All_multiclass_scaled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3ab6817",
   "metadata": {},
   "source": [
    "# Description\n",
    "\n",
    "The task is to compare the performance of classic ML models, Random Forest (RF), Support Vector Machine (SVM) and k-nearest neighbours algorithm (kNN) for multiclass classification of the complex boreal landscape using data from forest inventory data represented by plots. Each sample (a record) is the combination of spectral data (bands and derivatives) with terrain features assimilated pixel-wise.\n",
    "\n",
    "**Briefly**: performance of the models is evaluated by 5 fits for each model on random* train/test data. Models efficiency is evaluated using Kruskal–Wallis test by comparing average macro F1 scores for filtered and non-filtered data.\n",
    "\n",
    "*see description below\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "The structure of this notebook is the following:\n",
    "1) description\n",
    "2) libraries upload (data analysis, clustering, ML models, statistics, visualisation support and resulting models saving)\n",
    "3) data upload\n",
    "4) setting of helpers functions:\n",
    "\n",
    "* functions for data filtering (``get_cluster_pixels``, ``get_selection``) according to KMeans clusterisation results -- only data belonging to the dominant cluster is selected (see more in utils.py file and \"Clustering\" notebook)\n",
    "* function to get predictions (``get_predictions``) for optimisation of random train/test split and hyperparameters. This function considers the following problems of the label data from inventories used for ML:\n",
    "\n",
    "a) problem of overlapping the data from the same inventory plot in train and test. This must be avoided to reduce the possibility of overfitting. Thus, splitting using groups (by key) is used. As a result, in train and test data, there are no records with the duplicate keys (meaning from the same inventory plot) \n",
    "\n",
    "b) problem of missing classes. After train/test splitting, all classes must be represented in both parts of the data. To achieve this, a while-condition is added: train/test split is performed until the number of unique classes in train data is equal to the number of unique classes in test data, and these numbers are equal to the number of unique classes in the initial dataset. As a result, all classes are included in train and test data. \n",
    "\n",
    "c) problem of unbalanced data -- different classes is represented by different numbers of the records. Thus, SMOTE oversampling technique is used with a fixed random state. As a result, all classes are resampled to the dominant class number of pixels (records/samples).\n",
    "\n",
    "d) problem of unsuitable hyperparameters. For this search, the random grid space is used. Grid spaces are built for each model with main hyperparameters that should be considered.\n",
    "\n",
    "As a result of the whole step, a model variation with the best hyperparameters is selected. The function returns the best model fit, test dataset, corresponding labels and predictions of labels as a list.\n",
    "\n",
    "* function with metric descriptions (``metrics_description``): average and class-specified F1 score, precision and recall, average total accuracy and Kappa scores. The function is supplied with a confusion matrix plot (shown when the option is chosen as ``True``). All metrics are obtained for test data. \n",
    "* function for getting metrics **by classes** (``get_classes_metrics``) for each launch of model fit (for test data). As a result, the function returns a dataset with the number of launches, name of the class, F1 score, precision, and recall as columns.\n",
    "* function for getting **average metrics** (``get_metrics_average``) for each launch of model fit (for test data).\n",
    "\n",
    "5) data filtration using clustering procedure\n",
    "6) models built using random search CV and SMOTE options on filtered and non-filtered data\n",
    "* getting models fits for 5 random iteration\n",
    "* getting metrics -- average and by classes\n",
    "* plotting by class metrics distributions as boxplots\n",
    "7) getting statistics for the performance evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697a1db1",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7c6801b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "#clustering\n",
    "from shapely import affinity\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "#for PCA option\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "#test/train split and hyperparameters optimisation\n",
    "import random\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GroupShuffleSplit\n",
    "\n",
    "\n",
    "#ML\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, IsolationForest\n",
    "from sklearn.tree import DecisionTreeClassifier\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\n",
    "\n",
    "#visuals\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\": \"serif\",   \n",
    "    \"font.serif\": [\"Times\"],  \n",
    "    \"font.size\":9,\n",
    "    \"axes.labelsize\":9,\n",
    "    \"xtick.labelsize\" : 9,\n",
    "    \"ytick.labelsize\" : 9})   \n",
    "colors =[\n",
    "    '#117733',\n",
    "    '#50CE57',\n",
    "    '#23A28F',\n",
    "    '#5BD0AE',\n",
    "    '#88CCEE', #water\n",
    "    '#92462D', #soil\n",
    "    '#DE7486',\n",
    "    '#DDCC77',\n",
    "    '#AA4499',\n",
    "    '#0f62fe',\n",
    "    '#be95ff'\n",
    "]\n",
    "\n",
    "\n",
    "#model saving\n",
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e678d1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ef49af",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "263f7544",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('scalled_data_full.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111ee9d1",
   "metadata": {},
   "source": [
    "# Helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e48b735",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for data filtering\n",
    "from utils import get_cluster_pixels, get_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e715eea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for getting predictions\n",
    "def get_predictions(data, \n",
    "                    model,\n",
    "                    param_grid, \n",
    "                    cv: int=5, \n",
    "                    n_iter_search: int=3):\n",
    "    #test/train spliting considering key overlap problems and missed classes\n",
    "    while True:\n",
    "        train_inds, test_inds = next(GroupShuffleSplit(test_size=.25, \n",
    "                                                   n_splits=2#,random_state = 40\n",
    "                                                      ).split(data, groups=data['key']))\n",
    "        #because we need pixels from same plots to be separated in train and test\n",
    "\n",
    "        train = data.iloc[train_inds]\n",
    "        test = data.iloc[test_inds]\n",
    "        if len(train['class'].unique(\n",
    "        )) == len(test['class'].unique(\n",
    "        )) == len(filtered_df_a['class'].unique()): #because we need classes to be represented in train and test\n",
    "            break\n",
    "            \n",
    "    #function to be sure that keys in train and data are not the same\n",
    "    #if bool(set(test['key']) & set(train['key'])):\n",
    "    #    print('Dubles in train and test')\n",
    "    #else: print('No dubles found')  \n",
    "\n",
    "    #class balansing with smote\n",
    "    smote = SMOTE(random_state = 42)\n",
    "    X, y = smote.fit_resample(train.iloc[:,:-2], train['class'])\n",
    "    df_smote = pd.DataFrame(X, columns = train.iloc[:,:-2].columns.tolist())\n",
    "\n",
    "\n",
    "    #we set train Xy from SMOTE results\n",
    "    X_train = df_smote\n",
    "    y_train = y\n",
    "    X_test = test.iloc[:,:-2]   \n",
    "    y_test = test['class']\n",
    "    \n",
    "    #parameters optimisation\n",
    "    \n",
    "    gs = RandomizedSearchCV(model, \n",
    "                            param_distributions = param_grid,\n",
    "                            n_iter = n_iter_search, \n",
    "                            cv = cv, \n",
    "                            scoring= 'accuracy')\n",
    "    gs.fit(X_train, y_train)  \n",
    "    y_pred = gs.best_estimator_.predict(X_test)\n",
    "    model_fit = gs.best_estimator_\n",
    "\n",
    "    return model_fit, X_test, y_test, y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f222b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics_description(y_true, y_pred, confusion_matrix_on: bool=True):\n",
    "    dict_normal_names={7: \"Pine\", \n",
    "        5:\"Aspen\",\n",
    "        1:\"Birch\",\n",
    "        6:\"Silver fir\",\n",
    "        15:\"Burnt forest\", \n",
    "        13:'Deforestation', \n",
    "        14:'Grass',\n",
    "        12:'Soil', \n",
    "        16:'Swamp', \n",
    "        11:'Water body',\n",
    "        17:'Settlements'}\n",
    "    \n",
    "    print('Accuracy score: %.2f%%' %(accuracy_score(y_true, y_pred)*100))  \n",
    "    print('Precision score: %.2f%%' % (precision_score(y_true, y_pred, average= 'weighted')*100))\n",
    "    print('Recall score: %.2f%%' % (recall_score(y_true, y_pred, average= 'weighted')*100))\n",
    "    print('F1-Score: %.2f%%'%(f1_score(y_true, y_pred, average = 'macro')*100))\n",
    "    print('Kappa score: %.2f%%'%(cohen_kappa_score(y_true, y_pred)*100))\n",
    "    \n",
    "    \n",
    "    #dataframe with metrics by class\n",
    "    metrics_by_class = pd.DataFrame(\n",
    "            {\n",
    "                'names': list(map(dict_normal_names.get, list(np.unique(y_true)))),\n",
    "                'f1_scores': f1_score(y_true, y_pred,\n",
    "                           average=None).round(2).tolist(),\n",
    "                'precision_list': precision_score(y_true, y_pred, \n",
    "                                   average=None).round(2).tolist(),\n",
    "                'recall':recall_score(y_true, y_pred,\n",
    "                                   average=None).round(2).tolist()\n",
    "            }\n",
    "        )\n",
    "    display(metrics_by_class)\n",
    "    \n",
    "    #confusion matrix\n",
    "    if confusion_matrix_on is True:\n",
    "        data = confusion_matrix(y_true, y_pred)\n",
    "        df_cm = pd.DataFrame(data, columns=list(map(dict_normal_names.get, list(np.unique(y_true)))), \n",
    "                             index = list(map(dict_normal_names.get, list(np.unique(y_true)))))\n",
    "        df_cm.index.name = 'Actual'\n",
    "        df_cm.columns.name = 'Predicted'\n",
    "\n",
    "        #confusion matrix plot\n",
    "        f, ax = plt.subplots(figsize=(6, 10))\n",
    "        cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n",
    "\n",
    "        sns.heatmap(df_cm, cbar=False, annot=True, cmap=cmap, square=True, fmt='.0f',\n",
    "                    annot_kws={'size': 10})\n",
    "        plt.title('Actuals vs Predicted')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "55b93d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting dataset with metrics by class for each random prediction\n",
    "def get_classes_metrics(models_vector): #vector with model variations, y predicted and y true from the dataset\n",
    "    class_metrics_dataframe = pd.DataFrame()\n",
    "    count = 0 #counter of iteration\n",
    "\n",
    "    for i in models_vector:\n",
    "\n",
    "        dict_normal_names={7: \"Pine\",  \n",
    "            5:\"Aspen\",\n",
    "            1:\"Birch\",\n",
    "            6:\"Silver fir\",\n",
    "            15:\"Burnt forest\", \n",
    "            13:'Deforestation', \n",
    "            14:'Grass',\n",
    "            12:'Soil', \n",
    "            16:'Swamp', \n",
    "            11:'Water body',\n",
    "            17:'Settlements'}\n",
    "\n",
    "        count += 1 #counting\n",
    "        pred = i[2] #predicted values from random train set\n",
    "        true = i[3] #corresponding values from random test set\n",
    "        names_list = list(np.unique(true))\n",
    "\n",
    "        temp = pd.DataFrame(\n",
    "            {\n",
    "                'iteration':[count]*len(names_list), \n",
    "                'names': list(map(dict_normal_names.get, names_list)),\n",
    "                'f1_scores': f1_score(true, pred,\n",
    "                           average=None).round(2).tolist(),\n",
    "                'precision_list': precision_score(true, \n",
    "                                   pred, \n",
    "                                   average=None).round(2).tolist(),\n",
    "                'recall':recall_score(true, \n",
    "                                   pred, \n",
    "                                   average=None).round(2).tolist()\n",
    "            }\n",
    "        ) #dataset for each model \n",
    "\n",
    "        class_metrics_dataframe = pd.concat([class_metrics_dataframe, temp], ignore_index=True)\n",
    "    return class_metrics_dataframe    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1c52816c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting dataset with average metrics for each random prediction\n",
    "def get_metrics_average(models_vector): #vector with model variations, y predicted and y true from the dataset\n",
    "    average_metrics_dataframe = pd.DataFrame()\n",
    "    count = 0 #counter of iteration\n",
    "\n",
    "    for i in models_vector:\n",
    "\n",
    "\n",
    "        count += 1 #counting\n",
    "        pred = i[2] #predicted values from random train set\n",
    "        true = i[3] #corresponding values from random test set\n",
    "        #names_list = list(np.unique(true))\n",
    "\n",
    "        temp = pd.DataFrame(\n",
    "            {\n",
    "                'iteration':[count],#*len(names_list), \n",
    "                #'names': list(map(dict_normal_names.get, names_list)),\n",
    "                'f1_scores': f1_score(true, pred,\n",
    "                           average='macro').round(2).tolist(),\n",
    "                'precision_list': precision_score(true, \n",
    "                                   pred, \n",
    "                                   average='weighted').round(2).tolist(),\n",
    "                'recall':recall_score(true, \n",
    "                                   pred, \n",
    "                                   average='weighted').round(2).tolist()\n",
    "            }\n",
    "        ) #dataset for each model \n",
    "\n",
    "        average_metrics_dataframe = pd.concat([average_metrics_dataframe, temp], \n",
    "                                              ignore_index=True)\n",
    "    return average_metrics_dataframe  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e520fe",
   "metadata": {},
   "source": [
    "# Data filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "859b4767",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...........................................................................................................equal cluster\n",
      ".........................................equal cluster\n",
      ".............................equal cluster\n",
      ".....................equal cluster\n",
      "...............equal cluster\n",
      ".................................."
     ]
    }
   ],
   "source": [
    "clustered_df_a = pd.DataFrame() #initial dataset to filter\n",
    "\n",
    "for item in df.loc[df['class']<8].key.unique():\n",
    "    attmpt = get_cluster_pixels(df.loc[df['class']<8],\n",
    "                                key=item,\n",
    "                                correlation_threshold=0.7, \n",
    "                                max_number_of_clusters=4)\n",
    "    attmpt = get_selection(attmpt)\n",
    "    clustered_df_a = pd.concat([clustered_df_a, attmpt])\n",
    "    print('.', end='')\n",
    "#merge filtered forest data with non-forest pixels\n",
    "filtered_df_a = pd.concat([clustered_df_a, df.loc[df['class']>7]]).iloc[:,:-1]    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5542508c",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "13ca2753",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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 5 model iterations to evaluate average performance\n",
    "\n",
    "datavector_rf = []\n",
    "for i in range(5):\n",
    "    rf_model = get_predictions(data = filtered_df_a,\n",
    "                model = RandomForestClassifier(),\n",
    "                param_grid = random_grid)\n",
    "    datavector_rf.append(rf_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2580153e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 81.64%\n",
      "Precision score: 83.41%\n",
      "Recall score: 81.64%\n",
      "F1-Score: 80.76%\n",
      "Kappa score: 78.46%\n"
     ]
    },
    {
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       "  <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>Birch</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pine</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soil</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grass</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            names  f1_scores  precision_list  recall\n",
       "0           Birch       0.49            0.51    0.47\n",
       "1           Aspen       0.62            0.73    0.54\n",
       "2      Silver fir       0.84            0.95    0.76\n",
       "3            Pine       0.76            0.69    0.84\n",
       "4      Water body       1.00            1.00    1.00\n",
       "5            Soil       0.97            0.95    1.00\n",
       "6   Deforestation       0.89            0.99    0.82\n",
       "7           Grass       0.74            1.00    0.59\n",
       "8    Burnt forest       0.85            0.75    1.00\n",
       "9           Swamp       0.97            0.93    1.00\n",
       "10    Settlements       0.74            0.61    0.95"
      ]
     },
     "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 example\n",
    "metrics_description(datavector_rf[0][2], datavector_rf[0][3])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "dba74f02",
   "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>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>1</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>5</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration          names  f1_scores  precision_list  recall\n",
       "0           1          Birch       0.49            0.47    0.51\n",
       "1           1          Aspen       0.62            0.54    0.73\n",
       "2           1     Silver fir       0.84            0.76    0.95\n",
       "3           1           Pine       0.76            0.84    0.69\n",
       "4           1     Water body       1.00            1.00    1.00\n",
       "5           1           Soil       0.97            1.00    0.95\n",
       "6           1  Deforestation       0.89            0.82    0.99\n",
       "7           1          Grass       0.74            0.59    1.00\n",
       "8           1   Burnt forest       0.85            1.00    0.75\n",
       "9           1          Swamp       0.97            1.00    0.93\n",
       "10          1    Settlements       0.74            0.95    0.61\n",
       "11          2          Birch       0.26            0.27    0.25\n",
       "12          2          Aspen       0.63            0.56    0.73\n",
       "13          2     Silver fir       0.61            0.59    0.63\n",
       "14          2           Pine       0.78            0.87    0.70\n",
       "15          2     Water body       1.00            1.00    1.00\n",
       "16          2           Soil       0.91            0.92    0.90\n",
       "17          2  Deforestation       0.91            0.87    0.94\n",
       "18          2          Grass       0.94            1.00    0.89\n",
       "19          2   Burnt forest       0.97            0.94    1.00\n",
       "20          2          Swamp       0.97            0.95    0.99\n",
       "21          2    Settlements       0.66            0.79    0.57\n",
       "22          3          Birch       0.47            0.56    0.41\n",
       "23          3          Aspen       0.54            0.56    0.51\n",
       "24          3     Silver fir       0.60            0.43    1.00\n",
       "25          3           Pine       0.77            0.75    0.78\n",
       "26          3     Water body       1.00            1.00    1.00\n",
       "27          3           Soil       0.00            0.00    0.00\n",
       "28          3  Deforestation       0.91            0.98    0.85\n",
       "29          3          Grass       0.77            0.66    0.92\n",
       "30          3   Burnt forest       0.90            0.82    1.00\n",
       "31          3          Swamp       0.92            0.85    1.00\n",
       "32          3    Settlements       0.89            0.86    0.91\n",
       "33          4          Birch       0.54            0.41    0.81\n",
       "34          4          Aspen       0.60            0.81    0.48\n",
       "35          4     Silver fir       0.07            0.04    0.22\n",
       "36          4           Pine       0.77            0.77    0.76\n",
       "37          4     Water body       1.00            1.00    1.00\n",
       "38          4           Soil       0.46            1.00    0.30\n",
       "39          4  Deforestation       0.93            0.99    0.87\n",
       "40          4          Grass       0.63            0.61    0.66\n",
       "41          4   Burnt forest       0.92            0.86    1.00\n",
       "42          4          Swamp       0.65            0.49    0.96\n",
       "43          4    Settlements       0.82            0.71    0.97\n",
       "44          5          Birch       0.48            0.52    0.44\n",
       "45          5          Aspen       0.58            0.54    0.64\n",
       "46          5     Silver fir       0.60            0.58    0.63\n",
       "47          5           Pine       0.72            0.75    0.69\n",
       "48          5     Water body       1.00            1.00    1.00\n",
       "49          5           Soil       0.94            0.94    0.94\n",
       "50          5  Deforestation       0.87            0.82    0.92\n",
       "51          5          Grass       0.99            1.00    0.99\n",
       "52          5   Burnt forest       1.00            1.00    1.00\n",
