notebooks/modeling/DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install gitpath below if not already available on your environment.\n",
    "#! pip install git+https://github.com/maxnoe/python-gitpath\n",
    "import gitpath\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.preprocessing import PowerTransformer, RobustScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting the file path for the data file.\n",
    "file_path = str(gitpath.root()) + '/data/no_nans_data.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(file_path)\n",
    "df['Contract Years Left'] = df['Contract Years Left'].apply(lambda x:int(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Separating Data by Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "attack = df[df['Position']=='attack']\n",
    "midfield = df[df['Position']=='midfield']\n",
    "defence = df[df['Position']=='Defender']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Trees Regressor with all Features and Default Hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will first build a modeling workflow on the Attackers dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = attack.drop(['Player','Value','Club','Position','Nation','League'],axis=1)\n",
    "y = attack['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initiating DecisionTreeRegressor to calculate feature importance\n",
    "DtReg = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "DtReg.fit(X_train_scaled,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_train = cross_val_score(DtReg, X_train_scaled, y_train, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "cross_val_test = cross_val_score(DtReg, X_test_scaled, y_test, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Results\n",
    "cross_val_train_mean = round(-(cross_val_train.mean()),2)\n",
    "cross_val_train_max = round(-(cross_val_train.min()),2)\n",
    "cross_val_train_min = round(-(cross_val_train.max()),2)\n",
    "\n",
    "#Test Results\n",
    "cross_val_test_mean = round(-(cross_val_test.mean()),2)\n",
    "cross_val_test_max = round(-(cross_val_test.min()),2)\n",
    "cross_val_test_min = round(-(cross_val_test.max()),2)\n",
    "\n",
    "#Creating a Dataframe to display Validation Results\n",
    "results = pd.DataFrame(columns=['Train/Test','Avg RMSE','Max RMSE','Min RMSE'])\n",
    "results.loc[0] = ['Train',cross_val_train_mean,cross_val_train_max,cross_val_train_min]\n",
    "results.loc[1] = ['Test', cross_val_test_mean, cross_val_test_max, cross_val_test_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "x_ticks = list(range(0,max([results.iloc[0][2],(round(results.iloc[1][2])+1000000)]),2500000))\n",
    "y_ticks = [1,2,3,4]\n",
    "y_labels = ['Train Max RMSE','Train Min RMSE','Test Max RMSE','Test Min RMSE']\n",
    "\n",
    "train_max_pos = 1\n",
    "train_max = results.iloc[0][2]\n",
    "ax.barh(train_max_pos,train_max,color='navy')\n",
    "\n",
    "train_min_pos = 2\n",
    "train_min = results.iloc[0][3]\n",
    "ax.barh(train_min_pos,train_min,color='navy')\n",
    "\n",
    "test_max_pos = 3\n",
    "test_max = results.iloc[1][2]\n",
    "ax.barh(test_max_pos,test_max,color='orange')\n",
    "\n",
    "test_min_pos = 4\n",
    "test_min = results.iloc[1][3]\n",
    "ax.barh(test_min_pos,test_min,color='orange')\n",
    "\n",
    "ax.axvline(x = results.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
    "ax.axvline(x = results.iloc[1][1],label = 'Average Test RMSE',color='red')\n",
    "\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels([f'£ {a}' for a in x_ticks])\n",
    "ax.set_xlabel('Error Value in GBP (£)',fontsize=20)\n",
    "ax.set_ylabel('Validation Results',fontsize=20)\n",
    "ax.set_title('Comparing Train and Test RMSEs for Default Hyperparameter Decision Tree Model',fontsize=30)\n",
    "ax.legend(fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning. (Finding  Hyperparameters that may improve our model performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since a GridSearch would take a long time to run with all our features, we will perform a GridSearch for a model that would only take the top 10 most important features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print the features importance.\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "feat_importances = pd.Series(DtReg.feature_importances_, index=X_train.columns)\n",
    "\n",
    "feat_importances.nlargest(10).plot(kind='barh')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a list of top features\n",
    "top_features = list(feat_importances.nlargest(10).keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a newer model. This time only with important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = attack.drop(['Player','Value','Club','Position','Nation','League'],axis=1)[top_features]\n",
    "y = attack['Value']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper parameters range intialization for tuning \n",
    "\n",
    "parameters={\"splitter\":[\"best\",\"random\"],\n",
    "            \"max_depth\" : [1,3,5,7,9,11,12],\n",
    "           \"min_samples_leaf\":[1,2,3,4,5,6,7,8,9,10],\n",
    "           \"min_weight_fraction_leaf\":[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],\n",
    "           \"max_features\":[\"auto\",\"log2\",\"sqrt\",None],\n",
    "           \"max_leaf_nodes\":[None,10,20,30,40,50,60,70,80,90] }\n",
    "\n",
    "tuning_model=GridSearchCV(DtReg,param_grid=parameters,scoring='neg_root_mean_squared_error',cv=5,verbose=5)\n",
    "\n",
    "#function for calculating how much time taken for hyperparameter tuning\n",
    "\n",
    "def timer(start_time=None):\n",
    "    if not start_time:\n",
    "        start_time=datetime.now()\n",
    "        return start_time\n",
    "    elif start_time:\n",
    "        thour,temp_sec=divmod((datetime.now()-start_time).total_seconds(),3600)\n",
    "        tmin,tsec=divmod(temp_sec,60)\n",
    "        #print(thour,\":\",tmin,':',round(tsec,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting data to find best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from datetime import datetime\n",
    "\n",
    "start_time=timer(None)\n",
    "\n",
    "tuning_model.fit(X_train_scaled,y_train)\n",
    "\n",
    "timer(start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 11,\n",
       " 'max_features': 'log2',\n",
       " 'max_leaf_nodes': 40,\n",
       " 'min_samples_leaf': 9,\n",
       " 'min_weight_fraction_leaf': 0.1,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best hyperparameters \n",
    "tuning_model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using a Decision Tree Regressor with important features and better Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_values = list((tuning_model.best_params_).values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Traning model with all features using best Decision Tree Hyperparameters\n",
    "\n",
    "DtReg_Grid = DecisionTreeRegressor(max_depth=param_values[0],\n",
    "                                 max_features=param_values[1],\n",
    "                                 max_leaf_nodes=param_values[2],\n",
    "                                 min_samples_leaf=param_values[3],\n",
    "                                 min_weight_fraction_leaf=param_values[4],\n",
    "                                 splitter=param_values[5])\n",
    "\n",
    "DtReg_Grid.fit(X_train_scaled,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_train = cross_val_score(DtReg_Grid, X_train_scaled, y_train, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "cross_val_test = cross_val_score(DtReg_Grid, X_test_scaled, y_test, scoring=\"neg_root_mean_squared_error\",cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Results\n",
    "cross_val_train_mean = round(-(cross_val_train.mean()),2)\n",
    "cross_val_train_max = round(-(cross_val_train.min()),2)\n",
    "cross_val_train_min = round(-(cross_val_train.max()),2)\n",
    "\n",
    "#Test Results\n",
    "cross_val_test_mean = round(-(cross_val_test.mean()),2)\n",
    "cross_val_test_max = round(-(cross_val_test.min()),2)\n",
