Data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Models and training tools\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import copy\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "from BestModelFinders import ANNModel, init_weights, find_best_Ridge, find_best_KNN, find_best_RF, find_best_ANN, find_best_logistic\n",
    "\n",
    "# Statistics tools\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.anova import AnovaRM\n",
    "import statsmodels.formula.api as smf\n",
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HR_data = pd.read_csv('HR_data.csv')\n",
    "HR_data.head()\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create scatterplots of all input scatterplots against frustration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['HR_Mean', 'HR_Median', 'HR_std', 'HR_Min', 'HR_Max', 'HR_AUC']\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "\n",
    "for i, feature in enumerate(features):\n",
    "    plt.subplot(3, 2, i + 1)\n",
    "    plt.scatter(HR_data[feature], HR_data['Frustrated'], alpha=0.5)\n",
    "    plt.title(f'{feature} vs Frustrated')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Frustrated')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup folds for crossvalidation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_combinations = HR_data[['Cohort', 'Round']].drop_duplicates().values\n",
    "\n",
    "# Create folds (note that there are different individuals in different cohorts, so we do not have to account for individuals explicitly)\n",
    "folds = []\n",
    "for cohort, round_ in unique_combinations:\n",
    "    test_mask = (HR_data['Cohort'] == cohort) & (HR_data['Round'] == round_)\n",
    "    train_mask = ~((HR_data['Cohort'] == cohort) | (HR_data['Round'] == round_))\n",
    "    \n",
    "    test_data = HR_data[test_mask]\n",
    "    train_data = HR_data[train_mask]\n",
    "    \n",
    "    folds.append((train_data, test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtain errors for all different types of models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors = {'Baseline':[], 'Ridge':[], 'KNN':[], 'RF':[], 'ANN':[], 'Logistic':[]}\n",
    "for par_data, val_data in folds:\n",
    "    scaler.fit(par_data[inputcols])\n",
    "    x_par = scaler.transform(par_data[inputcols])\n",
    "    y_par = par_data['Frustrated']\n",
    "    x_val = scaler.transform(val_data[inputcols])\n",
    "    y_val = val_data['Frustrated']\n",
    "\n",
    "    baseline = np.mean(y_par)\n",
    "    baseline_errors = (y_val - baseline)**2\n",
    "    errors['Baseline'].append(baseline_errors)\n",
    "\n",
    "    ANN_model_params = find_best_ANN(x_par, y_par.values)\n",
    "    model = ANNModel()\n",
    "    model.load_state_dict(ANN_model_params)\n",
    "    ANN_errors = ((model(torch.tensor(x_val).float())).detach().numpy()-y_val.values)**2\n",
    "    errors['ANN'].append(ANN_errors)\n",
    "\n",
    "    Ridge_hyperparams = find_best_Ridge(x_par, y_par.values)\n",
    "    ridge_model = Ridge(alpha = Ridge_hyperparams)\n",
    "    ridge_model.fit(x_par, y_par)\n",
    "    ridge_errors = (ridge_model.predict(x_val)-y_val.values)**2\n",
    "    errors['Ridge'].append(ridge_errors)\n",
    "\n",
    "    KNN_hyperparams = int(find_best_KNN(x_par, y_par.values))\n",
    "    KNN_model = KNeighborsRegressor(n_neighbors = KNN_hyperparams)\n",
    "    KNN_model.fit(x_par, y_par)\n",
    "    KNN_errors = (KNN_model.predict(x_val)-y_val.values)**2\n",
    "    errors['KNN'].append(KNN_errors)\n",
    "\n",
    "    RF_hyperparams = int(find_best_RF(x_par, y_par.values))\n",
    "    RF_model = RandomForestRegressor(n_estimators = RF_hyperparams)\n",
    "    RF_model.fit(x_par, y_par)\n",
    "    RF_errors = (RF_model.predict(x_val)-y_val.values)**2\n",
    "    errors['RF'].append(RF_errors)\n",
    "\n",
    "    logistic_hyperparams = find_best_logistic(x_par, y_par.values)\n",
    "    logistic_model = LogisticRegression(C=logistic_hyperparams, penalty='l2',max_iter = 10**6)\n",
    "    logistic_model.fit(x_par, y_par)\n",
    "    logistic_errors = (logistic_model.predict(x_val)-y_val.values)**2\n",
    "    errors['Logistic'].append(logistic_errors)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The structure of errors_df was horrible, so I restructured below and save to a .csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for fold 0:\n",
      "\n",
      "24\n",
      "Data for fold 1:\n",
      "\n",
      "24\n",
      "Data for fold 2:\n",
      "\n",
      "24\n",
      "Data for fold 3:\n",
      "\n",
      "24\n",
      "Data for fold 4:\n",
      "\n",
      "18\n",
      "Data for fold 5:\n",
      "\n",
      "18\n",
      "Data for fold 6:\n",
      "\n",
      "18\n",
      "Data for fold 7:\n",
      "\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "unique_folds = errors_df['Fold'].unique()\n",
    "\n",
    "all_data = [0 for i in range(168)]\n",
    "\n",
    "# Loop through each fold and print the fold number\n",
    "for fold in unique_folds:\n",
    "    fold_df = errors_df[errors_df['Fold'] == fold]\n",
    "    print(f'Data for fold {fold}:')\n",
    "    # display(fold_df)\n",
    "    print()  # For better readability\n",
    "    # Extract the data where \"Model\" is \"Baseline\"\n",
    "    baseline_data = fold_df[fold_df['Model'] == 'Baseline']\n",
    "    ANN_data = fold_df[fold_df['Model'] == 'ANN']\n",
    "    Ridge_data = fold_df[fold_df['Model'] == 'Ridge']\n",
    "    KNN_data = fold_df[fold_df['Model'] == 'KNN']\n",
    "    RF_data = fold_df[fold_df['Model'] == 'RF']\n",
    "    Logistic_data = fold_df[fold_df['Model'] == 'Logistic']\n",
    "\n",
    "    baseline_data = baseline_data['Error'][baseline_data['Error'].index[0]]\n",
    "    ANN_data = ANN_data['Error'][ANN_data['Error'].index[0]]\n",
    "    Ridge_data = Ridge_data['Error'][Ridge_data['Error'].index[0]]\n",
    "    KNN_data = KNN_data['Error'][KNN_data['Error'].index[0]]\n",
    "    RF_data = RF_data['Error'][RF_data['Error'].index[0]]\n",
    "    Logistic_data = Logistic_data['Error'][Logistic_data['Error'].index[0]]\n",
    "\n",
    "    print(len(baseline_data))\n",
    "    for i in range(len(baseline_data)):\n",
    "        datapoint, baseline_error = baseline_data.index[i], baseline_data.values[i]\n",