       "53          5          Swamp       0.98            1.00    0.95\n",
       "54          5    Settlements       0.76            0.85    0.69"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#metrics by prediction on test data by class\n",
    "rf_class_metris = get_classes_metrics(datavector_rf)\n",
    "rf_class_metris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5c1c34b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f1 score plot\n",
    "plt.figure(figsize=(5,4))\n",
    "sns.boxplot(x = rf_class_metris['names'],\n",
    "            y = rf_class_metris['f1_scores'], palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('RF multiclass F1 score (filtered data)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.savefig('RF_multiclass_F1_score_filtered_data.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "069897a3",
   "metadata": {},
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "054a0cb1",
   "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 5 model iterations to evaluate average performance\n",
    "\n",
    "datavector_svc = []\n",
    "for i in range(5):\n",
    "    svc_model = get_predictions(data = filtered_df_a,\n",
    "                model = SVC(),\n",
    "                param_grid = svc_grid)\n",
    "    datavector_svc.append(svc_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "98d0dfc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 77.85%\n",
      "Precision score: 80.62%\n",
      "Recall score: 77.85%\n",
      "F1-Score: 72.24%\n",
      "Kappa score: 74.50%\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>Birch</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pine</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soil</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grass</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            names  f1_scores  precision_list  recall\n",
       "0           Birch       0.62            0.58    0.66\n",
       "1           Aspen       0.55            0.59    0.52\n",
       "2      Silver fir       0.36            0.30    0.44\n",
       "3            Pine       0.76            0.75    0.76\n",
       "4      Water body       1.00            1.00    1.00\n",
       "5            Soil       0.56            0.39    1.00\n",
       "6   Deforestation       0.72            0.93    0.59\n",
       "7           Grass       0.94            0.95    0.93\n",
       "8    Burnt forest       0.99            0.99    1.00\n",
       "9           Swamp       0.61            0.55    0.69\n",
       "10    Settlements       0.83            0.91    0.77"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#metrics example for one iteration\n",
    "metrics_description(datavector_svc[1][2], datavector_svc[1][3], confusion_matrix_on=True)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0337f1c4",
   "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>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>1</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>5</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration          names  f1_scores  precision_list  recall\n",
       "0           1          Birch       0.57            0.46    0.74\n",
       "1           1          Aspen       0.56            0.69    0.47\n",
       "2           1     Silver fir       0.41            0.37    0.46\n",
       "3           1           Pine       0.73            0.72    0.73\n",
       "4           1     Water body       1.00            1.00    1.00\n",
       "5           1           Soil       0.24            0.38    0.17\n",
       "6           1  Deforestation       0.97            1.00    0.93\n",
       "7           1          Grass       0.83            0.75    0.93\n",
       "8           1   Burnt forest       0.96            1.00    0.93\n",
       "9           1          Swamp       0.77            0.78    0.75\n",
       "10          1    Settlements       0.88            0.79    0.98\n",
       "11          2          Birch       0.62            0.66    0.58\n",
       "12          2          Aspen       0.55            0.52    0.59\n",
       "13          2     Silver fir       0.36            0.44    0.30\n",
       "14          2           Pine       0.76            0.76    0.75\n",
       "15          2     Water body       1.00            1.00    1.00\n",
       "16          2           Soil       0.56            1.00    0.39\n",
       "17          2  Deforestation       0.72            0.59    0.93\n",
       "18          2          Grass       0.94            0.93    0.95\n",
       "19          2   Burnt forest       0.99            1.00    0.99\n",
       "20          2          Swamp       0.61            0.69    0.55\n",
       "21          2    Settlements       0.83            0.77    0.91\n",
       "22          3          Birch       0.48            0.55    0.43\n",
       "23          3          Aspen       0.65            0.66    0.64\n",
       "24          3     Silver fir       0.07            0.05    0.09\n",
       "25          3           Pine       0.75            0.74    0.75\n",
       "26          3     Water body       1.00            1.00    1.00\n",
       "27          3           Soil       0.01            0.01    1.00\n",
       "28          3  Deforestation       0.90            0.84    0.98\n",
       "29          3          Grass       0.67            1.00    0.51\n",
       "30          3   Burnt forest       0.99            1.00    0.99\n",
       "31          3          Swamp       0.85            0.88    0.82\n",
       "32          3    Settlements       0.79            0.91    0.70\n",
       "33          4          Birch       0.47            0.44    0.50\n",
       "34          4          Aspen       0.53            0.57    0.50\n",
       "35          4     Silver fir       0.67            0.72    0.64\n",
       "36          4           Pine       0.72            0.70    0.74\n",
       "37          4     Water body       1.00            1.00    1.00\n",
       "38          4           Soil       0.24            0.58    0.15\n",
       "39          4  Deforestation       0.69            0.56    0.90\n",
       "40          4          Grass       0.45            0.29    1.00\n",
       "41          4   Burnt forest       0.99            0.98    1.00\n",
       "42          4          Swamp       0.99            1.00    0.99\n",
       "43          4    Settlements       0.49            0.85    0.34\n",
       "44          5          Birch       0.67            0.73    0.61\n",
       "45          5          Aspen       0.71            0.68    0.75\n",
       "46          5     Silver fir       0.63            0.76    0.54\n",
       "47          5           Pine       0.73            0.72    0.75\n",
       "48          5     Water body       1.00            1.00    1.00\n",
       "49          5           Soil       0.99            1.00    0.97\n",
       "50          5  Deforestation       0.96            0.97    0.96\n",
       "51          5          Grass       0.99            0.99    1.00\n",
       "52          5   Burnt forest       1.00            1.00    1.00\n",
       "53          5          Swamp       0.57            0.50    0.67\n",
       "54          5    Settlements       0.97            0.94    1.00"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#metrics by prediction on test data by class for each iteration\n",
    "svc_class_metris = get_classes_metrics(datavector_svc)\n",
    "svc_class_metris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0e9ebd38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f1 score plot\n",
    "plt.figure(figsize=(5,4))\n",
    "sns.boxplot(x = svc_class_metris['names'],\n",
    "            y = svc_class_metris['f1_scores'], palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('SVM multiclass F1 score (filtered data)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.savefig('SVC_multiclass_F1_score_filtered_data.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d5beae",
   "metadata": {},
   "source": [
    "## kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a037113c",
   "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 5 model iterations to evaluate average performance\n",
    "\n",
    "datavector_knn = []\n",
    "\n",
    "for i in range(5):\n",
    "    knn_model = get_predictions(data = filtered_df_a,\n",
    "                model = KNeighborsClassifier(),\n",
    "                param_grid = random_grid_knn)\n",
    "    datavector_knn.append(knn_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f4df3596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 79.55%\n",
      "Precision score: 81.57%\n",
      "Recall score: 79.55%\n",
      "F1-Score: 73.85%\n",
      "Kappa score: 76.29%\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>Birch</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pine</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soil</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grass</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            names  f1_scores  precision_list  recall\n",
       "0           Birch       0.55            0.46    0.67\n",
       "1           Aspen       0.56            0.63    0.51\n",
       "2      Silver fir       0.35            0.28    0.45\n",
       "3            Pine       0.66            0.72    0.61\n",
       "4      Water body       1.00            1.00    1.00\n",
       "5            Soil       0.93            0.95    0.90\n",
       "6   Deforestation       0.90            0.98    0.84\n",
       "7           Grass       0.85            0.76    0.98\n",
       "8    Burnt forest       0.95            0.91    1.00\n",
       "9           Swamp       0.59            0.42    1.00\n",
       "10    Settlements       0.78            0.72    0.85"
      ]
     },
     "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 example\n",
    "metrics_description(datavector_knn[0][2], datavector_knn[0][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f696ad3f",
   "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>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>1</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>5</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration          names  f1_scores  precision_list  recall\n",
       "0           1          Birch       0.55            0.67    0.46\n",
       "1           1          Aspen       0.56            0.51    0.63\n",
       "2           1     Silver fir       0.35            0.45    0.28\n",
       "3           1           Pine       0.66            0.61    0.72\n",
       "4           1     Water body       1.00            1.00    1.00\n",
       "5           1           Soil       0.93            0.90    0.95\n",
       "6           1  Deforestation       0.90            0.84    0.98\n",
       "7           1          Grass       0.85            0.98    0.76\n",
       "8           1   Burnt forest       0.95            1.00    0.91\n",
       "9           1          Swamp       0.59            1.00    0.42\n",
       "10          1    Settlements       0.78            0.85    0.72\n",
       "11          2          Birch       0.43            0.48    0.38\n",
       "12          2          Aspen       0.60            0.62    0.59\n",
       "13          2     Silver fir       0.37            0.76    0.24\n",
       "14          2           Pine       0.69            0.62    0.79\n",
       "15          2     Water body       1.00            1.00    1.00\n",
       "16          2           Soil       0.66            0.63    0.69\n",
       "17          2  Deforestation       0.83            0.91    0.76\n",
       "18          2          Grass       0.71            0.59    0.88\n",
       "19          2   Burnt forest       0.97            1.00    0.94\n",
       "20          2          Swamp       0.33            0.21    0.78\n",
       "21          2    Settlements       0.76            0.77    0.75\n",
       "22          3          Birch       0.43            0.60    0.33\n",
       "23          3          Aspen       0.63            0.63    0.62\n",
       "24          3     Silver fir       0.15            0.13    0.19\n",
       "25          3           Pine       0.67            0.58    0.79\n",
       "26          3     Water body       1.00            1.00    1.00\n",
       "27          3           Soil       0.77            0.76    0.79\n",
       "28          3  Deforestation       0.79            0.73    0.87\n",
       "29          3          Grass       0.95            0.96    0.94\n",
       "30          3   Burnt forest       0.59            1.00    0.42\n",
       "31          3          Swamp       0.74            0.81    0.68\n",
       "32          3    Settlements       0.81            0.83    0.78\n",
       "33          4          Birch       0.51            0.57    0.47\n",
       "34          4          Aspen       0.49            0.45    0.53\n",
       "35          4     Silver fir       0.34            0.92    0.21\n",
       "36          4           Pine       0.69            0.58    0.84\n",
       "37          4     Water body       1.00            1.00    1.00\n",
       "38          4           Soil       0.68            0.57    0.87\n",
       "39          4  Deforestation       0.89            0.89    0.88\n",
       "40          4          Grass       0.88            1.00    0.79\n",
       "41          4   Burnt forest       0.98            1.00    0.96\n",
       "42          4          Swamp       0.80            0.88    0.73\n",
       "43          4    Settlements       0.78            0.76    0.81\n",
       "44          5          Birch       0.57            0.62    0.53\n",
       "45          5          Aspen       0.56            0.51    0.61\n",
       "46          5     Silver fir       0.54            0.74    0.43\n",
       "47          5           Pine       0.61            0.50    0.79\n",
       "48          5     Water body       1.00            1.00    1.00\n",
       "49          5           Soil       0.17            0.12    0.28\n",
       "50          5  Deforestation       0.82            0.73    0.94\n",
       "51          5          Grass       0.52            0.99    0.35\n",
       "52          5   Burnt forest       0.96            1.00    0.93\n",
       "53          5          Swamp       0.43            0.92    0.28\n",
       "54          5    Settlements       0.85            0.82    0.89"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#metrics by prediction on test data by class for each iteration\n",
    "knn_class_metris = get_classes_metrics(datavector_knn)\n",
    "knn_class_metris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "cfab069d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f1 score plot\n",
    "plt.figure(figsize=(5,4))\n",
    "sns.boxplot(x = knn_class_metris['names'],\n",
    "            y = knn_class_metris['f1_scores'], palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('kNN multiclass F1 score (filtered data)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.savefig('kNN_multiclass_F1_score_filtered_data.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f359ee92",
   "metadata": {},
   "source": [
    "# Wihtout filtering "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0061b19b",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3b0251c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 76.41%\n",
      "Precision score: 77.31%\n",
      "Recall score: 76.41%\n",
      "F1-Score: 80.01%\n",
      "Kappa score: 71.92%\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>Birch</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pine</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soil</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grass</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            names  f1_scores  precision_list  recall\n",
       "0           Birch       0.44            0.65    0.33\n",
       "1           Aspen       0.62            0.54    0.73\n",
       "2      Silver fir       0.80            0.79    0.81\n",
       "3            Pine       0.80            0.78    0.83\n",
       "4      Water body       1.00            1.00    1.00\n",
       "5            Soil       0.72            0.56    1.00\n",
       "6   Deforestation       0.92            0.85    1.00\n",
       "7           Grass       0.80            1.00    0.67\n",
       "8    Burnt forest       1.00            1.00    1.00\n",
       "9           Swamp       0.78            1.00    0.64\n",
       "10    Settlements       0.92            0.94    0.90"
      ]
     },
     "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": [
    "#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",
    "\n",
    "#get 5 model results on random train/test pairs\n",
    "\n",
    "datavector_rf_wf = []\n",
    "for i in range(5):\n",
    "    rf_model_wf = get_predictions(data = df,\n",
    "                model = RandomForestClassifier(),\n",
    "                param_grid = random_grid)\n",
    "    datavector_rf_wf.append(rf_model_wf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f4a8c9ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 76.41%\n",
      "Precision score: 77.31%\n",
      "Recall score: 76.41%\n",
      "F1-Score: 80.01%\n",
      "Kappa score: 71.92%\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>Birch</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pine</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soil</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grass</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            names  f1_scores  precision_list  recall\n",
       "0           Birch       0.44            0.65    0.33\n",
       "1           Aspen       0.62            0.54    0.73\n",
       "2      Silver fir       0.80            0.79    0.81\n",
       "3            Pine       0.80            0.78    0.83\n",
       "4      Water body       1.00            1.00    1.00\n",
       "5            Soil       0.72            0.56    1.00\n",
       "6   Deforestation       0.92            0.85    1.00\n",
       "7           Grass       0.80            1.00    0.67\n",
       "8    Burnt forest       1.00            1.00    1.00\n",
       "9           Swamp       0.78            1.00    0.64\n",
       "10    Settlements       0.92            0.94    0.90"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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duzd+fn6MHTvWsDwA8ZcTaVr/YeISLt/W/Isxl/jomwm83rsnzz79BL5Fi/LtrDkFGdmCLY5pXuwl5zVxcXEEBwcTGxtrdJSbspcxVU7rMiKnQ5ceAGdnyyF46KGHOH36NHC1xBQtWvSOnsvb29uq+e6UX5EilPL1tZiXlJrKxdg4UtLSAPD0cCftiuVu7s37f6dejRoFkvHQn3/w1hPPUNm/NE1q1OaJ+o04fPZPAH7asYXYxMt0aRp82+sYYenSpdSuXRsfHx8AgoODWbx4MRkGHj4q7lsM/xIlbnv+qvW/UaNqFby9vABoWv9hVvy6nkyDDova4pjmxV5yXlOiRAkCbtira2vsZUyV07qMyOnwped6OTk5bNu2jUaNGnHp0iXee+89Jk+eTHp6OlOnTmXQoEGMHDmSrl27kpOTw+zZs1m5ciX9+/fnl19+MT/PgQMHGDJkCO3bt+f48eMGfkZXFfX2pkbFinw8bTqXk5NZuG49/Z7paH78YmwssQkJ1KxcqUDytKxTFzfXv4+sFvP2xs/bh+QraczduB5Pd3e+XraI/0z8io2Hfr/lOkYJDQ2lQoUK5uny5cuTlJREWFiYYZnu1N6DhykX4G+eLhvgT3JKKkdOnjYkj72Mqb3ktCf2MqbKaV1G5FTpAY4ePcpXX31Fx44diYuL4/XXX6dEiRKUKlWK7OxsPDw8uO+++9i/fz8DBgzg008/Zf78+RQpUoQnnniCl156iX379pmfr3Dhwnz55Zd06NCBRYsWGfiZ/e2jfi8TExdPl6HDCX74YepWDwQgOyeHFZu38HRwswLL4uTkZDF98kIEHYIasvfUCVxcnHmpdQcGdexKn1btGLd8CZFxsTddxygxMTEWe/W8/tpbEh2d/4cHreVSfDxehQubp73+Oj/qUlycIXnsZUztJac9sZcxVU7rMiKnSg9Qs2ZNBg8ezLJly2jVqhWdO3cmPj7e4tCXt7c3ZcuWxcfHh2rVqrFixQpq164NQJ06dXjvvffMy5YtW9b8f0pKSsF+MjcRl5hE4zoP8FD1QMbMmMmRM2eAq+fxPB3cLNdhvoJyMT6O7JxsHqn1ANEJCZT1K2E+OblJjdp4eXqy/4+TN13HKM7OzhZj5uLiYliWu+Vyw+dg1NfA9du3hzG1l5z2xF7GVDmty4icKj3XcXZ25rnnniMiIoJdu3bdctm0tDTOnz9vnr7ZMUiTyWTVjHcjPSODT3+YwUtPPckXb75B20aN+GT6D5hMJpZt2sTz739I69ffpPXrbwLwwocfMWXpsnzPlZWdzfJd23jziU4A+BQqRHrW3+eTODk5EeDrR/KVKzddxygBAQGkpqaap699XLp0aaMi3bFSJYqTdt3YXjvPK6/zfwqCvYypveS0J/YypsppXUbkdPjSk5OTYzF96tQpACpWrIjJZLppaWnYsCHjxo0jKiqKy5cvs3z58nzPerfOXIikkIc7Hu7uODk58Ua3LsQnJXE5OZm5o0ay9r8Tzf8Afvz4I4tzfvLLsp1b6fZIczz/ukS6SukyXIyLI+O64pOZnUW5EiVvuk52Tna+58xLUFAQERER5umoqCi8vLyoVauWIXnuxkO1a3Lhuqv1oi/F4lWoEDWq3mdIHnsZU3vJaU/sZUyV07qMyOnQpWfPnj0cPHiQ0NBQvv32Wz799FM+//xzRo8eTbFixfj99985cOAA0dHRbNq0idOnT/P7778D8NZbb1G+fHk6dOjAe++9R5s2bTh79izh4eHs3LmTy5cvExoayokTJwr8UtEbi1qZkiWIiU8g7a8bk2VnZ1OiWDGKGnil2ardOylfohTJV9I4dymaQ3/+QXxyMvdXqkzoiWMAJKamciUjgwbVatx0nb2nTt5qM/mma9euhIWFmQ9fhoaG0qNHD9zc3AzJc83NSnpe859p25qjp06T+tdVffvCDtOpQ1tcXY25fZetjumN7CXn9Wxhj/Ot2MuYKqd1GZHTyWTr3w0O7uLGDXe0/OXkZDbv/52vZs/h/57pSJtGDSlRrBihh4+wef9+qpYvR3RcPMEP16V6xYq51g/+v1eZP/qTO745YVLE7d+baNvRMD5bNA8Tf3/pubu68uPAd8kx5TBz/RpKFi1GVEI8HYIaUrV02Vuu4+Xpedvbrtaz820v+0+2b99OSEgIgYGBREZGMmDAANyteGO/xFOH72j5hMQkftu+k08nfscbfZ6nQ8vmlCzud9P5ALv2H2Dtlm1UrVSRi9ExvNa7J+53+AOnSNXad7T8reT3mFqLveQEiI+PZ+3atbz//vsMGjSIp59+Gn9//39esYDZy5gqp3UVdE6VHht3p6XHKHdSeoxkzdKT3+609BjFmqVHRCQ/OfThLREREXEcKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOARXowPIrflUq2R0hNuyad4BoyPclmo9jU5w+1wKFTI6gojIv4r29IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHrFpHt6FcHLWl2l+SE5JIT0jw+gYIiIFxtXoAJK/wo4eZ/OOXfgWK0rYseP85/nnqFShnPnxXXv3c/zUacqWDqBx/XoULlSoQPO5uLvS5r2ebJqwlNTYRABaDO5KqWpXM8afjeLX0XP/cR0jbNmyhQ0bNlCpUiXCw8MZOnQoHh4ehuW50c1e+5ycHJaGrOa7H35k1n/HUSbA3+ioZrY+ptcop/XZS1bltK6CzumQf0KbTCbeeecdo2Pku5ycHN4f8yX/6fUcz3Z6mk6Pt+fzid+aH//mu6kcPXGSXt278FizRwq88AAEtqxLkQA/83TRsiW4eDicNaNms2bUbDZPXP6P6xghMjKSoUOHMnDgQHr37o2fnx9jx441NNP1bvXaxydc5pGG9YmLTzA25A1sfUyvUU7rs5esymldRuR0yNKzc+dOQkJCOHz4sNFR8lViUjIXoqJISUkFoFAhT1LT0gD4ceESomIu0efZboblK/tQFaKOnbWYV635g2AykZOdTcL5GK4kpvzjOkZYunQptWvXxsfHB4Dg4GAWL15Mho0cLrrVa1/czxf/kiWNjJcnWx/Ta5TT+uwlq3JalxE5HbL07N27l2effZY5c+YYHSVfFStahNrVAxn+yWckXE5kzqKfePPlviQlJ/O/GbMpXMiT98d8xVPPv8gv638r0GyF/XwoVMybuD+j/p7pBN7+vtRs34D2H/Wm0YvtcHZxvvU6BgkNDaVChQrm6fLly5OUlERYWJiBqf52s9feltn6mF6jnNZnL1mV07qMyOlwpScuLo6SJUvy3HPP8csvv5CYePWckOTkZCZNmsTKlSt55plnAJg/fz59+vTh+++/54knnqBHjx5cvHgRgI0bNzJnzhxefvllVq1aRXZ2NhMnTuSll15i2rRpdOrUif/+97+GfZ7XfP7Bu0RFx9Cu2/M81uwRgh6qw/bQPbi6ujLglZcZNXww/fu9yIeff8O5iAsFksnJyYkqzepwatMBywdMsPGbxSwf/D/2zttA2brVqNG2/q3XMUhMTAze3t7maS8vLwCio6ONipRLXq+9LbOHMQXlzA/2klU5rcuInA5XetasWcPjjz9O5cqVefDBB/npp58A2Lp1K6mpqTzxxBMMGzYMgMaNG3PkyBG6devG8uXLKVasGF9//TURERFs3bqVnj17MmTIEEaMGEFqaip169bl4MGDdOnShe+++45Zs2YZ+akCEBsfz6ONGhBU90E+/PxrDh09xoWL0VQsV5Yif+1SbPloU3y8vdi5Z1+BZKrW8iFObzoIprwfz87M5tTGA+ybv4FKjWre1joFzdnZGefrripzcXExME3e8nrtbZk9jCkoZ36wl6zKaV1G5HS4q7f27NnDpUuXAChSpAjz5s2jd+/e1K1bl08++YSoqCgGDx4MXH0BvL29zU20c+fOfP3112zatIn4+HjmzZtHdnY2DRo0ID4+Hjc3NwoVKkSxYsUASEhIMOJTNLuSns4Hn33NrG/H4eHuzhcTv+O90V/Qq3sXrqSnm5dzcnKiXJnSJKWk3OLZrKdq8IPUeeYRi3kdPu7N8XX7OLRsm3neub0nqfdsyztap6AEBASQmppqnr72cenSpQs8S15u9tov/3E6Tk5ORsfLk62P6TXKaX32klU5rcuInA5VekJDQ3n22WcJCgoCID09nSZNmrB9+3YaNmzIsmXL+OKLL3jmmWdYtmxZrvVdXFzw8/MjIyODypUr8+yzzwLw/PPPYzKZiIyMLMhP5x/98Wc4np4eeP51+d/A1/qx8td11KxWhfMXIknPyMDD3R2AjIwMKpUvd6uns5qQD2ZYTHefPICQD2fmuvzcxc2VhPOX7midghIUFMShQ4fM01FRUXh5eVGrVi1D8tzoZq99wuXL+P5Vym2NrY/pNcppffaSVTmty4icDnV4a9WqVTz88MPmaQ8PD5o3b87cuXNZu3Ytnp6efPHFFwQHB5tfiMzMTEymq8dUdu3aRadOnWjSpAkLFy7k1KlTAKxdu5YDB2zjXJPrlS1dmuiYS6SlXQEgOzubkiVKUKt6IPUefIDN23cBkHA5kbQr6TzauKGRcfHx96V8vUDz9H2P3E/Yz9sNTHRzXbt2JSwsjJS/9o6FhobSo0cP3NzcDE521c1e+2JFiwKYv6Ztia2P6TXKaX32klU5rcuInA6zp2fZsmWsWrWKTp068eCDDwJXW2VCQgLbt2/n/vvvZ/HixXTu3JmAgAAeffRRLl26RHp6Ot9//z2JiYk4OzvTuXNnnJycePnll+nVqxdFihThlVdeoVWrVowfP57ExESOHz9uPuF58+bNNGvWzJDPuWgRHz4YMoCvv5tC9Sr3cTHmEp+8OwQnJyc+efcdJk77gfDz54m8GM2YEcNwczX2y6Gwrw8P92hBtRYPERl2hvjwKC4eCTc00834+fnx6aefMmbMGAIDA4mMjGTAgAFGxzK71WufcDmRDVuuHhL89bdNPN76MUqWKG5wYtsf02uU0/rsJatyWpcROZ1Mtvgnn404f/48vXr1YsOGDYZlSIn4w7Bt34mVI3PfRNAWdZ9se9/4N2Mvr71X2fuMjiAiclsc6vDW3VAnFBER+XdQ6bmFkJAQYmJiWL16tdFRRERE5B45zDk9d6Nfv37069fP6BgiIiJiBdrTIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCE4mUwmk9EhRApK4qnDRke4bV7lKxsd4ba4eBQ2OoKIyG3Rnh4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEblNyamppGdkGB1DRETukqvRAURsXU5ODsvWrGPy7Hn88M3nlPEvBcCOvfvZvGs3FcqW4dyFSPq/1BsPd3cAtu3Zx659v+NVuDDnIy8y9PV+eBfWu5GLiBjJyWQymYwOcTc2btzI0qVL8fX15ZdffqFly5aMGTMGgE6dOvHZZ59Rvnx51q1bR2RkJP369bN6hkuXLvHTTz9x7NgxateuzZ49e3j22Wdp1qyZ1bdlDVu2bGHDhg1UqlSJ8PBwhg4dioeHh9GxcsnPnImnDt/xOrHxCWRlZ/FE734s//5/lPEvxcWYS/R+ewhLpkzC28uLqXMXkpyawoCX+xKXkMB/hrzHwv9NwMXFhSlz5nMx5hIfvP3GHW3Xq3zlO856zfYdO/n8y6+5EBlJ0yaN+fjD9ylapMjfj+/cybFjJyhXtgxNmzbB6x4KmYuH9cqcvkaty15ygv1kVU7rKuicdnl4KysriyFDhvDhhx/y0UcfsXTpUjIzM82Pjxo1isqVK+Pu7o67uztbtmzJlxwzZszg/vvvZ9SoUTRv3pwBAwZQr169fNnWvYqMjGTo0KEMHDiQ3r174+fnx9ixY42OlYst5izuWwz/EiUs5q1a/xs1qlbB28sLgKb1H2bFr+vJzMwkZMMmKpYri4uLCwCtHm3K6t82E3/5coHkvRAZyYqfV/LFZ6MZ88lIduzcyTdjx5sf//yrrzly5Cgv9ulFm9at7qnwWJMtvvZ5UU7rs5esymldRuS0y9KTnJxMYmIi+/fvB6BMmTK0a9fO/Hjt2rVxdnbGxcWFYsWK5VuOY8eO4eLigpeXF1WqVCEwMBBPT8982969WLp0KbVr18bHxweA4OBgFi9eTIaNnaNiLzn3HjxMuQB/83TZAH+SU1I5cvI0ERejcHP7+8hx6VIlyczK4sQfZwokW2jobkZ+9AHVAwNp9VhLnuvRg737rn6v/DBzFlFR0bz8Yt8CyXIn7OW1V07rs5esymldRuS0y9JTrFgxOnToQP/+/Zk8eTLZ2dm0atUKgJMnT9KjRw/27t2ba73169dTs2ZNNm7cCMCiRYv45JNPgKuHy+bMmcPLL7/MqlWrSE9PZ+rUqQwaNIiRI0fStWtXi+cKCQnh7NmzrF27ltWrV3P+/HleffVVVqxYQWJiIt988w0jR47kjTfeoH///vk7ILchNDSUChUqmKfLly9PUlISYWFhBqbKzV5yXoqPt9hD4lWo0NX5cXEU8fHm3IVI82Nubm4AXE5MLpBsTz/1JO5/nVsEULy4HyVLliQxMYlJ3/6PwoULMezdEbR9/ClWrvqlQDLdDnt57ZXT+uwlq3JalxE57bL0AHz55Zf07duX8ePH07NnT6KiogCoVq0aLi4u5HWq0mOPPUZwcDCRkVd/IWVmZvLGG28QERHB1q1b6dmzJ0OGDGHEiBFkZGRw3333sX//fgYMGMCnn35q8VwdOnTA39+f1q1b065dO8qVK4ePjw8mk4kiRYpQokQJDh8+zOeff87bb7+d7+PxT2JiYvD29jZPe/11WCY6OtqoSHmyl5wuzs44O//97XP9x22DH+WP8HPsPXj1G3fvwUMAlCjuWyDZnJycLKYPHz5C925d2LptG26urgwZNJDPPv2EQW+/xbvvf8DZc+cKJNc/sZfXXjmtz16yKqd1GZHTbkuPq6srgwYNYv78+URHR/Pqq6+ai871v4Bu1L17d5YsWQJcPUxWrFgxNm3aRHx8PPPmzWP37t00aNCA+Ph4vL29KVu2LD4+PlSrVu0fM12/XW9vbypXroyXlxeVK9/9CanW4nzDL+lr55vYGnvJWapEcdKuXDFPp11JB8C/RAnuq1CeL0YMZfr8xXzx3VSOnvwDTw8PalWrWuA5z50/T1ZWFu3atCbiwgUqVqxoPqG5davHKOLjw/btOwo8V17s5bVXTuuzl6zKaV1G5LTL0rNp0ybS06/+kqlTpw6jR4/m8OHDxMfH/+O6wcHBXLp0ieXLl1O7dm0AMjIyqFy5Ms8++yzPP/88kydPpnz58vn6ORS0gIAAUlNTzdPXPi5durRRkfJkLzkfql2TC1F//zUSfSkWr0KFqFH1PgCaNazPt59+xDuv/odzFyLp0DIYzwK+ciIzM5NZs+fw8YcfAFC0aFGuXFfUnJycKF++HIlJSQWa62bs5bVXTuuzl6zKaV1G5LTL0gMwYcIE88dubm4EBATg63v18IHJZMrz8BZcbZZdunRhypQpNG7cGIAmTZqwcOFCTp06BcDatWs5cODAP2a4cTu32q7RgoKCiIiIME9HRUXh5eVFrVq1DEyVm63mvPF1faZta46eOk1qWhoA+8IO06lDW1xdLW99dejYcXbu/51XX3iuwLJeM/PH2fR7+SUKF756vlGtmjU4d/68+Q8GgPSMDO6zgT2RYLuv/Y2U0/rsJatyWpcROe2y9JQrV45p06bRt29fxo0bx6xZs5g0aRJOTk4cOXKE8PBwtmzZQlpaGlu2bOH8+fOcPn3avH6XLl144oknzLvVAgMDefnll+nVqxft2rUjJSWFGjVqsGnTJk6fPs3vv/+eK8OhQ4c4c+YM27ZtIy4ujjNnznDs2DF27drF5cuX2blzJ4cOHeLkyZMFNSy31LVrV8LCwkhJSQGunkDWo0cP80m2tsIWcyYkJrFszToA1m7eSkxsHMWKFuH9/q/zzdQfmL9iFecuRPLKC89arLfnwCEmzZjNt59+TLGiRfJ66nwzb/5Cqtx3H0lJSfxx5gy79+zh0qVY6gfV47dNm69+XgkJpKWlEdzs0QLNdjO2+NrnRTmtz16yKqd1GZHTbm9OKHdu+/bthISEEBgYSGRkJAMGDLC4ysdW5GfOu7k54Z1KuJzI2i3b8PH2otWjTXG9y+PUd3tzwl/XrmPA4Hcs9k55eHiwecNasnNyGDt+IqUDArhw4QI9unWlVq2ad7Wda6x5c0J9jVqXveQE+8mqnNZV0DlVesShFETpsZZ7uSNzQbJm6RERyU92eXhLRERE5E6p9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFyNDiC3lpOZYXSE25J0+pjREW6LV6UqRke4bXUD2xsd4bYcDN9kdAQRkduiPT0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpcdBJCcnk56eftPH4+LiyMzMLMBEVyWnppKekfud5G82X0RE5G65Gh3A1mzfvp23336b++67j8aNG3PkyBEqVarEoEGDaNu2LUuXLqVYsWJGx7xtOTk5LF6yhAkTJ7Fg3lzKli1rfuz53r3Zu3cfADVr1uSnRQsLNNfyteuZPGcB33/5KWX8S91yPsDhEyfZsnsvvkWKcPjkKV7q1pmK5crebBMOxa+EL2t3LsbNzfJbumOrXvxxMhyAhk3rUaN2VSLORbJt027SUtPMyxUq5MmCVVN5pdcQLpy/WKDZr7dlyxY2bNhApUqVCA8PZ+jQoXh4eBiW52aU0/rsJatyWldB53QymUymfHt2O9WzZ08aNWrEm2++SXp6Ok899RStW7emdevWPPjggwWaJSfz3vZ2XLp0iaysLFq0as26NavNpef48eP8tnETwcHNAChevDilSpa86+0knT52R8vHJiSQlZXNky+9wrIp/zWXm5vNz8nJodvrbzN/4je4urqy7/ARvl+wmEkjP7ij7XpVqnJHy99o+46dfPHVN1yIjKRpk8Z89MEIihYpwpat29i2YwfeXl6cO3ee998bjre39z1t6+HqHW572We6dyAmOtZccFxdXRg3dTSdWvcBYNCI14i7FM8P/5uX5/ovvdaT/kP70a5p9zsuPQfDN93R8jcTGRlJ586dWbNmDT4+PkyaNInk5GSGDRtmlee3FuW0PnvJqpzWZUROHd7Kg7Pz38Pi4eFBlSpVOHPmDA8++CA5OTkGJrtzJUqUICAgINf8ufPn4+zsjJubGzVr1LinwnM3ihcrhn+J4rc9PzE5hcjoGJL/2jtRyMOD1CtX8j3n9S5ERvLzylV8PuYTPh31MTt27uSbceOJjY1jzOdfMmTgAN547VUqVCjPZ198VaDZNq3fwdbfdnHh/EUunL9ImXKl2bXt6l68Xv/phn9AyZsWnpZtH2XXtr0FGTdPS5cupXbt2vj4+AAQHBzM4sWLybCxw5zKaX32klU5rcuInCo9/+DcuXPs27ePmjVr0q5dOyIjIzl37hyvvfYa33zzDe+//z5t27bl2LGrezqio6OZPn06Y8aMYfDgwYacJ/NPcnJy+PPPcCZPncqTT3dkyNChZNhgzusVK+JDrWpVGPHVWBISk5i7fCWvv9CzQDOE7t7Dxx++T/XAQFo91pJne3Rn3779/LxyFZUrV8LFxQWAtm3asDLkF+Li4gssW9wly221bPsIG9duw6eIN68N6Etqahqjv3mXlZvm0OHpVublSpf1p6R/ccIO3NmeuvwQGhpKhQoVzNPly5cnKSmJsLAwA1PlppzWZy9ZldO6jMip0nMTu3btYuzYsUybNo3PP/+cN954gytXrmAymShfvjyFChUiKiqKUaNG0bFjR5YtWwbA6NGjef755xk+fDh//PGHeb4tcXZ2ZuYP37Nt8yZGvPcua9etZ9r06UbH+kefDhlI1KVYnnjx/2jZpBH1HqhdoNt/+skncHd3N0+X8CtOyZIlORdxHjc3N/P8MqVLk5mZybHjxws03/WCGj3E3tADNA1uQFZWNl9/8i3vDfyUcZ9NZtRXwyhXoQzOzs50fe4pFv643LCc14uJibE4JOjl5QVc/UPCliin9dlLVuW0LiNy6kTmm2jYsCFvvvmmxbzrD3u5ubnh7+8PgL+/P+Hh4aSlpXH48GF++uknAGrVqkV2dnbBhb5Dnp6e9Hz2WTzcPZg6fTqvvfKK0ZFuKe7yZR6pX48/zp5j5Pj/UtLPj/urVyuw7Ts5OVlMhx05QveuXTh+4iT79v9unu/ufrUAJSRcLrBs16t5fyCnT/xJVmYWZcoFEP7HOZISkwFY98tmkhKTadKsPu4e7iycsxxbOa3P2dnZ4nvs2p4zW6Oc1mcvWZXTuozIqdJjRdcOZT377LPmebbyC+VW2rVtw6jRo42OcUtX0tP5aNxEfvhyDB7u7nw19Xs++GY8S/43MVcZKQjnzp8nKyuLtm1aU6VKFaZ9/wOhu/fQoH4Qobv3AFCqVIkCzwVXD21tWrcdgMsJSXgW8rR4/NzZC/gU8aZjtw70f+c/Fo+t2PAjs6YtZMIXUwss7zUBAQGkpqaap699XLp06QLPcivKaX32klU5rcuInDq8lYfs7Ow899CYTKZblpgiRYpQtGhRfvzxR3JycoiKiuLHH3/Mz6hWceXKFaoHBhod45b+OHueQh6eeHp44OTkxIAXexN/OZGExMQCz5KZmcmPs+fy8QfvA1C1yn2M/+YrJk+dxieffsbhI0cp5OnJ/bUL9vDbNc0ea8KW33YCcDTsBOUqlsHd4+/Dcu4e7pw5fZYnm/ekfvU25n8AT7V8wZDCAxAUFERERIR5OioqCi8vL2rVqmVInptRTuuzl6zKaV1G5FTpucHGjRs5ceIEmzdv5sCBA+b5oaGhxMbGsn79ei5dusSJEyc4fvw4ly5dYseOHRw9epRLly7x2WefsWTJEh555BHGjh1L165dDfxsrrqxqJ05c4ZfVq82z1+85CfefON1w3Pdan7ZgFJEx8aS9tcVW1nZ2ZQs7kexIkXyNWNeZv44h/+8/CKFCxe6miUrixbNg5k+5X+MeHcYZ8+e5cknHsfT0/Mfnsn6ypYPIC0ljcsJV8vgkUPH2bPzd4IfawJA0WJFKFTIk83rtxd4tn/StWtXwsLCSElJAa5+z/Xo0cPifClboJzWZy9ZldO6jMip+/TYuHu9T098QgJr163jw48+ZuCAt3n6ySc5dfo0Q4YOo3KlSjR79FFq1arJI02b3tN27vQ+PQmJSWzcuYtP/zuZ13v1pEPzZpQs7nfT+QA79v3Oxp27qFa5EtGXYmnRuCE1q97ZfXfu9T498xYsJMDfn4oVr15xEBsbR0pKCs3/ut/RgYMHGTh4KIsXzMXX1/eetnUn9+m55vkXu+Dm5sYPk/++PL1IUR/6D+3HxQtRlClXmoWzl3E07GSudQ+GbzL0Pj1w9eagISEhBAYGEhkZyYABAyxOHrcVyml99pJVOa2roHOq9Ni4ey09BeVOS49R7qX0/LpuPQMHv2OxJ8rDw4NN63/Fx8eHXaG7+W7yFD56fwSVKlW856x3U3qMYM3SIyKSn1R6bJxKj3Xd656evMTHx7P617X4+PjQrk1rXF2tc32ASo+IiHXp6i2Re+Tr68uz3bsZHUNERP6BTmQWERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEJxMJpPJ6BAiYr8OT5lvdITbUrtfD6MjiIjBtKdHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ3A1OoCtyMzMJCUlhWLFit3V+mlpaWRnZ+Pt7W3dYFa0ZcsWNmzYQKVKlQgPD2fo0KF4eHgYHSsX5bQ+e8pqD+xlPO0lJ9hPVuW0roLOafienpkzZ1K9enU+/fRTkpOTzdNjxowhMTERgM2bN9OwYUP27NmTLxkiIyPp2rUrGzZsuKv1T5w4Qbt27Th8+LCVk1lPZGQkQ4cOZeDAgfTu3Rs/Pz/Gjh1rdKxclNP6bC2rV1l/qnRpR40+nSnXqgnO7m4AeJcvTUDjupSsdz9lWzTC2e3vv8kKly7JfR1bX12nZWOcnI370WVr43kz9pIT7CerclqXETkNLz29e/emTp06FC5cGG9vb3r37k2tWrXw9PSkSJEiANSqVYuePXsSFBR00+eJj4/nzJkzd5WhdOnSVK9e/a7WBQgMDKRChQp3vX5BWLp0KbVr18bHxweA4OBgFi9eTEZGhsHJLCmn9dlSVjevwhSrVonzG3YQsXEn3mUD8G/4EC6eHgQ0rsvFnb8TszeMjMRkApo8DIBLIQ8KlfTjj+Xr+OOnNXiV8adotUoFnv0aWxrPW7GXnGA/WZXTuozIaXjpAXjmmWdYuXKleTo4OJiff/4Zk8kEwK+//kq7du1uun52djbvvvsu0dHRd53Bycnprte1B6GhoRbFrHz58iQlJREWFmZgqtyU0/psKWvhMqW4sHk36XGXSfozgrjDJ/EKKEGxapVIv5wEf33PJ54+S9GqFXHx9MDZxYXYg8fBZCIjMZmUizHm5YxgS+N5K/aSE+wnq3JalxE5baL0tG/fnsjISPbu3QtAVFQUUVFR5uljx44RGBhofmzUqFGsXr2a4cOHk5yczOnTpzlx4gQrV65kz549JCcn8/333zNx4kReeeUVkpKSOHLkCP369WPWrFm0bNmS9evX58px9OhR+vTpQ5MmTVi4cCEAGRkZTJo0iWXLljFixAjmzp1rXn7evHnMmTOHBQsWcP78eXPWhx9+mP/9738AnD9/np49e5KWlpZ/A3gbYmJiLM438vLyArinopgflNP6bCnr5ZN/YsrJMU9npV0hM/UK7kW8MWX/PT8jOQVnFxc8ixcjMznVPN/Fw52c9AwSTv5ZkLEt2NJ43oq95AT7yaqc1mVETps4kdnX15dGjRqxYsUKypQpQ+XKlWnatCkrVqygfPnylCpVyrzstm3b8PHxoV27dmzYsIHt27fTpk0bypQpwxNPPEFQUBAffvghr732Gv7+/rz55ptMnz6dt99+m5iYGFxcXJg9e7Z5d9r1PD09mTFjBps3b+a1116jSZMmLF++nEKFCtGxY0fat29Pq1atqFSpEk5OTvz+++98/vnnwNUCBFCjRg26dOnCpUuXAEhOTqZnz54UKlSoAEby5pydnXG+7jwIFxcXA9PcnHJany1nLVTSj/gjp/AsXozCASXM868VIBfPv09oLFqtEqXqP0BWShquhTzISr1S4HnBtsfzevaSE+wnq3JalxE5baL0AHTo0IEvvviCgIAAOnToQJkyZRg5ciTlypWjbdu25uU6depEbGwsP//8M5GRkaSkpOR6rp07d1KjRg0A/P39cXP760RJb2+qVq1KmTJl8sxQuXJlAJo1a0ZAQAAHDx5k/fr1vPnmmwB4eHgQHBzMb7/9RlJSEg888IB53etLVM+ePenSpQsDBw5ky5YtvPDCC/c4OvcuICCA1NS//2K+9nHp0qWNipQn5bQ+W83q5uMFzs4knjlHevxlSjxUk8KlS5EaGY1Xmat/6GSl/L2HNPGPc2QmpVC2eUP8Gz5ExG87Dcltq+N5I3vJCfaTVTmty4icNnF4C6BVq1akpKSwdetWKlasSKtWrcjKyuLXX3+lWrVq5uX27dvHxx9/TLt27Shbtqz5vJ/rJSUl0aVLF5599llGjBjBa6+9dsd5/Pz8KFasGK6uriQkJJjn+/r64urqSnx8vMX861WsWJGHHnqIFStWYDKZ8PT0vOPtW1tQUBARERHm6aioKLy8vKhVq5aBqXJTTuuzyaxOThR/oDoXNocCkJ6QyNlft1Kybi0Cmj5MoRJ+5GRmkRYTZ17FlJ1N6sUYIrfuwbN4MYOC2+h45sFecoL9ZFVO6zIip82UnqJFi9KwYUNatGgBXD3U1KZNG5o2bWqx3Pfff0/t2rVxc3MjMTGR7Oxs4OpusbS0NDIyMggKCmLs2LFkZmaSmJjIlClT7ihLSkoKWVlZBAUF0bZtW9asWWN+7PTp07Rt25b69euzcuVKczNNSUnhypW/d7c///zzTJo0ybzHyWhdu3YlLCzMvGcsNDSUHj16mPeC2QrltD5bzFqiTnUu7T+CKevq9y9OTiSfvUB4yEYubtuHe1EfEk7+iemv7+/rZaakknE5uYAT/80WxzMv9pIT7CerclqXETmdTHntKjHIokWLaNKkCWXLlgVg48aN+Pr68uCDD5qXWbBgAd988w3dunXD2dmZXbt2MXbsWNasWcOKFSsYNGgQVatWZdCgQZw8eZKgoCBGjhxJTEwM/fr1o127drz++usULVrUYtsbNmxgxYoVPPTQQyQkJNCxY0cqVapEVlYWY8eOxdXVldKlS+Ph4cEzzzxDRkYGI0aM4MiRIzz22GOEhYVRt25d+vXrh7u7OyaTie7duzN37lxcXW3jKOL27dsJCQkhMDCQyMhIBgwYgLu7u9GxclFO68vPrIenzL+j5X1rVSUzOZWMy0kAuBb2xNnNjeSzFwAoVKo45Vs15fSS1WSnZ+Dm44VrIU/SomMBKF6nOikXorlyKf6Otlu7X487Wv5W7OW1t5ecYD9ZldO6CjqnTZWef5Ps7GymT59Ov379jI4ikq/upPT4VC5H+VZNLW4RkZOVxfEfl5OTmUnh0qUoVe9+LmzZbS5F3hXKUK5FI1IuRJESGUN6XAIpF+786g5rlh4RsU8qPVaWnJzMsWPHOHr0KE2aNKFKlSpGRxLJV3e6pycvLh7uFKlSgZz0TC7/cTZf7sOj0iMiNnNOz7/FwYMHefXVV7l8+bIKj8htyk7PIP7IKS6fDjf0xoMi8u9mGyeb/Is0adKE3bt3Gx1DREREbqA9PSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDsHJZDKZjA4hIpLfstNTjY5w21w8ChsdQeRfSXt6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOIQ7Lj1hYWH5kUNEREQkX7neOOPrr78mJCQkz4VNJhMJCQns27cv34OJiIiIWFOu0vPggw/Stm1bfH19cy1sMpnYsGFDgQQTERERsaZcpadFixa4uLjcdIXatWvnayARERGR/JCr9FxfeE6dOsWCBQtISUnBZDKRk5PD7t27tbdHRERE7E6u0nO9OXPmcP/993P48GEeeOABEhMTadCgQUFlExEREbGaW1691aBBAzp37kyjRo149NFH6d27Nzt37iyobCIiIiJWc8s9Pfv372fhwoVMnjyZ//u//8PFxYWjR48WVDYRERERq3EymUymmz2YnZ3NuXPnqFSpErGxsYSEhFC3bl3uv//+gswoInLPstNTjY5w21w8ChsdQeRf6Zal50aZmZns3buXRo0a5Wcmm7Fx40aWLl2Kr68vv/zyCy1btmTMmDE3XX7UqFEEBgbSvXt3tm7dyrp16/joo48KLvA/2LJlCxs2bKBSpUqEh4czdOhQPDw8jI6Vi3Jan71kzc+c91p6tu/Yyedffs2FyEiaNmnMxx++T9EiRcyPp6am0aX7s0z937eULVvmnrZlrdJjL6872E9W5bSuAs9puoXq1aubatSoYf5Xs2ZN04svvnirVf41MjMzTUFBQabY2FiTyWQyRUREmAYNGnTLdcLDw83LX7p0yRQYGJjvOW/XhQsXTI0bNzYlJiaaTCaTaeLEiaYxY8YYnCo35bQ+e8ma3zmzrqTc9b+zZ06ZBg8cYDp8cL9p9aqfTfXqPWx6b/gwi2W+nTTBFBgYaAo/ffKetpV1JcUqn6+9vO4mk/1kVU7rMiLnLU9k/vbbbzl69Kj535o1a3jiiSfyr4HZkOTkZBITE9m/fz8AZcqUoV27drdcp0KFChQtWhSA4sWL53vGO7F06VJq166Nj48PAMHBwSxevJiMjAyDk1lSTuuzl6y2nDM0dDcjP/qA6oGBtHqsJc/16MHeffvNj69bv4FGDRsamDA3Wx7PG9lLVuW0LiNy3rL0tGzZ0mK6fPnyLFiwIN/C2JJixYrRoUMH+vfvz+TJk8nOzqZVq1YALFq0iEWLFjF+/Hg+/vhj0tPTuXTpEu+99x6TJ082OHneQkNDqVChgnm6fPnyJCUl2dx7qSmn9dlLVlvO+fRTT+Lu7m6eLl7cj5IlSwIQceEC0TEx1HnAts51tOXxvJG9ZFVO6zIi5y1LzwsvvECvXr3M/zp06EDhwo5zgt2XX35J3759GT9+PD179iQqKordu3ezfv16unbtSv/+/YmJieHbb7+lRIkSlCpViuzsbKNj5ykmJgZvb2/ztJeXFwDR0dFGRcqTclqfvWS15ZxOTk4W04cPH6F7ty5kZ2ezcNESenTralCym7Pl8byRvWRVTusyIuctL1lv3LgxQUFB5mlPT09q1qyZb2FsjaurK4MGDaJ169a8/fbbvPrqqzRo0IDKlSubl2nTpg3Tp09nwIABODvf8ZvWFxhnZ2eLfLd6qxEjKaf12UtWe8l57vx5srKyaNemNTN/nE33bl1s8nvfXsYT7CerclqXETlv+Z1au3ZtGjRoYP5Xo0YN1q9fn++hbMGmTZtIT08HoE6dOowePZrDhw+TmJhIQkKCeTlfX1+b/YK6XkBAAKmpf1+9cu3j0qVLGxUpT8ppffaS1R5yZmZmMmv2HD7+8AMA5i1YSIcnO/JQUEMeCrp6Tk+HpzryzfgJRsYE7GM8r7GXrMppXUbkzHNPz5EjRzhx4gQbN24kPj7eItDcuXP/8YTef4sJEyYwZMgQANzc3AgICKB79+68/vrrpKWlUahQIU6fPk379u2Bq+9Cb6uCgoI4dOiQeToqKgovLy9q1aplYKrclNP67CWrPeSc+eNs+r38EoULFwJg5bKfcHX9+8dorTp1CVmx7J4vWbcGexjPa+wlq3JalxE589zTU7VqVQ4ePMjZs2fZtWuX+d+RI0d466238i2MLSlXrhzTpk2jb9++jBs3jlmzZjFp0iQefPBB3nnnHT755BN++uknUlNT6dOnD5GRkfz+++8cOHCAmJgY1qxZA1zdY2QLunbtSlhYGCkpKcDVE8h69OiBm5ubwcksKaf12UtWW885b/5Cqtx3H0lJSfxx5gy79+xh67btRse6KVsfz+vZS1bltC4jct7y5oQXL14kICAAuLoX48aT+cS+bN++nZCQEAIDA4mMjGTAgAEWV6TYCuW0PnvJmp857+XmhL+uXceAwe9Y7M318PBg84a15stt4eqenrW/rLKZmxPay+sO9pNVOa2roHPesvTMnj2b/fv38/XXX5Odnc2iRYto3ry5uQiJiNgLvQ2FiNzyROYjR44waNAg4OpZ1c888wzDhg0rkGAiIiIi1nTL0tO4cWPKlPl7N21ERASHDx/O91AiIiIi1nbL+/RkZ2czefJkateuzblz55g6dSqPP/54QWUTERERsZp/fJf1LVu2sHz5ctLS0mjYsCFFihShY8eOBRRPRMQ6dE6PiPxj6blm+/btzJ07l99++02HuETE7qj0iMgtD28lJyezZMkS5s+fz6VLlwgODqZx48YFlU1ERETEavIsPUePHmX27Nn88ssvPPjgg1SvXp3ly5fj7u5uc29YJiIiInI7cl29NXToULp164a3tzc//fQTP/zwA1WrVjXfLKhUqVIFHlJERETkXuUqPZ9//jlTpkyhSJEi7N6923x7aBERERF7dssTmePj41mzZg179uzhueee4+GHH+bgwYPUqVOnIDOKiNwzncgsIrd99dbu3btZtWoVISEhhIaG5ncuERGrUukRkVtevXW9+vXrU79+ferWrZufeURERETyxW3v6RERsWfa0yMit3zvLREREZF/C5UeERERcQgqPSIiIuIQdE6PiIiNWfTqOKMj3Jau371tdASRO6I9PSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCa3498datWxk0aBBly5alefPmJCQkEBMTQ7du3Xj00Udvue7ixYtJT09n6dKlTJkyBT8/v/yKeVsiIyPp06cPq1evxsnJydAs92LLli1s2LCBSpUqER4eztChQ/Hw8DA6Vi7KaX32klU5756Luyut332OzROWkRqXeMv5vhX9KVPnPtKT0/Cr6M+RX0JJjoo3Kjpgm2OaF+W0vm3btvHZZ58RERHBI488wqhRoyhatGi+bMvJZDKZ8uWZgZ49e9KoUSPefPNNAE6fPs1//vMfnn32Wf7zn//kuU5ycjKvvfYas2bN4siRIwQGBuLqat1uFh8fT0JCApUrV76tZXJycggLC6NOnTpWzVGQIiMj6dy5M2vWrMHHx4dJkyaRnJzMsGHDjI5mQTmtz16yKuffFr067o7XqdG2Pg90bMqq9763KD255jtBuw97sWbkbEw5OZSoWpZaHRqwecLSO95m1+/evuN18qLX3rrsJSfAhQsXGDduHC+++CLnzp1j+PDhtG/fnlGjRuXL9vL18Jazs+XTV6lShQ8//JCxY8cSFRWV5zqnT5/mWg+rVauW1QtPdnY27777LtHR0be9jLOzM7Vr17ZqjoK2dOlSateujY+PDwDBwcEsXryYjIwMg5NZUk7rs5esynn3yjxYhahjZ29rvnthTwr7FcGtkDsAWRmZuHq4F0jOm7HFMc2Lclrfrl27+OSTT6hRowatW7emZ8+e7NmzJ9+2V+Dn9DRt2hRXV1c2b97M/PnzmTFjBr169eL48eOcP3+en3/+maioKGbPns2FCxdISkpi3Lhx/PTTTwwePJhff/0VgHnz5tG3b1+mTJlCcHAwmZmZuZ4P4Oeff2bVqlV88sknzJgxg9OnT3PixAlWrlzJnj17iIqKYtSoUaxevZrhw4eTnJyca5kNGzbQsmVL8+ewb98+pkyZwo8//kj//v2JjIwkLi6OoUOHMnLkSEaPHk27du3YvHlzQQ/vTYWGhlKhQgXzdPny5UlKSiIsLMzAVLkpp/XZS1blvDuF/XwoVMyL+PCo25qfkXKF+PAoGr3UHncvTwIfe5hDy7YVZORcbG1Mb0Y5ra9jx464u/9dukuUKEGpUqXybXsFXnpcXV0pWrQocXFxeHl50adPH1q3bs3HH39MuXLlaN26Nf7+/jz//POUKVOGMWPGUKNGDTp16sTw4cMZNmwYp0+fpm7duhw4cICOHTsyZcoUfv3111zPBzBz5kwaN27MiBEjqFmzJoGBgZQpU4YnnniCoKAgtm3bho+PD+3atSM7O5vt27fnWqZ58+ZcvHgRgMuXLzN8+HD69OnDCy+8QL169Rg4cCB+fn74+/tz8uRJhg8fzmuvvcaiRYsKenhvKiYmBm9vb/O0l5cXwC33eBlBOa3PXrIq511wcuK+Rx/g9OaDtzf/LzumhVDI14cnxrzM+X0niTl5vgDC3pxNjektKKf13XiebFhYGD169Mi37RV46cnJySEpKYmZM2cSHh7OvHnzuHz5MiVLlsxz+fXr13PfffcBULx4cR588EG2bNmCt7c3xYoVo1SpUlSvXp21a9fm+XyPPvooHTt2ZNmyZTRs2DDX83fq1IkXXniBn3/+mcjISFJSUnItc/1hur179+Ll5WVupm3btmXfvn0kJibi5uaGv78/zs7O+Pv7k5CQcK/DZTXOzs4Wn4eLi4uBaW5OOa3PXrIq552r1uIhTm8+BKbbm3+Np09hIg+dIfrEeRr0boNfpYD8D3sLtjSmt6Kc+evcuXNkZWXRvn37fNtGvl29dTO7d+8mJyeHmjVr0rBhQ+rXr29+LK9zql1dXS3Kg6+vb57n+WRkZOT5fK+//jqBgYGMGTOGU6dOMXjwYIv19u3bx4wZM/j666/Ztm1bnhn+KQ/kPn/J1gQEBJCammqevvZx6dKljYqUJ+W0PnvJqpx3rkpwHR7o2NRiXruPenHlckqe80+s38+RkJ006NOWdZ/PIyczm4e6Nadh33b88uGMAkxuyZbG9FaUM/9kZmYyc+bMfDuB+Zp8/U2dk5NjMR0VFcXHH3/MoEGDaNmyJZMmTSIhIYGsrCymT59OUlISJpPJoni0bduWNWvWmKfPnTtHixYtcm2radOmeT7fsmXLaN++PXPmzDGfY+Pi4kJaWhoZGRl8//331K5dGzc3NxITE8nOzs61zLU8JpOJhx9+mIyMDPOx0VOnTtGsWTOLXYm2KCgoiIiICPN0VFQUXl5e1KpVy8BUuSmn9dlLVuW8c6s/nMlPb00y/wNY/dEsQt7/Ic/5Ycu3UbR0cbIyMsnJvPqz7sDizXj4FMLdu1CB57/Glsb0VpQz//zwww/83//9H4ULFwYgKysrX7aTb3t6tmzZwqlTp0hNTSU7O5uEhAQuXrzI0KFDCQ4ONheH1q1bU6VKFd59910ANm3aRHh4OL///jsPPfQQgwcPZsyYMUyZMgV3d3feeOMNSpcuzaxZs4iJiWHz5s00a9aMrl275nq+IkWK8MMPP5CcnIy7uztvvfUWAM2bN2fChAm4ubnx6KOP8s0335CcnEy1atVYsmQJjzzyiMUyly9fBmDt2rW0adOGyZMnM336dBo1akRkZCRjxowhOTmZQ4cOERcXR3R0NJs3b+bMmTOcO3eO8uXL59cw37auXbsyb948UlJS8PLyIjQ0lB49euDm5mZ0NAvKaX32klU5C0bypcsUKuaNi7sr2RlZOLk4kZaQQkZymmGZ7GVMlTN/zJkzh6pVq5KYmEhiYiKxsbGkpKTkuYPjXuXrfXrEtmzfvp2QkBACAwOJjIxkwIABFmfN2wrltD57yaqcV93NfXrg6n1zbrxPT17z/WtWpGzdqiScj6Gwrzfn958i4eydn+Rqrfv0gF57a7OXnGvWrKF///4WR3g8PDzMFxlZm0qPiIiNudvSU9CsWXpECoJtn30rIiIiYiUqPSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BCeTyWQyOoSISH7Lyc40OsJtc3ZxMzrCbclIjDU6wm1xL1Lc6Aj/OqacbKMj3BYnZxeLae3pEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiJ3IDk5mfT0dKNjOIy4+Hgys7KMjiH5wIjvJdcC3ZqNycrKYvr06ezdu5cKFSoQExPDunXreOSRR5g8ebLR8axuy5YtbNiwgUqVKhEeHs7QoUPx8PAwOlYuyml99pLVlnPm5OSwePESJkycyIL58yhbtiwA27Zv5/PPv+TChQs80rQpH3/8IUWLFjU47VW2PJ7XpKal0f35vvxv4ljKlikNQO9+r7Jv/wEAalYPZOHsGQYmtGQPYwq2nTMnJ4dFixczYcJEFiyYT7m/vpe2bN3Kbxt+o1KlSvwZHs7Qd4ZYP7PJgQ0YMMD05ptvmq5cuWKet337dtMrr7xiYKr8ceHCBVPjxo1NiYmJJpPJZJo4caJpzJgxBqfKTTmtz16y5nfO7KyMe/oXdfGCKeL8WVNgYKDpbPgZU3ZWhunc2XDT4MGDTEcOHzKtXh1iqlevnum99969521ZQ0G87umXL93zv/+O+8YUGBho+uPoIVP65Uumg3t2miaO/cp0IHS76UDodtP508fueRvWou+lv+VkZ931v+ioi6YLEeevfi+dDTflZGeZIs6fMzVu3Nh0OSHelJOdZZo4YYLp009H39N2crKzcuV22MNbv/32G6tXr+aDDz6waJKNGzemc+fOBibLH0uXLqV27dr4+PgAEBwczOLFi8nIyDA4mSXltD57yWrrOUuUKEFAQIDFvNDQUEaN/Jjq1avTulUrnnvuWfbu3WtQQku2Pp4A63/bRMMGQRbz5i9agrOTM25ubtSoHkjJEiUMSpebPYwp2H7OvL6Xli1bTu1atcyZmwU3Y8mSn6ye2WFLz5o1a6hcuTIl8viGatWqFatXr6Zjx44sX76cRo0aERMTw9ixY1m2bBmff/45O3bsAODYsWPMmDGDWbNmMXDgQAB27NjBokWLmDRpEp999lmBfl43ExoaSoUKFczT5cuXJykpibCwMANT5aac1mcvWe0l5/Wefvop3N3dzdMlihenVMlSBib6m62P54XISKJjLvFA7VrmeTk5OYSfPcfUGbPo2L0nQ0d8RGZmpoEpLdn6mF5jLzmvFxoaSvkK5c3T5cuVu5r58GGrbsdhS09UVBRFihSxmHfu3DnGjh3LkCFDcHNz4+jRo9SqVYtZs2ZRqFAhfv31Vzp27EizZs2YOXMmAAsXLqRy5cr06tWL7t27AzBjxgzq1q3LG2+8QYsWLQr8c8tLTEwM3t7e5mkvLy8AoqOjjYqUJ+W0PnvJai85r+fk5GQxHXb4MN27dzMojSVbHs/s7GwW/bSc7l2esZjv7OzM9/+bxKY1q3h3yCDWb9zI9JmzDUqZmy2P6fXsJef1Ciqzw5YePz+/XINZvnx5ypQpw86dO3nssccAqFatGoGBgXh7e7Ny5Up+++039uzZQ0pKCgAtWrRg4MCBjB8/ngcffBC4uqeoZ8+ezJgxg6Agy123RnF2dsbZ+e+X28XFxcA0N6ec1mcvWe0l582cO3eOrKws2rVra3QUwLbHc86CRXTr3NEi3/U8PT14tltn3h0yiJ9DfingdDdny2N6PXvJeT1nFxdcnP/OmV+ZHbb0PPbYY1y4cIGzZ89azHdzc8PNzS3X8mlpafTr148KFSrQoEED8/zGjRvz448/sm3bNvr06YPJZOKZZ55h3LhxLFiwgMGDB+f753I7AgICSE1NNU9f+7h06dJGRcqTclqfvWS1l5x5yczMZNasHxn58UdGRzGz5fFcsPgnnujcg3pNm1OvaXMAnuzSg3GTvrNYrs1jLbkYZTt7J2x5TK9nLzmvF+DvT8r1mdPSACh9w7k/98phS0/79u1p2bIlH3/8scWJUtnZ2Xkuv337diIiIqhSpQpJSUnm5ZYtW0atWrWYM2cOsbGxJCYmsmzZMho3bsyiRYvYs2dPgXw+/yQoKIiIiAjzdFRUFF5eXtSqVesWaxU85bQ+e8lqLznzMmPmLPr160fhwoWBq7fDMJotj+eqnxayd9tG8z+AnxfP5+03XrVY7kp6OoFVqxiQMG+2PKbXs5ec16sXVI8L12e+eDFfMjts6XFycmLChAk0bdqUt956i88++4wvvviCPXv2MHz4cNasWQNcLTXZ2dnUrFmTjIwM/u///o+MjAzCw8NZu3Yte/fu5ZNPPmHNmjX06NGDokWLsnLlSiZNmsS6det46623DP5Mr+ratSthYWHmw3KhoaH06NEjz71aRlJO67OXrPaQ02Qy5Zo3d958qlapQlJSIn/88Qehu3ezZetWA9JZsofxvNGZP8NZvXa9eZx/Wr6C1//vPwan+pu9jKk95Lzxe6lb166EHf478+7du+nRvbvVMzuZ8vouln+l7du3ExISQmBgIJGRkQwYMMDiyhNboZzWZy9Z8zNnTva9XQUUn5DA2rXr+PDDjxg4cABPP/Ukv/9+gLcHDLT4Ae7h4cGWzRvNl97eDWcX6/ygz+/XPSMx1irP80D9JqxevoSz584z7P0PqVSxIo82aUzNGoE0bdzonp/fvUhxK6S8St9LV5ly8j4qcjvi4xNYu3YtH3z4IYMGDuSpp5/Cv1Qptm/fzi+/rKZaYLWrmd9++54zOzlbnhuk0iMiDuFeS09BslbpyW/WKj35zZqlR666l9JTkG4sPQ57eEtEREQci0qPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg7ByWQymYwOISIikl9SI8ONjnBbCpeuaHSEfz3t6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQXI0OIAVny5YtbNiwgUqVKhEeHs7QoUPx8PAwOlYuyml99pJVOa3LXnKC7WaNjYunXdfnyMrOtpg/ddxXvDpoaK75i2dMpUqligUZMU+2Op43KuicTiaTyZRvz/4Pdu/ezYgRI3B1daVdu3ZERUWRlpbGu+++S/HixfN9+yaTialTp5KcnMyff/7JhAkT8n2bRomMjKRz586sWbMGHx8fJk2aRHJyMsOGDTM6mgXltD57yaqc1mUvOSH/s6ZGht/1uktX/UKJ4sWpUrECAJnZ2Qwc8RHPd+2U5/wlM6be9bYKl7ZOWbKX196InIYe3qpfvz4PPvggDzzwAG+++SaffPIJACNHjrTqdvbt25fn/C1btpCSksLAgQN5+eWXrbrNf9p2QVu6dCm1a9fGx8cHgODgYBYvXkxGRobBySwpp/XZS1bltC57yQm2nbVZ40Y82qgBZUoHUKZ0AJEXL9Lg4YduOt8W2PJ4Xs+InIaf0+PsbBnhoYce4vTp01Z7/pCQEBYtWpTnY8eOHcPV9eoRvjp16lhtm9fExsYyePBgqz/v3QgNDaVChQrm6fLly5OUlERYWJiBqXJTTuuzl6zKaV32khNsO2txP1+L6d+2bie4SeObzrcFtjye1zMip+Gl53o5OTls27aNRo0aAXDq1Cnq1q3L+fPnOXfuHJ07d+ann37i7NmzDBo0iMmTJ/PEE08wadIkhg4dysiRIxk9ejTt2rVj8+bNZGVlsX79eo4fP87cuXMttrV37152797NwYMHmT17NtnZ2Zw+fZpJkyYxb9483nrrLU6cOEFiYiLffPMNI0eO5I033qB///4kJyfz/fffM3HiRF555RWSkpIAmDJlCr/++isvvvgiR48eJSwsjIiICKZMmcKFCxcKfDyvFxMTg7e3t3nay8sLgOjoaKMi5Uk5rc9esiqnddlLTrCvrHt+P0i9Bx+47flGsJfxNCKnTZzIfPToUb766is2b96Mp6cnY8aMAaBq1ar4+l5t0+XLl6datWoAVKhQgfT0dBISEpg1axbZ2dn8+OOP7N+/n5kzZ/LAAw+waNEimjVrRtOmTXFzc+O5556z2Ga9evXMe3eef/55srKyeP3115k7dy5+fn6UKlWK1157jZCQEEqUKMGuXbv4/vvviY6O5ssvv+S1117D39+fN998k+nTp9OrVy/Wr1/P3LlzqVu3LomJiQQHBwPQr1+/ghrKm3J2drbYq+bi4mJgmptTTuuzl6zKaV32khPsJ+uR4yeoUqkibm5utzXfKPYynkbktIk9PTVr1mTw4MEsW7aMVq1a0blzZ+Lj42+5jre3N9WqVcPPz4+SJUvi5uaGv78/zs7O+Pv7k5CQcEcZTp8+TXx8PH5+fgA0b96c6OhoTp8+jbe3N5UrV8bLy4vKlSuzc+dONmzYwLx58/D398fNzQ1fX1/c3Nzo0aMH58+fp0qVKnc7HPkiICCA1NRU8/S1j0uXLm1UpDwpp/XZS1bltC57yQn2k/W3rdtp1qTRbc83ir2MpxE5baL0XOPs7Mxzzz1HREQEu3btKtBtu7q6kpKSQmZmJnC1cRYpUiTP5pmUlESXLl149tlnGTFiBK+99homk4np06fTsmVLXnzxRTZu3Fig+f9JUFAQERER5umoqCi8vLyoVauWgalyU07rs5esymld9pIT7Cfrlh27eKRhg9uebxR7GU8jchpeenJyciymT506BUDFilcv3fP09CQ6Oprs7GyioqLu+KxuFxcXUlNTycnJISsry+Ixk8nEtSv2K1euzH333ceGDRsAiIuLo3jx4nnusQkKCmLs2LFkZmaSmJhoPmdn3759vPrqq4wYMYKtW7dabN/os+a7du1KWFgYKSkpwNUTyHr06GEzu2OvUU7rs5esymld9pIT7CNrRGQkhQsVoljRIrc130j2MJ5gTE5D79OzZ88eRowYQUZGBl26dCEhIYFDhw7RuXNnunTpAsDcuXOZNm0aLVu2xNPTE09PTzp06MCAAQOoUqUKw4YNo3DhwgwcOJC4uDi+/fZbZs6cyfLly5k3bx5OTk706tWLbt268dJLL5kHMyIignfffRdXV1c++OADKlasyNmzZxk7diz169cnLi6OJ554Al9fX0aPHs3hw4cZN24c1apVIyoqikGDBnHy5EmCgoIYOXIkaWlp9O3bl9dff53z58/z+OOPU7lyZd566y1ycnIYNGgQlStXNmqoAdi+fTshISEEBgYSGRnJgAEDcHd3NzRTXpTT+uwlq3Jal73khPzNei/36blmzqKfyMzKos+z3W5r/t2w1n16wH5e+4LOaWjpERERyW/WKD0FwZqlR/Jm+OEtERERkYKg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYfgZDKZTEaHEBGRv125dMHoCLfFs0QZoyP8qySePmp0hNtWpEpNoyPcFe3pEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIOJDklhfT0DKNjiJUlp6aSnqHX9Z+4Gh0gv2zcuJGlS5fi6+vLL7/8QsuWLRkzZozRsQy1ZcsWNmzYQKVKlQgPD2fo0KF4eHgYHSsX5bQ+e8mqnNazc/dejp88RdnSATRp2ABPTw9++nkV/536A7OnfkvZ0gFGR7RgD2MKtpczJyeHZWvWMvnHufww9gvK+PsDsGPvfjbvDKVC2TKcuxBJ/5f74OHuTmx8Ao/3eons7GyL51nwv4ncV6F8gecv6PF0MplMpnx7doNkZWXRuHFj1qxZg5+fHxcuXOCbb77hq6++MjqaYSIjI+ncuTNr1qzBx8eHSZMmkZyczLBhw4yOZkE5rc9esirn365cunBP63818Vv8fH158flnzfNi4+LIysqmzTPdCFk8zyqlx7NEmXt+DtBrf03i6aN3vE5sfAJZ2Vk80etllv8wmTL+/lyMiaF3/8Esmfot3l5eTJ27gOSUFAb850WWr1lLCT9fKv9VcLKyshk8agwL/zfxjrZbpErNO856IyNe93/l4a3k5GQSExPZv38/AGXKlKFdu3YGpzLW0qVLqV27Nj4+PgAEBwezePFiMmxsd6hyWp+9ZFVO65g5byFR0TEWhQeguJ8f/qVKGpTq1mx9TK+xxZzFfYvhX6KExbxV636jRtUqeHt5AdA0qB4rfl1HZmYmjzSoT9P6QZTx96eMvz+RUdE0eKiOEdENGc9/ZekpVqwYHTp0oH///kyePJns7GxatWrFsmXLqFmzJn/88Qfbtm2jVq1arFq1CoDvvvuO+fPnk5WVxejRowkJCeGjjz7ixIkTZGRk8O233zJo0CCmTJnC888/z7vvvsv69esZOnQojz/+OJGRkZw/f57+/fszduxYBg0axCOPPMKMGTOMHYy/hIaGUqFCBfN0+fLlSUpKIiwszMBUuSmn9dlLVuW8d4lJyXw3/QcKFy7Ee6M+5YluPQn5dZ3Rsf6RLY/p9ewl595DYZS7bk9e2dL+JKekcuTkaYr7FrNYduOOXTzasEEBJ7zKiPH8V5YegC+//JK+ffsyfvx4evbsSVRUFE8//TTly5cnPT2dpk2b0qBBA5ydrw6Bq6srnTp14ty5c5w4cYIOHTpw//33s3DhQtzd3alTpw5Hjx6lV69eTJ8+nZCQEIoUKcLnn39OgwYN+PXXXylXrhxubm54enry9ddfM2nSJL788kvOnDlj8GhATEwM3t7e5mmvv/4CiI6ONipSnpTT+uwlq3Leu227QnF1dWXQ668y+v13efvVfrw/+nPOnY8wOtot2fKYXs9ecl6Ki8ercGHztFehQn/Nj8u17N5DYTx8f60Cy3Y9I8bzX1t6XF1dGTRoEPPnzyc6OppXX30VgM6dO7Ns2TLzSVzLly8nMzMTAHd3dypXrszUqVNZs2YNhw8fJiUlBQA3NzeKFy+Op6cnHh4eFCtWjNKlSwPg5+dHUlKSebv+f51I9tBDD1GlShWOHDlSoJ97Xpydnc0FD8DFxcXANDennNZnL1mV895diLxIxfLlKFLk6uGCx5o3w8fbmx279xic7NZseUyvZy85XW7Ief3H1zt68hRVKpTHzc2toKJZMGI8/5WlZ9OmTaSnpwNQp04dRo8ezeHDh4mPj6djx46EhISwatUqhg4dypEjR1i0aBEtWrQAIDY2ln79+lG3bl0eeOABbnae941fRDdbztXVFT8/Pyt+dncnICCA1NRU8/S1j68VN1uhnNZnL1mV894VLeLDlSvp5mknJyfKly1DUlKygan+mS2P6fXsJWepEsVJS7tink7762vixnN/rh7aql+g2a5nxHj+K0sPwIQJE8wfu7m5ERAQgK+vL/7+/tSuXZuNGzdSs2ZNHn/8cVavXk3VqlUBWLVqFS4uLpQqVYrExMRcl/Vdk5OTc9Oic23PUWxsLBkZGQQFBVn5s7tzQUFBRET8vYs7KioKLy8vatUyZrfmzSin9dlLVuW8dzUDAzkfccHiPjzpGRlUqljhFmsZz5bH9Hr2kvOh2rW4cN0houhLl/AqVIgaVe+zWG5r6B6a1K9X0PHMjBjPf2XpKVeuHNOmTaNv376MGzeOWbNmMWnSJJycnADo0qULXbt2NX/81FNPmdetW7cuR44cYdCgQXh7e7Nnzx727NnDzp07uXDhAufOneP333/n0qVLrF+/ntjYWA4ePMjhw4eJ++t46Y4dO5g2bRqffvopn3/+uWG7Dq/XtWtXwsLCzIfrQkND6dGjh01ku55yWp+9ZFXOe1e7ZnWC6j7Epm3bAUi4fJm0K1do1rQxcPM90kaz5TG9nq3mvPF1faZdG46ePEVqWhoA+w4dplOHtri6/n1rvoiLURQu5EmxIkUKNOv1jBjPf+V9eow0bNgwGjRoQKdOnYyOksv27dsJCQkhMDCQyMhIBgwYgLu7u9GxclFO67OXrMp51b3cp+dyYiLj/zeV0v7+XIi8SLdnnqJm9UASLl9m/aYtjPz8a/q/2o8n2ramVMkS//yEt2Ct+/SAXnu4u/v0JCQm8tv2nXw64Vve6NuLDi2bU7K4H7v2H2Dt5q1UrVSRizExvNb7edyvKxNzl60gKzOLXl3v7neVNe7TAwX/uqv0WNmwYcOoX78+nTt3NjqKiNipe705YUGxZumRuys9RrFW6Slo/8rDW0Y5d+4cYWFhbNq0iXPnzhkdR0RERK6jPT0iIjZGe3ock/b05D/t6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQnEwmk8noECIi+c1kyjE6wm0z5WQbHeG2OLu4GR1BDHJh/TqjI9yWMo+1spjWnh4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYeg0iMiIiIOQaVHREREHIJKj4iIiDgElR4RERFxCCo9IiIi4hBUekRERMQhqPSIiIiIQ1DpEREREYeg0iMi8i+UnJxMenq60TFELKSkpZGRmWnY9l0N2/INNm7cyNKlS/H19eWXX36hZcuWjBkz5o6eIzo6mtWrV1OyZEnat2+fT0nt15YtW9iwYQOVKlUiPDycoUOH4uHhYXSsXJTT+uwlqz3kvHTpEs2btyArK8ti/sqVP1O1alWDUv0tJyeHxYuXMGHiRBbMn0fZsmUtHk9NTaVzl25Mmzo512NGsofXHpTzbuXk5LBq+3Z++Hkl374zhIDixQHYdfgwe44epbCnJxdiLvF2j+54FSpkse7eY8c4de48ASWK06BmTQp5et51DpvY05OVlcWQIUP48MMP+eijj1i6dCmZt2iCGRkZHD582Dy9b98+APz8/Dh79iynTp3K98x5uZbDFkVGRjJ06FAGDhxI79698fPzY+zYsUbHykU5rc9estpLzo0bN/Lf/05i3bp1rFu3jtWrf6Fq1So2UXgA4uLiaNbsUWJj4/J8fPbsOfz5558FG+of2Mtrr5x3LyE5mUa1axOflGSeF5+UxKRFi3mlUyf6PvEEZUuWZNLixRbrfbtkCSfOnqV761YE1617T4UHbKT0JCcnk5iYyP79+wEoU6YM7dq1u+nyY8aM4fjx4wDs3buXSZMmAeDq6krRokXzP3AeQkJCWLRokSHbvh1Lly6ldu3a+Pj4ABAcHMzixYvJyMgwOJkl5bQ+e8lqLzmbN29OcHAw5cqVpVy5skRERNCoUWOjY5mVKFGCgICAPB9bu24djRo1KuBE/8xeXnvlvHt+RYpQ0tfXYt7aXbuo4O+Pi/PVKtK83sOs372HhL+K0cJ164iJT+DZNm2slsMmSk+xYsXo0KED/fv3Z/LkyWRnZ9OqVStMJhPz589nxowZ9OrVi+PHjxMZGcmBAwfYuHEjGzduZP369YSHhzNlypRcz5ucnMz333/PxIkTeeWVV0hKSuLs2bO88cYbfPvtt3z22We0atWKdevWMXnyZJ599lnee+89gDy3HRcXx9ChQxk5ciSjR4+mXbt2bN68maysLNavX8/x48eZO3cu2dnZTJo0idWrV9OpUycSExMLekhzCQ0NpUKFCubp8uXLk5SURFhYmIGpclNO67OXrPaSs0SJEhbT69evp2XLFgaluX0REReIjo6hTp0HjI6Si7289sppXRcuxeLq6mKe9vfzIzMri1Pnz5OcmsoPq1ZRyMODT2fMpOeHH7Ju9+573qZNlB6AL7/8kr59+zJ+/Hh69uxJVFQUISEheHl50adPH1q3bs3HH39M6dKlCQwMpHnz5ua/uMqUKUO/fv3yfM7HH3+cN998Ezc3N6ZPn06FChXw8PAgJyeHYcOG8cYbbzB27Fh69+7NrFmzWLNmDYmJiXlu28/PD39/f06ePMnw4cN57bXXWLRoEa6urjRt2pTAwECee+45jhw5wsmTJ2nXrh0jR47E2dn4YY6JicHb29s87eXlBVw9D8qWKKf12UtWe8l5o9DQUIKCgoyOcUvZ2dksXLiQZ3t0NzpKnuzltVdO6yriVZjz0THmaXfXq6cZJ6akEHrkCK7OLrzauRPv9ulNv44d+XzWj0TExNzs6W6L8b+N/+Lq6sqgQYOYP38+0dHRvPrqq6xdu5bw8HDmzZvH5cuXKVmy5B09586dO9mwYQPz5s3D398fNzc3ANzc3ChTpgwA/v7++Pn54enpiZubG0WKFCEpKemm23Zzc8Pf3x9nZ2f8/f1JSEjItd2qVaty/PhxXnrpJTw9PS2++Izi7OxsUb5cXFxusbRxlNP67CWrveS8XljYYapWrYa7u7vRUW7px9lz6N69u038AZYXe3ntldO6WgYF8WdkJL+fOAHA/r/+L160KBdj4yjnXwqfwoUBaPbQQ3gXKsSeo0fvaZs2cfXWpk2baNSoER4eHtSpU4fRo0fTp08fSpQoQcOGDalfv755WZPJdNvPm5SURJcuXcxlJ6918/ohYDKZyMjIuOttu7m5sXjxYiZOnEjXrl2ZM2cOtWrVuu3c+SEgIIDU1FTz9LWPS5cubVSkPCmn9dlLVnvJeb3169fRokVzg1P8s3nz5jN27DiLee07PEGf3r0YOHCAMaGuYy+vvXJaV6XSpRnVrx8//rKaTfv3U7KYL57u7tSoWJGzUVGkZ/x9QZOTkxNlSpYkOTXtnrZpM7V/woQJ5o/d3NwICAggODiYSZMmkZCQQFZWFtOnTycpKQkXFxfS0tLIyMjAxcXF/ILeeJJWUFAQY8eOJTMzk8TExDzP+8nJycmzzDRt2jTPbd/MtRw5OTns27ePuLg4hg8fTt++fQkNDb3bYbGaoKAgIiIizNNRUVF4eXkZXsZupJzWZy9Z7SXn9TZu3ESzZs2MjvGP1qwO4cDv+8z/AH4JWWkThQfs57VXTutrUucBvu7/Fv27d+d8dDStGzTAw92dwPLluRATY3FPn4zMTCoE+N/T9myi9JQrV45p06bRt29fxo0bx6xZs5g0aRJdu3alTJkytG7dmueff5769etTpEgRmjVrxpw5c1i3bh01atQgOTmZr776ipiYGH7//XcOHz5MbGws7733HgcPHuSRRx5h+PDhdOnShaioKE6cOMGhQ4eIj49n69atnDp1itOnT3Po0CHi4uLYunVrntt2dnbm0KFD/Pnnn0RHR7N582bOnDnDuXPnqFevHmFhYUyZMoXMzEwGDBjAypUryc7O5qmnnjJ6iOnatSthYWGkpKQAV89D6NGjh3kvmK1QTuuzl6z2kvOa8+fPU7hwYXxvuCLFFtzJHnFbYC+vvXLem1t9XR45c4Y9R4/y0lNPAlC9YkUeCqzG9kOHALicnMyVjAwaP3BvJ+I7meztu0Pu2vbt2wkJCSEwMJDIyEgGDBhgk+ciKKf12UvW/MxpMuVY5XmumTlzJpmZmbz88stWfV4AU072Xa8bn5DA2rXr+PDDjxg4cABPP/UkpUqVslimZq37Wbd2zT3fnNDZxXq/RPU1al35nfPC+nV3tPzl5GS2/H6Ar+fOpV/Hp2ndoAElihUDYP/xE8wMCWHQc89S3v/vPTmJKSlMXb4cf18/LsbF8tSjjxJ43VVpt6PMY60splV6RMQhWLv05Kd7KT0FyZqlR+zLnZaevFxOTua3vfvwLlyIFg8/nC8nXN9YemziRGYRERFxLEW9vekYXLDnxNnEOT0iIiIi+U2lR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQVHpERETEIaj0iIiIiENQ6RERERGHoNIjIiIiDkGlR0RERByCSo+IiIg4BJUeERERcQgqPSIiIuIQVHpERETEIaj0iIiIiENwMplMJqNDiIiIiOQ37ekRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RERExCGo9IiIiIhDUOkRERERh6DSIyIiIg5BpUdEREQcgkqPiIiIOASVHhEREXEIKj0iIiLiEFR6RO7CgAEDCA8PNzrGv0Z6errREURu6eLFixbTGRkZ+hlgh1R6HFRcXJzREW7b1q1bjY6QS1ZWFqVKlbKYd/r0aYPS3Fx2djZLlixh4cKFAMyePdsmC8ZLL73E6tWrycjIMDrKXdm8ebPREW7L+fPnjY5gt37++WeL6ZycHL7++muD0vyz8PBwQkJC2LlzJ9nZ2UbHyVNycjKpqakAHDt2jJkzZxIfH5+v23QymUymfN2CGO7kyZMsWrSI5ORkrr3cJ06cYMmSJQYny+3bb79l5syZJCYmAmAymXBycuLo0aMGJ7M0atQoDh8+TOXKlc3zwsLCcv1gNNqQIUM4ffo0999/PyNHjiQ6OpqxY8cyZswYo6NZiIiIICkpia1bt2IymahZsyZNmjTB2dk2/i77+OOPmTdvHk5OTrkes9Wv0a1bt7Jw4UISEhKAqznDw8NtrqB9/vnntGzZknr16jF48GD27t3Lm2++SZcuXYyOBsD+/fsZPHgwERER5tffZDLh6urKY489xvjx4w1OmNuMGTP46quvqFKlCi4uLmRmZvLFF19Qs2ZNo6NZ6Ny5M6NGjcLDw4Nu3brRp08fsrOzefvtt/Ntm6759sxiM/r370+HDh1s7gs+L+vWrWPFihX4+/ub523atMnARHnz9vbm8ccfx9vb2zwvJyfHwER5K168OF9++SXz588HoGTJkmzZssXgVLmVLVsWgEqVKrF8+XKGDx9O2bJleeyxx3jssce47777DM3XqlUr3nzzTfz8/PJ8/Ndffy3gRP9s9OjRvPbaaxbfS7ZWeAC8vLyoX78+M2fO5Pjx46xatYp169YZHcusbt26LFq0iEOHDhEcHGx0nNsybdo05syZw4MPPghAbGwsn332GV9++aXBySz169ePWrVq8Z///IeuXbvy5ptvEhISkq/bVOlxAPfffz9vvPGGxbzHHnvMoDS39vjjj1O4cGGLeWXKlDEozc29/PLL+Pj4WMxr1aqVQWlurlSpUmRkZJj/Qp0zZw4lS5Y0OFVuU6ZMISYmhhUrVvDQQw8xevRomjVrRk5ODnPnzmX16tW89tprhuVr2rTpLR+vV69eASW5fY8++ihPPvmkxTyjy2Ne/P39OX36NN9++y0TJkzA29ubiIgIo2NZ8PPzw9/fnzNnzlC5cmU2bdrEtm3b6NmzJxUrVjQ6Xi6NGjUyFx64+sdPjRo1zNMRERHmPzSMdPLkSX755RfOnz/P+PHjOXfuHD/88AMdOnTIt23q8Na/1LFjx8zHRg8ePEjhwoUtysPOnTt57733jIp3Uy+//DKHDh2iUKFCwNXdyImJiezfv9/gZFcPF9SrVw8PDw9WrFhh8ZjJZGLXrl189tlnBqXLW0xMDKNHj+bw4cMAFCtWjDFjxlC1alWDk1mqV68eXbt25bnnnqNChQoWj+3Zs4fXX3+dXbt2GZQOFi1aROvWrSlSpAhTpkwhKyvL4vHff/+dadOmGZQub6NGjeLUqVMWe3r+/PNP8/ldtmLHjh3MnDmTVq1a8cwzz7Bs2TJWrlzJDz/8YHQ0Cy+99BJffPEF586d48UXX2TEiBFERkby+uuvGx0tl7Fjx1K4cGHza5+ZmcmOHTto1qwZJpOJTZs2MW7cOGNDcnXv+JEjR6hatSpubm7s27ePzMxMmjRpkm/b1J6ef6lff/2VjRs34uXllefj586ds8nS89BDD9G3b1/c3NzM83bs2GFgor/NnTuXgIAAqlatyq+//oqPj4/5nBOTycTx48cNTphbyZIlGTduHFeuXCEzMxMfHx+bPIl9zpw5Fn+JwtUf1G5ubtSsWdPwH9DHjx+nUaNGFCtWjPPnz1OqVCmL194W/3ZMSUnhoYcesvheunz5soGJ8ta4cWMaN25snm7Tpg2dO3c2MFHeevTogZ+fH6+++ir9+vWjU6dOuf74sRXh4eG4ubnx559/mud5eHiY/3CwlYsuFi5cSI8ePczTQUFBTJ06NV9Lj/b0/EuFh4dToUKFPE+8hKulp3z58gWc6p/l5OSQmppKfHw85cuX5+DBg9SpU8foWAB8//33NG/enPvuuy/P3cN//vknlSpVMibcTRw/fpxly5aZT2aFq3sBly5dalworv5CPnLkyE0fv3LlCocOHTL0kNbNxMbGUrx4cYt5Fy9eJCAgwKBEeYuLi8PPz4/k5GScnZ0pXLgwycnJFueh2YJBgwbRo0cP6tSpQ+/evYmPj6dHjx707dvX6GgWpkyZwoYNG3Bzc2P69Ons27ePDz/8kDVr1hgdLZe8fj4lJCRQrFgxwPif//v372fhwoWcOHGCwMBA8/y0tDQOHTrE+vXr823b2tPzL3X9ceY1a9awb98+hg8fTmZmJitWrKBNmzYGpru51atX895779GwYUP+97//ER0dzfLly3n66aeNjsbhw4d59tlnzR/f+EOlaNGiRsS6pf79+9OxY0eLHyy2cPmqi4sLQ4YMoUKFCphMJiIiIihatKj5F3JKSspNTxo2mo+PD+PHj2fjxo24uLjQvn17+vTpY3SsXDIzM3nuuefMh4abNWtmc1ftwdW9u/Xr12fChAmYTCZWrVplk0WiX79+dO3aFV9fX0wmExUqVOD77783OlaeXF1d+fHHH0lMTDTvhTx8+DDfffcdgOF/8NatW5eLFy+SmZlJgwYNzPOv/VzITyo9DuCXX37hxRdfBMDNzY3WrVvz3nvvMWHCBIOT5bZ06VIWLFjA7t27gasnB3fo0MEmSk/16tX5/fffcXV1Zc+ePfj6+pofM5lMbNu2jQEDBhiYMLcGDRrwyiuvWMyzhStQPD09mTp1KtWqVQOu7ubu1q2bxTJGH9K6mc8++4yYmBhefPFFfH19OXXqFFOnTs01zkYbM2YM7du3Z+zYseacc+bM4c033zQ6moWcnBw2b97MrFmzmD17Nq6urhw4cIDHH3/c6GgWkpOT+fHHH3Fzc+PVV19l5cqVNnNZ/Y1efvll6tevb/GHgy38sXO99u3b06ZNG1xcXCzm5/e9pFR6HEDr1q0tDhGlpaXZzHkyN2rRogWBgYHmv05Pnz5tM+chdO7cmRkzZnDs2DHOnj2b674sZ86csbnS4+vry/Dhwy0OvRw/fpxvv/3WwFRXXSs8QK4Tg69cucL69evz9X4dd6tYsWJ88MEH5ulHHnmEZcuWGRfoJoKCgnj++efN07Vq1TKf0G5LGjVqxMKFCxk3bhzVqlVjyZIlJCcnGx0rl8GDB+Pu7m4+ObhHjx689957TJw40eBkudWuXdviaxRs84a0O3bssLiXFFw9TSA/b62g0uMAEhISWLlyJbVr1+bcuXOMGzfuHy/BNUrZsmX55JNPuHz5MrGxsSxYsIBXX33V6FjA1cs+Bw0aBFzde9a+fXuLx8PCwoyIdUunTp3C09PT4hb6N95O3xY4OzszfPhw7r//fi5fvszKlStt5lyuG914C4X09HT27t1Lx44djQl0E6mpqWRnZ5v/kj5y5Ag7d+6ka9euBiezVL16dd5//33zdOfOnW3yUPG1W39cu+dV4cKFzXukbU2bNm3Ys2eP+WvVZDKxbt06evfubXAyS0bcS0qlxwH07NmTefPm8e6775KWlkbDhg1tbhf3NcHBwVSrVo1NmzaRmZnJtGnTLPYI2IobCw9c/aFoa4YPH065cuUs5sXGxhqU5uZ69OhBxYoVWbFiBfHx8XTu3JkXXnjB6Fh5MplMvPrqq1SuXJmEhAS2bNnC0KFDjY6VS/369c030ExISCA9PZ0pU6YYHSuXnTt3MmvWLFJSUjCZTOTk5PDnn3/a3H2vvLy8iI6OxsnJiezsbMaOHWuTP5sA1q9fT0hIiPkQvMlkIjY21uZKjxH3klLpcQDdu3fn008/NZ+Ea8uioqIYP348Bw8epHjx4nh7e9vsDxZblZiYSJEiRTCZTLi4uHDhwgXzYyaTifXr19OrVy8DE96cj48Pvr6+1K9fH3d3d6Pj5Klr165UrlyZ9evX4+zszH//+1+b3CtVt25dFi5cyN69e3F1deXhhx++6S0sjLR582Z69erFnj17aNCgAYmJiTb5Hmxdu3blq6++YseOHUycOJGgoCC++uoro2Pl6ezZs+zatcvie+jAgQMGJspbdnY2vXv3LtB7San0OID777/f4osKsKlLwa/Xv39/fH19GTRoEC4uLuzatYsvvviCd955x+hodqNjx46MHz+eBx54gBdffJErV65Y3LogLi7O5krPvHnz+O677wgODsbX15fJkyfTunVrmzlk9O6775KVlUXbtm157LHHqFChArt27eLIkSNs27aN//73v9SqVcvomLkUKVKEFi1amKfXrVtnc3tQatSoQaNGjbhy5QolSpQwn3yfn3flvRvvv/8+/fv356OPPjI6yj969NFHc8278TYLtsCIe0mp9DiAhIQEOnbsaHF898yZMzb57uUpKSkWb+zYokULm7yK59resypVqhgdJZfVq1czadIkpkyZwhNPPJHrjrG2eB7CkSNHWLduncVfprZ05+ANGzawbNkyAgICyMnJ4a233uLy5cvMmDEDV1dXJk+ebBNvPNm2bVu+/vpratWqRcuWLYmKijI/ZqtvjPrHH3/w+OOP8/PPP9OvXz8uXLhAenq60bFyycrKynUvptOnT9vkz4Djx4/TqVMn89VbJpOJixcvsnbtWoOTWXrnnXcsrjAriBPYVXocQM2aNXO9OaYtvoknQKdOnbhy5Yr5bSjA8v43v/76q03cY8iW956NHTuWDRs2ULVqVebMmUOdOnUs/vKrX7++genyVq9evVyHs64/LGe09u3bm3/hTZs2jbCwMObNm8cDDzwAwLZt24yMZzZz5kz8/f1xcnLiww8/pHnz5hbvDP7LL78YnDC3F154gZdffhlnZ2cmTZrE9u3bbfLNkUuVKkWfPn2oXLmyeV5YWBg///yzganyVrVqVTp16mSxB2Xnzp0GJspbbGwsb731FqVKleKbb75h4cKFNGjQIF/Pj1TpcQC9evXK9QvFFt94EK4e3//f//5nLmhZWVlcuXKF2bNnYzKZuHTpEgcPHjQ4pW3vPTtz5gw///wz7u7unDt3ju+//z7P3d22JCIigh9++IH77ruP+Ph41qxZY1PvDxYfH09CQgL79+9nwoQJvPTSS+bCA9jE1yRgsSeiaNGiFoc1s7KyLO4tZSuefPJJZs2aRdWqVfH09KRly5Y2+bYe3t7euf54zMnJMTDRzb388ssWhQfIdT8cW/DJJ5/Qrl078yXrvXr14oUXXmDevHn5tk2Vnn+p698g8fvvv7eLN0gEaNeuHR9//HGe36C29JeqLe89q127trnkli9f3nzr+WtsZY/U9fr168fUqVMZP3487u7utGnTxqauNOnYsSNt2rQhMTGRDh060L9/fwAyMjKYMGEC27dvNzjh3/78809iYmL4+eefLW5Il5qaysSJE1m8eLGB6XIbNmxYrrfL+fnnn3nqqacMSpS3l19+mUKFCnHq1ClcXV2pUqUKjz32mNGx8hQeHs6CBQssrojbvXs3GzZsMDqahRYtWvD888+bbwOQkpKS7+8LptLzL2WPb5AIV8+VuSYqKopDhw7RrFkz8y/x//znP0ZFs9CrVy8yMjIs3iPs2i9Co+3YscPil8iePXuYNGkScPUv002bNrFkyRKj4uXJzc2Njh07UqlSJXx9fQkKCrKpv0ybN2/Ojh07SE5OtjjcunPnTho0aECnTp0MTGepWLFifPHFF5w4cYIzZ86Y57u6uua6PNgW/PDDDxw7dsziZ5KTk5PNlZ6LFy/y2muvERMTg7e3NyVLlmT8+PEUKVLE6Gi5zJkzh/vvv5/Dhw/zwAMPkJiYaPF2D7aiUKFCrFq1itjYWPPPqfw+0V5vOOoArr1B4uXLl9m1axcVKlSgWLFiNvcGiXD1nJ7hw4dTvnx5OnXqRHBwMP7+/jZ3Z96QkBCL9whbt24dKSkpNvF2GQ0bNqRatWp5vtlsTk4OJ0+eJDQ01IBkN7do0SI+/vhjypQpg6+vL1lZWYwdO5YKFSoYHc0u5eTkcPr06Vy3e4iKisp1LprR1qxZg5ubGy1btjTPs5Vz967Xv39/nn76aZo3b46zszNxcXHMmzcv14UCtuDazVN//fVXHn74YUqUKMGQIUP48ssvjY6WS0hICGvXriUjI4MmTZrQvXt3XF3zb3+M9vT8S8XFxTFq1ChOnTpF48aNef3113nmmWe4cOECTk5OfPrppzzzzDNGx8zlpZdeon79+gwePJhHH32UMWPGEBISYnSsXGz5PcLGjRtH48aNb/q4rRyGu964ceP47rvvzOcepaSkMH36dN566y2Dk9knZ2dnypYty7p168xXxGRnZ7N48eJ8PV/idr3yyiu4uLjQuXNn2rZtyzvvvMPHH39s/mU3fPhwgxPm1rhxY4ti5ufnZ5OXgcPf72I+efJk/u///g8XFxebu2rvmmbNmlG3bl3znr6ffvop1/vwWZNKz7/U6NGjcXFxoXv37mzdupX/+7//o0ePHjz33HOcOnWKL7/80iZLz5kzZ/jyyy/ZuXMnK1asICUlhaVLl9rcPTts+T3CblV4wDbecPRGgYGBFidbe3l52eRbEdiTUaNGkZOTQ2RkJGXLliUxMZGGDRsaHQuArVu3Mm3aNBo1agRcfWuP119/HTc3N7Kysli/fr3N3U/o0qVLJPx/e/ceFGW9P3D8DQs4KSriEbxERzIT0ZNKIqYmaoCu6Tk4iCKQ1ni85a1Mo+g05TUtlZEoJ8pLanjljKKQ1xUV84bYUY86hJcRQoGUJRbGFtj9/cHPPa5oeWl9nl0+r5lmeL4PPt+P2LCf53m+389Hr8fDw4Oamhp0Oh0nTpwgKipK6dDqiIuLIz8/Hzc3N5YsWUJ6errqnpYD/Otf/2LXrl2WV4Rms5nS0lJJesTDa9iwIXPnzgUgOjqaYcOGMX78eAC6du2qypYJAG+88QZZWVmMGTOGpk2bsmfPHrp37650WHWouUeYPSgrK2P//v2W4/bt25OQkGC1HViNFWTtSf/+/QkLC2PHjh1otVo0Gg0JCQlKhwXUPhm9nfBAbb8tHx8fy/GVK1cUiOr3abVaRo0ahV6vx2Aw0K5dO1U2GwVITU21rDPz9PRUbUuXS5cuceTIEavXWbbuYShJj4O68729s7Nznbt7NW5dhdo7/IEDBwK1C3J37NjB/v37mTBhgsKRWbuzR5jRaFRtjzC1atSoEd9++y0dOnSwrD0qLy+nuLjY8j1qXHRrT44fP86FCxeIjY1lxowZdOrUiY0bN/L2228rHRpeXl5Wx3cmPKCereDTp09n3LhxdO7cmWeffZYdO3Zw8eJFXFxc8PX1vee6OTW4efMmq1evxsnJiX79+qmygCLUbhC4+9/a1q1SJOlxUDt37uTnn3+2HF+4cIGSkhLL8YkTJ5g4caISof0ug8FAamoqGzZs4JdffiE4OPgPX9coYcGCBUyZMsUu+pmpkYuLC8uXL1flYnpHMX78eA4fPoynpydjxoxh1apVzJgxQ+mwgNreUEaj8Z791aqqqlTzpKdt27aWp+IpKSnExMTw/PPPW87n5+fXSdjU4Pbv9urqarZs2cLcuXP5+9//rqpdhlBbLy4iIsKqMerFixdtWgJCkh4HVV1djclksmxT9/Pzs5wzmUx1Clcp7fz586xbt47vv/+eLl260KFDB7Zt24abm5vV3b9atG/fnvT0dAwGA507d1ZlYqZ2kvDYVkFBgWXdXkBAAJ07d+bkyZMKR1UrICCAWbNm8dFHH1m1IcjPz2fOnDn07t1bwej+x93dnWXLlqHRaDhz5gylpaVW59Va7ywjIwONRsPWrVs5d+4cr776Kl27dlU6rDoWLVrE5MmTadmyJc7OzphMJpsXeJWkx0HNmzfvd9fCZGdnP8Fofl9cXBwZGRlER0fz73//m7Zt25KUlGS5C7z7UbgaREZGWr5OS0tj6NChDBkyRHWv4UT9Yw/FCceOHcusWbPo27cvvr6+NGnShOLiYgoLCxkwYIBqClOOGzeOQ4cO1akjBLVPJe4u+qoWs2bNstTl+eqrr6xuetUkJCSkTlNhW7cgkTo9QhWOHDlCTk4OXl5eDB48mFWrVjFlyhSlw7qvAwcOcP36dVJTUykpKSEiIoLIyEjV1UCxF/v37ycwMNCqwrV4NHq9nvj4eHJzc2nVqpVl3MXFhX79+qkmoYDaf/fdu3dTVFSEp6cnr7zyClqtVumw7iknJ4eAgACrscLCQksrGjX54Ycf6NWrFyaTiYyMDPbv38+IESNUs3vvtuXLl2MymWjTpo1l7MyZM3z44Yc2m1OSHqEqt/suZWdnEx0dTUBAgCrbJvj7+9OvXz9GjBhBcHCwahc02osBAwaQkpJi9cqrvLycxo0bKxiV/bpfcUJRP1y5coVbt26xbds20tPT8ff3JzIyUnVtM6ZOnYqbm5vV2q7c3FybVoyXpEeo1okTJ0hPTycjI0N1FYR1Op1VoTLxeNauXYtGo7H6kN63bx/vvfeeglHZtzVr1tCxY0cCAwNZunQpBw8eZNq0afL/bT3g7++Pj48PERERhIeHq3KJANx7IXhxcbFN45U1PUK1AgMDCQwMpFu3bkqHUse9PjguXbqk2u71apeVlUVJSYnVdtX8/HxJeh7D1atXGT16NFu3biUtLY1169Zx6NAhpcNyGLfXS6mpR9xtn3zyiaU6/N2Lr9WkWbNmJCYm4urqyqRJk0hOTmb48OE2nVOSHqF6amjtAJCcnHzfnQVms5mff/5ZdV2M7cVbb71VZwHjhQsXFIrGMQQEBKDX60lISCA+Pp6nn36aqqoqpcOyWxs2bLCqvqzRaEhOTrYUfVVafHw81dXVDBw4kH/84x+UlJQwceJEzp07R8uWLUlKSqJTp05Kh2ll5syZuLm5WdZCRkVF8cEHH9i06KMkPUI8oNatWxMbG4uHh8c9z8td9KPz8fEhMTERNzc3Jk6c+ETu+BydyWRi3LhxREREEBoaSmpqKps3b2b06NFKh2ZXbvexys3NtaoSXllZydmzZ1WT9Oh0OrZu3UrLli0xmUxMnTqV0tJSVq1ahYuLC8nJySxbtkzpMK107tyZKVOmsGHDBqC2k8Dtfoa2IkmPEA8oJCQENzc3S+2ju3Xp0uUJR+Q4lLjjc3RDhgxhyJAhluOBAwcSERGhYET2qVu3bly/fp2qqirLNnCofdLz7rvvKhiZNa1Wa9kI8M0333D27FnWr1/P3/72NwAOHz6sZHj31KhRI4qLi3FycqKmpoaEhASbL76XpEeIB1RQUICvry8mk4mcnByruh1ms5lDhw7xzjvvKBih/VLijs/R/fTTT8yePRsvLy+WLl3Kpk2b6NGjh2r77qmZVqslLCyszvqdgoIChSKqq7S0FL1ez6lTp0hMTGTs2LGWhAfg9OnTCkZ3b5GRkSxevJgjR47w+eef0717dxYvXmzTOSXpEeIBvfvuu8yfP5+OHTuyePFiXFxcrLaqX716VZKeR6TEHZ+jmzdvHoMGDUKv1wMwevRoXnvtNdavX69sYHbqyJEjbNq0yfLzhNqt4QcPHlQuqDuEh4cTFhbGr7/+yuDBg5k+fToARqORxMREm7Z2eFTu7u58/PHHVmM3b9606ZyyZV2IR5CXl8dzzz1nNXb+/HmbVxN1VAaDwXLHV1FRQffu3YmPj1ftVlt7sHr1al5//XXLAtyysjJCQ0NVV/7BXmi1Wt58802rAqQHDx5k5syZCkZlraamBoPBQNOmTS1jt5Oyp59+WhW7S3ft2sVPP/103/P//e9/Wb58uc3mlyc9QjyCOxOe0tJSmjVrJgnPY8jNzbW64zMajZw8eVKSnsfw1FNPkZ6ezo0bN8jOziYpKYmQkBClw7JbL7/8MkOHDrUaU0MScSeNRmOV8AD07dtXoWjurbi4GL1ef98NIUaj0abzS9IjxAO6c0voK6+8QnFxMZMmTVL1llC1s4c+UfZq+PDh7Nq1i7y8PM6dO0doaCgjRoxQOiy7VVNTw5gxY6ye9Fy5coVNmzYpGJX9GTRoEJ6envesb1RVVUVJSYlN55ekR4gHdPeW0GnTpql+S6jaeXh48Omnn5Kbm8vly5ct4y4uLnXuqsXDCQkJITk5mcGDBysdikOoqKiga9euuLq6WsbKysoUjMg+tWjRwvL1119/zbhx4yzHrq6ubNiwgRkzZthsfkl6hHhA9rglVO08PDxISkq6Z5+ooqIihaJyDKNGjaJZs2ZWY1lZWfTp00ehiOybj48PUVFRNG/e3DJmMBgUjMh+nTp1iqSkJK5evWpV36yyshKDwSBJjxBqYI9bQu2Bs7Mzbdq0Ye/evZYPkZqaGrZs2SI7jR7D0aNHSU5OtqzxMJvN3Lhxw6rAnnhwO3fuJDo62mrs1q1buLu7KxSR/erWrRtvvvkmOp2O4OBgy7hGo6FDhw42nVuSHiEekD1uCbUXc+fOxWQyce3aNdq0acOvv/5KUFCQ0mHZtbCwMOLi4qw+lPfs2aNgRPZt6NChfPPNN1ZPJA8dOsSSJUsUjMp+vfjiixgMBquCj0ajEZ1Ox6BBg2w2r2xZF+Ih2MOWUHu0e/duwsLC2LFjB1qtFo1GQ0JCAm+//bbSodm1S5cukZWVhUajYcCAAbRq1UrpkOzWO++8g9FotEoiz58/z9atW5ULyk6dO3eO3NxcMjMz6devn2W8srKSlJQUduzYYbO5JekRQihu3rx5NGnShNjYWGbPnk2nTp1YuXIlR48eVTo0u7Vnzx7ee+89unTpQrNmzSgoKGDy5Mmq28JsL/Lz8/Hx8bEau3z5Mr6+vgpFZL+MRiMLFy7kxx9/tHqdpdFo6Nu3L2FhYTabW5IeIYTiiouLOXz4MMOGDSMnJ4dVq1bx8ssvyxbrxzBr1izef/99PD09LWNr1qyRhqOP6O4nOkajkZKSEiZPnqxMQA7g+vXrls0hAOXl5TRu3Nimc8qaHiGEInJycggICADAy8uLYcOGARAQEGAZF4+uV69eVgkPwG+//aZQNPZv3bp1Vut5ioqKpI/ZYyovL2fmzJmW/nCbN2+2eX84SXqEEIqYNGkSr732GoMGDarT0kM8vqtXr7J7926effZZ9Ho9O3futHm1W0eWkJBg9XrLZDKxcOFCBSOyf0r0h3O22ZWFEOJ3LF68mFGjRnHixAk++ugjvvzySy5evKh0WHattLQUk8kEwNixYzlw4AAxMTG89dZbALz//vsKRmffNBoNhYWFlv9Onz6NTqdTOiy71r9/f2JjY/nLX/4C1BaAtPXvAFnTI4RQheLiYtLS0ti9eze9evViwIABvPDCC0qHZVdGjBjBnDlz8PPzq7NeQjyebt264enpye2PzIYNGxITE8OoUaMUjsx+bdy4EXd3d65cuUJQUBBJSUm0bt2aBQsW2GxOeb0lhFCEwWCwbP89efIkaWlp7Ny5k+rqatq1a0dpaanCEdqf8PBw/Pz8ANi3bx8xMTFW50+fPi2J5CPasmUL7dq1UzoMhzJy5EgyMjLIy8vjzJkzhIWFERkZadM55UmPEEIRixYtokGDBmzfvp2ioiL69OnD0KFDCQkJoUGDBkqHZ5dSU1NJS0vDbDZTVFRk1RzTbDZz9epVDhw4oGCE9q+srIxjx47x17/+1ebVgx3RvRo3T5w4kXPnztGqVSu++OIL/P39bTa/JD1CCEX4+fnh7+9PcHAw169fx9fXF61WW6cWing4BoOBS5cusX37dkJDQy3jZrOZzMxM4uLiFIzOvty8eZO5c+eSl5fHSy+9xOTJkxk2bBiFhYU4OTmxYMECy65D8WB69uxp1bg5JiaG4uJi5s+fj4uLC2vXrrVp42Z5vSWEUER8fLxVzZjr16+zbds2srKy6N+/P1qtVioIPwJ3d3deeOEFmjdvTps2bSzjBoPBpnfQjmj+/PloNBpGjhxJVlYWEyZMICoqiujoaPLy8vjss88k6XlIdzduPnPmzBNt3CxJjxBCEREREZavT506RVpaGhkZGVRUVNCgQQOaNGnC8OHDFYzQvlVWVhITE4O3tzdLly5l06ZNNq+B4mgaNmzI3LlzAYiOjmbYsGGMHz8egK5du8rP8hEo3bhZkh4hhCI+//xzGjZsyPbt27l27Ro9e/YkLi6OkJAQmjRponR4dm/evHlotdonWgPF0dy5JsrZ2dmqIzhAs2bNnnRIdk/pxs2ypkcIoQg/Pz+6dOlCeHg4Wq0WDw8PpUNyKKtXr+b1119nw4YNREVFUVZWRmhoKMePH1c6NLsxZMgQq6cQFy5csOyOAzhx4gR79+5VIjS7pmTjZnnSI4RQxN1resSf66mnniI9PZ0bN26QnZ1NUlISISEhSodlV6qrqzGZTDg719bxvTPhMZlMuLq6KhWaXdNoNFYJD/DEGuHKkx4hhHAwxcXFHDp0iBs3brBmzRo6duxI//79iYyMlA/qh5CdnU337t0f+bxQH0l6hBDCgaxYsYKlS5fi4uJC06ZNKS8vx2QyMWvWLGJjY5UOTwhFyestIYRwEFlZWaSkpPDll1/Sp08fNBoNZrOZ48ePM2fOHNq2bUufPn2UDlMIxUjDUSGEcBCbNm3i66+/Jjg4GI1GA4CTkxNBQUGsXLmS7777TuEIhVCWJD1CCOEgWrRocd+dL97e3tKAVNR7kvQIIYSD+KO6MVJXRtR3sqZHCCEcRGZm5h+enzZt2pMJRggVkqRHCCEcREFBAUePHsXJyanOObPZTGFhoQJRCaEekvQIIYSDWLJkCb17977v+aysrCcYjRDqI3V6hBBCCFEvyEJmIYQQQtQLkvQIIYQQol6QpEcIIR5CSUmJXV9fiPpMkh4hhEP74Ycf6NGjB1FRUSxbtowJEybwySefYDQaH+o6VVVVfPrpp8yYMQOAa9euMXDgQP7MZZGrV69m5MiRf9r1hBDWJOkRQji0Xr160b59e3r37s306dNJTEwkMzOTxMTEh7qOq6srwcHBlmNvb28+++yze24Pv1NOTs4DzxESEvJQMQkhHo4kPUIIh+fs/L9fdQ0aNKBdu3Zcvnz5sa/ZqVOn3/2ejIwMNm/e/FjzCCH+PFKnRwhRr+Tn55OTk0NsbCxvvPEGL730Et999x179+4lNTWVW7duodPp+OCDD+jQoQNFRUWsXbuW559/nmPHjlmuo9PpmD17NgcOHAAgOzub3NxcSktLKSgoYPbs2ezbt4/Lly+TkpJCdHQ027dvR6/Xs2/fPiZOnEjPnj0pLy/nq6++on379uTl5Sn1YxGiXpCkRwhRLxw7dozq6mr0ej2LFi3C29ublStXsmjRIoKDg9m9ezeNGjUiKioKjUbD7NmzSUlJYcaMGSxcuBAfHx8aNmzIt99+C0C/fv2YNGkSAL/88gtr1qwhMTERs9nMlClTMBgM9O7dG1dXV6Kjo/nxxx8pLCxkwoQJtGnThri4OA4cOMCcOXOIiIigZ8+eXLhwgfT0dCV/TEI4NEl6hBD1QlBQEFOnTrUcFxQU4OHhgZeXF15eXixfvpznnnuO9evXU1ZWRosWLSgoKCAvLw8fHx8AGjdubPnzd74y0+l0PPPMMwA4OTnxxRdf1Jl/z549/Pbbb6xfv56Kigr8/PwwGAxkZGTw8ccfA+Du7m6Lv7oQ4v9J0iOEEIDRaCQoKIjAwEDL2OnTp6moqKCqqgpXV9f7/tnKykry8/PrXO/u486dOxMeHg7AP//5T27evEl1dTVlZWU0atToz/vLCCHuSRYyCyEcXk1NDTU1Nb/7Pb179yYpKQm9Xk91dTUrVqzA29ubxo0bs3XrVgAqKiq4desWgGWrutlspkePHuzbtw+dTkdNTQ3p6emUlpai0WiorKzEZDIRFBTEihUruHbtGmazmc2bN2MwGGjfvj0bN26sc30hxJ9Pkh4hhEPLzMwkNzeXgwcP8p///AcAk8nE3r17KSkp4eDBgwBERkbSunVrQkNDiY2NJTAwEG9vb5YuXcqKFSuYMmUKJ0+epHHjxpw+fZrvv/8eqH1t5e/vT3x8PB9++CGvvvoqzZs3x9vbmxdffJGzZ8+SnJxMcHAwwcHBhIeHEx4ejpeXF8888wyLFy8mMzOTsWPHotPp8PHxkcagQtiINBwVQgghRL0gT3qEEEIIUS9I0iOEEEKIekGSHiGEEELUC5L0CCGEEKJekKRHCCGEEPWCJD1CCCGEqBck6RFCCCFEvfB/cQpdAhlMLN4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#metrics example\n",
    "metrics_description(datavector_rf_wf[0][2], datavector_rf_wf[0][3])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "393276f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#metrics by prediction on test data by class\n",
    "rf_class_metris_wf = get_classes_metrics(datavector_rf_wf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e9439b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "sns.boxplot(x = rf_class_metris_wf['names'],\n",
    "            y = rf_class_metris_wf['f1_scores'], palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('RF multiclass F1 score (non-filtered data)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.savefig('RF_multiclass_F1_score_nonfiltered_data.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a6c18d",
   "metadata": {},
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0462ef21",
   "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 5 model iterations to evaluate average performance on non-filtered data\n",
    "datavector_svc_wf = []\n",
    "for i in range(5):\n",
    "    svc_model_wf = get_predictions(data = df,\n",
    "                model = SVC(),\n",
    "                param_grid = svc_grid)\n",
    "    datavector_svc_wf.append(svc_model_wf) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "99defc42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 79.01%\n",
      "Precision score: 83.75%\n",
      "Recall score: 79.01%\n",
      "F1-Score: 69.26%\n",
      "Kappa score: 74.56%\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>Birch</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pine</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soil</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grass</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            names  f1_scores  precision_list  recall\n",
       "0           Birch       0.47            0.33    0.84\n",
       "1           Aspen       0.58            0.72    0.49\n",
       "2      Silver fir       0.58            0.41    0.97\n",
       "3            Pine       0.81            0.86    0.76\n",
       "4      Water body       1.00            1.00    1.00\n",
       "5            Soil       0.17            0.13    0.24\n",
       "6   Deforestation       0.98            0.97    0.99\n",
