiukpIS+Pr6Go1duHCBpxOQWXGNGpGE/bRIA8AijUgivvrqq3Zjcrkca9euZYsOMhsWakRERCY4d+4ckpKSjDYQrFmzBtbW1vj666+RlZUlMB1ZCm4mICIiMsG3336LX/3qV1AoFLCysoK/vz9qa2sRGxsLANi6davghGQJWKgRERGZYOrUqYazdzMyMuDv74/7l327uLiIikYWhIUaERGRCZydnQEA1dXVOHjwINzc3HDz5k1UVlbiiSee4CkiZBYs1IiIiEwwZMgQTJgwAXV1dfjwww+Rl5eH6dOnIzExES0tLXjyySdFRyQLwPYcREREJrpz5w70ej0cHR0NY1VVVSguLsb48eNhbc3nIdQxLNSIiIhMpFKpoFarDdcZGRlYsWKFwERkaVioERERmWDHjh3YtGkT7O3t4ejoCLVaDQcHB5w6dUp0NLIgfCZLRERkgoyMDOTk5ODo0aOYO3culEol6urqRMciC8OGt0RERCYICgpC7969YW1tjZs3b8LLyws7duwQHYssDJ+oERERmcDHxwfHjx/H2LFjsWjRIri7u+Pq1auiY5GF4Ro1IiKiDlIqlcjLy0NISAieffZZ0XHIgrBQIyIiIpIorlEjIiIikigWakREREQSxUKNiIiISKJYqBERWTC1Wo2KigqLuxdRd8FCjYhIArKzszFjxgyMGzcOGzduxF//+ldMnjwZmzdvNvkzz5w5g+nTp2P//v0AgNbWVkycOBHffPPN//m79x+LZMq9iMg82EeNiEgCQkNDUVJSgosXLyI6OhoA8MILLyAyMhK//vWvMWDAgF/8mcHBwfDz8zNcy2QyxMfHY+DAgQ/9vb/97W+oqalBbGysyfciIvPgEzUiIgmxsrIy/Hy3H5dOpzP582QymdF1UFAQ9Hr9Q3+noaEBDg4OHb4XEXUcn6gREUnUoUOH8NJLLyEnJwdRUVH4wx/+gLi4OHz66afQ6XQoLS1FY2Mj8vPz8ac//QkeHh5oaGhARkYGbt26heLiYly7dg0REREA2s6mTE5ORlJSEiZMmAC9Xo/9+/fj8uXLUCqV6NmzJ1577TXk5eVBLpfj1q1biIqKwqVLl37xvYjIPFioERFJTGFhIf73v//B3t4eycnJsLa2xsaNG6FSqbB+/Xq4urrirbfeQnp6OgCgsbERycnJ+Oijj7Bs2TLExcXBx8cHLS0tWLJkieFzZ8yYgU2bNhmuN27ciB49eiAuLg4AsHv3bnh7e+OZZ55BYGAgIiIicO3aNSQnJ//iexGRebBQIyKSGH9/f/j7+xuNtba2YtKkSbC1tcXevXvR3NyML774AgDQ0tKCwYMH4/vvv8eFCxfg4+MDoG0a1d3d3ehzrK3vfe0fOHAAKSkphuvIyMh2WfLz802+FxF1HAs1IqIuwtbWFgCg0Wjg4ODQbpqxqKgIzc3N0Ov1kMvbliBrtdoHfl5jYyNqa2sfek9z3YuITMPNBEREXczzzz+PoqIiFBcXG8ZUKhWeffZZ2NjYIC0tDQBQVVWFwsLCB35OcHAwdu3ahZaWFqNxuVwOlUpl1nsRkWlYqBERSUB+fj4KCgqgVCqRlZVlGNfpdIb1Yfv27UNNTQ18fX2RmJiIt99+G6+//joSExNRWloKe3t7bNiwAbt27UJERAQyMzMRGBiI/Px8nD9/HidOnEBtbS1yc3Nx8+ZNrFmzBvb29pg7dy7i4+Oxd+9eAMDo0aORmpqKhIQEDBo0yKR7EZF5yFpbW1tFhyAiIiKi9vhEjYiIiEiiWKgRERERSRQLNSIiIiKJYqFGREREJFEs1IiIiIgkioUaERERkUT9P1ZA7RT/e2tzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_description(datavector_knn_wf[3]['y test data'], datavector_knn_wf[3]['y predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7c9db2db",
   "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>iteration</th>\n",
       "      <th>f1_scores</th>\n",
       "      <th>precision_list</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iteration  f1_scores  precision_list  recall\n",
       "0          1       0.66            0.79    0.78\n",
       "1          2       0.61            0.79    0.72\n",
       "2          3       0.63            0.82    0.77\n",
       "3          4       0.49            0.70    0.71\n",
       "4          5       0.61            0.85    0.81\n",
       "5          6       0.66            0.81    0.73\n",
       "6          7       0.55            0.74    0.76\n",
       "7          8       0.58            0.77    0.76\n",
       "8          9       0.60            0.81    0.81\n",
       "9         10       0.61            0.64    0.65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_class_metrics = get_classes_metrics(datavector_knn_wf)\n",
    "display(get_metrics_average(datavector_knn_wf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "805001fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "# plot a bar chart\n",
    "ax = sns.barplot(x = 'names', y = 'f1_scores', palette=colors, data=knn_class_metrics, estimator=np.mean, ci=95, capsize=.3)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('kNN multiclass F1 score')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0, 1)\n",
    "#plt.savefig('kNN_multiclass_F1_score_scale_2.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fec35149",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_best_model = get_best_model(datavector_knn_wf)\n",
    "knn_worst_model = get_worst_model(datavector_knn_wf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ad7e2b",
   "metadata": {},
   "source": [
    "# Saving models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6ee1fdaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn_worst_model.joblib']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(svc_best_model, 'models/svc_best_model.joblib')\n",
    "dump(rf_best_model, 'models/rf_best_model.joblib')\n",
    "dump(knn_best_model, 'models/knn_best_model.joblib')\n",
    "\n",
    "dump(svc_worst_model, 'models/svc_worst_model.joblib')\n",
    "dump(rf_worst_model, 'models/rf_worst_model.joblib')\n",
    "dump(knn_worst_model, 'models/knn_worst_model.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb43299",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1b7ae4",
   "metadata": {},
   "source": [
    "According to average scores SVC should show the best performance in its best iteration.\n",
    "Among classes, the highest certainty in predictions can be expected for trees, barrens, open water.\n",
    "Next step is to obtained classified maps of the region of interest."
   ]
  }
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