    "        ANN_error = ANN_data[i]\n",
    "        Ridge_error = Ridge_data[i]\n",
    "        KNN_error = KNN_data[i]\n",
    "        RF_error = RF_data[i]\n",
    "        Logistic_error = Logistic_data[i]\n",
    "        # print(datapoint, baseline_error, Ridge_error, KNN_error, RF_error, ANN_error, Logistic_error)\n",
    "\n",
    "        all_data[datapoint]=({\n",
    "            'Fold': fold,\n",
    "            'Baseline Error': baseline_error,\n",
    "            'Ridge Error': Ridge_error,\n",
    "            'KNN Error': KNN_error,\n",
    "            'RF Error': RF_error,\n",
    "            'ANN Error': ANN_error,\n",
    "            'Logistic Error': Logistic_error\n",
    "        })\n",
    "\n",
    "final_df = pd.DataFrame(all_data)\n",
    "final_df\n",
    "final_df.to_csv('final_errors.csv', index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do repeated measures one-way anova:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First I have to reshape the data to long format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>Subject</th>\n",
       "      <th>Fold</th>\n",
       "      <th>Model</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Baseline Error</td>\n",
       "      <td>0.757545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Baseline Error</td>\n",
       "      <td>9.794582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Baseline Error</td>\n",
       "      <td>3.498285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Baseline Error</td>\n",
       "      <td>0.521605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Baseline Error</td>\n",
       "      <td>10.743827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>163</td>\n",
       "      <td>6</td>\n",
       "      <td>Logistic Error</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>164</td>\n",
       "      <td>6</td>\n",
       "      <td>Logistic Error</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>165</td>\n",
       "      <td>7</td>\n",
       "      <td>Logistic Error</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>166</td>\n",
       "      <td>7</td>\n",
       "      <td>Logistic Error</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>167</td>\n",
       "      <td>7</td>\n",
       "      <td>Logistic Error</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1008 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Subject  Fold           Model      Error\n",
       "0           0     0  Baseline Error   0.757545\n",
       "1           1     0  Baseline Error   9.794582\n",
       "2           2     0  Baseline Error   3.498285\n",
       "3           3     1  Baseline Error   0.521605\n",
       "4           4     1  Baseline Error  10.743827\n",
       "...       ...   ...             ...        ...\n",
       "1003      163     6  Logistic Error  36.000000\n",
       "1004      164     6  Logistic Error   4.000000\n",
       "1005      165     7  Logistic Error   1.000000\n",
       "1006      166     7  Logistic Error   4.000000\n",
       "1007      167     7  Logistic Error   4.000000\n",
       "\n",
       "[1008 rows x 4 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_long = pd.melt(final_df.reset_index(), id_vars=['index', 'Fold'], var_name='Model', value_name='Error')\n",
    "df_long.rename(columns={'index': 'Subject'}, inplace=True)\n",
    "df_long"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I can do ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>F Value</th>\n",
       "      <th>Num DF</th>\n",
       "      <th>Den DF</th>\n",
       "      <th>Pr &gt; F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <td>11.276563</td>\n",
       "      <td>5.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>1.534747e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Value  Num DF  Den DF        Pr > F\n",
       "Model  11.276563     5.0   835.0  1.534747e-10"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aovrm = AnovaRM(df_long, 'Error', 'Subject', within=['Model'])\n",
    "res = aovrm.fit()\n",
    "res.anova_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check assumptions, first, is data normally distributed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Q-Q plot\n",
    "stats.probplot(final_df['ANN Error'], dist=\"norm\", plot=plt)\n",
    "plt.title('Q-Q Plot of Residuals')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Already no, what about with log transformation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIh5DwUdEREQ8Ro4LPpGRkXTr1o0iRYqQN29eqlevzu+//+7uskRERCQLyFGHlF66dIkGDRrQpEkT1qxZQ2BgIAcPHqRQoULuLk1EREQuXYIDB+Chh9xWQo4KPlOmTCE4OJhFixbZ2sqXL+/GikRERISLF2HmTJg9G/LlgyNHIG9et5SSo4a6vvrqK2rXrk3nzp0pVqwY999/Px9++GGa98TFxREbG5vkS0RERJzg/HkYPhzKloXx4yE2FooWhRMn3FZSjgo+R44cYe7cuVSqVInvvvuOl19+mQEDBvDJJ5+kes+kSZMICAiwfQUHB2dixSIiIjnQuXPw1ltQrhxMmgRXrkCNGrBiBezcCf/5j9tKsxiGYbjtuzuZj48PtWvXZsuWLba2AQMG8Ntvv/Hzzz+neE9cXBxxcXG217GxsQQHBxMTE4O/v7/LaxYREckxzpyBadNg7ly4ds1su/9+GDMG2rYFL9f1t8TGxhIQEJDu53eOmuNTsmRJqlSpkqStcuXKrFixItV7fH198fX1dXVpIiIiOVdUFEydCvPnw/XrZlvt2mbgadMGLBb31nebHBV8GjRowIEDB5K0/fXXX5QtW9ZNFYmIiORgkZEwZQosWACJoyd165qBp1WrLBV4EuWo4PPaa69Rv359Jk6cyFNPPcXWrVtZsGABCxYscHdpIiIiOceJEzB5Mnz0Edy8abbVr28GnkcfzZKBJ1GOmuMD8M033zBs2DAOHjxI+fLlGTx4MH369LH7fnvHCEVERDzO33+bgefjjyE+3mxr2NAMPE2bujXw2Pv5neOCz91S8BEREbnD0aPm6qywsH8DT+PGMHq0+c8s0MPjkZObRURExIkOH4aJE2HxYrh1y2xr1swMPI0aube2DFLwERERkaQOHoQJE+D//g+sVrOtRQsz8DRo4N7a7pKCj4iIiJj27zcDz2efQUKC2da6NYwaBfXqubc2J1HwERER8XR795pHSixdColTfx9/3Aw8Dz7o3tqcTMFHRETEU/35pxl4vvzy38DTrp0ZeGrVcm9tLqLgIyIi4ml27oS33zbPzkrUoYMZeGrWdFtZmUHBR0RExFNs324GnlWrzNcWC3TqBCNHwn33ubW0zKLgIyIiktP9/juMGwdff22+tljg6afNwFO1qntry2QKPiIiIjnVr7+aged//zNfe3lBly4wYgRUruze2txEwUdERCSn2bLFDDzffWe+9vKCbt1g+HC45x731uZmCj4iIiI5RUSEGXjWrzdfe3tD9+5m4KlY0b21ZREKPiIiItndpk1m4Nm40XydKxf07AnDhkFIiDsry3IUfERERLIjwzCDTmgobN5stuXODb16wdChUK6cW8vLqhR8REREshPDMIeyQkPhp5/MNh8f6N3bDDxlyri3vixOwUdERCQ7MAxzsnJoKPzyi9nm6wt9+sBbb0FQkHvryyYUfERERLIywzCXo48bB1u3mm158kDfvvDmm1CqlHvry2YUfERERLIiwzA3HBw3DrZtM9vy5oWXX4YhQ6BECffWl00p+IiIiGQlCQmwerUZeHbsMNvy5YN+/eCNN6BYMbeWl90p+IiIiGQFCQkQHm6epbVrl9lWoAD07w+DB0NgoHvryyEUfERERNzJaoXly83As2eP2ebnBwMGwGuvQZEi7q0vh1HwERERcQerFZYtg/HjYd8+sy0gAAYONL8KF3ZvfTmUgo+IiEhmunULPv/cDDx//WW2FSxo9u4MGGD+WVxGwUdERCQz3LoF//d/MGECHDpkthUubM