       "7           Grass       0.73            0.86    0.64\n",
       "8    Burnt forest       0.99            0.97    1.00\n",
       "9           Swamp       0.39            0.44    0.34\n",
       "10    Settlements       0.93            0.99    0.88"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/y9Gjx/h10yaC294HQJnSpSlbtuwd5XQrYptyXBhjagvKaXuOklU5bcuMnE5/eMvV1XoI7r77bk6ePAlAlSpVKFGixG3dl4+Pj03z3a7SJUviX6a01bw2QffQpmkTKpQrR4Vy5YiOjaNZo7ssy2cvWUZsfMI/Ljy2dvHiRaKiokhNTQWgaNGipKenm5zK2qJFi2jQoAG+vr4ABAcHs3DhQjLt4JBiWNguPnz/PerUCaTj/ffzZJ8+lj07P86di6urKx4eHtSrW/eOC48t2fOYXks5bc9RsiqnbZmR0+lLz7Vyc3PZtm0bLVu25MKFC7z11ltMmTKFjIwMpk2bRkhICB9++CG9e/cmNzeXWbNmsXz5cgYPHsyqVass97N//36GDx9O165dOXr0qImP6IrSJUtaTW/auYv7mjUFICUtjWlzF1DUy4v3x0+i50uvsWbzVhNS/pefnx8NGzZk6NChJCYm8t133zF06FBTM10vLCyMKlWqWKYrV65MSkoK4eHhJqa64tFHHqZIkSKW6dKlS1G2XFlyc3OJiIhg6rTpPNK9B8NDR5JpR+fz2POYXks5bc9RsiqnbZmRU6UHOHLkCF988QXdu3cnISGBl19+mTJlylCuXDlycnLw9PSkRo0a7Nu3jyFDhvDJJ58wd+5cihcvzkMPPcRzzz3H3r/eSQMUK1aMMWPG0K1bNxYsWGDiI8vf3kOHaVK/PgC/7f0ddzd3Xnu2P+8PfoWXn+7HhxMnczb6vKkZx40bR3R0NG3btqVLly60aNHC1DzXi4uLs9qr5+3tDUBsbKxZkSxcXFyspg8dPswTvXvj6urKjG+/YeumX3n7zZGsW7+eb7751qSUednzmF5LOW3PUbIqp22ZkVOlB6hXrx7Dhg1j8eLFdOzYkZ49e5KYmGh16MvHx4eKFSvi6+tL7dq1Wbp0KQ0aNACgUaNGvPXWW5Z1K1asaPk3LS2tcB/M3zhy8k9qVK6Eh8eVc9ijY+OoUqE8xf/6wWvfsjm+xbzZuf+AmTGJj4+nffv2tGjRgtDQUPbv329qnuu5urpa/Xy4ubmZmObGIiPPkp2VTZfOD1jmeXl58WTfPrz95giWLFtuYjprjjKmyml7jpJVOW3LjJwqPddwdXXlySefJCoqip07d9503UuXLnH27FnL9I2OQdrbeeKbdu7i3r8ObQEU9/XhckaGZdrFxYWKAf6kmFjWLl++TGhoKIMHD2batGk89thjDBs2zK7GMiAgwOo8o6vfly9f3qxIeWRlZTFz1iw+eP/dfJd3fuABzp83d4/etRxhTEE5C4KjZFVO2zIjp9OXntzcXKvpEydOAFC1alUMw7jhL9oWLVowbtw4YmJiuHjxIkuWLCnwrLawbfde2tzTxDJdt0Z1zp6PsbqmT2ZWFtX+2ltlhuPHj1O0aFG8vLxwcXFhxIgRxMfHk5iYaFqm6wUFBREVFWWZjomJwdvbm/p/HTa0B9/P/IGBLzxPsWLFAMjOzrZafvlyBnUCa5sRLV+OMKagnAXBUbIqp22ZkdOpS8/u3bs5cOAAYWFhTJ48mU8++YTPPvuMUaNGUbJkSX7//Xf2799PbGwsmzZt4uTJk/z+++8AvPbaa1SuXJlu3brx1ltv8cADD3DmzBkiIiLYsWMHFy9eJCwsjGPHjhEfH1+oj+tGRe1cTCxFvbwoUdzXMq9erZrcc1d9tu7aA8DF5BQuXc7g3qB7CiVrfipXrsz58+ctrT8nJwd/f3/8/PxMy3S93r17Ex4ebjl8GRYWRp8+ffDw8DA52RVz5s6jZs0apCSn8Oefp9i1azdz5s5j1eo1lp+PhT//zCsvv2xy0v+y9zG9Sjltz1GyKqdtmZHT6a/TY+9u9zo9F5NT+HVHGKO/nsrLTz1J13b3Ufav6wzNWbaC7OxsnnrM+qPpF1NSmTzrRwLKlCE6No4eXTpRt+btXQjQltfpgSsXrFq7di1169bl/PnzPPDAA9x1111/f8NCtH37dlauXElgYCDR0dEMGTLE6lNTd+qfXqfnl7XrGBJifTjQ09OTLz7/jPc++IBq1arR9r77qF+vLve2aXPHOW11nR4o+DG1FeW0PUfJqpy2Vdg5VXrs3O2WHrPYuvTIPy89hc2WpUdEpCA59eEtERERcR4qPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BXezA8jNJYSfNjvCLTm9LtzsCLek0ctPmB3hlmWlJJkd4Za4lQ4wO4LI/wTDyDU7wi1zcXHMfSaOmVpERETkNqn0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo/IP5CamkpGRobZMURE8rjR65Net8Dd7ABiWwdO/cn0NSs4eyGO5oF1CenxOB7u/32aL2dm8srk8Yx65jn8/UqRmJrCU2M+ISc31+p+/vPqUKqW8y/wvK7ubgT2fZA/F68nMyUN74rlqHhvUzz9fEk+dY4za7dj5ObiXtSLBv/qgYubdU8/MmsZGQkXCzznVbm5uSxc+BMTJk5k3tw5VKxYEYADBw7y68aNlPLz4+DBg7z00otUr1690HLl5+DhI2zauh2/kiUIP/wH/zfgaapVrcJvYbv4YsJXRJ+PoXWLZrwbOpzixX1NzXrVli1b2LBhA9WqVSMiIoLQ0FA8PT3NjpWHctqeo2S155y5ubksWLCQCRMmMG/ePCpVqnjT+fagsMfTKff0GIbBG2+8YXYMm0tMTeFYVCRjB77MpJdeY/+fJ9mwf6/VOkt+20ZU/AXLdNjRI7zb7xlmDA1lxtBQpr8+nCplyxVK4QEoc3ddvPyKA+Be1Iti5UpzbP5qjs5ZiU9lf/zqXikOxatX5M/lGzn83aIrX98v4VJ8UqEWHoCEhATatr2P+PgEy7zc3FxCR4xk0Esv8tRT/en9eG8++nhUoea6Xm5uLm99OIr/+9cz9Hu8F70efZjR/x5H9PkYlq/+hdHvv8NH74xkx649jJv8H1OzXhUdHU1oaChDhw7lmWeeoVSpUowdO9bsWHkop+05SlZ7z5mQkEBwcFvi4+Nvab7ZzBhPpyw9O3bsYOXKlRw6dMjsKDaVmZ1Nr3uDcXN1pULpMjSoWg1Xl/8+xdsOh3N3zVpWt2lepx7NA+vi71cKf79SxCQm5FmnoJSoUZnUyPOWaRd3N+L2HQHDIPNiKmnnYsEwAEg+FUVKxDkyU9LITEmjSHFvq9sWljJlyhAQEGA172JyMlFRUaSmpQFQrGhR0tMvFXq2ayWnpHAu+jxpf2UqWtSL9PRL7Nq7j/dGDCewVk3uD25Ln56PsXf/AVOzXrVo0SIaNGiAr++VvU7BwcEsXLiQzMxMk5NZU07bc5Ss9p4zv9enm803mxnj6ZSlZ8+ePfTt25fZs2ebHcWm/Ev6Wb5PTk/Dt2hR2jduAkBMUiIJKcnUqVTZ6jZ+PtaHNX47cpiWdesXeFYPX288fIqSHvPfdx5ZKWmW7928ipBzOZOEo6cAyL502er2JWpW5uKpswWe81b4lSxJw4Z3ERIyjMSkJGbMmMnQIYNNzVSyRAka1KtL6LsfkHTxIj/Mnc9rLw3k4a6dKVKkiGW9UqX8KFumjIlJ/yssLIwqVapYpitXrkxKSgrh4eEmpspLOW3PUbI6Sk5HYcZ4Ol3pSUhIoGzZsjz55JOsWrWK5ORk4MoJXpMmTWL58uU89thjAMydO5dnn32Wb7/9loceeog+ffpw/vyVvQsbN25k9uzZPP/886xYsYKcnBwmTpzIc889x/Tp0+nRowdfffWVKY9x3b49vDJ5PKdjY7iYlkZObi6rdu3kwWYt//a2B06d5K6qBXwuiosLZe6qzYUDx/Jd7Fe3OnX6PohX6ZK4F/XKdx2fiv6kRcUWZMrbMvbfX3L+fAzt2nXggc6daN68udmRGPPR+5yPiaPTo73o2D6YZvc0wcXFxWqdw38cpfdjj5iU0FpcXBw+Pj6WaW9vbwBiY+3neQblLAiOktVRcjoKM8bT6UrPmjVrePDBB6levTqNGzfm559/BmDr1q2kp6fz0EMPMWLECABatWrF4cOHefzxx1myZAklS5bkyy+/JCoqiq1bt9KvXz+GDx/O22+/TXp6Ok2aNOHAgQP06tWLr7/+mpkzZ5ryGO+7qxHDe/UhKTWVb39ZydId2+jWrAWurjd/uo9HnaVqOX+rE58LQtnGdbhwMP/CA5B0/AwRa7bhXsyLCm2a5FletGwpLidcxLju5GszXbgQT7vgtrRo3pyRI99ivx0cMopPSKBtm1Y0u+du3vloNAcOHbZafjbqHNnZ2TzQob1JCa25urpa/Yy6ubmZmObGlNP2HCWro+R0FGaMp9N9emv37t1cuHDlRN7ixYszZ84cnnnmGZo0acLHH39MTEwMw4YNA648AT4+PpYm2rNnT7788ks2bdpEYmIic+bMIScnh+bNm5OYmIiHhwdFixalZMmSACQlJZnxEPH08KBhtRq8/HB3pq9ewR+RZ/hu7WqrdZ4f/wU9Wt/HgAe6Wub9duQQLQrh0FaZRoGUb21dZuo+/Qhx+44Qvf13jJwc0s7FcnZjGBXubZrn9iVqVibZTg5tAVy+fJkRI99k/rw5eHp6MmrUaN54I5TVq1fm2bNSaJkyMnj7o0+Y/c0UPIsU4dOx43nz/Y9ZNn82Li4uZGVnM2veAt4dMdyUfPkJCAggPT3dMn31+/Lly5sVKV/KaXuOktVRcjoKM8bTqUpPWFgYffv2JSgoCICMjAxat27N9u3badGiBYsXL+bzzz/nscceY/HixXlu7+bmRqlSpcjMzKR69er07dsXgP79+2MYBtHR0YX5cP5W2eIlqFC6DO8++bTV/K7vhDJ98DD8/UpZzd959A9Gt763wHMdmbnUavru1/rzx8ylZF5zTg9AVko6GUnJeW5fvHpFTi46UqAZb8eJEycoWrQoXl5XDsWFhg5n8ZIlJCYmUqpUqb+5dcE4+eepK5n++ujnsNdeYdnKh0lMukgpv5L8MGc+zz/Tn2JFiwKQnZ2NewHv4fs7QUFBHDx40DIdExODt7c39esXfBG/Hcppe46S1VFyOgozxtOpDm+tWLGCe+65xzLt6elJu3bt+PHHH1m7di1eXl58/vnnBAcHW56IrKwsjL8+QbRz50569OhB69atmT9/PidOnABg7dq17N+/v/Af0HXOJyZwJDLCMr3r+FH6BHe45dsW9SxC8WLeBRXvbxUp7k2xgP+eVOtbtQIxu8LzrJObmU3OZfM+LXH15+GqSpUqExMTY3mXkpOTg79/Ofz8/PK7eaGoVLECMbFxpF+6ZMlUrmwZ/EqWYN5Pi6hRvSopKamcOh3B7n2/s21nmGlZr+rduzfh4eGWT5yFhYXRp08fPDw8TE5mTTltz1GyOkLO61+f/m6+mcwYT6fZ07N48WJWrFhBjx49aNy4MXClVSYlJbF9+3buuusuFi5cSM+ePQkICOC+++7jwoULZGRk8O2335KcnIyrqys9e/bExcWF559/nqeffprixYvz4osv0rFjR8aPH09ycjJHjx61nPC8efNm2rZtWyiPMSI2hi8WzqNR9RrcVa06tSpUJLBipVu67W9HDtGiTr0CTnhzXqVKUuWB1qSejSHtXCyX4hK4FJtgtU6JGuYe2kpMSmLt2nUArFy1mkcfeZhy5crx8Ucf8tlnY6hbrw7no8/z+WefmnZoC6BE8eK8P/INvpjwFXVr1+J8bCyfvPc26zduZvS/x1u9AHoWKcL65YtMy3pVqVKl+OSTTxg9ejSBgYFER0czZMgQs2PloZy25yhZ7T1nYmIia9euBWDVqpU88sij+PuXu+F8s5kxni6GPdY/O3H27FmefvppNmzYYFqGP+cvNm3btyP5fKrZEW5Jo5efMDvCLctMsq8Lid2IV2n7u/6HiCMyDPv5cMbfcXFxzANFjpm6EKkTioiI/G9Q6bmJlStXEhcXx+rVq/9+ZREREbFrOrxl53R4y7Z0eMv2dHhLxDZ0eKvgOWZqERERkduk0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRcDMMwzA4hUlgyk+PNjnDLihQvbXYEEZH/KdrTIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6nFBqaioZGRlmxxAT6LkXEWfmbnaAf2rjxo0sWrQIPz8/Vq1aRYcOHRg9ejQAPXr04NNPP6Vy5cqsW7eO6OhoBg4caPMMFy5c4Oeff+aPP/6gQYMG7N69m759+9K2bVubb8sWcnNzWbBgAePHj2f+/PlUqlTJ7Ej52rJlCxs2bKBatWpEREQQGhqKp6en2bGs/LZzF0ePHadixfK0admCYsWKAZB+6RJP9B/AfyaOpWKF8ian/C8997alnLbnKFmV07YKPafhgLKysoygoCAjPj7eMAzDiIqKMkJCQizLw8PDjczMTCM7O9tYvXq10b9//wLJMWbMGGPbtm1GamqqceLECePo0aNGampqgWzLFuLi4ozo6GgjMDDQiIyMNDtOvs6dO2e0atXKSE5ONgzDMCZOnGiMHj3aZvefcfHCHX99/P67xuQJY/Nd9tW4fxuBgYHGn0cO3vF2bEnPve0op+05SlbltC0zcjrk4a3U1FSSk5PZt28fABUqVKBLly6W5Q0aNMDV1RU3NzdKlixZYDn++OMP3Nzc8Pb2pmbNmgQGBuLl5VVg27tTZcqUISAgwOwYN7Vo0SIaNGiAr68vAMHBwSxcuJDMzEyTk13x/awfiYmN47lnnsqzbP2vm2jRPMiEVH9Pz73tKKftOUpW5bQtM3I6ZOkpWbIk3bp1Y/DgwUyZMoWcnBw6duwIwPHjx+nTpw979uzJc7v169dTr149Nm7cCMCCBQv4+OOPgSuHy2bPns3zzz/PihUryMjIYNq0aYSEhPDhhx/Su3dvq/tauXIlZ86cYe3ataxevZqzZ8/y0ksvsXTpUpKTk/n3v//Nhx9+yCuvvMLgwYMLdkD+h4SFhVGlShXLdOXKlUlJSSE8PNzEVFckp6Tw1dRvKFasKG++9yHdHuvNitW/AHAuOprYuAs0bFDf5JSOy56f+2spp+05SlbltC0zcjpk6QEYM2YMAwYMYPz48fTr14+YmBgAateujZubG4Zh5LnN/fffT3BwMNHR0QBkZWXxyiuvEBUVxdatW+nXrx/Dhw/n7bffJjMzkxo1arBv3z6GDBnCJ598YnVf3bp1w9/fn06dOtGlSxcqVaqEr68vhmFQvHhxypQpw6FDh/jss894/fXXC3w8/lfExcXh4+Njmfb29gYgNjbWrEgW237bgbu7OyGDX+GTD95lyKuDePuDj4k8e5YFPy/hiV6PmR3Rodnzc38t5bQ9R8mqnLZlRk6HLT3u7u6EhIQwd+5cYmNjeemllyxFx9X1xg/riSee4KeffgKuHCYrWbIkmzZtIjExkTlz5rBr1y6aN29OYmIiPj4+VKxYEV9fX2rXrv23ma7dro+PD9WrV8fb25vq1avf4aN1Hq6urlbj6ObmZmIaa1HnoqlWpTIlihcHoGP7dhT39WHN2g083rP7TX/u5O/Z83N/LeW0PUfJqpy2ZUZOh3yV3rRpk+Vjt40aNWLUqFEcOnSIxMTEv71tcHAwFy5cYMmSJTRo0ACAzMxMqlevTt++fenfvz9TpkyhcuXKBfoYJH8BAQGkp6dbpq9+X768+Z+EKlG8OJcuX7ZMu7i4UKlSRb6aMo2HevahaZt2NG3TDoCHe/Vh3KSvTUrqmOz5ub+Wctqeo2RVTtsyI6dDlh6ACRMmWL738PAgICAAPz8/AAzDyPfwFlxplr169WLq1Km0atUKgNatWzN//nxOnDgBwNq1a9m/f//fZrh+OzfbrtyaoKAgoqKiLNMxMTF4e3tTv77558rUr1uHs2ejrK5zk5mRyRejP2bPto2WL4BlC+fy+isvmZTUMdnzc38t5bQ9R8mqnLZlRk6HLD2VKlVi+vTpDBgwgHHjxjFz5kwmTZqEi4sLhw8fJiIigi1btnDp0iW2bNnC2bNnOXnypOX2vXr14qGHHrLsVgsMDOT555/n6aefpkuXLqSlpVG3bl02bdrEyZMn+f333/NkOHjwIKdOnWLbtm0kJCRw6tQp/vjjD3bu3MnFixfZsWMHBw8e5Pjx44U1LLfE3ktZ7969CQ8PJy0tDbhyolufPn3w8PAwORk0qF+PoKb3sHHLNgCSki5y6fJl2t7XxuRkt0bPvW0op+05SlbltC0zcroY9v5KKDaTmJjI2rVreeeddwgJCeHRRx/F39/f7Fh5bN++nZUrVxIYGEh0dDRDhgyhSJEiNrnvzOT4O7r9xYvJjPvqa8oH+HPuXDSP9+pB/bp1rNZp2Kw1q5f8dMcXJyxSvPQd3f5aeu5tSzltz1GyKqdtFXZOlR5xKndaegqTLUuPiIg46OEtERERkdul0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU3AxDMMwO4TcmJGbY3aEW5Jy6pjZEW6JZ+nSZke4ZeOenWx2hFsSuvh9syOIiNwS7ekRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj1OIjU1lYyMDLNj5JGank5GZqbZMZxK0eLFcHXL/7/+zZaJY7PX1wCRwuRudgB7s337dl5//XVq1KhBq1atOHz4MNWqVSMkJITOnTuzaNEiSpYsaXbMW5abm8uChQuZMGEi8+bNpVLFilbL09PT6dGzF9OnT8uzrKBzLV6zlik//Mh3Yz+ngr+/1fJLly/T/9WhTPz4vTzLdu7bz7E/T1HRvxwtmzahWNGihZI5Pj6BTg/3IDsnx2r+ojkzSUxK4ssJkzkdcYb72rRi1Htv4eHhUSi5AIqV8GbQN0Nxc3ezmj/9la+IPxvHk6MGULlBVQDOn4zm+5AplnVutqywbdmyhQ0bNlCtWjUiIiIIDQ3F09PTtDw34ig54a/XgAULGD9+PPPnz6dSpUpmR8qXI4zphQsXCA4OJjs722r+ihUrqFWrlkmpbiwsLIzPPvuMU6dO0a5dOz799FOKFClidiwrhf28q/Rcp3Xr1tSuXZuWLVvy6quvkpGRwSOPPIKHhwfjxo1zqMIDkJCQQHDbtrz33vv5Lv9h1ixOnz5dqJkAEi8m06ZZU0ZP/Drf5XOXLOdM1Lk888dO+5ZSJUvyTO8eBR0xj03btjN+zGhqVK8GQHZ2Nq+/8SYlS5Rgy/YdzPrmP0Sdi+bpFwaxbNUaejzyUKFlq9UskJ9Hz+VCZBwAbm6u9BjZh/izcZSt6s+pfSdYN30VAGlJqZbb3WxZYYuOjiY0NJQ1a9bg6+vLpEmTGDt2LCNGjDAtU34cJedVCQkJBAcH8+6775od5YYcZUx//fVXJk+eTM2aNYErrwEvv/yyXRaeCxcucPDgQebPn8/Zs2fp27cvS5YsoXfv3mZHszDjedd+7Hy4uv53WDw9PalZsyanTp2icePG5Obmmpjs9pUpU4aAgIB8l61du45WLVsWcqIrSvuVxL9MmXyX/bp9B83vbpRn/qyfFxN7Id6UwgMQ3KY197VpRcUK5alYoTznos/TollTMjIzebZ/X9zc3KhSuRL33N0IN9fC/a91Ytcx/txznOTYJJJjkyhRriSnD5wC4J5uzTAMg5ysHGJPnSct8b/F5mbLCtuiRYto0KABvr6+AAQHB7Nw4UIy7ezwp6PkvOpmrwH2wlHGtH379gQHB1OpUiUqVapEVFQUrVq1MjtWvjIyMnjuuedwc3OjatWqBAUF4ebm9vc3LERmPO8qPX8jMjKSvXv3Uq9ePbp06UJ0dDSRkZEMGjSIf//737zzzjt07tyZP/74A4DY2Fi++eYbRo8ezbBhw8jKyjL5EeQvKiqK2NhYGjXKWy7MFB0TS1x8Ag3qBFrNT0lNZeqsuRQr6sV7X4zjsedeZPWvmwo1W+nSpaymN2zaQrv77qVC+f/+Qkm6eJHixX3p1uWBQs2WfjHNarp2i7qcCDsKLi74VShNy1738fykl3loSA9crx4Cu9kyE4SFhVGlShXLdOXKlUlJSSE8PNy0TPlxlJyOxFHGtMx1b9TWrVtHhw4dTEpzcxWvOV0hMTGR4sWL8/DDD5uYKC8znneVnhvYuXMnY8eOZfr06Xz22We88sorXL58GcMwqFy5MkWLFiUmJoaPPvqI7t27s3jxYgBGjRpF//79GTlyJH/++adlvj3Jyclh3vz59O3bx+woVnJycvh51Rp6Pdglz7Lf9uzD3d2Nwc8N4INhr/PqgKf5YOxEzkZHm5D0it1799G0SWPL9NKVq3n8qX9x4uQpEhOTTMsFUOWuakQejgDDYO473zPpmTH8MmUFgS3r0bJHmysr3WyZCeLi4vDx8bFMe3t7A1feSNgTR8npSBx1TMPCwggKCjI7xk0tXryYxx57jOPHj5OQkGB2HCtmPO8qPTfQokULhgwZwgcffEBwcDBgfdjLw8PDssvY39+fpKQkLl26xKFDh/j555+ZM2cO9evXJ+e6k17twQ8/zKLPE09YPR57MG/pCnp265JvrnMxsVSpUIHivlf+g7Rv0wpfb2927P29kFNecfjIUWrWqG51svIDHdrzyQfvEJ+QwLhJ+Z+rVBj8a5bnQmQcudn//dnLzsxm36pdrJu2kgbtGlutf7NlhcnV1dXqube3XfFXOUpOR+KIYxoeHk6tWrXs7sTg63Xp0oUxY8Zw4cIFvvjiC7PjWDHjedeJzDZ09VBW3759LfMMwzArzg3NmTOHf48dazWva9duPPvsM4QMHWpSKli4YhVfzfjBal6vF17myccepUJAOS5f83FbFxcXKpUPICU17fq7KRQbNm0h+N7WVvO8vDwJanI3b70RwpcTvjIlF0Bgi7qc2HU032V/bDtMp4EP3vaywhAQEEB6erpl+ur35cuXNytSvhwlpyNxxDFdt24d7du3NzvG3/Ly8qJZs2a89957fP7552bHsWLG867Sk4+cnJx899AYhnHTElO8eHFKlCjBDz/8QL9+/YiLi2PNmjU8/fTTBRn3tq1Zs9pqum69+qxatbJQP7Ken5+nW+8dadatOwunfUUFf38OHzvB2fPnycjMxPOvd1YZmZlUq2zOx283b9vO1L5j810W4F+OqiblAqgZFMiu92bmu8y9iDuxp2Nue1lhCAoK4uDBg5bpmJgYvL29qV+/vmmZ8uMoOR2JI47pxo0b+e6778yOccvKly9P1apVzY5hxYzn3b6Ob9iBjRs3cuzYMTZv3sz+/fst88PCwoiPj2f9+vVcuHCBY8eOcfToUS5cuMBvv/3GkSNHuHDhAp9++ik//fQT9957L2PHjrWLjwfa494muL1c9QNr0bThXWzZuQuApORkLl/O4L7mhX88/ey5cxQtWpSSJUpYpvcfPGRZvnX7Dl4YYE7RLVGuJJmXM7mccgmAUhVKU7dNA8vyxp3uYeucX/92mRl69+5NeHg4aWlX9t6FhYXRp0+fQr3e0a1wlJzXstfXgKscbUwjIyMpVqwYfn5+Zke5ocjISH7//XfL9ObNm3nxxRfNC5QPM5537em5Trt27di9e3ee+c2bN+fAgQOW6Z9//tny/ZgxYyzflylTxq5OXk5MTGLt2rUArFq5ikcefQT/cuVMTnWltPy6fQcAazdvo1uHdpS97tNR1/tw+BC+mvEDZ6LOcS4mllEjQnB3L/wf4V83bSX4vv8e2jr55ynefP9jmje9h3vubky9uoE0qFe30HPBX5/a2nXMMu1bpgQdX+jGPQ825889xzl/4hynfj/5t8vMUKpUKT755BNGjx5NYGAg0dHRDBkyxLQ8N+IoOa9KTEy0vAasXLmSRx99FP/rLvhpNkcb0/Xr19v9oa0TJ07wxhtv0KJFC4KCgqhfvz4NGzY0O5YVM553F8Pe3wI4OSPX/k6Ezk/KqWN/v5Id8Cxd2uwIt2zcs5PNjnBLQhe/b3YEEZFbosNbIiIi4hRUekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCi6GYRhmhxARx5UeHWF2hFtSrHxVsyOIiMm0p0dEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIKVLT0sjIyDQ7hog4EXezA9iLrKws0tLSKFmy5D+6/aVLl8jJycHHx8e2wWxoy5YtbNiwgWrVqhEREUFoaCienp5mx8pDOW3PnrLm5uayaMUqJn/7PT9MnkCF8gEAbA/bzabtv1G1UiXOnI1iyEsD8fQsYnXbS5cu0Xfgy0z+/BPL7cxgT+N5M46SExwnq3LaVqHnNEw2Y8YMIzAw0Bg1apSRkpJimf7kk0+MixcvGoZhGJs2bTKaN29u7Nq1q0AynDt3znj00UeNn3766R/d/ujRo0bbtm2NHTt22DiZ7Zw7d85o1aqVkZycbBiGYUycONEYPXq0yanyUk7bK+isaedO39bXmfB9xp+/7zICAwON43t2GGnnThsnfw8zWrZobpw/dshIO3fa+Pfoj40P3x6Z57YTxoy2ut3tfNmKozz3jpLTMBwnq3Lalhk5TT+89cwzz9CoUSOKFSuGj48PzzzzDPXr18fLy4vixYsDUL9+ffr160dQUNAN7ycxMZFTp079owzly5enTp06/+i2AIGBgVSpUuUf374wLFq0iAYNGuDr6wtAcHAwCxcuJDPTvg4vKKft2VvW0qX88C9X1mrestVrqRdYG18fbwDubdGcxStWk5WVZVlnw5attLinSaFmzY+9jeeNOEpOcJysymlbZuQ0vfQAPPbYYyxfvtwyHRwczLJlyzAMA4BffvmFLl263PD2OTk5vPnmm8TGxv7jDC4uLv/4to4gLCzMqphVrlyZlJQUwsPDTUyVl3LaniNk3f37ASpVqGCZrlQhgNS0NA4dPQbAufMxxF1I4K56dc2KaOEI4wmOkxMcJ6ty2pYZOe2i9HTt2pXo6Gj27NkDQExMDDExMZbpP/74g8DAQMuyjz76iNWrVzNy5EhSU1M5efIkx44dY/ny5ezevZvU1FS+/fZbJk6cyIsvvkhKSgqHDx9m4MCBzJw5kw4dOrB+/fo8OY4cOcKzzz5L69atmT9/PgCZmZlMmjSJxYsX8/bbb/Pjjz9a1p8zZw6zZ89m3rx5nD171pL1nnvu4T//+Q8AZ8+epV+/fly6dKngBvAWxMXFWZ1v5O195R31nRTFgqCctucIWS8kJODjXcwyXazYle/jLsSTk5PDT8tW0PvRh8yKZ8URxhMcJyc4TlbltC0zctrFicx+fn60bNmSpUuXUqFCBapXr06bNm1YunQplStXply5cpZ1t23bhq+vL126dGHDhg1s376dBx54gAoVKvDQQw8RFBTEe++9x6BBg/D39+fVV1/lm2++4fXXXycuLg43NzdmzZpl2Z12LS8vL2bMmMHmzZsZNGgQrVu3ZsmSJRQtWpTu3bvTtWtXOnbsSLVq1XBxceH333/ns88+A64UIIC6devSq1cvLly4AEBqair9+vWjaNGihTCSN+bq6oqr6387rpubm4lpbkw5bc8Rsrpdn/Ga7+f8vJhejzxktdxMjjCe4Dg5wXGyKqdtmZHTLkoPQLdu3fj8888JCAigW7duVKhQgQ8//JBKlSrRuXNny3o9evQgPj6eZcuWER0dTVpaWp772rFjB3XrXtkN7u/vj4eHBwA+Pj7UqlWLCtfsRr9W9erVAWjbti0BAQEcOHCA9evX8+qrrwLg6elJcHAwv/76KykpKTRs2NBy22tLVL9+/ejVqxdDhw5ly5YtPPXUU3c4OncuICCA9PR0y/TV78uXL29WpHwpp+05QtZyZcuQfs3e0EuXLwMQUK4sE6d9y8Sp31qt3/3p5+jfuwevDXyuUHOCY4wnOE5OcJysymlbZuS0j7dOQMeOHUlLS2Pr1q1UrVqVjh07kp2dzS+//ELt2rUt6+3du5cPPviALl26ULFiRct5P9dKSUmhV69e9O3bl7fffptBgwbddp5SpUpRsmRJ3N3dSUpKssz38/PD3d2dxMREq/nXqlq1KnfffTdLly7FMAy8vLxue/u2FhQURFRUlGU6JiYGb29v6tevb2KqvJTT9hwha5NGdxF9PsYyHRN3Ae9ixagbWJuls2ewc+0KyxfA4pnfmFJ4wDHGExwnJzhOVuW0LTNy2k3pKVGiBC1atKB9+/bAlUNNDzzwAG3atLFa79tvv6VBgwZ4eHiQnJxMTk4OcGW32KVLl8jMzCQoKIixY8eSlZVFcnIyU6dOva0saWlpZGdnExQUROfOnVmzZo1l2cmTJ+ncuTPNmjVj+fLllmaalpbG5b/enQL079+fSZMmWfY4ma13796Eh4db9oyFhYXRp08fy14we6GctmePWa9/s9LjwW4cPnqM9PQre3v2/H6AXo88iIe73eyMtrDH8cyPo+QEx8mqnLZlRk4XI79dJSZZsGABrVu3pmLFigBs3LgRPz8/GjdubFln3rx5/Pvf/+bxxx/H1dWVnTt3MnbsWNasWcPSpUsJCQmhVq1ahISEcPz4cYKCgvjwww+Ji4tj4MCBdOnShZdffpkSJUpYbXvDhg0sXbqUu+++m6SkJLp37061atXIzs5m7NixuLu7U758eTw9PXnsscfIzMzk7bff5vDhw9x///2Eh4fTpEkTBg4cSJEiRTAMgyeeeIIff/wRdzt54d6+fTsrV64kMDCQ6OhohgwZQpEiRf7+hoVMOW2vILOmR0fc1vpJF5PZsGUrH30xjtcGPseDD3SkXJnS7Ni9l19+3UStGtU4HxPHK88/m2/GJu0eYMWcmbd9ccJi5ave1vo34yjPvaPkBMfJqpy2Vdg57ar0/C/Jycnhm2++YeDAgWZHESlQt1t6zGLL0iMijsk+dkH8D0lNTeWPP/7gyJEj3H///WbHERERkb/YzTk9/ysOHDjASy+9xMWLF6lZs6bZcUREROQvOrwlIndEh7dExFFoT4+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFPQX1kXEbEzuVmZZke4Ja4eRcyOIHJbtKdHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU7jt0hMeHl4QOUREREQKlPv1M7788ktWrlyZ78qGYZCUlMTevXsLPJiIiIiILeUpPY0bN6Zz5874+fnlWdkwDDZs2FAowURERERsKU/pad++PW5ubje8QYMGDQo0kIiIiEhByFN6ri08J06cYN68eaSlpWEYBrm5uezatUt7e0RERMTh5Ck915o9ezZ33XUXhw4domHDhiQnJ9O8efPCyiYiIiJiMzf99Fbz5s3p2bMnLVu25L777uOZZ55hx44dhZVNRERExGZuuqdn3759zJ8/nylTpvB///d/uLm5ceTIkcLKJiIiImIzLoZhGDdamJOTQ2RkJNWqVSM+Pp6VK1fSpEkT7rrrrsLMKCLiVHKzMs2OcEtcPYqYHUHktty09FwvKyuLPXv20LJly4LMZDc2btzIokWL8PPzY9WqVXTo0IHRo0ffcP2PPvqIwMBAnnjiCbZu3cq6det4//33Cy/w39iyZQsbNmygWrVqREREEBoaiqenp9mx8lBO23OUrI6Q88KFCwQHB5OdnW01f8WKFdSqVcsm27iT0rNt+3Y+GzOGc+eiubdNaz547z1KlChxw/l3wpalxxGee1BOWyv0nMZN1KlTx6hbt67lq169esa//vWvm93kf0ZWVpYRFBRkxMfHG4ZhGFFRUUZISMhNbxMREWFZ/8KFC0ZgYGCB57xV586dM1q1amUkJycbhmEYEydONEaPHm1yqryU0/YcJauj5Jw/f76xceNGIzIy0oiMjDROnTpldOvWzabbyMnM+EdfkRGnjWEhIcbhgweM1StXGk2bNjXeenPkDef/0+1c/bIVR3nuldO2zMh50xOZJ0+ezJEjRyxfa9as4aGHHiq4BmZHUlNTSU5OZt++fQBUqFCBLl263PQ2VapUsbxzKl26dIFnvB2LFi2iQYMG+Pr6AhAcHMzChQvJzLSv3ejKaXuOktVRcrZv357g4GAqVapEpUqViIqKolWrVmbHAiAsLIyPPnifOnXq0Knj/TzZtw979uy94Xx74SjPvXLalhk5b1p6OnToYDVduXJl5s2bV2Bh7EnJkiXp1q0bgwcPZsqUKeTk5NCxY0cAFixYwIIFCxg/fjwffPABGRkZXLhwgbfeeospU6aYnDx/YWFhVKlSxTJduXJlUlJS7O5vqSmn7TlKVkfJWaZMGavpdevW5XmtNMujjzxCkSL/PeRUpnRpypUrd8P59sJRnnvltC0zct609Dz11FM8/fTTlq9u3bpRrFixAgtjb8aMGcOAAQMYP348/fr1IyYmhl27drF+/Xp69+7N4MGDiYuLY/LkyZQpU4Zy5cqRk5Njdux8xcXF4ePjY5n29vYGIDY21qxI+VJO23OUrI6S83phYWEEBQWZHQMAFxcXq+nwQ4d54vHeN5xvLxzluVdO2zIj500/st6qVSur/8xeXl7Uq1evwMLYG3d3d0JCQujUqROvv/46L730Es2bN6d69eqWdR544AG++eYbhgwZgqvrbf/R+kLj6upqle9mf2rETMppe46S1VFyXis8PJxatWpZ7UWxF5GRkWRnZ9Glc+dbmm8mR3nuldO2zMh509/SDRo0oHnz5pavunXrsn79+gIPZQ82bdpERkYGAI0aNWLUqFEcOnSI5ORkkpKSLOv5+fnZ7Q/UtQICAkhPT7dMX/2+fPnyZkXKl3LanqNkdZSc11q3bh3t27c3O0YeWVlZzPxhFh9e9+nRG803m6M898ppW2bkzLf0HD58mMWLF7No0SIWL15s+Vq4cCGTJk0qsDD2ZsKECZbvPTw8CAgI4IknnmDLli1cunQJgJMnT9K1a1fgyl+hN279CgCFKigoiKioKMt0TEwM3t7e1K9f38RUeSmn7TlKVkfJea2NGzcSHBxsdow8ZsycycCBL1hOR7j68fobzTebozz3ymlbZuTM9/BWrVq1WLhwIWfOnGHnzp2W+W5ubrz22msFFsaeVKpUiYEDB3L48GEaN27Mn3/+yaRJk2jYsCFvvPEGH3/8MU2bNiU9PZ0XXniB6Ohofv/9d1xcXIiLi2Pv3iufjNi0aZNdvCj27t2bOXPmkJaWhre3N2FhYfTp0wcPDw+zo1lRTttzlKyOkvOqyMhIihUrhp+fn9lRrPw4dy61atYkJTmZlORkLsTHk5aWTvT56Hznt2+n16dbpZy2ZUbOm16c8Pz58wQEBABX9mJcfzKcOJbt27ezcuVKAgMDiY6OZsiQIXZ5LoJy2p6jZHWUnAAzZswgKyuLF154web3/U8vTvjL2rW8PjTEao+zp6cn77/7Dm++/U6e+Vs2/mr5uPA/YcuLEzrKc6+ctlXYOW9aembNmsW+ffv48ssvycnJYcGCBbRr185ShERExPb0ZyhECsZNT2Q+fPgwISEhwJVDW4899hgjRowolGAiIiIitnTT0tOqVSsqVKhgmY6KiuLQoUMFHkpERETE1m56nZ6cnBymTJlCgwYNiIyMZNq0aTz44IOFlU1ERETEZv72r6xv2bKFJUuWcOnSJVq0aEHx4sXp3r17IcUTEXE+OqdHpGD8bem5avv27fz444/8+uuvOsQlIlKAVHpECsZND2+lpqby008/MXfuXC5cuEBwcLDd/DVhERERkduRb+k5cuQIs2bNYtWqVTRu3Jg6deqwZMkSihQpYnd/sExERETkVuT59FZoaCiPP/44Pj4+/Pzzz3z33XdWf1CvXLlyhR5SRERE5E7lKT2fffYZU6dOpXjx4uzatYu0tDQzcomIiIjY1E1PZE5MTGTNmjXs3r2bJ598knvuuYcDBw7QqFGjwswoIuJUdCKzSMG45U9v7dq1ixUrVrBy5UrCwsIKOpeIiNNS6REpGDf99Na1mjVrRrNmzWjSpElB5hEREREpELe8p0dERAqH9vSIFIyb/u0tERERkf8VKj0iIiLiFFR6RERExCnonB4RcQqGkWt2hFvWuFp7syPckgMRm8yOIHJbtKdHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiNyG1NRUMjIyzI4BgLdPMYp4FjE7hojDcC+oO966dSshISFUrFiRdu3akZSURFxcHI8//jj33XffTW+7cOFCMjIyWLRoEVOnTqVUqVIFFfOWREdH8+yzz7J69WpcXFxMzXIntmzZwoYNG6hWrRoRERGEhobi6elpdqw8lNP2HCWrPefMzc1lwYKFTJgwgXnz5lGpUkUAtmzZyq+/Xsl8+nQEoaFvFHhmFxcXevR5iFdC/kW/7i9x7ux5AFq3bUa7Tm2I+DOSKtUr8eWor8nMyLS6bYs2TanboBZRkdFs27SLS+mXaNqiMSFvDaJajcps2bCDt0I+ITsru0Afw/Xs+bm/lnLaVmHndDEMwyioO+/Xrx8tW7bk1VdfBeDkyZO88MIL9O3blxdeeCHf26SmpjJo0CBmzpzJ4cOHCQwMxN3dtt0sMTGRpKQkqlevfkvr5ObmEh4eTqNGjWyaozBFR0fTs2dP1qxZg6+vL5MmTSI1NZURI0aYHc2Kctqeo2Qt6JyGkXtHt79w4QLZ2dm0a9eedevWUalSRaKjo+nVqzerV6/C19eXr776ipSUVEaMCL2jbTWu1v6my0uV8cPD3Z21OxfSpc0TnDt7Hv/yZZm7bCoPt+9Pakoa//faM/gU9+bLjydbbhfy9iASLiTy3X/mWN3Xwz0e4IfpC6hYuTwzf5rEhDHTWDRv5d/mPBCx6Z8/yGvoZ9S2lPPGCvTwlqur9d3XrFmT9957j7FjxxITE5PvbU6ePMnVHla/fn2bF56cnBzefPNNYmNjb3kdV1dXGjRoYNMchW3RokU0aNAAX19fAIKDg1m4cCGZmZl/c8vCpZy25yhZ7T1nmTJlCAgIsJq3ePFiGjSob8nctm0wP/30U4FnTriQSMz5OKt5j/TqwuHwY6SmpAGw5dcdPPZ4N9w9rryGPv3C4/gHlLUqPACenkX4fuo8cnNziYyIYu+uA+Tk3FlBvF32/txfpZy2ZUbOQj+np02bNri7u7N582bmzp3LjBkzePrppzl69Chnz55l2bJlxMTEMGvWLM6dO0dKSgrjxo3j559/ZtiwYfzyyy8AzJkzhwEDBjB16lSCg4PJysrKc38Ay5YtY8WKFXz88cfMmDGDkydPcuzYMZYvX87u3buJiYnho48+YvXq1YwcOZLU1NQ862zYsIEOHTpYHsPevXuZOnUqP/zwA4MHDyY6OpqEhARCQ0P58MMPGTVqFF26dGHz5s2FPbw3FBYWRpUqVSzTlStXJiUlhfDwcBNT5aWctucoWR0l57XCwsKoXPnazJX+ynyo0LM0a3k3kRHnLNNnz5yjeAlfGjSqg29xHwYNGUB6+iVG/ftNlm+aTbdHOwIQHfXfN6AlShYn+WIqKxevLdTsjvLcK6dtmZGz0EuPu7s7JUqUICEhAW9vb5599lk6derEBx98QKVKlejUqRP+/v7079+fChUqMHr0aOrWrUuPHj0YOXIkI0aM4OTJkzRp0oT9+/fTvXt3pk6dyi+//JLn/gC+//57WrVqxdtvv029evUIDAykQoUKPPTQQwQFBbFt2zZ8fX3p0qULOTk5bN++Pc867dq14/z5K8fML168yMiRI3n22Wd56qmnaNq0KUOHDqVUqVL4+/tz/PhxRo4cyaBBg1iwYEFhD+8NxcXF4ePjY5n29vYGuOkeLzMop+05SlZHyXkte8pcplxp0lLTLNPpaekAlPMvQ5vg5mRn5/Dlx5N5a+gnjPt0Ch99MYJKVSpY1n+4R2fmr5xOrTrV8StVslCz29M43oxy2pYZOQu99OTm5pKSksL3339PREQEc+bM4eLFi5QtWzbf9devX0+NGjUAKF26NI0bN2bLli34+PhQsmRJypUrR506dVi7dm2+93fffffRvXt3Fi9eTIsWLfLcf48ePXjqqadYtmwZ0dHRpKWl5Vnn2sN0e/bswdvbmyJFrnxionPnzuzdu5fk5GQ8PDzw9/fH1dUVf39/kpKS7nS4bMbV1dXqcbi5uZmY5saU0/YcJauj5LyWq6sbbm72kTk3J9fqsNS131eoFEDEn5GkJKcCsG7VZlKSU2ndtpllnV9W/Mqbr39M6TJ+vD7yxcILjuM898ppW2bkLLBPb93Irl27yM3NpV69erRo0YJmzf77ny6/c6rd3d2tyoOfn1++5/lkZmbme38vv/wygYGBjB49mhMnTjBs2DCr2+3du5cZM2bw5Zdfsm3btnwz/F0eyHv+kr0JCAggPT3dMn31+/Lly5sVKV/KaXuOktVRcl4rIMCftLRrM18CoHz5gBvdpMDEnI+jmHdRy3TRYl4AnD8XS/ESxfEq6mW1fuSZc/gW/++77IyMTPaEHWDU22MJeeulwgn9F0d57pXTtszIWaC/qXNzrU+Gi4mJ4YMPPiAkJIQOHTowadIkkpKSyM7O5ptvviElJQXDMKyKR+fOnVmzZo1lOjIykvbt836yoU2bNvne3+LFi+natSuzZ8+2nGPj5ubGpUuXyMzM5Ntvv6VBgwZ4eHiQnJxMTk5OnnWu5jEMg3vuuYfMzEzLMccTJ07Qtm1bq1109igoKIioqCjLdExMDN7e3tSvX9/EVHkpp+05SlZHyXmtpk2DOHfu2sznTcu8N+wAFSr9t2z5ly9HakoaR8KPcST8GJWqVrC6pk8RzyKcOnkmz/2cPxdLxKmoPPMLkqM898ppW2bkLLDSs2XLFk6cOMGGDRsYN24c77//Pu+99x6hoaE888wz9O7dmwoVKtCpUyf69+9v2UOzadMmIiIi+P333wEYNmwYmZmZTJ06lRkzZvDKK69Qvnx51q1bR1xcnKXI5Hd/xYsX57vvvmPGjBls2bKF1157DYB27doxYcIEdu3axX333ce3337Ll19+Se3atfnpp5+Ijo62WmfVqlUArF27Fh8fH6ZMmcK3337L/PnzWbt2LaNHjyY1NZWDBw9y+vRpYmNj2bx5M6dOnSIyMrKghvi29O7dm/DwcMvhu7CwMPr06YOHh4fJyawpp+05SlZHyHn9nuDHH+9NePghS+Zdu3bRp88TpmT+ac5yGjSsQ9FiV/b2BLVozILZS8nOzuHwwaPs3vE7wfe3Bq6csFy0qBeb12+nYuUAGjX57y+Ze9u3YNqkHwo1uyM896CctmZGzgK9To/Yl+3bt7Ny5UoCAwOJjo5myJAhlnOT7Ily2p6jZC3InHd6nZ7ExETWrl3Lu+++R0jIUB555FH8/cuxfft2Vq1aRe3aVzO/fseZ/+46PSVKFqdjl7a899lwxo7+D8t//oW42HhatGlKl4c7cPyPPylfsRwTxkwnKzMLgOIlfBkcOpDz52KoUKk882ct5kj4cdp2aMUn494i7Ld97N25n2N/nCRs+75bymmr6/SAfkZtTTnzp9IjIk7hTktPYfq70mMvbFl6RAqDfZ99KyIiImIjKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAUXwzAMs0OIiBS03JwssyPcMlc3D7Mj3JLsSylmR7gl7kV9zY4gdkJ7ekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFd7MDmCk7O5tvvvmGPXv2UKVKFeLi4li3bh333nsvU6ZMMTuezW3ZsoUNGzZQrVo1IiIiCA0NxdPT0+xYeSin7TlKVnvOuW37dj77bAznzp3j3jZt+OCD9yhRogRbt25jw6+/Uq1aVSIizvDG8GF2k9mex/Oq9EuX6N2nH1MnT6JixQps/20Hn385lnPR52nTqiXvv/sWJYoXNzumhSOMKThOzqvS09N57LHH+Oabb6hUqVLBbchwYkOGDDFeffVV4/Lly5Z527dvN1588UUTUxWMc+fOGa1atTKSk5MNwzCMiRMnGqNHjzY5VV7KaXuOkrWgc+ZkZ/7jr8gzEcawYSHG4UMHjdWrVxpNmzY13nrrTeNsZITRqlVLIykx3sjJzjQmTBhvfDJq1B1tKyc70yaPtzCe96z05Dv+mjxxvBEYGGicPv6HEXHymDFs6OtG+O97jFXLlxhN77nHeHNE6B1vw1b0f6ng/Oc//zECAwONyMjIAt2O0x7e+vXXX1m9ejXvvvuuVftt1aoVPXv2NDFZwVi0aBENGjTA19cXgODgYBYuXEhmZqbJyawpp+05SlZ7zhkWFsZHH35AnTp16NSxI08+2Zc9e/aweMlS6te/JnPb+/jp55/JzMwyObF9j+