21_RegressionOnTrees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning to predict risk of crimes based on the location, time and presence of trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import altair as alt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>fair_tree_count</th>\n",
       "      <th>good_tree_count</th>\n",
       "      <th>poor_tree_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-74.25</td>\n",
       "      <td>40.5004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-74.25</td>\n",
       "      <td>40.5004</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-74.25</td>\n",
       "      <td>40.5004</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-74.25</td>\n",
       "      <td>40.5004</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-74.25</td>\n",
       "      <td>40.5004</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Longitude  Latitude  hour_of_day  day_of_week  F  M  fair_tree_count  \\\n",
       "0     -74.25   40.5004            0            0  0  1               20   \n",
       "1     -74.25   40.5004            0            1  0  0               20   \n",
       "2     -74.25   40.5004            0            2  0  1               20   \n",
       "3     -74.25   40.5004            0            3  1  0               20   \n",
       "4     -74.25   40.5004            0            4  0  0               20   \n",
       "\n",
       "   good_tree_count  poor_tree_count  \n",
       "0              154                0  \n",
       "1              154                0  \n",
       "2              154                0  \n",
       "3              154                0  \n",
       "4              154                0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset from parquet file\n",
    "trees_crimes_people_df = pd.read_parquet('matched_data.parquet')\n",
    "trees_crimes_people_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dividing the dataset into the feature matrix and the target vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slide the dataframe and move colums 4 and 5 to df Y. Keep the rest in df X\n",
    "Y = trees_crimes_people_df.iloc[:, 4:5]\n",
    "\n",
    "X = trees_crimes_people_df.iloc[:,[0,1,2,3,6,7,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline: Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  54.72092320155289\n",
      "MAE:  4.676501485650232\n",
      "MAPE:  4838356067768301.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train)\n",
    "Y_pred = lr.predict(X_test)\n",
    "print(\"MSE: \", mean_squared_error(Y_test, Y_pred))\n",
    "print(\"MAE: \", mean_absolute_error(Y_test, Y_pred))\n",
    "print(\"MAPE: \", mean_absolute_percentage_error(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the linear regression model using matplotlib\n",
    "plt.scatter(Y_test, Y_pred)\n",
    "plt.xlabel(\"True Values\")\n",
    "plt.ylabel(\"Predictions\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test different regression models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Perceptron, Lasso, ElasticNet\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Convert Y_train and Y_test to 1D array\n",
    "Y_train = Y_train.to_numpy().ravel()\n",
    "Y_test = Y_test.to_numpy().ravel()\n",
    "\n",
    "names = [\n",
    "    \"Lasso\",\n",
    "    \"Elastic Net\",\n",
    "    # \"Linear SVM\",\n",
    "    \"Decision Tree\",\n",
    "    \"Random Forest\",\n",
    "    # \"AdaBoost\",\n",
    "    \"Neural Net\",\n",
    "    \"K-Nearest Neighbours\"\n",
    "]\n",
    "\n",
    "regressors = [\n",
    "    Lasso(alpha=0.1),\n",
    "    ElasticNet(random_state=0),\n",
    "    # SVR(kernel=\"linear\", C=0.025),\n",
    "    DecisionTreeRegressor(max_depth=5),\n",
    "    RandomForestRegressor(max_depth=5, n_estimators=10, max_features=1),\n",
    "    # AdaBoostRegressor(),\n",
    "    MLPRegressor(alpha=1, max_iter=1000),\n",
    "    KNeighborsRegressor(3),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] - Categorical technique:  Just one\n",
      "[INFO] - Regressor:  Lasso\n",
      "[INFO] - Regressor:  Elastic Net\n",
      "[INFO] - Regressor:  Decision Tree\n",
      "[INFO] - Regressor:  Random Forest\n",
      "[INFO] - Regressor:  Neural Net\n",
      "[INFO] - Regressor:  K-Nearest Neighbours\n"
     ]
    }
   ],
   "source": [
    "categorical_technique_list = [\"Just one\"]\n",
    "X_train_list = [X_train]\n",
    "X_test_list = [X_test]\n",
    "\n",
    "# Create a dictionary to store the dataframes of the results for each method\n",
    "results_dict = {}\n",
    "\n",
    "MSE_per_dataset_df = pd.DataFrame(columns=[\"Dataset Name\"].append(names))\n",
    "MAE_per_dataset_df = pd.DataFrame(columns=[\"Dataset Name\"].append(names))\n",
    "MAPE_per_dataset_df = pd.DataFrame(columns=[\"Dataset Name\"].append(names))\n",
    "\n",
    "for technique,X_train,X_test in zip(categorical_technique_list,X_train_list,X_test_list):\n",
    "    print(\"[INFO] - Categorical technique: \", technique)\n",
    "    MSE_line = {\"Dataset Name\": technique}\n",
    "    MAE_line = {\"Dataset Name\": technique}\n",
    "    MAPE_line = {\"Dataset Name\": technique}\n",
    "\n",
    "    for regressor,method_name in zip(regressors,names):\n",
    "        print(\"[INFO] - Regressor: \", method_name)\n",
    "        regressor.fit(X_train, Y_train)\n",
    "        Y_pred = regressor.predict(X_test)\n",
    "        # create a dataframe with the Y_test and Y_pred\n",
    "        Y_test_Y_pred_df = pd.DataFrame({'Y_test': Y_test, 'Y_pred': Y_pred})\n",
    "        # append the dataframe to the dictionary\n",
    "        results_dict[method_name] = Y_test_Y_pred_df\n",
    "        # calculate the MSE, MAE and MAPE\n",
    "        MSE_line[method_name] = mean_squared_error(Y_test, Y_pred)\n",
    "        MAE_line[method_name] = mean_absolute_error(Y_test, Y_pred)\n",
    "        MAPE_line[method_name] = mean_absolute_percentage_error(Y_test, Y_pred)\n",
    "\n",
    "\n",
    "    # using pandas concat to append the new line to the dataframe\n",
    "    MSE_per_dataset_df = pd.concat([MSE_per_dataset_df, pd.DataFrame([MSE_line])], ignore_index=True)\n",
    "    MAE_per_dataset_df = pd.concat([MAE_per_dataset_df, pd.DataFrame([MAE_line])], ignore_index=True)\n",
    "    MAPE_per_dataset_df = pd.concat([MAPE_per_dataset_df, pd.DataFrame([MAPE_line])], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Dataset Name</th>\n",
