{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic classification using LightGBM\n",
    "Solve the Titanic as classification problem with LightGBM  \n",
    "  \n",
    "Data: [Titanic: Machine Learning from Disaster | Kaggle](https://www.kaggle.com/c/titanic/data)  \n",
    "Model: LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "import lightgbm\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_value = 89\n",
    "random.seed(seed_value)\n",
    "np.random.seed(seed_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../../data/titanic'\n",
    "train_raw = pd.read_csv(os.path.join(data_path, \"train.csv\"))\n",
    "# test_raw = pd.read_csv(os.path.join(data_path, \"test.csv\"))\n",
    "train_raw, test_raw = train_test_split(train_raw, test_size=0.1, random_state=seed_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 90 entries, 291 to 811\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    90 non-null int64\n",
      "Survived       90 non-null int64\n",
      "Pclass         90 non-null int64\n",
      "Name           90 non-null object\n",
      "Sex            90 non-null object\n",
      "Age            73 non-null float64\n",
      "SibSp          90 non-null int64\n",
      "Parch          90 non-null int64\n",
      "Ticket         90 non-null object\n",
      "Fare           90 non-null float64\n",
      "Cabin          23 non-null object\n",
      "Embarked       88 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 9.1+ KB\n"
     ]
    }
   ],
   "source": [
    "test_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_raw.pop('Survived')\n",
    "test_labels = test_raw.pop('Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_feature_list = ['PassengerId', 'Name', 'Ticket', 'Cabin']\n",
    "drop_labels = train_raw.drop(drop_feature_list, axis=1)\n",
    "test_drop_labels = test_raw.drop(drop_feature_list, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_fill_nan = drop_labels.fillna(drop_labels.median())\n",
    "test_fill_nan  = test_drop_labels.fillna(drop_labels.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search categorical data that has missing value\n",
    "#### 欠損値を持つカテゴリ特徴量を探す"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Missing Values of Train Data ----\n",
      "[]\n",
      "---- Missing Values of Train & Test Data ----\n",
      "['Embarked']\n"
     ]
    }
   ],
   "source": [
    "categorical = []\n",
    "\n",
    "print(\"---- Missing Values of Train Data ----\")\n",
    "for i in train_fill_nan.columns:\n",
    "    if train_fill_nan[i].dtype == 'object' and train_fill_nan[i].isnull().any() == True:\n",
    "        categorical.append(i)\n",
    "        \n",
    "print(categorical)\n",
    "\n",
    "\n",
    "print(\"---- Missing Values of Train & Test Data ----\")\n",
    "for i in test_fill_nan.columns:\n",
    "    if test_fill_nan[i].dtype == 'object' and test_fill_nan[i].isnull().any() == True:\n",
    "        categorical.append(i)\n",
    "        \n",
    "print(categorical)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolate the missing values with mode value\n",
    "#### 最頻値を用いて，欠損値の補間"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fill_values = {\n",
    "    'Embarked': train_fill_nan['Embarked'].mode().values[0]\n",
    "}\n",
    "\n",
    "train_fill_categorical = train_fill_nan.fillna(fill_values)\n",
    "test_fill_categorical = test_fill_nan.fillna(fill_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.get_dummies(train_fill_categorical)\n",
    "test_data = pd.get_dummies(test_fill_categorical)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Data\n",
    "#### Train 用 と Validation 用のデータセットに分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, validation_data, train_labels, validation_labels = train_test_split(train_data, \n",
    "                                                                                train_labels,\n",
    "                                                                                test_size = 0.1,\n",
    "                                                                                stratify=train_labels,\n",
    "                                                                                random_state=seed_value\n",
    "                                                                               )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box of each model's results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_results  = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(preds, data):\n",
    "    y_true = data.get_label()\n",
    "    N_LABELS = 2\n",
    "    reshaped_preds = preds.reshape(N_LABELS, len(preds) // N_LABELS)\n",
    "    \n",
    "    y_pred = np.argmax(reshaped_preds, axis=0)\n",
    "    acc = np.mean(y_true == y_pred)\n",
    "    \n",
    "    return 'accuracy', acc, True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'task': 'train',\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'multiclass',\n",
    "    'num_class': 2,\n",
    "    'verbose': 2,\n",
    "    'seed': seed_value\n",
    "}\n",
    "\n",
    "evaluation_results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_train_data = lightgbm.Dataset(train_data, label=train_labels)\n",