7f6d/f7O0Rl1PwERERcaX4eFi8GCZOhCNHzLYiRcwVWv36mfN5JNMo+IiIiLjCzZsQFgaTJsGxY2ZbYKC5B8/LL5srtiTTKfiIiIg4U1wcLFxoBp4TJ8y24sXNXZb79oX8+d1bn4dT8BEREXGGGzfgo49g8mSIjDTbSpY0z9Hq08fchFDcTsFHRETkbly/Dh9+CFOmwKlTZlvp0uZJ6b17m8dMSJah4CMiIpIR167B/PkwdSqcPm22BQXBsGHQq5d5kKhkOQo+IiIijrh6FebOhWnT4OxZs61MGRg+HHr2BF9ft5YnaVPwERERscfly/DBBzB9Opw/b7aVL28Gnu7dwcfHvfWJXbzcXYArTZ48GYvFwqBBg9xdioiIZFexseYePOXKmfN2zp+HChXMlVsHDsALLyj0ZCM5tsfnt99+Y/78+dx3333uLkVERLKj6GiYMwdmzoRLl8y2SpVg5Ejo2hVy5diP0BwtR/b4XLlyhWeffZYPP/yQQoUKubscERHJTi5dgrFjzR6e0aPN1/feax43sW+fOayl0JNt5cjg069fP9q0aUPz5s3dXYqIiGQXFy7AqFFm4AkNhZgYqFLFPFB092549lnw9nZ3ldmW1QqbNpm/zk2bzNfukOMi69KlS9m+fTu//fabXdfHxcURFxdnex0bG+uq0kREJCs6fx7eeccc1rpyxWyrVs3s7enYEbxyZB9BpgoPh4ED4eTJf9uCguDdd6FDh8ytJUf9bZ44cYKBAwfy6aefksfO/RMmTZpEQECA7Ss4ONjFVYqISJZw9qy5q3K5cubxEleuQI0asGIF7NwJnTsr9DhBeDh06pQ09IC5uXWnTub7mcliGIaRud/SdVatWsWTTz6J921dkVarFYvFgpeXF3FxcUneg5R7fIKDg4mJicHf3z/TahcRkUxy+rS5JH3uXHMTQoAHHjB7eJ54AiwW99aXiaxWiIiAqCjzdI2GDZ07mme1mrnyztCTyGIxe36OHr377xsbG0tAQEC6n985aqirWbNm/Pnnn0nann/+ee69917eeuutZKEHwNfXF19tNiUikvNFRZm7LM+bZ56rBVC7NowZA23aZNvAk9HwkhnDTxERqYceAMMwz3GNiIDGjZ3zPdOTo4KPn58f1apVS9KWP39+ihQpkqxdREQ8RGSkeY7WggXmyekAdeuagadVqywZeOwNMxkNL4nDT3eO+SQOPy1f7pzwExXl3OucQYOXIiKSM504Af36QUiIOXE5Lg7q14fvvoOff4bWrbNk6AkPN4eHmjQxtwtq0sR8fedcmIzOnbFazbCU0kSXxLZBg5yz6qpkSede5ww5ao6PM9g7RigiIlnU33+bk5UXLoT4eLOtYUOzh6dp00wJO2n12KT1Xmo9MYklJ/bE3M3cmU2bzDCVno0b7374KbHOyMiUg5Y75viox0dERHKGo0ehTx+oWNE8NT0+3vzk3rgRNm+GZs0yJfSk1WOT1nuO9MQ4MnfmTpk5/OTtbQ67QfJffeLrWbMyd3ukHDXHR0REPNDhwzBhAixe/O/4TLNm5iqtRo0ytZS05s507JjyPYlDU2PH2h9m7ia8ZPbwU4cOZk9VSnORZs3K/H18FHxERCR7+usvM/B8+um/gadFCzPwNGiQ6eXY02OTEsMwez8Se0bSkzhEZo+UrmvY0Awd6Q0/NWxo3/ewR4cO0K6da5fO20vBR0REspf9+2H8ePPsg4QEs611azPwPPRQppVx51wdqzXtHpu0GAZcvGjftYmhIaPhJXH4qVMn87rb73fl8JO3d+YtWU+Lgo+IiGQPe/fC22/DsmX/flo//rgZeOrUsesRd4aV+vVhy5akvRCQvGfizrbz5+G115IGncKF7/5HLFzYPBM1vTBzt+Elqw0/ZSat6rqDVnWJiGQxf/5pBp7ly//9hG/Xzgw8DzyQ5q23B52DB+HDD5N+0Ht7J122XaSI+c8LF9Juc5XQUHOuD6QcZu7cXyelfXyCg+0PL67euTkz2fv5reBzBwUfEZEsYudOGDcu6YY0HTqYJ6jXrJniLekFnazq9mXdq1c7FmZyUni5Gx55ZIWIiOQA27ebgWf1avO1xWKO6YwaBdWrp3pbSr0fWcXtw1HpDU05OhE4q8ydyS4UfEREJGv47Tcz8HzzjfnaYoGnn4aRI6Fq1VRvs1rNxV1jxmRSnXYoXDjpZOXEuTNg37wahRnXUfARERH3+vVXc3LLmjXmay8v6NLFDDz33gskHc4pVsy87OxZczhrwQJzdVNW8sUXZnhJqccmqyzr9lQKPiIi4h5btpiBZ+1a87W3Nzz7LIwYAf/5j+2yrDyEdafEuTqNG2toKqtS8BERkcwVEWEOaa1fb7729obu3WH4cKzlK5q9IdvMnp2ICDMbZQfuOoJBHKPgIyIimWPTJjPwbNwIgJErF1Ete/J782Hkrx5CxP+Zh6jbu5FfVuMJe+DkBAo+IiLiOoYBGzdijA3FErEZgIRcudleoxe9Dw1j13/Lwn/dV15G9/EJDoYZMyAwUHN1shsFHxERcT7DMIeyQkPhp5+wAHH48BEvMOXWW5zYVsYtZQUFmQe4V6p0dzs3K+RkX9rA8A7awFBE5C4YBnz3nRl4fvkFgBv48iF9mMJbRBKUqeWULg0vvvhv0FFgybm0gaGIiGQew4D//c+cw7N1KwC3cufhY+++hN54kyhKZXpJoaHmAjEFHbmdgo+IiGScYcDXX5uBZ9s2AK5b8vKB8TLT4odwJr5EppfkyFlV4nkUfERExHEJCeaREuPGwY4dAMTlyse7t/ox3XiDcxTLtFLunLej4SxJi4KPiIjYxWqFiB8S8FodTuXlbxN4ahcAVyjAHPrzzq3BnCfQ5XWMGWOGm7NnFXTEcQo+IiKSosRjIiIjYcM6KyxfzmtX36YaewCIxY/ZDGAmr3GRIi6vR0NY4gwKPiIiAiQNOt9/b45kRV+08jTLGMl4qrAPgGgCeJeBzGIQ0RRySS2FC8Orr6pnR5xPwUdExMMlnm7+7rv/7prszS268DkjGc89/AXAJQoyk9eYzQBiKOi076+QI5lJwUdExIOFh5v73CTuSpyLeJ7lU0YwgUocAuAChXmHwczhVS7jnP3NihY1h61Kl1bIkcyl4CMi4kHuHM5atMhsz0U83VnMcCZSgSMAnKcI03mD9+nHFfyc8v0TD/KcP19zdcQ9FHxERHKo20POmTPw00+wbh1cvvzvNbm5SU/CGM5EyvE3AGcJZBpDmMvLXKWAU2vSQZ7ibgo+IiI5TEpzdu7kQxy9WMgwJlGGEwCcpjhTeZP59OUa+Z1Wz5gxcM89mrsjWYOCj4hIDrJ8OfTuDbGxKb/vyw1e4COGMpkgIgE4RUmm8BYLeJEb5HVaLVp+LlmRgo+ISDZntcKmTTBqFPz8c8rX5OE6L7KAt5hCKaIAOElpJjOUj3iBOPLcdR2FCkG7dtC8uSYtS9al4CMiko0khpxNm8xTIyIjYcUKuHIl5evzco2XmMebTKUEZwA4TjCTGMZCenETX4drSAw4TZuaq8ECAxV0JPtQ8BERyaJSmpz8v//BjRvp35ufK7zMXN5gOsU5C8AxyjKR4XxCD4cCj3pyJCdR8BERyWKsVnj7bXjnnaQrsOxRgMv0431eZwaBnAfgCOWZwAiW8Bzx+Nj1nHz5YN48c56Ogo7kJF7uLsCZJk2aRJ06dfDz86NYsWK0b9+eAwcOuLssERG7hYdDwYIQGupY6PEjluFM4BjlmMwwAjnPISrQk0XcwwEW0tvu0AOwZAk89xw0bqzQIzlLjurx+eGHH+jXrx916tTh1q1bDB8+nBYtWrB3717y53fe0kwREWe5fThr3Tr45BPH7g8gmgHM5jVmUohoAA7wH8Yzks/pgtXBf81rJZbkdBbDMAx3F+Eq586do1ixYvzwww80atTIrntiY2MJCAggJiYGf3/nbM0uIp7tzrk6587ByZNw9Sps2AAxMY4/syCXGMQsBvIuBTEfsI97eZtRLONpEki7myZ/fujcWROUJeew9/M7R/X43Cnmn3+bFC5c2M2ViIinCg+HgQPNoOMMhbnAa8xkALPxxxwL201V3mYUy+mUauBp3Bjq1wcvL/PPGsIST5Vjg09CQgKDBg2iQYMGVKtWLdXr4uLiiIuLs72OTW3XLxEROyX28KxaZe6e7AxFOM/rzKA/7+GHuXZ9F9UZx2jC6YCRypRNf3/46COzd0dEcnDw6devH7t37+bHH39M87pJkyYRGhqaSVWJSE5z5746J06YgcfR1VipCeQsbzCdV/iAAlwF4A9qMo7RrKZdqoGnQAEYMgRGjFDPjsjtcuQcn/79+7N69Wo2b95M+fLl07w2pR6f4OBgzfERkXSFh8OLL5pzZJytOKcZwjReZi75uA7ANh4glDF8TVvAkuwePz9o2RJeeklDWeJ5PHKOj2EYvPrqq6xcuZJNmzalG3oAfH198fV1fOdSEfFs4eHQsaPzn1uSU7zJVPoyn7yYOxVupQ6hjOF/PEZi4MmTB9q0gXr1oEQJTUwWsVeOCj79+vXjs88+Y/Xq1fj5+XH69GkAAgICyJvXeQfviYhnShzWWr8eZsxw7rNLc5K3mEIfPiQPZi/0zzxEKGP4jpbkzWuhQ2uoUkWTk0XuRo4a6rJYknf9AixatIiePXva9QwtZxeRRIlBZ8MG+PFH2LrVvuMiHBHMcYYymd58jC83AfiRBoQyhvU0ByyMGWMeQKqgI5I6jx3qEhHJiNtDzvHj5h47330H16655vuV5RhDmUwvFuJDPAA/0IhQxrCRJoCFIkVgwQJtJijiTDkq+IiI2Ov21Vh798K337ou5NyuPEcYxiR6EkZubgGwgSaMYzR/+DemeXMYqeEsEZdR8BERj+PK1VipCeEwI5hAdxaTCysAB8s1Z9tjo7HWb8hYTU4WyRQKPiLiUVy1Gis1lfiLEUzgWT61BZ4zNVtS/P3RVKpfn0qZV4qIoOAjIh7CaoXvv4dnn82c73cP+xnJeLrwOd4kAHD+wcco9M4oijd4KHOKEJFkUt7yU0QkBwkPh+LFzc39nL0q606V2UtEUBf2UoVufGqGnrZtYetWiv76X7wVekTcSj0+IpKjpLQ6a+VK13/favzJ2z5v0y5+OZaT/6wwbd/eXIf+wAOuL0BE7KLgIyI5xvLl0Ls3ZOZZw/exk3He42hnDeefbXjMSUQjR0LNmplXiIjYRUNdIpKtJc7dqV/fPIE8M0KPry/0vn87e/7Tnp3UNEOPxQJPPQW7dpkJTKFHJEtSj4+IZDtWK0REmKegf/hh5uy/kzcvtG4NbzX9jTprxmH57zfmGxYLPPOMeQx61aquL0RE7oqCj4hkC4lzdz74wPWbDebNC61aQYMGtx0A6vMr3uNDof8a8yIvL+ja1Qw8997rumJExKkUfEQkS7NaYcIEmDYNrlxx7fcaMACefPKOjQS3bIHQUFi71nzt7Q3dusHw4fCf/7i2IBFxOgUfEcmyMmuycopnYkVEmIHn++/N197e0L27GXgqVnRtQSLiMgo+IpJl3H5+1rp18Ouvrv1+FguMGWMuwLL18GzaZAaeTZvM17lyQc+eMGwYhIS4tiARcTkFHxFxG3cdFJpo2TJzJRiGAd9vgHHjYPNm883cuaFXLzPwlC2beUWJiEsp+IiIW7hjz51EtqGtJw1Yu84MPD/9ZL7p4wMvvABDh0JwcOYXJyIupeAjIpkmsYdn1Cj4+WfXfz8fH2jTBurVg+hocyFW48bQ+BED73XfQv1x8Msv5sW+vuaR7W++CUFBri9ORNxCwUdEMkVm9vDkyWOOUI0YcdvcHTCHtP77XzPw/Pbbvxe/9BIMGQKlSrm+OBFxKwUfEXEpq9U8EX3ZMtd/Lz8/GDzY7FFKFni++soc0tq+3WzLmxdeeQXeeMPcrEdEPIKCj4i4zPLl5vzgy5dd9z3y5TOn5CTbfwcgIcHc3nncONi502zLnx/69YPXX4dixVxXmIhkSQo+IuI0mbUcvXJlM+g0bWrO2UkSdsAMPCtWwNtvw59/mm0FCsCrr5pdQkWLuqYwEcnyFHxE5K5l5u7Kr78O06enUciXX5qBZ+9es83f39ySedAgczmXiHg0BR8RyZDMPig0MBDef/+ffXdSKmbpUhg/HvbvN9sCAsywM3AgFCrk2uJEJNtQ8BERh4WHm3ni5EnXfY8CBaBjR3j00X8OCb1z/g7ArVvw2Wdmd9Nff5lthQrBa6+Zw1oFC7quQBHJlhwOPidOnMBisRD0zz4XW7du5bPPPqNKlSq8+OKLTi9QRLKW8HDo1MlcKOVMefLA00+nE3QSxcfD//2fGXgOHzbbChc2x8H69zeHt0REUuBw8OnatSsvvvgizz33HKdPn+bRRx+latWqfPrpp5w+fZrRo0e7ok4RcbPEics9ejg39KS6505Kbt6ExYth4kQ4etRsK1rUXJL+yivmenYRkTR4OXrD7t27efDBBwH44osvqFatGlu2bOHTTz8lLCzM2fWJSBawfDmULAnNmztv8nLevOYBoVeuwOjR6YSemzdh/nz4z3+gTx8z9BQrZs6mPnoU3npLoUdE7OJwj098fDy+vr4ArF+/nieeeAKAe++9l6ioKOdWJyJu48rjJZ56ypyak24PT1wcfPwxTJ4MJ06YbSVKmMdK9O1rbuIjIuIAh3t8qlatyrx584iIiGDdunW0atUKgFOnTlFES0VFsj2r1dzvr2BBs4fH2aHn9dfNXZzTDD03bsB770GFCuZmgydOmF1O774LR46Yk5cVekQkAxzu8ZkyZQpPPvkk06ZNo0ePHtSoUQOAr776yjYEJiLZU3i4eU7nhQvOf3aay9ETXb9uHps+ZQok9iCXLm1OAurd25wQJCJyFyyG4fg0RavVSmxsLIVu2xvj2LFj5MuXj2LZfAv42NhYAgICiImJwV8rQ8SDhIeby8edpW5dM+SUKGHHKq2rV805PFOnwpkzZltwsBl4evUyT04XEUmDvZ/fGdrHxzAMtm3bxuHDh+natSt+fn74+PiQT13PItmS1Wruy+MMRYvCBx+k07OT6MoVmDvXnKR87pzZVrYsDB8OPXuCj49zihIR+YfDwefvv/+mVatWHD9+nLi4OB599FH8/PyYMmUKcXFxzJs3zxV1iogLRUQ4ZzPCsWNh5Eg7Ji1fvmyOe82YAefPm23ly5tr2rt3h9y5774YEZEUODy5eeDAgdSuXZtLly6RN29eW/uTTz7J999/79TiMur999+nXLly5MmTh7p167J161Z3lySSpa1efffPGDLEXJ6eZuiJiTE3HSxXzhzGOn8eKlaERYvgwAFzHo9Cj4i4kMM9PhEREWzZsgWfO7qgy5UrR2RkpNMKy6hly5YxePBg5s2bR926dZk1axYtW7bkwIED2X7+kYgrhIfDrFkZv9+uScvR0TB7Nsycaf4ZzD15Ro6ELl0gl07PEZHM4XCPT0JCAlarNVn7yZMn8csCG4i988479OnTh+eff54qVaowb9488uXLx8KFC91dmkiWc/MmPP+84/fly2ceeL5xo7n4KtXQc+mS2Q1Urpz5z+houPde+PRT8/T0555T6BGRTOVw8GnRogWzbvvPQ4vFwpUrVxgzZgyPPfaYM2tz2M2bN9m2bRvNmze3tXl5edG8eXN+dvZmJCLZXHi42VsTG2v/PQUKQGioec+770LjxqkMbV24YPbmlC1rbgoUEwNVq5onqO/eDV272jERSETE+Rz+T60ZM2bQsmVLqlSpwo0bN+jatSsHDx6kaNGifP75566o0W7nz5/HarVSvHjxJO3Fixdn//79Kd4TFxdHXFyc7XWsI58CItmUo0vXH3zQPB4r1aCT6Px5c8Lye+/9e7ZF9ermmRQdOoCXw/+tJSLiVA4Hn6CgIHbu3MnSpUvZtWsXV65coXfv3jz77LNJJjtnF5MmTSI0NNTdZYhkGqvV3KTQEVOmmKEnVWfPwvTp5jr2q1fNtpo1zcDTrp0Cj4hkGRkaXM+VKxfdunVzdi13rWjRonh7e3MmcQO0f5w5c4YSJUqkeM+wYcMYPHiw7XVsbCzBwcEurVPEnSZMcGxn5iJFzM0HU3T6tLkHz9y55q7LALVqmYGnbVuwWO66XhERZ3I4+CxevDjN97t3757hYu6Wj48PtWrV4vvvv6d9+/aAORn7+++/p3///ine4+vrazt0VSSns1rNuTmOGDAgheGtU6fMXZbnzzfP1QKoU8ecwPzYYwo8IpJlORx8Bt6xvWt8fDzXrl2z7dzszuADMHjwYHr06EHt2rV58MEHmTVrFlevXuX5jCxdEclhJkyAixftv75IEXNPQZuTJ81xrw8/NE9OB3joITPwtGypwCMiWZ7DwefSpUvJ2g4ePMjLL7/MkCFDnFLU3Xj66ac5d+4co0eP5vTp09SsWZNvv/022YRnEU+zfLmZTxyxYME/vT3Hj8PkyfDxx+YaeIAGDcwHNm+uwCMi2UaGDilNye+//063bt1SXT2VXeiQUsmJli0zV5AnJNh3vb+/uZlyhweOmYFn4UKIjzffbNTIDDxNmijwiEiW4dJDSlN8UK5cnDp1ylmPE5G7YLWa529FRpqdNBs32n+vvz+c+/UIPjMmwdNhcOuW+UaTJmbgeeQRl9QsIpIZHA4+X331VZLXhmEQFRXFe++9R4MGDZxWmIhkTHi4edJ6Rg4drcAh1teeiE+1xWZ6AnMoa/ToNJZ2iYhkHw4Hn8TVUoksFguBgYE0bdqUGTNmOKsuEcmA8HDo1AkcHcCuxF+MYALPeX2K14Z/Ak/LlmbgqV/f+YWKiLiJw8Enwd5JAiKSqaxWs6fHkdBzL/sYwQS68DneJEAC5nL00aOhbl2X1Soi4i46HVAkh4iIsH94qwp7GMl4nmYZXphJKap2W0rOHQ21a7uwShER97Ir+Ny+s3F63nnnnQwXIyKOS5zI/OWX6V9bjT8Zxdt0Yrkt8KyiPQWmjqb5kPtdXKmIiPvZFXz++OMPux5m0dJWkUxl70TmGuxgFG/TkXBb23I68jajGL60Bu2fdnGhIiJZhF3BZ6Mja2FFJFPYM5H5AbYxirdpz2oAErDwJZ15m1HsoRpDhsDTCj0i4kE0x0ckm7h9b54zZ8zjJ1ILPbX5jdGMoy3fAGbgWcozTGAEe6mKvz988RF07pyJP4CISBaQoeDz+++/88UXX3D8+HFuJm5f/4/w8PBU7hKRjLJ3SKsuvzCacTzGGgCsePEZXZnACA5wLwUKQOgQ8/ytZAePioh4AC9Hb1i6dCn169dn3759rFy5kvj4ePbs2cOGDRsICAhwRY0iHslqhU2bYNAg6Ngx7dBTn5/4lpb8Qj0eYw238CaMHlRmH91ZwgHuBWDVKnOlukKPiHgqh3t8Jk6cyMyZM+nXrx9+fn68++67lC9fnr59+1KyZElX1Cjicezt4WnIZkYzjuZ8D8AtvPmEHkxiGIepmOTa4GBo3NhFBYuIZBMO9/gcPnyYNm3aAODj48PVq1exWCy89tprLFiwwOkFinia5cvT6+ExaMxGNtCEzTxCc74nnlwsoA+VOMgLfJws9FgsMGuWenpERBzu8SlUqBCXL18GoHTp0uzevZvq1asTHR3NtWvXnF6gSE53+6Tldetg8eLUrjRoygbGEEojIgC4SW4+pjeTGcpxyqZ4V5EisGABdOjgmvpFRLITh4NPo0aNWLduHdWrV6dz584MHDiQDRs2sG7dOpo1a+aKGkVyJKvVXJn17rtw8WJaVxo8yjrGEEoDtgAQhw8f8QKTGcpJglO8q3Bhc7hME5lFRP5ld/DZvXs31apV47333uPGjRsAjBgxgty5c7NlyxY6duzIyJEjXVaoSE6Q2LuzejUsXAixsWldbdCKbxlDKA/xKwA38GUBLzKFtzhF6RTvKlwYvvjCnM+jwCMikpTFMOw70tDLy4s6derwwgsv8Mwzz+Dn5+fq2twiNjaWgIAAYmJi8Pf3d3c5koPYO2EZDNrwX0Yzjgf5DYDr5GEeLzGVNzlN6osILBZzjpCGtUTE09j7+W335OYffviBqlWr8vrrr1OyZEl69OhBRESEU4oVyenSn7AMYPAEq/md2nxDWx7kN66Sj+m8TnmOMpiZaYae4GCFHhGR9Njd45Po6tWrfPHFF4SFhREREUHFihXp3bs3PXr0oESJEq6qM9Oox0ec7csvoUsXc5grJRYSaM8qRjOOmuwE4Ar5eZ9+zOB1zlEsxfv694cKFSAwEEqXhoYNNbQlIp7L3s9vh4PP7Q4dOsSiRYtYsmQJp0+fplWrVnz11VcZfVyWoOAjzpI4eXnMmJTft5BAR1Ywire5jz8BuEwB5vAq7zCYCxRN8b7gYHNpunp2RET+lSnBB8weoE8//ZRhw4YRHR2NNbX/rM0mFHzEGcLDYcAAc4n6nbyw0pkvGcXbVGUvADH4M5sBzOQ1LlE4xWcOGABPPqmeHRGRlNj7+Z3hQ0o3b97MwoULWbFiBV5eXjz11FP07t07o48TydYSV2tFRcGBAxAamvwab27xNMsYyXgqsx+AaAKYxSDeZSDRFErx2dqHR0TEeRwKPqdOnSIsLIywsDAOHTpE/fr1mT17Nk899RT58+d3VY0iWVp6q7W8uUVXPmMk4/kPBwG4SCFm8hqzGUAsKZ9xp314REScz+7g07p1a9avX0/RokXp3r07vXr14p577nFlbSJZXng4dOoEKQ0Y5yKebvwfI5hARQ4DcIHCzOB13qM/l0naFVuoELRrB82ba7KyiIir2B18cufOzfLly3n88cfx1r+NRbBazR6ZO0NPbm7SncUMZyIhHAXgHEWZzht8wCtc4d89sLy84NVXoX17BR0Rkcxgd/DJ7qu1RJwtIiLp8FZubvI8ixjGJMrxNwBnKMY0hjCXl7lG8uHgpUuhc+fMqlhERDI8uVnE061ebf7Thzh68zFDmUwZTgAQRQmm8ibz6ct18iW7NyjIPKNLE5ZFRDKXgo9IBoSHw9xZN+jHRwxlMkGY69YjKcUU3uJD+nCDvCneGxqqCcsiIu6i4CPioJsx19nWfQFHmEIpogA4QRCTGcrH9CaOPCne5+1tDm116pSZ1YqIyO0UfETsYLXCT2uvcnHyfOpFTGWCcQaAvynDJIaxiOe5iW+az/j8c4UeERF3syv4ODKx+YknnshwMSJZ0epPr/Bnv7m8GDONYpwD4CjlmMhwPqEH8fikeb+OmBARyTrsCj7t27dP8tpisXD7SRcWi8X25+x+ZIWIzeXL7H75fep/OoN2nAfgMCFMYARLeI5b5E7z9pEjoVkzLVMXEclKvOy5KCEhwfa1du1aatasyZo1a4iOjiY6Opr//e9/PPDAA3z77beurjdVx44do3fv3pQvX568efNSoUIFxowZw82bN91Wk2RTMTEwYQJGuXJU+3QYgZznIBXpQRj3sp9F9Eoz9FgsZi/P2LHQuLFCj4hIVuLwHJ9BgwYxb948Hn74YVtby5YtyZcvHy+++CL79u1zaoH22r9/PwkJCcyfP5+KFSuye/du+vTpw9WrV5k+fbpbapJsJjoaZs+GmTMhOhoLsJ97GM9IlvIMVgf+7zJrlgKPiEhW5HDwOXz4MAULFkzWHhAQwLFjx5xQUsa0atWKVq1a2V6HhIRw4MAB5s6dq+Ajabt4Ed59F+Pdd7HExABwLrAyw6+NYuHVp0jA/gQTGAjz5mk+j4hIVuVw8KlTpw6DBw9myZIlFC9eHIAzZ84wZMgQHnzwQacXeDdiYmIoXLiwu8uQrOrCBbN3Z/ZsuHwZC7CbqoxjNCvOdXQo8IAZek6eBJ+05zqLiIgbORx8Fi5cyJNPPkmZMmUIDg4G4MSJE1SqVIlVq1Y5u74MO3ToEHPmzEm3tycuLo64uDjb69jYWFeXJu527hy88w689x5cuQLATu5jHKNZyZMY9k19s0mc2z9vnkKPiEhWZzGMlM6VTpthGKxbt479+/cDULlyZZo3b55kdZezDB06lClTpqR5zb59+7j33nttryMjI3nkkUdo3LgxH330UZr3jh07ltDQ0GTtMTEx+Pv7p3CHZFtnz8L06fDBB3D1KgB/5r6fUfGj+YonHA48ibRcXUTE/WJjYwkICEj38ztDwSfRjRs38PX1dUngSXTu3DkuXLiQ5jUhISH4/POf2qdOnaJx48Y89NBDhIWF4eWV9odZSj0+wcHBCj45yenTMG0azJ0L168DcKJYLV45O4ZveBxw/H+//v7Qqxe0a6fl6iIiWYG9wcfhoa6EhAQmTJjAvHnzOHPmDH/99RchISGMGjWKcuXK0bt377sq/E6BgYEEBgbadW1kZCRNmjShVq1aLFq0KN3QA+Dr64uvb9o77ko2deoUTJ0K8+fDjRsA/JH7QUbEj2HN2dZkJPAkCg839+gREZHsxeG+/fHjxxMWFsbUqVNtvSwA1apVS3dYyZUiIyNp3LgxZcqUYfr06Zw7d47Tp09z+vRpt9UkbnLyJLz6KoSEmEeg37jBhXvq0YpveSD+F9bwGBkNPYl79DRu7NSKRUQkkzjc47N48WIWLFhAs2bNeOmll2ztNWrUsM35cYd169Zx6NAhDh06RFBQUJL37mI0T7KT48dh8mT4+GNI3Ljy4YexjhxDzd7NOHkXPTzw7yRm7dEjIpJ9OdzjExkZScWKFZO1JyQkEB8f75