       "      <th>Method</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>56.739021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>59.148555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>41.315855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>42.999616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Neural Net</td>\n",
       "      <td>51.181149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Just one</td>\n",
       "      <td>K-Nearest Neighbours</td>\n",
       "      <td>9.153458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset Name                Method        MSE\n",
       "0     Just one                 Lasso  56.739021\n",
       "1     Just one           Elastic Net  59.148555\n",
       "2     Just one         Decision Tree  41.315855\n",
       "3     Just one         Random Forest  42.999616\n",
       "4     Just one            Neural Net  51.181149\n",
       "5     Just one  K-Nearest Neighbours   9.153458"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE_per_dataset_df\n",
    "# unpivot the dataframe\n",
    "MSE_per_dataset_df = MSE_per_dataset_df.melt(id_vars=['Dataset Name'], var_name='Method', value_name='MSE')\n",
    "MSE_per_dataset_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Dataset Name</th>\n",
       "      <th>Lasso</th>\n",
       "      <th>Elastic Net</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Neural Net</th>\n",
       "      <th>K-Nearest Neighbours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Just one</td>\n",
       "      <td>4.633886</td>\n",
       "      <td>4.730247</td>\n",
       "      <td>3.610396</td>\n",
       "      <td>3.786157</td>\n",
       "      <td>4.026148</td>\n",
       "      <td>1.753851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset Name     Lasso  Elastic Net  Decision Tree  Random Forest  \\\n",
       "0     Just one  4.633886     4.730247       3.610396       3.786157   \n",
       "\n",
       "   Neural Net  K-Nearest Neighbours  \n",
       "0    4.026148              1.753851  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE_per_dataset_df\n",
    "# unpivot the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Dataset Name</th>\n",
       "      <th>Method</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>4.633886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>4.730247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>3.610396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>3.786157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Just one</td>\n",
       "      <td>Neural Net</td>\n",
       "      <td>4.026148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Just one</td>\n",
       "      <td>K-Nearest Neighbours</td>\n",
       "      <td>1.753851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset Name                Method       MAE\n",
       "0     Just one                 Lasso  4.633886\n",
       "1     Just one           Elastic Net  4.730247\n",
       "2     Just one         Decision Tree  3.610396\n",
       "3     Just one         Random Forest  3.786157\n",
       "4     Just one            Neural Net  4.026148\n",
       "5     Just one  K-Nearest Neighbours  1.753851"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE_per_dataset_df = MAE_per_dataset_df.melt(id_vars=['Dataset Name'], var_name='Method', value_name='MAE')\n",
    "MAE_per_dataset_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Dataset Name</th>\n",
       "      <th>Lasso</th>\n",
       "      <th>Elastic Net</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Neural Net</th>\n",
       "      <th>K-Nearest Neighbours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Just one</td>\n",
       "      <td>4.513637e+15</td>\n",
       "      <td>4.613325e+15</td>\n",
       "      <td>2.172915e+15</td>\n",
       "      <td>3.048903e+15</td>\n",
       "      <td>2.658984e+15</td>\n",
       "      <td>6.993451e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset Name         Lasso   Elastic Net  Decision Tree  Random Forest  \\\n",
       "0     Just one  4.513637e+15  4.613325e+15   2.172915e+15   3.048903e+15   \n",
       "\n",
       "     Neural Net  K-Nearest Neighbours  \n",
       "0  2.658984e+15          6.993451e+14  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAPE_per_dataset_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-332aaa2555fb4883859e2f284ca6a059\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-332aaa2555fb4883859e2f284ca6a059\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-332aaa2555fb4883859e2f284ca6a059\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-fb547f70c24c7f62d950e73c55b8888d\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"Method\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"MSE\"}}, \"height\": 300, \"width\": 300, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-fb547f70c24c7f62d950e73c55b8888d\": [{\"Dataset Name\": \"Just one\", \"Method\": \"Lasso\", \"MSE\": 56.73902110173415}, {\"Dataset Name\": \"Just one\", \"Method\": \"Elastic Net\", \"MSE\": 59.14855472761319}, {\"Dataset Name\": \"Just one\", \"Method\": \"Decision Tree\", \"MSE\": 41.315854541214705}, {\"Dataset Name\": \"Just one\", \"Method\": \"Random Forest\", \"MSE\": 42.999616495001476}, {\"Dataset Name\": \"Just one\", \"Method\": \"Neural Net\", \"MSE\": 51.1811488973038}, {\"Dataset Name\": \"Just one\", \"Method\": \"K-Nearest Neighbours\", \"MSE\": 9.153458012169162}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For plot the MSE, MAE and MAPE in a bar chart for each method\n",
    "alt.Chart(MSE_per_dataset_df).mark_bar().encode(\n",
    "    x='Method',\n",
    "    y='MSE',\n",
    ").properties(\n",
    "    width=300,\n",
    "    height=300\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] - Method:  Lasso\n",
      "        Y_test  Y_pred\n",
      "Y_test  1.0000  0.4098\n",
      "Y_pred  0.4098  1.0000\n",
      "\n",
      "[INFO] - Method:  Elastic Net\n",
      "          Y_test    Y_pred\n",
      "Y_test  1.000000  0.362688\n",
      "Y_pred  0.362688  1.000000\n",
      "\n",
      "[INFO] - Method:  Decision Tree\n",
      "          Y_test    Y_pred\n",
      "Y_test  1.000000  0.627193\n",
      "Y_pred  0.627193  1.000000\n",
      "\n",
      "[INFO] - Method:  Random Forest\n",
      "          Y_test    Y_pred\n",
      "Y_test  1.000000  0.639178\n",
      "Y_pred  0.639178  1.000000\n",
      "\n",
      "[INFO] - Method:  Neural Net\n",
      "          Y_test    Y_pred\n",
      "Y_test  1.000000  0.513486\n",
      "Y_pred  0.513486  1.000000\n",