    "lgbm_validation_data = lightgbm.Dataset(validation_data, label=validation_labels, reference=lgbm_train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 50 rounds.\n",
      "[20]\tTrain's multi_logloss: 0.344026\tTrain's accuracy: 0.872222\tValidation's multi_logloss: 0.541238\tValidation's accuracy: 0.753086\n",
      "[40]\tTrain's multi_logloss: 0.269737\tTrain's accuracy: 0.902778\tValidation's multi_logloss: 0.559097\tValidation's accuracy: 0.740741\n",
      "[60]\tTrain's multi_logloss: 0.218793\tTrain's accuracy: 0.913889\tValidation's multi_logloss: 0.594013\tValidation's accuracy: 0.753086\n",
      "Early stopping, best iteration is:\n",
      "[16]\tTrain's multi_logloss: 0.367465\tTrain's accuracy: 0.868056\tValidation's multi_logloss: 0.542608\tValidation's accuracy: 0.765432\n"
     ]
    }
   ],
   "source": [
    "history = lightgbm.train(\n",
    "    params, \n",
    "    lgbm_train_data,\n",
    "    valid_sets=[lgbm_train_data, lgbm_validation_data],\n",
    "    valid_names=['Train', 'Validation'],\n",
    "    evals_result=evaluation_results,\n",
    "    feval=accuracy,\n",
    "    num_boost_round=500,\n",
    "    verbose_eval=20,\n",
    "    early_stopping_rounds=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_accuracy = evaluation_results['Train']['accuracy']\n",
    "valid_accuracy = evaluation_results['Validation']['accuracy']\n",
    "train_loss = evaluation_results['Train']['multi_logloss']\n",
    "valid_loss = evaluation_results['Validation']['multi_logloss']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Training Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Point of Marker`  : Best Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7767b1db70>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ou8/8s84a8Tv+MQ8AAAAAIVBaVaGFOSt8oc5PeasOadu1xJi4BiGON8DZ+5ogBwCA8EXIE0EIeULDNWeRnPc861eLfu5OWTPS93HF4aHCWaX7PnxRT3z15j6/UBjWva/+ee5fNLbn4CB3BwAAAABHlp2le/Tj+uW+7deWbV4vj+lp8n26te2ood36KDM1XZmp6erezrsyp01CMkEOAAARiJAnghDyhIZZ61LV+TfL3F3iq9mmjlfU9ReHsKvgyS0s0N/e+Y/e/2nOPsecfNR43XPW5erZoUsQOwMAAACAw9f2kiJ9v/pnzV+3TPPWLtXqrRuadZ8+HbtpbK/BGtNzkMb0HKy0Vu1auFMAABBKhDwRhJAndGpe/kC1r31cX4h2KPbNh2XEx4auqSBbmLNCt775pBbmrGj0vNVi1R+OOVW3nXqp2iW2CnJ3AAAAAHB4KCzZrXs+eEEv/fCx3B53k661GBYN7tJTY3oO0thegzW6xyC1SUgOUKcAACAcEPJEEEKe0PHs2qOq8/8quev/ge24YprsZx4bwq6CzzRNffjz97r97aeVu6PxvysSomN149SL9Jdjz1VsVHSQOwQAAACAyFRV49S/v5ylhz57XeXVlQd1jcNm19BufTS252CN7TVYozIHKDEmLsCdAgCAcELIE0EIeUKr+u7/yP39T773RqdUxbx8jwyLJYRdhUaNq1YvfPeB7vvwRRWVlzQ6pmNKW915xh91/ugTZLVYg9whAAAAAEQGj8ej/y38Une886y27Nmx37GxjmiNyhygsb0Ga2zPwRrWva9iHFFB6hQAAIQjQp4IQsgTWu5la1V9/QN+taj7r5NteP8QdRR6JZXleujT1/Tk12+quram0TED03vovnOv1O/6jQhydwAAAAAQ3uauWaJb3nxCv2xc0+j5GEeUJvYZpjF1oc6QLr1kt9mC3CUAAAhnhDwRhJAntEzTVNUf7pC5cauvZj16kKLvuTqEXYWHzUXbddd7z2nWgi+1r//ej+0/Uveec6UGpGcGuTsAAAAACC/rt2/WbW89pU+WzG30vGEYunDMFP39jD+pU0rbIHcHAAAiCSFPBCHkCb3aj79TzWOv1RcMQzGv3y9L+zahayqMLN20Vre99ZTmrPqp0fOGYeiiMSfqjjP+yBcqAAAAAI44u8qKdd9HL+r5Oe/L1eCZrw0d02eo7p92lQZ17hnk7gAAQCQi5IkghDyhZ1ZVq/LcG6WKKl/NPm2KHJedFcKuwotpmvpqxUL931tPaeWWvEbHxDiidPVx03TDiRcqgYeCAgAAADjMOWtr9PQ37+hfH7+skqryRsf06tBF953zF00ZNFqGYQS5QwAAEKkIeSIIIU94cD45U673v60vJMYr9s2HZDjsoWsqDLk9br027zPd9f7z2l68q9ExSTHxOnXoBJ09crKO6TNUNit7SwMAAAA4fJimqXcXz9bf3vmPNu7c2uiYNvHJuv303+vS8afyvB0AANBkhDwRhJAnPHg2b1PVjNv9ao6bL5X9+DEh6ii8VTir9PgXs/TI52+owlm1z3FtE5J1+rCJOnvkZI3uMUgWiyWIXQIAAABAy1qYs0K3/O8JZeVmN3o+yubQX447RzdNna6k2PggdwcAAA4XhDwRhJAnfFTd9JA8v6z2vbf06qaYp2/fzxXYXlKkez/4r178/iN5TM9+x3ZMaaszh0/S2SOP1bBufdiqAAAAAEDE2Lhzq25/+z96d/G3+xxzzshjdfdZf1aXNh2C2BkAADgcEfJEEEKe8OGa94ucf3/Krxb91O2y9u4Woo4ix5qtG/W3t5/WJ0vnHdT4rm076uwRk3X2yMnqn5ZB4AMAAAAgLBVXlulfH7+ip795WzWu2kbHHN1joO4/9yqNyOgX5O4AAMDhipAnghDyhA/T7VbVhbfI3LHbV7MdN1pRf/19CLuKLPlFhXp38bd6O+sb/bJxzUFd07tjV509YrLOGvE79ezQJcAdAgAAADgclFdXKqcwX+u352vjrq1yud2yWa2yW21+HzarVbZf1ey+2m/P7b2HzWrTR798r/s+fFFF5SWN9tC9XSfdc/YVOm3oMfzgGgAAaFGEPBGEkCe81LzxqWpffK++YLcp9s2HZCQlhK6pCJVbWOALfLILcg/qmsGde+qskd7Ahy0OAAAAgCNbda1TeTu2+MKc3MJ8rS/MV05hgbYX7wpZXylxCbrl5Bn68+/OlMNmD1kfAADg8EXIE0EIecKLuadUlefdJNW6fDX7H86U47wTQ9hV5Fu1JU/vLPIGPjmF+Qd1zYiMfjp7xGSdMXySOqa0DXCHAAAAAELB5XZp067tdUHOZuUUFiin0HvcXLRd4fS9B7vVpj//7kzdcvIMtYpPDHU7AADgMEbIE0EIecJP9X3Py/3tQt97I7W1Yl67X4bVEsKuDg+maWrppnV6e9E3emfRN8ovKjzgNYZhaFyvIZo26jidMXySkmLjg9ApAAAAgIPh8XhU466Vs7ZWTleNnLW1qnHVqrrWKaertu59Td3rGm0vKdL67ZuVW1ig9YX52rBzi1xud6h/GQd06tBjdO/ZVygjla/ZAQBA4BHyRBBCnvDjXpWr6qvu86tF/eMq2UYPDlFHhyePx6Os3JV6Z9E3em/xbG0vKTrgNVE2h04aMk4XjDlBk/uNlN1mC0KnAAAAwJGhutapeWuX6uvsLK3eskFVtU6/kKa6tkbO2hrVuGp9oU2t23XgGweIYRhKb5WqzNR0xUXHyOV2qdblUq3bJZfH7T26vcfaBudq3S65PXvrbl/dY3p+c/+jMwfqrrP+pLE9+XoQAAAEDyFPBCHkCT+maar68n/Is36Tr2Yd1k/R/7o+hF0d3twet+atXaq3s77R+z/N0e6K0gNe0zYhWeeMOk4XjJ6iwV168qBTAAAAoIlM09TabZv0dXaWvsnO0g9rflF1bU2o2/qN9kmtldk+XZnt0r3H1HT1aJ+u7u06Kdoe1WLzeDye+kDI7ZLdalN8dGyL3R8AAOBgEfJEEEKe8FT7+VzVPPSyXy3m5XtlSW8fmoaOILUul2avWqy3s77RR798r7LqygNe06djN50/+gRNO/p4pbVqF4QuAQAAgMhUXFmmOat+0jfZWfpqRZYKdh94C+VgSIlLUGZqZ/WoC3HqP9KUEBMX6vYAAACCipAnghDyhCez2qnKaTdKZfUBg+3MYxV1xbQQdnXkqa516otlC/S/hV/ps6XzDrgVhGEYOqbPUF0weopOHTqBn7oDAABASNW6XFqev14Lc1Zocd4qVdfWqGubDspITVNGuzRlpKYprVU7WS3WgPXg9rj1y8a1+iY7S19nZ2lR7kq5PcF5Bo7DZleUza4om0MOu/d1Uky8MupW4mSm1q/MaR2fFJSeAAAAIgEhTwQh5AlfzmfelOvtr+oLcTGKffNhGTEttx0ADl5ReYneXfSt3vjxcy3KXXnA8bGOaJ06dIIuGDNFx/QZGtAvnAEAAABJ2l1eqkW52VqQs1wLc7L1U94qVdZU