dV6zb8SovmzSzTYbt288G7b1MnsDYdO7Snb5/H2btvn4kJrTnCmILj5Lxq7dq1tGrVqlC25bSlZ82aNVSvXp0yZcrkWdaxY0dWr15N9+7dWbJkCS1btiQuLo6xY8eyePFiPvvsM3777TcA/vjjD2bMmMHMmTMZOnQoAL/99hsLFixg0qRJfPrpp4X6uG4kLCyMKlWqWKYrV65MSkoK4eHhJqbKSzltz1Gy2nPORx99hCJFilimy5QuTbmy5QgL20WVypUt8ytVupL50CHzM9vzeAKcOxdNbGwcjRreZZn36MMPXjfOpShbtqwZ8fJl72N6laPkBIiKiiI2NpZGjRoVyvactvTExMRQ/LrjxJGRkYwdO5bhw4fj4eHBkSNHqF+/PjNnzqRo0aL88ssvdO/enbZt2/L9998DMH/+fKpXr87TTz/NE088AcCMGTNo0qQJr7zyCu3bty/0x5afuLg4fHx8LNPe3t4AxMbGmhUpX8ppe46S1Z5zuri4WE2HHzrEE088/ldmb8t8b+9iAMTGxhVqvvzY83jm5OQwf+HP9Hm8l9X8vON8hCd62c+ed3se02s5Ss6cnBzmzZtH3759C22bTlt6SpUqlecHoHLlylSoUIEdO3Zw//33A1C7dm0CAwPx8fFh+fLl/Prrr+zevZu0tDQA2rdvz9ChQxk/fjyNGzcGruwp6tevHzNmzCAoKKhwH9gNuLq64ur636fbzc3NxDQ3ppy25yhZHSVnZGQk2dnZdOnSGTc3V1yvyWlPme15PGf9OJfHe/e0yne9yLNnyc7OpvMDHQsx2c3Z85hey1Fy/vDDD/Tp0+emPwe25rSl5/777+fcuXOcOXPGar6HhwceHh551r906RIDBw6kSpUqNG/e3DK/VatW/PDDD2zbto1nn30WwzB47LHHGDduHPPmzWPYsGEF/lhuRUBAAOnp6Zbpq9+XL1/erEj5Uk7bc5SsjpAzKyuLmTN/4MMP3gfA398686VLl4Arj8Vs9jyec+cv5MFHe9CkeWuaNG8NwIPdezJ2wiQAsrKy+WH2HD54720zY+Zhz2N6LUfJ+eOPP9K5c2caNmxIw4YNAejSpQtffvllgW3TaUtP165d6dChAx988IHVyV05OTn5rr99+3aioqKoWbMmKSkplvUWL15M/fr1mT17NvHx8SQnJ7N48WJatWrFggUL2L17d6E8nr8TFBREVFSUZTomJgZvb2/q169vYqq8lNP2HCWrI+Sc8f1MBg4cSLFiVw5j3X13Y6vM58+f/ytzPbMiWtjzeK5atoh9YdstXwArFv/EkNdeAeD7WbN44bkBFCtaFLhyeRF7YM9jei1HyfnLL79w8OBByxfA6tWrCQkJKbBtOm3pcXFxYcKECbRp04bXXnuNTz/9lM8//5zdu3czcuRI1qxZA1wpNTk5OdSrV4/MzEz+7//+j8zMTCIiIli7di179uzh448/Zs2aNfTp04cSJUqwfPlyJk2axLp163jttddMfqRX9O7dm/DwcMthubCwMPr06ZPvXi0zKaftOUpWe8/545y51KpZk5SUZP7880/Cdu2iUsWKHAo/RFralXfSu3bt5oknHreLzPY+njcyZ94CataoQUpKKn+eOs2u3XvYuv03s2MBjjOmjpLTDC6GYRhmh5DCsX37dlauXElgYCDR0dEMGTLE6pMS9kI5bc9RshZkztycf/4x8l9+WcvrQ4Zy7culp6cnWzZv5ODBcFatXk1g7dpER0fz+uuD7zizq5ttfjkV9POefSnFJvfT4O4gflmxlENHjjB0+Ig847xp3Rp8fX1ucg83517U1xYxAf1fKkh16tRh/fr1BXpxQpUeEXEKd1J6CputSk9Bs1XpKWi2LD3i2Jz28JaIiIg4F5UeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCi2EYhtkhRERECsr5jRvMjnBLAtp1MDvC/zzt6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIU3M0OIIVny5YtbNiwgWrVqhEREUFoaCienp5mx8pDOW3PUbIqp205Sk6wr6y7Dh/hqwULiUlIoFn9+gzv/yS+3t6W5buPHOFE5FnKlylNs/r1KeblRdihw2zbv5/K/v6cjY3lpV498fTwMCU/2Nd43kxh53QxDMMosHv/G7t27eLtt9/G3d2dLl26EBMTw6VLl3jzzTcpXbp0gW/fMAymTZtGamoqp0+fZsKECQW+TbNER0fTs2dP1qxZg6+vL5MmTSI1NZURI0aYHc2Kctqeo2RVTttylJxQ8FnPb9xwy+vGJCQwfclS+nTqSFTcBT77fibtmjZl+FP9APhqwUL8fH15sktny21iExIY+MmnzProA3yKFmXG8hWkXbrEy7173VbOgHYdbmv9G3GU596MnKYe3mrWrBmNGzemYcOGvPrqq3z88ccAfPjhhzbdzt69e/Odv2XLFtLS0hg6dCjPP/+8Tbf5d9subIsWLaJBgwb4+voCEBwczMKFC8nMzDQ5mTXltD1HyaqctuUoOcG+su47epTh/ftRs1Il2ja5m8faBXPgxAkA5q1dR1xiklXhAVj92w4Cq1bBp2hRAFredRcrt20nKzu70PODfY3nzZiR0/RzelxdrSPcfffdnDx50mb3v3LlShYsWJDvsj/++AN39ytH+Bo1amSzbV4VHx/PsGHDbH6//0RYWBhVqlSxTFeuXJmUlBTCw8NNTJWXctqeo2RVTttylJxgX1k7t2xJkWsOS/kVL06ZEiVISU/nu2XLKerlyajvZvDk2++yNiwMgN+PHaNCmbKW21QoW4bUS5c4GhFR6PnBvsbzZszIaVfn9OTm5rJt2zZatmwJwIkTJ+jduzfLli3DMAxef/11+vXrR1BQEOPHjycwMJBly5bRpUsXIiMj8fb2xs3NjS1btvDmm2/SunVr1q9fz6lTp/jxxx958sknLdvas2cPu3btAmDWrFn07duX06dPs2rVKkqXLs1vv/3GK6+8QkBAANOnTyc1NZXY2Fjc3NwYNWoU8+fPJy0tjUOHDjFmzBh8fX2ZOnUq1apVY+7cuQwfPpzY2FiioqKYOnUqDz30EBUqVDBlXAHi4uJo3LixZdr7r+PTsbGxZkXKl3LanqNkVU7bcpScYF9ZXVxcrKaPRkTwSPB9hB06hJubK4N69sDX25tNe/fy4fRvqV+9OvEXk6lXvbrlNsW8vAC4kHSxULNfZU/jeTNm5LSL0nPkyBG++OILNm/ejJeXF6NHjwagVq1a+Pn5AVcaYO3atQGoUqUKGRkZJCUlMXPmTHJycvjhhx/Yt28f33//PQ0bNmTBggW0bduWNm3a4OHhYVV4AJo2bWrZu9O/f3+ys7N5+eWX+fHHHylVqhTlypVj0KBBrFy5kjJlyrBz506+/fZbYmNjGTNmDIMGDcLf359XX32Vb775hqeffpr169fz448/0qRJE5KTkwkODgZg4MCBhTWUN+Tq6mq1V83Nzc3ENDemnLbnKFmV07YcJSfYb9ZzcXFk5+TQvmlTZq9eQ+Vy/pYTmts2aYJPsaLsOnw4T37X64pTYbPX8byeGTlNP7wFUK9ePYYNG8bixYvp2LEjPXv2JDEx8aa38fHxoXbt2pQqVYqyZcvi4eGBv78/rq6u+Pv7k5SUdFsZTp48SWJiIqVKlQKgXbt2xMbGcvLkSXx8fKhevTre3t5Ur16dHTt2sGHDBubMmYO/vz8eHh74+fnh4eFBnz59OHv2LDVr1vynw1EgAgICSE9Pt0xf/b58+fJmRcqXctqeo2RVTttylJxgn1mzc3JYsH4Dw5/qD4BvsWJcvuZcExcXFyqUKUtq+iXK+pXk0uUMy7Kr65Ur5Ve4of9ij+OZHzNy2kXpucrV1ZUnn3ySqKgodu7cWajbdnd3Jy0tjaysLOBK4yxevHi+zTMlJYVevXrRt29f3n77bQYNGoRhGHzzzTd06NCBf/3rX2zcuLFQ8/+doKAgoqKiLNMxMTF4e3tTv359E1PlpZy25yhZldO2HCUn2GfW+WvX0b9rF4r+9fHpwCpVOBcXR8ZfvyMAMrOzqBIQQKNatTgfH2+ZH5uYSDEvLwKvOV+lMNnjeObHjJyml57c3Fyr6RN/nSVftWpVALy8vIiNjSUnJ4eYmJjbPqvbzc2N9PR0cnNzyb7uTHrDMLj6if3q1atTo0YNNmy48tHGhIQESpcune8em6CgIMaOHUtWVhbJyclMnTqVc+fOsXfvXl566SXefvtttm7darV9s8+a7927N+Hh4aSlpQFXTiDr06cPHiZeRyI/yml7jpJVOW3LUXKC/WVdtHETVcuXJ/XSJSLOn+f3Y8dISE7m7jqBbD9wAICLqalczsikdaOGPHTfvRyNiCD98mUA9h87ziNt78PdpMNK9jaeN2JGTlOv07N7927efvttMjMz6dWrF0lJSRw8eJCePXvSq9eV6xv8+OOPTJ8+nQ4dOuDl5YWXlxfdunVjyJAh1KxZkxEjRlCsWDGGDh1KQkICkydP5vvvv2fJkiXMmTMHFxcXnn76aR5//HGee+45y2BGRUXx5ptv4u7uzrvvvkvVqlU5c+YMY8eOpVmzZiQkJPDQQw/h5+fHqFGjOHToEOPGjaN27drExMQQEhLC8ePHCQoK4sMPP+TSpUsMGDCAl19+mbNnz/Lggw9SvXp1XnvtNXJzcwkJCaH6NSe6mWH79u2sXLmSwMBAoqOjGTJkCEWKFDE1U36U0/YcJaty2paj5ISCzXo71+nZtHcv702dzrW/Got4eLBozGfk5uYyddFi/EuVIjo+nu7BbS17c3YfOcKG3XuoUbECsQmJPP/oI1afArsVtrpODzjOc1/YOU0tPSIiIgXtdkqPmWxZeiR/ph/eEhERESkMKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKLoZhGGaHEMdn5OaYHeGWuLi6mR1BRCRfhpFrdoRb5uLimPtMHDO1iIiIyG1S6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUesSupqalkZGSYHUNExGHd6HVUr6/gbnaAgrJx40YWLVqEn58fq1atokOHDowePdrsWKbasmULGzZsoFq1akRERBAaGoqnp6fZsQDIzc1lwcKFTJgwkXnz5lKpYkUAwsJ28fmYMZw6dYrg4GA+Hf0JRYoUMTntFfY8ntc7cOAAGzZsoFSpUhw4cIBBgwZRo0YNs2Pl4Shjqpy25yhZ7Tlnbm4uCxYsZMKECcybN49KlSredL49KPTxNP4HZWVlGUFBQUZ8fLxhGIYRFRVlhISEmJzKXOfOnTNatWplJCcnG4ZhGBMnTjRGjx5ts/vPzcm+o6/YmPPGuaizRmBgoHHmTIRl3rRpU42szAzj1J8njVatWhnz5827o+3YSkGPpy3l5OQYDzzwgJGZmWkYhmGEhYUZzz77rMmp8nKUMVVO23OUrAX+Opqbc0dfsbExxrlzUX+9jp752/l38mULZjzv/5OHt1JTU0lOTmbfvn0AVKhQgS5dupicylyLFi2iQYMG+Pr6AhAcHMzChQvJzMw0OdkVZcqUISAgwGpeRkYGz/3rX7i5uVG1alWaNm2Kq5t9/Mja+3he6+LFi0RFRZGamgpA0aJFSU9PNzlVXo4ypsppe46S1d5z5vc6erP5ZjNjPO3jN4iNlSxZkm7dujF48GCmTJlCTk4OHTt2ZPHixdSrV48///yTbdu2Ub9+fVasWAHA119/zdy5c8nOzmbUqFGsXLmS999/n2PHjpGZmcnkyZMJCQlh6tSp9O/fnzfffJP169cTGhrKgw8+SHR0NGfPnmXw4MGMHTuWkJAQ7r33XmbMmGHuYPwlLCyMKlWqWKYrV65MSkoK4eHhJqa6uYoV/7sLNjExiRLFi/PwQw+ZmOi/HGk8/fz8aNiwIUOHDiUxMZHvvvuOoUOHmh0rD0cZU+W0PUfJ6ig5HYUZ4/k/WXoAxowZw4ABAxg/fjz9+vUjJiaGRx99lMqVK5ORkUGbNm1o3rw5rq5XhsDd3Z0ePXoQGRnJsWPH6NatG3fddRfz58+nSJEiNGrUiCNHjvD000/zzTffsHLlSooXL85nn31G8+bN+eWXX6hUqRIeHh54eXnx5ZdfMmnSJMb8dT6K2eLi4vDx8bFMe3t7AxAbG2tWpFu2ePESevTswbHjx0lITDQ7DuB44zlu3Diio6Np27YtXbp0oUWLFmZHysNRxlQ5bc9RsjpKTkdhxnj+z5Yed3d3QkJCmDt3LrGxsbz00ksA9OzZk8WLF5OTkwPAkiVLyMrKAqBIkSJUr16dadOmsWbNGg4dOkRaWhoAHh4elC5dGi8vLzw9PSlZsiTly5cHoFSpUqSkpFi26+/vD8Ddd99NzZo1OXz4cKE+9vy4urpaCh6Am5ubiWluT5cunfn8s8+Jj4/nyy++NDsO4HjjGR8fT/v27WnRogWhoaHs37/f7Eh5OMqYKqftOUpWR8npKMwYz//J0rNp0ybLx/IaNWrEqFGjOHToEImJiXTv3p2VK1eyYsUKQkNDOXz4MAsWLKB9+/bAlV8OAwcOpEmTJjRs2BDDMPLdxrVPFHDD9dzd3SlVqpQNH90/ExAQYHUex9XvrxY3e+bl5UWzZkG89+47HDlyxOw4gGON5+XLlwkNDWXw4MFMmzaNxx57jGHDht3wZ9YsjjKmyml7jpLVUXI6CjPG83+y9ABMmDDB8r2HhwcBAQH4+fnh7+9PgwYN2LhxI/Xq1ePBBx9k9erV1KpVC4AVK1bg5uZGuXLlSE5OtuwRul5ubu4Nf2lc3XMUHx9PZmYmQUFBNn50ty8oKIioqCjLdExMDN7e3tSvX9/EVLcnICCAqlWrmh0DcKzxPH78OEWLFsXLywsXFxdGjBhBfHw8iXZyqPAqRxlT5bQ9R8nqKDkdhRnj+T9ZeipVqsT06dMZMGAA48aNY+bMmUyaNAkXFxcAevXqRe/evS3fP/LII5bbNmnShMOHDxMSEoKPjw+7d+9m9+7d7Nixg3PnzhEZGcnvv//OhQsXWL9+PfHx8Rw4cIBDhw6RkJAAwG+//cb06dP55JNP+Oyzz/Dw8Cj8QbhO7969CQ8PtxyuCwsLo0+fPnaR7arrS+TZs2f5/ff/HobZvGUL//fi/xV2rHw5wnheVblyZc6fP295F5WTk4O/vz9+fn4mJ7PmKGOqnLbnKFkdIeeN3ozb255dMGc8XQx7HAkHNmLECJo3b06PHj3MjpLH9u3bWblyJYGBgURHRzNkyBCbXejPyM1/j9itSkxMYu3atbz73nuEDB3KI48+wuFDhwkdMYIWzZsTFBREnTqBtGzZ8o624+Jqu2PGBTmetrZlyxbWrl1L3bp1OX/+PA888AB33XWX2bHycJQxVU7bc5SsBfo6auTe0e0TExOvvI6++x4hIUN55JFH8fcvd8P5d8LFxTb7TAr7eVfpsbERI0bQrFkzevbsaXaUQnWnpaew2LL0iIjY0p2WnsJkq9JT2BwztZ2KjIwkPDycTZs2ERkZaXYcERERuYb29IhNaE+PiMid0Z6egueYqUVERERuk0qPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIiIhTUOkRERERp6DSIyIiIk5BpUdEREScgkqPiIiIOAWVHhEREXEKKj0iIiLiFFR6RERExCmo9IiIiIhTcDEMwzA7hIhIQcvNyTI7wi1zcXGM96Murm5mRxCTRK5YZXaEW1L5wa5W047xP0tERETkDqn0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IyG1ITU0lIyPD7Bh/y1FyihQmd7MDXLVx40YWLVqEn58fq1atokOHDowePfq27iM2NpbVq1dTtmxZunbt+vc3cDJbtmxhw4YNVKtWjYiICEJDQ/H09DQ7Vh7KaXvbtm3j008/JSoqinvvvZePPvqIEiVKmB0rD3se09zcXBYu/IkJEycyb+4cKlasCMCBAwf5deNGSvn5cfDgQV566UWqV69uas4FCxcyYcJE5s2bS6W/cl6Vnp5Oj569mD59Wp5lZrlw4QLBwcFkZ2dbzV+xYgW1atUyKVX+7Pln9Fr2lnP/iRNMWbaEs7GxtKhfnzf69sPD3Z1dfxzht0OHqFS2LFFxcfzfI49SxMMD4KbL/im72NOTnZ3N8OHDee+993j//fdZtGgRWVlZN1w/MzOTQ4cOWab37t0LQKlSpThz5gwnTpwo8Mz5uZrDHkVHRxMaGsrQoUN55plnKFWqFGPHjjU7Vh7KaXvnzp1jyZIljBkzhs8++4zt27fzxRdfmB0rD3sf04SEBNq2vY/4+ATLvNzcXEJHjGTQSy/y1FP96f14bz76eJSJKa/kDG7blvj4+HyX/zBrFqdPny7cUH/j119/ZfLkyaxfv57169ezZs0aatWqZXeFx95/Rq+yt5yJKSkcjTzDxMFD+HroMPYdP8663buJTUzksx9n869uD9KjbTAlfXz4duUKgJsuuxN2UXpSU1NJTk5m3759AFSoUIEuXbrccP3Ro0dz9OhRAPbs2cOkSZMAcHd3N+3d68qVK1mwYIEp274VixYtokGDBvj6+gIQHBzMwoULyczMNDmZNeW0vZ07d/Lxxx9Tt25dOnXqRL9+/di9e7fZsfKw9zEtU6YMAQEBVvMuJicTFRVFaloaAMWKFiU9/ZIZ8Szyy3nV2rXraNWyZSEn+nvt27cnODiYSpUqUalSJaKiomjVqpXZsfKw95/Rq+wtZ2ZWFo+374CbqysVy5alYY2auLq68MvuXQRWqoxP0aIANK9fn1U7d5CVnX3TZXfCLkpPyZIl6datG4MHD2bKlCnk5OTQsWNHDMNg7ty5zJgxg6effpqjR48SHR3N/v372bhxIxs3bmT9+vVEREQwderUPPebmprKt99+y8SJE3nxxRdJSUnhzJkzvPLKK0yePJlPP/2Ujh07sm7dOqZMmULfvn156623APLddkJCAqGhoXz44YeMGjWKLl26sHnzZrKzs1m/fj1Hjx7lxx9/JCcnh0mTJrF69Wp69OhBcnJyYQ9pHmFhYVSpUsUyXblyZVJSUggPDzcxVV7KaXvdu3enSJEilukyZcpQrlw5ExPlz5HG9Cq/kiVp2PAuQkKGkZiUxIwZMxk6ZLDZsfIVFRVFbGwsjRo1MjtKHmXKlLGaXrduHR06dDApzY05ys+oveX0L1XK8v3FtDR8ihbl/qZB7D9xgvLXPPflS5Um7fJljkVG3nTZnbCL0gMwZswYBgwYwPjx4+nXrx8xMTGsXLkSb29vnn32WTp16sQHH3xA+fLlCQwMpF27drRr147g4GAqVKjAwIED873PBx98kFdffRUPDw+++eYbqlSpgqenJ7m5uYwYMYJXXnmFsWPH8swzzzBz5kzWrFlDcnJyvtsuVaoU/v7+HD9+nJEjRzJo0CAWLFiAu7s7bdq0ITAwkCeffJLDhw9z/PhxunTpwocffoirq/nDHBcXh4+Pj2Xa29sbuHIelD1RTttzcXGxmg4PD6dPnz4mpbkxRxrTa43995ecPx9Du3YdeKBzJ5o3b252pDxycnKYN38+ffva3/Oen7CwMIKCgsyOkYej/Izaa85fdoXx4pdjOH0+mqTUVBKSk/H28rIsL/bX9/HJF2+67E6Y/9v4L+7u7oSEhDB37lxiY2N56aWXWLt2LREREcyZM4eLFy9StmzZ27rPHTt2sGHDBubMmYO/vz8ef50A5eHhQYUKFQDw9/enVKlSeHl54eHhQfHixUlJSbnhtj08PPD398fV1RV/f3+SkpLybLdWrVocPXqU5557Di8vL6sfPrO4urpalS83NzcT09yYchasyMhIsrOz7fJEf0cd0wsX4mkX3JYWzZszcuRb7N9/wOxIefzwwyz6PPGEXbwB+zvh4eHUqlXLau+kvXCUn1F7zRnc+G5G9nuKxJRUpi9fhqurC67XvCmz+v4my+6EXXx6a9OmTbRs2RJPT08aNWrEqFGjePbZZylTpgwtWrSgWbNmlnUNw7jl+01JSaFXr16WspPfbfN7ETAMg8zMzH+8bQ8PDxYuXMjEiRPp3bs3s2fPpn79+recuyAEBASQnp5umb76ffny5c2KlC/lLDhZWVl8//33fPTRR2ZHyZcjjunly5cZMfJN5s+bg6enJ6NGjeaNN0JZvXplnj1sZpozZw7/vu5E1q5du/Hss88QMnSoSanyt27dOtq3b292jHw5ys+oveb0LFKERjVr8lrPXkxdtoRyJf24lPnfyypc/uuco7IlS1K2RMkbLrsTdlP7J0yYYPnew8ODgIAAgoODmTRpEklJSWRnZ/PNN9+QkpKCm5sbly5dIjMzEzc3N8sTev1JWkFBQYwdO5asrCySk5PzPe8nNzc33zLTpk2bfLd9I1dz5ObmsnfvXhISEhg5ciQDBgwgLCzsnw6LzQQFBREVFWWZjomJwdvb2/Qydj3lLDjfffcd//d//0exYsUA8nw82GyOOKYnTpygaNGieHl54eLiQmjocOITEkhMTDQ7mpU1a1ZzYP/vli+AVatW2l3hgSuXLwkODjY7Rr4c5WfU3nOW8ytJxTJluatGDWIS/vt/Je5iEsU8PaldqfJNl90Juyg9lSpVYvr06QwYMIBx48Yxc+ZMJk2aRO/evalQoQKdOnWif//+NGvWjOLFi9O2bVtmz57NunXrqFu3LqmpqXzxxRfExcXx+++/c+jQIeLj43nrrbc4cOAA9957LyNHjqRXr17ExMRw7NgxDh48SGJiIlu3buXEiROcPHmSgwcPkpCQwNatW/PdtqurKwcPHuT06dPExsayefNmTp06RWRkJE2bNiU8PJypU6eSlZXFkCFDWL58OTk5OTzyyCNmDzG9e/cmPDyctL8+ZRIWFkafPn0se8HshXIWjNmzZ1OrVi2Sk5M5efIkYWFhbNmyxexYVhxhTK9/g1SpUmViYmIsb7xycnLw9y+Hn5+fGfEsbmePuD2JjIykWLFipo/fjTjCzyjYX87o+HgOX3OZhLAjR3iyUycebNmKo5FnuPTXRTQPnDzJQ63b4O7mdtNld8LFcNT/HXLbtm/fzsqVKwkMDCQ6OpohQ4bY5XFz5bStNWvWMHjwYKtfhJ6enmzbts3ykVZ7UZBjmptz42t/3YrEpCTWrl3He++9z9ChQ3j0kYcpV64cW7duY+3addStV4fz0ed54IFONGjQ4I625eLyz9+PJiYmsXbtWt597z1Chg7lkUcfwf+6T+vVrVefdevW3vHFCV1cbXuuyIwZM8jKyuKFF16w6f3akqP8vy/onJErVt3yujsOHeLTH2dxd63aNKxRgxoVKtCkdiAAe44dZeO+fdQoX57YpCQGdHuQIu7uf7vsVlV+0Pr8RZUeEXEKd1p6CtOdlJ7CZOvSI47jdkqPma4vPY7xP0tERETkDqn0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUVHpERETEKaj0iIiIiFNQ6RERERGnoNIjIiIiTkGlR0RERJyCSo+IiIg4BZUeERERcQoqPSIiIuIUXAzDMMwOISIiIlLQtKdHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU1DpEREREaeg0iMiIiJOQaVHREREnIJKj4iIiDgFlR4RERFxCio9IiIi4hRUekRERMQpqPSIiIiIU1DpEfkHhgwZQkREhNkx/mdkZGSYHUHkps6fP281nZmZqdcAB6TS46QSEhLMjnDLtm7danaEPLKzsylXrpzVvJMnT5qU5sZycnL46aefmD9/PgCzZs2yy4Lx3HPPsXr1ajIzM82O8o9s3rzZ7Ai35OzZs2ZHcFjLli2zms7NzeXLL780Kc3fi4iIYOXKlezYsYOcnByz4+QrNTWV9PR0AP744w++//57EhMTC3SbLoZhGAW6BTHd8ePHWbBgAampqVx9uo8dO8ZPP/1kcrK8Jk+ezPfff09ycjIAhmHg4uLCkSNHTE5m7aOPPuLQoUNUr17dMi88PDzPC6PZhg8fzsmTJ7nrrrv48MMPiY2NZezYsYwePdrsaFaioqJISUlh69atGIZBvXr1aN26Na6u9vG+7IMPPmDOnDm4uLjkWWavP6Nbt25l/vz5JCUlAVdyRkRE2F1B++yzz+jQoQNNmzZl2LBh7Nmzh1dffZVevXqZHQ2Affv2MWzYMKKioizPv2EYuLu7c//99zN+/HiTE+Y1Y8YMvvjiC2rWrImbmxtZWVl8/vnn1KtXz+xoVnr27MlHH32Ep6cnjz/+OM8++yw5OTm8/vrrBbZN9wK7Z7EbgwcPplu3bnb3A5+fdevWsXTpUvz9/S3zNm3aZGKi/Pn4+PDggw/i4+NjmZebm2tiovyVLl2aMWPGMHfuXADKli3Lli1bTE6VV8WKFQGoVq0aS5YsYeTIkVSsWJH777+f+++/nxo1apiar2PHjrz66quUKlUq3+W//PJLISf6e6NGjWLQoEFW/5fsrfAAeHt706xZM77//nuOHj3KihUrWLdundmxLJo0acKCBQs4ePAgwcHBZse5JdOnT2f27Nk0btwYgPj4eD799FPGjBljcjJrAwcOpH79+rzwwgv07t2bV199lZUrVxboNlV6nMBdd93FK6+8YjXv/vvvNynNzT344IMUK1bMal6FChVMSnNjzz//PL6+vlbzOnbsaFKaGytXrhyZmZmWd6izZ8+mbNmyJqfKa+rUqcTFxbF06VLuvvtuRo0aRdu2bcnNzeXHH39k9erVDBo0yLR8bdq0uenypk2bFlKSW3fffffx8MMPW80zuzzmx9/fn5MnTzJ58mQmTJiAj48PUVFRZseyUqpUKfz9/Tl16hTVq1dn06ZNbNu2jX79+lG1alWz4+XRsmVLS+GBK29+6tata5mOioqyvNEw0/Hjx1m1ahVnz55l/PjxREZG8t1339GtW7cC26YOb/2P+uOPPyzHRg8cOECxYsWsysOOHTt46623zIp3Q88//zwHDx6kaNGiwJXdyMnJyezbt8/kZFcOFzRt2hRPT0+WLl1qtcwwDHbu3Mmnn35qUrr8xcXFMWrUKA4dOgRAyZIlGT16NLVq1TI5mbWmTZvSu3dvnnzySapUqWK1bPfu3bz88svs3LnTpHSwYMECOnXqRPHixZk6dSrZ2dlWy3///XemT59uUrr8ffTRR5w4ccJqT8/p06ct53fZi99++43vv/+ejh078thjj7F48WKWL1/Od999Z3Y0K8899xyff/45kZGR/Otf/+Ltt98mOjqal19+2exoeYwdO5ZixYpZnvusrCx+++032rZti2EYbNq0iXHjxpkbkit7xw8fPkytWrXw8PBg7969ZGVl0bp16wLbpvb0/I/65Zdf2LhxI97e3vkuj4yMtMvSc/fddzNgwAA8PDws83777TcTE/3Xjz/+SEBAALVq1eKXX37B19fXcs6JYRgcPXrU5IR5lS1blnHjxnH58mWysrLw9fW1y5PYZ8+ebfVOFK68UHt4eFCvXj3TX6CPHj1Ky5YtKVmyJGfPnqVcuXJWz709vndMS0vj7rvvtvq/dPHiRRMT5a9Vq1a0atXKMv3AAw/Qs2dPExPlr0+fPpQqVYqXXnqJgQMH0qNHjzxvfuxFREQEHh4enD592jLP09PT8sbBXj50MX/+fPr06WOZDgoKYtq0aQVaerSn539UREQEVapUyffES7hSeipXrlzIqf5ebm4u6enpJCYmUrlyZQ4cOECjRo3MjgXAt99+S7t27ahRo0a+u4dPnz5NtWrVzAl3A0ePHmXx4sWWk1nhyl7ARYsWmReKK7+QDx8+fMPlly9f5uDBg6Ye0rqR+Ph4SpcubTXv/PnzBAQEmJQofwkJCZQqVYrU1FRcXV0pVqwYqampVueh2YOQkBD69OlDo0aNeOaZZ0hMTKRPnz4MGDDA7GhWpk6dyoYNG/Dw8OCbb75h7969vPfee6xZs8bsaHnk9/qUlJREyZIlAfNf//ft28f8+fM5duwYgYGBlvmXLl3i4MGDrF+/vsC2rT09/6OuPc68Zs0a9u7dy8iRI8nKymLp0qU88MADJqa7sdWrV/PWW2/RokUL/vOf/xAbG8uSJUt49NFHzY7GoUOH6Nu3r+X7619USpQoYUasmxo8eDDdu3e3emGxh4+vurm5MXz4cKpUqYJhGERFRVGiRAnLL+S0tLQbnjRsNl9fX8aPH8/GjRtxc3Oja9euPPvss2bHyiMrK4snn3zScmi4bdu2dvepPbiyd7dZs2ZMmDABwzBYsWKFXRaJgQMH0rt3b/z8/DAMgypVqvDtt9+aHStf7u7u/PDDDyQnJ1v2Qh46dIivv/4awPQ3vE2aNOH8+fNkZWXRvHlzy/yrrwsFSaXHCaxatYp//etfAHh4eNCpUyfeeustJkyYYHKyvBYtWsS8efPYtWsXcOXk4G7dutlF6alTpw6///477u7u7N69Gz8/P8sywzDYtm0bQ4YMMTFhXs2bN+fFF1+0mmcPn0Dx8vJi2rRp1K5dG7iym/vxxx+3WsfsQ1o38umnnxIXF8e//vUv/Pz8OHHiBNOmTcszzmYbPXo0Xbt2ZezYsZacs2fP5tVXXzU7mpXc3Fw2b97MzJkzmTVrFu7u7uzfv58HH3zQ7GhWUlNT+eGHH/Dw8OCll15i+fLldvOx+us9//zzNGvWzOqNgz282blW165deeCBB3Bzc7OaX9DXklLpcQKdOnWyOkR06dIluzlP5nrt27cnMDDQ8u705MmTdnMeQs+ePZkxYwZ//PEHZ86cyXNdllOnTtld6fHz82PkyJFWh16OHj3K5MmTTUx1xdXCA+Q5Mfjy5cusX7++QK/X8U+VLFmSd9991zJ97733snjxYvMC3UBQUBD9+/e3TNevX99yQrs9admyJfPnz2fcuHHUrl2bn376idTUVLNj5TFs2DCKFCliOTm4T58+vPXWW0ycONHkZHk1aNDA6mcU7POCtL/99pvVtaTgymkCBXlpBZUeJ5CUlMTy5ctp0KABkZGRjBs37m8/gmuWihUr8vHHH3Px4kXi4+OZN28eL730ktmxgCsf+wwJCQGu7D3r2rWr1fLw8HAzYt3UiRMn8PLysrqE/vWX07cHrq6ujBw5krvuuouLFy+yfPlyuzmX63rXX0IhIyODPXv20L17d3MC3UB6ejo5OTmWd9KHDx9mx44d9O7d2+Rk1urUqcM777xjme7Zs6ddHiq+eumPq9e8KlasmGWPtL154IEH2L17t+Vn1TAM1q1bxzPPPGNyMmtmXEtKpccJ9OvXjzlz5vDmm29y6dIlWrRoYXe7uK8KDg6mdu3abNq0iaysLKZPn261R8BeXF944MqLor0ZOXIklSpVspoXHx9vUpob69OnD1WrVmXp0qUkJibSs2dPnnrqKbNj5cswDF566SWqV69OUlISW7ZsITQ01OxYeTRr1sxyAc2kpCQyMjKYOnWq2bHy2LFjBzNnziQtLQ3DMMjNzeX06dN2d90rb29vYmNjcXFxIScnh7Fjx9rlaxPA+vXrWblypeUQvGEYxMfH213pMeNaUio9TuCJJ57gk08+sZyEa89iYmIYP348Bw4coHTp0vj4+NjtC4u9Sk5Opnjx4hiGgZubG+fOnbMsMwyD9evX8/TTT5uY8MZ8fX3x8/OjWbNmFClSxOw4+erduzfVq1dn/fr1uLq68tVXX9nlXqkmTZowf/589uzZg7u7O/fcc88NL2Fhps2bN/P000+ze/dumjdvTnJysl3+DbbevXvzxRdf8NtvvzFx4kSCgoL44osvzI6VrzNnzrBz506r/0P79+83MVH+cnJyeOaZZwr1WlIqPU7grrvusvqhAuzqo+DXGjx4MH5+foSEhODm5sbOnTv5/PPPeeONN8yO5jC6d+/O+PHjadiwIf/617+4fPmy1aULEhIS7K70zJkzh6+//prg4GD8/PyYMmUKnTp1sptDRm+++SbZ2dl07tyZ+++/nypVqrBz504OHz7Mtm3b+Oqrr6hfv77ZMfMoXrw47du3t0yvW7fO7vag1K1bl5YtW3L58mXKlCljOfm+IK/K+0+88847DB48mPfff9/sKH/rvvvuyzPv+sss2AMzriWl0uMEkpKS6N69u9Xx3VOnTtnlXy9PS0uz+sOO7du3t8tP8Vzde1azZk2zo+SxevVqJk2axNSpU3nooYfyXDHWHs9DOHz4MOvWrbN6Z2pPVw7esGEDixcvJiAggNzcXF577TUuXrzIjBkzcHd3Z8qUKXbxhyc7d+7Ml19+Sf369enQoQMxMTGWZfb6h1H//PNPHnzwQZYtW8bAgQM5d+4cGRkZZsfKIzs7O8+1mE6ePGmXrwFHjx6lR48elk9vGYbB+fPnWbt2rcnJrL3xxhtWnzArjBPYVXqcQL169fL8cUx7/COeAD169ODy5cuWP0MB1te/+eWXX+ziGkP2vPds7NixbNiwgVq1ajF79mwaNWpk9c6vWbNmJqbLX9OmTfMczrr2sJzZunbtavmFN336dMLDw5kzZw4NGzYEYNu2bWbGs/j+++/x9/fHxcWF9957j3bt2ln9ZfBVq1aZnDCvp556iueffx5XV1cmTZrE9u3b7fKPI5crV45nn32W6tWrW+aFh4ezbNkyE1Plr1atWvTo0cNqD8qOHTtMTJS/+Ph4XnvtNcqVK8e///1v5s+fT/PmzQv0/EiVHifw9NNP5/mFYo9/eBCuHN//z3/+Yylo2dnZXL58mVmzZmEYBhcuXODAgQMmp7TvvWenTp1i2bJlFClShMjISL799tt8d3fbk6ioKL777jtq1KhBYmIia9assau/D5aYmEhSUhL79u1jwoQJPPfcc5bCA9jFzyRgtSeiRIkSVoc1s7Ozra4tZS8efvhhZs6cSa1atfDy8qJDhw52+Wc9fHx88rx5zM3NNTHRjT3//PNWhQfIcz0ce/Dxxx/Tpcv/t3fnUVHX+x/HnzCAR0UDTEgJr2YqLqWSiDuoiGJaEKKIqHXMfSvTXLqdQtRyJZHySLnkgitdRSFX3HBH6qqpB3G5QiiQAjpwbITh9wc/5zqCXZfG73eG9+OcznG+Y/N9ZcW8v9/v5/N+9zJsWR8yZAiDBw9m/fr1JjunFD0W6uEBiStWrDCLAYkAvXr1Ijw8vML/QdV0parmu2fNmzc3FLlubm6G1vMPqOWO1MNGjBjB999/z+LFi7Gzs8PPz09VO00CAgLw8/Pjzp079O7dm4kTJwKg0+mIiori6NGjCif8r2vXrpGbm8v27duNGtIVFRWxZMkStmzZomC68qZNm1ZuXM727dt55513FEpUsQ8//JCqVauSnp6OjY0NDRs2pHv37krHqtB//vMfNm7caLQj7tSpUyQlJSkdzUjXrl0JCwsztAEoLCw0+VwwKXoslDkOSISytTIPZGdnc/bsWbp06WL4Eh8+fLhS0YwMGTIEnU5nNCPswReh0o4dO2b0JZKSkkJ0dDRQdmV68OBB4uLilIpXIVtbWwICAqhfvz6Ojo60adNGVVemPj4+HDt2DK1Wa/S49fjx47Rt25b33ntPwXTGHBwcmDdvHmlpaVy9etVw3MbGptz2YDVYuXIlFy9eNPqZZGVlpbqi5+bNm4wZM4bc3Fzs7e2pXbs2ixcvpmbNmkpHK2fdunW0aNGC3377jTfeeIM7d+4YjXtQi6pVq5KQkMCtW7cMP6dMvdBeBo5WAg8GJBYUFHDixAnq1auHg4OD6gYkQtmanunTp+Pm5sZ7772Ht7c3Li4uquvMm5iYaDQjbO/evRQWFqpiXIaXlxeNGjWqcNisXq/n0qVLnDx5UoFkj7d582bCw8OpW7cujo6OFBcXExkZSb169ZSOZpb0ej2XL18u1+4hOzu73Fo0pe3atQtbW1u6detmOKaWtXsPmzhxIu+++y4+Pj5YW1tz+/Zt1q9fX26jgBo8aJ66e/duPDw8ePnll5kyZQrz589XOlo5iYmJ7NmzB51OR4cOHRgwYAA2Nqa7HyN3eizU7du3iYiIID09nfbt2zN27FgCAwPJysrCysqKOXPmEBgYqHTMcoYNG4anpyeTJ0+mc+fOfPXVVyQmJiodqxw1zwj75ptvaN++/WPfV8tjuId98803LF261LD2qLCwkOXLlzNhwgSFk5kna2trXF1d2bt3r2FHTElJCVu2bDHpeoknNWrUKDQaDUFBQfTs2ZNPP/2U8PBww5fd9OnTFU5YXvv27Y0KMycnJ1VuA4f/TjFftmwZI0eORKPRqG7X3gNdunShdevWhjt9P/30U7k5fH8nKXos1OzZs9FoNAwYMIDk5GRGjhxJSEgIoaGhpKenM3/+fFUWPVevXmX+/PkcP36c+Ph4CgsL+de//qW6nh1qnhH2VwUPqGPg6KMaN25stNi6evXqqhxFYE4iIiLQ6/XcuHEDV1dX7ty5g5eXl9KxAEhOTuaHH36gXbt2QNloj7Fjx2Jra0txcTH79u1TXT+hP/74g/z8fBwcHCgpKSEpKYlTp04REhKidLRypk6dSkZGBnZ2dixcuJCEhATV3S0H+Oc//8muXbsMjwhLS0vJy8uTokc8vWrVqhEREQFAaGgogYGBjBgxAoBWrVqpcmQCwAcffEBycjJDhw7lpZdeYs+ePbRp00bpWOWoeUaYOSgoKGD//v2G140aNSIyMtJoO7AaO8iak65du+Ln58eOHTvw9/dHo9EQGRmpdCyg7M7og4IHyuZtubm5GV5fu3ZNgVR/zd/fn4EDB5Kfn49Wq6Vhw4aqHDYKEBcXZ1hn5uTkpNqRLleuXOHYsWNGj7NMPcNQih4L9fBze2tr63JX92rcugplV/g9e/YEyhbk7tixg/379zNy5EiFkxl7eEaYTqdT7YwwtapevTo//vgjTZo0Maw9unv3Ljk5OYbfo8ZFt+bk5MmTXLx4kbCwMCZNmkTz5s3ZuHEjH3/8sdLRcHZ2Nnr9cMED6tkKPnHiRIYPH06LFi147bXX2LFjB5cvX8bGxoYGDRpUuG5ODW7fvs2qVauwsrLCx8dHlQ0UoWyDwKP/rk09KkWKHgu1c+dOfv/9d8Prixcvkpuba3h96tQpRo0apUS0v6TVaomLi2PDhg388ccfeHt7/8/HNUqYM2cO48aNM4t5ZmpkY2PD0qVLVbmY3lKMGDGCI0eO4OTkxNChQ1m5ciWTJk1SOhZQNhtKp9NVOF/t/v37qrnTU79+fcNd8djYWAYNGkTjxo0N72dkZJQr2NTgwc/24uJitmzZQkREBO+8846qdhlCWb+4oKAgo8Goly9fNmkLCCl6LFRxcTF6vd6wTd3d3d3wnl6vL9e4SmkXLlxg7dq1/Pzzz7Rs2ZImTZqwbds27OzsjK7+1aJRo0YkJCSg1Wpp0aKFKgsztZOCx7QyMzMN6/Y8PDxo0aIFp0+fVjhVGQ8PD6ZMmcIXX3xhNIYgIyODmTNn0rFjRwXT/Ze9vT2LFy9Go9Fw9uxZ8vLyjN5Xa7+zxMRENBoNW7du5fz587z99tu0atVK6VjlzJ07l7Fjx/LKK69gbW2NXq83eYNXKXos1KxZs/5yLUxKSsoLTPPXpk6dSmJiIqGhofz000/Ur1+f6Ohow1Xgo7fC1SA4ONjw6/j4ePr27UufPn1U9xhOVD7m0Jxw2LBhTJkyhS5dutCgQQNq1qxJTk4OWVlZdOvWTTWNKYcPH87hw4fL9RGCsrsSjzZ9VYspU6YY+vIsW7bM6KJXTXx9fcsNFTb1CBLp0yNU4dixY6SmpuLs7Ezv3r1ZuXIl48aNUzrWYx08eJCbN28SFxdHbm4uQUFBBAcHq64HirnYv38/np6eRh2uxbPJz89nxowZpKWlUadOHcNxGxsbfHx8VFNQQNm/9927d5OdnY2TkxPdu3fH399f6VgVSk1NxcPDw+hYVlaWYRSNmhw9epQOHTqg1+tJTExk//799O/fXzW79x5YunQper0eV1dXw7GzZ8/y+eefm+ycUvQIVXkwdyklJYXQ0FA8PDxUOTahWbNm+Pj40L9/f7y9vVW7oNFcdOvWjdjYWKNHXnfv3qVGjRoKpjJfj2tOKCqHa9euce/ePbZt20ZCQgLNmjUjODhYdWMzxo8fj52dndHarrS0NJN2jJeiR6jWqVOnSEhIIDExUXUdhJOSkowalYnns2bNGjQajdGX9L59+5g2bZqCqczb6tWradq0KZ6enixatIhDhw4xYcIE+e+2EmjWrBlubm4EBQUREBCgyiUCUPFC8JycHJPmlTU9QrU8PT3x9PSkdevWSkcpp6IvjitXrqh2er3aJScnk5uba7RdNSMjQ4qe53D9+nWGDBnC1q1biY+PZ+3atRw+fFjpWBbjwXopNc2Ie+Crr74ydId/dPG1mjg6OhIVFYWtrS2jR48mJiaGfv36mfScUvQI1VPDaAeAmJiYx+4sKC0t5ffff1fdFGNz8dFHH5VbwHjx4kWF0lgGDw8P8vPziYyMZMaMGbz66qvcv39f6Vhma8OGDUbdlzUaDTExMYamr0qbMWMGxcXF9OzZk3fffZfc3FxGjRrF+fPneeWVV4iOjqZ58+ZKxzQyefJk7OzsDGshQ0JC+Oyzz0za9FGKHiGeUN26dQkLC8PBwaHC9+Uq+tm5ubkRFRWFnZ0do0aNeiFXfJZOr9czfPhwgoKC6NGjB3FxcWzevJkhQ4YoHc2sPJhjlZaWZtQlvKioiHPnzqmm6ElKSmLr1q288sor6PV6xo8fT15eHitXrsTGxoaYmBgWL16sdEwjLVq0YNy4cWzYsAEomyTwYJ6hqUjRI8QT8vX1xc7OztD76FEtW7Z8wYkshxJXfJauT58+9OnTx/C6Z8+eBAUFKZjIPLVu3ZqbN29y//59wzZwKLvT8+mnnyqYzJi/v79hI8APP/zAuXPnWL9+PW+88QYAR44cUTJehapXr05OTg5WVlaUlJQQGRlp8sX3UvQI8YQyMzNp0KABer2e1NRUo74dpaWlHD58mE8++UTBhOZLiSs+S3fp0iXCw8NxdnZm0aJFbNq0ibZt26p27p6a+fv74+fnV279TmZmpkKJysvLyyM/P59ffvmFqKgohg0bZih4AM6cOaNguooFBwezYMECjh07xpIlS2jTpg0LFiww6Tml6BHiCX366afMnj2bpk2bsmDBAmxsbIy2ql+/fl2KnmekxBWfpZs1axa9evUiPz8fgCFDhjB48GDWr1+vbDAzdezYMTZt2mT484SyreGHDh1SLtRDAgIC8PPz486dO/Tu3ZuJEycCoNPpiIqKMuloh2dlb2/Pl19+aXTs9u3bJj2nbFkX4hmkp6fz+uuvGx27cOGCybuJWiqtVmu44issLKRNmzbMmDFDtVttzcGqVat4//33DQtwCwoK6NGjh+raP5gLf39/xowZY9SA9NChQ0yePFnBVMZKSkrQarW89NJLhmMPirJXX31VFbtLd+3axaVLlx77/m+//cbSpUtNdn650yPEM3i44MnLy8PR0VEKnueQlpZmdMWn0+k4ffq0FD3PoWrVqiQkJHDr1i1SUlKIjo7G19dX6Vhmq3PnzvTt29fomBqKiIdpNBqjggegS5cuCqWpWE5ODvn5+Y/dEKLT6Ux6fil6hHhCD28J7d69Ozk5OYwePVrVW0LVzhzmRJmrfv36sWvXLtLT0zl//jw9evSgf//+SscyWyUlJQwdOtToTs+1a9fYtGmTgqnMT69evXBycqqwv9H9+/fJzc016fml6BHiCT26JXTChAmq3xKqdg4ODsybN4+0tDSuXr1qOG5jY1Puqlo8HV9fX2JiYujdu7fSUSxCYWEhrVq1wtbW1nCsoKBAwUTmqXbt2oZff//99wwfPtzw2tbWlg0bNjBp0iSTnV+KHiGekDluCVU7BwcHoqOjK5wTlZ2drVAqyzBw4EAcHR2NjiUnJ9OpUyeFEpk3Nzc3QkJCqFWrluGYVqtVMJH5+uWXX4iOjub69etG/c2KiorQarVS9AihBua4JdQcWFtb4+rqyt69ew1fIiUlJWzZskV2Gj2H48ePExMTY1jjUVpayq1bt4wa7Iknt3PnTkJDQ42O3bt3D3t7e4USma/WrVszZswYkpKS8Pb2NhzXaDQ0adLEpOeWokeIJ2SOW0LNRUREBHq9nhs3buDq6sqdO3fw8vJSOpZZ8/PzY+rUqUZfynv27FEwkXnr27cvP/zwg9EdycOHD7Nw4UIFU5mvt956C61Wa9TwUafTkZSURK9evUx2XtmyLsRTMIctoeZo9+7d+Pn5sWPHDvz9/dFoNERGRvLxxx8rHc2sXblyheTkZDQaDd26daNOnTpKRzJbn3zyCTqdzqiIvHDhAlu3blUulJk6f/48aWlpHDhwAB8fH8PxoqIiYmNj2bFjh8nOLUWPEEJxs2bNombNmoSFhREeHk7z5s1ZsWIFx48fVzqa2dqzZw/Tpk2jZcuWODo6kpmZydixY1W3hdlcZGRk4ObmZnTs6tWrNGjQQKFE5kun0/H111/z66+/Gj3O0mg0dOnSBT8/P5OdW4oeIYTicnJyOHLkCIGBgaSmprJy5Uo6d+4sW6yfw5QpU5g+fTpOTk6GY6tXr5aBo8/o0Ts6Op2O3Nxcxo4dq0wgC3Dz5k3D5hCAu3fvUqNGDZOeU9b0CCEUkZqaioeHBwDOzs4EBgYC4OHhYTgunl2HDh2MCh6AP//8U6E05m/t2rVG63mys7Nljtlzunv3LpMnTzbMh9u8ebPJ58NJ0SOEUMTo0aMZPHgwvXr1KjfSQzy/69evs3v3bl577TXy8/PZuXOnybvdWrLIyEijx1t6vZ6vv/5awUTmT4n5cNYm+2QhhPgLCxYsYODAgZw6dYovvviC7777jsuXLysdy6zl5eWh1+sBGDZsGAcPHmTQoEF89NFHAEyfPl3BdOZNo9GQlZVl+OvMmTMkJSUpHcusde3albCwMF5++WWgrAGkqX8GyJoeIYQq5OTkEB8fz+7du+nQoQPdunXjzTffVDqWWenfvz8zZ87E3d293HoJ8Xxat26Nk5MTD74yq1WrxqBBgxg4cKDCyczXxo0bsbe359q1a3h5eREdHU3dunWZM2eOyc4pj7eEEIrQarWG7b+nT58mPj6enTt3UlxcTMOGDcnLy1M4ofkJCAjA3d0dgH379jFo0CCj98+cOSOF5DPasmULDRs2VDqGRRkwYACJiYmkp6dz9uxZ/Pz8CA4ONuk55U6PEEIRc+fOpUqVKmzfvp3s7Gw6depE37598fX1pUqVKkrHM0txcXHEx8dTWlpKdna20XDM0tJSrl+/zsGDBxVMaP4KCgo4ceIE//jHP0zePdgSVTS4edSoUZw/f546derw7bff0qxZM5OdX4oeIYQi3N3dadasGd7e3ty8eZMGDRrg7+9frheKeDparZYrV66wfft2evToYTheWlrKgQMHmDp1qoLpzMvt27eJiIggPT2d9u3bM3bsWAIDA8nKysLKyoo5c+YYdh2KJ9OuXTujwc2DBg0iJyeH2bNnY2Njw5o1a0w6uFkebwkhFDFjxgyjnjE3b95k27ZtJCcn07VrV/z9/aWD8DOwt7fnzTffpFatWri6uhqOa7Vak15BW6LZs2ej0WgYMGAAycnJjBw5kpCQEEJDQ0lPT2f+/PlS9DylRwc3nz179oUObpaiRwihiKCgIMOvf/nlF+Lj40lMTKSwsJAqVapQs2ZN+vXrp2BC81ZUVMSgQYNwcXFh0aJFbNq0yeQ9UCxNtWrViIiIACA0NJTAwEBGjBgBQKtWreTP8hkoPbhZih4hhCKWLFlCtWrV2L59Ozdu3KBdu3ZMnToVX19fatasqXQ8szdr1iz8/f1faA8US/Pwmihra2ujieAAjo6OLzqS2VN6cLOs6RFCKMLd3Z2WLVsSEBCAv78/Dg4OSkeyKKtWreL9999nw4YNhISEUFBQQI8ePTh58qTS0cxGnz59jO5CXLx40bA7DuDUqVPs3btXiWhmTcnBzXKnRwihiEfX9Ii/V9WqVUlISODWrVukpKQQHR2Nr6+v0rHMSnFxMXq9Hmvrsj6+Dxc8er0eW1tbpaKZNY1GY1TwAC9sEK7c6RFCCAuTk5PD4cOHuXXrFqtXr6Zp06Z07dqV4OBg+aJ+CikpKbRp0+aZ3xfqI0WPEEJYkOXLl7No0SJsbGx46aWXuHv3Lnq9nilTphAWFqZ0PCEUJY+3hBDCQiQnJxMbG8t3331Hp06d0Gg0lJaWcvLkSWbOnEn9+vXp1KmT0jGFUIwMHBVCCAuxadMmvv/+e7y9vdFoNABYWVnh5eXFihUrWLduncIJhVCWFD1CCGEhateu/didLy4uLjKAVFR6UvQIIYSF+F99Y6SvjKjsZE2PEEJYiAMHDvzP9ydMmPBiwgihQlL0CCGEhcjMzOT48eNYWVmVe6+0tJSsrCwFUgmhHlL0CCGEhVi4cCEdO3Z87PvJyckvMI0Q6iN9eoQQQghRKchCZiGEEEJUClL0CCGEEKJSkKJHCCGeQm5urll/vhCVmRQ9QgiLdvToUdq2bUtISAiLFy9m5MiRfPXVV+h0uqf6nPv37zNv3jwmTZoEwI0bN+jZsyd/57LIVatWMWDAgL/t84QQxqToEUJYtA4dOtCoUSM6duzIxIkTiYqK4sCBA0RFRT3V59ja2uLt7W147eLiwvz58yvcHv6w1NTUJz6Hr6/vU2USQjwdKXqEEBbP2vq/P+qqVKlCw4YNuXr16nN/ZvPmzf/y9yQmJrJ58+bnOo8Q4u8jfXqEEJVKRkYGqamphIWF8cEHH9C+fXvWrVvH3r17iYuL4969eyQlJfHZZ5/RpEkTsrOzWbNmDY0bN+bEiROGz0lKSiI8PJyDBw8CkJKSQlpaGnl5eWRmZhIeHs6+ffu4evUqsbGxhIaGsn37dvLz89m3bx+jRo2iXbt23L17l2XLltGoUSPS09OV+mMRolKQokcIUSmcOHGC4uJi8vPzmTt3Li4uLqxYsYK5c+fi7e3N7t27qV69OiEhIWg0GsLDw4mNjWXSpEl8/fXXuLm5Ua1aNX788UcAfHx8GD16NAB//PEHq1evJioqitLSUsaNG4dWq6Vjx47Y2toSGhrKr7/+SlZWFiNHjsTV1ZWpU6dy8OBBZs6cSVBQEO3atePixYskJCQo+cckhEWTokcIUSl4eXkxfvx4w+vMzEwcHBxwdnbG2dmZpUuX8vrrr7N+/XoKCgqoXbs2mZmZpKen4+bmBkCNGjUMf//Dj8ySkpKoV68eAFZWVnz77bflzr9nzx7+/PNP1q9fT2FhIe7u7mi1WhITE/nyyy8BsLe3N8U/uhDi/0nRI4QQgE6nw8vLC09PT8OxM2fOUFhYyP3797G1tX3s31tUVERGRka5z3v0dYsWLQgICADgww8/5Pbt2xQXF1NQUED16tX/vn8YIUSFZCGzEMLilZSUUFJS8pe/p2PHjkRHR5Ofn09xcTHLly/HxcWFGjVqsHXrVgAKCwu5d+8egGGremlpKW3btmXfvn0kJSVRUlJCQkICeXl5aDQaioqK0Ov1eHl5sXz5cm7cuEFpaSmbN29Gq9XSqFEjNm7cWO7zhRB/Pyl6hBAW7cCBA6SlpXHo0CH+/e9/A6DX69m7dy+5ubkcOnQIgODgYOrWrUuPHj0ICwvD09MTFxcXFi1axPLlyxk3bhynT5+mRo0anDlzhp9//hkoe2zVrFkzZsyYweeff87bb79NrVq1cHFx4a233uLcuXPExMTg7e2Nt7c3AQEBBAQE4OzsTL169ViwYAEHDhxg2LBhJCUl4ebmJoNBhTARGTgqhBBCiEpB7vQIIYQQolKQokcIIYQQlYIUPUIIIYSoFKToEUIIIUSlIEWPEEIIISoFKXqEEEIIUSlI0SOEEEKISuH/AHEpix9HiCTuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#metrics example\n",