SiMqJnz54YhpHil+Rwx45B375QsaI5j+fmTXjkEdiwATZvJsK3OScj734eWlAQLF+uScwiItmZwz0+VapUISIigrJlyyZpX758Offff7/TChNJ15EjMHEifPIJ3LpltjVtCqNHm8HnH1FRGXt86dLw4otQqRKULKlJzCIiOYHDwWf06NH06NGDyMhIEhISCA8P58CBAyxevJhvvvnGFTWKJHXoEEyYAEuWQOKhuI8+agae245SSVSypOPfIjQURoxQ0BERyWkcHupq164dX3/9NevXryd//vyMHj2affv28fXXX/Poo4+6okYR019/QffucM89EBZmhp5WrWDLFli7NsXQA2ZPTVDQv3N00hIcDCtWmBlKoUdEJOdxqMfn1q1bTJw4kV69erFu3TpX1SSS1L59MH48LF0KCQlmW5s2MGoU1K2b7u3e3ubirk6dzPCT0rSvQYO0J4+IiCdwqMcnV65cTJ06lVuJ8ylEXGnPHnjmGahaFT77zAw9bdvCb7/BN9/YFXqsVti0CeLiYOxYc97O7RJ7eGbONJeoK/SIiORsDs/xadasGT/88APlypVzQTkiwK5d8Pbb5hKqRO3bm+NPDkygDw+HgQPNbX0SlS5tzt/RhGUREc/kcPBp3bo1Q4cO5c8//6RWrVrkz58/yftPPPGE04oTD7NjB4wbBytX/tvWsaM5pFWjhkOPCg83h7buHNY6dcrs+Vm+XJsQioh4IofP6krrGAiLxYI1cZVNNmXvWR/iRNu2mYHnq6/M1xYLdO5sBp5q1Rx6VOLQ1lNPwcWLKV9jsZiTnY8eVW+PiEhOYe/nt8OruhISElL9yu6hRzLZ1q3w+ONQu7YZeiwW6NIFdu+GZcscDj3h4VCuHDRvnnroAbMX6MQJiIi4u/JFRCT7cXio63Y3btwgT548zqpFPMUvv5gTbb791nzt5QVdu5ob59x7b4YemdrQVloyurGhiIhkXw73+FitVt5++21Kly5NgQIFOHLkCACjRo3i448/dnqBkoP89BO0aAH16pmhx9sbevSA/fvNzQgzGHqsVnMSs6Onk2RkY0MREcneHA4+EyZMyJKns0sWtnkzNGtmbjC4bh3kygW9esGBA+ZGhJUq3dXjIyKSrtxKT+IJ6w0b3tW3FRGRbMjh4JN4Ovuzzz6L920zQ919OrtkMYYBGzeaS6cSDwzNlQv69DF3YP74Y6hQwSnfypEhK52wLiLi2Rye45NVT2eXLMIw4PvvzVVaibOHc+eG3r1h6FC443BbZ3BkyCooyAw9OmFdRMQz6XR2cQ7DMM/LGjfOPDsLwMfH7OF56y1zbMnJrFYzW0VGQmAgnD+f+jyfwoXhiy+0O7OIiKfT6exydwwD1qwxA8+vv5ptefLAiy/Cm28mPyPCSVLalTkliUNbH35oTjMSERHPptPZJWMM49/zstq0MUNP3rzw2mtw5Ih5KqgLQ0+nTvZNaA4KMndp1tCWiIhABnZuzum0c3M6DMPcbHDcONi+3WzLlw9eeQXeeAOKF3fpt7dazU0K0wo9gYHmoaOlS+ssLhERT2Hv5/ddbWAoHiQhAVatMgPPzp1mW/780L8/DB4MxYplShn2LF0/d84MPTqLS0RE7mRX8ClUqBCWxMkS6biY1lkBkv0kJMCKFeZp6X/+abb5+cGrr5rDWkWLZkoZiROZV6yw73rtyiwiIimxK/jMmjXL9ucLFy4wfvx4WrZsSb169QD4+eef+e677xg1apRLihQ3sFrhyy/NwLN3r9nm72/OKB40yFwm5YRvERFhhpSSJf8dlrqz/fx5M2M5skmhdmUWEZGUODzHp2PHjjRp0oT+/fsnaX/vvfdYv349q1atcmZ9mc7j5/jcumUeEDp+vHmUBEDBgmbYGTAAChVyyrdJaVVWUJB5RunnnzsWcm6nk9dFRDyTvZ/fDgefAgUKsGPHjmSbGB46dIiaNWty5cqVjFWcRXhs8Ll1Cz77zAw8Bw+abYUKmfN3Xn0VAgKc9q0ycqCoPRJHY7WKS0TE89j7+e3wcvYiRYqwevXqZO2rV6+mSJEijj5O3C0+HhYtMg8I7dHDDD1FisDEiXDsGIwc6dTQk9EDRe2hpesiIpIeh1d1hYaG8sILL7Bp0ybq1q0LwK+//sq3337Lhx9+6PQCxUVu3oTFi82Ac/So2Va0KAwZAi+/bE5gdgFHDxS1R//+0LGjlq6LiEj6HA4+PXv2pHLlysyePZvw8HAAKleuzI8//mgLQpKFxcWZJ6JPmgR//222FStm7rL80kvmEnUXcsVqq44dtXRdRETs41DwiY+Pp2/fvowaNYpPP/3UVTWJK9y4AQsXwuTJcOKE2VaihHmO1osvmpsQZgJnrrZKnMjcsKHznikiIjmbQ3N8cufOzQp7N1KRrOHGDZgzBypWhH79zNBTqhTMnm0eLTFoUKaFHjBDSlDQvxORMyrx/lmzNLwlIiL2c3hyc/v27bP9knWPcO2amQpCQsxl6JGRZuJ47z04fNhcqZU3b6aX5e1tHuMFdxd+NJFZREQywuE5PpUqVWLcuHH89NNP1KpVi/x3zAkZMGCA04qTDLh6FebNg2nT4MwZs61MGRg2DJ5/Hnx93VsfZlhZvjz5Pj7BwfDMM8n38QkOhhkzzDO47tzsUERExBEO7+NTvnz51B9msXDkyJG7Lsqdsu0+PleuwAcfwPTp5mFVYJ7mOXy4uUzdxyfTS0ptZ+b03k/vPhERkTu5bAPDnC7bBZ/Ll83hqxkz4MIFsy0kBEaMgOeeg9y53VJWajszv/uuhqdERMT5XLaBYaLz589z/vz5jN7uUnFxcdSsWROLxcKOHTvcXY5rxMSYuywn9upcuGBOYA4LgwMHoFcvt4aeTp2S79cTGWm2/7MLgoiISKZzKPhER0fTr18/ihYtSvHixSlevDhFixalf//+REdHu6hEx7355puUKlXK3WW4RnQ0hIaagWfUKLh4Ee65B5YsgX37zGGtXA5P3XKatHZmTmwbNMi8TkREJLPZ/Ql58eJF6tWrR2RkJM8++yyVK1cGYO/evYSFhfH999+zZcsWCjnpEMuMWrNmDWvXrmXFihWsWbPGrbU41cWL5iqtd9+F2FizrXJlM/w89VSWmQST3s7MhmGuqI+I0KaDIiKS+ewOPuPGjcPHx4fDhw9TvHjxZO+1aNGCcePGMXPmTKcXaa8zZ87Qp08fVq1aRb5M3JvGpS5cgHfeMffiuXzZbKtWzQw8nTqBV4ZHK13C3p2ZXbGDs4iISHrs/tRctWoV06dPTxZ6AEqUKMHUqVNZuXKlU4tzhGEY9OzZk5deeonatWvbfV9cXByxsbFJvrKEc+dg6FBzSGviRDP03HefuQ58506zlyeLhR6wf2dmZ+7gLCIiYi+7PzmjoqKoWrVqqu9Xq1aN06dPO6Wo2w0dOhSLxZLm1/79+5kzZw6XL19m2LBhDj1/0qRJBAQE2L6Cg4Od/jM45MwZ86DQcuVgyhRzmfr998PKlfDHH+bBVFkw8CRKb2dmi8Xcl0fHTIiIiDvYvZy9dOnSLFu2jIcffjjF9yMiInj66ac5deqUUws8d+4cFxKXaaciJCSEp556iq+//hrLbZ+4VqsVb29vnn32WT755JMU742LiyMuLs72OjY2luDg4Mxfzn76NEydam4+eP262Va7NoweDY8/fvdnPGSixFVdkHSSc+KPoB2XRUTE2Zy+j0+vXr04fPgw69atw+eOzfDi4uJo2bIlISEhLFy48O4qz6Djx48nGaY6deoULVu2ZPny5dStW5egoCC7npPp+/icOmX27CxYYJ6rBfDggzBmDLRuna0Cz+1S2scnONicn63QIyIizub04HPy5Elq166Nr68v/fr1495778UwDPbt28cHH3xAXFwcv//+u/uHiv5x7Ngxypcvzx9//EHNmjXtvi/Tgs/Jk+ZJ6R99BIk9TvXqmYGnRYtsG3hupx2YRUQks9j7+W33qq6goCB+/vlnXnnlFYYNG0ZiXrJYLDz66KO89957WSb0ZGnHj8OkSbBwIdy8abY9/LAZeJo1yxGBJ5G3t5asi4hI1pKhIysuXbrEwYMHAahYsSKFCxd2emHu4rIen2PHzMCzaBHEx5ttjzxiBp7GjXNU4BEREclsTu/xuV2hQoV48MEHM1ycxzlyxNxd+dYt83XTpuak5UcecW9dIiIiHsZ9Zxt4kpAQM+R4eZmBJ5WVcSIiIuJaCj6Z5auvIKfsJi0iIpJNZd2d8HIahR4RERG3U/ARERERj6HgIyIiIh5DwUdEREQ8hoKPiIiIeAwFHxEREfEYCj4iIiLiMRR8RERExGMo+IiIiIjHUPARERERj6HgIyIiIh5DwUdEREQ8hoKPiIiIeAydzi4ZYrVCRARERUHJktCwIXh7u7sqERGRtCn4iMPCw2HgQDh58t+2oCB4913o0MF9dYmIiKRHQ13ikPBw6NQpaegBiIw028PD3VOXiIiIPRR8xG5Wq9nTYxjJ30tsGzTIvE5ERCQrUvARu0VEJO/puZ1hwIkT5nUiIiJZkeb4iE16E5ajoux7jr3XiYiIZDYFHwHsm7BcsqR9z7L3OhERkcymoS6xe8Jyw4ZmGLJYUn6OxQLBweZ1IiIiWZGCj4dzZMKyt7fZAwTJw0/i61mztJ+PiIhkXQo+Hs7RCcsdOsDy5VC6dNLrgoLMdu3jIyIiWZnm+Hi4jExY7tAB2rXTzs0iIpL9KPh4uIxOWPb2hsaNnV6OiIiIS2moy8NpwrKIiHgSBR8PpwnLIiLiSRR8RBOWRUTEY2iOjwCasCwiIp4hx/X4/Pe//6Vu3brkzZuXQoUK0b59e3eXlKmsVti0CT7/3PynIweGJk5Y7tLF/KdCj4iI5DQ5qsdnxYoV9OnTh4kTJ9K0aVNu3brF7t273V1WprHn2AkRERFPZjGMlPbszX5u3bpFuXLlCA0NpXfv3hl+TmxsLAEBAcTExODv7+/ECl0r8diJO/82Eycoa66OiIjkZPZ+fueYoa7t27cTGRmJl5cX999/PyVLlqR169Ye0ePjyLETIiIinizHBJ8jR44AMHbsWEaOHMk333xDoUKFaNy4MRcvXkz1vri4OGJjY5N8ZTeOHjshIiLiqbJ88Bk6dCgWiyXNr/3795OQkADAiBEj6NixI7Vq1WLRokVYLBa+/PLLVJ8/adIkAgICbF/BwcGZ9aM5TUaOnRAREfFEWX5y8+uvv07Pnj3TvCYkJISofz7Vq1SpYmv39fUlJCSE48ePp3rvsGHDGDx4sO11bGxstgs/GT12QkRExNNk+eATGBhIYGBgutfVqlULX19fDhw4wMMPPwxAfHw8x44do2zZsqne5+vri6+vr9PqdYfEYyciI1Oe52OxmO/r2AkREfF0WX6oy17+/v689NJLjBkzhrVr13LgwAFefvllADp37uzm6lxLx06IiIjYJ8v3+Dhi2rRp5MqVi+eee47r169Tt25dNmzYQKFChdxdmsslHjuR0j4+s2ZpKbuIiAjkoH18nCW77uOTyGrVsRMiIuJ57P38zlE9PvLvsRMiIiKSXI6Z4yMiIiKSHgUfERER8RgKPiIiIuIxFHxERETEYyj4iIiIiMdQ8BERERGPoeAjIiIiHkPBR0RERDyGgo+IiIh4DAUfERER8RgKPiIiIuIxFHxERETEYyj4iIiIiMdQ8BERERGPoeAjIiIiHkPBR0RERDyGgo+IiIh4DAUfERER8RgKPiIiIuIxFHxERETEYyj4iIiIiMdQ8BERERGPoeAjIiIiHkPBR0RERDyGgo+IiIh4DAUfERER8RgKPiIiIuIxFHxERETEYyj4iIiIiMdQ8BERERGPoeAjIiIiHiNHBZ+//vqLdu3aUbRoUfz9/Xn44YfZuHGju8sSERGRLCJHBZ/HH3+cW7dusWHDBrZt20aNGjV4/PHHOX36tLtLExERkSwgxwSf8+fPc/DgQYYOHcp9991HpUqVmDx5MteuXWP37t3uLk9ERESygBwTfIoUKcI999zD4sWLuXr1Krdu3WL+/PkUK1aMWrVqubs8ERERyQJyubsAZ7FYLKxfv5727dvj5+eHl5cXxYoV49tvv6VQoUKp3hcXF0dcXJztdWxsbGaUKyIiIm6Q5Xt8hg4disViSfNr//79GIZBv379KFasGBEREWzdupX27dvTtm1boqKiUn3+pEmTCAgIsH0FBwdn4k8nIiIimcliGIbh7iLScu7cOS5cuJDmNSEhIURERNCiRQsuXbqEv7+/7b1KlSrRu3dvhg4dmuK9KfX4BAcHExMTk+Q5IiIiknXFxsYSEBCQ7ud3lh/qCgwMJDAwMN3rrl27BoCXV9JOLC8vLxISElK9z9fXF19f37srUkRERLKFLD/UZa969epRqFAhevTowc6dO/nrr78YMmQIR48epU2bNu4uT0RERLKAHBN8ihYtyrfffsuVK1do2rQptWvX5scff2T16tXUqFHD3eWJiIhIFpDl5/hkNnvHCEVERCTrsPfzO8f0+IiIiIikR8FHREREPIaCj4iIiHgMBR8RERHxGAo+IiIi4jEUfERERMRjKPiIiIiIx1DwEREREY+h4CMiIiIeQ8FHREREPIaCj4iIiHgMBR8RERHxGAo+IiIi4jEUfERERMRjKPiIiIiIx1DwEREREY+h4CMiIiIeQ8FHREREPIaCj4iIiHgMBR8RERHxGAo+IiIi4jEUfERERMRjKPiIiIiIx8jl7gI8gdUKEREQFQUlS0LDhuDt7e6qREREPI+Cj4uFh8PAgXDy5L9tQUHw7rvQoYP76hIREfFEGupyofBw6NQpaegBiIw028PD3VOXiIiIp1LwcRGr1ezpMYzk7yW2DRpkXiciIiKZQ8HHRSIikvf03M4w4MQJ8zoRERHJHAo+LhIV5dzrRERE5O4p+LhIyZLOvU5ERETunoKPizRsaK7eslhSft9igeBg8zoRERHJHAo+LuLtbS5Zh+ThJ/H1rFnaz0dERCQzZZvgM2HCBOrXr0++fPkoWLBgitccP36cNm3akC9fPooVK8aQIUO4detW5hZ6mw4dYPlyKF06aXtQkNmufXxEREQyV7bZwPDmzZt07tyZevXq8fHHHyd732q10qZNG0qUKMGWLVuIioqie/fu5M6dm4kTJ7qhYlOHDtCunXZuFhERyQoshpHSTjNZV1hYGIMGDSI6OjpJ+5o1a3j88cc5deoUxYsXB2DevHm89dZbnDt3Dh8fH7ueHxsbS0BAADExMfj7+zu7fBEREXEBez+/s81QV3p+/vlnqlevbgs9AC1btiQ2NpY9e/a4sTIRERHJKrLNUFd6Tp8+nST0ALbXp0+fTvW+uLg44uLibK9jY2NdU6CIiIi4nVt7fIYOHYrFYknza//+/S6tYdKkSQQEBNi+goODXfr9RERExH3c2uPz+uuv07NnzzSvCQkJsetZJUqUYOvWrUnazpw5Y3svNcOGDWPw4MG217GxsQo/IiIiOZRbg09gYCCBgYFOeVa9evWYMGECZ8+epVixYgCsW7cOf39/qlSpkup9vr6++Pr6OqUGERERydqyzRyf48ePc/HiRY4fP47VamXHjh0AVKxYkQIFCtCiRQuqVKnCc889x9SpUzl9+jQjR46kX79+CjYiIiICZKPl7D179uSTTz5J1r5x40YaN24MwN9//83LL7/Mpk2byJ8/Pz169GDy5MnkymV/vtNydhERkezH3s/vbBN8MouCj4iISPbjcfv4iIiIiKQn28zxySyJHWDaz0dERCT7SPzcTm8gS8HnDpcvXwbQknYREZFs6PLlywQEBKT6vub43CEhIYFTp07h5+eHxWJxdznZQuLeRydOnNC8KCfR79Q19Ht1Pv1OnU+/04wxDIPLly9TqlQpvLxSn8mjHp87eHl5ERQU5O4ysiV/f3/9n9TJ9Dt1Df1enU+/U+fT79RxafX0JNLkZhEREfEYCj4iIiLiMRR85K75+voyZswY7ZDtRPqduoZ+r86n36nz6XfqWprcLCIiIh5DPT4iIiLiMRR8RERExGMo+IiIiIjHUPARERERj6HgI05z7NgxevfuTfny5cmbNy8VKlRgzJgx3Lx5092lZWsTJkygfv365MuXj4IFC7q7nGzr/fffp1y5cuTJk4e6deuydetWd5eUrW3evJm2bdtSqlQpLBYLq1atcndJ2d6kSZOoU6cOfn5+FCtWjPbt23PgwAF3l5XjKPiI0+zfv5+EhATmz5/Pnj17mDlzJvPmzWP48OHuLi1bu3nzJp07d+bll192dynZ1rJlyxg8eDBjxoxh+/bt1KhRg5YtW3L27Fl3l5ZtXb16lRo1avD++++7u5Qc44cffqBfv3788ssvrFu3jvj4eFq0aMHVq1fdXVqOouXs4lLTpk1j7ty5HDlyxN2lZHthYWEMGjSI6Ohod5eS7dStW5c6derw3nvvAeaZfMHBwbz66qsMHTrUzdVlfxaLhZUrV9K+fXt3l5KjnDt3jmLFivHDDz/QqFEjd5eTY6jHR1wqJiaGwoULu7sM8WA3b95k27ZtNG/e3Nbm5eVF8+bN+fnnn91YmUjaYmJiAPTvUCdT8BGXOXToEHPmzKFv377uLkU82Pnz57FarRQvXjxJe/HixTl9+rSbqhJJW0JCAoMGDaJBgwZUq1bN3eXkKAo+kq6hQ4disVjS/Nq/f3+SeyIjI2nVqhWdO3emT58+bqo868rI71REPEe/fv3YvXs3S5cudXcpOU4udxcgWd/rr79Oz54907wmJCTE9udTp07RpEkT6tevz4IFC1xcXfbk6O9UMq5o0aJ4e3tz5syZJO1nzpyhRIkSbqpKJHX9+/fnm2++YfPmzQQFBbm7nBxHwUfSFRgYSGBgoF3XRkZG0qRJE2rVqsWiRYvw8lKnYkoc+Z3K3fHx8aFWrVp8//33tsm3CQkJfP/99/Tv39+9xYncxjAMXn31VVauXMmmTZsoX768u0vKkRR8xGkiIyNp3LgxZcuWZfr06Zw7d872nv7LOuOOHz/OxYsXOX78OFarlR07dgBQsWJFChQo4N7isonBgwfTo0cPateuzYMPPsisWbO4evUqzz//vLtLy7auXLnCoUOHbK+PHj3Kjh07KFy4MGXKlHFjZdlXv379+Oyzz1i9ejV+fn62OWgBAQHkzZvXzdXlHFrOLk4TFhaW6geJ/meWcT179uSTTz5J1r5x40YaN26c+QVlU++99x7Tpk3j9OnT1KxZk9mzZ1O3bl13l5Vtbdq0iSZNmiRr79GjB2FhYZlfUA5gsVhSbF+0aFG6Q+NiPwUfERER8RiagCEiIiIeQ8FHREREPIaCj4iIiHgMBR8RERHxGAo+IiIi4jEUfERERMRjKPiIiIiIx1DwEfEwmzZtwmKxEB0d7e5SHGKxWFi1apXTnleuXDlmzZrltOdltmPHjmGxWGw7eWfXv1eRzKbgI5KDpHfi+9ixY91dYrrGjh1LzZo1k7VHRUXRunXrzC8oC+jZs6ftnLFEwcHBREVFUa1aNfcUJZJN6awukRwkKirK9udly5YxevRoDhw4YGsrUKAAv//+uztK4+bNm/j4+GT4fp33lpS3t7d+JyIZoB4fkRykRIkStq+AgAAsFkuSttsPNd22bRu1a9cmX7581K9fP0lAAli9ejUPPPAAefLkISQkhNDQUG7dumV7//jx47Rr144CBQrg7+/PU089xZkzZ2zvJ/bcfPTRR5QvX548efIAEB0dzQsvvEBgYCD+/v40bdqUnTt3AuZ5b6GhoezcudPWS5V47tOdQ10nT56kS5cuFC5cmPz581O7dm1+/fVXAA4fPky7du0oXrw4BQoUoE6dOqxfv96h36XVamXw4MEULFiQIkWK8Oabb9KjR48kPS8pDZfVrFkzSc/aO++8Q/Xq1cmfPz/BwcG88sorXLlyxfZ+WFgYBQsW5LvvvqNy5coUKFCAVq1a2ULs2LFj+eSTT1i9erXtd7Jp06ZkQ10p+fHHH2nYsCF58+YlODiYAQMGcPXqVdv7H3zwAZUqVSJPnjwUL16cTp06OfQ7EsmOFHxEPNSIESOYMWMGv//+O7ly5aJXr1629yIiIujevTsDBw5k7969zJ8/n7CwMCZMmABAQkIC7dq14+LFi/zwww+sW7eOI0eO8PTTTyf5HocOHWLFihWEh4fbPqA7d+7M2bNnWbNmDdu2beOBBx6gWbNmXLx4kaeffprXX3+dqlWrEhUVRVRUVLJngnky+COPPEJkZCRfffUVO3fu5M033yQhIcH2/mOPPcb333/PH3/8QatWrWjbti3Hjx+3+/czY8YMwsLCWLhwIT/++CMXL15k5cqVjv6a8fLyYvbs2ezZs4dPPvmEDRs28Oabbya55tq1a0yfPp0lS5awefNmjh8/zhtvvAHAG2+8wVNPPWULQ1FRUdSvXz/d73v48GFatWpFx44d2bVrF8uWLePHH3+kf//+APz+++8MGDCAcePGceDAAb799lsaNWrk8M8nku0YIpIjLVq0yAgICEjWvnHjRgMw1q9fb2v773//awDG9evXDcMwjGbNmhkTJ05Mct+SJUuMkiVLGoZhGGvXrjW8vb2N48eP297fs2ePARhbt241DMMwxowZY+TOnds4e/as7ZqIiAjD39/fuHHjRpJnV6hQwZg/f77tvho1aiSrGzBWrlxpGIZhzJ8/3/Dz8zMuXLhg52/DMKpWrWrMmTPH9rps2bLGzJkzU72+ZMmSxtSpU22v4+PjjaCgIKNdu3ZpPqNGjRrGmDFjUn3ul19+aRQpUsT2etGiRQZgHDp0yNb2/vvvG8WLF7e97tGjR5LvaxiGcfToUQMw/vjjD8Mw/v17vXTpkmEYhtG7d2/jxRdfTHJPRESE4eXlZVy/ft1YsWKF4e/vb8TGxqZaq0hOpDk+Ih7qvvvus/25ZMmSAJw9e5YyZcqwc+dOfvrpJ1sPD5hDPzdu3ODatWvs27eP4OBggoODbe9XqVKFggULsm/fPurUqQNA2bJlCQwMtF2zc+dOrly5QpEiRZLUcv36dQ4fPmx37Tt27OD++++ncOHCKb5/5coVxo4dy3//+1+ioqK4desW169ft7vHJyYmhqioKOrWrWtry5UrF7Vr18YwDLvrBFi/fj2TJk1i//79xMbGcuvWLdvvMV++fADky5ePChUq2O4pWbIkZ8+edej73Gnnzp3s2rWLTz/91NZmGAYJCQkcPXqURx99lLJlyxISEkKrVq1o1aoVTz75pK0mkZxKwUfEQ+XOndv2Z4vFApBkqCg0NJQOHTokuy9xro498ufPn+T1lStXKFmyJJs2bUp2bcGCBe1+bt68edN8/4033mDdunVMnz6dihUrkjdvXjp16sTNmzft/h728PLyShaE4uPjbX8+duwYjz/+OC+//DITJkygcOHC/Pjjj/Tu3ZubN2/aQsbtfxdg/n04GrDudOXKFfr27cuAAQOSvVemTBl8fHzYvn07mzZtYu3atYwePZqxY8fy22+/OfR3IZLdKPiISDIPPPAABw4coGLFiim+X7lyZU6cOMGJEydsvT579+4lOjqaKlWqpPnc06dPkytXLsqVK5fiNT4+Plit1jTru++++/joo4+4ePFiir0+P/30Ez179uTJJ58EzBBw7NixNJ95u4CAAEqWLMmvv/5qm/dy69Yt25ykRIGBgUlW0sXGxnL06FHb623btpGQkMCMGTPw8jKnVH7xxRd215HInt/JnR544AH27t2b6t8hmL1YzZs3p3nz5owZM4aCBQuyYcOGFAOvSE6hyc0ikszo0aNZvHgxoaGh7Nmzh3379rF06VJGjhwJQPPmzalevTrPPvss27dvZ+vWrXTv3p1HHnmE2rVrp/rc5s2bU69ePdq3b8/atWs5duwYW7ZsYcSIEbZl9uXKlePo0aPs2LGD8+fPExcXl+w5Xbp0oUSJErRv356ffvqJI0eOsGLFCn7++WcAKlWqZJtQvXPnTrp27WrrzbLXwIEDmTx5MqtWrWL//v288soryTYHbNq0KUuWLCEiIoI///yTHj164O3tbXu/YsWKxMfHM2fOHI4cOcKSJUuYN2+eQ3Uk/k527drFgQMHOH/+fJJepdS89dZbbNmyhf79+7Njxw4OHjzI6tWrbZObv/nmG2bPns2OHTv4+++/Wbx4MQkJCdxzzz0O1yeSnSj4iEgyLVu25JtvvmHt2rXUqVOHhx56iJkzZ1K2bFnAHIpZvXo1hQoVolGjRjRv3pyQkBCWLVuW5nMtFgv/+9//aNSoEc8//zz/+c9/eOaZZ/j7778pXrw4AB07dqRVq1Y0adKEwMBAPv/882TP8fHxYe3atRQrVozHHnuM6tWrM3nyZFvoeOeddyhUqBD169enbdu2tGzZMklPjT1ef/11nnvuOXr06EG9evXw8/Oz9SAlGjZsGI888giPP/44bdq0oX379knm6tSoUYN33nmHKVOmUK1aNT799FMmTZrkUB0Affr04Z577qF27doEBgby008/pXvPfffdxw8//MBff/1Fw4YNuf/++xk9ejSlSpUCzKHF8PBwmjZtSuXKlZk3bx6ff/45VatWdbg+kezEYtztQLKIiIfo2bMn0dHRTj06Q0Qyl3p8RERExGMo+IiIiIjH0FCXiIiIeAz1+IiIiIjHUPARERERj6HgIyIiIh5DwUdEREQ8hoKPiIiIeAwFHxEREfEYCj4iIiLiMRR8RERExGMo+IiIiIjH+H942+bNEVbpmQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Q-Q plot\n",