7/cau9Wmbm07KiM1Td3bpal7u07eAKhdmrq06dCs51Bu3bNT365cpK+zs/Rt9qKD2g55L4th0bDufTSp73C1T26tKJtDUTa7HHZHXWBjV5TdURfgOBRltyva7pCjblxU3TiHzc6WygAAAM1EyBNBCHnCl2frDlVNv01q8LnuuG667CdNCGFXkKSc7fmaueALzfzxC23ate2A4zskt9G0o4/XBaNPUL+0jCB0CAAAgMOdaZrKKczXgvXLtTBnhRbkrNCarRtbdA6rxarOrVN9q3661x0z2qWpa9sOvufROGtr9OP65fo6O0tfr1io7ILcJs3TMaWtjus/SpMHjNTEPsPUKj6xRX8dAAAAaBpCnghCyBPeqm97TO6sFb73lu5pin7uTn4iLUx4PB79mLNcM+d/rncXz1ZpVcUBrxnUuYfOHz1F54w6Vu2TWgehSwAAAATK7vJSWSyGEqPjZLFYAjpXVY1TP29crYXrV2hhjvejqLwkoHPuj2EY6pTSTh1T2ig7P/eAK4YairI5NK7XYE0eMFLH9h+pPh278TUOAABAGCHkiSCEPOHNlbVcztse96tFP3aLrAN6hKgj7EtVjVOfLp2nmT9+rq9WZB1wn3Grxarf9RuuU4dO0JRBY9QhuU2QOgUAAEBzmKapDTu3aO6apZq7donmrl2izUXbJXkDj4ToWCXFxispJl5JsfFKjk1QYkyckmITlBwbr8SYeO8x1nvcO27vNVF2h99824p3eVforPduvbZ009oDPiNyX3q276xRmQPULrGVNuzcqrwdBcopzFdZdeWBL24hvTt21eT+3lBnbM/Bio2KDtrcAAAAaBpCnghCyBPeTI9HVdNvk7ltp69mnThC0bf/KYRd4UAKS3br7ayvNfPHL7Rk09qDumZotz46afBYTR0yTv3TMvhJRgAAgBAzTVPrt2+uC3S8wc7WPTsPfGEzRdsdvmCourbGFyA15z5Du/XRqMwBOrrHQI3M6K82Ccm/GWeapnaVFSt3R4HydhQot3CL97jDezzUVUJJMfGa2G+Yju0/UpP7j1Tn1u0P6X4AAAAIHkKeCELIE/5q3/pCNc++XV+wWhUz6wFZWv/2CzWEn1Vb8jTzxy80a8GXB/1NgfTWqZo6eJymDh6jcb2G/OanOgEAANDyTNPU6q0bvKHOmqWav26ptpcUhbqtA2qf1FqjegzQ0ZkDNSpzgAZ36SmHzX7I991TUaq8HVu8IVDh3vDHGwA19vtiGIaGduvjC3VGdO8rm9V2yH0AAAAg+Ah5IgghT/gzS8tVee6NUk2tr2a/+FQ5pp8Swq7QVG6PW9+v/kUzf/xCH/z8nSqcVQd1XUJ0rCb3H6mThozV8QNHq3V8UoA7BQAAODJ4PB5lF+T6VurMX7tUu8qLQ93WflkMi/qnZejoHgM0MnOARvcYqM6t2wd9FXh5daUvANqye4faJ7fRxL7D+LcqAADAYYKQJ4IQ8kQG54MvyfXFPN97o3WyYmb+S4aNn4yLROXVlfpkyTx9unSuvlqxUKVVFQd1ncWw6OgeAzR18DidNGSserTvHOBOAQAADh9uj1vLNq/X3LVLNG/NEs1fv0x7KsqafJ92iSka22uIxvUaorE9ByspNl4lleUqripTaWWFSirLVFLlfyyuLFdpZbmKq7zHkspyFVeWy+mq2e9cCdGxGpnR37f12rDufZUYE9fc3wIAAADgoBDyRBBCnsjgXrdJ1Zff7VeLuuNy2SYMC1FHaCk1rlrNX7dMnyyZq0+XztOmXdsO+toe7Ttr6uCxmjpkrEZl9Gc7DAAAAHn/fbVp1zblFhYopzBfuTu2KGf7Zi3KW3nQP1zTUIfkNhpXF+qM6z1YPdt3abGVM9W1TpVUVqi0yhv67A2Galy16p+Wob6duslqsbbIXAAAAMDBIuSJIIQ8kaPqL/fKszrP994yqJdiHrk5hB2hpZmmqVVb8vTp0nn6dOk8Lc5bpYP9u65VXKJOGDRaJw4eq+5tO6nW7fJ+uGrrXrtV43tdq1qXW7Vul2rc3lqNq1Yut9v32nt0yVV3n2i7Q5P6DddJg8fJYrEE+HcCAABg/5y1Ndq4a6tyC71bhuUW5iu3sEC5Owq0add2eUxPs++d3jq1PtTpNUTd23UK+nZoAAAAQCgR8kQQQp7I4fpmoZz/fN6vFvPCXbJ048/scLW9pEhfLPtRny6dp29XLlJVjTPULal3x666aepFOnvEsbKzXSAAAAig6lqnNuzY6g1xdhR4Q5y6ICe/qPCQgpyGurXt2GClzhB1adOhRe4LAAAARCpCnghCyBM5zJpaVZ53k1Rcv2+47ZRjFHXNRSHsCsFSVePUnFU/6bOl8/TpsvnaXrwrpP10adNBN5x4oS4ae6Ki7VEh7QUAAIQPt8et/2fvvsPrrOv/j78+ZydNmjZtmjajbdo03QsKLVCQIYiCgsgWAVHGF5ENBVFUFNlDBRVUhiBT+MkURdmUVaCL7p026UybZp39+f2RQ9qb0jZNc3LnJM/H5bnOud/3fe77Fb+13+vKq5/7boyE1RiNqCkaVkOkSY3RiBojTWqMhtUQCaspGt72ORJWQzSc+k6TGiMRbarfoqXrV2t1zfpWr2reE+WFpTp4+ITmZ+oMn6jSPoXtfg0AAAAgk1HyZBBKnswS/euzij320rZBVlDZT94h0yPLvVDocMlkUp+uXNh8W7dP39HsysWuZenfq68u+dqp+uGhxysnlO1aDgAAkH6bG7ZqTuUSzVq1WLNXLdb8quWqa2poKW4aImFF4lG3Y7bom9NLQwqLVV5YqqH9SlQxYJAOqhivAb36uh0NAAAA6NQoeTIIJU9mSa7bpKYzpknJbX/+Az8+Xf7jj3AxFdy2atNavTzzXb088x1NXzxb0XhMAZ9ffq9Pfq9X/tTn5pm3ed6y36eA1ye/zye/d9t3Wr7v88vn9eqNeTM0d/XSnWbI79FTPzryZP3fV09U7x49O/CnBwAA7c1aq5Ubq1vKnNmVze+rNq11O9oOCnJ7aWiqxCkvLNGQwhIN7VeioYUl6pWd63Y8AAAAICNR8mQQSp7ME77+HiXe/bRl2wwcoKwHfsXDYJFW1lq9POtd3fLCw/po2Wc7PS43lK3zDj9BPz7qVBXm5XdgQgAA0BaRWFTzq5Zr9qrFzaVO5WLNWbVEtU31bkdrUdgzX0MKm0ucof1KNKRficoLSzWkX7HysnPcjgcAAAB0OZQ8GYSSJ/MkPpmn8FV3OGah266Qd59RLiVCd2Kt1ZvzP9YtLz6sN+Z/vNPjQv6Azj7kW7rs66drYJ/+HZgQAADsTE39Vs2pXOxYoTO/arniiURar5sVCKpHIEtZwaCyAyH1CGY1z4JZygqElN3yufk9J5StwQVFGtqvWEP7lSg3q0da8wEAAABwouTJIJQ8mcdaq6Zzfia7qrpl5p26j0K//JGLqdAdfbB0rm578W96aeY7Oz3G5/Xq9AOO1pXHfE/D+g/swHQAAHRfdU0NWli9UguqVmhB9QotqFqh2ZWLVblp3V6fO+Dza/OfiEsAACAASURBVEzJUI0rHaaxA8s1oFdfZQdCza/gju9Z/qA8Hs/e/1AAAAAAOgwlTwah5MlMsf/3P0XveWzbwGOU9fdb5enH7bHQ8eZULtFtL/1Nz3z4mpI2+aXHGGN0wqTDdfWxZ2rcwGEdnBAAgK5pU31tc5FTtVwLqldqwZrm99U1e1/mSM3P3Bs3cJjGD6zQ+IHDNG7gMFX0HyS/z9cu5wcAAADQOVHyZBBKnsxk6xvVeMqVUjjSMvN/9xgFzjnBxVTo7pasrdTtLz+iv0//1y5v+/L18Qfq6mPP0pTysR2YDgCAzGSt1draTakyp7nQmV+1QgurV2j91s3tdp2ygiKN267MGVc6TCX5/XjuIwAAANANUfJkEEqezBW5+xHFX3hj26BXrrIfv00m4HctEyBJqzat1d2vPKYH33xe4Vh0p8d9ZcQ+uvrYs3TYqEn8AgkAAEnVWzZq1spFLbdY+/xV21TfbtcI+PwaXTykuchJvcaWlCsvO6fdrgEAAAAgs1HyZBBKnsyVXL5aTT/8uWMWvPZc+b46xaVEgNP6rTX6/b+f1H2vPaO6cONOjxtcUKST9v+qTp58pMaUDu3AhAAAuCeZTGph9UpNXzxL0xfP1nuLZ2v5hqp2O7/HeFRWUKSRRYM1vGiwRhWXaVxphYYP4HZrAAAAAHaNkieDUPJktqbLb1Vy1sKWbc+oocr6/U9cTATsaHPDVt33v2d0z6tPaVN97S6PHVVcppMnH6mTJx+psn7FHZQQAID0i8Si+njFAk1fNEvvLZ6t95fMUU3D1r0+r9/r07D+pRpRVKYRAwZrRFHza1j/UoX8wXZIDgAAAKC7oeTJIJQ8mS3+5keK3PAnxyz0x+vlrRjkUiJg5+rDjXrgzed19yuPqXrLxt0eP2nIKJ08+UiduP8RGtCrbwckBACg/dTUb9X7S+a0rNT5ZPkCReI7v43p7mQHQho+YFBLiTOiaLBGDBisIf2K5fOyMgcAAABA+6HkySCUPJnNxuNqOn2a7KYtLTPvV6codO25LqYCdi0Si+rRd1/W7/79hBatXbXb440xOmT4RJ085Ugdv+9hys/p2QEpAQBoPWutVmyoarnt2vTFszW/anmbzpUbytao4iEaWVSm4UWDNLKoTCOKBqs0v1Aej6edkwMAAADAjih5MgglT+aL/u05xR5+ftvAY5T111/JM3CAe6GAVrDWaubKRXrqg1f19Af/1ZrN63f7Hb/XpyPHTNZJk4/UsROnKieU3QFJAQBwiifiml25pPnWa0vmaPri2VrbilWqX6Ykv1AHDhunAyvG6YDycRpdMkRej7edEwMAAABA61HyZBBKnsxnt9Sp8bvTpHCkZeY9dD+FfnaBi6mAPZNMJjV9yWw9/f6revaj17Wxfstuv5MdCOkbEw7SyZOP1FFjpyjoD3RAUgBAdxSORTRj+Xy9u3Cm3k0VO/Xhxj0+jzFGY0qG6sBh43RA6jWwT/80JAYAAACAtqPkySCUPF1D9C/PKPb4y45Z1p9/Kc8Q/u+JzBOLx/X6/Bl66v1X9fwnb6quFb9E65Wdq+P3PVQnTzlSh4yYyL+ABoBOLJFMaP3WzVpTs15VWzZoTc0GVW3eoDWb1ysci6q8sFSjS4ZodPFQVQwYqIDP3+EZ65oa9MHSuXpn4Uy9s2imZiyb36bn6WQFgtpvyGgdUD5WB1aM1+ShY5SXnZOGxAAAAADQfih5MgglT9dgt9ar8YxrpIamlpn34H0U+sWPXEwF7L2maESvzJ6uJ99/Va/Mmt6qX7D1z+uj8QMrZEzzv5g2Mqn3FGOccyN9vnfbfNvnz+dej0c9glnqEcxSbihbPUKp92CWckLZLZ+b92UrJ5ilnFAWhROAbiUci6h680at2bxeazZv0JrN2wqcqs0bVLV5o6q3bFQimWjV+Xxeryr6D9So4iEaUzJUo4qHaHTJEA3uW9Suz6fZVF+r6Ytm6Z1FM/XOwpmatWpxqzNuryC3lw4YNl4HDBurA4eN14RBFa6UVAAAAACwNyh5MgglT9cRffg5xf72vGMW+tP18g4b5FIioH3VNtbrhU/e0lMfvKrX5s1o0y/f3JAVCKYKn+xtr2BWS0nUr2e+Dhw2XlOHT1DPrB5ux92p1TXrtWLDGhXm9dHgvkXy+3xuRwLQQay1qm2q16a6Wm2s36KNdVu0ceuW1Coc52qc1txusz1kB0IaWVym0cVDNLpkaPPKn5KhKuyZL2PMbr+/ZvMGvbPw05ZiZ96a5W3KUV5YqoMqxuuA8uZn6pQXlrbq+gAAAADQmVHyZBBKnq7D1jeq8YxpUt22W1t5p4xX6MaLXUwFpMeGrZv17IzX9NT7/9X0xbPcjtMuvB6vJpWN1KEj99VhoyZpcvkYhfxBV7JYa7VyY7XeXvhp6jVTKzZUtez3eb0aUlCsiv4DNWzAIA0rLNWwAQNV0X+gCnJ78wvOLiSeiGvZ+jWaX7VC89cs07yq5Vq5sVpej1dBn18hf0ABX0BBf/PnoC+gQGoe9AcU9PlT79uOCfgCLd8N+rcdnxvqoZL8fjxfqwM0RsItZc2mui3aWF+rTXVbtKm+Vpvqa5tLnLot2lS/RZvqarWpoVbxRGYU631y8lKrfYZqdPEQjSoZotHFQ7SxbkvLrdfeXTRTy7f7O621jDEaV1qugyom6KCK8TqoYoIK8/LT8FMAAAAAgLsoeTIIJU/XEv37S4o98KxjFrrnOnlHDnEpEZB+qzat1T8+/J+efv9VzVy1yO047SbkD+iAYeN02MhJOnTUJE0cVCGfNz2rZ6y1WrputaPUWV2zrk3n6pWdq2H9mwufigEDWz4PLSzpkNKqpn6r/vbOi3rts49UF25QbqiHjhi9v7439Rjl5/Rs83k31m3RB0vnalH1Khkj+Txe+X2+5nevTz6vVz6vc9uf2vb5vnzu9/r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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5), dpi=200)\n",
    "\n",
    "sns.lineplot(\n",
    "    color='#f44262',\n",
    "    data = np.array(train_loss),\n",
    "    label = 'Train Loss'\n",
    ")\n",
    "sns.lineplot(\n",
    "    color='#086039',\n",
    "    data = np.array(valid_loss),\n",
    "    label = 'Validation Loss'\n",
    ")\n",
    "\n",
    "\n",
    "sns.lineplot(\n",
    "    color='#f44262',\n",
    "    x = [np.array(train_loss).argmin()],\n",
    "    y = [np.array(train_loss).min()],\n",
    "    marker='o',\n",
    ")\n",
    "sns.lineplot(\n",
    "    color='#086039',\n",
    "    x = [np.array(valid_loss).argmin()],\n",
    "    y = [np.array(valid_loss).min()],\n",
    "    marker='o',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77601f6b00>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5), dpi=200)\n",
    "\n",
    "sns.lineplot(\n",
    "    color='#a2fc23',\n",
    "    data = np.array(train_accuracy),\n",
    "    label = 'Train Accuracy'\n",
    ")\n",
    "sns.lineplot(\n",
    "    color='#00dfc3',\n",
    "    data = np.array(valid_accuracy),\n",
    "    label = 'Validation Accuracy'\n",
    ")\n",
    "\n",
    "\n",
    "sns.lineplot(\n",
    "    color='#a2fc23',\n",
    "    x = [np.array(train_accuracy).argmax()],\n",
    "    y = [np.array(train_accuracy).max()],\n",
    "    marker='o',\n",
    ")\n",
    "sns.lineplot(\n",
    "    color='#00dfc3',\n",
    "    x = [np.array(valid_accuracy).argmax()],\n",
    "    y = [np.array(valid_accuracy).max()],\n",
    "    marker='o',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fare</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sex_female</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Parch</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sex_male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     features  importance\n",
       "0        Fare         400\n",
       "1         Age         292\n",
       "2      Pclass          52\n",
       "3  Sex_female          32\n",
       "4  Embarked_S          28\n",
       "5       SibSp          20\n",
       "6  Embarked_C          12\n",
       "7       Parch           8\n",
       "8  Embarked_Q           2\n",
       "9    Sex_male           0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances  = pd.DataFrame(\n",
    "    {'features': history.feature_name(),\n",
    "     'importance': history.feature_importance()\n",
    "    }\n",
    ").sort_values('importance', ascending=False)\n",
    "importances.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CvwSOdvc7K4Hb8FK3fXeaJA0ihxoox8/f4u+DzGyBIoRpcczcaCE5DzJGOcfdX6BzEuQ5gGWRVxXA/0GnhWmSTJVof8OAB2LTgu7+trtPjHCTk4n2OsbMVjez5c1sQzNbLCzaadWWkxmnPM8yHwvhwenAz4C7zOywUrXpuO2BR4GjgL2QAvIRlHvqiZKrJc79K6SkegXlrdjRzLbo7e+WJL1NmQ8ir0GAWZB1eDLwOBT1UUsC25jZSsgwdzHkFbAfcIm7/9LdDwC+EsetDuxrZrP0/S0nSa/xEWDp+Psy6NTfdaLypnob+C2aP4DCn24b641kYPICcCGKKPNu4PNdVYw55KLI4HxBpOg/xt3vAcZXc8JzgDtQ+oRtgV2b1wL9nVyIJEkv0FVHYGZLoPwrc6J8Aee4+0t0XpyOR+E7iHqHQCa5HsrEoDS1waW49l7q7hcVS+lQYK1jZregwWwsChXzCWSNmyQ9SrTXlgqm6McWQxZgr6M+8PFSv7byzz4v6S8UpZG734EsFp9B7bh4K7Rqq6sCiwL/AfYvlnHeyEl5GMojswlaSPwYCZqTZLqI9jkMtcs3gHeb2b6xryRWXsLMVjGzrwE3IuvLe5An/53AuWa2bCpIe44QKhWvyZ2QF8hZyFio5A3dwMzmaPHc7wZeRl4F2wNrAue7+32lQnXu4e7+X2T4Buqbdjflz0uSAUvl9blofL6OPJmTAUII2IeHlX+ZM62LDCd3AK5H6Q1ejzVrEbSehgzZRiAvg92nPHuSDFg2ic97o9T93RRUntbPoN/F3SjM7/uBtXr1TpNeI975X5CDAsgAcsUu6jpSTC4I3Ad8x90fKdE3oq9d28xuRp5Uw4AHo+6AiqCQSqok6UGqiVURCizXVOWDyBrsJnf/Ziio6vqHoRiz66HO5CcobFsyhHEx0cwWjnCRk6mUAWUB969q38Jm9gtk+b8GstI4ANjG3X9Vxz1Okp4gFqIeC81RpqSwzSyL5h/PIqV8UaaOCGvz+czsM2b2RTP7rpltbWZztThPkvQZlYfJhSjckAOfMbPVo/02W0AuH5/312EszWwnM3uCRizx36LF6n7ufnevfYFk0BLKkHeAq5BVJsB3zewyM9vXzH4G/Aj4A8pZtDLwHtQPT0CGUTsB55kSMSc9QPQLK5jZ5cAFwHJIWHAaEtauhkJadVJyh8DhQRQafBjw3tj1fOxvNgIpSvSzgBuQYmtFJAhOkgFLNe7eGJ/LEL+X9PrsvzS/m8r47FTgLmBeYEdgEeD37v5qVbejmk/9AHgYjVGfNoVATZIBS9W2i2fg7Ej2Nz08hHLcguYHK/TArSVtwt2fQBE3/o3aw0HdVF8XKSdv98g9Gn3mgmZ2LpoDroXCzH8NWN/dLx9ohr85uCdJD1JZNW5tZi8Cp5jZklWVohn/V32cmX3MzB