    "metrics_description(datavector_svc_wf[0][2], datavector_svc_wf[0][3])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1c14fed8",
   "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>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>1</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>Water body</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>Birch</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5</td>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>Pine</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>Soil</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5</td>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5</td>\n",
       "      <td>Grass</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5</td>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5</td>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>5</td>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration          names  f1_scores  precision_list  recall\n",
       "0           1          Birch       0.47            0.84    0.33\n",
       "1           1          Aspen       0.58            0.49    0.72\n",
       "2           1     Silver fir       0.58            0.97    0.41\n",
       "3           1           Pine       0.81            0.76    0.86\n",
       "4           1     Water body       1.00            1.00    1.00\n",
       "5           1           Soil       0.17            0.24    0.13\n",
       "6           1  Deforestation       0.98            0.99    0.97\n",
       "7           1          Grass       0.73            0.64    0.86\n",
       "8           1   Burnt forest       0.99            1.00    0.97\n",
       "9           1          Swamp       0.39            0.34    0.44\n",
       "10          1    Settlements       0.93            0.88    0.99\n",
       "11          2          Birch       0.54            0.59    0.50\n",
       "12          2          Aspen       0.65            0.65    0.65\n",
       "13          2     Silver fir       0.11            0.07    0.23\n",
       "14          2           Pine       0.71            0.70    0.73\n",
       "15          2     Water body       0.97            0.93    1.00\n",
       "16          2           Soil       0.68            0.80    0.59\n",
       "17          2  Deforestation       0.73            0.58    0.99\n",
       "18          2          Grass       0.88            1.00    0.78\n",
       "19          2   Burnt forest       0.96            0.99    0.93\n",
       "20          2          Swamp       0.82            0.78    0.86\n",
       "21          2    Settlements       0.11            0.95    0.06\n",
       "22          3          Birch       0.56            0.67    0.47\n",
       "23          3          Aspen       0.49            0.49    0.48\n",
       "24          3     Silver fir       0.48            0.49    0.47\n",
       "25          3           Pine       0.68            0.62    0.75\n",
       "26          3     Water body       1.00            1.00    1.00\n",
       "27          3           Soil       0.33            0.42    0.28\n",
       "28          3  Deforestation       0.91            0.86    0.97\n",
       "29          3          Grass       0.63            0.82    0.51\n",
       "30          3   Burnt forest       1.00            1.00    1.00\n",
       "31          3          Swamp       0.85            1.00    0.74\n",
       "32          3    Settlements       0.75            0.71    0.80\n",
       "33          4          Birch       0.46            0.67    0.35\n",
       "34          4          Aspen       0.49            0.36    0.76\n",
       "35          4     Silver fir       0.54            0.74    0.42\n",
       "36          4           Pine       0.75            0.74    0.75\n",
       "37          4     Water body       1.00            1.00    1.00\n",
       "38          4           Soil       0.56            0.88    0.41\n",
       "39          4  Deforestation       0.94            1.00    0.88\n",
       "40          4          Grass       0.33            0.21    0.71\n",
       "41          4   Burnt forest       0.97            1.00    0.95\n",
       "42          4          Swamp       0.27            0.50    0.18\n",
       "43          4    Settlements       0.90            0.83    0.98\n",
       "44          5          Birch       0.54            0.63    0.46\n",
       "45          5          Aspen       0.45            0.34    0.65\n",
       "46          5     Silver fir       0.08            0.14    0.06\n",
       "47          5           Pine       0.72            0.82    0.65\n",
       "48          5     Water body       1.00            1.00    1.00\n",
       "49          5           Soil       0.55            0.40    0.89\n",
       "50          5  Deforestation       0.86            0.88    0.84\n",
       "51          5          Grass       0.74            1.00    0.59\n",
       "52          5   Burnt forest       0.99            0.99    0.99\n",
       "53          5          Swamp       0.47            0.57    0.40\n",
       "54          5    Settlements       0.76            0.69    0.85"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#metrics by prediction on test data by class for each iteration\n",
    "svc_class_metris_wf = get_classes_metrics(datavector_svc_wf)\n",
    "svc_class_metris_wf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ceeda0a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eN+fZ2Niwfv16vv/+e3bs2JGpH6TU25MVZ86coWTJkkyfPp0dO3bw4MEDzp49i62tLbdu3eL69etp5v/nn3+ws7OjVKlSeHp6pnnvxYsXVKlShQ8++CDdsuzs7IiIiOB///tfmunbt29PN7BSejD+1w/umxo2bIiXlxfR0dFp6ga891lspUqV0vyXUUC/rzc/c5ndF++zTNWqVTl79ux716lq1ar4+/tn2GyoVqv59ttvcXJy0jSLv2ubgoODmT17NkOGDEn39yNFVFQU8LpjWVEjYZYLbGxscHd3T9O19tWrV5w+fZpBgwZl+OVNuXese/fu6b7v7OzMoEGD2LZtGwsWLODVq1ea91QqFTNmzNC0vb8p5YgzdcjEx8eTkJCgeZ1y1JoyT0rTz5EjR4iMjNQE0KNHjzRB9ObRromJCQAPHjzA29sbRVE0TSEpZY0ePZr79+8zcuRIDh06xJ49e/jtt9+oW7dumrqmrDckJISAgAB8fHxQqVT4+PgQERFBcHAw8fHx7Nq1C3jdxNKxY0dNE97+/fs1X+7evXtjYmLyVtNqipiYGABiY2PTff+/9mV6vLy8NIFVv359qlatSrly5ejevTsWFhaMHTuWTZs2cezYMb7++mtsbGwwMDDgyy+/5NixYwQEBACvm5L++ecfTRNYSrmpm5js7e2pXr063333HWvXruXChQv8+uuvFC9ePN1tbtSoEcbGxmlaEODtzwC8/rulTB8zZoymBSGFu7s77dq1o379+pr9k3r5lHr+1/5K+Uyl/kym/H/qbX3z85FyHevhw4f4+vpqRtvJaF9kZf8NHz6cq1evsnPnThRFISgoCF9fX/z8/NK9PtWjRw9Kly7NnDlziIyMJCkpSXOA4uHhQWRkJFFRUVy8eJHQ0FCOHDmCvr4+oaGhvHz58q1tevLkCUlJSZw9e5bw8HBNr+GXL18SGhqqKdfPz4/atWu/1UxdJOR9n5Oi4ZNPPlHs7OyUDh06KBMnTlScnZ2V1atXKwkJCe9c7uuvv/7PdZ88eVL57LPPlNatWyv9+vVTPvvsM2Xx4sXKy5cv053/1atXyoYNGxQ7OztlwoQJir+/v3Lz5k3F0dFRqV69urJ7924lMjJSWbNmjabnpUqlUhRFUVauXKk0aNBA6dq1q+Lp6alMnjxZWb58ueLv769cunRJ6dq1q1KzZk1l7969Snx8vBITE6P069dP+fTTT5UDBw4ot2/fVoYNG6bY2dkpf/zxhxIVFaUoiqJs375dad26tfLRRx8pX331labn5u3bt5XBgwcrdnZ2yrp165SIiAjl2rVrSqtWrZSWLVsqmzdvVv755x+lfv36ys8//6wEBAQodnZ2iouLi7Jr1y5l7ty5mtEsBg8erHTr1k3ZuHGjsnjxYuXChQvp7p9du3YpLVu2VOzs7JRBgwYp586dy3Dfnzt3Thk+fLhiZ2enbNy4UXn48GGG8y5btkxp2rSpsnz5cmXjxo3K5s2bNe/5+PgogwYNUmrXrq3Zt6nt3r1bGTx4sLJkyRJlxowZml5xQUFByqxZsxQ7Oztl7NixipeXl2YZf39/ZciQIUrdunWVjh07Kn/99VeGdVMURZk9e7amd5yiKMrLly+Vb775RrGzs1OWLVumREVFKTt37lSqV6+u9OnTR9Pz8cGDB8qoUaOUGTNmKEuWLFHmz5+v6Wl68+ZNxcHBQWnSpIly4cIFJSQkRJk0aZJiZ2enLF68WDPfm65du6YMGDBA8znx8/NTQkNDlVWrVil2dnbK0KFDlfv376f7+UhKSlLGjh2rNG/eXNmwYcM790V29t8ff/yhNGvWTGnZsqXy66+/KpMnT1Z+++035cGDB+lu0/Xr15Vu3bop9evXV0aOHKmsXLlS+eKLL5Tjx48rCQkJyqJFi5T69esrAwcOVLy8vJQvv/xSadGiheLp6fnWNiUnJyvffvutUr9+fWXUqFGKv7+/0qdPH6V9+/bKvXv3NGUOHjxY2b179zv/7oWVjqIU9QG9hCiaQkND+eyzz/jzzz81I6AI7eXr66tpTs9u0602kmZGIYooc3Nzpk2blqeDU4vckZCQwKpVq/j999+LZJAByJmZEEXcxYsXCQoKSneEDVHwJSYmap7Q8eYtH0WJhJkQguTk5CzdkC8KBvn7SZgJIYQoBIp2lAshhCgUJMyEEEJoPQkzIYQQWq/A9eH09vbOcBQLIYQQRUd8fDy1a9d+r3kLXJgZGhpqHkMhhBCi6MrMU7OlmVEIIYTWkzATQgih9STMhBBCaD0JMyGEEFpPwkwIIYTWkzATQgih9STMhBBCaD0JMyGEEFrvP2+ajo6O5vz583Tq1Cnd9zdv3oyuri6xsbHY2Njg6OiY45UUQggh3uWdYfb48WMWLlzIixcv0g2zq1evcuXKFVasWAHA4MGDqV+/PuXLl8+d2gohhBDpeGczY8WKFXFwcMjw/YMHD2Jvb695Xa9ePdzd3XOudkIIIcR7yNbYjHfu3KFVq1aa15aWlty4cSO7dRIiW27evElAQEC67/n6+hIREZHpdZYuXZqqVaum+56NjQ316tXL9DqFdggICCAqKird9yIjIylVqlS675mYmGBjY5ObVROpZCvMoqKiMDIy0rw2MDAgPDw8u3USIsvCwsIYNGgQefkAdR0dHc6fP4+ZmVmelSnyRlhYGI6OjiQnJ2d6WT09Pc6ePSufizySrTAzNzcnISFB8zouLi7DoxQh8oKZmRk7duzI8zMz+cEqnMzMzDh69Gi6Z2YqlQoXFxcWLFiAra3tW++bmJjI5yIPZSvMqlWrxosXLzSvw8LCqFOnTrYrJUR21KtXT5r9RKZcvXqVZ8+eZWqZJ0+epPk3PTdv3kx3erly5WjUqFGmyhPvlqUw8/PzIzo6mgEDBrB+/Xr69u0LgI+PD3PmzMnRCgohRG66e/cuQ4cOzfLyy5Yty9Jy+/bto2bNmlkuV6T1zjALCgriypUrBAUFcfPmTc3R7vHjxwkMDGTmzJk0aNCAjRs3YmRkRO/evbG0tMyTiguRk06dOsW8efP44Ycf3tmDVxQ+KddXW5RpTOliJTO1rDo5AQPdYplaJiIhGo8XV/L0um5RoKMUsD368OFDedK0yFOxsbF07tyZ4OBgLC0tOXLkCMbGxvldrTxVlHvs3blzhz59+jCkci8sjcrmennBcc/Z+ugv9u7dS61atXK9PG2WmTzI1jUzIQqDdevWERISAkBISAiurq5MmjQpn2uVd6THnigMJMxEkebv74+rq6umyUdRFNatW0e3bt2oVKlSPtcub0iPPVEYSJiJIktRFObOnfvWtYuU6WvXrkVHRyefape3/qup0NbWttA3ib2MD8/0MnFJ8RjpGeZ6OeK/SZiJIkulUuHh4fHW9KSkJDw8PFCpVBneWyYKD1NTU4wMDTkSdDLPyjQyNMTU1DTPyisKJMxEkWVra0uLFi24ePEiSUlJmul6enrY29un26wmCh8rKysOu7tnevSi/2qCfRdTU1OsrKwytYx4NwkzUWTp6Ogwffp0nJyc0p1eVJoYxetAyyhc3tXT810KQ09PbSJhJoq0SpUqMWrUKNasWYOiKOjo6DB69GgqVqyY31UTBcD79PR0cXFJd7r09MxbEmaiyBs9ejT79+8nODgYCwsLRo0ald9VEgXEu3p6wn/fgydBlnckzESRZ2xszIwZMzQjgBS1G6bFu0lToXaQMBMCcHBwkGGshNBi73zStBBCCKEN5MxMiCIiMDAwS93PU/+bGdL9XOQlCTMhioDAwEC6dulCXHx8lpbPqMfeuxgZGnLY3V0CTeQJCTMhioDw8HDi4uOZ2MyBCqUy18MuRh1PCYPMDdn0NDKMFRdPER4eLmEm8oSEmRBFSIVSZlQxL5Pf1RCFQEF7bJCEmRBCiEwpiI8NkjATQgiRKQXxsUESZkIIITKtoD02SO4zE0IIofUkzIQQQmg9aWYUogh5GhlWqMoRIoWEmRBFQGRkJAArLp7Kl3KFyG0SZkIUASn3/GTlpumsSLlpOqN7jYTIaRJmQhQhctO0yAxtGs9TwkwIIcRbAgMD6dq1K3FxcVlaPkvjeRoZcfjw4SwFmoSZEEKIt4SHhxMXF8cIp9mUL1M5U8u+iouiuJFJppYJevGIjYdmZnk8TwkzIYQQGSpfpjIVy9XI72r8J7nPTAghhNaTMBNCCKH1pJlRCFGgFLRHiwjtIGEmhCgwCuKjRYR2kDATQhQYBfHRIkI7SJgJIQqUgvZoEaEdpAOIEEIIrSdhJoQQQutJmAkhhNB6cs1MiCIko+eMhb6KITZRnen1GesbYF68xHuXI0RukTAToggwNTXFyNAwT59nZmRoiKmpaZ6VJ4o2CTMhigArKysOu7tn+DiP4OBgYmJiMr3eEiVKYGlpme572XmchxCZJWEmRBFhZWWVYbhIV3ftIKOjZEzCTAghtICMjvJuEmZCCKEFZHSUd5MwE0IILSGjo2RM7jMTQgih9STMhBBCaD0JMyGEEFpPwkwIIYTWkzATQgih9aQ3oxBCFCCBgYEZjtSSEZVKlebfzCgsI7VImAkhRAERGBhI5y5dUcfHZWl5FxeXTC9jYGjEEffDWh9oEmZCCFFAhIeHo46PQ7fWt+iUqJipZZWEaHSKlczcMjGPUd9ZSHh4eOEOs82bN6Orq0tsbCw2NjY4Ojq+Nc9ff/2Fjo4OYWFhxMbGMmHChFyrrNA+WR1LDorGeHJCpEenREV0TD7M3DK5VBdtkWGYXb16lStXrrBixQoABg8eTP369SlfvrxmnoCAAE6fPs2yZcsAGDduHL6+vlStWjWXqy20QXbGkoOiMZ6cECJnZBhmBw8exN7eXvO6Xr16uLu7M2rUKM20+Ph4bt++zatXryhevDgxMTHvPNIWRUt2xpKDojGenBAiZ2QYZnfu3KFVq1aa15aWlty4cSPNPB9++CHVq1dnxIgRODg4MGTIEMqWLZtrlRXaR8aSE0LkhQzvM4uKisLIyEjz2sDAIN3uorNmzeL58+ds3LgRc3PzXKmkEEII8S4Zhpm5uTkJCQma13FxcW81IarValxcXDh48CBDhw5lzJgxPH36NPdqK4QQQqQjwzCrVq0aL1680LwOCwujTp06aeY5d+4cJiYmlCxZkgkTJvDJJ59w7Nix3KutEEIIkY4Mw2zAgAFcvHhR89rHxwcnJycA/Pz88PLywszMLE3gWVpaUq5cuVysrhBCCPG2DDuA1KpViwYNGrBx40aMjIzo3bs3lpaWABw/fpzAwEBmzpxJmzZtWLVqFZaWlpiYmNCpU6c8q7wQQggB/3HTtLOzc7rTR48erfn/MWPG5GyNhBBCiEySUfOFEEJoPQkzIYQQWk/CTAghhNaTMBNCCKH1JMyEEEJoPXmemRBCiAw9e/FIK8qRMBNCCPGWyMhIADYcmpkv5WaWhJkQQoi3pIzFO9JpNuXKVM718p69eMSGQzOz/BgxCTORbYGBgek+UeFdVCpVmn8zy9TUVOsf8y6ENlAymB4WFUJ8/KtMr8/QsDhmJhbvXc77kjAT2RIYGEiXrl2Jj4vL0vIuLi5ZWs7QyAj3w4cl0ITIJaamphgZGbExD5sZjYyMMDU1zdKyEmYiW8LDw4mPi6PmiJ6UKJ+5B7MmvIqjWHGj/57xDTFBz7m7cT/h4eESZrns1KlTzJs3jx9++AEHB4f8ro7IQ1ZWVhw+fDjDVpfg4GBiYmIyvd4SJUpoxvl9U3ZaXCTMRI4oUb4sJhXL53c1RA6KjY1lzpw5BAcHM2fOHJo1a4axsXGOrDuvm6alWTprrKysMtxvBe0J8RJmQoh0rVu3jpCQEABCQkJwdXVl0qRJ2V5vYGAgXbt2IS4uPkvLZ6Vp2sjIkMOH3SXQCjEJMyHEW/z9/XF1dUVRXl+WVxSFdevW0a1bNypVqpStdYeHhxMXF8+Ez5pToXzpTC0b80pNieIGmVrmaVAEK9dfkGbpQk5GABFCpKEoCnPnztUE2X9Nz0uZDTJRdMiZmRAiDZVKhYeHx1vTk5KS8PDwQKVSUbVq1SyvP+Wm2JXrL2R5HdkpVxROEmZCiDRsbW1p0aIFFy9eJCkpSTNdT08Pe3t7bG1ts7X+lJtis9LMmBUpzYxZvRlXaAcJM5EjYp69KJRlFUU6OjpMnz4dJyendKfr6OjkSDkVypemSiXzHFmXEBJmIltSmm7ubvgr38oWOa9SpUqMGjWKNWvWoCgKOjo6jB49mooVK+Z31YRIl4SZyJaUppuaI3tRolyZPCkz5tkL7m74S5qNctno0aPZv38/wcHBWFhYMGrUqPyukhAZkt6MImdkoYdbwqusDYGVlbJE5hkbGzNjxgysrKyYMWNGjt0wLURukDMzkS2mpqYYGhlxd+P+PC3XMBtjuIn35+DgIMNYCa0gYSayxcrKCvd3jN+WEZVKhYuLCwsWLMhS7zgZnkgIkZqEmci2d43f9l9sbW0L3BhvQgjtI9fMhBBCaD0JMyGEEFpPwkwIIYTWkzATQgih9STMhBBCaD3pzShyVUBAAFFRUW9Nf5+nBpuYmGBjY5NrdctvGe0beD1UV0YjnBT2/SJEVkiYiVwTFhaGo6MjycnJGc7zrqcG6+npcfbsWczMzHKjevnqffZNRgrzfhEiqyTMRK4xMzPj6NGjWTr7gNdnIIX1B/td++a/bigvzPtFiKySMBO5SprDMvZf+0ZuKBfi/UkHECGEEFpPwkwIIYTWkzATQgih9STMhBBCaD0JMyGEEFpPejPmELkBVggh8o+EWQ6QG2CFECJ/SZjlALkBVggh8peEWQ6RG2CFECL/SAcQIYQQWk/CTAghhNaTMBNCCKH15JqZECJfPA2KSHd6aNgrYuMSMr0+Y6NimJsVf+9yROEiYSaEyFOmpqYYGRmycv2FPCvTyMgQU1PTPCtP5D0JMyFEnrKysuLwYXfCw8PTfT84OJiYmJhMr7dEiRJYWlqm+56pqSlWVlaZXqfQHhJmQog8Z2VllWG4yC0sIiukA4gQQgitJ2EmhBBC672zmXHz5s3o6uoSGxuLjY0Njo6O6c4XGhrKzp07sbKyonHjxlSoUCFXKiuEEEKkJ8Mwu3r1KleuXGHFihUADB48mPr161O+fPk08/n7+/PNN9+wYsWKDC++CiGEELkpw2bGgwcPYm9vr3ldr1493N3d08yTmJjIxIkTGT9+vASZEEKIfJNhmN25cydNQFlaWnLnzp0085w4cYJHjx7h5+fH2LFjmThxItHR0blXWyGEECIdGYZZVFQURkZGmtcGBgZv3Rfy77//4ujoyMiRI1m9ejUvX75kw4YNuVZZIYQQIj0Zhpm5uTkJCf83pExcXNxbT0tOTEykbNmyAOjo6NCsWTNu3bqVS1UVQggh0pdhmFWrVo0XL15oXoeFhVGnTp0081SuXJng4GDNawsLC0xMTHKhmkIIIUTGMgyzAQMGcPHiRc1rHx8fnJycAPDz88PLy4vu3btz9+5dzRmcr68vXbp0yeUqCyGEEGll2DW/Vq1aNGjQgI0bN2JkZETv3r01HUKOHz9OYGAgM2fOZPbs2SxduhQbGxvKlStHu3bt8qzyQgghBPzHTdPOzs7pTh89erTm/xs1akSjRo1ytlYFVGBgYIaDo2ZEpVKl+Tczsjo4akBAAFFRUem+FxkZ+da1zxQmJibY2NhkujwhhMhvMtDwewoMDKRL1y7Ex8VnaXkXF5dML2NoZIj7YfdMBVpYWBiOjo4kJydnujw9PT3Onj2LmZlZppcVQoj8JGH2nsLDw4mPi6fqMDuMy739AMB3SXyViH7xzO3q2Gev8N18n/Dw8EyFmZmZGUePHk33zEylUuHi4sKCBQuwtbV9630TExMJMiGEVpIwyyTjcsUpUbFkflfjnf6rqdDW1lYesyGEKFRk1HwhhBBaT8JMCCGE1tPqZkbptScKOm3pASuEttPaMJNee6KgCwwMpEuXLsTH52EPWEND3N0z1wNWiMJAa8NMeu2Jgi48PJz4+HhaWBSnlIFeppZVJyVjoJe5qwCR6iQ8Ql5lugesEIWB1oYZSK89oR2ULCyT2SDLajlCFBZaHWZCFGSRkZEAXAh5lS/lClGUSJgJkUtSOiA1tyhO6Uw2M2ZFhDqJCyGvMuz4JERhVuDD7OrVqzx79ixTyzx58gSAs2fPZrpHWLly5bRirEnpJac9Shvo8YFhgf+qCaHVCvQ37O7duwwdOjTLyy9btixLy+3bt4+aNWtmudzcFhgYSOcuXVDnYS85A0NDjkgvOSFEAVWgw0xRXl/Sjq9siGKsk7mFE8n01unEKhg+iteUW1CFh4ejjo/HoG1zdM1KZ2pZJV6NjqFBppZJDotAfeKC9JITQhRYBTrMUiSX0SfZJPevOehGJcGjrJ3t5Adds9LoljXP72oIIUS+k+GshBBCaD0JMyGEEFpPwkwIIYTWkzATQgih9STMhBBCaD0JMyGEEFpPwkwIIYTWkzATQgih9STMhBBCaD0JMyGEEFpPwkwIIYTW04qxGQuScO9QYp9l7mGLSXFJ6BllbmzJ+JdxmZpfCCGKMgmz96Sj83rU/ieHH+dLuUIIITImYfaeatasyZYtW7L0oNBly5bxxRdfYG1tnally5UrV6CfqyaEEAWFhFkmZOUJ1Hfu3GHZsmW0atWKWrVq5Wh9ksMicnR9+V2OEEJklYSZFoqMjARAfeJCvpQrhBAFjYSZFipVqhRAlp40nRUpT5pOKVcIIQoarQgznZjkzN9DkKBAscx1ntCJSc5sKflKnjQtROGkxAQUqnLyQoEOM1NTUwwMDeBubJ6VaWBogKmpaZ6VJ4QQKVKa8pPvLMiXcrVZgQ4zKysrjrgfITw8PFPLqVQqXFxcWLBgAba2tpla1tTUFCsrq0wtI4QQOSGlKV+3lgs6JWxyvTwlJoDkOwsKxSWEAh1m8DrQMgqXgIAAoqKiMr1OExMTbGxy/4MihBBZoVPCBh2TD/O7GlqlwIdZRsLCwnB0dCQ5OePrXC4uLulO19PT4+zZs5iZmeVW9YQQQuQhrQ0zMzMzjh49muGZWWRkZIanziYmJhJkQghRiGhtmAEFqqkwoyZPlUqV5t83SZOnEEJkn1aHWUEhTZ5CCJG/JMxygDR5CiFE/pIwyyHSVCiEEPlHHs4phBBC60mYCSGE0HoSZkIIIbSehJkQQgitJ2EmhBBC60mYCSGE0HoSZkIIIbSehJkQQgitJ2EmhBBC671zBJDNmzejq6tLbGwsNjY2ODo6ZjjvmTNn+N///sf8+fNzvJJCCCHEu2QYZlevXuXKlSusWLECgMGDB1O/fn3Kly//1rzR0dGsWbOGihUr5l5NhRBCiAxk2Mx48OBB7O3tNa/r1auHu7t7uvPu3r2bLl265HzthBBCiPeQYZjduXMHS0tLzWtLS0vu3Lnz1nzXr1+nRo0aGBsb504NhRBCiP+QYZhFRUVhZGSkeW1gYEB4eHiaedRqNVeuXKF58+a5VkEhhBDiv2QYZubm5iQkJGhex8XFvfVMrt27d9O3b9/cq50QQgjxHjLsAFKtWjVevHiheR0WFkadOnXSzHPo0CG2bdsGvO4EEhsby+PHj9mxY0cuVVcIIQo/JeZx5pdJiEanWMlcL6egyjDMBgwYwPr16zVnXj4+PsyZMwcAPz8/oqOj2bVrl2b+v/76i8uXL/PLL7/kcpWFEKJwMjU1xcDQCPWdhXlWpoGhEaampnlWXm7JMMxq1apFgwYN2LhxI0ZGRvTu3VvTIeT48eMEBgZSt27dPKuoEEIUdlZWVhxxP/xW/4T/olKpcHFxYcGCBdja2mZqWVNTU6ysrDK1TEH0zpumnZ2d050+evTot6b16tWLXr165UythBCiiLKysspyuNja2lKrVq0crpF2kOGshBBCaD0JMyGEEFpPwkwIIYTWkzATQgih9STMhBBCaD0JMyGEEFpPwkwIIYTWkzATQgih9STMhBBCaD0JMyGEEFpPwkwIoRVOnTpFu3btOHXqVH5XRRRAEmZCiAIvNjaWOXPmEBgYyJw5c4iNjc3vKokCRsJMCFHgrVu3jpCQEABCQkJwdXXN5xqJgkbCTAhRoPn7++Pq6oqiKAAoisK6devw9/fP55qJguSdj4ARQmRfpDop08uok5Ix0MvcsWZWyinoFEVh7ty5miB7c/ratWvR0dHJp9qJgkTCTIhcYmpqiqGhIR4hr/KsTENDw0Lx1OAUKpUKDw+Pt6YnJSXh4eGBSqWiatWq+VAzUdBImAmRS6ysrHB3d5enBmeDra0tLVq04OLFiyQl/d+Zp56eHvb29pneP6LwkjATIhfJU4OzR0dHh+nTp+Pk5JTudGliFCmkA4gQokCrVKkSo0aN0gSXjo4Oo0ePpmLFivlcM1GQSJgJIQq80aNHY2FhAYCFhQWjRo3K5xqJgkbCTAhR4BkbGzNjxgysrKyYMWMGxsbG+V0lUcDINTMhhFZwcHDAwcEhv6shCig5MxNCCKH1JMyEEEJoPQkzIYQQWk/CTAghhNaTMBNCCKH1JMyEEEJoPQkzIYQQWk/CTAghhNaTMBNCCKH1JMyEEEJoPRnOSgghtERAQABRUVFvTVepVGn+fZOJiQk2Nja5Wrf8JmEmhBBaICwsDEdHR5KTkzOcx8XFJd3penp6nD17FjMzs9yqXr6TMBNCCC1gZmbG0aNH0z0zA4iMjKRUqVLpvmdiYlKogwwkzIQQQmsU9qbC7JAOIEIIIbSehJkQQgitJ2EmhBBC60mYCSGE0HoSZkIIIbSehJkQQgitJ2EmhBBC60mYCSGE0HoSZkIIIbSehJkQQgitJ2EmhBBC60mYCSGE0HoSZkIIIbSehJkQQgitJ2EmhBBC673zeWabN29GV1eX2NhYbGxscHR0fGue9evXs3HjRnR1dfn666/p3r17rlVWCCGESE+GZ2ZXr17lypUrDBkyhDFjxrB9+3aCgoLSzOPp6Ym1tTVnz55l7Nix/PDDD7x48SLXKy2EEEKklmGYHTx4EHt7e83revXq4e7unmaeEiVK0LFjR3R1dXF2dsbY2JgnT57kXm2FEEKIdGQYZnfu3MHS0lLz2tLSkjt37qSZ56OPPtL8f1JSErq6utja2uZCNYUQQoiMZRhmUVFRGBkZaV4bGBgQHh6e4YpOnTrFwIEDKVWqVI5WUAghhPgvGYaZubk5CQkJmtdxcXEZBtWrV684f/48EydOzPkaCiGEEP8hwzCrVq1ams4cYWFh1KlTJ915N2zYwJQpU9DXf2fnSCGEECJXZBhmAwYM4OLFi5rXPj4+ODk5AeDn54eXlxcAe/bsoXfv3pQqVYrExETOnDmTy1UWQggh0srwVKpWrVo0aNCAjRs3YmRkRO/evTUdQo4fP05gYCC+vr5Mnz49zXKzZ8/O3RoLIYQQb3hnu6Czs3O600ePHq35/x49euRohYQQQojMkuGshBBCaD0JMyGEEFpPuh8KkU8CAgKIiop6a7pKpUrz75tMTEywsbHJ1boJoW0kzITIB2FhYTg6OpKcnJzhPC4uLulO19PT4+zZs5iZmeVW9YTQOhJmQuQDMzMzjh49mu6ZGUBkZGSGgxSYmJhIkAnxBgkzIfKJNBUKkXOkA4gQQgitJ2EmhBBC60mYCSGE0HoSZkIIIbSehJkQQgitJ2EmhBBC60mYCSGE0HoSZkIIIbSehJkQQgitJ2EmhBBC60mYCSGEyDGnTp2iXbt2nDp1Kk/LlTATQgiRI2JjY5kzZw6BgYHMmTOH2NjYPCtbwkwIIUSOWLduHSEhIQCEhITg6uqaZ2VLmAkhhMg2f39/XF1dURQFAEVRWLduHf7+/nlSvjwCRoslh0VkehklXo2OoUGulyOEKDoURWHu3LmaIHtz+tq1a9HR0cnVOkiYaSFTU1MMDA1Rn7iQZ2UaGBpiamqaZ+UJIbSHSqXCw8PjrelJSUl4eHigUqmoWrVqrtZBwkwLWVlZccTdnfDw8Ewtp1KpcHFxYcGCBdja2mZqWVNTU6ysrDK1jBCiaLC1taVFixZcvHiRpKQkzXQ9PT3s7e0z/XuTFRJmWsrKyirL4WJra0utWrVyuEZCiKJKR0eH6dOn4+TklO703G5iBOkAIoQQIgdUqlSJUaNGaYJLR0eH0aNHU7FixTwpX8JMCCFEjhg9ejQWFhYAWFhYMGrUqDwrW8JMCCFEjjA2NmbGjBlYWVkxY8YMjI2N86xsuWYmhBAixzg4OODg4JDn5cqZmRBCCK0nZ2aFUEBAAFFRUW9NV6lUaf59k4mJCTY2NrlaNyGEyA0SZoVMWFgYjo6OJCcnZziPi4tLutP19PQ4e/YsZmZmuVU9IYTIFRJmhYyZmRlHjx5N98wMIDIyklKlSqX7nomJiQSZEEIrSZgVQtJUKIQoaqQDiBBCCK0nYSaEEELrSZgJIYTQehJmQgghtJ6EmRBCCK0nYSaEEELrSZgJIYTQehJmQgghtJ6EmRBCCK0nYSaEEELrSZgJIYTQegVubMb4+HgePnyY39UQQgiRz+Lj4997Xh1FUZRcrIsQQgiR66SZUQghhNaTMBNCCKH1JMyEEEJoPQkzIYQQWk/CTAghhNaTMBNCCKH1JMyEEEJoPQkzIYQQWk/CTAghhNaTMBNCCKH1JMyEEEJoPQkzIYQQWk/CTAghhNaTMBPpkocpZE5u7a/k5ORcWa/If0lJSZr/T0xMzMeaFA4SZuItiqKgo6MDwIkTJwgICMjTsrVRyv7KKefOnSM8PBxdXV35oSukvL292bVrFwEBATx79ixNuOU0bfleZaeeEmb5IPXRdsofLzc/yO8rpQ4pP8xnzpxhz549BAcH59mXIadDIS+tWbOG9evXZ3s9QUFBHD9+nI4dOxIcHIy+vr5WB1rqz45ardb8f0H4zOcna2trli1bxueff461tTV6enq5sk+SkpI036u4uLgcX39OSv3bk/qz8j4KbZhdu3aNBw8eaL5Ix48f5/Lly/j7++drvZKTk9HVfb3b161bx2+//UZ4eDh6enr5+oO1Y8cO7ty5o3l99epVDh06hKOjI40aNcr1kDl9+jS//vorf//9N8+fP8/VsnLKm3+v1q1bU758eSDrR5h+fn6UL1+e2bNn4+TkRPfu3bU60FKf5e/cuZOZM2eyYsUKkpKScvTH++zZs7i5ueXIuvJK6dKlmT17NlFRUcyaNQsgx38HFEVBT08PgJkzZzJp0iTmzJmDSqXKsTJy2oMHD9izZw9XrlzJ1HKFMsyCg4M5duwYv//+O76+vpw6dYqlS5eyZ88eVqxYgbe3d77US1EUTZAdPXoUf39/wsLCcHR0JDQ0NN9+sE6cOIGPjw9ly5YFXh89x8XFUbx4cQ4fPsz169dztXxPT09u375NcnIymzZt4v79+7laXnaEhoaiUqlQFAV9fX3g9YFSYGAgNWrUICYmhmfPnmUp/Pft24e7uzuxsbEATJ8+na5du9KtWzetDbSU/XD9+nUePHhAs2bNePToEdOnT9cEWnbP+m/evMmDBw8oU6ZMmnUV5OuNbm5u7N27l08++YR//vmHEydO8P333wMQHh6eIy0hqQ8kdu3aRVRUFD179uTKlSusXbu2wBw0pj4De/HiBUePHmX58uXUrFkTLy8vAgICiI6O/s/16Cja0piaScnJyfzwww/ExcVRs2ZNevfuTWJiInv27CEgIIARI0ZQo0aNfKnb5s2b8fT0ZPXq1cDrH63Tp0/zv//9j5IlS+ZpXTZu3EhkZCTdu3encuXKnDt3jujoaNq3b8+LFy/Yv38/KpWKESNGUKtWrVypw+nTp/n000/x9fXl/PnzDB06lBMnTtCgQQPMzMwKTNOjoih8++23BAcH88MPP1CjRg22b9/OL7/8Qs+ePenYsSMmJibcvHmT/v37Y2BgkKn1BwcHk5SUhJWVFf7+/lSqVAmAuXPncujQIQ4ePIilpWVubFqu8vDwYPv27TRu3JgRI0agUqlYtWoVJiYmfPnll5iammZqfal/pOPj4zl06BBOTk4EBQURFBTEs2fP6NmzZy5sSc7w9fXl9u3b1KxZk8qVK2NgYEB0dDRdu3alQoUKVK9eHRcXF4yMjHKkvB07duDv78+4ceMwMzPj+fPnfPHFFzg4ODBmzJgcKSM77ty5Q2BgIB9++CFWVlasXLmS48ePY2hoyJMnT2jWrBndunWjXbt271xPoTszUxRF05Q3f/58ypQpw9mzZ7l37x4WFhY4OTlRrVo15syZw8mTJ/OkTqmPEKOiokhISODRo0d89dVXwOsfq/bt29OoUSNOnz6dJ3UCiI2NRU9PjyFDhlC5cmViYmI4ffo0f//9N6dPn6Zs2bJ069aN2rVrM2vWLHbv3p3tI8b0lr9z5w6TJ08mMDCQYcOGoaOjQ2RkZIEKMnh9lvHNN99Qs2ZN1q9fj4+PD05OTqxduxY7OzumTp3KwYMHOX78uObs6n33l6IoWFpaYmVlxeXLl1m2bBlHjhwBXh/sODk50bt3b65du6ZZt7YwNDTEysqKEydOcOjQIWxtbfn666+5desWLi4umb42kvKZ8PHxwdDQkOvXrzN+/HiGDBnC3LlzcXV15ZdffsmNTcmSlM9AcnIyycnJHDt2DLVaTVBQEJs2bWLevHl4e3vz999/07JlS0aMGJGtIEvddBsVFUVISAj+/v6cPn2aFy9eULZsWebOnUtUVFS2ty0nJCQk4OrqyuLFi9HV1WXSpEmMGzeORYsWcezYMTp16vReZ2aFLsx0dHTQ1dXl+vXreHt7a46gd+7cyb///kulSpVo2LAhxsbGVK1aNU/qlNK0eOvWLR49esSoUaNYuHAhjx8/5uuvvwZg6tSpDB48mE8++SRP6gSvj2qDgoLw8vLiypUr+Pr6Mn36dD766CP++ecfTp48SYUKFWjcuDEmJiY0btw4W+GS+oh6z549rFy5Eg8PDz777DMiIyO5cOECL1++ZNOmTezevZvw8PAc2tKckZycTLly5Zg2bRrPnj1jyZIlhISEYG9vz+DBg9m+fTvNmjVDX1+ftWvXAu/XoSX1fgEoXrw4lSpV4sqVK2kCrV69ekyZMoX4+Pjc2cAcdvnyZa5evYqhoSFTp06lcePGHDp0iKNHj2JpaUmfPn34/vvvM30GC6+bLVevXs2DBw/4/vvvmTZtGnv27MHd3Z2tW7dSvXp1EhIScmGrMi/lbxsdHY2uri5du3blwIEDLF26FG9vb/T09Lhw4QKGhoaMGzcOGxubbJWXco3s9OnTxMbG8tVXX+Ho6IiHhweenp7ExsZy7949ihUrlu1ty6rUB3lly5alcuXKKIqCu7s7+vr6ODk5UapUKU6ePEliYiKtWrX6z3UWymZGb29vNm3aRKlSpRg0aBBVq1Zl5syZREdH07p1a1q1aoW+vn6eNult2bKFW7ducebMGQYPHsy4ceO4d++e5ghy0aJFms4DiYmJmusxue3kyZOsWrWKGjVqMHHiRMqVKwfA2rVrefr0KZUqVaJbt24YGxtTokSJLJeTuuPLv//+y5YtW6hYsSIBAQG0aNGCNm3a8O2336Krq8uTJ09YvHgxNWvWzJFtzGmenp5ERUXx999/k5SUxNixY6lWrVqav9n+/fupX78+VapUee/1enl5YWRkRIkSJShfvjyurq4EBATQqlUr2rRpg6+vL8bGxlhbW+fGZmVb6r/xrVu3cHNzIykpiUOHDjF+/HicnZ3ZunUr+/fvZ+DAgTg7O2t+eDPD398fT09PevXqBbz+vhQvXpyHDx9y4sQJSpQoQefOnTE3N8/R7cuOM2fOsGHDBmxsbOjYsSPNmzdHV1cXRVF4/vw5Bw4cYNiwYRgaGma5jNT7/48//mD//v00bNiQJk2a0LNnT/bv38/58+cxNzencuXKODs759TmZdnTp08pW7Ysurq6HDt2jOPHj2Nvb0/fvn25cuUK//zzDwMHDnyv71He/GLmspQjW0VRePbsGTdv3mTOnDkYGhpq/rizZ89m4sSJrF69mhYtWmS6nT47UnqoDR06lM2bN2NmZoahoSEmJiZ06dKFY8eOpbkWkldBBtC4cWNq1qyJr68v3t7eFC9enFKlSjFmzBjmzJmDm5sbXbt2zXKQ/frrr4wfPx4TExMATTNmr169aNWqFUeOHOHEiRMUL16cP/74A3h9xpgyf0Fz584dvLy8GDNmDB06dGDGjBm4uroyatQorK2tNfUuU6YMkZGRGa5HrVZjYGCg+exevXqVAwcO8PTpU169ekXv3r0ZNWoU27ZtY/PmzahUKj777LMs/fjntn/++Yf27dtrvmvPnj3j33//ZcaMGSQnJ9O1a1dGjBiBlZUVXbp0wcvLi5YtW2Z5W6KiooiKimLevHmEh4dz+/ZtmjZtyrRp03jy5AljxozJ9yBLfbZ99+5d/Pz8mDJlCps2beLJkyfo6ekRGRnJqlWriIiIYOrUqTkWZHfu3KF69eocOXIENzc3rl27ho6ODj179kRXVxd3d3dq1679VotAXkhdppubGxs2bKBKlSp0796dzp07o1arOXPmDP/++y/169fn66+/fu/9ovVhlnrnXLt2jbp162pOYVOmX7lyBRMTE6ZOnYqOjg4ffPBBrtYppZdWCmNjYy5dukRgYCA1a9akSZMmeHl5kZSURN++fRk0aBA6OjppPpC55c0PsImJCT/99BNHjhxh06ZNxMfH06ZNG4yMjBgxYgRjxozBwsIiy+UlJiYSFRWFiYkJCQkJvHz5kpCQEA4cOICFhQWOjo4YGxuzdOlSbt68ydSpU7PU7JRb3txfpUqVwtfXFx8fH2rUqMGcOXP47rvvmD17NoMHD6Zr164kJCRw6NAhKlWqRN26dd/6m86aNYukpCSmTJmCmZkZvr6+BAQE8NNPP/H48WOuXbvG1q1bqVKlCq1ateLff/+lQ4cOBTLI/P392bJlCx999JHmgOz58+fcvXsXeN3E3rhxY9atW8f169fp3LkzCxYswNjYONNlBQUFkZycjJ2dnSYgWrVqxciRI9myZQtPnz7lp59+ytHty4rUn5mAgADMzc1p1qwZxYsXp1mzZvTt2xdPT090dHTo2rUrxYsXz9bBderfjUWLFnHp0iXq169PmzZt6NKlC4qicOvWLR48eMCYMWOIiYnhxo0bqFQqWrVqRaNGjXJis99Lyn65ceMG/v7+mu/9+vXr0dHRoUePHnzwwQfs2bOHJk2aZCrgtT7MUnbOuXPnWLlyJaNGjeL69euULVuWJk2aYGpqSnBwMImJiXnSezH1fR27du3CzMwMU1NTqlevjr+/Px06dADA3d2dmjVrUr9+fc1yuR1k8H/7KyEhgWLFimm+CClHRfv378fPz48WLVpo6pYVq1ev5vHjx+jq6vLixQuePn1KuXLl6N27N1WrVmXz5s3s2LGDoUOH0qhRIxo3bkz//v3zZB9kRsr+unfvHvD6wMTMzIyLFy+SlJRE7dq1adWqFVZWVnTu3Bl43bVarVanOVtJsXXrVp48eULbtm05fvw4ffv25cmTJ3h5edGzZ08qVqyIubm55sJ9o0aNmDdvHsWLF8/bDX9PlSpVYv369RgYGGgCvm7duvj7+zN//nxNd/OU5tOU/8+sGzducOzYMQIDA2nRogV9+/alb9++REdHExMTQ5s2bTTN9Pkt5TPj7u7OgQMHaN26NYcPH6ZXr170798feB34VapUoU6dOtkuL+UzduzYMUxNTfniiy/47rvvqFixIoMHD6Znz548ePCAgIAASpcuzaBBg1iyZAnHjx9nwIAB2S4/s7y8vFi+fDkAQ4YMoUePHiiKwrZt2wgODmbAgAF88sknmT5r1MprZm5ublSvXl1zTeXy5cts2bKFL7/8kmrVqnHs2DFWrFhByZIl0dfX58GDB+zYsSNT1y+yIvUR0sKFCzl+/Dhly5YlIiICOzs76taty/bt2ylXrhxhYWG4ubnlaZNiiv3793PmzBl+++039PX10xxJrl69mr1797Jt2zbN9bPMcnNzw9/fnzJlynDo0CFKly5NfHw8YWFhrFy5Emtra65evcq+ffsICgpi5MiR2Nvb5+sF6XfZs2cPt27d4vTp03Ts2JGQkBDUajWPHz/G3NwcS0tLFi5cqLkGoqOjo2lGTC1lv3z55ZfA69EYjIyMCAoKYvny5Xz88cf07dsXeH3vWnBwMM7OzvnSHPQ+4uLiUBQFY2NjfHx8cHZ2pnfv3kybNo0zZ86wdOlSjI2NcXJyQq1W06VLl/duFUn9XfL19eXMmTOMHDmS7du34+7uTvv27RkxYoSmE85XX31VoG5buH79On/++SdffvklFSpUYO3atezdu5eRI0dy//59Tpw4wbZt27Ld2SPF3r17OXToEDNmzKBq1apcunSJH3/8keHDh9O2bVv09fUxMzPT7NPQ0FASEhLyZZ9FR0dz/fp1du/ejampKZ9//jlWVlYsWrSII0eOsGfPniw1E2tdmAUHB7Njxw4GDRqEpaUliYmJHD58GDc3N4yNjVm0aBHFixfn2rVr+Pn5ERYWRtu2bbG1tc2zOu7btw9dXV169uxJYGAgly9fxt3dnS+//JKEhASio6Np3ry5ZgSEvGw+unz5MsePH2fgwIFUrFhRU3bKj0doaChJSUmaG6gza/bs2ajVaubNmwe8DoINGzZw+PBhFixYwPXr11m0aBE2NjacOnWK5cuX8/vvv1OxYsUc28ac9M8//6Crq0vbtm25ceOG5naOxo0bk5iYyPPnz+nVq9dbBwXpcXV1RVEURo8eTUBAADExMahUKkJDQ7l58yZ6enq8fPmSgQMHEhgYSJcuXTAzM8urTc20oKAgwsPDKVasGAYGBqjVapydnRkwYACTJ08mIiKCnTt3UqFCBerXr//eHVdSWg3g9efVysqKoUOHsmzZMvz8/ChVqhSbN2+mRo0aNGnShFq1amWrKTwnpP7bh4eHs3HjRjw9PWndujUTJkwAXt/vFRQURHBwMJ999hnVq1fPkfKSkpI4ffo0p06dIjAwkDlz5mBtbc2NGzcYMGAAgwcPZvr06QB5cikjI4cPH+b27dsAfPPNN1y5coV9+/ZhYmKiOWgrUaIEVlZWWStA0SKxsbFpXnt4eCgeHh6KoijK48ePlWnTpilTpkxRkpKS8rReqcu7dOmS0qtXL2X48OGaaYGBgcrcuXMVb2/vNMslJibmWR0VRVH8/f0VFxcXpVOnTsrjx48VRVFydF+dPXtWadiwoTJ27Fjl9OnTiqIoSmhoqPLzzz9r5pk5c6bi7OysnDlzRomIiFCioqJyrPyc8ObfZPfu3cqBAwc0r318fJTx48crd+/efedy6bl586ZSvXp1pUOHDkqTJk2UmjVrKh9//LHSokULpUaNGsru3buV1atXK9u2bVOePHmSMxuUi9zd3ZWuXbsqs2fPVl6+fKkoiqI8efJEadiwobJ48eJMr+/06dNKcnKy5vXdu3eVNm3aKCEhIcrz58+ViIgIZffu3YqiKMquXbuUfv36KU+fPs2ZjcmG1N+hyMhIzWf66NGjyrRp05R169ZlOH9WpN5HN2/eVB4+fKgoiqJEREQo8+fPV8aOHasEBQUpiqIohw8fVhISErJVXk7w8vJSNm3apBw4cEDp16+fcvDgQUVRFOXChQtK7969lVGjRilxcXHZKkNrzszWrl3L9evXiY+Pp1u3bnTt2pVp06ZRrFgxHB0d+eSTT3j8+DGbNm3izp07TJkyJdv3Rb0PJdURkqenJ/b29rx8+ZJvvvkGU1NTlixZArxudqxVq5bmgmxeNxul3JgaFhbGpk2b8PX15fvvv8/RptekpCQCAwOZP38+Ojo6ODo6EhAQQK1atWjdurVmvnHjxnH//n327t2b773OAF6+fAmQpgksMjKSUqVKsWfPHq5fv87w4cOxs7MDYPfu3dja2tKoUaNM/y29vLw4ffo0FhYWWFpaUqtWLeLj41m7di0lSpTgu+++K7DNipD2865Wq5k1axaBgYFMmTKFqlWrUqJECcLCwrC3t6ddu3YsXLjwvTp7fPHFF3h4eNCvXz+mTp3KmTNn2LdvHxMnTtTs97t37/LVV1/Rpk0bSpYsybBhw/J8xJzUvL29qV27tua1p6cnO3fu5MmTJ9StW5eRI0fi7e2Np6cnBgYGDBs2DBsbmxz7+3p4eODh4UFkZCS2traMHDmSiIgIXF1dOXbsGIsXL9bULy9v94G0Pcx1dHT4+++/qVChAklJSTx8+JDu3btz5coVateujZubGy1btsz2b1HButqegYMHD1KiRAkGDx6MnZ0dGzZs4MmTJ0yYMIGKFSty/Phxzpw5Q8WKFWnXrh3x8fGUK1cuT34QUso4ffo0rq6u+Pv788EHH7Bo0SIiIiKYNGkSHh4eVKpUifbt26dZJq+ktKWPHDmS2NhYhg8fTu3atVm4cCGenp45dhOunp4eNjY2TJ06lYSEBFavXo2iKJogSynnm2++YcuWLQUiyNRqNTNnzuTPP/8kJCQEgO+++44JEybw+eefY2FhgUqlYvny5SxYsID9+/ejq6tLw4YNgcz/LevWrcukSZPo378/n376KRYWFtjY2FC/fn3N/iioQQb/VzeVSoWenh4///wzrVq1YtmyZXh7e5OQkICpqSkzZ85k8uTJ7xVkO3fuxMzMjO3bt9OwYUOeP39OdHQ0Xl5emuZqgPLly9OvXz+uXr1Kx44d8zXIdu7cyebNmwkODgZeD8z95MkTvv76a+bNm4enpyeurq44OjpibW3NpUuXNE2nWf37pj7vuHLlChcuXMDFxYVy5cpx8+ZNNmzYQOnSpWnevDlNmjRJ0+EtP4IM0Ax8YGJigru7O/7+/vTu3Rt9fX18fHxITk5m6NChOXJQXeDPzGbNmsWzZ8804xiqVCp+/PFH5s6di4GBAcnJyRw4cIDY2FiqVq3Kp59+io6OTq5fa0j9B7t27RoqlYq+ffsSGRmJv78/tWrVIiwsjKlTpxISEsKhQ4eAvG+zfvToER4eHrRu3Zrvv/+ezp07a67J/Pbbb9y/f5/t27dTunTpHC33yZMn/PTTTxQrVozevXvj4OCQo+vPKUlJSezcuVMzVl5cXBze3t44OzuzfPlyypQpg7OzM/7+/gQFBVGyZEmGDh2aprNHVkRHR2uumapUKs6cOUOPHj1yrENAbrp//z6bNm3CwMAAR0dHmjZtytq1a7ly5QrVqlWjQoUK9O7d+716LS5duhQDAwM+//xzzbT4+Hh27txJ+/bt+e6779DT02Pjxo1p3s/OPVnZdfv2bU6dOsWkSZM0044cOUL16tU1owqFhYVprlWVK1eO4sWL51hnC19fX0xNTZk+fTrffPMNjx49Qk9Pj4MHD/Lq1SsmTpxI7dq10dHRyfNr8qnt27ePAwcO0LJlS7p168bs2bNp2rQp5cuXJygoiN27d7N169YcO6gt0Gdmly5d4urVq8TFxbFz504AKleuzNOnT1m2bBkdOnRg0aJFODg48Pz5c3bs2JGrQZaYmMiJEyeAtEdXZcuW5d69ezg5OTFu3DgGDx7M3Llz0dPTY+HChVhYWGiGQMrrUc8jIiIoUaIE0dHRDBw4kD59+nDnzh0iIyPp1q0bq1atyvEgg9fPavrhhx9Qq9UcOHCAtWvXau49Kkj09PRwdnamc+fO3Lx5k4CAAMaPH0+TJk3YunUr+vr6HDp0iN69ezNx4kSGDx+e7SCD12eEKaMbfPnll/Ts2bPABlnqYaF8fX05ePCgpqPP7t278fT0ZMyYMXTu3JmEhASaN2/+XkEWFBSEv7+/JsiSk5NRq9Xs3buXPXv28Pvvv7NlyxZ0dHTo2LEjZ86cQVGUfA0yACsrK9RqtWa0frVaja+vL1OnTtXMY2ZmxldffYW5uTlVqlTJVpClPt84e/Ysx44d07T+xMXF4evry6effkqTJk2wtramZs2ams9mXgZZ6nqmDKb8+eef8+eff3LkyBEmTZpEaGgoBw8e5O+//2bhwoU52jpToM/MEhMTCQ0NZf369ZrHR8THx2Nra0vbtm15+PAhffr0Ye7cuVStWpWyZcvm+lA/vXr1wszMjEaNGtGzZ0/KlStHYmIiKpWKS5cu0bx5c0qWLMnWrVtxdHSkdu3axMbG0q1bN8zNzVm/fn2eNI+cO3eO4OBg2rRpw4IFC7C2tmbixIkAbN++nXr16uXIPS7/5fnz5zg7OxMbG8uePXuy3N0/t6QOJQ8PD3bt2kWtWrXo2LGjpunjjz/+SHPmkFPUajUeHh5YW1tTrVq1HF9/TvHz86NChQrExcURGRnJ6tWrGT16NN7e3jx+/JiHDx9SrVo12rVrR6VKld67Sevx48d88803uLq6UqJECfT09DQ31t+/f5/Fixdja2vL5MmTmTBhAsuXL9c8SSA/RUdHc+DAAa5evYqJiQlz5swBYNiwYYSFhfHzzz8TFRWFr68vjo6O2RqkIXVLTkJCApGRkRw4cIDmzZtTo0YN7t+/z5w5c9DX16dZs2aMGzcOeHvghrx0/PhxIiMjKVOmDK1atcLPz48JEybQv39/2rZti7W1Na9evcrxeycL9JmZvr4+FhYWjBw5kipVqnDo0CEiIiLo0qULRkZG1KlTh0GDBqEoCh9//HGuBlnKyPejRo3CysqKoKAg+vfvz6JFi/Dw8MDOzo6+fftSoUIFrly5QsOGDTX3wRkaGjJy5Ejmz5+fa0GWemT+5ORkQkNDMTMzw9zcnGrVqhETE8OiRYtYtWoV69evz/VRUFKULVuWYcOGsXnz5gIVZG8+VRvQ3Izr6+vL2bNnef78OZ6enlkareJ9GBgY4ODgUKCDLDExkb/++ovFixdz4sQJSpUqxYgRIzAwMCAoKIhx48bxwQcfcPfuXQwMDDJ1bcbAwEDTxJryw6uvr0+5cuWwt7dn2rRphIeHExERwbZt2wpEkAGULFkSW1tbYmJigNfXywBWrlyJnZ0dW7du5eDBg3Ts2DHL37OUc4yUIJs6dSorV67E2NiY/v37o1KpSExMpHTp0nTv3p3y5cszatQozfL5dUbm6enJqlWr8PDw4LfffuPWrVtUqVKFDRs2sGLFCmbPnq15VmJOK9BnZqk9f/6c1atX4+fnR9u2bXF2dsbHx4eVK1fi4uKSZ000AQEBHDx4kAkTJuDu7s6WLVt48OABLVu2pHLlyhQrVoyoqChcXFzQ09PTHFnl1rWylDOLlPV7eXnx4YcfoigKf/31F02aNKFMmTL4+Piwb98+DA0NGTJkSJ4O4puf97akJ/XZ2MWLF6lbt26asSfPnDnD/v37MTY25sMPP2TEiBEFqv55bc+ePaxatYoRI0YwdOhQ4PXTgFeuXMmHH34IvB7JIbPN1YqiMG3aNG7cuMGMGTOwt7dHURTNaDienp4cPnyY7777rsCN1fny5UtevnzJ8ePHefHiBe3ataN58+aa99O7af59JCQkkJSUlKaZNjw8nMWLF+Pn50fJkiX5+OOPsbCwoGPHjm8daOXnGdnFixe5efMmffr0wdTUlHXr1uHp6cl3331HzZo1OXHiBFWqVMm1e361Jszg9Q3Ta9asITAwUNMLrFu3bnl+F/v169epWbMmR48epXTp0tSsWZPVq1eze/du2rdvz9KlS/NkrMUzZ85w+/ZtRo0ahaGhIadOneLzzz9n9OjRNG/enI8++oirV6+m6Raf1110C5rUQbZhwwY8PT1ZsmQJJUuWTPOej48P4eHhNGnSJEeukWkrb29vjIyMePz4MZs3b6Zr16506dKF2NhYfvvtNx4/fszMmTOzfHbp5+fHF198QVJSEpMmTcLR0RF4fbZz8eJF+vTpk+9n9O8KJn9/f9zc3FCr1VSuXJm2bdtibm6epc9LTEwM48aNo0KFCkyfPp2SJUuyZMkSunXrRtWqVbl+/Tpubm7cu3ePV69e0b59e8aPH59v4ZW6+31oaCi//PILYWFhODs707JlS2JjY9m3bx9btmzh22+/pVOnTrn6HdKqMIPXHRq++uorfH192b59e75cND916hTu7u60bdtW8+Xz8/PD3d2dcePGvddoENkVFxfHhQsXcHd3B2D+/PkoisKpU6fw8/Pj6NGjWFhYYGpqyqxZs/K1G3NBtH37doKCgujYsSN169bVTE/v71bQzizziqenJydPnqRp06a0a9eOf/75h02bNtG/f38sLCwwMTHB1tY2282wDx8+5Oeff8bT05NatWphZ2dHVFQU33//PRUqVMihrck6Hx8fVCoVdnZ2WFlZvdVEFhQUxIIFC3j27BkrV67MUqeGuLg4Vq5cSUJCAh999BG6urqUK1eOyZMnU6ZMGZycnKhUqRIVKlTA1tYWb29vFEXho48+yqnNzLLw8HBMTU2JjY1l48aNPHv2jI4dO9KiRQt8fX35+eef+eGHH3J9FCatCzN4PYBnnTp18rUNfd++fTg4OGBubv7WqX1un/2cOXOG1q1bk5CQgL+/P5s2bUKtVjNnzhyMjIxITEwkPj6eTZs24efnR9OmTenTp0+RPLNI8eaF9F27drFr1y769u1Lr169JOzf8OzZM7y8vGjfvj1qtZro6GhMTEzw8vJi7dq1REREsGTJkhw7a4qJieHevXs8efKEypUrY2VlRZkyZXJk3dl1+/Zt5s2bR5kyZVi0aFGaR/ekOH36NFWrVs3ywfWaNWuwtLSkR48eb7137tw5bt++zZ9//kmZMmWYOnUqTZs2zerm5KizZ8+ybt06rKys6NChAy1btmTFihW8evWKOnXq0Lp1awwMDLL1LMT3pZVhlp9NPillX7hwASMjIxo0aJCnZacc6SxatEhzC4Kfnx/r1q0jPj6eefPmadrbk5OTSUxMLBDdmfNT6s/L0aNHsba25sMPP+Tu3bssWbKEHj160Llz51zr6KEN3vxO+fj48Msvv1CuXDkePnzIixcvMDExYdmyZZQuXRo9Pb1cuaWjoEi9P0JCQli+fDkRERG0bNlSMxh06jEksyM5OZmffvoJa2trBgwYgK+vL0+fPsXf3x9FUejatSs2Njbcvn2bc+fO0apVqzQjj+QVHx8fypYtq+nUcvfuXa5cuULjxo1xdXWlYcOGDBo0CIDvv/8ePz8/Vq1alWeDI2hlmBUEoaGhdOvWjdGjRzNs2LA8KTPlZtGUoZa8vb2pUKECpqammkBTq9X8+uuvBfLZV/nNzc2N8+fP8/z5cypVqsScOXM4e/YsGzduxMLCgv79++fpwUlBdPHiRQwMDKhSpQqPHj3i5s2bfPjhh3z44Ydcu3YNc3Nz7O3t87uaeebx48eUKVMGY2Njjh8/zv/+9z8aNGiAs7NzjoUZvL5GOHToUEqVKkV8fDxqtRpLS0vi4+OJjY3lwIED2NjYEB0dnS+tCD4+Puzfvx8TExN69+5N+fLlUalUREdHY2pqqrm+6enpScmSJTE1NaVYsWJZHzQ4CyTMsighIYHt27czePDgPOlQMWvWLBITE/n66681D3T86quv6NKlCwMHDqR06dL4+fmxfft27t27x7Rp0/K0x2JBlPqLHxwcjKenJz169GDFihVUr16dVq1akZCQwMGDBzl16hSrV68u0gcBN27cYOPGjejr6/Pw4UM2btyIubk5vr6+vHz5kqdPn+Lg4JCnT2nPa6nPyA4cOICrqytVqlShU6dOdO7cGXd3d06ePElCQgJNmzZlwIABOfaZefDgATdv3sTS0pLy5ctrns/2008/UbNmzTw7aE5PTEwMJ0+e5MqVK1haWtK7d2/Cw8OZMWMGTk5ODBkyBHg9dJ6VlZVmuLe8VHS7tWVTsWLFGD58OJD718jefKBjr169qFy5MjNnzmTlypXo6urSv39/qlSpgoODAzdu3JBrQPzfUXVkZCS6uroEBQVx8uRJGjZsiL29PU+fPuX27dv079+fgQMH5tnTvguKNwcNvnHjBgsXLuTcuXP4+flhbm7Os2fP8PPzY+XKlaxevbpQBxn8332H169fR6VSsWrVKry9vdm2bRsAXbp0oWzZsuzZswd7e/scPfipVq1aur1CK1SoQKlSpXKsnMxKSEigRIkSODk5YWZmxj///MPevXsZM2YMgwcPZvHixZph2U6fPs3WrVvzpZ4SZjkgN4PMzc2N0NBQXF1dgde9nlK+QI0aNWLSpEn8/vvvFC9eHHt7exo0aICrq2uh/9H5LyljLj5+/JjOnTvTr18/Hj16xNWrV1m/fj2A5gGkKfsz5f6moiB1kO3YsYOGDRtSpkwZDhw4gKGhIaNGjdIMuTVkyBAaN25cZK6RXb58mc2bNxMVFcWAAQPo1KkTycnJ/PXXXwQHBzN8+HCaNGmSa3UJDg5GX1+f+Ph4rly5gomJCd26dcu18v7L3bt3efLkCR999BGffPIJiYmJnD59mrVr1zJhwgRMTEy4desWycnJrFu3Lt865kmYFXAvXrzQdAVOeaCjr68vr169onnz5jRo0ICpU6fyxRdfcP78eRYvXlykgyzlzEpPTw9HR0cWLVqEn58farWaX375hWHDhjF8+HAiIyOB149zSVGUenumbOvNmzc1TVvBwcHcu3eP8ePHA3Ds2DFUKhVJSUn5emaQ21IH2c6dOwkODqZ9+/acP3+eZcuWMXbsWDp37oxKpWLbtm04OTnlak9LfX19zp07xy+//EKVKlXYtm1bmgEY8krKNUFjY2P++OMPzMzM2LJlC59++inJycmcOXOGDRs20K5duwIxkLhcMyvgbt26Rb9+/ahUqRLh4eFERUVRvHhxkpKSKFasGH/++SdVq1Zl7969NGzYMEefT6ZtUv8ovXz5kg8++ICQkBBmzpyJtbU13377LQYGBty4cQN4/TiW/Hjad0Fx7do15s2bR48ePRg6dCivXr1i3rx5REdH8/TpU4KDg1m7dm2RufZ69+5dXF1dmTBhAra2tpw7d47Dhw9jYGDAwIEDMTY2xsjISHMtKzclJiZy48YN6tevj76+fr40f9+5c4eKFSsSEhLCnj178Pb2pmPHjjg7OwOvbxFKua46d+7cXBmiKjMkzLTAmw90rF27Nmq1mhUrVlCyZEl++OGHInWtJ7XExESSk5PTjNBw8uRJ3NzcqFu3Lv379ycuLo4ff/wRW1tbihUrhoODAx9//DGQv8P/5LU3u98/evSIU6dO4erqyvTp03F0dCQuLo5nz54REhKCjY1Nnvxw55fU+yMgIIB58+ZRvHhxxowZo3kW2MWLF/n9998xMjJi9erV7/U0gJyWX6P2rF+/nqCgIOrWrUuHDh24efMma9asoWXLlowcORK1Ws2ePXto0aIFlStXzvP6vUmaGbVA3bp104xSkaJx48aapyQXxSBTq9UMHz6czz//nJYtWwKvnyulKApffPEFP//8M69evWLEiBHMmzePDRs2EBkZmWbUhKISZKkPdq5evUpwcDCWlpYMGDCASpUqMW/ePIyNjfn000+pXLlygfhxym0pQfbgwQNMTEwYP348Gzdu5MyZM+jr6/Phhx/SrFkzunfvjr29fb4EGeTtgzVT09PT4/z581SoUAFFUWjWrBlqtZp169Zx/fp1WrRoQb9+/XLs9oTsKnq/gFosOjqa2NhYoqOjuXXrFk+fPqVTp075Xa188/LlS5o0aUJwcHCapwbcv38fgPbt2xMQEMCWLVu4dOkSn3/+ObNmzdJcfygKVqxYwePHjzVBdvfuXY4fP86tW7eYMmUKS5cupVq1asyYMYPx48ezePFi1Gp1Ptc6d6VujLp69SpTpkzhxIkT1KlTh7Fjx/Lw4UPOnTuHh4cHarWagQMHFolwf1Pfvn357rvv8Pb2xs3NjbCwMFq1aqVprm/atGmBCTKQMzOtolarOX/+POvWrSM6OpqtW7fm+vPbCqKUs4zy5cszZswYTpw4ofmxrlq1Ko0bN8bLy4tmzZrRrFkzlixZwuPHjzXjaELROJO9ePEiMTExxMbGoigKT5484fjx40yaNIkSJUrQvHlz9u3bh5eXFx07dqR379507949S6O9a4OUZsWUM7KEhATq1avHsGHDuHLlCnp6evTu3ZvPP/+cadOmce7cOerUqVNo90eKlIGUU/bPkSNHOH/+PMnJyXz//fcoisLhw4cxMjLSjHq0aNGi/K72WyTMtIi5uTmdOnXCxMQEa2vrIhlkMTExrF69mg4dOlC3bl2KFy9OSEgIW7ZsYejQoVSvXh14fY/Z7du3MTQ0pG3btnTr1k0zwndR6LUYHR3NhQsX6NevH1WqVEGtVlOiRAmSkpI0Q5y1bt1aM1/nzp2ZPn16oR32LD4+nsTERM0YgW5ubuzatQtXV1f69OmDsbExJ06cIDk5mXbt2vHZZ59RrVq1Qn07ArwejCEpKYkpU6ZgZmbG7du3iYyMpGPHjmzZsgV3d3cGDRrEq1evOH/+PA8ePCiwo+RIBxChNZKSknBxccHd3Z0ePXrQrFkzzcCsu3fvpmHDhlStWhV4PRi1m5sbcXFxbNq0CT09vSITZCmOHDnCnj176N69u2bw3s8//5yGDRsyZswYypQpQ3BwMB4eHnTv3r3QXj9cu3YtN27cID4+HicnJ3r06IG/vz/Tp0+ndOnS/Prrr5QoUQI3Nzfc3Nxo1qwZw4cPz7drZHll69atnDlzhrZt26Knp0e/fv3w8PBAX18fQ0ND/Pz86Nq1K56entSrV0/TWaqgBryEmdAKSUlJxMfHExUVxe3btzl+/Dj79++na9eu1K1blzJlylC5cuU0A7CmPG27qI3skdr06dP5999/+emnn2jYsKHmPrKaNWtSrlw5ypcvT7du3Shbtmx+VzVXHDx4kKioKCpXrsy5c+e4cOECS5Ys0Rz0jB07Fh0dHZYvX86NGzf4999/6du3b54Njptf3Nzc8Pf358svvwQgNjYWY2Nj/v33X06ePEmVKlXo2bMnurq6bN26lTZt2hSIx/G8izQzigIvJiaGadOm0blzZzp27IilpSWWlpY8f/6cuLg4jh07xrVr16hbty6LFi2iYsWKAJofpKIUZKnPPl+9ekXjxo0pVaoUq1at4uuvv6ZWrVps3bqVe/fuERkZSbNmzQptkM2aNYtnz56xevVqAMqXL4+Xlxf6+vrcvn2bOnXqsGbNGsaOHcvIkSN58eIFa9euLfRBBukPxhAQEEBAQABeXl6UL1+eP//8k+joaHbv3k3Hjh3zucb/Tc7MRIG3dOlS/vjjDz755BPKly/P5MmTMTc3x9vbm/j4eKytrdmwYQO3b99m0KBBtGvXrtBftE/Pm0/KNjQ0pFy5chgbG7N06VJu3LjBjBkzisSN9ZcuXeKnn36iTJkydOrUiQEDBpCcnEzbtm35+OOPOXbsGI0bN+aPP/7AyMiIW7duaQ6SioL0BmMwMjLCxMSE4OBg+vXrh5GREY8ePWLy5MlaceO8hJko8FIeDnnixAn+/PNPIiIicHBwoHTp0nz44YeaXoqenp6UKFGiQDx9Nz9dvnyZVatWYWBggL6+PitWrODly5ccOnSInTt3Mn78eLp161aoz1YTExMJDQ1l/fr1PHjwgGbNmhEfH4+trS1t27bl4cOH9OnTh2nTpjF06ND8rm6+eHMwhlq1amke6gvw448/EhcXpzXXDqWZURR4BgYGmJub07dvX/r27YurqyteXl5s27aNDz74ADMzM5o1a1aknrOVwt/fn8DAQOLi4mjcuDERERH4+/vzxx9/4O7uTkhICLq6uhgbG9O0aVM8PDyoV69eoQ4yeH2jsYWFBSNHjmTt2rUcOnSIpk2b0qVLFwDq1KnDoEGDivQDWTMajKFWrVqawRi0qXerhJnQGinNaKNGjSI6OprOnTuzYcMGzU3Thf0H+k1//PEHAQEB3L59G7VaTXh4OJMnT6ZYsWKcO3cOfX19Pv/8cwICArh27RqdOnViyZIlhXrQ4DdZWloybtw4AFQqFdu3b8fZ2RkfHx+eP3/OiBEj8rmG+S86OlozRqlKpSIwMFDTS1ibev9KM6PQKm92r/fx8eHly5e0aNEiH2uV9w4cOEBUVBSDBw8mNjaWYsWKMXPmTC5duoS1tTU1a9ZkxIgRWFhYcPjw4SL/wx0cHMyaNWsIDAzEwsICGxsbunXrVmSukb1LaGhooRiMQcJMCC0zc+ZMXr16xcKFC4G0A9EuWLCADRs2YG9vT7ly5QgMDOTevXts3rxZc0N5URUREcFXX32Fr68v27dvx8bGJr+rVGCo1Wo8PDywtrZO9wGh2kDCTAgtcvbsWb755hsaNGjAkCFDNGekKUMSAYwbNw5ra2s6depEQEAADRs21NyuUNS5u7tTp06dfHuApMg9EmZCaJHExESCgoKYP38+ycnJ9OvXjzZt2gD/F2iTJ0+mTp06fPbZZ/lc24KnqI0CU5QUrSvmQmg5fX19bGxs+O6770hOTmb37t2cPHkSQHNmVrduXc0IF3KsmpYEWeElYSaEFqpYsSI//vjjW4F248YNbt68qbnuIT/eoqiQZkYhtNjjx4+ZO3cuJUqU0JyN9enTh3LlyuVzzYTIWxJmQmi5kJAQhgwZQkJCAlu2bNHKbtVCZJeEmRCFwK5du2jcuDG2trb5XRUh8oWEmRCFQFEcAUWI1CTMhBBCaD05lBNCCKH1JMyEEEJoPQkzIYQQWk/CTAghhNaTMBNCCKH1/h+c4oZfTA57SAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f1 score plot\n",
    "plt.figure(figsize=(5,4))\n",
    "sns.boxplot(x = svc_class_metris_wf['names'],\n",
    "            y = svc_class_metris_wf['f1_scores'], palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('SVC multiclass F1 score (non-filtered data)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.savefig('SVC_multiclass_F1_score_nonfiltered_data.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad610af",
   "metadata": {},
   "source": [
    "## kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "128d923b",
   "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 5 model iterations to evaluate average performance on non-filtered data\n",
    "datavector_knn_wf = []\n",
    "\n",
    "for i in range(5):\n",
    "    knn_model_wf = get_predictions(data = df,\n",
    "                model = KNeighborsClassifier(),\n",
    "                param_grid = random_grid_knn)\n",
    "    datavector_knn_wf.append(knn_model_wf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e212b389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 66.40%\n",
      "Precision score: 81.35%\n",
      "Recall score: 66.40%\n",
      "F1-Score: 62.23%\n",
      "Kappa score: 60.82%\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>Birch</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspen</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Silver fir</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pine</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water body</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soil</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Deforestation</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grass</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burnt forest</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Swamp</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Settlements</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            names  f1_scores  precision_list  recall\n",
       "0           Birch       0.43            0.31    0.75\n",
       "1           Aspen       0.52            0.68    0.43\n",
       "2      Silver fir       0.47            0.31    0.95\n",
       "3            Pine       0.65            0.91    0.51\n",
       "4      Water body       1.00            1.00    1.00\n",
       "5            Soil       0.91            0.94    0.88\n",
       "6   Deforestation       0.76            0.94    0.64\n",
       "7           Grass       0.23            0.13    1.00\n",
       "8    Burnt forest       0.98            0.96    1.00\n",
       "9           Swamp       0.16            0.09    1.00\n",
       "10    Settlements       0.72            0.72    0.72"
      ]
     },
     "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 example\n",
    "metrics_description(datavector_knn_wf[0][2], datavector_knn_wf[0][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "22bfd94c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#metrics by prediction on test data by class for each iteration on non-filtered data\n",
    "knn_class_metris_wf = get_classes_metrics(datavector_knn_wf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d90c11c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f1 score plot\n",
    "plt.figure(figsize=(5,4))\n",
    "sns.boxplot(x = knn_class_metris_wf['names'],\n",
    "            y = knn_class_metris_wf['f1_scores'], palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('kNN multiclass F1 score (non-filtered data)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.savefig('kNN_multiclass_F1_score_nonfiltered_data.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e956f5f6",
   "metadata": {},
   "source": [
    "# Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0d1fbb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stat_results(dataframe):\n",
    "    KW_results = scipy.stats.kruskal(*[group[\"f1_scores\"].values for name, \n",
    "                      group in dataframe.groupby(\"model\")])\n",
    "    if KW_results[1]>0.05:\n",
    "        return 'there is no significant difference between means'\n",
    "    else: \n",
    "        return 'there is significant difference between means'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1bd794df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get datasets with average metrics \n",
    "\n",
    "avg_svc_wf = get_metrics_average(datavector_svc_wf)\n",
    "avg_svc = get_metrics_average(datavector_svc)\n",
    "\n",
    "avg_svc_wf['model'] = 'svc_wf'\n",
    "avg_svc['model'] = 'svc'\n",
    "svcs = pd.concat([avg_svc, avg_svc_wf])\n",
    "\n",
    "\n",
    "avg_knn_wf = get_metrics_average(datavector_knn_wf)\n",
    "avg_knn = get_metrics_average(datavector_knn)\n",
    "\n",
    "avg_knn_wf['model'] = 'knn_wf'\n",
    "avg_knn['model'] = 'knn'\n",
    "knns = pd.concat([avg_knn, avg_knn_wf])\n",
    "\n",
    "avg_rf_wf = get_metrics_average(datavector_rf_wf)\n",
    "avg_rf = get_metrics_average(datavector_rf)\n",
    "\n",
    "avg_rf_wf['model'] = 'rf_wf'\n",
    "avg_rf['model'] = 'rf'\n",
    "rfs = pd.concat([avg_rf, avg_rf_wf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5fa0aa54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF on filtered and non-filtered data: there is no significant difference between means\n",
      "SVM on filtered and non-filtered data: there is no significant difference between means\n",
      "KNN on filtered and non-filtered data: there is no significant difference between means\n"
     ]
    }
   ],
   "source": [
    "#STATS RESULTS\n",
    "print('RF on filtered and non-filtered data:', \n",
    "      get_stat_results(rfs))\n",
    "print('SVM on filtered and non-filtered data:', \n",
    "      get_stat_results(svcs))\n",
    "print('KNN on filtered and non-filtered data:', \n",
    "      get_stat_results(knns))"
   ]
  }
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