    "cross_val_test_min = round(-(cross_val_test.max()),2)\n",
    "\n",
    "#Creating a Dataframe to display Validation Results\n",
    "results_att = pd.DataFrame(columns=['Train/Test','Avg RMSE','Max RMSE','Min RMSE'])\n",
    "results_att.loc[0] = ['Train',cross_val_train_mean,cross_val_train_max,cross_val_train_min]\n",
    "results_att.loc[1] = ['Test', cross_val_test_mean, cross_val_test_max, cross_val_test_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "x_ticks = list(range(0,max([results_att.iloc[0][2],(round(results_att.iloc[1][2])+1000000)]),2500000))\n",
    "y_ticks = [1,2,3,4]\n",
    "y_labels = ['Train Max RMSE','Train Min RMSE','Test Max RMSE','Test Min RMSE']\n",
    "\n",
    "train_max_pos = 1\n",
    "train_max = results_att.iloc[0][2]\n",
    "ax.barh(train_max_pos,train_max,color='navy')\n",
    "\n",
    "train_min_pos = 2\n",
    "train_min = results_att.iloc[0][3]\n",
    "ax.barh(train_min_pos,train_min,color='navy')\n",
    "\n",
    "test_max_pos = 3\n",
    "test_max = results_att.iloc[1][2]\n",
    "ax.barh(test_max_pos,test_max,color='orange')\n",
    "\n",
    "test_min_pos = 4\n",
    "test_min = results_att.iloc[1][3]\n",
    "ax.barh(test_min_pos,test_min,color='orange')\n",
    "\n",
    "ax.axvline(x = results_att.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
    "ax.axvline(x = results_att.iloc[1][1],label = 'Average Test RMSE',color='red')\n",
    "\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels([f'£ {a}' for a in x_ticks])\n",
    "ax.set_xlabel('Error Value in GBP (£)',fontsize=20)\n",
    "ax.set_ylabel('Validation Results',fontsize=20)\n",
    "ax.set_title('DecisionTreeRegression Results-Attackers',fontsize=30)\n",
    "ax.legend(fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the above workflow for Midfielders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "#Splitting the data\n",
    "\n",
    "X = midfield.drop(['Player','Value','Club','Position','Nation','League'],axis=1)\n",
    "y = midfield['Value']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=0)\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Pre-processing\n",
    "\n",
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Fitting data into first DecisionTree to get important features\n",
    "\n",
    "#Initiating DecisionTreeRegressor\n",
    "DtReg = DecisionTreeRegressor()\n",
    "\n",
    "DtReg.fit(X_train_scaled,y_train);\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Extracting Most Important Features\n",
    "\n",
    "feat_importances = pd.Series(DtReg.feature_importances_, index=X_train.columns)\n",
    "\n",
    "#Creating a list of top features\n",
    "top_features = list(feat_importances.nlargest(10).keys())\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Repeating Workflow. This time only with most important features.\n",
    "\n",
    "#This is because of computational constraints from GridSearching over 500 features.\n",
    "\n",
    "X = midfield.drop(['Player','Value','Club','Position','Nation','League'],axis=1)[top_features]\n",
    "y = midfield['Value']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=0)\n",
    "\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Pre-processing\n",
    "\n",
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#GridSearching improved hyperparameters\n",
    "\n",
    "# Hyper parameters range intialization for tuning \n",
    "\n",
    "parameters={\"splitter\":[\"best\",\"random\"],\n",
    "            \"max_depth\" : [1,3,5,7,9,11,12],\n",
    "           \"min_samples_leaf\":[1,2,3,4,5,6,7,8,9,10],\n",
    "           \"min_weight_fraction_leaf\":[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],\n",
    "           \"max_features\":[\"auto\",\"log2\",\"sqrt\",None],\n",
    "           \"max_leaf_nodes\":[None,10,20,30,40,50,60,70,80,90] }\n",
    "\n",
    "tuning_model=GridSearchCV(DtReg,param_grid=parameters,scoring='neg_root_mean_squared_error',cv=5,verbose=5)\n",
    "\n",
    "tuning_model.fit(X_train_scaled,y_train)\n",
    "\n",
    "\n",
    "#Extracting the best Parameters\n",
    "param_values = list((tuning_model.best_params_).values())\n",
    "\n",
    "############################################################################\n",
    "        \n",
    "# Traning model with all features using better Decision Tree Hyperparameters\n",
    "\n",
    "#Initiating a new DecisionTreeRegressor\n",
    "DtReg_Grid = DecisionTreeRegressor(max_depth=param_values[0],\n",
    "                                 max_features=param_values[1],\n",
    "                                 max_leaf_nodes=param_values[2],\n",
    "                                 min_samples_leaf=param_values[3],\n",
    "                                 min_weight_fraction_leaf=param_values[4],\n",
    "                                 splitter=param_values[5])\n",
    "\n",
    "DtReg_Grid.fit(X_train_scaled,y_train);\n",
    "\n",
    "cross_val_train = cross_val_score(DtReg_Grid, X_train_scaled, y_train, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "cross_val_test = cross_val_score(DtReg_Grid, X_test_scaled, y_test, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "\n",
    "#Train Results\n",
    "cross_val_train_mean = round(-(cross_val_train.mean()),2)\n",
    "cross_val_train_max = round(-(cross_val_train.min()),2)\n",
    "cross_val_train_min = round(-(cross_val_train.max()),2)\n",
    "\n",
    "#Test Results\n",
    "cross_val_test_mean = round(-(cross_val_test.mean()),2)\n",
    "cross_val_test_max = round(-(cross_val_test.min()),2)\n",
    "cross_val_test_min = round(-(cross_val_test.max()),2)\n",
    "\n",
    "#Creating a Dataframe to display Validation Results\n",
    "results_mid = pd.DataFrame(columns=['Train/Test','Avg RMSE','Max RMSE','Min RMSE'])\n",
    "results_mid.loc[0] = ['Train',cross_val_train_mean,cross_val_train_max,cross_val_train_min]\n",
    "results_mid.loc[1] = ['Test', cross_val_test_mean, cross_val_test_max, cross_val_test_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNcAAALfCAYAAABCaCRzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAB9m0lEQVR4nOzdd5glVZ038O8PGIJkJEhSUFiMiIJKUByiigLioiAqYMR9lVVcXwxrwIS6KriugVdXBUSHpGBg3RURMCyoIIiigqijokgQQRBEGM77R92ZudPT3dNd0zPTg5/P89ynb1WdOnVu3dB9v31OnWqtBQAAAACYvBWWdQMAAAAAYHklXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BsASVVVtcLtgCdU/e1D/7CVRPzAxVXXM0Pt95rJuD0uX53/RpvL3VVWdOHS+txin3GpV9fqq+m5V3VJV9w7tt85k6poKU3msqjp8qK7Dp6aFAP2stKwbAPD3qqraGJv+luTPSW5N8uskP0jy3STntNbuXErNYwkY5zmfrLe11o6ZorqWqsGXqV+NsbkluS3JDUkuS/KFJF9orf1t6bQOxraI9+/tSW5KckWSryT5XGvtL0ulYcuJQZDz6sHi5a21s5dZY0aoqhOTHDa06o9JNpnIZ09VbZfu82rYC1trJ05V+/qqqvsl+WaS7Zd1WwDu64RrANPPyknWH9wekmT3wfpbquqkJG9trd26rBoHS1AlWWtw2yrJs5P8uKoObK1dtUxbBuNbY3DbIsl+Sd5UVQe31i5apq2aXtZJ8tbB/ZOSnL3MWrJo90+yb5LPT6DsC5dwWxbHyzM/WPtxko8n+X2SOYN1AmCAKSJcA5geDhi6X0nWTrJuku2S7JruC9s6SV6V5B+r6rmttW8v3Sb201qrJVz/Fkuy/il2wDjbHpnkHYP7VyZ50zhlfzZlLVq2bkzysqHlSve6f0KS5yVZPd15+VpVPaq19uel30QmatCb8phl3IylZeR7ea0kj0nygnTBzAOT/FdVbdda+/XSbhyL5Z5035EOzyLCtaqakeSQEfuNaRn8vtpn7qGTPKW19vvRCrXWDk/3eAHoSbgGMA2MNzymqirJ05J8MMnWSTZLck5V7dxau3KpNJApsYjn+ZahxZum05CpJeiOMR7np6rq/Um+lWSjdEHFkUnetRTbBmMa43V7clW9O8mFSR6a7h8ib0ry0qXXMqbAfyd5RpKnVtVGrbXrxyn7jHS9zJPkq+l6u00nmw9+Xj9WsAbA1DChAcA01zr/lWSHJHN7q62V5Iyq8jnOfVJr7eeZ35Mv6YbawbTWWrshyWuHVnndLn9OTnJvuk4Iz19E2cMHP69O8r9LsE19rTL4edcybQXA3wFfygCWE4Mhcc9Jcstg1cOSHDTePlW1clW9uKq+VFW/raq/DmYLu6KqPjCZmbqq6h+q6t+q6vtVdWNV3V1Vt1bVD6rqI1W1x6CX3cj9FjlbaFVtUlVvr6qLqurmQd1/qqqfV9WFVfXWqnrcGPtOePa1qtq7qj5TVb+sqjuq6raq+llVnVBV417wuaq2GHosJw7WrT+YIe9Hg7puG5yPNwwuJD2lqmrmUBuOGazbpqo+WFU/rao/jzVr2qCt/1pV36qqP1TV3wbP47eq6uiqWnMS7XjqYMa3nw8e8x1V9YvBuidO3SPOt4bu/8PSaFdVrVBVL6yq86vqpkEd1wxeIw8flBl3VsRl/TxV1WMH7f3R4Fh3V9UNVfWTqvpyVR1ZVVuOse+KVfWCQbm5nxl3Du7/oKo+XlXPGu31vajzMqLs5lX1nkGdN1fVXVX1u8FxD6+qFRex/wVzjzW07qCqOndw3u6qql9X1aer6qGLOmdTbPh1u2FVrT1e4araqao+Nnh+bhmc899U1WlV9fRFHWwxn7PJfH72mumyBp+dWXAik8OGXivDty1G7LtqVf2fwfN63eB5vX3Qlu9V1b9X1dOqG545VX6b5Btz2znO49ogXa/ypLuG3CJN9BxW1UpV9cqq+t/qfhfeXt3vqg9U1YMWse+8GTSTzC37oFHO9eFD+0x4Bs+qWqOqXj14Tn4/eE5uru5vg7cPzsuUqKpnDF7Xfxi8rmdX1WeraqcedS3W30OjnaPB++pLg/fr34Y/j4b227+qTq/5f3f8dfBZ98Pq/h55XlWtO9nHA0xDrTU3Nzc3t2VwS3cNlNZ9FE9qv/cO7XvuOOV2SPLL4eOMcrsryRGLON5KSY5Pdz2Z8epqSZ48zuO8YIz6n55uhshF1X3LGPvPHmyfPc5jWCPJlxZR/71J/j3JCmPUscVQ2RMH5/faceq7LMl6k3heZ07gXA2XOSbJoUnuGOXYh4/Y7/B0M9CO9/j/kGSnRbRxgyTnTeC5+s8kMyZwHsd8zgZltxkqe+eSbNegnrXSDekba/87kzx3cO7nrps5nZ6nwfHuncC5OHuUfddP8r0J7NuSPHOMY495XobKHTHG+Ri+XZFki3HquGCo7KpJzhqnrr8medpE34vjHHNenYsot8qI4280RrnVk8yawLn+SpI1x6hjcZ+z2ZnAe3EiZcd6/rPge35Rty2G9ntIkp9PcL/tFvO5PXGorh3TXfNx7vJjx9jnqMH2Oeku1/D6oX0OH2OfRZ7vJBummyV8rMf6pyR7jmjz8Hk7fILn7PAxHv9477unJbl+EfX+Ocl+EzzXox4ryYojyo28zUly9IjHOuo5H9S32H8PjWjPNulms16onqHyq6V7707kuXj14n4+ubm5Lfuba64BLH8+l+6PyiTZuapmtNbuHi4w+K/u15PM7alwXrrrwfw23RfRndJ94b9fkhOq6q7W2okjD1RVle6CznOHNs1JN8Pb+UluGOz/sCRPSTf5wqQmL6iqTZOcmi78SpJzkpybbjazFdJ9yXh0kr3STfIwadX1gPlqkrk9l25J8ql0X15WGqw/NN0srf+c7g/ily1U0YI2H7R1vSSfTXc+bk/y8CSvSHdB8+3SXSfv0D7tnoBdkvxruufkk0m+ky5E2CZdAJMkqapXDdqRdF8ePp+uZ80fB+1/apL9013b7OtV9bjW2k9GHqyq1ktyUbovvEnykyRnpBsOdW+SR6T7orNZkhdn/gXBF8cjhu7/ZrQCU9WuwWv9rHQTiCTd6+ST6ULSFdO9Tg5L9wXrvyfxGJba81RV+2f+bIx3pgtuLk5yc7r3/WbpvmTuNUZbP5Fkbg/Rawb7Xz2oa61Bm3dNN+FEL1V1RJIThlZ9Od176ZZ0vRNfmGTLJI9K8u2qekxr7cZFVPupJM9Mcmm6z5PfpAudnpdk53Rh1ylVtU1r7aa+bZ+E4dftXek+KxdQVauk+4zecbDqN+nO95WDfbZK99mxTbp/QJxdVXu11u4dUdUSf86mwA3pJoDYMMn/G6w7P8mHxig79/14RrrzkCSXJzkzXUByd7qJTx6WZLd0n7VT7QvpQqK10n1e/GCUMocNfp7XWru2Fu64PWlVtXK6z5fHDFbdkO6fAj9OF8Y+Nck/pjs3l49RzTcyf8KNj6f758PIyWOS0R/TeG37xySnpfs8nJMuODov3efYmumei4MG988avF6/MUZ1i/KhzD+/f0vXM/Db6T7TH5/us/y9mcCMs1P199AIx6cLGn+R5DNJrhrs/+ShMseme+8myXVJTkn3/r493d88Ww2OvWuA+4Zlne65ubm5/b3eMsFeEKPst2K6P87m7v/oEdvXTPdFrQ3KjdpjI90fdr8eKrf+KGWOHjrOr5M8apx2bZ/kQeM8zgtG2fbaoe1Hj1N3JXnSGNtmZ/zeFK8bOsbPkmwySpnHpAsx5pZ7xihltsiC/2n+U5InjFJuy8G2lq6330LHG6OdM8c7V6OUaen+YH/4Ip6Tu4ce+9ZjlHt6ui8wLcl3xyhz1tBx35RRevil+8LwP0PlnrqI8zjqczYoNyPdNYzmlj1xCbfrxUPbr0my2ShlHpsuqBp+DmZOl+cp83tJ3JNk53GOt2qSx49Yt2G6L8wtyfeTrD7O/g/K6O/1YxZxXrbI/B5r9yR5zihlRvb2OGOMNlww4hy/M0mNKLPCiNfHmJ8xE3yPTugzO13wsaj38vFDZT6WZOUx3gMnDZV7+RJ4zmZnEe/FiZad4PM/7vt5qOwOQ2W/nGTFcco+PMn9F/O5PXHoeDsO1n1isHzTyOcn3e+MueUPGaxb7J5r6T7D5tZx2WiPK124NrIn+RZ9jjfG41+ornT/ULp1sP0PSR43Rj2PSxeUt3Th1UI9hSdwrCdlfu/bPyXZfpQy26T7XB0+Bwud80zt30Mnjjje6SNfF0NlVxw6D7OTbDjOud8gyUMX5/Xr5uY2PW6uuQawnGmtzUk3HHGukdc3eWnmzxD2T621r45RzzXpeokk3X/EF/ivdlWtkS6YSrov9M9orf1onHZd2lr79YQexHxbDd3/xDh1t9bat8baPpZBL4CjBov3JHl2G2XGtNbaZemGqs31+glU/8+tte+OUtevknxksLhikj0m1ejJOaKN0stsyFvT9dS6K93z9/PRCrXWzknynsHi46tq5+HtVfXYdD2DkuRTrbV3toV70KS1dnuSg9N9CUuS10z0gQyOU1W1blU9NV3vi7nX1ZmT5P2jlJ/Kdr166P7zW2vXjizQWvvBGPsuylJ5njL//XRla23Mi6u31v7aWvveiNUPzvxr8X6utfaXcfb/dY/3ejK/Z2iSfKC1dvoodd+Z5JB0X5yT5B+ralHX2/tGa+1NrbU2oq57k/zfoVVP6dHmCamqNatq16r6UpIDhza9Z5SyGyf5P4PF81pr/9Ra+9vIcq3rkfySdL21koVfe0vjOVtWhn83fGrwe29UrbWftNb+uATacOLg5/3TzQo67PDBzz+nC3AX2+D31ZGDxb+lC58Xelyttc+nC2eXpv+brhdf0v0e/f5ohQbr575ON0vy7B7H+pfM7wX/6tbapaMc56p0741FmZK/h0ZxbZIXjva+Hdgg83vbf7F1E52MqrV2Y2vtZ4s4HrAcEK4BLJ/+NHT//iO2vWDw87p0QxbH1LohG3PDpr1HbH5auuFoSffFbcxgbTHcMXT/EWOW6m/ndMPokuSriwgHz0zXYylJdqmqDcep98Z0w3PHMjwU5uETaWgPv07Xo2NUgwskzx2S8sXBl4fxnDJ0f+Rr4QVD9xcKuYa11v6U5L8Gi7sOhr+NZYGLbKfrrXBzFhzGe3eS57bWfjzK/lPSrqp6SJJHDha/21q7eJyqPpuul+NELc3nae77abNaxEX0R7Gk34tJ8qzBz3uSfGCsQq2bvOWjg8XK/AB1LP8+Tl3XpOtBk0zhe3HkxeHThSwXJtl3qNhrWmujDSF+Trph6Mk45yGZF7CdNljcesRF15fGc7asLPPH1lr7TrprviVDQ8mrmzzhkMHiaYNAeCrskq43YpJ8eayQfeC4dJ+XS9xgiO7zBovfm8A/uk5L9x5PFv6MWtSxVsn8SSJuyIKfdwsY/LPhp4uocqr+HhrpU+OF2emGZc+1pP4GAKYZ11wDWD4N/3NkXm+NwRfqbQeL1yXZbwLXgbl98PNhI9YPz674pR5tnIhzM79n2Req6t3phoEt1Guop8cP3f/aBNszt8fEEzJ2KHLJeD0pkvxu6P6SmgXs2yN76oywS+a/Tv5aVc9cRH3Ds+2NfC08afDzb0m2qaptFlHXKkM/H5xFfwEay9fS9fqaPcb2qWrXDkNlzh+vgtba3VX1ncy/DuGiLM3n6dx0w9XWS3JhVb03yTmDsGpRrkz3xXKTJC8efKH+RLov04v9JX4QVj9osPjD8XpyDHwtyTsG9xd1vbDxwtCkez9uniX3XhzpsiSHjhEIJ/Nft0k3m+gzF1HfcLsflm6YWbKEn7Nl7NvpAorVkrx1EEKf1Fq7Yim346R0Q46fVlUbDl63+6a7pl8yv3fbVBieEfu88Qq21q6rqp9k/j8FlqRHZP4/2m6ewOs16f6uWCcLf0YtyqMzP3i+YBG/Z5PuPI16jCn+e2ikcQPG1tqtVfW9dH+D7FlVZyX5jyTfaiOukQvcdwjXAJZP6wzdv3no/uaZ/0X9sZnccJWRXzw3G7rfNxwZV2vtq1X1uXS9ADZI99/446rq5+mut/XNJF+ZwBfxsWw8dP/qCZQfLrPxmKW6a/CM566h+6tO4Lh9/G4R27cYun9oJjexwsjXwty6Vs7kh0CNF2iMvMj23Is8H54uiNkzXc+Ddyy059S2a5Oh+78cWXAUEykz19J8nt6Tbvjaw9N9Sf1ckjlVdXm6iRTOT/I/o/W0aa3NGUw28Pl05/NFg9stVXVRurDjf0YbojVBS+q9mEz8/TheL8rJOmDo/mrpnsfnpQsiHpPklVX1f8YIubYYun/iJI877zlfCs/ZMtNau7mqjkp3PbqV0g01fE1V3ZDud8O30vVGHvV30yICoDtaaxP5Z0uSnJzk7YM2PC/dcMzDB9uuHm/4dQ/Dn0OL6sE6t8zSCNe2GLr/1MFtoiYbaPc5B2OZyr+HRlrU53rSTW50XrrhtM8c3P5SVd9N9978epLv3AeCcGDAsFCA5cxg9svh4Gt4Jr3JDgUbNmPE8lpD92/PkvP8dNdOuXJo3dbpZgr7ZJLfV9XnBtcpmqw1h+6PN4RjruHHueaYpZbScJxFWNRQpMV5Law8Ynkq6xp2R2vt7KHbKa21Y9I9/2el+zvl7VU11vVvpqpdqw+3aQL7TuS1NNdSe54GQ193TPKuJNcPVq+YbsKEf053Tq+vqrcPru+UEft/JV1Pi7PTDcdNuiD/aYM6L6mqHw2uiTdZS+q9mGXx5XTE63ZWa+3d6WY4/Y9BkSPS9XgazVQ+50vyOVumWmv/L90MlOdl/mfuhulCig8k+UlVfaeqHj/K7meNc/v4JNrw28wf5n/4oAfm3GGLJ03m8UzAGkP3p/pzaHEsqc//0UzlOZjKv4dGWuRQ4NbaJelmsj15qPzqSXZP8pZ0/zz8RVU9v38zgelEuAaw/HlU5k8p/5ckwxdKH/5CemJrrSZzG3Gc4aFka2QJaZ1PttYemeQh6UK1/5f517pZMclzk3y3qjYao5qx3DZ0f/UxS803/DhvG7PU8mH4tXD4JF8LM8eoa/ZkX1OttQsm2/DBsJlDM/9aWcdX1YNGKTpV7Rr+gna/LNpEXksTNZXPU1prt7XW3pSuB8hj010g/bTM7921ZpI3J/lSjTJGqrX2w9baAemu5fi0dL0GL8z84OaRSf6rqp43ct9FuM+/FwfDf49Kcslg1euqarQhrXOf83vSzaY4mef8xFGOu6Ses5GW+veG1tqFrbU904VqByT5tyTfzfywbeck366qmUuwGScOfm6b7tqOKw2Of/IUH2f4s2Bpfw6NZ7hdx0zy9brFYhxrcc/BVP491Etr7VettcPSDavdPd1ssP+d+WHbFkk+U1VvnIrjAcuWcA1g+XPI0P3/bQtev2N4qMLiXgR6+Lpnk71uSi+ttV+21k5urb28tfYP6XrcXDbYvHkWnPlvIq4bur/1BMoPl1loVtHlzFS+FubWtXlVrTVuySnSuhk+/3WweL90vXCWVLuGn+sHT6D8RMpM1FQ+T/O01u5trV3WWvtwa+3gdBN7HJD5w8ifkvkTKYy2/22ttf9urb1lEOJtnPkzFFa64dsrTqJJfxfvxdZdI2rubIkrZPSJNuY+5yslWdRMqJM5dp/nbO6Q2XF7GA2C2PXGK7Mktdb+2Lpegq9rre2Y5IGZP6nMjIw4z1Mc+Hwh8//ZNPcC+ee1qbs26FzDr/Otxiw1uTJTYYl8Ro1hKs/B0mz3uFo3Q/P5rbV3tdaeli4sfl3mXzP3LVU1cnIqYDkjXANYjgyGRr50aNUnh7e31m7K/J5s21fV5ulv+IK9E714+5Rqrf0gC84I+cSxyo7he0P395pA+eEy3xuz1PLhm5n/h/v+VbU4v/MvHPxcMQvOhrikfS7zr6lzSFWN/II0Ve26ZOj+buMVHMwUuPNiHGukqXyexjQI285ONxxprgm/nwbhxmsy/1xtmImFZHP3vyHdzKlJsl1VbbCIXYZn61uu3outm01x7sQYT6yqfUYUuXDo/gFZQib4nN0y+Ln+4LU9lkdm8XtKDQ/fXayeQa2136Xr5fyHwartq2q1xalznGPdmeT0EatPXAKHGn6d7z5ewcHfAkvln17p/sE1N1zcu6qWZI+5H6aboCZJnjyBAH/M8zTFfw9Nqdba7a21f0t3vcSkux7k48bZBVgOCNcAlhNVtWa6P/DXGaz6aZIzRik69zowKyR592Ic8quZ38vlkKp61GLUtThmD92f7EQ8/5v5X76eXlUPH6tgVT0r8794frv1n0RhWhi0/78Hi/+Q5MWLUd3w8Ke3LOEvV/MMegH922Cxkrx1SbSrtfaLzL/m3xOqasdxij8v82cKXGxT/DxNxOyh+30mtlqc/ed+kVwpyavHKjT4rPs/g8WWyU9WMR0Mf/aOfN2emvkBwlFV9YAl3JbZQ/dHPmdzw4cZWXAW05H+eQraMTxMb7E/Q1pr92TBHtZLcqK2T6QbjvrddP94WhKvye9k/rUS96uqh4xT9tXp/qmwxA0+hz87WFw7yRIbwthauyvJfw0WN8qCPfUXUFVPSzd5y3im6u+hJWX20H0TDcJyTrgGMM1V52npeh/M7Wny5yTPbqNfyPsjmd9D5HlVdfxoFy8fqn+tqvrnqtpzeH1r7S/pZh9MuiFDXx4vYKuq7ca4LtaYquotVbXXInrr/J+h+z+cTP2ttb9l/rColZKcMdrECFW1bbrrvM31npFlllNvyvzrLv3Hoi6cXFUPrKr3DS7aPU9r7buZH4z8Q7rXwpjXv6uqlarqmVX1f8YqMwknZf5QoQOrat7seFPcruOH7p9SVZuOsv9j0s1oO9Wm5Hmqqo8Pn59R9lspC/Z8/eHQtqdU1auqasyLgFfVVpnfu/P2JL8Yr52j+I/Mv9bQ0VX1j6McY9Ukp2T+rIGfb639fGS56a61dm6SubN0Pn6491rrLpI/d+KD+yf5n8G5HdXgd8AeVfWvI9ZPxXP230P331FVC82oWlUvSTfpzGJprd2c5NbB4najXfNv6JjPq6oXjtcbbRCCP2aw+MvW2hK7Nl9r7XuttR0Ht13bKDPuTsEx7s7818XKSU6vqoWG4g5mQn3NyPVL2LGZ38vxDVX12vF+b1fVBlX1psHv1sn6wND9f6+q7Uapf+uM6Lk/hin5e2iyquoxVfXmRfw+Wj/JsweLLckVi3NMYNmTkANMA4M/luctprvo+HrpZpraNcmWQ9uvTfLc1trw7JrztNb+MqjvwnQzfr46yXOq6vR0f7z9eVD/lulmmdst3ZCEF4xS3fvTBXr7JXlQksuq6qwkFyS5IclqSbZJN4Rrh0Fdvx6lnrHsnuRtSf5QVf+T5PJ0Pc1WSPfler/M701xV/oFGx9IN2Twien+y31lVX0qyQ/S/R7cJd3worlfKj/RWjunx3GmndbaD6rqn9L1ulgl3YWT/yXJF9MNt7wrXU/Ih6Y7D49P9/r791Gqe1G6AOtR6Z7nX1bVmUkuSnex/FXTXePpseleD+tlYl9+FvUY/lZVx6V7Lc7tvfbsoSJT1a5PpeuVtlu6iTV+XFWfzPzXyRPTvU6S5EuZP1R6sWeqnMLn6aVJXlpVV6YblvjjdL1PV093nbiDM7935tVJzhzad+MkH0zyb1V1froeOr9MN2Pf+umGLD0n83scfXCyAUNrbXZVHZXkhHTn9Myq+mK6niq3DNr2osy/pt3vkrxiMseYZt6d+ef4mMzvkZMkb0j3+b5Hugvl/2RwLr6Z7jNwRrqeO49OF45tkm7WzOFrD07Fc3Z2utfYVumGO39/8Lr/fZIHpJuZc/d0vbUekvmhZ1/fSDcU9iFJTquqL2R+aJMkFw7auHW69/p/VNW5Sb6fboKTu9INb33SoG1ze28du5jtmi7el+TAdK+Nx6Z7XXwiXc/a1ZM8Nck/pgspL08yc2k0qrV2bVUdnO6zb+VBO19WVZ9P14v+jnR/b2ydbsbiJ6V7bi7ocaxvV9VH0/1jbd0kF1fVSUm+ne7z9vHpeviunu71+8xx6prKv4cmY+0kb0/y1qr6Trpe9Fenm5xlvXS/rw7J/OsYfra19pvFPCawrLXW3Nzc3NyWwS3dfyonc/tTui/T60yw/m3SBQMTqfuvSZ46Rj0z0v33d84E6tl1nMd5wSjbvjHB9t2YZO8x2jd7UGb2OOdijSRfXsQx7k3Xa2CFMerYYqjsiYs49xMuO7TPzPHO1ShljpnEa23fdF/YJ3Kub0qy/hj1rJluSNtEX7