    "stats.probplot(np.log(final_df['ANN Error']), dist=\"norm\", plot=plt)\n",
    "plt.title('Q-Q Plot of Residuals')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "not either. Therefore I need to use non-parametric alternative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Friedman Test: Statistic=24.731, p=1.570e-04\n"
     ]
    }
   ],
   "source": [
    "# Remove the Fold column\n",
    "df_wide = final_df.drop(columns=['Fold'])\n",
    "# display(df_wide)\n",
    "\n",
    "# Perform the Friedman test\n",
    "stat, p = stats.friedmanchisquare(*[df_wide[model].values for model in df_wide.columns])\n",
    "\n",
    "\n",
    "print(f'Friedman Test: Statistic={stat:.3f}, p={p:.3e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make pairwise comparisons with paired t.tests. Use setup II from the 02450 machine learning book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paired t-test between Baseline Error and Ridge Error: mean=-13.75, std = 39.03, t=-0.680, p=0.518\n",
      "Paired t-test between Baseline Error and KNN Error: mean=0.05, std = 0.44, t=0.229, p=0.826\n",
      "Paired t-test between Baseline Error and RF Error: mean=-0.37, std = 0.58, t=-1.235, p=0.257\n",
      "Paired t-test between Baseline Error and ANN Error: mean=-2.53, std = 2.50, t=-1.958, p=0.091\n",
      "Paired t-test between Baseline Error and Logistic Error: mean=-1.88, std = 2.36, t=-1.540, p=0.167\n",
      "Paired t-test between Ridge Error and KNN Error: mean=13.80, std = 38.66, t=0.690, p=0.513\n",
      "Paired t-test between Ridge Error and RF Error: mean=13.38, std = 39.17, t=0.660, p=0.531\n",
      "Paired t-test between Ridge Error and ANN Error: mean=11.21, std = 37.16, t=0.583, p=0.578\n",
      "Paired t-test between Ridge Error and Logistic Error: mean=11.87, std = 38.23, t=0.600, p=0.568\n",
      "Paired t-test between KNN Error and RF Error: mean=-0.42, std = 0.85, t=-0.961, p=0.368\n",
      "Paired t-test between KNN Error and ANN Error: mean=-2.59, std = 2.31, t=-2.164, p=0.067\n",
      "Paired t-test between KNN Error and Logistic Error: mean=-1.93, std = 2.45, t=-1.526, p=0.171\n",
      "Paired t-test between RF Error and ANN Error: mean=-2.16, std = 2.65, t=-1.577, p=0.159\n",
      "Paired t-test between RF Error and Logistic Error: mean=-1.51, std = 2.48, t=-1.176, p=0.278\n",
      "Paired t-test between ANN Error and Logistic Error: mean=0.65, std = 2.28, t=0.555, p=0.596\n"
     ]
    }
   ],
   "source": [
    "for model1, model2 in combinations(df_wide.columns, 2):\n",
    "    # display(final_df[final_df['Fold'] == 0])\n",
    "    rjs = []\n",
    "    for i in range(8):\n",
    "        rjs.append(np.mean(final_df[final_df['Fold'] == i][model1] - final_df[final_df['Fold'] == i][model2]))\n",
    "    # print(rjs)\n",
    "    rjs = np.array(rjs)\n",
    "    mean = np.mean(rjs)\n",
    "    s2 = 1/7 * np.sum((rjs - mean)**2)\n",
    "    o2 = (1/8+1/7)*s2\n",
    "    t = mean/np.sqrt(o2)\n",
    "    print(f'Paired t-test between {model1} and {model2}: mean={mean:.2f}, std = {np.sqrt(s2):.2f}, t={t:.3f}, p={2*stats.t.sf(np.abs(t), 7):.3f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the same comparisons with normal paired t-tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Error vs. Ridge Error: Mean=-15.71, sd = 58.01 Statistic=-3.511, p=0.00057\n",
      "Baseline Error vs. KNN Error: Mean=0.05, sd = 2.49 Statistic=0.244, p=0.80718\n",
      "Baseline Error vs. RF Error: Mean=-0.34, sd = 3.61 Statistic=-1.231, p=0.22000\n",
      "Baseline Error vs. ANN Error: Mean=-2.65, sd = 8.35 Statistic=-4.123, p=0.00006\n",
      "Baseline Error vs. Logistic Error: Mean=-2.08, sd = 5.13 Statistic=-5.247, p=0.00000\n",
      "Ridge Error vs. KNN Error: Mean=15.76, sd = 56.84 Statistic=3.594, p=0.00043\n",
      "Ridge Error vs. RF Error: Mean=15.37, sd = 58.21 Statistic=3.423, p=0.00078\n",
      "Ridge Error vs. ANN Error: Mean=13.06, sd = 55.40 Statistic=3.055, p=0.00262\n",
      "Ridge Error vs. Logistic Error: Mean=13.64, sd = 56.79 Statistic=3.113, p=0.00218\n",
      "KNN Error vs. RF Error: Mean=-0.39, sd = 3.73 Statistic=-1.357, p=0.17658\n",
      "KNN Error vs. ANN Error: Mean=-2.70, sd = 7.35 Statistic=-4.764, p=0.00000\n",
      "KNN Error vs. Logistic Error: Mean=-2.12, sd = 5.32 Statistic=-5.171, p=0.00000\n",
      "RF Error vs. ANN Error: Mean=-2.31, sd = 8.26 Statistic=-3.625, p=0.00038\n",
      "RF Error vs. Logistic Error: Mean=-1.73, sd = 6.35 Statistic=-3.536, p=0.00053\n",
      "ANN Error vs. Logistic Error: Mean=0.58, sd = 9.52 Statistic=0.788, p=0.43154\n"
     ]
    }
   ],
   "source": [
    "# Make pairwise comparisons between the 6 models with paired t.tests\n",
    "\n",
    "# Perform the pairwise comparisons\n",
    "for model1, model2 in combinations(df_wide.columns, 2):\n",
    "    stat, p = stats.ttest_rel(df_wide[model1], df_wide[model2])\n",
    "    diff = df_wide[model1] - df_wide[model2]\n",
    "    print(f'{model1} vs. {model2}: Mean={np.mean(diff):.2f}, sd = {np.std(diff-np.mean(diff),ddof =1):.2f} Statistic={stat:.3f}, p={p:.5f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Means within each fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0\n",
      "Baseline: 2.825445816186557\n",
      "ANN: 6.7994231348872605\n",
      "Ridge: 2.825457504326401\n",
      "KNN: 2.670118454806096\n",
      "RF: 3.0855064247921384\n",
      "Logistic: 5.041666666666667\n",
      "Fold 1\n",
      "Baseline: 5.854938271604937\n",
      "ANN: 8.89029778044321\n",
      "Ridge: 5.5892517540408555\n",
      "KNN: 5.401023644752018\n",
      "RF: 5.692652329749104\n",
      "Logistic: 11.541666666666666\n",
      "Fold 2\n",
      "Baseline: 5.141803840877915\n",
      "ANN: 4.373255481726328\n",
      "Ridge: 5.148866954344389\n",
      "KNN: 4.83616255144033\n",
      "RF: 5.737366255144033\n",
      "Logistic: 6.208333333333333\n",
      "Fold 3\n",
      "Baseline: 2.00960219478738\n",
      "ANN: 9.273163358037772\n",
      "Ridge: 112.34898403287805\n",
      "KNN: 2.8697916666666665\n",
      "RF: 2.035224597442561\n",
      "Logistic: 6.041666666666667\n",
      "Fold 4\n",
      "Baseline: 4.335069444444445\n",