6nIbT6HbApElrd05v3nPRPygS/+vwSSox4Si2gaDHozBP1D4/6OyMBxknApu7+g7DCSZIeo0y6Ky+RnVGbu9jM9ohtJbH7r1EOvqWAz5vZRma2JfAlM/spCoV6OvL0Owj4DXBRC6V/kvQZlTfVE8CvgP8io5PDYntp+6V/LgvRd8X2VczseiSsXhiFYvgksJu7X5f5NZIZpbKwvQwpPZ9DCZQ3Q/3wbsBWKBTcK8DTwO3Iu/rvwNtI8LsmsKeZLU0yw1Tztg2Ac1GI7xdQuKttgW+4+3fd/Q53/4+7v97FqU5E1tKj4v+x8dksAK6V5MfG5/LofSfJgKXyMHwTGTWNAT41o+czs+GmpPNJL1CtBabwyK08Ow+Mz+WQoP7u2D+5X6uUWrcC56M8PCsh74AkGbBU3lILx+frSPk+Ped4DbgNKXABPtozd5f0NdWa8R9obQnyhN+0i7qLxL8ln+kIMzsEGaJ/Es3lT0Yyv+Pc/enevP/eIpVUSTITRFSrkj9leHgOHIKEqnOhBeKyFrH+gdnic0wcs5qZXYcssxdBIVd2oyG0eqfvvk3SHzCzYXWImMqibN6oskWUUr8MbrcjZdQnzex/wOGx/XfISv8gd/9383XMbKSZrdfCMjdJpolor0VAv7UptOQvUOiyDYGTzWyMu78TFuLP0vAQ3Q3F4v4dcAyKTz8KhaF6CE26JgEbI6+VVp5ZSdInVP3kZShEzVvA1ma2Y+wfXhkOPA+8BixhZlegBeW6sf0QYOvIvfBadf4yn1jKzGZrumaSdEkl4Ps+Cis9kYal7kQUBvBnwJHALu6+urvv5e4boL765qi7GVJWJTNIJaD9LPKWugfYE/ieu9/v7q9A154gVXist9C4WIRa25vZ7D5lvtFa8HUbEl6MRFEZsg9JBizVb+QqtIZ2YCczWzR+J9Mry9oQ+EAP3mLCZHnIsGotsIaZHWJm3zazzwO4+9uxXrgGyUlKv/Sh2N/RdM7ybk9B8pExKN9ehjZLBixVu74hPscR3tDT0p9V4/nf0XrZgeXMbPmuj0r6K5WR2UvA5cB1sesQMxtT6kX/6ihXKcAmptyjDwFHx7Y/ICeHA9z93urYklJhzvr//kwqqZJkBomJlsdicfaYmC0H7I0EU4fF33dUoX6eAN4BVjKzS4B/AuvTEFpt5e6/qN3eK2XCEtZFQsVkcGCNmLJuZiua2RFmdjRqG68Ct0bVw4slYCUQfRa1o7HAAjSs9D/VSuFZroMEUvujeN9JMt1Ee13CzM5HC881kJfJD1EC0K3c/c2oW5Sv3wLOQ1Zgs9Ow7L8QJZP9KAqPuiONkAYfA+boo6+VJFMQbX1YhEr9Bco3BfB/ZjYy5gNlnH4MKbEWBj4c204FNnP3Y9z9yXLesmCI4xdGIcDWKtfs9S+WtJ2Znd9Vfetj7n4CCuf3MWBL4NMoT9HewEnufl1cs3i3XoS8ql4C5ieUVANhIdtfMbNPIE92Q8/2ilbzsK6OrzwJLkACepBX5g5TufQw5MU5CZijEmwkSZ/Tg/3a9Uh4Zygfx+fq/VO5B4u19HBknxLkRQAAIABJREFUwHe2mc0/M/eVNKjkIR1mNs7MfoVCIh+NwpoeaY0cK0X2eGB1iuUtvHfrMacoId39KeAMJFtZCq0rkmRAUvVZ45kB79BqHfIsCoNqKGzzhO6PTAYAd6P5+FtIPvzJal+Zx92JZIIbIqXUYsgwaXdgV3f/cz3XLHNAM5sVRUoYG//36/l9KqmSZAYpiz5TEupXzWxNFNN/caSgOt7db3D3Os7sY6iTeQ9KhAwKa7UZcGwXQqsO5Aa8FxlzdlATE/KxZnYyGnAOQwqqI1AIl+VohOQpIdRKP34lDeuK15Ag6pfIjXwKTJ5/W6FQNNsCa82ARWKSYGbvAk4APo7a2zFI0XSou5/m7tc21S9Ci+JptRnyMNnN3Xd29yuBh9z9BWQpdjWayC9B9oFJm6mEZn8GrgBeRols92+qdyPKvWCoTz7B3b/g7ne0OGeZTyyJhDH7Ia+J2ZrrJoMHU6jTv5vZfKGg7LEx2N0fcPdL3P2K8Nh7CZgU84wyv3wnBIxvISOBV+LwdZu8ApPpZ7v4vBk41bsI51lZuE7x7qux8kgU7mxB5EmwWAgZWikAnqWhqHpuWoT4SdKT9HS/VrXzo+JzDmBnM1u7aX9LigIFeewsjRTxi3Z3TDLtlL7IzPZFQtYdUQjZi1HYqYOInDvFkMfdH0JrBZBh2yblXM2nj89zkPf6CODDZpbhzZIBSU94h0a90cjIE2TAMmuv3HDSZ8Rc/BoUthtkALlwU7Unacj7JqL54cbAebWTQ3XO0la+C3wbGa31ewPIFEgmyUxgZhsB+8a/P0LeKH8Dzi2eA1GvaKsvRd5Uw5HF6iHuvo8rLn2nzqISWi0N/BT4JpqYjejFr5S0ETNbAPg58IXY9HPgEyiXxGFIcVXa0jdCUFHCwryGQiJMRAu4L1Uefp0EINGGNgQOQJ5XVwHnpDAjmUF2QUr3l5Fw/VhXSKPXoVMIs8neInHc2+7+pLtf4+7/dSWKx8xGVIoAR6FQZwNepCmfX5L0JGFxPVXL76o//SXyWgX4qpktHkKYknvhiPicHVjTIq9aWHV3GstDQfVlYCPU1q8Ij61kEGJmGyLP0TWBb/XB9azMK5vmm2VO8QSNnEf3eIuQcsnUiT7EaKyx5yEsnON3X5RSZTz0eDfNoa6svAN3vwWtAwxYB/hqHNvqHe2K8lEB3NTz3zBJuqY3+rXKq/A6FLIUZLT0vXp/V/1V/O4+jH5DCyFlxxM9cW/JZHZHXmqzIgHrFmgtu5+7/6zJYLf0dUcipfoiwFZmtipM4U3lscZ9B8lZnkF96t5mNpYk6WOmZY3QHV14h87P9HmHDnf3t2mkEXkCeDDnbP2X6TDWeBAp+J9E49y+9c4wgLwFGQKM0CZ/jphzNl8nDIkPQv3xGCSr6fekkipJZo6/I0XC6yg0z3rAbe7+Uj2IVZOsiTQm7bOgsH8lPujw5oHPzJZBioR1kNDqlq6sMZNBwUeQovN14OtoYLrY3S9392ORFeAlaIBZCPhKHFcET+egXCnjgVWBc8xscqxvM5s7lJ5fRgu9DZCA9Th3fzYnN8n0YmZz0Ag/dDVwfihMJwv8KwGDV8dNEYKoCPdKHxeChXWr898MPNuT3gZJUgjlqIfgdxYz29DMVjHlVpi7qlfnDLwHGZ88hcKsHhLb3442fgNwFlqErgecambjXOFWSzufI9r5CUj4/BbynL2KZDDzIGo7k5DAbbUYp3vFEKkrq8lKILIrUNr5Td0dk3RN9CFOIzTtLDSSmk+hKDSzHYDDzOwkM/udmR1uZqsARdFdctoegwRRcwP7mdkXzGze6jzDQsi7MxIC34jG5CTpS3q7XzsIhToaDnwwfjfvLjtbCOhGorBJByDvqauAT7pCZSXTQTdKwBWRIeW8wAXAga4w88+38qSrZCITgK/F5vWBLU35azuFoqrWEFeh8N+jUAjlTXr4KyZJS6xvvEN3MbN1mva3OrYYsCyJcl6CQuxPoGF0lPQTYm42rMy1TRGTzBR+bwpiTncDkvcB7G9mq5R+M7b9FLgv/j7CzDZHeUg7KTnNbCHCWADlpf02klv3f9w9S5YsM1DQYhOUgPVatCicCJwIDJvKsddH/Uko3N8cTftnRflYLo16TwOfb/d3zjLNbeN9yMJvhek8rrSLm4H5mvaNjM8VUX6D0t7WjO0j4vO9KIRfR5TXgF+h/D8XoVi2Zd/VwMrtfl5ZBm5Bk6LSpo7pos4CSCG6HrKuHFntsxb1hwNzIcHejXHuW0tbz5KlJ0tzG0Shde9AAuEOZK12LwrnOybqDCvjfLTvi1G+ybeAdWP76PicD4VjHR/nuwc4DRkLfAuFsXk99r2OjBNGtPu5ZOmTtrcl8I9499e16R5GIG/t/8R9XAPM0+5nM1ALMDw+d4znOSHGrw/G2LY8UiQdCdwfdUrfUMoTwOn1O4rP/ao6b8VccW+Uo+UQlEC79CN7xTFTjLFZsvRm6a1+rfpt7VS19fL7+ggwV1V3MWB14FDkrVPG3q2RIHd4u5/TQCrdzUmQgqrIKtaYgXPfFMffAGzSRZ0y31oFyVx2aPczyTI0Coo881y00dNiW7dyvuk8fy3TuaEes7sav5EH1cnVfGDvdj+nLC3f0/Dq76WBHyBj8r8DfwQ+A4yL/cOajv0QCsPdgZT/zWvVL6PoMh0xHp6JnBqWROH/9kLRvUobOQVYtN3PZJqfXbtvIEuW/li6m7y2GjBQLoqnoxM4P7ZNMYBVE+yVkWB3UhzzN+D/kLv815DiqgitXkEL0BRa9fOCBBCnVwPC34CtpvHYBZEwtAP4Zt1eWtTdBC3KOpBV2fCm/QvH5OU5Ogs+Jsbno8CeTcekICPL5NJdH9hUbyzyyutAVj0rI8vx9YCPxaToeeBxGor5K4Dt4/iy8JwLhfDYHPhitOvSbv8LbNPuZ5JlcBdkcHJL1e7KmP4CDYX/cU3HFGOVHWkI+f9Y7S/C5ZWB45v64+bya2Dp5nNnGXylajezAd+JttUBfLxuN71wvXrBPDz63N2RErYDCZY3rI/JMsPPfDgK5VP3J3cjT/inWszLXqzGyLLvoLo9IOV4Ef6/gRRVE5r6kduBD7X7+2cZeqUv+zWk6L21avcvAP9GRnqXxW/voWr/5cB72v2MBlppHgeAg4Hl4+9hyAOkGOpeO53nLjKRdar3dCJhpJljUJb+UJAH5unIEK0DWC2290h/hgzZXql+AyeXvgop1JuVF2NQXvIno/5vgNmq/T2mQMvSI+93DDJcL+/3HTrP2e4AFm9+f8CcSDZc6m0V20u/OQcy2Hi9qvNM/P9ste05YPd2P4fpLWUykSQJk8MEmDfiW78HDR4j0cLyn179aIr7ppkti1wod4xdy7n7g3Woq+oYc3c3s82QBWQJZTUJLWo7aITi/A1ym3+oN75v0rOY2SLIImxRFILPkODhc8CV7j6+dvltOnZxZMG8DPBddz+4RZ3SdsYixeihsWsnd7+47K/qjwM+jjy7JqIJzYPAL13JGWnVRpOhS7iSd5R2FGGHxiPvzkfdfYpYxma2F5pIjUMKpdGoD5sLTc5A/edY9JsoiqeN3P0JM5sL2BY4A/WDw1CfOwn4CXCot0gGmiQ9QYQD2hkJ1RZGSvyzgduAx5DAbX8aY/Un3P1XcWyZA4xGFnKfQr+V3d3952Y20pVLoVxrkzjPu5Gl9x1oAXG+Kz59mYd43Zcng49qPP