      "\n",
      "[INFO] - Method:  K-Nearest Neighbours\n",
      "          Y_test    Y_pred\n",
      "Y_test  1.000000  0.930604\n",
      "Y_pred  0.930604  1.000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# display the feature importance for each method\n",
    "for method_name in names:\n",
    "    print(\"[INFO] - Method: \", method_name)\n",
    "    print(results_dict[method_name].corr())\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intrinsic Explainability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://machinelearningmastery.com/calculate-feature-importance-with-python/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Feature 0, Score: 1.00415\n",
      "Feature: Feature 1, Score: 0.87001\n",
      "Feature: Feature 2, Score: 25.11611\n",
      "Feature: Feature 3, Score: 3.25005\n",
      "Feature: Feature 4, Score: 82.81600\n",
      "Feature: Feature 5, Score: 86.77275\n",
      "Feature: Feature 6, Score: 66.65607\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "# define the model\n",
    "model = KNeighborsRegressor(3)\n",
    "\n",
    "# fit the model\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "# perform permutation importance\n",
    "results = permutation_importance(model, X_test, Y_test, scoring='neg_mean_squared_error')\n",
    "\n",
    "# get importance\n",
    "importance = results.importances_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Longitude, Score: 1.00415\n",
      "Feature: Latitude, Score: 0.87001\n",
      "Feature: hour_of_day, Score: 25.11611\n",
      "Feature: day_of_week, Score: 3.25005\n",
      "Feature: fair_tree_count, Score: 82.81600\n",
      "Feature: good_tree_count, Score: 86.77275\n",
      "Feature: poor_tree_count, Score: 66.65607\n"
     ]
    },
    {
     "data": {
      "image/png": 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9Dt6vFXj7+czps5zd5/a/WLRoEcaNG4fBgwdj1qxZKFWqFPT19TF16tR/HWayvgdy+j45cuSIWltePsf/pobsrsTM6fs0O/n5eQL8/2uTn89HXvd/z549kZGRgR9++AGdO3eGUqlEnTp1MHv2bDRv3vyD7yU72X19njx5gr1796oOg+ZUt1wxzOiwUqVKAQC2b98OJyenHPslJCTgt99+w/Tp0zFx4kRVe2pqquq4bV6YmJggNTVVo/2ff/5R1fKu96/YyOqzdOnSHK9aye8vn4/l8ePH2bYZGRmhePHiMDAwgJ6eHuLi4jT6ZZ3L8P4+yM8VK8ePH8ejR48QFhamGo0B8J/mo7GxscGpU6egVCpzDDSlSpWCQqHA77//rgoI78qu7V25fSbe1759e7Rv3x6pqak4c+YMgoKC0L17dzg7O6NBgwZ5fFfZK1myJDIyMvD8+XO1XzjZfV1zk9PnICtMZX2NJ02ahE6dOmW7jcqVK6vdL4grl/Kz33OyceNGNG7cGCtXrlRrT0pK+td1Ze2nnL5Psvs58bGVLFkyX9+nH1N+Ph/52f99+vRBnz598OrVK5w8eRLTp09HmzZtcPPmTTg5OakCYWpqqtr3bE6fh+w+k6VKlUL16tUxZ86cbJ9TUNOBaAsPM+mwTz/9FAYGBrhz5w68vLyyvQFvP7hCCI1fTGvXrkVmZqZa2/t/8b7L2dkZly9fVmu7efOmxrB6Tnx8fGBlZYVr167lWG/WXzratnPnTrW/BpOSkrB371588skn0NfXh5mZGerVq4edO3eq7RulUomNGzeibNmyGsO22clp/2b9cHn/a7R69ep//Z5atWqFlJSUXCcWa9OmDYQQePjwYbZfDw8Pj1xfw9nZGfHx8WonCqelpeHQoUM5PsfY2Bi+vr6qkzwvXLiQvzeWjawA+P6J2Fu2bMnXdjZv3gwhhOr+vXv3cPr0adX8KpUrV0bFihVx6dKlHD/D5ubm/+3N5EF+9ruxsXG2388KhULj83b58mW1k96zng9k/zPhfQ0aNICpqSk2btyo1v7gwQMcP34cTZs2/eA28iqn99W0aVNcu3YNUVFRau0bNmyAQqGAn5/fR6vhffn5fOR1/7/LzMwMrVq1wuTJk5GWloarV68CgGok6v2fz3v37s1z7W3atMGVK1dQvnz5bOuWe5jhyIwOc3Z2xsyZMzF58mTcvXsXLVu2RIkSJfDkyROcPXsWZmZmCAwMhIWFBRo1aoQFCxagVKlScHZ2Rnh4ONatW6cxcZu7uzsAYM2aNTA3N4eJiQlcXFxQsmRJ9OzZEz169MDQoUPRuXNn3Lt3D/Pnz8/zPBPFixfH0qVLERAQgOfPn+Ozzz6Dra0tnj59ikuXLuHp06caf6Voi76+Ppo3b46xY8dCqVRi3rx5SExMVE0WBry9SqN58+bw8/PDV199BSMjI6xYsQJXrlzB5s2b8/QXeFY4WLx4MQICAmBoaIjKlSvD29sbJUqUwODBgzF9+nQYGhri559/Vh3e+De6deuGkJAQDB48GDdu3ICfnx+USiX+/PNPVK1aFV27doWPjw8GDhyIPn364Pz582jUqBHMzMwQFxeHU6dOwcPDA0OGDMnxNbp06YJp06aha9euGD9+PFJSUrBkyRKNkDxt2jQ8ePAATZs2RdmyZfHy5UssXrxY7byg/6Jly5bw8fHBuHHjkJiYiNq1ayMiIgIbNmwAgFwPtb0rPj4eHTt2xIABA5CQkIDp06fDxMQEkyZNUvVZvXo1WrVqhU8//RS9e/dGmTJl8Pz5c1y/fh1RUVH45Zdf/vP7+ZC87nfg7WcuLCwMe/fuhb29PczNzVG5cmW0adMGs2bNwvTp0+Hr64sbN25g5syZcHFxUbvKzNzcHE5OTtizZw+aNm0Ka2tr1c+R91lZWWHq1Kn45ptv0KtXL3Tr1g3Pnj1DYGAgTExMMH369I+2Dzw8PLBlyxZs3boVrq6uMDExgYeHB8aMGYMNGzagdevWmDlzJpycnLBv3z6sWLECQ4YMydMfHf9FXj8fed3/AwYMgKmpKXx8fGBvb4/Hjx8jKCgIlpaWqlMM/P39YW1tjX79+mHmzJkwMDBAaGgo7t+/n+e6Z86ciSNHjsDb2xsjR45E5cqVkZKSgpiYGOzfvx+rVq1C2bJlP+7O0iYpzz4u6vI6z8zu3buFn5+fsLCwEMbGxsLJyUl89tlnavOkPHjwQHTu3FmUKFFCmJubi5YtW4orV65ke4VScHCwcHFxEfr6+mpXlyiVSjF//nzh6uoqTExMhJeXlzh+/HiOVzP98ssv2dYbHh4uWrduLaytrYWhoaEoU6aMaN26dY79s+R2NdP7+yjr7P6nT5+qtWfNbfH+NufNmycCAwNF2bJlhZGRkfD09BSHDh3SqCFrnhkzMzNhamoq6tevr5p/4kM1ZZk0aZJwcHAQenp6aleOZc05UqxYMWFjYyP69+8voqKiNK7wef89vP+e3/XmzRsxbdo0UbFiRWFkZCRKliwpmjRpIk6fPq3W78cffxT16tVTva/y5cuLXr16qV1tk5P9+/eLmjVrClNTU+Hq6iqWLVumUctvv/0mWrVqJcqUKSOMjIyEra2t8Pf3F7///ruqT25XM73/dczuKo3nz5+LPn36CCsrK1GsWDHRvHlzcebMGQFA7Yq97Lw7z8zIkSOFjY2NMDY2Fp988km2++DSpUviiy++ELa2tsLQ0FDY2dmJJk2aiFWrVmnUmN3nIGuemfdlzQHyPgBi2LBham152e9CvL0Sx8fHRxQrVkxtnpnU1FTx1VdfiTJlyggTExNRq1YtsXv37myvlDp69Kjw9PQUxsbGeZpnZu3ataJ69erCyMhIWFpaivbt22tcDZOfz3F2YmJiRIsWLYS5uXm288x0795dlCxZUhgaGorKlSuLBQsW5Guemfdl9zXI7meSEHn7fOR1/69fv174+fmJ0qVLCyMjI+Hg4CC++OILcfnyZbXXPHv2rPD29hZmZmaiTJkyYvr06WLt2rU5zjOTnadPn4qRI0cKFxcXYWhoKKytrUXt2rXF5MmTNea7khuFEO+MuRIVMjExMXBxccGCBQsKbL0rks6mTZvw5Zdf4o8//oC3t3eO/cLCwuDn54dffvkl1xPUiUieeJiJiGRh8+bNePjwITw8PKCnp4czZ85gwYIFaNSoUa5BhogKP4YZIpIFc3NzbNmyBbNnz8arV69gb2+P3r17Y/bs2VKXRkQS42EmIiIikjVemk1ERESyxjBDREREssYwQ0RERLJW6E8AViqVePToEczNzQtk2nEiIiL6+IQQSEpK+uAadEARCDOPHj2Co6Oj1GUQERHRv3D//v0Pzk5c6MNM1joZ9+/fh4WFhcTVEBERUV4kJibC0dExT+uhFfowk3VoycLCgmGGiIhIZvJyighPACYiIiJZY5ghIiIiWWOYISIiyWVkZGDKlClwcXGBqakpXF1dMXPmTCiVSrV+169fR7t27WBpaQlzc3PUr18fsbGxOW63cePGUCgUGrfWrVur+jg7O2fbZ9iwYQX2funjKvTnzBARke6bN28eVq1ahfXr16NatWo4f/48+vTpA0tLS4waNQoAcOfOHTRs2BD9+vVDYGAgLC0tcf36dZiYmOS43Z07dyItLU11/9mzZ6hRowY+//xzVdu5c+eQmZmpun/lyhU0b95crQ/pNoYZIiKSXEREBNq3b68aMXF2dsbmzZtx/vx5VZ/JkyfD398f8+fPV7W5urrmul1ra2u1+1u2bEGxYsXUgoqNjY1an2+//Rbly5eHr6/vv34/pF08zERERJJr2LAhjh07hps3bwIALl26hFOnTsHf3x/A2wlQ9+3bh0qVKuHTTz+Fra0t6tWrh927d+frddatW4euXbvCzMws28fT0tKwceNG9O3blxOtygjDDBERSW7ChAno1q0bqlSpAkNDQ3h6emL06NHo1q0bACA+Ph7Jycn49ttv0bJlSxw+fBgdO3ZEp06dEB4enqfXOHv2LK5cuYL+/fvn2Gf37t14+fIlevfu/THeFmkJDzMREZHktm7dio0bN2LTpk2oVq0aLl68iNGjR8PBwQEBAQGqE4Hbt2+PMWPGAABq1qyJ06dPY9WqVXk6JLRu3Tq4u7ujbt26ufZp1aoVHBwcPs4bI61gmCEiIsmNHz8eEydORNeuXQEAHh4euHfvHoKCghAQEIBSpUrBwMAAbm5uas+rWrUqTp069cHtv379Glu2bMHMmTNz7HPv3j0cPXoUO3fu/G9vhrSOh5mIiEhyr1+/1lhMUF9fXzUiY2RkhDp16uDGjRtqfW7evAknJ6cPbn/btm1ITU1Fjx49cuwTEhICW1tbtcu2SR44MkNERJJr27Yt5syZg3LlyqFatWq4cOECFi1ahL59+6r6jB8/Hl26dEGjRo3g5+eHgwcPYu/evQgLC1P16dWrF8qUKYOgoCC17a9btw4dOnRAyZIls319pVKJkJAQBAQEwMCAvxrlhl8xIiKS3NKlSzF16lQMHToU8fHxcHBwwKBBgzBt2jRVn44dO2LVqlUICgrCyJEjUblyZezYsQMNGzZU9YmNjdUY4bl58yZOnTqFw4cP5/j6R48eRWxsrFp4IvlQCCGE1EUUpMTERFhaWiIhIYELTRIREclEfn5/85wZIiIikjWGGSIiIpI1njNDREQFznniPqlL+OhivuVVT7qCIzNEREQkawwzREREJGsMM0REEsnIyMCUKVPg4uICU1NTuLq6YubMmaqJ4tLT0zFhwgR4eHjAzMwMDg4O6NWrFx49evTBbQcHB6Ny5cowNTWFo6MjxowZg5SUFNXjQUFBqFOnDszNzWFra4sOHTpoTEhHJBc8Z4aISCLz5s3DqlWrsH79elSrVg3nz59Hnz59YGlpiVGjRuH169eIiorC1KlTUaNGDbx48QKjR49Gu3btcP78+Ry3+/PPP2PixIn48ccf4e3tjZs3b6oWTvz+++8BAOHh4Rg2bBjq1KmDjIwMTJ48GS1atMC1a9dyXFGaSFcxzBARSSQiIgLt27dXTZ/v7OyMzZs3q4KKpaUljhw5ovacpUuXom7duoiNjUW5cuVy3K6Pjw+6d++u2m63bt1w9uxZVZ+DBw+qPSdrKv/IyEg0atToo71HIm3gYSYiIok0bNgQx44dw82bNwEAly5dwqlTp+Dv75/jcxISEqBQKGBlZZXrdiMjI1Xh5e7du9i/f3+uaw4lJCQAAKytrf/FOyGSFkdmiIgkMmHCBCQkJKBKlSrQ19dHZmYm5syZg27dumXbPyUlBRMnTkT37t1znRG1a9euePr0KRo2bAghBDIyMjBkyBBMnDgx2/5CCIwdOxYNGzaEu7v7R3lvRNrEMENEJJGtW7di48aN2LRpE6pVq4aLFy9i9OjRcHBwQEBAgFrf9PR0dO3aFUqlEitWrMh1u2FhYZgzZw5WrFiBevXq4fbt2xg1ahTs7e0xdepUjf7Dhw/H5cuXcerUqY/6/oi0hWGGiEgi48ePx8SJE9G1a1cAgIeHB+7du4egoCC1MJOeno4vvvgC0dHROH78+AfXqZk6dSp69uyJ/v37q7b76tUrDBw4EJMnT1ZbiHHEiBH49ddfcfLkSZQtW7YA3iVRwWOYISKSyOvXrzVWeNbX11ddmg38P8jcunULJ06cQMmSJf/1doUQyFpbWAiBESNGYNeuXQgLC4OLi8tHeEdE0mCYISKSSNu2bTFnzhyUK1cO1apVw4ULF7Bo0SL07dsXwNt5aD777DNERUXht99+Q2ZmJh4/fgzg7Ym6RkZGAIBevXqhTJkyCAoKUm130aJF8PT0VB1mmjp1Ktq1awd9fX0AwLBhw7Bp0ybs2bMH5ubmqu1aWlrC1NRU27uC6D9hmCEiksjSpUsxdepUDB06FPHx8XBwcMCgQYMwbdo0AMCDBw/w66+/AgBq1qyp9twTJ06gcePGAIDY2Fi1kZgpU6ZAoVBgypQpePjwIWxsbFTBKcvKlSsBQLWNLCEhIao5aYjkQiGyxhwLqcTERFhaWiIhIeGDx5mJiKhgcKFJyq/8/P7mPDNEREQkawwzREREJGs8Z4aIqADx8ApRwePIDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyZqkYSYjIwNTpkyBi4sLTE1N4erqipkzZ6otsiaEwIwZM+Dg4ABTU1M0btwYV69elbBqIiIi0iWShpl58+Zh1apVWLZsGa5fv4758+djwYIFWLp0qarP/PnzsWjRIixbtgznzp2DnZ0dmjdvjqSkJAkrJyIiIl0haZiJiIhA+/bt0bp1azg7O+Ozzz5DixYtcP78eQBvR2WCg4MxefJkdOrUCe7u7li/fj1ev36NTZs2SVk6ERER6QhJw0zDhg1x7Ngx3Lx5EwBw6dIlnDp1Cv7+/gCA6OhoPH78GC1atFA9x9jYGL6+vjh9+nS220xNTUViYqLajYiIiAovSZczmDBhAhISElClShXo6+sjMzMTc+bMQbdu3QAAjx8/BgCULl1a7XmlS5fGvXv3st1mUFAQAgMDC7ZwIiIi0hmSjsxs3boVGzduxKZNmxAVFYX169fju+++w/r169X6KRQKtftCCI22LJMmTUJCQoLqdv/+/QKrn4iIiKQn6cjM+PHjMXHiRHTt2hUA4OHhgXv37iEoKAgBAQGws7MD8HaExt7eXvW8+Ph4jdGaLMbGxjA2Ni744omIiEgnSDoy8/r1a+jpqZegr6+vujTbxcUFdnZ2OHLkiOrxtLQ0hIeHw9vbW6u1EhERkW6SdGSmbdu2mDNnDsqVK4dq1arhwoULWLRoEfr27Qvg7eGl0aNHY+7cuahYsSIqVqyIuXPnolixYujevbuUpRMREZGOkDTMLF26FFOnTsXQoUMRHx8PBwcHDBo0CNOmTVP1+frrr/HmzRsMHToUL168QL169XD48GGYm5tLWDkRERHpCoUQQkhdREFKTEyEpaUlEhISYGFhIXU5RFTEOE/cJ3UJH13Mt63z/RzuB8qv/Pz+5tpMREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQ65OHDh+jRowdKliyJYsWKoWbNmoiMjFTrc/36dbRr1w6WlpYwNzdH/fr1ERsbm+t2X758iWHDhsHe3h4mJiaoWrUq9u/fn+/X1kUGUhdAREREb7148QI+Pj7w8/PDgQMHYGtrizt37sDKykrV586dO2jYsCH69euHwMBAWFpa4vr16zAxMclxu2lpaWjevDlsbW2xfft2lC1bFvfv34e5uXm+XltXMcwQERHpiHnz5sHR0REhISGqNmdnZ7U+kydPhr+/P+bPn69qc3V1zXW7P/74I54/f47Tp0/D0NAQAODk5JTv19ZVPMxERESkI3799Vd4eXnh888/h62tLTw9PfHDDz+oHlcqldi3bx8qVaqETz/9FLa2tqhXrx527979we02aNAAw4YNQ+nSpeHu7o65c+ciMzMzz6+tyxhmiIiIdMTdu3excuVKVKxYEYcOHcLgwYMxcuRIbNiwAQAQHx+P5ORkfPvtt2jZsiUOHz6Mjh07olOnTggPD891u9u3b0dmZib279+PKVOmYOHChZgzZ06eX1uXKYQQQuoiClJiYiIsLS2RkJAACwsLqcshoiLGeeI+qUv46GK+bZ3v53A/5I2RkRG8vLxw+vRpVdvIkSNx7tw5RERE4NGjRyhTpgy6deuGTZs2qfq0a9cOZmZm2Lx5c7bbrVSpElJSUhAdHQ19fX0AwKJFi7BgwQLExcXl6bW1LT+/vzkyQ0REpCPs7e3h5uam1la1alXVlUqlSpWCgYFBrn1y2m6lSpVUQSbrOY8fP0ZaWlqeXluXMcwQERHpCB8fH9y4cUOt7ebNm6qTdY2MjFCnTp1c++S03du3b0OpVKo9x97eHkZGRnl6bV3GMENERKQjxowZgzNnzmDu3Lm4ffs2Nm3ahDVr1mDYsGGqPuPHj8fWrVvxww8/4Pbt21i2bBn27t2LoUOHqvr06tULkyZNUt0fMmQInj17hlGjRuHmzZvYt28f5s6dq7bdvLy2rmKYISIi0hF16tTBrl27sHnzZri7u2PWrFkIDg7Gl19+qerTsWNHrFq1CvPnz4eHhwfWrl2LHTt2oGHDhqo+sbGxqnNhAMDR0RGHDx/GuXPnUL16dYwcORKjRo3CxIkT8/XauoonABMRFSCe+PoW9wPlF08AJiIioiKDMwATERFpCUeoCgZHZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWJA8zDx8+RI8ePVCyZEkUK1YMNWvWRGRkpOpxIQRmzJgBBwcHmJqaonHjxrh69aqEFRMREZEukTTMvHjxAj4+PjA0NMSBAwdw7do1LFy4EFZWVqo+8+fPx6JFi7Bs2TKcO3cOdnZ2aN68OZKSkqQrnIiIiHSGgZQvPm/ePDg6OiIkJETV5uzsrPq/EALBwcGYPHkyOnXqBABYv349SpcujU2bNmHQoEHaLpmIiIh0jKQjM7/++iu8vLzw+eefw9bWFp6envjhhx9Uj0dHR+Px48do0aKFqs3Y2Bi+vr44ffp0tttMTU1FYmKi2o2IiIgKL0nDzN27d7Fy5UpUrFgRhw4dwuDBgzFy5Ehs2LABAPD48WMAQOnSpdWeV7p0adVj7wsKCoKlpaXq5ujoWLBvgoiIiCQlaZhRKpWoVasW5s6dC09PTwwaNAgDBgzAypUr1fopFAq1+0IIjbYskyZNQkJCgup2//79AqufiIiIpCdpmLG3t4ebm5taW9WqVREbGwsAsLOzAwCNUZj4+HiN0ZosxsbGsLCwULsRERFR4SVpmPHx8cGNGzfU2m7evAknJycAgIuLC+zs7HDkyBHV42lpaQgPD4e3t7dWayUiIiLdJOnVTGPGjIG3tzfmzp2LL774AmfPnsWaNWuwZs0aAG8PL40ePRpz585FxYoVUbFiRcydOxfFihVD9+7dpSydiIiIdISkYaZOnTrYtWsXJk2ahJkzZ8LFxQXBwcH48ssvVX2+/vprvHnzBkOHDsWLFy9Qr149HD58GObm5hJWTkRERLpC0jADAG3atEGbNm1yfFyhUGDGjBmYMWOG9ooiIiIi2ZB8OQMiIiKi/4JhhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZO1fhZmXL19i7dq1mDRpEp4/fw4AiIqKwsOHDz9qcUREREQfYpDfJ1y+fBnNmjWDpaUlYmJiMGDAAFhbW2PXrl24d+8eNmzYUBB1EhEREWUr3yMzY8eORe/evXHr1i2YmJio2lu1aoWTJ09+1OKIiIiIPiTfYebcuXMYNGiQRnuZMmXw+PHjj1IUERERUV7lO8yYmJggMTFRo/3GjRuwsbH5KEURERER5VW+w0z79u0xc+ZMpKenAwAUCgViY2MxceJEdO7c+aMXSERERJSbfIeZ7777Dk+fPoWtrS3evHkDX19fVKhQAebm5pgzZ05B1EhERESUo3xfzWRhYYFTp07h+PHjiIqKglKpRK1atdCsWbOCqI+IiIgoV/kKMxkZGTAxMcHFixfRpEkTNGnSpKDqIiIiIsqTfB1mMjAwgJOTEzIzMwuqHiIiIqJ8yfc5M1OmTFGb+ZeIiIhISvk+Z2bJkiW4ffs2HBwc4OTkBDMzM7XHo6KiPlpxRERERB+S7zDToUOHAiiDiIiI6N/Jd5iZPn16QdRBRERE9K/kO8xkiYyMxPXr16FQKODm5gZPT8+PWRcRERFRnuQ7zMTHx6Nr164ICwuDlZUVhBBISEiAn58ftmzZwiUNiIiISKvyfTXTiBEjkJiYiKtXr+L58+d48eIFrly5gsTERIwcObIgaiQiIiLKUb5HZg4ePIijR4+iatWqqjY3NzcsX74cLVq0+KjFEREREX1IvkdmlEolDA0NNdoNDQ2hVCo/SlFEREREeZXvMNOkSROMGjUKjx49UrU9fPgQY8aMQdOmTT9qcUREREQfku8ws2zZMiQlJcHZ2Rnly5dHhQoV4OLigqSkJCxdurQgaiQiIiLKUb7PmXF0dERUVBSOHDmCv//+G0IIuLm5cdVsIiIiksS/nmemefPmaN68+ceshYiIiCjf8n2YaeTIkViyZIlG+7JlyzB69OiPURMRERFRnuU7zOzYsQM+Pj4a7d7e3ti+fftHKYqIiIgor/IdZp49ewZLS0uNdgsLC/zzzz8fpSgiIiKivMp3mKlQoQIOHjyo0X7gwAG4urp+lKKIiIiI8irfJwCPHTsWw4cPx9OnT9GkSRMAwLFjx7Bw4UIEBwd/7PqIiIiIcpXvMNO3b1+kpqZizpw5mDVrFgDA2dkZK1euRK9evT56gURERES5+VeXZg8ZMgRDhgzB06dPYWpqiuLFi3/suoiIiIjyJN/nzLzLxsYGkZGROHDgAF68ePGxaiIiIiLKszyPzCxYsADJyckIDAwEAAgh0KpVKxw+fBgAYGtri2PHjqFatWoFUykRERFRNvI8MrN582a4ubmp7m/fvh0nT57E77//jn/++QdeXl6qoENERESkLXkOM9HR0ahevbrq/v79+9G5c2f4+PjA2toaU6ZMQURERIEUSURERJSTPIeZ9PR0GBsbq+5HRETA29tbdd/BwYGT5hEREZHW5TnMVKhQASdPngQAxMbG4ubNm/D19VU9/uDBA5QsWfLjV0hERESUizyfADxkyBAMHz4cv//+O86cOYMGDRqonUNz/PhxeHp6FkiRRERERDnJc5gZNGgQDAwM8Ntvv6FRo0aYPn262uOPHj1C3759P3qBRERERLnJ16R5/fr1Q79+/bJ9bMWKFR+lICIiIqL8+E+T5hERERFJjWGGiIiIZI1hhoiIiGRNZ8JMUFAQFAoFRo8erWoTQmDGjBlwcHCAqakpGjdujKtXr0pXJBEREemcPIeZu3fvQghRIEWcO3cOa9asUZthGADmz5+PRYsWYdmyZTh37hzs7OzQvHlzJCUlFUgdREREJD95DjMVK1bE06dPVfe7dOmCJ0+e/OcCkpOT8eWXX+KHH35AiRIlVO1CCAQHB2Py5Mno1KkT3N3dsX79erx+/RqbNm36z69LREREhUOew8z7ozL79+/Hq1ev/nMBw4YNQ+vWrdGsWTO19ujoaDx+/BgtWrRQtRkbG8PX1xenT5/+z69LREREhUO+5pn52LZs2YLIyEicP39e47HHjx8DAEqXLq3WXrp0ady