NvX8S5GfM5G+X5++PQ8/TIqW7XoJ61Mv8aaKPd7kw3c+07h9Y9Zro8T4NzM5F9f5hkixH7HjrBfe9NF+gs9D5JFyDNLTdznMf58sG5HO84PxrZxhF1XDC37ATO64TLLqKeNpl60v2T4GdD+z19xPaV033m3DPBc3/SVD9ng3q2TxfCjrX/xUk2yCI+ayfy/KcLje4Y51hbDMq9ZYKP7W9JXrc4z+vgeCcO1bljzzpeP1TH4WOUGfccDspsmPF/d/8pyZ4j2jzqe2Uixxvl8Y/3vtsxXe/HiTw3tyV5VJ9jpQvmThqn7jnpJjg6fFHnfFDfYv89NNFzNCi76wSP1dLN5rza4r6G3dzclv1NzzWA6efudP9N/XO6P4x/kK5HwlfaJHqKtNauqqrt031hf1aSndL1RFg93R+9v073JfsbSb7UWvvTGPXcneQVVfWxdEODdk83c+eag3p+ke6/sme07kLek/GMdP/h3mPQvq3SDZNq6b7sXZnu2m+fbK3dMsm6hx/D7Un2raqnpPtCuku6XiFz0vWOuSDJx1trl45ZyXKstfblqtoy3WPfJ91QqvXTfYG5NV3PlUuSfC3J11o3nHa0em5LcnBVvXdQ15PTzdq3drovJNele86+meSLrbVfTlH7b6+qD6f7wr1Q77Wpaldr7c9VNTPdF7ZD0/UoWi3da+S8JP/eWvvJ4L0w182ZIlPwPD0g3ftz93SByZbpAsO/pbuW02XpelKd1rrrKA37TLqeMHukO3+PSNczatV0wwl/la7nyKdaa5ct5uM8oarOSdcr7SnpAtf7pQtQL0t3LcnPjNLG5Upr7d7Ba/JTg1VvTXLO0Pa/JTmyqv49XU+c3dL12ls385+zn6Y7719prf1oxCGm5DlrrV06GPL/uiRPS7JZuvDzZ+mG6H6itXb3OKM4J6y1dvng99Jr0n32b57uuR/pXUn+J91redd0vTY3SvfPntuS/Dxd78z/bK1dM8r+y63W2g1V9YQkR6TrTfvwdD09f5uu9+O/t9Z+vajh40uobRdX1TZJDkrXe/dx6YLXVdP9zfLLdO/hryc5p3WXl+hznDlJDquqM9KF8Y9P91l2fbpr0/1Ha+2iqjp8gvVNyd9Dk2j/Nwfnafd07+tt072v7pcuXP5NutD6pNbaNxfnWMD0Ua21Zd0GAIDlRlVdmm7I1q1J1m3+mAIA+LtmQgMAgAmqqp3SBWtJN3xXsAYA8HdOuAYAkKSqHllV9x9n+8OTzBpa9f/GKgsAwN8P11wDAOg8M8m/VtV56a4jODvdNRA3THfdpwPSXfMpSc5srX11GbQRAIBpRrgGADDfqkmePriN5bR0kx4AAIAJDe5r1l9//bbFFlss62YAwHLn7rvvzq233po///nPufPOO3PPPfdkzpw5qarMmDEja6yxRu5///tnzTXXXNZNBeA+5peDOUofvO44ha66qvu5zTZLvD3Awi699NKbWmsbjLZNz7X7mC222CKXXHLJsm4GAAAAE3TQmd3P0w4cp9DMmd3PCy5Ywq0BRlNVvx5rmwkNAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE8rLesGAAAAAEvGn//859xwww25++67l3VTYNqaMWNGNtxww6y11lq99heuAQAAwH3Qn//851x//fXZdNNNs9pqq6WqlnWTYNppreXOO+/M7373uyTpFbAZFgoAAAD3QTfccEM23XTT3O9+9xOswRiqKve73/2y6aab5oYbbuhVh3ANAAAA7oPuvvvurLbaasu6GbBcWG211XoPnxauAQAAwH2UHmswMYvzXhGuAQAAAEBPwjUAAAAA6Em4BgAAAExrrbVsueWWqapcc801y7o5S9WJJ56Yqhr3tsUWW/Su/5hjjsn6668/JW2dOXPmvDattNJK2WKLLXLEEUfkxhtvXKDc3Me01lpr5c4771yonj322CNVlcMPP3yB9V/+8pezyy67ZJ111slaa62VRzziEXn5y1+e22+/fV6ZLbbYYtRztNJKK03JYxzNkqsZAAAAYApcdNFFmT17dpLk1FNPzZve9KZl26Cl6OlPf3ouuuiiectnnnlmPvCBDyywbpVVVuld/0te8pLsu+++i9XGYbvttluOPfbY3HPPPfnBD36QN73pTfnFL36Rr3/96wuVba3lnHPOyYEHHjhv3fXXX58LL7wwa6yxxgJlZ82alUMOOSRHHHFE3vSmN6WqcsUVV+Skk07KLbfcskD5Qw45JEceeeQC+y/J6w8K1wAAAIBpbdasWVl99dXzyEc+MrNmzVpq4dqdd965zGdc3WCDDbLBBhvMW77kkkuSJDvuuOOY+0ym3Ztttlk222yzxWvkkPXWW29e2574xCfmjjvuyBve8Ib8/ve/zyabbLJA2X333TennnrqAuHa6aefnoc85CFZc801Fyj74Q9/OPvss09OOOGEeeue+tSn5uijj05rbYGyG2+88bjnZ6oZFgoAAABMW3PmzMkZZ5yR/fbbLy960Yvyk5/8JFdccUWS5C9/+UtWX331fPSjH11ovx122CEveMEL5i3/5je/ycEHH5z11lsv97vf/fKUpzwlV1111bzts2fPTlXls5/9bA499NCss84683p0nXzyyXniE5+Y9dZbL+uuu2522223eSHXsA9/+MPZfPPNs/rqq+eZz3xmzjvvvFRVLrjggnll7r333rznPe/JVlttlVVWWSX/8A//kJNOOmmxzlFV5bjjjsurX/3qbLDBBnnUox6VJDnnnHOy1157ZcMNN8xaa62VHXfcMV/72tcW2HfksNALLrhgXpuf/exnZ4011siDH/zgUc/xRDz60Y9Okvz2t79daNvBBx+cc845J7fddtu8daeeemoOPvjghcrecsstecADHjDqMZb1rLjCNQAAAGDa+sY3vpHrr78+Bx98cA488MDMmDEjs2bNSpKsvvrqecYznpHTTjttgX1++ctf5tJLL81BBx2UJLn55pvzxCc+MVdddVVOOOGEnH766fnLX/6SPffcc6Frfr32ta/NmmuumTPOOCNvfOMbk3TB26GHHpozzjgjn/vc57LZZptl1113zS9/+ct5+5111lk58sgjs99+++Wss87Ktttumxe/+MULPZ4jjzwy73znO/Oyl70s55xzTg444IC86EUvyle+8pXFOk/ve9/7ct111+Uzn/lMPvShDyVJfvWrX2XffffNZz7zmXz+85/PzjvvnKc97Wn5zne+s8j6XvrSl+bRj350zjrrrMycOTOveMUr8r3vfW/S7frNb36TFVZYIQ960IMW2jZz5sysu+66Ofvss+eVveiii0YN1x772Mdm1qxZ+fCHP5zf//734x6ztZZ77rlngducOXMm3faJMiwUAAAA/k687cLkJzcuutyS8PANkrc+efL7zZo1K+uss06e+tSnZuWVV85ee+2VU089Nccee2yqal7oNjzs8LTTTsu6666bvffeO0ly/PHH5y9/+Usuv/zyrLfeekmSXXbZJVtssUU+9alP5RWveMW84+244475yEc+skAb3vKWt8y7f++992avvfbK97///Zxyyinzth177LHZZ5995u27995756abbsrHPvaxeftec801+djHPpZPf/rTOeyww5Ike+65Z6677rq87W1vyzOe8YzJn6CBBzzgAQuFjK985SsXaPduu+2WK6+8Mp/85Cezyy67jFvfc5/73HnDb2fOnJkvf/nL+cIXvpDHP/7x4+43N9iaM2dOLr300rz73e/Oy172slF7na2wwgp59rOfnVNPPTUveMELcuqpp2bbbbfNwx72sIXKHnvssfnRj36UI488MkceeWS23HLLPPOZz8zRRx+9UN3HHXdcjjvuuAXWPfnJT16gB+FU0nMNAAAAmJbuuuuunHXWWTnggAOy8sorJ+lCn9mzZ+fiiy9OkjztaU/LGmuskTPOOGPefqeddtoC+3z961/PXnvtlbXWWmteT6Y111wz22+//ULDO5/+9Kcv1I6f/vSnOeCAA7LRRhtlxRVXzIwZM3LVVVfl6quvTtINXb388suz3377LbDfyOXzzjsvK6ywQg444IAFelXtscceufzyyxerd9Vo7b722mtz2GGHZdNNN81KK62UGTNm5Gtf+9q8do9nbjCZJDNmzMjWW2+da6+9dpH7feELX8iMGTOy6qqrZpdddslGG200ryfdaA4++OCce+65ufnmm8ccEpokm2++eS699NJ8/etfz7/8y79kvfXWy/HHH59tt912oXY9//nPz/e///0Fbv/v//2/Rba9Lz3XAAAA4O9En55jy9JXv/rV3HLLLdlnn31yyy23JOl6Ua2yyiqZNWtWdtppp6y66qrZf//9c9ppp+VVr3pVrrrqqvzwhz/M+973vnn13HTTTbn44osX6tmVJHvssccCyxtttNECy7fddlv23nvvbLTRRjnuuOPyoAc9KKuuumpe8pKX5K9//WuS5MYbb8w999yzwMQDSRZavummmzJnzpysvfbaoz7e6667rvfkAiPbfe+992a//fbLbbfdlre//e3Zaqutsvrqq+ctb3lLbrjhhkXWt8466yywvPLKK897vOPZfffd8973vjd33XVXvvzlL+e9731v3vSmN+W9733vqOV32mmnbLLJJjn22GNz2WWX5fOf//yYda+44orZY4895j1nX/va17LPPvvkAx/4QI4//vh55TbaaKPssMMOi2zrVBGuAQAAANPS3GurPfvZz15o2+mnn57jjz8+K664Yg466KDsu++++c1vfpPTTjstG2ywQXbfffd5Zddbb73st99+efOb37xQPSNnpRx5cfyLLroo1157bc4999w89KEPnbf+1ltvnXd/gw02yEorrZQbb1xwzO3I5fXWWy8rrbRSvvOd72SFFRYeTLjhhhsutG6iRrb7mmuuyWWXXZavfvWreepTnzpv/chrzE21ddddd16wtcsuu+TGG2/MBz/4wbzyla/M5ptvPuo+Bx10UN7//vfnCU94QrbccssJH2vvvffOox/96PzsZz+bkrb3JVwDAAAApp3bb789X/nKV/Lc5z43L3vZyxbYdtlll+U1r3lNzj///Oy5557Ze++9s+666+b000/PaaedlgMPPDArrrjivPJ77LFHTj/99DziEY/IaqutNql2zA2jVllllXnr/vd//zezZ8/O9ttvn6TrUbXddtvli1/8Yo444oh55b70pS8tUNfuu++eOXPm5NZbb81ee+01qXZM1mjt/vWvf53vfOc72XbbbZfosYe97W1vyymnnJLjjz9+oeugzXXYYYflqquuyiGHHDJmPTfccMNC4eNf//rXXHvttfNmR11WhGsAAADAtPPFL34xd9xxR171qlflCU94wgLbdtlll7zrXe/KrFmzsueee2bGjBk54IADctxxx+W6667LRz/60QXKv+Y1r8kpp5yS3XffPUceeWQ23XTTXH/99bnwwgvzxCc+Mc997nPHbMeOO+6YNdZYIy996Utz9NFH59prr80xxxyTTTfddIFyb3zjG/OsZz0rr3zlK7PffvvlO9/5Ts4555wkmddLbZtttsnLX/7yHHzwwTn66KOzww475K9//WuuvPLKXH311fnP//zPqTh1SZKHPvSh2WyzzfIv//Ivecc73pHbbrstb33rWxdq95K22Wab5bDDDssnPvGJvOUtb1louGmSPPzhD583Y+hYnvKUp+ShD31o9t1332y++eb5wx/+kA9/+MP505/+tECgmXTDa+dek2/YYx/72HnX4ZtKJjQAAAAApp1Zs2Zl6623XihYS7oL7D/nOc/JF77whdx1111JugvjX3fdddlkk03ypCc9aYHy66+/fi6++OI89KEPzVFHHZW99947Rx99dG699dZF9uLaaKONcsYZZ+QPf/hD9t9//3zwgx/MCSeckK222mqBcgcccEA+9KEP5eyzz84zn/nMfP/738/73//+JMlaa601r9xHPvKRvPnNb87JJ5+cffbZJ4cffnjOOeec7Lrrrr3O01hWWWWVfOELX8hKK62UAw88MG9+85vzhje8IU9+8tK/8N7rX//6/PWvf11g5tTJOvroo3PHHXfkda97Xfbcc88cddRRWXvttfPtb387O+200wJlP/e5z2WnnXZa6DaRa831Ua21JVIxy8YOO+zQRs50AgAAwPR10Jndz9MOHKfQzJndzwsumHC9P/3pT/Owhz2sb7OYAu985zvzrne9KzfffPOkh6Oy9I33nqmqS1tro86SYFgoAAAAwGK68cYb8+53vzu77bZb7ne/++Vb3/pW3vve9+bFL36xYO0+TrgGAAAAsJhWXnnl/OxnP8vJJ5+cW2+9NRtvvHFe9apX5R3veMeybhpLmHDtvubmS5PP1aLLAQAA9x2HuNwPLGtrr712/uu//mtZN4NlwIQGAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAADCttday5ZZbpqpyzTXXLOvmLFUnnnhiqmrc2xZbbLFYx7jhhhtyzDHHZPbs2ZNuz/rrr5/ddtst3/zmNxcqO7fMZz/72YW2feYzn5m3fdivf/3rvOAFL8gDH/jArLrqqtl8882z//77L1D/McccM+a5OOWUUyZ/AhbTSkv9iAAAAACTcNFFF80Lfk499dS86U1vWrYNWoqe/vSn56KLLpq3fOaZZ+YDH/jAAutWWWWVxTrGDTfckLe97W2ZOXPmhIO6b3zjG1lttdXyhz/8Iccee2ye8pSn5IorrsjWW2+9QLk11lgjs2bNyvOe97wF1p966qlZY401cvvtt89b96c//Sk77rhjNt5447z73e/OJptsktmzZ+dLX/pSLrroouy6667zyq699tr57//+74XatdVWW03ikU8N4RoAAAAwrc2aNSurr756HvnIR2bWrFlLLVy78847s9pqqy2VY41lgw02yAYbbDBv+ZJLLkmS7LjjjsuqSUmSxz3ucVljjTWSJI9//OOz2Wab5Ywzzsgb3/jGBcrtu+++OfPMM/OnP/0p6667bpLk5ptvzrnnnptnP/vZ+dznPjev7Jlnnpnrr78+P/zhD7PhhhvOW//CF74wrbUF6l1ppZWW+TmYy7BQAAAAYNqaM2dOzjjjjOy333550YtelJ/85Ce54oorkiR/+ctfsvrqq+ejH/3oQvvtsMMOecELXjBv+Te/+U0OPvjgrLfeernf/e6XpzzlKbnqqqvmbZ89e/a8IYyHHnpo1llnney7775JkpNPPjlPfOITs95662XdddfNbrvtNi/kGvbhD384m2++eVZfffU885nPzHnnnZeqygUXXDCvzL333pv3vOc92WqrrbLKKqvkH/7hH3LSSSct1jm6+eabc8QRR2SjjTbKqquump133jnf/e53FyjzyU9+Mo94xCOy2mqrZf3118+Tn/zkXHnllZk9e3Ye9ahHJUl22223UYdqLsomm2ySDTbYIL/97W8X2rbTTjtlk002yec///l56z7/+c9nk002yU477bRA2VtuuSUrr7xy1ltvvYXqmWyblibhGgAAADBtfeMb38j111+fgw8+OAceeGBmzJiRWbNmJUlWX331POMZz8hpp522wD6//OUvc+mll+aggw5K0oVPT3ziE3PVVVflhBNOyOmnn56//OUv2XPPPXPnnXcusO9rX/varLnmmgv0wpo9e3YOPfTQnHHGGfnc5z6XzTbbLLvuumt++ctfztvvrLPOypFHHpn99tsvZ511Vrbddtu8+MUvXujxHHnkkXnnO9+Zl73sZTnnnHNywAEH5EUvelG+8pWv9Do/d911V/bcc8+ce+65ed/73pezzz47G2ywQfbcc8/84Q9/SJJ885vfzMtf/vI8//nPz1e/+tV86lOfys4775xbb701G2+88bxron3kIx/JRRddtMCQ04m4/fbbc/PNN2fLLbdcaFtV5aCDDpr3nCVdT8SDDz54obKPfexjc9ddd+UFL3hBLr300tx7773jHveee+5Z6LYsGBYKAAAAfy9e/erk8suXzbG32y754AcnvdusWbOyzjrr5KlPfWpWXnnl7LXXXjn11FNz7LHHpqrmhW6///3vs8kmmyRJTjvttKy77rrZe++9kyTHH398