8A8qrZFHnQLBHbW84VZuJ6I939HTObDSlklwG2Qd6tIOXGge5+bU9dcyAT4cnmc/f/dVPHWv1Oq3e7EjLKWKjaXeb4byMh+5No7ncnsAIy8Ngz6o4H3uvuj5rZaFcY0W2RQnsCssT9BQoBOCfwkLtfWt1Hj7ahJJkavd2v1b85M1sehcvahkZ4zMIEFBbuTuTlX8bsybngZvQehiJmtjEKSfw+4EJ33zm2z4GUgwuj53zI9Kwrq/ZyLvBJZOxzpLtf2CtfJElmADPbEuWYXR243t037KHzDneF7vs4WoOMQ/Kaf6F19c1l3W1m86LcVTugdA/zxmnuA/4KXOTuGeK3H2Fm6wPfRzkaQXO2u5GR0XtRqphFUWi/b7v7HU3Hr4rCv2+KjCg39UipUNXZAuUF3x3JHkehdezbKILSMe7+RtQdMDK/VFIlQ4oQ7r/j7m+0EOjX8UKXQIPRR9APfn7gTaQ0OrvVIGBmu6EBZXmqCVyLevUE29Ck7OPIPXMsGpieBn7mShKbDBDMbAFkrbcSeo+jkNDhMeBX7v61bo4dhwSWcyDB/MHNA1FT/a2QxdlSaMDboFYgtFoImnKulDwonZQRSdLUB34AWUyuhIT070IKzm8BV7n702Y2yt0nmPL17IkshUo87GLFcysSor2KrMe/CWyHhHTHuPshcb33A39CFkD/RZOxn7j7/b3+xZMhjZl9EIXfWxYlfP8+8G+v8j+acvmdjPIg3IsMA+6NfUVRtS7ylloTCVrWcvfXS+x67xwnvCgM5qwVsANpAZHMOC2MAfZDXvQLAIe7+9E92RbMbDUaSZPXRf3vQvHpSJj8rar/b6l8GSqY2eeRwOjHwPeajSS6mj81ze9Lv/BBtJbYBnmWANyFvD2eQX3NW03nuRT1NbMAJ7r7/nV7MLOLUTjcB4Cj3f28pmuP8Mxfm/QxfdmvNc1XVwHeD6wBLA48guarjwCXVPVyfJ0KIZcYVj8nM1sDOB+Fq/oP8BfgKHf/n5nNj1ITrA1c4e5bTuf1Sj9ZDISGo5wp33H3+4b6WJS0l0qJOhsKqftlZIi2s7tf2JNjrZntigziVo1NLwL/Q56Kc6L5wMpIzgjyFF266TSPoNBwp7n7oz1xX8mMEUZKJ6F8e7chZdNNwEshhx6OQkkWmfIP0HzzmUp5OQrYDRkIzAF8xd1/FOdvlmMviBRfbyL5y8Pu/nTsGwFMGlB9qfcDd64sWXq7oJj730cLum27qTccTaiLi+QEFKqqdsN9iYgtX46Jz/mQcqG4cW5Qrt3FtYZVfxsNL4MF2v28ssxQGytK/1/SCN20HLJwKeFYjgZWbPX+47PkIPsbsNBUrjMXjRBqHcDXuru3clx9jiyDvyALnd901+811Z8PxTUu7aqEa3k1Pl8GLq7q1+1qLRSa9DNIEbVSi/OPiz60I/rLkrdnFIo1vzqwVLufW5ahUWLcvSja4xnAsk37h8Vv6PCmecCxRC7Jpt/AYTFn6ACOKOdocV1r+szwHEOgUOUyi//nj8/3oUTIJdzbIrG9R/KmICHx5VX7fRIZt5xQrtWT1xvIJd7Rn+I53UzM5WNfp1w2SCCwPbARsAQwqqtniQQMswBzd3FNo5F7dC1kTV1yVMxZnxMZj5T+6A80chpkWPAsfV7a1a91cS/lNzKseVuW6XqO8yKvcZDSqAMJURejc+6vYTTCJP+FFvP+Vu2l6f8SzvQb1Rj1mXY/gyxZ3DvN0z+APGE6kPFxj8zf6byGeDeSBTWH8a1DA/8b+ASSbW6LDOgeiD72GuCrVOHjsrSlzYyJuVlHvM81YYrQqbNHm3o06j0MfKpFu1uKRlqFp5BXcqf203zupn0Dcn1ZQoolyaAlPJyeBA5AFgfzRXif5nqLo/wpx8am3yLvgO3Q4vNzSAM+Fikb1gZwabrN3Z9DwuBb4/jvx/6JJbxAjVdW1a6e5FUXz8z0l07ayS3IMnktNJjsDJwX+74KnG5mC3uTxXK0kTL5WQflnZqCqDvK5TX1YFwL4KDwAKS5vUW78vr/nvmqSX/GzBYCzkGJor9uZrNPpf66yOV8T9QOz0JKp82Q9f2FqL191My+HpY+dbu6xd1Pcfcz3f0yd78rzjuiuswjqN0CvO3ub8exE9z9Dne/1d3/O7PfPUmmBXd/BY3ZFyKPhAfKvrBg2xUJ9w9HQuZHYvdeSKFa+uThsf0CGnOAfc1sWZeVcNlfrutNnxmSa5ATY31HtIflzOx04FIz+yfyMJ0PedEb8sgD9cMze91hMa88DbXzfZFX1Xbuvr+7P2lmw6PekPUyqMKAdQAHoVApawHbhoVqmUtNMrMFzOxslB/qTCQYuhX4kymU9+S1QcHlGf+2u78UmyavwaNduMu7cgTwTzRODgMmeHhyVeuNu4BT4/A1UDQGPL2nkj6mXf1aV8RvpPaysqHcr80I4dHxP7Re/RSSd9wK/MjdH/dG+LER8ZzPiUNXAtZsJWOpibYyd3jAQSMCw3eRvOYnaP2RJG2lnru7+83AlcgDelHUv0Gj/c4QZR0Q/dZ/3H17tOY+EPgV+u2dj5TEn0YhgC9w94nu/ht33xd55GyJDJZPdPfHZuaekplmlvh8CXlH/aOWl5jZyuh93ogU/45kzVuZ2QqlGkDIRH6N1p8LIlniZJplfLUMMHYNzPVlb2i+smTpDwVpp2+mYYFwE1IYzNpF/SOR18DDSCE1F1NaQq5Xne/HNFmFoZjYRyFvgw5gr9ie1o1DpACfjXd/Pw2vqVFIePlc7PsjLTxbULiKP0ede2mylmiqOwp5Uv0LhUfrAI6PfQPSaiJLz5boj/ZBCaU7gC90U3dhpGTvQJOmLVFIg9oadQFksVU8qjbo4lwtrcuQ1VDtqbpTXT9Llp4sLcbv5vZYktO+i8oyOLYtiRYFpa0+gsJrLQ3cE9t+QST4bjr2szGP6ED5/9r+LLL0jxLj9mFVu3oNhUatvfQmxed6ccxMzR+n1r8O9fkCsEkX278b7+HR+O0Xa/9NkQKpWDY/F+/s7dj2AnDYTN7TijSSop/Z6n0B81T3cQMKLzrk32eWvi/t6Ney9Or73BuFjHoV5fTqQEYNXdUfh9asxbNzjamcf/GYX91Bw0u0eJGOaff3z5KF/uUdWuYeI6ttRe7YpQdNu5/hYC3TOsdCnvbjWmzfn4Y8sHioHhp/P4OUk508glHame/RiPL1/nY/h94u6UmVDDrMbEEz+xkStK6FLLi+Bmzv7ud7JI9rOmYl1EGMRsqqX7j7y96I/z7alPC81l5vgxareMNy620k6L0p6nzHzMa4vKny9zaIqSwXronPZZDgH3efgAalI2LfpsBZZrZjU7u4HSXZfgm5fB9oZsvE+SdbppnZaCQIXQSFpTkjdu1nZou6rNSyvQ1xoj+6GrUpgIPNbLHmetFW9kQeVzeikBtXuvsb3rBGnQP4ELLWAsXH3teUh635uh5eVpMtWc1sLuSVsg8KiXqyRyJrjxlYkvQExfKxGr/XCy/Teas6Vtqnuz/v7i+X48xsO+DvyIvaUb89zt1/jQTQf4rT7AxsUh1XLC4vpjEH2NGUbzBJAD6FvHRAFrJbIeOnBZF33vU0rHJPgJn3jmnuX8vcoMlzaMhhZpua2VPARaYcjJ2spoEfAU8gK9cdiPkc8kJbChkbfSzK5ihH6BvA3MARZra3mc0Z551mS+vwgD4c5QwDCX0nE/O74e7+InBcbH4f8PEy7k7P9ZKkB+jzfi3peaox4QwUTWZ2FLZ+AlLCd8WTKBINqC/8bBXZo46kUPKmfBbJUAwZCeHu78Tnmz30dZJkhuiH3qETQ8b4TuWZPSn2tVw/57q654lHX8s2ZpvKIfe5+8PVGnEFM7sOzdvmRePimu6+n7sfjUJIzoeMhD8Q5yjRNp6lIV8eAXy/ab46+Gi3lixLlp4sKKlhsdaahKz+V5yG4+ZHnlHfaLFvWbRYrc/7evx9MXK7hc4WF/uizqYDuXl22p9l8BZkIXZjvPtjWuz/MoonXCwmDgZmq/Yvj5ROHUjgcSlVnhQUcuojyGrtZRTqZTOU+6oDJSdu+3PI0n8KypnxSLSP47qoszsSvk9hVY5y8NVeJS9Wf3+SJotYGpY/s6GJ1m7A76pjrgJWbvdzyTK4C4rVfjey6H4h+t2vEJa6tLA0RN6DJXfPX4F1q33FmvGQqi3/jSqHWtX2P4qs3hZu93PI0j8KEtiW2PNXAsu0qDM/Wri+EfX2iO3pddDz7+OLNBJMX9ZFnc/F/vHIkGPX+P8KWuR8QML6Mv+7G9ixm+tPkUcACWy/Ef3Vy13N5+rjgOto5DPo8npZsvRGyX5tYJVqjtKVB0ax4N+geq8dwDr1/hbHzYcUW8WT7kpg+XJNpORfD4WcLdEYuozukCVLOwvpHZqlc3uo51zzI2OLP0T5PLDcVI5/F40cVc8An6/2jYj2dkLsfwt58jfnIp0dGTA9QZW7arCWtt9Aliw9UVAcz6eqgeMalNdnluk4x4Ittn0yOoNy3p8DI2ksVF9C1mOjo37pSJZEeYjKcSvE9lRUDfKCkiXeFu/9eCJpYRngkLfepjRCUb6NXMfHVudYHgk/S/u5Pyb2Z6KwgWVidGLUXxiF/etAsbxHt/s5ZOk/BYXpO45GeKK1qn21QqmVW/oRMWEqbfFIZEVeFqM30CSUiL73h0hx+m/kOVUm9IdlP5ilN0tM5I+r2uzr1d8dwOldtUFkRNARbfaj0X+XUpRUH6PzAnX/ag4wxXnJpO1Z3AE2iv53PJGQvml/mT9+iEbY32e7a1tZZug9lLnYSKTgeZoIPdui7mxIWd0RY94vom/YsKleCVU1GlnBltB/5zWPjy2uMQrlt9gcGSWVfuoXwNJdvfuqvWwa9Z8khGVZsvRVyX5tYJSpzUNobbTzQySc7wBOnYZrLBLz/qKMfBnJY/6GFPvjaax7v0wK9bP00wJ8pmr7FyCl7fJIxrMHDeOQDuAf7b7fLH3WLj5NQ1FZZBsTgIeA1egiDCMK4deBwvztXs1Dh1d1fli1qbtpnRZk/nY/g74qGQ4qGSy8ihQDoA7gSne/0d3fmtYTuPvToHAfZjaXmZ0EnIsUALeixd9uLpf0a1Es+LHIZf39cY7ifvsIcst8JE6/V2wfkmFVhgrhjv0m8JfYtIGLDmBYJJl9292vBi5DQs6RaMJzSgk54+73xbZLkavvMkgwuiewExKY/hBZVODuT6H2CMpj1V1YhmSI4e7PIAHbrUhh+o0qJFoJeTbe5ZZuAGb2nnBLPwwJ0S5FArPDXUk8r