7dy/HbaampiI1NVV1PzEx8SNVS0RERLoozyMzCoUCCoVCo+3fun//PkaNGoWff/4ZJiYmub7uu4QQub5uUFAQLC0tVTdHR8d/XSMRERHpvjyPzAgh0Lt3b9XK2SkpKRg8eDDMzMzU+u3cuTNP24uMjER8fDxq166tasvMzMTJkyexbNky3LhxA8DbERp7e3tVn/j4eI3RmndNmjQJY8eOVd1PTExkoCEiIirE8hxmAgIC1O736NHjP71w06ZN8ddff6m19enTB1WqVMGECRPg6uoKOzs7HDlyRLWAZVpaGsLDwzFv3rwct2tsbKwKXERERFT45TnMhISEfNQXNjc3h7u7u1qbmZkZSpYsqWofPXo05s6di4oVK6JixYqYO3cuihUrhu7du3/UWoiIiEi+JD0B+EO+/vprvHnzBkOHDsWLFy9Qr149HD58GObm5lKXRkRERDpCp8JMWFiY2n2FQoEZM2ZgxowZktRDREREuk9nljMgIiIi+jcYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaItCgoKAh16tSBubk5bG1t0aFDB9y4cUOtT+/evaFQKNRu9evXz3W7V69eRefOneHs7AyFQoHg4OB/9dpERHLEMEOkReHh4Rg2bBjOnDmDI0eOICMjAy1atMCrV6/U+rVs2RJxcXGq2/79+3Pd7uvXr+Hq6opvv/0WdnZ2/+m1iYjkxkDqAoiKkoMHD6rdDwkJga2tLSIjI9GoUSNVu7GxcY6hJDt16tRBnTp1AAATJ078T69NRCQ3HJkhklBCQgIAwNraWq09LCwMtra2qFSpEgYMGID4+HitvTYRkdwwzBBJRAiBsWPHomHDhnB3d1e1t2rVCj///DOOHz+OhQsX4ty5c2jSpAlSU1ML/LWJiOSIh5mIJDJ8+HBcvnwZp06dUmvv0qWL6v/u7u7w8vKCk5MT9u3bh06dOhXoaxMRyRHDDJEERowYgV9//RUnT55E2bJlc+1rb28PJycn3Lp1S+uvTUQkBwwzRFokhMCIESOwa9cuhIWFwcXF5YPPefbsGe7fvw97e3utvzYRkRwwzBBp0bBhw7Bp0ybs2bMH5ubmePz4MQDA0tISpqamSE5OxowZM9C5c2fY29sjJiYG33zzDUqVKoWOHTuqttOrVy+UKVMGQUFBAIC0tDRcu3ZN9f+HDx/i4sWLKF68OCpUqJCn1yYikiueAEykRStXrkRCQgIaN24Me3t71W3r1q0AAH19ffz1119o3749KlWqhICAAFSqVAkREREwNzdXbSc2NhZxcXGq+48ePYKnpyc8PT0RFxeH7777Dp6enujfv3+eX5uISK44MkOkRUKIXB83NTXFoUOHPridsLAwtfvOzs4f3PaHHicikiuOzBAREZGsMcwQERGRrPEwE9FH5Dxxn9QlfHQx37aWugQiolxxZIaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGRN0jATFBSEOnXqwNzcHLa2tujQoQNu3Lih1kcIgRkzZsDBwQGmpqZo3Lgxrl69KlHFREREpGskDTPh4eEYNmwYzpw5gyNHjiAjIwMtWrTAq1evVH3mz5+PRYsWYdmyZTh37hzs7OzQvHlzJCUlSVg5ERER6QoDKV/84MGDavdDQkJga2uLyMhINGrUCEIIBAcHY/LkyejUqRMAYP369ShdujQ2bdqEQYMGSVE2ERER6RCdOmcmISEBAGBtbQ0AiI6OxuPHj9GiRQtVH2NjY/j6+uL06dOS1EhERES6RdKRmXcJITB27Fg0bNgQ7u7uAIDHjx8DAEqXLq3Wt3Tp0rh3716220lNTUVqaqrqfmJiYgFVTERERLpAZ0Zmhg8fjsuXL2Pz5s0ajykUCrX7QgiNtixBQUGwtLRU3RwdHQukXiIiItINOhFmRowYgV9//RUnTpxA2bJlVe12dnYA/j9CkyU+Pl5jtCbLpEmTkJCQoLrdv3+/4AonIiIiyUkaZoQQGD58OHbu3Injx4/DxcVF7XEXFxfY2dnhyJEjqra0tDSEh4fD29s7220aGxvDwsJC7UZERESFl6TnzAwbNgybNm3Cnj17YG5urhqBsbS0hKmpKRQKBUaPHo25c+eiYsWKqFixIubOnYtixYqhe/fuUpZOREREOkLSMLNy5UoAQOPGjdXaQ0JC0Lt3bwDA119/jTdv3mDo0KF48eIF6tWrh8OHD8Pc3FzL1RIREZEukjTMCCE+2EehUGDGjBmYMWNGwRdEREREsqMTJwATERER/VsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0SkE1auXInq1avDwsICFhYWaNCgAQ4cOJBj/507d6J58+awsbFR9T906JBan9DQUCgUCo1bSkpKQb8dItIihhki0glly5bFt99+i/Pnz+P8+fNo0qQJ2rdvj6tXr2bb/+TJk2jevDn279+PyMhI+Pn5oW3btrhw4YJaPwsLC8TFxandTExMtPGWiEhLDKQugIgIANq2bat2f86cOVi5ciXOnDmDatWqafQPDg5Wuz937lzs2bMHe/fuhaenp6pdoVDAzs6uQGomIt3AkRki0jmZmZnYsmULXr16hQYNGuTpOUqlEklJSbC2tlZrT05OhpOTE8qWLYs2bdpojNwQkfxxZIaIdMZff/2FBg0aICUlBcWLF8euXbvg5uaWp+cuXLgQr169whdffKFqq1KlCkJDQ+Hh4YHExEQsXrwYPj4+uHTpEipWrFhQb4OItIxhhoh0RuXKlXHx4kW8fPkSO3bsQEBAAMLDwz8YaDZv3owZM2Zgz549sLW1VbXXr18f9evXV9338fFBrVq1sHTpUixZsqTA3gcRaRfDDBHpDCMjI1SoUAEA4OXlhXPnzmHx4sVYvXp1js/ZunUr+vXrh19++QXNmjXLdft6enqoU6cObt269VHrJiJp8ZwZItJZQgikpqbm+PjmzZvRu3dvbNq0Ca1bt87T9i5evAh7e/uPWSYRSYwjM0SkE7755hu0atUKjo6OSEpKwpYtWxAWFoaDBw8CACZNmoSHDx9iw4YNAN4GmV69emHx4sWoX78+Hj9+DAAwNTWFpaUlACAwMBD169dHxYoVkZiYiCVLluDixYtYvny5NG+SiAoER2a07OTJk2jbti0cHBygUCiwe/fuDz4nPDwctWvXhomJCVxdXbFq1SqNPjt27ICbmxuMjY3h5uaGXbt2FUD1H9+KFSvg4uICExMT1K5dG7///nuu/X/++WfUqFEDxYoVg729Pfr06YNnz56pHm/cuHG2k6Tl5a92ktaTJ0/Qs2dPVK5cGU2bNsWff/6JgwcPonnz5gCAuLg4xMbGqvqvXr0aGRkZGDZsGOzt7VW3UaNGqfq8fPkSAwcORNWqVdGiRQs8fPgQJ0+eRN26dbX+/oio4HBkRstevXqFGjVqoE+fPujcufMH+0dHR8Pf3x8DBgzAxo0b8ccff2Do0KGwsbFRPT8iIgJdunTBrFmz0LFjR+zatQtffPEFTp06hXr16hX0W/rXtm7ditGjR2PFihXw8fHB6tWr0apVK1y7dg3lypXT6H/q1Cn06tUL33//Pdq2bYuHDx9i8ODB6N+/vyq87dy5E2lpaarnPHv2DDVq1MDnn3+utfdF/866detyfTw0NFTtflhY2Ae3+f333+P777//D1URkRwwzGhZq1at0KpVqzz3X7VqFcqVK6eaIKxq1ao4f/48vvvuO1WYCQ4ORvPmzTFp0iQAb4fjw8PDERwcjM2bN3/09/CxLFq0CP369UP//v0BvH0fhw4dwsqVKxEUFKTR/8yZM3B2dsbIkSMBAC4uLhg0aBDmz5+v6vP+HCNbtmxBsWLFGGaIiAoxHmbScREREWjRooVa26efforz588jPT091z6nT5/WWp35lZaWhsjISI26W7RokWPd3t7eePDgAfbv3w8hBJ48eYLt27fneghp3bp16Nq1K8zMzD5q/UREpDs4MqPjHj9+jNKlS6u1lS5dGhkZGfjnn39gb2+fY5+sEyJ10T///IPMzMx81e3t7Y2ff/4ZXbp0QUpKCjIyMtCuXTssXbo02/5nz57FlStXPnj4ggqG88R9Upfw0cV8y3OviHQRR2ZkQKFQqN0XQmi0Z9fn/TZdlJ+6r127hpEjR2LatGmIjIzEwYMHER0djcGDB2fbf926dXB3d+fJnkREhRxHZnScnZ2dxkhFfHw8DAwMULJkyVz7vD/qoUtKlSoFfX39fNUdFBQEHx8fjB8/HgBQvXp1mJmZ4ZNPPsHs2bPV5g55/fo1tmzZgpkzZxbcmyAiIp3AkRkd16BBAxw5ckSt7fDhw/Dy8oKhoWGufby9vbVWZ34ZGRmhdu3aGnUfOXIkx7pfv34NPT31j6y+vj6A/49WZdm2bRtSU1PRo0ePj1g1ERHpIo7MaFlycjJu376tuh8dHY2LFy/C2toa5cqV05gYbPDgwVi2bBnGjh2LAQMGICIiAuvWrVO7SmnUqFFo1KgR5s2bh/bt22PPnj04evQoTp06pfX3lx9jx45Fz5494eXlhQYNGmDNmjWIjY1VHTZ6f1+0bdsWAwYMwMqVK/Hpp58iLi4Oo0ePRt26deHg4KC27XXr1qFDhw6q0SsiIiq8GGa07Pz58/Dz81PdHzt2LAAgICAAoaGhGhODubi4YP/+/RgzZgyWL18OBwcHLFmyRG2OGm9vb2zZsgVTpkzB1KlTUb58eWzdulWn55gBgC5duuDZs2eYOXMm4uLi4O7ujv3798PJyQmA5iRpvXv3RlJSEpYtW4Zx48bBysoKTZo0wbx589S2e/PmTZw6dQqHDx/W6vshIiJpKMT74/OFTGJiIiwtLZGQkAALCwupy6FCjlfw/B/3xVvcD29xP7zF/ZB3+fn9zXNmiIiISNZkEWbyu34PERERFR06f85Mftfv0TYOGb7F/UBERFLR+ZGZd9fvqVq1KoKDg+Ho6IiVK1dKXRoRERHpAJ0OM/9m/R4iIiIqWnT6MNO/Wb8nNTUVqampqvsJCQkA3p4VXRCUqa8LZLtS+jf7ivvhLe6H/+O+eIv74S3uh7e4H/K/3bxcdK3TYSZLftbvCQoKQmBgoEa7o6NjgdRWGFkGS12BbuB+eIv74f+4L97ifniL++Gtgt4PSUlJsLS0zLWPToeZf7N+z6RJk1QT0QGAUqnE8+fPUbJkSVksvJidxMREODo64v79+0V6rhzuh//jvniL++Et7of/4754qzDsByEEkpKSNGZ4z45Oh5l31+/p2LGjqv3IkSNo3759ts8xNjaGsbGxWpuVlVVBlqk1FhYWsv1QfkzcD//HffEW98Nb3A//x33xltz3w4dGZLLodJgBPrx+DxERERVtOh9mPrR+DxERERVtOh9mAGDo0KEYOnSo1GVIxtjYGNOnT9c4fFbUcD/8H/fFW9wPb3E//B/3xVtFbT8U+oUmiYiIqHDT6UnziIiIiD6EYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIZOX+/fs5PnbmzBktVkJEuoJhhohkpXnz5nj27JlG+x9//IGWLVtKUJE09PX1ER8fr9H+7Nkz6OvrS1CRNGbOnInXrzUXb3zz5g1mzpwpQUXS2bBhg9pCy1nS0tKwYcMGCSrSHoYZHZaRkYGjR49i9erVSEpKAgA8evQIycnJElemfXfu3MGUKVPQrVs31Q/wgwcP4urVqxJXpj2hoaHZ/tAuaj755BO0aNFC9T0BACdPnoS/vz+mT58uYWXaldOsGqmpqTAyMtJyNdIJDAzM9mfi69evs110uDDr06cPEhISNNqTkpLQp08fCSrSHllMmlcU3bt3Dy1btkRsbCxSU1PRvHlzmJubY/78+UhJScGqVaukLlFrwsPD0apVK/j4+ODkyZOYM2cObG1tcfnyZaxduxbbt2+XukStmDRpEkaOHInPP/8c/fr1g7e3t9QlSWLNmjX4/PPP0bp1axw+fBgRERFo164dZs+ejVGjRkldXoFbsmQJAEChUGDt2rUoXry46rHMzEycPHkSVapUkao8rRNCZLuI8KVLl2BtbS1BRdLJaV88ePAgz2scyRXDjI4aNWoUvLy8cOnSJZQsWVLV3rFjR/Tv31/CyrRv4sSJmD17NsaOHQtzc3NVu5+fHxYvXixhZdr14MED7Nu3D6GhofDz84OLiwv69OmDgIAA2NnZSV2e1igUCmzevBmtW7dG06ZNcfnyZQQFBWH48OFSl6YV33//PYC3v7hWrVqldkjJyMgIzs7OReKPnRIlSkChUEChUKBSpUpqv8QzMzORnJxcZNbw8/T0VO2Lpk2bwsDg/7/aMzMzER0dXegPwXIGYB1VqlQp/PHHH6hcuTLMzc1x6dIluLq6IiYmBm5ubkXqcEPx4sXx119/wcXFRWNfVKlSBSkpKVKXqHXx8fHYuHEjQkND8ffff6Nly5bo168f2rZtCz29wnf0+PLlyxptSUlJ6NatG1q3bo0hQ4ao2qtXr67N0iTj5+eHnTt3okSJElKXIon169dDCIG+ffsiODhYbeQhK9Q1aNBAwgq1J+twWmBgIMaNG6c2Wpe1Lzp37lyoDz9yZEZHKZVKZGZmarQ/ePBAbXSiKLCyskJcXBxcXFzU2i9cuIAyZcpIVJW0bG1t4ePjgxs3buDmzZv466+/0Lt3b1hZWSEkJASNGzeWusSPqmbNmlAoFGrniWTdX716NdasWaMaYs/u+6YwOnHihNQlSCogIAAA4OLiAm9vbxgaGkpckXSyzhVzdnZGly5dYGJiInFF2seRGR3VpUsXWFpaYs2aNTA3N8fly5dhY2OD9u3bo1y5cggJCZG6RK35+uuvERERgV9++QWVKlVCVFQUnjx5gl69eqFXr15F6qTPJ0+e4KeffkJISAju3r2LDh06oF+/fmjWrBnevHmDKVOmYPv27bh3757UpX5U+Xk/Tk5OBViJ7sjMzERoaCiOHTuG+Ph4KJVKtcePHz8uUWXap1Qqcfv27Wz3Q6NGjSSqSjppaWnZ7oty5cpJVFHBY5jRUY8ePYKfnx/09fVx69YteHl54datWyhVqhROnjwJW1tbqUvUmvT0dPTu3RtbtmyBEAIGBgbIzMxE9+7dERoaWmQuQ23bti0OHTqESpUqoX///ujVq5fGCY6PHj1C2bJlNX6IUeEzfPhwhIaGonXr1rC3t9c48TPr3JrC7syZM+jevTvu3buncYVXURqpA4Bbt26hb9++OH36tFp7URi1ZJjRYW/evMHmzZsRFRUFpVKJWrVq4csvv4SpqanUpUnizp07uHDhApRKJTw9PVGxYkWpS9Kqfv36oX///rmeByCEQGxsbKEfnfjpp5+watUqREdHIyIiAk5OTggODoaLiwvat28vdXlaUapUKWzYsAH+/v5SlyKpmjVrolKlSggMDMw21BX2q3je5ePjAwMDA0ycODHbfVGjRg2JKit4DDNEJCsrV67EtGnTMHr0aMyZMwdXrlyBq6srQkNDsX79+iJzLomDgwPCwsJQqVIlqUuRlJmZGS5duoQKFSpIXYrkzMzMEBkZWaQuzc/CE4B1yK+//prnvu3atSvASqQ3duzYPPddtGhRAVaiW169eoXw8HDExsYiLS1N7bGRI0dKVJV2LV26FD/88AM6dOiAb7/9VtXu5eWFr776SsLKtGvcuHFYvHgxli1blu3cIkVFvXr1cPv2bYYZAG5ubvjnn3+kLkMSDDM6pEOHDmr33796I6sNQKE+9gm8vVLpXZGRkcjMzETlypUBADdv3oS+vj5q164tRXmSuHDhAvz9/fH69Wu8evUK1tbW+Oeff1CsWDHY2toWmTATHR0NT09PjXZjY2O8evVKgoqkcerUKZw4cQIHDhxAtWrVNK7m2blzp0SVadeIESMwbtw4PH78GB4eHhr7oahcqg8A8+bNw9dff425c+dmuy8sLCwkqqzgMczokHdP2jx69CgmTJiAuXPnokGDBlAoFDh9+jSmTJmCuXPnSlildrx7qGDRokUwNzfH+vXrVXNqvHjxAn369MEnn3wiVYlaN2bMGLRt2xYrV66ElZUVzpw5A0NDQ/To0aNIzHybxcXFBRcvXtQ4L+jAgQNwc3OTqCrts7KyQseOHaUuQ3KdO3cGAPTt21fVlvWHYGE/6fV9zZo1AwA0bdpUrb0o7AueM6Oj3N3dsWrVKjRs2FCt/ffff8fAgQNx/fp1iSrTvjJlyuDw4cOoVq2aWvuVK1fQokULPHr0SKLKtMvKygp//vknKleuDCsrK0RERKBq1ar4888/ERAQgL///lvqErUiJCQEU6dOxcKFC9GvXz+sXbsWd+7cQVBQENauXYuuXbtKXSJp0Ycu2y/sJ8O/Kzw8PNfHfX19tVSJ9nFkRkfduXMn27PwLS0tERMTo/2CJJSYmIgnT55ohJn4+Hi1xQYLO0NDQ9VhxtKlSyM2NhZVq1aFpaUlYmNjJa5Oe/r06YOMjAx8/fXXeP36Nbp3744yZcpg8eLFDDJFUFEKKx9SmMPKh3BkRkc1atQIhoaG2LhxI+zt7QEAjx8/Rs+ePZGWlvbBBF6Y9OrVC+Hh4Vi4cCHq168P4O3cEuPHj0ejRo2wfv16iSvUjhYtWqB3797o3r07Bg8ejAsXLmDkyJH46aef8OLFC/z5559Sl6h1//zzD5RKZZGadymLi4tLrif+3r17V4vVSGfDhg25Pt6rVy8tVSK9kydP5vp4YZ5AkGFGR92+fRsdO3bEjRs3VLM2xsbGolKlSti9e3eROnP/9evX+Oqrr/Djjz8iPT0dAGBgYIB+/fphwYIFMDMzk7hC7Th//jySkpLg5+eHp0+fIiAgAKdOnUKFChUQEhJSqOeQeF9GRgbCwsJw584ddO/eHebm5nj06BEsLCzU1qUpzN5fZDU9PR0XLlzAwYMHMX78eEycOFGiyrTr/bWp0tPT8fr1axgZGaFYsWJ4/vy5RJVpX3brsr2/AGdhxTCjw4QQOHLkCP7++28IIeDm5oZmzZoV2cswX716hTt37kAIgQoVKhSZEEPq7t27h5YtWyI2Nhapqam4efMmXF1dMXr0aKSkpBSJFaNzs3z5cpw/f75ILXnyvlu3bmHIkCEYP348Pv30U6nL0ZqEhAS1+1kBd+rUqZgzZ47GicGFCcMMEclKhw4dYG5ujnXr1qFkyZKqVdTDw8PRv39/3Lp1S+oSJXX37l3UrFkTiYmJUpciqfPnz6NHjx5F5sT43Jw8eRJjxoxBZGSk1KUUGJ4ArKNmzpyZ6+PTpk3TUiXS8/Pzy3U0qjAvqOfp6ZnnkbioqKgCrkY3nDp1Cn/88QeMjIzU2p2cnPDw4UOJqtId27dv11izqyjS19cvMlc6foiNjQ1u3LghdRkFimFGR+3atUvtfnp6OqKjo2FgYIDy5csXqTBTs2ZNtfvp6em4ePEirly5goCAAGmK0pJ3J1JMSUnBihUr4Obmplqf6cyZM7h69SqGDh0qUYXap1Qqsz32/+DBA5ibm0tQkTTeD7pCCDx+/BhPnz7FihUrJKxMu96fOV0Igbi4OCxbtgw+Pj4SVSWNy5cvq93P2hfffvttoT+njoeZZCQxMRG9e/dGx44d0bNnT6nLkdyMGTOQnJyM7777TupStKJ///6wt7fHrFmz1NqnT5+O+/fv48cff5SoMu3q0qULLC0tsWbNGpibm+Py5cuwsbFB+/btUa5cuSJzrkhgYKDafT09PdjY2KBx48ZFam2e9096VSgUsLGxQZMmTbBw4ULV1aBFgZ6eXrYzx9evXx8//vhjof5cMMzIzJUrV9CmTZsiN9dMdm7fvo26desWmasVLC0tcf78eY3Vwm/dugUvLy+Nk/8Kq0ePHsHPzw/6+vqq937r1i2UKlUKJ0+eLJKXaRMBmhMIZgVcExMTiSrSHh5mkpmXL18WmV9aHxIREVEkvkmzmJqa4tSpUxph5tSpU0VqPzg4OODixYvYvHkzoqKioFQq0a9fP3z55ZcwNTWVujytyszMxO7du3H9+nUoFAq4ubmhXbt20NfXl7o0SWT9bV5Ur/gsyhMIMszoqCVLlqjdzzr2+dNPP6Fly5YSVSWNTp06qd3P2hfnz5/H1KlTJapK+0aPHo0hQ4YgMjJSbfLAH3/8sUidQwW8DXZ9+/ZVW4+nqLl9+zb8/f3x8OFDVK5cGUII3Lx5E46Ojti3bx/Kly8vdYlas2HDBixYsEB1JVulSpUwfvz4Ink4Pjw8HN99950q4FatWhXjx48v9OvY8TCTjnJxcVG7nzVc2KRJE0yaNKlInejYu3dvtb+03t0XLVq0kLAy7du2bRsWL16sWpuratWqGDVqFL744guJK9MeBwcHNG7cWHWrVKmS1CVJwt/fH0II/Pzzz6qrl549e4YePXpAT08P+/btk7hC7Vi0aBGmTp2K4cOHw8fHB0II/PHHH1i+fDlmz56NMWPGSF2i1mzcuBF9+vRBp06dVPvi9OnT2LVrF0JDQ9G9e3epSywwDDNEhczmzZvRrl27Qjup4ObNmxEeHo6wsDDcvHkTpUuXhq+vLxo3bgxfX19UrVpV6hK1wszMDGfOnIGHh4da+6VLl+Dj44Pk5GSJKtMuFxcXBAYGaixbsH79esyYMQPR0dESVaZ9VatWxcCBAzUC3KJFi/DDDz8U6gWKNec+Jp3Qt2/fbBdRfPXqVZEbWnd1dcWzZ8802l++fAlXV1cJKtJtgwYNwpMnT6Quo8B069YNq1atwt9//424uDh8//33MDAwwIgRI+Du7i51eVpjbGyc7c+I5ORkjTl4CrO4uDh4e3trtHt7eyMuLk6CiqRz9+5dtG3bVqO9Xbt2hT7UMczoqPXr1+PNmzca7W/evPngwmqFTUxMTLbziqSmpnKStGwUhcHW5ORkHDx4EMHBwVi8eDG2b98ODw8PjBw5UurStKZNmzYYOHAg/vzzTwghIITAmTNnMHjwYLRr107q8rSmQoUK2LZtm0b71q1bNU6WL+wcHR1x7NgxjfZjx47B0dFRgoq0hycA65jExETVD6akpCS1q1QyMzOxf//+InPp6buTYR06dAiWlpaq+5mZmTh27BicnZ0lqIykVK9ePVy+fBnu7u5o3LgxvvnmG3zyySewsrKSujStWrJkCQICAtCgQQMYGhoCeLsAZ7t27TQWoSzMAgMD0aVLF5w8eRI+Pj5QKBQ4deoUjh07lm3IKczGjRuHkSNH4uLFi/D29lbti9DQ0EL/meA5Mzoma9KjnCgUCgQGBmLy5MlarEoaWZNhZTcJlKGhIZydnbFw4UK0adNGivJ0lrm5uWq9osLI2toaCoUCzZo1U50EXFTOk8nO7du3cf36ddVitBUqVJC6JK2LjIzE999/r7Yfxo0bB09PT6lL07pdu3Zh4cKFahcJjB8/Hu3bt5e4soLFMKNjwsPDIYRAkyZNsGPHDrU1VoyMjODk5AQHBwcJK9Q+FxcXnDt3DqVKlZK6FFko7GEGeDtte1hYGMLDw/H7779DT08Pvr6+8PPzw+DBg6Uuj4i0jGFGR927dw/lypUrspM/0b9XFMLMuyIjI7Fs2TJs3Lgxx3WbCqPPPvsMXl5emDhxolr7ggULcPbsWfzyyy8SVaZd+/fvh76+Pj799FO19kOHDkGpVKJVq1YSVaZ9586dg1KpRL169dTa//zzT+jr68PLy0uiygoez5nRIVnnAejp6SEhIQF//fVXjn2rV6+uxcq0b8mSJRg4cCBMTEw0JhB8X2E+6fPd/RAbGwtHR8cPBlwnJyfVORSF0YULFxAWFoawsDD8/vvvSEpKQo0aNTBq1Cj4+flJXZ7WhIeHY/r06RrtLVu2LDLrlQHAxIkT8e2332q0CyEwceLEIhVmhg0bhq+//lojzDx8+BDz5s3Dn3/+KVFlBY8jMzpET08Pjx8/hq2tbY4LhgFvzyEp7H99uri44Pz58yhZsqTGBILvUigUuHv3rhYr0y4DAwM8evQItra20NfXR1xcXJE5ATwnBgYG8PT0VM0t06hRI1hYWEhdltaZmpri4sWLqFy5slr733//DU9Pz2yvhiyMTE1Ncf36dY2LAWJiYlCtWjW8evVKmsIkULx4cVy+fFljVDY6OhrVq1fP9lL+woIjMzokOjoaNjY2qv8XZe++/6K8LxwcHLBjxw7VbK8PHjxASkpKtn3LlSun5eqk8fz58zyFl8I+eaC7uzu2bt2qsZTFli1b4ObmJlFV2mdpaYm7d+9qhJnbt28X2q99ToyNjfHkyRONMBMXFwcDg0L+614Q6bjAwEDx6tUrjfbXr1+LwMBACSrSntWrVwsjIyOhp6eX402hUAg9PT2pS9U55ubm4s6dO1KXUWD27NkjDAwMRK9evURoaKgIDQ0VPXv2FAYGBmLXrl1Sl6c1AwYMEB4eHuL27duqtlu3bonq1auLfv36SViZ9nXp0kX4+vqKly9fqtpevHghfH19xeeffy5hZQWPh5l01LtzrLxLoVDAxMQEFSpUyPXwS2GS0+GVZ8+ewdbWttAfcktKSsK9e/dQvXp1HD16FCVLlsy2X40aNbRcmW4rCidC79u3D3PnzsXFixdhamqK6tWrY/r06fD19ZW6NK1JSEhAy5Ytcf78eZQtWxYA8ODBA3zyySfYuXNnkZp/6OHDh2jUqBGePXumuiz94sWLKF26NI4cOVKoJ85jmNFROZ0zk9WmUCjQsGFD7N69GyVKlJCoSu3Q09PDkydPVIfgshw/fhxdunTB06dPJapMu9avX4+uXbvC2NhY6lJkoSiEmbwo7IfbgLcn+x45cgSXLl1ShbpGjRpJXZYkXr16hZ9//lltX3Tr1q1QXxQAMMzorGPHjmHy5MmYM2cO6tatCwA4e/YspkyZgqlTp8LS0hKDBg1CvXr1sG7dOomrLRglSpSAQqFAQkICLCws1K7iyczMRHJyMgYPHozly5dLWKX2RUZG4vr161AoFKhatSpq1aoldUk6iWHmLQsLC1y8eLHI7wcPDw/s37+/UI9O5FXr1q2xdu1a2NvbS13KR1PIzwiSr1GjRmHNmjVqC6g1bdoUJiYmGDhwIK5evYrg4OBCvehkcHAwhBDo27cvAgMD