/vKXv+Tyyy+f1ytql112yRZbbJFPfepTecUrXjHveDvuuGM+8pGPLNCGt7zlLfPu33vvvdlrr73y/e9/P6eccsq8bccee2z22Wefefvuvffeuemmm/Kxj31s3r7XXHNNPvaxj+XTn/50DjvssCTJnnvumeuuuy5ve9vb8oxnPGPS5+eUU07Jj3/841x55ZXzrne25557ZptttskHPvCBvO9978v3vve9bLvttnnDG94wb7/99ttv3v1tt902SfLwhz98wkMt58yZk3vuuSfXX399Xve612XjjTfOC1/4wlHLHnzwwXn/+98/L+y78MILc9xxx+Xb3/72AuX22GOPHHXUUfngBz+YU089NWuuuWb22muv/NM//VP23HPPBcr+8Y9/zIwZMxY61q9+9avFnuBhsvRcAwAAAKalu+66K2eddVYOOOCArLzyykmS5z73uZk9e3YuvvjiJMnTnva0rLHGGjnjjDPm7XfaaactsM/Xv/717LXXXllrrbXm9XBac801s/322y80vPPpT3/6Qu346U9/mgMOOCAbbbRRVlxxxcyYMSNXXXVVrr766iRd0HT55ZcvEFglWWj5vPPOyworrJADDjhggd5We+yxRy6//PLMmTNn0ufo61//erbffvtsueWWC/TeevKTnzzvsW233Xa57LLLctRRR+Wb3/xm/va3v036OCOts846mTFjRjbbbLN8/vOfz5lnnrnAteGGPeYxj8nWW2+dM844I6effnq23nrrbLfddqOWPe6443L11Vfnfe97X2bOnJn//u//zt57750TTjhhgXJrr712vv/97y90mxuwLk16rgEAAMDfix49x5alr371q7nllluyzz775JZbbkmSzJw5M6usskpmzZqVnXbaKauuumr233//nHbaaXnVq16Vq666Kj/84Q/zvve9b149N910Uy6++OKFho8mXW+pYRtttNECy7fddlv23nvvbLTRRjnuuOPyoAc9KKuuumpe8pKX5K9//WuS5MYbb8w999yzULg0cvmmm27KnDlzsvbaa4/6eK+77rpsttlmEzs5Ix7baL24HvKQhyTperJ9+tOfzoc+9KH8+7//e9ZYY408//nPz/ve976svvrqkzreXN/85jez6qqr5uqrr87RRx+dgw8+OD/60Y/GrO+ggw7KqaeemtbaqENCh2211VZ57Wtfm9e+9rW56aabsvfee+eNb3xjjjjiiHnXXltppZWyww479Gr7VBOuAQAAANPS3Ot0PfvZz15o2+mnn57jjz8+K664Yg466KDsu++++c1vfpPTTjstG2ywQXbfffd5Zddbb73st99+efOb37xQPWuuueYCyyMvnH/RRRfl2muvzbnnnpuHPvSh89bfeuut8+5vsMEGWWmllXLjjTcusO/I5fXWWy8rrbRSvvOd72SFFRYeTDg8Q+ZErbfeetlhhx0WGH461yqrrDLv/mGHHZbDDjssN954Y77whS/kqKOOylprrZX3vOc9kz5m0vVGW2ONNfK4xz0uD3zgA7Prrrvmwx/+cF73uteNWv7ggw/OO97xjiTJpz71qQkfZ/31188LX/jC/PM//3NuuOGGhcLP6UC4BgAAAEw7t99+e77yla/kuc99bl72spctsO2yyy7La17zmpx//vnZc889s/fee2fdddfN6aefntNOOy0HHnhgVlxxxXnl99hjj5x++unzZsucjLkTHgwHVf/7v/+b2bNnZ/vtt0+SrLjiitluu+3yxS9+MUccccS8cl/60pcWqGv33XfPnDlzcuutt2avvfaaVDvGsscee+RrX/taHvjAB04onNtggw1yxBFH5Atf+EJ+8pOfJMm84bNze+JN1pOe9KTss88++eAHP5hXv/rVC5yruR72sIfNex6HQ8phN95446hDS3/+859nlVVWGbPH37ImXAMAAACmnS9+8Yu544478qpXvSpPeMITFti2yy675F3veldmzZqVPffcMzNmzMgBBxyQ4447Ltddd10++tGPLlD+Na95TU455ZTsvvvuOfLII7Ppppvm+uuvz4UXXpgnPvGJee5znztmO3bcccesscYaeelLX5qjjz461157bY455phsuummC5R74xvfmGc961l55Stfmf322y/f+c53cs455yTJvF5q22yzTV7+8pfn4IMPztFHH50ddtghf/3rX3PllVfm6quvzn/+539O+jwdeuihOeGEEzJz5sy89rWvzYMf/OD88Y9/zPe+97084AEPyFFHHZW3vvWtufnmmzNz5sysv/76ueyyy3LhhRfO67X2wAc+MKuttlpOOumkrL322pkxY8akh1z+67/+a3bZZZeccsopo86SmmSh66aNdNJJJ+Wzn/1sDj300Dz60Y/O3XffnfPOOy8f/ehH80//9E9ZddVV55W955575l13b9jmm2++0HOzpJnQAAAAAJh2Zs2ala233nqhYC1JZsyYkec85zn5whe+kLvuuitJN+zwuuuuyyabbJInPelJC5Rff/31c/HFF+ehD31ojjrqqOy99945+uijc+utt86bKXMsG220Uc4444z84Q9/yP77758PfvCDOeGEE7LVVlstUO6AAw7Ihz70oZx99tl55jOfme9///t5//vfnyRZa6215pX7yEc+kje/+c05+eSTs88+++Twww/POeeck1133bXXeVp11VVz/vnnZ6+99spb3/rW7L333nnVq16Vn//853n84x+fJHnc4x6Xn/zkJ3n5y1+epzzlKfnYxz6WY445Jq961avm1fGJT3wil156aZ785CfncY973KTbsfPOO2e33XbL+9///rTWej2WffbZJzvvvHM+8YlPZP/9989BBx2U8847L//xH/+R448/foGyt956a3baaaeFbp/+9Kd7HXtxVN8HzPS0w4OrXfLOZd0KAABgqTrE97rl2UFndj9PO3CcQjNndj8vuGDC9f70pz/Nwx72sL7NYgq8853vzLve9a7cfPPNkx6OytI33numqi5trY3anc+wUAAAAIDFdOONN+bd7353dtttt9zvfvfLt771rbz3ve/Ni1/8YsHafZxwDQAAAGAxrbzyyvnZz36Wk08+Obfeems23njjvOpVr5o3Qyb3XcI1AAAAgMW09tpr57/+67+WdTNYBkxoAAAAAAA9CdcAAADgPsokhjAxi/NeEa4BAADAfdCMGTNy5513LutmwHLhzjvvzIwZM3rtK1wDAACA+6ANN9wwv/vd73LHHXfowQZjaK3ljjvuyO9+97tsuOGGveowoQEAAADcB6211lpJkt///ve5++67l3FrYPqaMWNGNtpoo3nvmckSrgEAAMB91FprrdU7MAAmxrBQAAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0NO0DNeq6v5Vdfng9oeq+t3Q8soT2H9mVe08xrbDq6pV1R5D6w4YrDtwsPyfVfXwSbT38Kq6cdC+n1XVUUPbjhnUvdXQuqMG63YYLL+oqn5UVVdU1Y+rav/B+hOr6ldDj/1/J9omAAAAAJa8lZZ1A0bTWvtjku2SLpxKcntr7f2TqGJmktuTjBVG/SjJc5OcN1g+OMkPh47/kkk1uHNaa+2VVXX/JFdV1Zmttd8OHe/gJO8cLB+Y5CdJUlWbJfnXJI9trd1aVWsk2WCo3v/bWjuzR3sAAAAAWMKmZc+10VTV9lV1YVVdWlX/U1UbD9b/c1X9ZNDr69Sq2iLJy5McNejt9aRRqvtWksdX1YxBmLVVksuHjnXBUK+y26vqXVX1w6q6uKo2Gq+dg2DwmiQbD60+O8nc3mgPTnJrkhsH2zZMclu6MDCttdtba7+azLkBAAAAYNlYXsK1SvIfSQ5srW2f5FNJ3jXY9vokj2mtbZvk5a212UlOSHJ8a2271tq3RqmvJfl6kqekC72+NM6xV09ycWvt0Um+meSl4za06oFJVk1yxdDqPyf5bVU9Ml2PudOGtv0wyfVJflVVn66qfUdU+b6hYaGfHe/YAAAAACxdy0u4tkqSRyY5t6ouT/KmJJsNtl2R5LNV9fwk90yizlPTDdU8OMmsccr9LclXBvcvTbLFGOUOqqork/wyyb+31v46xvGemeSsuStba3OSPDXdUNGrkxw/GAo71/8dhITbtdaeN9qBq+plVXVJVV1y423jPBIAAAAAptTyEq5VkiuHQqZHtdb2Hmx7epKPJNk+yaVVNaHryLXWvpcusFu/tXb1OEXvbq21wf05Gfs6dae11h6R5ElJPlBVDxix/ctJXpDkN621P49oS2utfa+19u50Adw/TuQxDO3/8dbaDq21HTZYczJ7AgAAALA4lpdw7a4kG1TVTkkyuFbaI6pqhSSbt9bOT3J0knWSrJHuGmYTiZnekOSNU9nQ1tpFST6T5FUj1t+Z5HWZP5w1SVJVm1TVY4dWbZfk11PZJgAAAACWjGk5W+go7k03bPJDVbV2unZ/MN0wylMG6yrdddZuqaovJzmzqvZPcuQY111La+2rS6i9703yg6o6dsTxTh2l7Iwk76+qTZL8Nd1EBy8f2v6+qnrT0PLjW2t/m+oGAwAAADB5NX/EI/cFOzy42iXvXNatAAAAlqpDfK9bnh10ZvfztAPHKTRzZvfzgguWcGuA0VTVpa21HUbbtrwMCwUAAACAaUe4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhppWXdAKbYetsnh1yyrFsBAAAA8HdBzzUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQ00rLugFMsZsvTT5Xy7oVAAAATNQt53c/P7fb2GVuGPz0fY/lwSFtWbdgqdJzDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgp5UmWrCqVkyySmvtjhHrd0+yf5I7kny8tfarqW0iAAAAAExPk+m59v4kN1fV2nNXVNXBSc5NcmSS1yX5XlVtPrVNBAAAAIDpaTLh2q5Jzm+t3Tq07q1JbklyaJKjk6yT5DVT1TgAAAAAmM4mE65tnuSauQtV9eAk2yT5j9baKa219yf5apKnTm0TAQAAAGB6mky4tlaSPw8t75KkJfnvoXVXJtlsCtoFAAAAANPeZMK165JsObS8Z5I7k1w6tG6NJPf0bUxV3b+qLh/c/lBVvxtaXnkC+8+sqp3H2HZ4VbWq2mNo3QGDdQf2bfNQXSdW1a8Gbf3hiONcUFW/qaoaWnd2Vd0+uL9CVX2oqn5cVT+qqu9X1ZaDbbMH6+aehw8tblsBAAAAmBoTni00ycVJ9quqZyT5a5IDk5zXWrt7qMyDk/yub2Naa39Msl2SVNUxSW4fDDedqJlJbk/yv2Ns/1GS5yY5b7B8cJIf9mjqWP5va+3MqtotyceTbD207ZZ0vf2+XVXrJNl4aNtBSTZJsm1r7d6q2izJX4a279Zau2kK2wkAAADAFJhMz7VjB+W/mOR/kqyc5F1zN1bVWunCre9OYftSVdtX1YVVdWlV/U9VbTxY/89V9ZOquqKqTq2qLZK8PMlRgx5eTxqlum8leXxVzaiqNZJsleTyoWO9ZdBr7MdV9fHqrDRYN3NQ5t1V9a5R6h52UZJNR6w7NV2YlyTPSvKFoW0bJ7mutXZvkrTWrm2t/WlR5wYAAACAZWvC4Vpr7UdJnpDk+MFt59bacJC2bZKvJZk1he2rJP+R5MDW2vZJPpX5gd7rkzymtbZtkpe31mYnOSHJ8a217Vpr3xrtYST5epKnJNk/yZdGbP9wa+1xrbVHJlktyTNaa/ckOTzJx6pqr3QTNrxtEe1+apKzR6w7L8muVbViupDttKFtpyfZdxAKfqCqHjNi3/OHhoUetYhjAwAAALCUTGZY6NyA7bVjbPt2km9PRaOGrJLkkUnOHVyubMV0135LkiuSfLaqzs7CQdZ4Tk3yz0nWTvIvSd44tG23qjo6yf2SrJdugoYvt9aurKrPJPlykp1aa38bo+73VdW/JdkwyY4jts1Jd34OSrJaa2323Euwtdauraptkuw+uJ1XVc9urc0dvjrusNCqelmSlyXJA9efyCkAAAAAYCpMuOdaVX2jqg5dRJnnV9U3Fr9Z86tMcuWgJ9p2rbVHtdb2Hmx7epKPJNk+yaVVNaGgsLX2vXSB3fqttauH2r5qko+m6yX3qCSfSLLq0K6PSnfdtI3Gqf7/phtq+qYkJ42y/dR0PfFOH6Vdd7XWvtpa+7/phuA+cyKPZ7Dvx1trO7TWdthgzYnuBQAAAMDimsw112Ym2WIRZR6U5Ml9GzOKu5JsUFU7JcngWmmPqKoVkmzeWjs/ydFJ1kk3U+ltSSYSL70hC/ZYS+YHaTcNrsc2bwbRqnpWkvsn2TXJhwYTEoxqcN20f0+yQlU9ZcTmbyV5d0YMna2qx1bVJoP7K6QbYvvrCTwOAAAAAJahyYRrE7FaknumsL5704Vc762qH6abfGDndMNDT6mqHyW5LN111m5JN2zzgHEmNEiSDHqInT9i3S3peqv9KN0w0+8nSVWtn+Q9SV486On24XTh2Zhaay3JO9MFfwusb629f5Qhnhsm+XJV/TjdcNd7BseZa/iaayePd2wAAAAAlp7qcqAJFKy6N8lbW2vvGGVbJXlgkrOSrNVa22pKW8mE7fDgape8c1m3AgAAgIk66Jau78dp6+w2dqG53/PetOTbA4vtkIllTcuTqrq0tbbDaNvG7blWVfdW1ZyqmjNYdczc5eFbup5Wv0yyXbrrigEAAADAfd6iJgH4ZpK5ceOuSX6TZPYo5eYk+WOS85L851Q1DgAAAACms3HDtdbazLn3B8NCP91ae/uSbhQAAAAALA8W1XNt2JZJbllC7QAAAACA5c6Ew7XW2q+XZEMAAAAAYHkzZrhWVW/pWWcbbUZRAAAAALivGa/n2jE962xJhGsAAAAA3OeNF67tttRaAQAAAADLoTHDtdbahUuzIQAAAACwvFlhWTcAAAAAAJZXwjUAAAAA6Gm8a64toKruTTdZwaK01tqE6wUAAACA5dVkQrBvZvRwbZ0k/5BktSQ/THLLYrcKAAAAAJYDEw7XWmszx9pWVWsmOT7JzkmetfjNAgAAAIDpb0quudZauy3Jy5Lck+RdU1EnAAAAAEx3UzahQWvt3iTnJ3nmVNUJAAAAANPZVM8WumqSdae4TgAAAACYlqYsXKuqhyZ5dpJrpqpOAAAAAJjOJjyhQVV9apw6Nk+yS5IVk/zLFLQLAAAAAKa9CYdrSQ5fxPafJXlfa+3T/ZsDAAAAAMuPyYRrW46x/t4kf2qt3T4F7QEAAACA5caEw7XW2q+XZEMAAAAAYHkzJRMaVNW6VbX6VNQFAAAAAMuLCYdrVbVHVf1bVa07tG7DqrowyU1Jbq6q45ZEIwEAAABgOppMz7UjkzyrtfanoXXvT/KkJNck+WOSV1XVc6awfQAAAAAwbU0mXHt0km/PXaiq1ZIcmOTc1to2SbZJ8tskL5/SFgIAAADANDWZcG3DJL8fWn5CklWTnJgkrbXbknwlXcgGAAAAAPd5kwnX7kqy2tDyk5K0JN8cWvfnJOtNQbsAAAAAYNqbTLj2qyS7Dy3/Y5Kft9Z+N7Ru83STGwAAAADAfd5kwrWTkjyqqr5bVd9K8qgknxtR5rFJrpqqxgEAAADAdLbSJMp+LMmOSQ5KUkm+nOS9czdW1eOTPCzJrKlsIAAAAABMVxMO11prdyc5pKpe3i2220YU+WWSxySZPXXNAwAAAIDpazI915IkrbU/j7H+prjeGgAAAAB/RyYdrlXVBukmM3hYktVbay8ZWr9lkh+11u6c0lYCAAAAwDQ0qXCtql6c5ENJVk133bWW5CWDzRsluSjJy5J8cgrbCAAAAADT0oRnC62qvZJ8PMnVSQ5IN8HBPK21Hye5Mskzp7B9AAAAADBtTabn2uuSXJfkya21P1fVY0Ypc0WSnaakZQAAAAAwzU2451qSHZJ8ZawJDQauTfKAxWsSAAAAACwfJhOurZzkL4sos06SOb1bAwAAAADLkcmEa7OTbL+IMk9IclXv1gAAAADAcmQy4doXkzypqp492saqemGSbZN8fioaBgAAAADT3WQmNPi3JAcnmVVVByZZO0mq6pVJnpTkWUl+nuQ/prqRAAAAADAdTThca639qaqenOTkJMO91z40+PmtJIe01hZ1XTYAAAAAuE+YTM+1tNZ+k2RmVW2bZKck909ya5KLW2uXLoH2AQAAAMC0Nalwba7W2hVJrhi5vqpWSnJEa+0ji9swAAAAAJjuJjOhwZiqc1iSqzN/mCgAAAAA3KctsudaVa2b5JVJHpfk7nTXVjuhtfbXwfZnpJvsYJvBLmctmaYCAAAAwPQybrhWVesn+V6SByWpwepnJtmvqvZKckKSFw22fSXJW1prly+pxgIAAADAdLKonmuvT7JFkh8m+Wy6EO0FSZ6c5Jwkeyf5bpJXtda+t+SaCQAAAADTz6LCtacl+XWSJ7TW/pYkVfXhJD9LsleSU5M8r7XWlmgrAQAAAGAaWtSEBlsk+a+5wVqStNbuTDcENEneLFgDAAAA4O/VosK11ZJcP8r6GwY/fzm1zQEAAACA5ceiwrVx6bUGAAAAwN+zRV1zLUm2q6pDR65Lkqp6QebPIjpPa+3kxW8aAAAAAExvEwnX9h/cRqokJ46xj3ANAAAAgPu8RYVrJy2VVgAAAADAcmjccK219sKl1RAAAAAAWN5MZFgoy5P1tk8OuWRZtwIAAICJOnPw88Bx5gz8+Mzu5yEXLOHGAJO1WLOFAgAAAMDfM+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Gmlye5QVSsm2SbJuklWHK1Ma+2bi9kuAAAAAJj2JhWuVdWbkxyVZO1FFB01dAMAAACA+5IJh2tVdXSStyW5Nclnkvw2yT1LqF0AAAAAMO1NpufaS5P8LsljW2s3LqH2AAAAAMByYzITGmye5GzBGgAAAAB0JhOuXZ8eEyAAAAAAwH3VZMK105PsVVWrLKnGAAAAAMDyZDLh2luSXJfkzKracgm1BwAAAACWG5MZ5nllkhlJNkmyT1XdmuSWUcq11tpDpqBtAAAAADCtTSZcWyHJPUl+M7SuRik32joAAAAAuM+ZcLjWWttiCbYDAAAAAJY7k7nmGgAAAAAwZDLDQhdQVWslWTvJra21P09dkwAAAABg+TCpnmtVtWJVvb6qrknypySzk/ypqq4ZrO8d1gEAAADA8mbCYVhVrZzkv5M8OUlL8tsk1yXZOMkWSd6V5KlVtXdr7W9T31QAAAAAmF4m03PtNUlmJjknycNaa1u01nYaTHSwTZIvJ3nSoBwAAAAA3OdNJlw7JMmPkzyztfbz4Q2ttV8keVaSK5M8b+qaBwAAAADT12TCta2SfLW1du9oGwfrv5rkIVPRMAAAAACY7iYTrv0tyRqLKLN6krv7NwcAAAAAlh+TCdeuSHJgVW0w2saqWj/JgUl+OBUNAwAAAIDpbjLh2oeTbJDke1X14qp6cFWtVlVbVtULk3x3sP3DS6KhAAAAADDdrDTRgq2106tquySvT/LxUYpUkn9rrZ0+RW0DAAAAgGltwuFakrTW3lhVX0ry4iSPSbJ2kluTXJbkU621i6a+iQAAAAAwPU0qXEuS1trFSS5eAm1hClx66e9T9bZl3QwAAGCaaO2ty7oJAPdpk7nmGgAAAAAwZMyea1X1wMHd37XW5gwtL1Jr7TeL3TIAAAAAmObGGxY6O0lL8rAkVw8tL0pbRL0AAAAAcJ8wXgh2crqg7NYRywAAAABAxgnXWmuHj7cMAAAAAH/vTGgAAAAAAD1NOFyrqjlV9eZFlPnXqrpn8ZsFAAAAANPfZHqu1eA2kXIAAAAAcJ831cNC103y1ymuEwAAAACmpfFmC01V7Tpi1RajrEuSFZM8MMnzklw1RW0DAAAAgGlt3HAtyQVJ2uB+S3LY4DaaSnJvkn+ZkpYBAAAAwDS3qHDt7elCtUrylnRh24WjlJuT5I9Jzm+t/WwqGwgAAAAA09W44Vpr7Zi596vqsCRnt9Y+tKQbBQAAAADLg0X1XJuntbblkmwIAAAAACxvpnq2UAAAAAD4uzHhnmtJUlWV5MAkT0myaZJVRinWWmt7TEHbAAAAAGBam3C4VlWrJPmvJDPTTXAwd6KDudrQegAAAAC4z5vMsNDXJdktyTuTbJAuSDsmySZJDkny2ySnJll5apsIAAAAANPTZMK1Zyf5QWvtra21P85d2Vr7Q2vt1CS7J3lGkldPbRMBAAAAYHqaTLj2kCTfGVpuSWbMW2jtl0nOSXL4lLQMAAAAAKa5yYRrdyf569DybemGhw77dZIHL26jAAAAAGB5MJlw7dp0M4TOdXWSnUaUeUySmxe3UQAAAACwPJhMuPadJDsPLZ+d5FFV9cmqenpVvS/JnkkumLrmAQAAAMD0tdIkyn4uyeZVtUVrbXaSDybZP8kL011nrZJck+T1U9tEAAAAAJieJhyutdYuyFCvtNbaHVW1S7qAbasks5N8ubV2x9Q2EQAAAACmp8n0XFtIa+2eJJ+forYAAAAAwHJlMtdcAwAAAACGjNlzraoO7Vtpa+3kvvsCAAAAwPJivGGhJyZpQ8s1Ynk0c8sI1wAAAAC4zxsvXHvhKOuelWTfJBemm9zgD0kekGS3JLsm+VKSs6a2iQAAAAAwPY0ZrrXWThperqp9kjw1yf6ttS+PKP62qto/yelJTpjyVgIAAADANDSZCQ3+NclZowRrSZLW2heTnJ3kzVPQLgAAAACY9iYTrj06yTWLKHNNkm37NwcAAAAAlh+TCdf+li5gG8+jk9zdvzkAAAAAsPyYTLh2XpJ9quqVVVXDG6pzZJKnJfn6VDYQAAAAAKar8WYLHen16WYF/fckr66qbye5PslGSZ6YZMskNw/KAQAAAMB93oTDtdbaL6pqxyQfTbJnkgePKHJukle01n45he0DAAAAgGlrMj3X0lq7JsneVbVpksckWTvJrUkua639bgm0DwAAAACmrUmFa3MNgjRhGgAAAAB/1yYzoQEAAAAAMGTMnmtV9akkLckbW2vXD5YnorXWXjwlrQMAAACAaWy8YaGHpwvX3ptuVtDDJ1hnSyJcAwAAAOA+b7xwbcvBz9+NWAYAAAAAMk641lr79XjLAAAAAPD3zoQGAAAAANDTeBMaPLBvpa213/TdFwAAAACWF+Ndc212uskJJqstol4AAAAAuE8YLwQ7Of3CNQAAAAD4uzDehAaHL8V2AAAAAMByx4QGAAAAANCTcA0AAAAAepr0xANV9bgkT0myaZJVRinSWmsvXtyGAQAAAMB0N+FwraoqyYlJnp+k0k12UENF2tB64RoAAAAA93mTGRb6yiQvSPKZJDukC9I+mGTnJG9McluSU5M8eGqbCAAAAADT02SGhR6W5Kq5s4h2HdlyS2vt4iQXV9X/JLk4yblJPj3F7QQAAACAaWcyPde2SfKNEevmhXOttcuSfCXJ/5mCdgEAAADAtDeZcK2S3Dq0/Jck640o8/MkD13cRgEAAADA8mAy4drv0s0QOtcvk2w/oszW6UI3AAAAALjPm0y49r0sGKZ9Ncnjq+rNVfWIqnpFkv3TXXdtUqrq/lV1+eD2h6r63dDyyovYd4eq+tAkjze7qr41Yt3lVfXjxazzR1V1RVVdWFUPGtrWquozQ8srVdWNVfWVwfJGVfWVqvphVf2kqv5rsH6Lqrpz6FxcXlWHTqZdAAAAACw5k5nQ4PNJdqiqLVtrv0ryb0mek+RtSY5JN2z05iSvn2wjWmt/TLJdklTVMUlub629f+72qlqptXbPGPtekuSSyR4zyZpVtXlr7bdV9bApqnO31tpNVfW2JG9K8tLB+r8keWRVrdZauzPJXul6As719iTnttb+PUmqatuhbb9orW3Xoy0AAAAALGHj9lyrqv2qaoUkaa2d3Vp72CBYS2vt5iSPSXJ0ko8neUOSR7XWfjYVDauqE6vquKo6P8l7q+rxVfW/VXXZ4Oc2g3Izh3qAHVNVn6qqC6rql1X1z+Mc4vQkBw3uPzfJrKFj961zrouy4BDapOvp9/TRjpdk4yTXzl1orV0xgWMAAAAAsIwtaljo2Ul+XVVvHx7mOFdr7dbW2vtba//UWntva+26KW7fPyTZs7X2L0l+lmTX1tpjkrwlybFj7PPQJE9J8vgkb62qGWOUOzPJswb3903y5XHaMdE653pqunM37NQkB1fVqkm2TfLdoW0fSfLJqjq/qv61qjYZ2vaQEcNCn7SIYwMAAACwlCxqWOh5SXZPN8TxjVV1brpeal9qrc1Z0o1LcsbQcdZOclJVbZ2kJRkr4DqntXZXkruq6oYkG2WoV9iQm5P8qaoOTvLTJHeM046J1nl+VW2U5IZ052ye1toVVbVFul5r/zVi2/9U1YPThXJPS3JZVT1ysHmRw0Kr6mVJXtYtrT1eUQAAAACm0Lg911preyV5cJJ3JbkuXe+tM5P8tqreNQiElqThmUffkeT81toj0/U0W3WMfe4auj8n4weIp6XrNTZrnDKTqXO3JA9KcmW666iN9KUk7x/teK21m1trn2utvSDJ95Psuog2De/78dbaDq21HZL7TXQ3AAAAABbTImcLba39urX25nSh0X5JvpJk/XTXWLu6qr5WVQdW1WQmR+hj7cyfBODwKarzrHQTM/zPFNWXwYQFr05yaFWtN2Lzp5K8vbX2o+GVVbV7Vd1vcH/NJA9J8pupahMAAAAAS8Yiw7W5Wmv3tta+0lrbP8kD0w17nJ1kz3Q9wH5XVe8dDNtcEv4tybur6jtJVpyKCltrtw2uFfe3qahvqN7r0vVOe8WI9dfOnRF0hO2TXFJVV6SbDOE/W2vfH2wbec21iUyoAAAAAMBSUK21xaugao8kL0nyzCQrJ2mttSXdi40xVG3SkiOWdTMAAIBporW3LusmsAgHndn9PO3AcQrNnNn9vOCCJdwaYDRVdWl3Oa6FTUUIdmGS9ZJsmW42TQAAAAD4u9A7XKuqbdL1WDs03TXYKsmvknxyapoGAAAAANPbpMK1qlo1yXPShWq7pAvU7k7yhSSfaK19bcpbCAAAAADT1ITCtaraLslLkxySZK10odovkvxnkk+31m5YUg0EAAAAgOlq3HCtqo5IF6o9Jl2g9rckZyT5eGvtG0u+eQAAAAAwfS2q59rHBj+vTvKJJCe11m5ask0CAAAAgOXDosK1Wel6qV24NBoDAAAAAMuTccO11trzllZDAAAAAGB5s8KybgAAAAAALK+EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ5WWtYNYGptv/0mueSSty7rZgAAAAD8XdBzDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPS00rJuAFPr0kt/n6q3LetmAAAAMEEbvvKwJEk9+6Qxy5yf2UmS3XzfW2pae+uybgLLCT3XAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPS0zMK1qrp/VV0+uP2hqn43tLzyIvbdoao+NMnjza6qb41Yd3lV/bhP+0fUs0VV3Tmo7ydVdXJVzRhsm1lVrapePFT+MYN1rx0s71hV3x3s/9OqOmaw/vCqunHovFxeVQ9f3PYCAAAAMDVWWlYHbq39Mcl2STIIk25vrb1/7vaqWqm1ds8Y+16S5JIeh12zqjZvrf22qh7WY//x/KK1tl1VrZjk3CTPSfLZwbYfJTkoyScHywcn+eHQvicleU5r7YeD/bcZ2nZaa+2VU9xWAAAAAKbAtBoWWlUnVtVxVXV+kvdW1eOr6n+r6rLBz20G5WZW1VcG94+pqk9V1QVV9cuq+udxDnF6upArSZ6bZNbQsbeoqm9V1Q8Gt50H6w+oqq9XZ+OqurqqHjDWAVprc5J8L8mmQ6t/k2TVqtqoqirJU5N8dWj7hkmum7t/a+0nEzphAAAAACxT0ypcG/iHJHu21v4lyc+S7Npae0yStyQ5dox9HprkKUken+Stc4dkjuLMJM8a3N83yZeHtt2QZK/W2mPTBXAfSpLW2llJ/pDkFUk+keStrbU/jNX4qlo1yROS/Pcox352kp2T/CDJXUPbjk9yVVWdVVVHDOqY66ARw0JXG+vYAAAAACxdy2xY6DjOGPT+SpK1k5xUVVsnaUnGCs3Oaa3dleSuqrohyUZJrh2l3M1J/lRVByf5aZI7hrbNSPLhqtouyZx0Id9cRyb5cZKLW2uzMrqHVNXlSbZOcmZr7YoR209Pclq6IHBWupAtSdJae3tVfTbJ3kkOSderbuZg8yKHhVbVy5K8rFtae7yiAAAAAEyh6dhz7S9D99+R5PzW2iPT9TRbdfRdFugFNifjh4anJflIhoaEDhyV5Pokj06yQ5LhSRU2TXJvko2qaqxz9ovW2nZJtkqyY1XtN7xx0Nvt7iR7JTlv5M6ttV+01j6WZI8kj66q+4/zGEbu+/HW2g6ttR2S+010NwAAAAAW03QM14atneR3g/uHT1GdZyX5tyT/M8qxrmut3ZvkBUlWTLqJFZJ8Ol2Psp8mec14lbfWrkvy+iRvGGXzW5K8bqhnXgbHePrgWmxJ1/NtTpJbJv6QAAAAAFgWpnu49m9J3l1V38kg7FpcrbXbWmvvba39bcSmjyY5rKouTjckdG4Pujcm+VZr7VvpgrWXTGCm0bOT3K+qnjTi2P/bWjt7lPIvSHfNtcuTfCbJ84YCuJHXXNt5lP0BAAAAWAaqtbas28AUqtqkJUcs62YAAAAwQRu+8rAkyQ0fPmnMMufn00mS3fLCpdImktbeuqybwDRSVZd2l+Na2HTvuQYAAAAA05ZwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANBTtdaWdRuYQjvssEO75JJLlnUzAAAAmKCDzux+nnbgOIVmzux+XnDBEm4NMJqqurS1tsNo2/RcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoSbgGAAAAAD0J1wAAAACgJ+EaAAAAAPQkXAMAAACAnoRrAAAAANCTcA0AAAAAehKuAQAAAEBPwjUAAAAA6Em4BgAAAAA9CdcAAAAAoCfhGgAAAAD0JFwDAAAAgJ6EawAAAADQk3ANAAAAAHoSrgEAAABAT8I1AAAAAOhJuAYAAAAAPQnXAAAAAKAn4RoAAAAA9CRcAwAAAICehGsAAAAA0JNwDQAAAAB6Eq4BAAAAQE/CNQAAAADoqVpry7oNTKGqui3JVcu6HZBk/SQ3LetGwIDXI9OF1yLTidcj04XXItOJ1yNjeVBrbYPRNqy0tFvCEndVa22HZd0IqKpLvBaZLrwemS68FplOvB6ZLrwWmU68Hvn/7d15kBzlecfx7y/CCAwFCMnGwhwrLgMpQFyKuO/DwgGVwRXAFEggysaJgcJOAoEYSIKxwZgjwkUAG3H44AiHCMYELEQAcRYREke4ZZAFEghLXBZI6Mkf7zuoGfXszszOsrOj36eqq1vv+/bb746e6lk9evvtZvixUDMzMzMzMzMzsyY5uWZmZmZmZmZmZtYkJ9c6z+X9PQCzzLFo7cTxaO3CsWjtxPFo7cKxaO3E8WgN8wsNzMzMzMzMzMzMmuSZa2ZmZmZmZmZmZk1ycs3MzMzMzMzMzKxJTq4NUJL+QdKzkp6WdEBJ/WBJ10t6UdIjkrr6YZjWpuqIn1MkPSNphqTfS9qwUPexpOl5m1woH5Fj7YUceyvnckm6JMfiDEnbFc45UNJzue7UQvnaku7Ofd0taUih7rTc/rmysdvAU0c8jpP0ZiHuJhTqjslx8oKkYwrljkdrWB2xeGEhDp+XtKBQ53uj9Uod8be7pCckLZF0WFVdW94LJW0vaWauu0SSWveJWV9qNh4ljZT0UD5vhqS/KdRNkvRK4V45Mpc7Hq2mXt4b2/K72bHYoSLC2wDbgC8AfwBWISVI1y5p8x3gsnx8OHB9f4/bW3tsdcbPXsDn8/EJxfgB3qvR7w3A4fn4MuCEfDwGuBMQMBp4JJcPAl4CNgJWBp4Etsx15wGn5uNTgR/n4y1zu8HAiHz+oP7+TL31eTyOAyaWlK8NvJz3Q/LxEMejt76Kxar23wV+Ufiz743e+jT+gC5ga+Aa4LBCedveC4FHgZ3yde4Evtrfn7W3Po/HzYBN8/G6wOvAWvnPk4ptC+c4Hr21PBZzXVt+NzsWO3PzzLWBaWPguYhYFBFLI+LtkjaHAFfn45uAfZwRt6zH+ImIeyPig/zHh4H1uuswx9bepFiDFHtj8/EhwDWRPAysJWk4MAp4MSJejoiPgN/ktpVzrq7R128i4sOIeAV4MfdjA1c997NaDgDujoi3I+JPwN3AgY5Ha1KjsXgE8OvuGjgWrQH1fDfPiogZwNKqqra8F+b+1oiIhyIiSP/wrZxj7a3peIyI5yPihXw8B5hHSpB0x/FotfTm3ljK90brK06uDUyrAB/10ObLwGsAEbEEWAgM7eNx2cBQT/wUHUf6H5VPzpf0uKSHJY3NZUOBBTnWAGaTYhAKsVhVV6scYJ2IeB0g77/YQ182cNUbj4fm6fk3SVo/l9WKB8ejNaPue6PSo/IjgCnF831vtF5o9Lu5qF3vhV/Ox2V9WXvrTTx+QtIo0iyflwrF5+Tv8wslDc5ljkerpbex2I7fzY7FDuXk2gAjaSvgSmC3/Oz4ObWalpRF343MBoIG4qfS/ihgB+D8QvEGEbEDcCRwkaSN6T7eatU1E6OO6w7SQDzeDnRFxNbAPSz738FmYsvxaMtp9N5IWm7hpoj4uFDme6M1pYn4W66LkrJ2uBc6LgegFsRjpZ/hwLXA+IiozCg6Ddgc2JH0GPM/VpqXdOF4XMG1KBbb8bvZsdihnFwbYCJiJjABuD8iRkbE6TWazgbWB5C0ErAm0MjjVtaBGogfJO0LnA4cHBEfFvqYk/cvA1OBbYG3SNOmV8rN1gPm5ONPYrGqrlY5wNz8S1nll7N5PfRlA1C98RgR8wsxeAWwfT6uFQ+OR2tII/fG7HCqHgn1vdGa1UT8VWvXe+FsPr2shONyAGhBPCJpDeAO4Iz8aF2l79fz43YfAlex7PF1x6MtpxWx2KbfzY7FDuXkWueaDByTjw8DpuRnus16JGlb4D9IibV5hfIhlSn8koYBuwDP5Ni6lxRrkGLvtnw8GTg6v31nNLAwT5l+DNg0v61nZdI/VicXzjmmRl+HK70NdwSwKWlBUOtglV9YsoOBZ/PxXcD+OS6HAPsDdzkerS9J+gpp0fiHCmW+N1p/ast7Ye7vXUmj8xpHRxfOsQ6V4+YW0rpVN1bVVRIQIq0x9VSucjxay7Xrd7NjsYNFG7xVwVv9G+km8CJpDbXpeRtf0m4V4Mbc9lFgo/4eu7f+3xqIn3uAuYU2k3P5zsBM0ptvZgLHFc7ZKMfaizn2BudyAZeS1tuYCexQOGcM8HyuO71QPhT4PfBC3q9dqDs9t38Ov1lnQG8NxOO5wNM57u4FNi/UHZv7eLF4ruPRW1/EYm57FvCjqjLfG731efyRHqWbDbwPzAeeLtS15b2QtLTEU7luIqD+/ry99W08AkcBiwvnTgdG5ropOd6eAq4DVnc8euvDWGzb72bHYmduyn+5ZmZmZmZmZmZm1iA/FmpmZmZmZmZmZtYkJ9fMzMzMzMzMzMya5OSamZmZmZmZmZlZk5xcMzMzMzMzMzMza5KTa2ZmZmZmZmZmZk1ycs3MzMzMAJDUJSkkTervsVS045jalaRj82c1qpf9HJr72adVYzMzM+tkTq6ZmZlZR8lJgZ62Pft7nI2QtFke9x8lDeqh7S657ZOf1fg6hZKxkq6X9Iqk9yUtkjRb0m8lnShpaMl5s0pibHH++7pJ0uiSc7pKzlkiaa6kOyR9tcGxrw78G3B7RDzazc93oqTpkj6Q9J6kGZLOrWp6M/AE8FNJ/veCmZlZD1bq7wGYmZmZ9ZGzu6mb9VkNohUi4nlJ9wF7AAcBk7tpPiHvL+/zgX02/ghsASzsy4tIWhe4HtgV+AC4F7gFWAR8CdgZuBg4R9ImETG3pJuLgQX5eHVga+DrwCGSDo6IO0vOWQhclI9XAbYBxgBjJJ0UEZfU+SOcCAwHftRNmx8CpwJzgeuAd4AtgWOB0yqNIiIk/Zj0eRwO/KrOMZiZma2QFBH9PQYzMzOzlpEUABGh/h5LK0k6EvglaWbSwTXarAHMAQSsGxENJaQkdQGvAFdHxLheDXgAkbQa8BCwFXAjcEJEzC9pNxo4FxgfEbMK5bOADYERxfJc9z3gJ8B9EbFnobyL9Fn/ISK6qs4ZD/wCeB/4YkR80MP4B+W+FkXEZjXarArMBxYDm0TEm4W6NSLinar2g4E3gKcjYtfurm9mZrai8zRvMzMzW6FJOqvyqKikIyU9kh+Xm1VPfW4zXNKl+fHAjyS9KelmSduXXG9c7m+cpAMlTZW0sJIU7MZ/kpIjY/IsqzJHAqsBN0TEQknrSvqBpAclvZHHNkfSryRt0cBnNLXW+Io/T0ndepImSnpZ0oeS5kuaLGnHBq5duuaapEm5vEvStyTNzI9wzpV0uaQ1670G8H1SYu1+4IiyxBpARDwM7A281kDf/533X2jgnEmkxNpqwF/W0X4/YH3STLNatgRWBR4sJtYAqhNruexD4FZgF0mb1zVqMzOzFZSTa2ZmZmbJ90izhV4FJgLVj/CV1ksaATwOfAd4CbgAuIv0+OY0SV+rcb3DgP8C3gUuA27obnA52XEtMAgYX6NZ5ZHQK/J+d9JjgAtIybkLgYfztR+TtE131+wNSdsB00mfy3PAvwO35zE9IGlMiy51Xt6eBC4lPUZ6POmRznodl/f/GhEfd9cwkm7bVNk37x9v4JxPXbKBazzQTZtKsvGtBq79YFX/ZmZmVsJrrpmZmVlHknRWjapFEVG2LtXewE4R8b81zqtVfxmwLnBGRJxTuP7PgP8Brpa0YUS8V3XeGGBMRPyuhx+l6HLgZOA4ST+MwvoekkYC25Me45uWi6cA60TEu8VOclLtQdL6XA0tnF8PSSuRkoWrA3tFxH2FunWBx4CfS+rKScPeGA1sFRGvFq49BdhL0qhai/sXxrMBadbXEtLfV2+cLGlBPl6NNBtuP2AahTXN6jA+n/8+8HQd7SuPbXaXwKv8p/rSBsbxWN7vTkoom5mZWQkn18zMzKxTnVmjfCHli75f3k1irbRe0nrA/qTZbOcV6yJimqRfA0eRFrW/pqq/2xpMrBERz0p6gJRM2Qe4p1B9fGWchfbzavTzpKQpwP6SPhcRixsZRx0OAjYGflJMrOVrz5F0HmkR/32A3/byWv9SSazl/pdIugrYDRgFdJtcI72sAGB+WaJP0lhgZFXx1IiYWtLXSSVlr5FeCPBGjeuvVUgEr0J6CUIl4flPEfHnWgMv2ABYXGOduC5gHLBRLhpZlXiu9bNQGPMGdYzBzMxsheXkmpmZmXWkJl5o0FMSpqx+27y/v0aCagopubYtyyfXerpeLVeQkmvHk5NrebH6b5LebHltsbGkg4BvAzsAw1j+979hwOtNjqWWnfJ+wxozCDfN+y3ofXKtbLZWZU20IXWcX4mTWo9fjgWOKSmfWlL2yQsNJK0CbAL8gDTra2fS31G1NVmWCP4YeJv0yPHEiKj3sxkK/KlGXRefTjRvk7eiqTXOfTvvh9U5DjMzsxWSk2tmZmZmSa2ZRd3VV9axqpWcqpSv1cT1armBNOtrrKRhEfEW8I08lusi4pMki6QTgYtJiZe7STPsPiAlksaSkiyDmxxHd4bm/Td6aLd6C661oKRsSd4PquP8yt/RMEmDq2ev5bemjgOQNIFl69l1KyIWAU9J+iawI3CkpIkR8VBV0+XeFtqEP5NmvZWNYyogSfuSYqCRN8GuWujfzMzManByzczMzCzpaeH4svqFef+lkjqA4VXtGrle+SAiFkm6DvgucDTwU0oeCc1rj51NSuJtFxGfSgBK2on6La30GRFLqurWKmlf+XkPiYjJDVznMxcRr0p6jbTu2u6kBFQr+18s6QnSDLJRQHVyrRXmAZv2wSO+lSRp6ePFZmZmlvhtoWZmZmbNq6zBtmtOZlXbK++faPF1K0m0CZI2Jz0m+n8RcX+hzTBS4mtaSWJtdWC7Bq5XmQ23fkndDiVlD+f9bg1coz/9PO9Pl9QXvx9XHk/tq9+9Z+T9V1rc7+Z5P73F/ZqZmXUUJ9fMzMzMmhQRs0kznbpIb/H8hKS/Ao4kJaZuafF1nyIlsLZgWaKt+nHFeaRHQLfPybTKuD5HelS0kXW0KuvDHV8slLQPcERJ+9uAl4C/lTSmrENJO0n6fANj6EsXAE8BewC/lLR2jXZrNdqxpB1ZlmS8r7u2vTA170e3uN9Kf/e2uF8zM7OO4sdCzczMrCPVWEi/4taImN6iS30beBA4X9L+pAX21yetN7YUGB8R77boWkWXk5IfuwEfAlcXKyNiqaRLgFOBmZJuA1YmzaZbm5Qw2Yv6XAX8PXCapG2AZ4DNSG+1vAU4tOraiyV9HbgLuEPSNNLspw9In82OpLdXDs9l/Soi3pN0AHAjcDhwcH6b6nPAR8A6pJl+I4F3WTZjsdrJkhbk48oLDQ4m/c49MSJaPYOx4lbSOnwHAFcWKwpvO631ttCKiyJiQVXZ/qQ17aa0aJxmZmYdyck1MzMz61RndlM3ixY96hYRL0vaATgDGAPsCbwD/A44JyIea8V1SlwPXEh6kcHNETG/pM0/A28CE4BvkdZCuzuP9ex6LxQR8yTtAZxPWpdsD1IScT9gBFXJtXzOjJyIOwX4GjCelGx8nZScOhN4q94x9LWImCNpN+AQ0ozDUcC+pLeJzgdmkmYnXlfjswY4qXC8lPS2zanAlRFxQ9+MPM2glHQ78NeShhRfasHybzste1sowCQKL4eQtBkpeXtxRPR7AtTMzKydKaKptXTNzMzMzKxNSNqZNIPylIi4sAX9XQD8HbBFRLzc2/7MzMw6mZNrZmZmZmYdQNINpJmFG/Vmtpmk4aQ1834WEd9v1fjMzMw6lZNrZmZmZmYdQNIGwLHAjRHxdC/62Ym03trFJeuwmZmZWRUn18zMzMzMzMzMzJr0F/09ADMzMzMzMzMzs4HKyTUzMzMzMzMzM7MmOblmZmZmZmZmZmbWJCfXzMzMzMzMzMzMmuTkmpmZmZmZmZmZWZOcXDMzMzMzMzMzM2vS/wP3DgoIQLjmBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "x_ticks = list(range(0,max([results_mid.iloc[0][2],(round(results_mid.iloc[1][2])+1000000)]),2500000))\n",
    "y_ticks = [1,2,3,4]\n",
    "y_labels = ['Train Max RMSE','Train Min RMSE','Test Max RMSE','Test Min RMSE']\n",
    "\n",
    "train_max_pos = 1\n",
    "train_max = results_mid.iloc[0][2]\n",
    "ax.barh(train_max_pos,train_max,color='navy')\n",
    "\n",
    "train_min_pos = 2\n",
    "train_min = results_mid.iloc[0][3]\n",
    "ax.barh(train_min_pos,train_min,color='navy')\n",
    "\n",
    "test_max_pos = 3\n",
    "test_max = results_mid.iloc[1][2]\n",
    "ax.barh(test_max_pos,test_max,color='orange')\n",
    "\n",
    "test_min_pos = 4\n",
    "test_min = results_mid.iloc[1][3]\n",
    "ax.barh(test_min_pos,test_min,color='orange')\n",
    "\n",
    "ax.axvline(x = results_mid.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
    "ax.axvline(x = results_mid.iloc[1][1],label = 'Average Test RMSE',color='red')\n",
    "\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels([f'£ {a}' for a in x_ticks])\n",
    "ax.set_xlabel('Error Value in GBP (£)',fontsize=20)\n",
    "ax.set_ylabel('Validation Results',fontsize=20)\n",
    "ax.set_title('DecisionTreeRegression Results-Midfielders',fontsize=30)\n",
    "ax.legend(fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the above workflow for Defenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "#Splitting the data\n",
    "\n",
    "X = defence.drop(['Player','Value','Club','Position','Nation','League'],axis=1)\n",
    "y = defence['Value']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=0)\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Pre-processing\n",
    "\n",
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Fitting data into first DecisionTree to get important features\n",
    "\n",
    "#Initiating DecisionTreeRegressor\n",
    "DtReg = DecisionTreeRegressor()\n",
    "\n",
    "DtReg.fit(X_train_scaled,y_train);\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Extracting Most Important Features\n",
    "\n",
    "feat_importances = pd.Series(DtReg.feature_importances_, index=X_train.columns)\n",
    "\n",
    "#Creating a list of top features\n",
    "top_features = list(feat_importances.nlargest(10).keys())\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Repeating Workflow. This time only with most important features.\n",
    "\n",
    "#This is because of computational constraints from GridSearching over 500 features.\n",
    "\n",
    "X = defence.drop(['Player','Value','Club','Position','Nation','League'],axis=1)[top_features]\n",
    "y = defence['Value']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=0)\n",
    "\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#Pre-processing\n",
    "\n",
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)\n",
    "\n",
    "############################################################################\n",
    "\n",
    "#GridSearching improved hyperparameters\n",
    "\n",
    "# Hyper parameters range intialization for tuning \n",
    "\n",