0WCibWBj5nZmOqS9yGB27LIuvJhFAppcXc/KvvBpJf5IAor+TJwNPKY+CpS5IMWn/s2h+KKMGjrR53L3f3X3pkSnmgtNOe4Pf7fF1gVphzjY0zIpO0JKJ9ZWXfdAlMk5S7zx2vR/PEpZH15VFTJEG7TSauwd+7uMRd7B/UFi3iEnm0+zt3HA9+OzVshhdDjSIBQn7OEqnrb3f+A5mcAGwNrtrivzc1sWTPbHTgWhcr6LfL+dODrwG7u/lCcd4oxs2ovVwMfR+PrX6f6UJKkZ8l+rR8TIaomz0PMbC0z+56ZnWBm50VY0rk9JKBmNqwKF38SynnswKZm9r5Sp8V1hrv7k8AuwKlIgDsnUmKuA3wYCfgvRRFoTvIM+Zj0QyIk5aHISOUq4Jvu/hd3v8/d33T3s5Es5m/IiHN1M9sjjh3R1XmTgU2kfTkeRTT6C1Je3oGUVuOQ99Mm0DLc4sbxeRtKKVPC+E2q0nnU87z3AFtWYVPLnPTZ+H/Qt7NUUiWDAnd/CVlOv4KEoh8ys9Wh9WSqbDezeVqcaxIScu2IBp/vAxu5+w1xzPDYfn8csg6wdTlXNTn/E/J+2cPdD+yZb5r0Z7yRC+BR1EbmN7PVYt8kV1zhVUL4/x1gOAqdBvAJ4Odm9j4zm8XdH0CT/R1QKMq7UPiEX6KEifu5+yswuc2VfAnjy6KkL75z0r+phHS3AxfF39shYdgUVBOrPYB1kSfp3u6+QyixysToEWQ9BFIIvLdcz91fQB6mn0P5O7Z09/1jAZskM01XsbjNbHbgANS37gEc7e4/c/eT4v/yG/g8UrAWofWwEAS/EPuXqM5ZlFhmZu9GffLzSJD8DvKcfler+0mF7OBmOsfZkg/wJWRxPlmAW52v9NdXoJC+AAeZ2TKxmJ3pGPTTEEd/wFMJZovgdWEzW7IIWQvu/mD9DsxsqeZzufuVSBgB+p0PizGuO36KhPELoDVCnQtsTuA0pOg6GynQt0TC+vOAJd39+zGf7PZ9V4KLi1IZnvQU2a/1b5oMa7rMeRfzcY++ZAkzuwBF8TgQ9Tu7oL7o0iJkh8ZaNpTk56CwqIuiOf3UlOa3uvv+SFi7H/IIPQatedeNtcQDPfAYkqS3WBH1ZW8C57j7g/XOUMg+iwyFb47N3zWz0Z456Ac0XcnPzGxWGka7+yPD8U+hfu7gqLYxsJeZLRnHlP55DtSmAP4VbWR4NX97JxSjmwK/R9E+QP1zMYDspPQaCgr+/BElg4mbUDg0UNzjrc1sjLdIImxmyyLl0/fMbJmmfWOQpnzBOOePgddjwtYBdLgSFpcJsaEF5nowWStu7v6iux/s7j/rlW+b9EtiIHkWmAV5SZUEi/OY2anIimJ95EZ+JsrX83MUo3ZpJNwoC4G33f0yd/+Su6+CQml80t3/US1SRqDwk6vGLdxaFiV9842T/kKrhX5lrVNixF8eu75pZnN1cZ4PoknYMJQf7ZexfRjy/gO141GofS8GfNrMxlbX+627n+Huv8oFadJTVMLnYhW8spmtFsojUE6pNYCfu/tv6om8u9+KvCJeRGE7dipWajFPGIY8Ul8FVipCm+J5gRatX0BKqSuBvwN7o1jkv+/t7570H6Id1gmUx5m8T1cIRWldtyj274rPeZFF+RTCxar/vJ+GMBcUq34K4e903vNYk+fOicV4pivh5kAm3ksRzC5lZkcgT6UrgDvNbKkQElh1zNJmdjVwnZktF7/5ejz9DhJYAYwxsw3juK6e3/+QZxTAzmY2a1E6ufuryHtqFPJSuBhZ4K7l7p9y98eLAGNq77uFtW6SzDDZr/VvitAz+qfJ37GrfqDUM7PPAHci7493gD+iOcwFKPTeesAZZrZ2tXYsMsIzkIfcSGArM9usvpcW91gEr3919x+6ItAc4u7fdPcbZ+b7J0kfkd6hQxBmjtEoAAAgAElEQVSTh32ZO46KbeVdLowMdy8GznX356PffcndzwROjnrrAzvD5HXl8JC/XBf71zWzeV2G68WIaixaW66IjNy/hDz2v+bul/Xy1+63pJIqGTS4Qpydg0JNzY7CTa0f+0pHMJ+Z7YsWq/shy+pxTadaGIXxewv4g7s/Fp1WmWR7aNRnAR5AQtt3A7uY2dL19ZKhRTWY/QWFnVwAWNHM9kT5Aj4b+y9HIaiOcfcrkOX/t2LfGsBxphAMi8V5S19dhCSTrf+Rle7nkOfANUgYkwwhrBG6rwju1zOznc1sY+schu/faIL1MrAKEYa0Be+v6p/jCmGJu3dUfdvqyJOqhFD7FLDxYBUOJP2DagGxjJldjMJO/h64x8zOQvHCnwN+18XxdyEBMcijcMMibAvhzN9otOmTzOwoM/sSsjr+FQrtdw9wiru/7u7nuvuDVlnFJYObUJJ6GCQtYGY/Qp7zv0Vt4w4zO9nM1opDigD2XtSnjkbCQmghzKiEIZfGp9NZODhdYT7MbKSZbYSMYc5G/f5GMLjmqpVwdJKZzWpm30O/5cNQqL7lo+pOUa/+7rsgK9j5kOK5k+Dc3f9FCNSRcmnVItBodS+hiBqP3l0HylVQC7dOAT6CvDm/5DJouz2+x/BagJEkfUH2a/2XUDQVQ1nMbGuUf/hcM7vVzI4zs81bHDcSeY0fg0Lv/QYZ1e4DbOfuu6Dcm1cimeCJZrYoTO5Hh7v768hY90XkTbWPmY3yFgbAcdzkZ29T8fRKkr6iK6VqF6R36BDEw6jRzPYCfm9mvwPOjr51lqh2jMtRoVDa1eEoL9X8yEmiyFFKf3g5MoBcEzjGzNaJdez7Ua7vr8fxp7rCR49z95/E/QzN/tP7QWKsLFl6qiBBfUl83oE02/PFvu2Q8qDsuwPYpMU5FkDKgA7gi7FtRLV/BJr0dSCB1SHx959RbPu2P4cs7S0oh9RN0S7GV23un8hDZUUi0XbULwkUD0FK1lJ/ry7O/y5gBaRofTTqPgrsUJ8vy+AvVEmngQ2RUOGlqg2t11R/SeStVxJ4Lt18LqT07EDefqX/rNvrWODXSEm1Q7TrDuAIppKcOUuW6S00EtGW9rkj8lbtiLG6jNcTo3QAK9THNJ1vLhT+sgMpuN7XtH9fFM63/IYmVX8/gMJXtv25ZGlviXng+KqNPI2Mm0pbeQ7lpSz1F0ahj8r+98T2KdpobN8eWb0/E/Vvr/ZN0xiP4tqfXF1zIsqv0Pbn14vvZWfkyVS+81XA94j8U8CKVd3Sp8wbY+ckFMp2/dheJ7WeP37/HUjY+54urj+yah8dyFNh0am9u67aQZYsfVmyX+tfhc5z/A/QWY5Rl9eRMc08Vf314v29jAwhF2px/nlR9JgJNObxY1pc+7xoD88De07P+8qSpV0FKczrcXxc9B8rALM31R0RnzvSWF9sVs7TzTVOrH6Hv+2Bex4L7I68GFeb2vWzzHjbaPp/NeDGLvrXu5Cx+bLNYxuNNereNGQnP6Sz7HgJlKuvA3gj+uQHacxVJyKZ3kga89LhQ/m9t/0GsmTp6QIshDxKOpDA/zDkYVU6mleAzzUdU5QEhsIFlQn3v4Dlq3qjkbXl3WjSvh7Sru/R7u+dpX8VFAqqtLnHUM60dZonRVG3DEizIEvEvwBbNNdBC5RTkJfA3dX5/w6s3e7vnKVH2k3pi4bV/0/lmAWBs6r28DSyjPwrstQe3VR/KxpC+B+3ON8OwMOx/7j63lDC0H3QYvXGuPbHga3b/eyyDO4SE/ZRwA3RNi9AccF3RIu5p6rJ/gFTOVdZhHag/AyzV/tmQbHBb45zPRrn/h4wa7ufQ5b2legDDQkDi3L0CmSN/gGkBPkGjYXuvSiHZDl+b2Qt2QFc03zupv8PqNp5advdtuvq2AWjXZfjOoCfAQu0+xn20nsZhhb3ByABQUf0EzsgIfos3R0bn7siAexElE928rqgqrs7DcXT14Gx9TmazvtDJPh9DCm4hqywIUv/Ltmv9b9CZ8H6gvE9y3d+BnmkXY/m8m/H9oeBr1fHFSPac4CF6/cR/eVGyFOuFsY+TbWepCG4fz8KTzoJeZyX86VyPUu/LHRWsi4A/CjacOmrHkTK7rWiTvltrIi8RzuAM5vPVZ2zKCc2oLNBW1FsjZjO+231mzyw3c9xMJbmd4MUg3+s2sWPkTHSHdW7eB1YsEXbqueI10bde4Btmq6xIJLvFSOQYvxxG00GxVlSSZVlkBYkgCqDxZtVB3MiMFtVb4oBBAnCdqdhqX09cpX/GnASjQXwj8hFZ5amUk1ajox28jKyHBw7jcc3L+jqwe+jKORCBxJ+3Al8ut3fOUuPtJtOE56pLfyqyfSywGXVhOcHSDG1WDfHvAvl2Cj94rr1PSBvq8uq/UehUCp7oNAqRdm/Q7ufW5bBX4DNkJfDV5CCtSPa+bxVm50VeVCUNnse4dncapyOcf7SqPsPwnOiro8WLcsAK9PZEyK9BYdwQfkf70OCwR8Ai7eosyQS9nWgnH7LxvZ3IWFjWZx+s25b1fEro5j096MoANcC/6ESDHdxb7MjQ5dbqt/CTVM7bjAUFIK2CJZ+TmVgVtVp1RfURmoXIiXV88Cusb0em0cDV8c17qYRbaEWKFuMwcXQ45pW182SpT+V7Nf6Z0GKpiLPmISE6qsC74r9SyDvp9ri//2xb0VaRORAwtITm877Exqeoj8F5m5x3LHRPl5nkHquZRl8hfQOzdL6mc8C/B+KrrFrPPdfoBQuo6LOEkjWVt7Lwa3eKw3Z30ZIPjcBOL/qp4uyfy6k1DwYOBT4WNN5UulfnkW7byBLlt4oyNr/3Kqjv5cqNAcK2ded6+6CKMF6Rww8zcqus2jhNp8lSynIyvatmPx8ZAaO7yT0KNuQouCraMHWpXVwloFRCAvW6v+tkbfcecCtyANv826OPzLa2VMoL9SYpv1dTarXpuGRcnmZkFX7d6IRsrJYab5BY5L/teZjsmTp6RJjcQnh93ukrHoSWKKpXlFWlVCW9wO7T+Xca9EQ0hwHzF+fq0X9YbmAGBqFbixgkdde8dQpQtp6vJ6bRliPjuibV6na6IbIG7oDhWb9LVKwzItC0XwI+EPs/04cs+zU7hf4IHBRdd2ngU+2+1n24Tsr1sePAmtO57G1RfQjcZ6/APO2eL8fqp7xqyg8buk75kE5B8p7eIghIEjPMjBK9msDpyDhaO0xdg2KxjHFug+YjYan1cvAsdW+WZvqbo0UheW859IQpB5OQ26yHdXaMz4XRYrCd1CYqrXa/ZyyZGlVSO/QLN0/91WRx1QHckQ4KfrOtao6pf9bG3ngdSC5zOKxvau14jk05qItI3e1