1ZYzMDIygrOzMxo0aCBhhdoVHx+Prl27IiwsDFZWVhBCICEhAX5+ftiyZYvGyBURUDTW6sqLmJgYpKenS12GTjh58mShu9qNYUZH3blzJ9srNiwsLFSXIlesWBH//POPtkvTmoCAAABvL9P29vYu9MOkHzJixAgkJibi6tWrqun7r127hoCAAIwcORKbN2+WuEIiImlw1WwdVbt2bYwfP17tfJCnT5/i66+/Rp06dQAAt27dUp3wVpj5+vqqgsybN2+QmJiodisqDh48iJUrV6qtQ+Tm5obly5fjwIEDElZW8JYsWaK6JD02NjZPow2FffJAIvo/hhkdtW7dOkRHR6Ns2bKoUKECKlasiLJlyyImJgZr164FACQnJ2Pq1KkSV1rwXr9+jeHDh8PW1hbFixdHiRIl1G5FhVKpzPaXs6GhIZRKpQQVac/YsWNVwdXFxSVPJ31fuXKF50cQFRE8zKSjKleujOvXr+PQoUO4efMmhBCoUqUKmjdvrlpNukOHDtIWqSXjx4/HiRMnsGLFCvTq1QvLly/Hw4cPsXr16mynMS+smjRpglGjRmHz5s2qxUYfPnyIMWPGoGnTphJXV7A4eSAR5YZXM5HOK1euHDZs2IDGjRvDwsICUVFRqFChAn766Sds3rwZ+/fvl7pErbh//z7at2+vGnFQKBSIjY2Fh4cH9uzZU6gPOa5ZswYjRoxARkZGjn2ypiwo7PMO5Ze7uzsOHDhQ5EepeHXb/xXGfcEwo8OOHTuGY8eOIT4+XuMwwo8//ihRVdpXvHhxXL16FU5OTihbtix27tyJunXrIjo6Gh4eHkhOTpa6RK06cuQI/v77bwgh4ObmhmbNmkldklZw8kBNL1++xPbt23Hnzh2MHz8e1tbWiIqKQunSpVGmTBmpy9O6lJQUmJiYZPvYpk2b0L59+0I9305eBQUFYciQIYVqQkEeZtJRgYGBmDlzJry8vGBvb//BlZILM1dXV8TExMDJyQlubm7Ytm0b6tati71796pdrl1UNG/eHM2bN8/x8cI6n4a5uTnc3d0REhICHx+fIj954OXLl9GsWTNYWloiJiYGAwYMgLW1NXbt2oV79+5hw4YNUpeoFUqlEnPmzMGqVavw5MkT3Lx5E66urpg6dSqcnZ3Rr18/AED37t0lrlQ7fvrpJ6xatQrR0dGIiIiAk5MTgoOD4eLigvbt2wMAJk2aJHGVBUCriydQntnZ2YkNGzZIXYZOWLRokVi8eLEQQojjx48LU1NT1XpFwcHBElene4oXL16o1yTKcv78efHTTz+JjRs3isjISKnL0bqmTZuK8ePHCyHUv+Z//PGHcHJykrAy7QoMDBSurq5i48aNwtTUVLUftm7dKurXry9xddq1YsUKUapUKTF79my1fRESEiIaN24scXUFi2FGR1lbW6stnEb/d+/ePbFjxw5x8uRJ0adPH6nL0TmFPcw8efJE+Pn5CYVCIUqUKCGsrKyEQqEQTZo0EfHx8VKXpzUWFhaqnxHvfs1jYmKEsbGxlKVpVfny5cXRo0eFEOr74fr168LKykrK0rSuatWqqkVG390Xf/31lyhZsqSElRU8Xpqto/r3749NmzZJXYZOKleuHDp16gQLCwusX79e6nJIy96dPPD58+d48eIFrly5gsTERIwcOVLq8rTGxMQk23mWbty4UaRmg3748CEqVKig0a5UKovcjL/R0dGqBSbfZWxsjFevXklQkfbwnBkdlZKSgjVr1uDo0aOoXr26xvwiixYtkqgyImkdPHgQR48ezXbywBYtWkhYmXa1b98eM2fOxLZt2wBAdXXbxIkT0blzZ4mr055q1arh999/h5OTk1r7L7/8ku0v9sLMxcUFFy9e1NgXBw4cgJubm0RVaQfDjI66fPkyatasCeDt5F/vKsonAxMV5ckD3/Xdd9/B398ftra2ePPmDXx9ffH48WM0aNAAc+bMkbo8rZk+fTp69uyJhw8fQqlUYufOnbhx4wY2bNiA3377TerytGr8+PEYNmwYUlJSIITA2bNnsXnzZgQFBakmWy2seGk2ydalS5dQq1YtzivynsI4h8S72rdvj5cvX2pMHvjll1+iRIkS2LVrl8QVatfx48cRFRUFpVKJWrVqFZlL9d916NAhzJ07F5GRkar9MG3atCI1Upflhx9+wOzZs3H//n0AQJkyZTBjxgzVVV2FFcOMDDx48AAKhaLIzRvRqVOnXB9/+fIlwsPDi0SYSU9PR4sWLbB69WpUqlQp176FfT6Nojx5YE5SUlJgbGzMUVtS+eeff6BUKmFrayt1KVrBE4B1lFKpxMyZM2FpaQknJyeUK1cOVlZWmDVrVpEZSre0tMz15uTkhF69ekldplYYGhriypUrefpl1b1790IbZADA0dERUVFR2LdvH0aPHo2RI0di//79iIyMLFJBRqlUYtasWShTpgyKFy+O6OhoAMDUqVOxbt06iavTrpcvX2Lt2rX45ptv8Pz5cwBAVFQUHj58KHFl2peRkYGjR49ix44dMDU1BQA8evSo8E8uKum1VJSjiRMnChsbG7FixQpx6dIlcfHiRbF8+XJhY2MjvvnmG6nLIwmMHTtWTJgwQeoyZMPd3V3ExsZKXUaB4fwqb126dEnY2NiIChUqCAMDA9V+mDJliujZs6fE1WlXTEyMqFKliihWrJjQ19dX7YtRo0aJQYMGSVxdweIJwDpq/fr1WLt2Ldq1a6dqq1GjBsqUKYOhQ4cWqRP86K20tDSsXbsWR44cgZeXl8boC69wUxcTE1OoL83dsGED1qxZg6ZNm2Lw4MGq9urVq+Pvv/+WsDLtGjt2LHr37o358+fD3Nxc1d6qVasiM+tvllGjRsHLywuXLl1SW+6jY8eO6N+/v4SVFTyGGR31/PlzVKlSRaO9SpUqqmFUKlquXLmCWrVqAQBu3ryp9hjPlSh6OL/KW+fOncPq1as12suUKYPHjx9LUJF0Tp06hT/++ANGRkZq7U5OToX+kBvDjI6qUaMGli1bhiVLlqi1L1u2DNWrV5eoKpLSiRMnpC6BdAjnV3mLkwf+n1KpzPaCiAcPHqiNWhVGDDM6av78+WjdujWOHj2KBg0aQKFQ4PTp07h//z72798vdXlEJDHOr/IWJw/8v+bNmyM4OBhr1qwB8HZfJCcnY/r06fD395e4uoLFS7N12KNHj7B8+XL8/fffEELAzc0NAwcOxIwZM/Djjz9KXR5pmZ+fX66Hk44fP67FanRfYZ9vB+D8KgCQmJgIf39/XL16FUlJSXBwcFBNHrh///5CfWXf+x4+fIgmTZpAX18ft27dgpeXF27duoVSpUrh5MmThfoybYYZmeFEcUXXmDFj1O6np6fj4sWLuHLlCgICArB48WKJKtNNhTnMZGRkYM6cOejbty8cHR2lLkcncPLAt968eYMtW7aoBdwvv/xSdZl2YcUwIzMMM/S+GTNmIDk5Gd99953UpWhFdHQ0XFxcPtivsE8eWLx4cVy5cgXOzs5SlyKZjIwMmJiY4OLFi3B3d5e6HEmlp6ejcuXK+O233wr9OkzZ4aR5RDLXo0ePInXYsUKFCvDz88PGjRuRkpKSY7/CPnlgs2bNEBYWJnUZkjIwMICTkxP/uMPbiTVTU1OL7JWNDDNEMhcREQETExOpy9CaS5cuwdPTE+PGjYOdnR0GDRqEs2fPSl2W1rVq1QqTJk3CV199hc2bN+PXX39VuxUVU6ZMwaRJkzhlBYARI0Zg3rx5yMjIkLoUreNhJh3D9YgoJ+9/NoQQiIuLw/nz5zF16lRMnz5dosqkkZGRgb179yI0NBQHDhxAxYoV0a9fP/Ts2bNIXJKrp5fz36IKhaLI/Izw9PTE7du3kZ6eDicnJ43RuKioKIkq076OHTvi2LFjKF68ODw8PDT2xc6dOyWqrOAxzOiYPn365KlfSEhIAVdCuub9z4aenh5sbGzQpEmTInX1yvtSU1OxYsUKTJo0CWlpaTA0NESXLl0wb9482NvbS10eFbAZM2bkemilKIX8D/3+KMy/NxhmiEiWzp8/jx9//BFbtmyBmZkZAgIC0K9fPzx69AjTpk1DUlJSoT78tGHDBnTp0gXGxsZq7WlpadiyZUuRWYSVCGCYIZKdyMhIXL9+HQqFAm5ubkVqtlfg7RpUISEhuHHjBvz9/dG/f3/4+/urHXa5ffs2qlSpUqjPHdDX10dcXJzG3CHPnj2Dra1tkTnM5OrqinPnzqmtRQS8PSRfq1Yt3L17V6LKtK9JkybYuXMnrKys1NoTExPRoUOHQj0XFWcAJpKJ+Ph4dO3aFWFhYbCysoIQAgkJCfDz88OWLVuKxHkiALBy5Ur07dsXffr0gZ2dXbZ9ypUrh3Xr1mm5Mu0SQmR7eOXBgwewtLSUoCJpxMTEZBvcUlNT8eDBAwkqkk5YWBjS0tI02lNSUvD7779LUJH2MMwQycSIESOQmJiIq1evomrVqgCAa9euISAgACNHjsTmzZslrlA7bt269cE+RkZGCAgI0EI12ufp6QmFQgGFQoGmTZvCwOD/P8YzMzMRHR2Nli1bSlihdrx7xdahQ4fUAlxmZiaOHTuWp/mICoPLly+r/n/t2jW1BTYzMzNx8OBBlClTRorStIaHmYhkwtLSEkePHkWdOnXU2s+ePYsWLVrg5cuX0hQmkdevXyM2NlbjL9HCvhBrYGCg6t9x48ahePHiqseMjIzg7OyMzp07a6ycXNhkHVZUKBR4/9eYoaEhnJ2dsXDhQrRp00aK8rRKT09PNUqX3a90U1NTLF26FH379tV2aVrDkRkimVAqlTA0NNRoNzQ0hFKplKAiaTx9+hS9e/fGwYMHs328sJ8rknV1jrOzM7p06fLBOYY2b96Mdu3aFboJBLM+8y4uLjh37hxKlSolcUXSiY6OhhACrq6uOHv2rNohZyMjI9ja2kJfX1/CCgseJ80jkokmTZpg1KhRePTokart4cOHGDNmDJo2bSphZdo1evRovHz5EmfOnIGpqSkOHjyI9evXo2LFikVqsriAgIA8TZY4aNAgPHnyRAsVSSM6OjpPQcbDwwP379/XQkXa5+TkBGdnZyiVSnh5ecHJyUl1s7e31wgyrVu3RlxcnETVFgyOzBDJxLJly9C+fXs4OzvD0dERCoUC9+7dQ/Xq1fHTTz9JXZ7WHD9+HHv27EGdOnWgp6cHJycnNG/eHBYWFggKCkLr1q2lLlGn8EyCt2JiYpCeni51GTrh5MmTePPmjdRlfFQMM0Qy4ejoiKioKBw9ehTXr1+HEAJubm5FbnXgV69eqS5Htra2xtOnT1GpUiV4eHgUqdleiej/GGaIZOTYsWM4fvw44uPjoVQqcfHiRWzatAkAisxik5UrV8aNGzfg7OyMmjVrYvXq1XB2dsaqVas44y9REcUwQyQTgYGBmDlzJry8vGBvb19kV8cdPXq06nj/9OnT8emnn2Ljxo0wMjLC+vXrJa6OiKTAMEMkE6tWrUJoaCh69uwpdSmS+vLLL1X/9/T0RExMDP7++2+UK1euSF/RQlSUMcwQyURaWhq8vb2lLkMSY8eOzXPfRYsWFWAluiEzMxOnTp1C9erVUaJEiVz7Ojk5ZXtJP1FhwjBDJBP9+/fHpk2bMHXqVKlL0boLFy6o3Y+MjERmZiYqV64MALh58yb09fVRu3ZtKcrTOn19fXz66ae4fv36B8PMlStXtFSVblu9ejVKly4tdRkFJj09HQMHDsTUqVPh6uqaa99vvvkG1tbWWqpMOzgDMJEOe3dEQqlUYv369ahevTqqV6+u8dd2URiRAN6+z7CwMKxfv171i/zFixfo06cPPvnkE4wbN07iCrWjTp06+Pbbb4vUHENZlixZkue+I0eOLMBKdIuVlRWioqI+GGYKI4YZIh3m5+eXp34KhaJQr4j7rjJlyuDw4cOoVq2aWvuVK1fQokULtUkFC7PDhw9jwoQJmDVrFmrXrq0xw6+FhYVElRW899dcevr0KV6/fq1aLfrly5coVqwYbG1ti9Sq2X369IGHh0e+DssWFjzMRKTDTpw4IXUJOicxMRFPnjzRCDPx8fFISkqSqCrty1pMsl27dmpXtmWtpl2Yl3WIjo5W/X/Tpk1YsWIF1q1bpzrseOPGDQwYMACDBg2SqkRJVKhQAbNmzcLp06ezDbiFeZSKIzNEJCu9evVCeHg4Fi5ciPr16wMAzpw5g/Hjx6NRo0ZF5vLs8PDwXB/39fXVUiXSKl++PLZv3w5PT0+19sjISHz22Wdqwaewy22VcIVCUahHqTgyQ0SysmrVKnz11Vfo0aOHanp6AwMD9OvXDwsWLJC4Ou0pKmHlQ+Li4rJdpiAzM7NQr0mVnaIU3N7HkRkikqVXr17hzp07EEKgQoUKhW5V6OxcvnwZ7u7u0NPTw+XLl3PtW716dS1VJa22bdsiNjYW69atQ+3ataFQKHD+/HkMGDAAjo6ORWrx0Xdl/WovKpNrMswQEcmEnp4eHj9+DFtbW+jp6UGhUGS7kGRhP2fmXU+fPkVAQAAOHjyousIvIyMDn376KUJDQ1XreBUVGzZswIIFC3Dr1i0AQKVKlTB+/PhCP9kmDzMREclEdHQ0bGxsVP8nwMbGBvv378fNmzdx/fp1AEDVqlVRqVIliSvTvkWLFmHq1KkYPnw4fHx8IITAH3/8gcGDB+Off/7BmDFjpC6xwHBkhoiICoWidmjlfS4uLggMDESvXr3U2tevX48ZM2YU6gDMkRkiIhm7du0aYmNjkZaWptberl07iSrSvqJ6aOV9cXFx2S554u3trVqctbBimCEikqG7d++iY8eO+Ouvv9TOnckalSgq58wU5UMr76tQoQK2bduGb775Rq1969atqFixokRVaQcPMxERyVDbtm2hr6+PH374Aa6urjh79iyePXuGcePG4bvvvsMnn3widYlaUZQPrbxvx44d6NKlC5o1awYfHx8oFAqcOnUKx44dw7Zt29CxY0epSywwDDNERDJUqlQpHD9+HNWrV4elpSXOnj2LypUr4/jx4xg3bpzG4pyFlYmJCa5cuYIKFSqotd+6dQseHh5ISUmRqDJpREZG4vvvv8f169chhICbmxvGjRunMalgYcPDTEREMpSZmYnixYsDeBtsHj16hMqVK8PJyQk3btyQuDrtKcqHVrJTu3ZtbNy4UeoytI5hhohIhtzd3XH58mW4urqiXr16mD9/PoyMjLBmzZoitWpyYGAgunTpgpMnT2Z7aKWoyczMxO7du3H9+nUoFAq4ubmhXbt20NfXl7q0AsXDTEREMvHuDMCHDh3C69ev0bFjR9y9exdt2rTB33//jZIlS2Lr1q1o0qSJ1OVqTVE9tPK+27dvo3Xr1njw4AEqV64MIQRu3rwJR0dH7Nu3D+XLl5e6xALDMENEJBP6+vqIi4uDra0tXF1dce7cOZQsWVL1+PPnz1GiRIkiO89KUefv7w8hBH7++WdYW1sDAJ49e4YePXpAT08P+/btk7jCgsMwQ0QkEyVLlsT+/ftRr1496Onp4cmTJ6oZgYuyonpo5X1mZmY4c+YMPDw81NovXboEHx8fJCcnS1RZweM5M0REMtG5c2f4+vrC3t4eCoUCXl5eOf7Cvnv3rpark0ZRPrTyPmNjYyQlJWm0Jycnw8jISIKKtIcjM0REMnLw4EHcvn0bI0eOxMyZM2Fubp5tv1GjRmm5MmkU5UMr7+vVqxeioqKwbt061K1bFwDw559/YsCAAahduzZCQ0OlLbAAMcwQEclQnz59sGTJkhzDTFFRlA+tvO/ly5cICAjA3r17VSuIp6eno3379ggNDYWlpaXEFRYcHmYiIpKhkJAQqUvQCUX50Mr7rKyssGfPHty+fRvXrl0DALi5uWlMKFgY6UldABER0b/Vpk0bDBw4EH/++SeEEBBC4MyZMxg8eHCRWmwzy7p169ChQwd8/vnn+Pzzz9GhQwesXbtW6rIKHEdmiIhItpYsWYKAgAA0aNBA49BKcHCwtMVp2dSpU/H9999jxIgRaNCgAQAgIiICY8aMQUxMDGbPni1xhQWH58wQEZHs3b59W23SvKJwaOV9pUqVwtKlS9GtWze19s2bN2PEiBH4559/JKqs4HFkhoiIZGvs2LEabWFhYVAoFDAxMUGFChXQvn171ZVOhVlmZia8vLw02mvXro2MjAwJKtIejswQEZFs+fn5ISoqCpmZmap5Zm7dugV9fX1UqVIFN27cUK3X5ObmJnW5BWrEiBEwNDTEokWL1Nq/+uorvHnzBsuXL5eosoLHMENERLIVHByM33//HSEhIbCwsAAAJCYmol+/fmjYsCEGDBiA7t27482bNzh06JDE1RasESNGYMOGDXB0dET9+vUBAGfOnMH9+/fRq1cv1TlFADQCj9wxzBARkWyVKVMGR44c0Rh1uXr1Klq0aIGHDx8iKioKLVq0KNTnjABvR6nyQqFQ4Pjx4wVcjXbxnBkiIpKthIQExMfHa4SZp0+fIjExEcDb+VfS0tKkKE+rTpw4IXUJkuE8M0REJFvt27dH3759sWvXLjx48AAPHz7Erl270K9fP3To0AEAcPbsWVSqVEnaQqlA8TATERHJVnJyMsaMGYMNGzaortgxMDBAQEAAvv/+e5iZmeHixYsAgJo1a0pXKBUohhkiIpK95ORk3L17F0IIlC9fHsWLF5e6JNIihhkiIiKSNZ4zQ0RERLLGMENERESyxjBDREREssYwQ0RERLLGMENEH1Xv3r2hUCg0brdv3/7P2w4NDYWVldV/L5KIChXOAExEH13Lli0REhKi1mZjYyNRNdlLT09XW6uGiOSLIzNE9NEZGxvDzs5O7aavr4+9e/eidu3aMDExgaurKwIDA1UTnQFvF7/z8PCAmZkZHB0dMXToUCQnJwMAwsLC0KdPHyQkJKhGe2bMmAHg7Vozu3fvVqvBysoKoaGhAICYmBgoFAps27YNjRs3homJCTZu3AgACAkJQdWqVWFiYoIqVapgxYoVqm2kpaVh+PDhsLe3h4mJCZydnREUFFRwO46I/hWOzBCRVhw6dAg9evTAkiVL8Mknn+DOnTsYOHAgAGD69OkAAD09PSxZsgTOzs6Ijo7G0KFD8fXXX2PFihXw9vZGcHAwpk2bhhs3bgBAvidGmzBhAhYuXIiQkBAYGxvjhx9+wPTp07Fs2TJ4enriwoULGDBgAMzMzBAQEIAlS5bg119/xbZt21CuXDncv38f9+/f/7g7hoj+O0FE9BEFBAQIfX19YWZmprp99tln4pNPPhFz585V6/vTTz8Je3v7HLe1bds2UbJkSdX9kJAQYWlpqdEPgNi1a5dam6WlpQgJCRFCCBEdHS0AiODgYLU+jo6OYtOmTWpts2bNEg0aNBBCCDFixAjRpEkToVQqP/S2iUhCHJkhoo/Oz88PK1euVN03MzNDhQoVcO7cOcyZM0fVnpmZiZSUFLx+/RrFihXDiRMnMHfuXFy7dg2JiYnIyMhASkoKXr16BTMzs/9cl5eXl+r/T58+xf3799GvXz8MGDBA1Z6RkQFLS0sAb09mbt68OSpXroyWLVuiTZs2aNGixX+ug4g+LoYZIvrossLLu5RKJQIDA9GpUyeN/iYmJrh37x78/f0xePBgzJo1C9bW1jh16hT69euH9PT0XF9PoVBAvLcyS3bPeTcQKZVKAMAPP/yAevXqqfXT19cHANSqVQvR0dE4cOAAjh49ii+++ALNmjXD9u3bc62HiLSLYYaItKJWrVq4ceOGRsjJcv78eWRkZGDhwoXQ03t7bcK2bdvU+hgZGSEzM1PjuTY2NoiLi1Pdv3XrFl6/fp1rPaVLl0aZMmVw9+5dfPnllzn2s7CwQJcuXdClSxd89tlnaNmyJZ4/fw5ra+tct09E2sMwQ0RaMW3aNLRp0waOjo74/PPPoaenh8uXL+Ovv/7C7NmzUb58eWRkZGDp0qVo27Yt/vjjD6xatUptG87OzkhOTsaxY8dQo0YNFCtWDMWKFUOTJk2wbNky1K9fH0qlEhMmTMjTZdczZszAyJEjYWFhgVatWiE1NRXnz5/HixcvMHbsWHz//fewt7dHzZo1oaenh19++QV2dnac64ZIx/DSbCLSik8//RS//fYbjhw5gjp16qB+/fpYtGgRnJycAAA1a9bEokWLMG/ePLi7u+Pnn3/WuAza29sbgwcPRpcuXWBjY4P58+cDABYuXAhHR0c0atQI3bt3x1dffYVixYp9sKb+/ftj7dq1CA0NhYeHB3x9fREaGgoXFxcAb6+WmjdvHry8vFCnTh3ExMRg//79qpEjItINCvH+gWYiIiIiGeGfF0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGsMM0RERCRrDDNEREQkawwzREREJGv/A+hdslx7vEsJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the feature names from the dataset\n",
    "feature_names = list(X_test.columns)\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "    print('Feature: %s, Score: %.5f' % (feature_names[i], v))\n",
    "\n",
    "# plot feature importance\n",
    "pyplot.title(\"Feature Importance using permutation to measure\")\n",
    "plt.bar(feature_names, importance)\n",
    "plt.xticks(rotation=90)\n",
    "pyplot.xlabel(\"Features\")\n",
    "pyplot.ylabel(\"F Score\")\n",
    "\n",
    "# Display the score rounded to two decimal places on top of each bar\n",
    "for i in range(len(importance)):\n",
    "    plt.text(i, importance[i], \"{:.2f}\".format(importance[i]), ha='center', va='bottom')\n",
    "    \n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lime\n",
    "import lime.lime_tabular\n",
    "from lime import submodular_pick\n",
    "from lime.lime_tabular import LimeTabularExplainer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kelvi\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Get feature names from the input DataFrame\n",
    "feature_names = X_train.columns.tolist()\n",
    "\n",
    "# Create the LIME explainer object\n",
    "explainer = LimeTabularExplainer(X_train.values, feature_names=feature_names, class_names=['target'], mode='regression')\n",
    "\n",
    "# Generate explanations for a single prediction\n",
    "exp = explainer.explain_instance(X_test.values[0], knn.predict, num_features=len(feature_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_fn = lambda x: knn.predict(x.reshape(1,-1)).astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the explanation \n",
    "%matplotlib inline\n",
    "fig = exp.as_pyplot_figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'exp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[1], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mexp\u001B[49m\u001B[38;5;241m.\u001B[39mshow_in_notebook(show_all\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'exp' is not defined"
     ]
    }
   ],
   "source": [
    "exp.show_in_notebook(show_all=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('good_tree_count <= 45.00', -3.429858684164526),\n",
       " ('12.00 < hour_of_day <= 17.00', 2.051814230762995),\n",
       " ('fair_tree_count <= 7.00', -1.9886936470437124),\n",
       " ('poor_tree_count <= 1.00', -0.8124282280605661),\n",
       " ('1.00 < day_of_week <= 3.00', -0.1356113631573724),\n",
       " ('Longitude <= -73.99', -0.0672759357488189),\n",
       " ('Latitude <= 40.62', 0.04793590965560265)]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.as_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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