      "ANN: 6.748247207164997\n",
      "Ridge: 4.335092125413221\n",
      "KNN: 4.375283446712018\n",
      "RF: 4.822208745435986\n",
      "Logistic: 2.888888888888889\n",
      "Fold 5\n",
      "Baseline: 3.846450617283951\n",
      "ANN: 4.460979385859954\n",
      "Ridge: 3.846461953015034\n",
      "KNN: 3.910817901234568\n",
      "RF: 3.3974871964208253\n",
      "Logistic: 3.5\n",
      "Fold 6\n",
      "Baseline: 7.481674382716049\n",
      "ANN: 8.005234563789655\n",
      "Ridge: 7.481654342096636\n",
      "KNN: 7.578512396694214\n",
      "RF: 8.33425160697888\n",
      "Logistic: 8.166666666666666\n",
      "Fold 7\n",
      "Baseline: 5.01929012345679\n",
      "ANN: 8.240609293997988\n",
      "Ridge: 4.902451673321912\n",
      "KNN: 4.452222222222223\n",
      "RF: 6.372008062484252\n",
      "Logistic: 8.166666666666666\n"
     ]
    }
   ],
   "source": [
    "for i in range(8):\n",
    "    print('Fold', i)\n",
    "    print('Baseline:', np.mean(errors['Baseline'][i]))\n",
    "    print('ANN:', np.mean(errors['ANN'][i]))\n",
    "    print('Ridge:', np.mean(errors['Ridge'][i]))\n",
    "    print('KNN:', np.mean(errors['KNN'][i]))\n",
    "    print('RF:', np.mean(errors['RF'][i]))\n",
    "    print('Logistic:', np.mean(errors['Logistic'][i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean errors of all models over all 168 datapoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.47766479276896\n",
      "20.1909721879964\n",
      "4.430674756116055\n",
      "4.820745187945399\n",
      "7.132491799029145\n",
      "6.553571428571429\n"
     ]
    }
   ],
   "source": [
    "for model in ['Baseline Error', 'Ridge Error', 'KNN Error', 'RF Error', 'ANN Error', 'Logistic Error']:\n",
    "    print(np.mean(final_df.mean()[model]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robustness of models when changing data calculations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Error standard deviation:\n",
      "1.725245169581837\n",
      "\n",
      "Ridge Error standard deviation:\n",
      "38.02187685602264\n",
      "\n",
      "KNN Error standard deviation:\n",
      "1.547901930100556\n",
      "\n",
      "RF Error standard deviation:\n",
      "2.0369347531699784\n",
      "\n",
      "ANN Error standard deviation:\n",
      "1.8769989950431802\n",
      "\n",
      "Logistic Error standard deviation:\n",
      "2.8135200482186713\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for model in ['Baseline Error', 'Ridge Error', 'KNN Error', 'RF Error', 'ANN Error', 'Logistic Error']:\n",
    "    print(model,\" standard deviation:\",sep=\"\")\n",
    "    print(np.std([final_df[final_df['Fold'] == i].mean()[model] for i in range(8)], ddof=1))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test functions and show hyperparameter optimization graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.57535911e-01, 9.79482121e+00, 3.49818443e+00, 1.68164897e-02,\n",
       "       4.53535801e+00, 7.57601471e-01, 1.27600236e+00, 4.53522796e+00,\n",
       "       1.27595647e+00, 1.68165128e-02, 1.27595570e+00, 1.68243916e-02,\n",
       "       7.57460939e-01, 1.27613166e+00, 1.68136170e-02, 9.79484454e+00,\n",
       "       1.67997945e-02, 1.67948786e-02, 1.27604237e+00, 1.70537411e+01,\n",
       "       4.53559811e+00, 1.68112409e-02, 4.53534811e+00, 7.57492842e-01])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.825457504326401"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ridge_hyperparams = find_best_Ridge(x_par, y_par.values)\n",
    "ridge_model = Ridge(alpha = Ridge_hyperparams)\n",
    "ridge_model.fit(x_par, y_par)\n",
    "ridge_errors = (ridge_model.predict(x_val)-y_val.values)**2\n",
    "display(ridge_errors)\n",
    "np.mean(ridge_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.16704391e-01, 9.38705297e+00, 3.83159801e+00, 4.07424174e-03,\n",
       "       4.25939339e+00, 9.57899502e-01, 1.00000000e+00, 4.00000000e+00,\n",
       "       1.00000000e+00, 4.07424174e-03, 1.00000000e+00, 7.24309642e-03,\n",
       "       8.37030330e-01, 1.13173382e+00, 4.07424174e-03, 9.38705297e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.04300589e+00, 1.60000000e+01,\n",
       "       4.25939339e+00, 4.07424174e-03, 4.17202354e+00, 8.76414667e-01])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.670118454806096"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KNN_hyperparams = int(find_best_KNN(x_par, y_par.values))\n",
    "KNN_model = KNeighborsRegressor(n_neighbors = KNN_hyperparams)\n",
    "KNN_model.fit(x_par, y_par)\n",
    "KNN_errors = (KNN_model.predict(x_val)-y_val.values)**2\n",
    "display(KNN_errors)\n",
    "np.mean(KNN_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.55328798e-01, 7.36734694e+00, 3.44897959e+00, 3.83219955e-01,\n",
       "       2.62131519e+00, 2.04081633e+00, 0.00000000e+00, 6.85941043e+00,\n",
       "       2.26757370e-01, 7.34693878e-01, 9.07029478e-01, 5.10204082e-01,\n",
       "       2.26757370e-01, 1.53287982e+00, 5.66893424e-02, 1.11111111e+01,\n",
       "       9.07029478e-03, 5.80498866e-01, 2.62131519e+00, 1.63832200e+01,\n",
       "       8.16326531e+00, 1.09750567e+00, 6.13151927e+00, 3.62811791e+00])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "3.220710506424792"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RF_hyperparams = int(find_best_RF(x_par, y_par.values))\n",
    "RF_model = RandomForestRegressor(n_estimators = RF_hyperparams)\n",
    "RF_model.fit(x_par, y_par)\n",
    "RF_errors = (RF_model.predict(x_val)-y_val.values)**2\n",
    "display(RF_errors)\n",
    "np.mean(RF_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 16,  1,  1,  9,  0,  4,  9,  4,  1,  4,  1,  0,  4,  1, 16,  1,\n",
       "        1,  4, 25,  9,  1,  9,  0], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "5.041666666666667"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_hyperparams = find_best_logistic(x_par, y_par.values)\n",
    "logistic_model = LogisticRegression(C=logistic_hyperparams, penalty='l2',max_iter = 10**6)\n",
    "logistic_model.fit(x_par, y_par)\n",
    "logistic_errors = (logistic_model.predict(x_val)-y_val.values)**2\n",
    "display(logistic_errors)\n",
    "np.mean(logistic_errors)"
   ]
  }
 ],
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