    "parameters={\"splitter\":[\"best\",\"random\"],\n",
    "            \"max_depth\" : [1,3,5,7,9,11,12],\n",
    "           \"min_samples_leaf\":[1,2,3,4,5,6,7,8,9,10],\n",
    "           \"min_weight_fraction_leaf\":[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],\n",
    "           \"max_features\":[\"auto\",\"log2\",\"sqrt\",None],\n",
    "           \"max_leaf_nodes\":[None,10,20,30,40,50,60,70,80,90] }\n",
    "\n",
    "tuning_model=GridSearchCV(DtReg,param_grid=parameters,scoring='neg_root_mean_squared_error',cv=5,verbose=3)\n",
    "\n",
    "tuning_model.fit(X_train_scaled,y_train)\n",
    "\n",
    "\n",
    "#Extracting the best Parameters\n",
    "param_values = list((tuning_model.best_params_).values())\n",
    "\n",
    "############################################################################\n",
    "        \n",
    "# Traning model with all features using better Decision Tree Hyperparameters\n",
    "\n",
    "#Initiating a new DecisionTreeRegressor\n",
    "DtReg_Grid = DecisionTreeRegressor(max_depth=param_values[0],\n",
    "                                 max_features=param_values[1],\n",
    "                                 max_leaf_nodes=param_values[2],\n",
    "                                 min_samples_leaf=param_values[3],\n",
    "                                 min_weight_fraction_leaf=param_values[4],\n",
    "                                 splitter=param_values[5])\n",
    "\n",
    "DtReg_Grid.fit(X_train_scaled,y_train);\n",
    "\n",
    "cross_val_train = cross_val_score(DtReg_Grid, X_train_scaled, y_train, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "cross_val_test = cross_val_score(DtReg_Grid, X_test_scaled, y_test, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "\n",
    "#Train Results\n",
    "cross_val_train_mean = round(-(cross_val_train.mean()),2)\n",
    "cross_val_train_max = round(-(cross_val_train.min()),2)\n",
    "cross_val_train_min = round(-(cross_val_train.max()),2)\n",
    "\n",
    "#Test Results\n",
    "cross_val_test_mean = round(-(cross_val_test.mean()),2)\n",
    "cross_val_test_max = round(-(cross_val_test.min()),2)\n",
    "cross_val_test_min = round(-(cross_val_test.max()),2)\n",
    "\n",
    "#Creating a Dataframe to display Validation Results\n",
    "results_def = pd.DataFrame(columns=['Train/Test','Avg RMSE','Max RMSE','Min RMSE'])\n",
    "results_def.loc[0] = ['Train',cross_val_train_mean,cross_val_train_max,cross_val_train_min]\n",
    "results_def.loc[1] = ['Test', cross_val_test_mean, cross_val_test_max, cross_val_test_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "x_ticks = list(range(0,max([results_def.iloc[0][2],(round(results_def.iloc[1][2])+1000000)]),2500000))\n",
    "y_ticks = [1,2,3,4]\n",
    "y_labels = ['Train Max RMSE','Train Min RMSE','Test Max RMSE','Test Min RMSE']\n",
    "\n",
    "train_max_pos = 1\n",
    "train_max = results_def.iloc[0][2]\n",
    "ax.barh(train_max_pos,train_max,color='navy')\n",
    "\n",
    "train_min_pos = 2\n",
    "train_min = results_def.iloc[0][3]\n",
    "ax.barh(train_min_pos,train_min,color='navy')\n",
    "\n",
    "test_max_pos = 3\n",
    "test_max = results_def.iloc[1][2]\n",
    "ax.barh(test_max_pos,test_max,color='orange')\n",
    "\n",
    "test_min_pos = 4\n",
    "test_min = results_def.iloc[1][3]\n",
    "ax.barh(test_min_pos,test_min,color='orange')\n",
    "\n",
    "ax.axvline(x = results_def.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
    "ax.axvline(x = results_def.iloc[1][1],label = 'Average Test RMSE',color='red')\n",
    "\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels([f'£ {a}' for a in x_ticks])\n",
    "ax.set_xlabel('Error Value in GBP (£)',fontsize=20)\n",
    "ax.set_ylabel('Validation Results',fontsize=20)\n",
    "ax.set_title('DecisionTreeRegression Results-Defenders',fontsize=30)\n",
    "ax.legend(fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Model Performance for all three Positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attackers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Train/Test</th>\n",
       "      <th>Avg RMSE</th>\n",
       "      <th>Max RMSE</th>\n",
       "      <th>Min RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>14645838.77</td>\n",
       "      <td>17853648.07</td>\n",
       "      <td>13111885.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>18420271.01</td>\n",
       "      <td>22033897.38</td>\n",
       "      <td>13987068.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Train/Test     Avg RMSE     Max RMSE     Min RMSE\n",
       "0      Train  14645838.77  17853648.07  13111885.01\n",
       "1       Test  18420271.01  22033897.38  13987068.28"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_att"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Midfielders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Train/Test</th>\n",
       "      <th>Avg RMSE</th>\n",
       "      <th>Max RMSE</th>\n",
       "      <th>Min RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>13509705.19</td>\n",
       "      <td>15052857.95</td>\n",
       "      <td>11881107.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>13637991.79</td>\n",
       "      <td>16259954.48</td>\n",
       "      <td>11782082.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Train/Test     Avg RMSE     Max RMSE     Min RMSE\n",
       "0      Train  13509705.19  15052857.95  11881107.24\n",
       "1       Test  13637991.79  16259954.48  11782082.80"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_mid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Train/Test</th>\n",
       "      <th>Avg RMSE</th>\n",
       "      <th>Max RMSE</th>\n",
       "      <th>Min RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>10424269.55</td>\n",
       "      <td>12191782.81</td>\n",
       "      <td>9745567.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>12187829.81</td>\n",
       "      <td>14583295.48</td>\n",
       "      <td>9000880.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Train/Test     Avg RMSE     Max RMSE    Min RMSE\n",
       "0      Train  10424269.55  12191782.81  9745567.61\n",
       "1       Test  12187829.81  14583295.48  9000880.77"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_def"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing RMSEs together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "x_ticks = list(range(0,int(max([max(results_att['Avg RMSE']),max(results_mid['Avg RMSE']),max(results_def['Avg RMSE'])])+1000000),2500000))\n",
    "y_ticks = [1,2,3,4,5,6]\n",
    "y_ticks = [1,2,3,4,5,6]\n",
    "y_labels = ['Defence Train RMSE','Defence Test RMSE',\n",
    "            'Midfield Train RMSE','Midfield Test RMSE',\n",
    "            'Attack Train RMSE','Attack Test RMSE',]\n",
    "\n",
    "#Defence Results\n",
    "def_train_pos = 1\n",
    "def_train = results_def.iloc[0][1]\n",
    "ax.barh(def_train_pos,def_train,color='limegreen')\n",
    "\n",
    "def_test_pos = 2\n",
    "def_test = results_def.iloc[1][1]\n",
    "ax.barh(def_test_pos,def_test,color='limegreen')\n",
    "\n",
    "#Midfield Results\n",
    "mid_train_pos = 3\n",
    "mid_train = results_mid.iloc[0][1]\n",
    "ax.barh(mid_train_pos,mid_train,color='dodgerblue')\n",
    "\n",
    "mid_test_pos = 4\n",
    "mid_test = results_mid.iloc[1][1]\n",
    "ax.barh(mid_test_pos,mid_test,color='dodgerblue')\n",
    "\n",
    "#Attack Results\n",
    "att_train_pos = 5\n",
    "att_train = results_att.iloc[0][1]\n",
    "ax.barh(att_train_pos,att_train,color='salmon')\n",
    "\n",
    "att_test_pos = 6\n",
    "att_test = results_att.iloc[1][1]\n",
    "ax.barh(att_test_pos,att_test,color='salmon')\n",
    "\n",
    "\n",
    "#Labels\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels([f'£ {a}' for a in x_ticks])\n",
    "ax.set_xlabel('Error Value in GBP (£)',fontsize=20)\n",
    "ax.set_ylabel('Validation Results',fontsize=20)\n",
    "ax.set_title('DecisionTreeRegression Average Results',fontsize=30);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (learn-env)",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}