OGa6vO6GQmn7DWTJ0lslJrbFG+pyIg799HQEKLzPnch76uEYzKZb4ZBl6JRqUPtwtL3xhKVEVwPadJ63U3zlLAO3MIOx5mkIBZajYQ32E5oUVFO59qzIM6XEod+l6dwjUXzmm5CS6qWYwP2Bphw+WbL0Rqna4qHRRp+PNvjHburOU/0mfkUjN1VXC4PjaSi1tm/3d87S622qxPdvOYY2txOajJGQFeRj0WbWb3H8fnQ2aPopsHrzNdAC+d6q3uNRbqZhFPDP5r621fwVWCracW0ZfPT0zHUHegEWp2EpfXg39azVZ1OdE2h4CxzcxXnOqJ51Cen3V5T/6sVq39diLE1Pqiy9VrJfG3wFKQV/H9/7GaYS9gspBEuklwtpEb0DGZ+VnLW3U+Xljvf3cRo5Uv5EZw/yMscqeb9vosppmyVLfyukd2iWrp//csCfo208E+/5uG7qH4EMdd8EjumiTlHmL4MM1DtQ6oXlYnurkJE5N+zqmbf7BrJk6a2CFoZH0/AAOJbIOzG1ToHOwuOxKJbtyu3+TlkGTok2c1+0vxNiW1rhZ5k8kYm/ZyjWfBx7YOx7DHjvDNzHisCv4xz/JqwpY18d8uy9aAG8RrufXZbBVeheWFyH8ithvDqIPGo0CeSqBcL+1W/pwOZ6TccsRkM4dzEwX7ufSZZeaWfrIA+ZCYSAtrvxGNgGWUEeQGdP13mR9eUbdA4PswUSTJQ2ejWwOU35AJuusQqdPRPq8gsi58c0fLf6HBfR5GU4FAqwPPKw7CC8CJrfL13kEKi2jYzPZWjkIfgXjVA/9bi9HFKYl7H5xuhvXkLGcReTxhxZerlkvza4C/JOK0qlPxCKwVbvGHmrlX7r8Rb756RhCPd7Goa71tT3Taye+5eqfWV+NRrYtN3PJksW9+4Nz0nv0Czdv4tP01j/dQBHt6hT2sKCNKLLPAh8oLk9NdUvxpWvETLALNNXhpEkgxR3fwe5sd+F4o5uggYcPHqQbo7tqP5+xd2fcfc7e+9uk0HIGGRxAbCOmc1Vt6tk6OLukwDM7BAkWNsNcOQNtTmwt7uvD2yK3NBBoT92NbM1qlO9q5wyzjO93IeEac8hId8+1T16fL7i7v9y91vd/Z8zcI0kmQITw6t25vW+2NYRdd5Av4NJUWUDM5vV3SeVukFHHHcCWkTMh4RsH+jqPtz9cRTm4X7gW+7+XI99yaQ/sRFqDyOQdwtdjcdmtgnKybcIcErTfHEBlK9gEnCvmY0zsz8gQcVyqB19DtjJ3a9097e7uiF3v8Pd90Eeq18ADkL5/9Zx913d/Skz63KdVu37LRIeb+zuH3P3R6fyLAYjI5AXpQOrmdmi9fs1M4v+ZH4z+zJwmJldYGanm9l+ZjZ3rBlw9wdRuJbX0Tv9Ymwv/Q/ufj/w3fh3NMolsT4KIbqhu+/o7neb2bDu3mGSzCTZrw1ubgLOjL/XA7Y2szHRl1lT3SeRR5wDj5nZqKY6a8Y5AC5093th8txrYtQdgZTvr0W9vdH7p8y33P1td7+6Z79mknTGzFaLz+Fd7C/rhInx/0JN++dCEW1AHtEPRP2y/t4PKaU+G3XOBraO/qv0oX8BDkOK+rEoJ+5lSBl8PvKS2QK4Lf6nXMfMRrS456XQvOFPwA6x+dvIQ+u8aXgsSQ9Q9YuXAdchIw/KZ/3uoq8d5u5PI6Xny8j7bvK8sEVfDPICfg6FYr2r57/FEKDdWrIsWXqzoBwSn6OhJT+dsOIiXSyz9HKhYYFzN7BUu+8nS/8ozFys+WOqfRfQCJ+yGDPgqYdizJ8S53mRCI+WJcuMFGRt9sGp1KktGedFytGDUF6obalCyNDZ++FKGtaOJTxlcxijkpx2q6j7JloUztmqfov7yXnBICo0rBoXQQr5SSgs3JrN77465rhoOz9vVYdGLr9bqz78VeBbwLgW5ysW6CuU/a2u29VxWab5Xf+GRljQM+KdG7J2/gwKUfYmjTyzHTRy0v0D2K0615xIQF5Clm3a/E6QFfZdNDwT5sr3l6UvSvZrQ6cA70GRDjpQ2MQP1+2gagt7V33bD1ucZ18akRfKfKi5DeyJBLU/Bp4o56Ibz7ksWXqykN6hWfqmnZUoHlug0KcdwH+b9zfVHYkUW5OQ5/yOrdpnNTauTUbnmOGS1l3JoMZlDfFrlCwRYGPCssKjB0mSXqRYT4xHCoAkAS38b4u/nwOudPcb3f2t5oruPh55eoxHgrOlzGye2P3n+FwNhTLt6MrqrCvc/QkUXvBuZIX57en9MkkCYGYHIQXSNWa2eFf1vGHJeBCy0P4JUiTtjxZufzKzz5rZnNGmR8ehR6G44fMDO5jZgu7utVW2h1Wlu/8e+CMRmgYJqluO+9X9jMh5weAi5oC4+5PAJSj58RhkHTv53RfMbDZg3fj37+U01f5ZgL/Fv6vE5zlIyHGYuz9c1S2WvuUae3V13bp+ac+t6iTd8p34nAc96z8iYdeFKM/UHqg/KGPkS0iJBQrhc6qZLQ/g7q8igdJzyMNkXzMb6bKaLe/nJZS39vfAZ9z95fpm8v0lvUX2a0OK/yBDMpCn5jZmtiCoHcQcaQ2kpBqOFO7HtTjPo6jPW5SGHGTyuzCz9yFjoYloHnYxUvhf6t14ziVJD5PeoUmvU7WVK5Fn2yvAkmb2pdhudd3wpnoHKfCfQm10n2qdWq9DJ8XnTe7+nJmN6MLbKumGVFIlgx5X+J5TUW6XccBWZvYe6OTymSS9wQ3AR939A80CjGToEsKt49CkaD7gQ2a2OnSazNa8CDwUf6/j7kXh+RzKhwHKvdOlAKDu6yLkUa3M+gfy5hoPXD4j3ykZupjZ2mZ2C43wV/+gxfyyElatbGZXR/25kXXwJWjx+CSwOPp9HBWHTgBw9xuRV+EwFMJvp9jeaQFbte394nMVYEszW6K771EUXMngour7foNCe7yF2sNOsb/uC9+HBIGvowTYndpXGBLcgQR+Btzh7nuGkUGndlgWwWY2r5kdg5SwO5cwNs2U+l0JZJLucfe/A0ciy2lQzsXFUFjc2ZFH1FUoosKHgU8An0dCYJCQ/6jqfJcgRZejMFm7tbjmee6+jbs/nWH9kr4k+7WhQczpL6JhlLYJYXRjZmPM7HjgFmANpFQ6xhXGuJmnUYhvgBPMbAUzm83MFjKz9VDftzxwjrtfh8Ifzx9/J0mvUo2fZyPDcgc+a2Zrxv5WBpibx+f57v5GXccVzvJ2FI3kSrSG3gL1gd8BtnD3M2I9Xu5heHyuYGbjmq8byqpT3f14dz/C3W8udbrr3yqjgsvd/cPufu20P5mktwjFkwO/RIa6AAeY2fwtFE/lHV6NlI0TUQjVT5cqXV3H3SemAeT0kxPqZKjwJyTcAtgA+Eixmk5FVdJbxITkMmgdnzgZ0sxMrPlZYvs/kVDBUb6q90PryXwlWPgk6gvHVfteAn6AwhWd2XxsknRFtMWDkIDkARQuZht3f6S5buX1tB/yan4UWSXuCHza3bcGtkO5JMcAXzazreO40n9+G/gfsBCwfVj/0rSYmBSLj/8AJ8bmXYBVe/TLJwOCygryDRSa5d+x6xAzGx3tZVRs2zQ+b3b3e+rzVP3y1cCNKLTSKma2V/H2q/rmcsw8wO5RxgOHuvttJL2Cux+JhAYXI0vpa9FYeyBSMn3R3fdx96ujnBbbi7HHx8ys7id+HPtmBT5vTbmuClMTUiVJT5P92tDB3f9HI1TYskgZeSiy6C/GOD8DVi1rzhbn+AdS0v8PeZ78GnnP/RQZp22Lon+cE/Wf76WvkyRT4OkdmvQxVZu7DSmenkFGkgeXKnX9ap35Y+BeZPz0dTNbNpVQPU8qqZIhQUziz0SxmOdBQrENY192LEmvk1b6SY0rtMA5yLJxdjRxXj/2uXVOuL47jfwa/3T3t8zMwlryEmQ9OQo4ySIkEXRWVsX5NgC+hKzIV4rtZXL+RE60kxlgU6RYehs4FAlKXunGq+BLwKdQ2JmDgXPd/SnC+8Hd7wTOouE5eKaZzePuE0Mg9wQNxdMqwM5xXFcC4kORB8XXuxLeJIOXotysFqPXIaval5B3QRHwTYz+siSWv7o+Po716HefR0LhW2LXt4DDzWxseCRgZvOF0cBxURaMY34Z+9M4qpdw94uRUnollJx8PXc/wd2vd/eHoJPF9DB3vxWNoyAh8ObVuf6OvBhGof5mhS6umWNn0mdkvzYk+SNwXvy9A/IaHYsiduwA7OPuT7aae1XbzkFKKZDX1Mqov5sNhU3b3t1vaT4+SfqC9A5NpoaZDW/u42Zm3KnOdSEa+xz4jJmtHmNj7UlXxtt7URs14Hrg2Rm9ftI1qaRKhhJ30JicLYHywiRJkrSLmYk1XyZl56IwIOOR6/mpZrZunGOSmY0Oq9ftUV6ONZE15W1RJ5X0yczwLhSO7zngsWhPVibzlTB4eCwktojjTnH3C8KApF4k7otCfSwX9W5GAmJoWLWdiMJ4jAU2N7ON49g6hnhHeEuPR0ndf9xcJxnchOB1Yvy9vZmtH7t+heaDAF8xsyWiva6C+uE3kUdBd8Ylf0Tt8DGU++AbwPVmdo6ZXYL69StohAL5NnBAWAlnv9v7uLtPcPeXvUWuxkqpVLZfH5/DUFisWpB/Gho7l3OFekmStpH92tDE3V8DzkBGaaPRnP9EFLZsct6oVoLzSrj6iLsfhgx1T0Gh1c4DPuzuH629S5Kkr0nv0GRquPukmNO928w2jW0zPO5UfePjSFH1X6S078qDr+hOTgBWc/ed3f2VGb1+0jWppEqGDDEpPw9ZmC2Z1kJJkrQTn4lY8zFJGxYL1x8gK0hQ6KJLzOx0MzsaOBYtRC9C4c5uBb7tLcKxJckM8BpSIs0GvBcmt80tzexiFEKmtPXZkLAMlH9qMmb2YTO7FzgJhfi4DtgK+Li7Px3n8Air9Q4SjoE8G3Y1s9mLRXg5ZxHEFSF1CPdSiDZEiPawopldhTxlvhlt4E7gUhRGdQHg8DhkE2BOFPLon92c02KhfAkS1t4Yu9+HvAS3Bz6KQrT+EYVgOtTdx3fjYZj0IM2Cha48naIvAVgdWW07ahe4vDfN3R919wPd/eFK2Z4kbSH7tSHNLUhRBTAC9VnAtBngVJETfu3uX3T3vdz9U6l8T9pNeocm04KZjTSzM1C4vSVj20yNP00efNejfnVrM9sx9rfypnrF3e8w0SpfWjKTWK7XkyRJkqR9xEToAmQ48ksUAnB/5CkCCqH2zWKt2sU55kMJ4dcH5kbCtubJ9fEoFvebPfoFkiGNmd2DlEUXAj9Hnn/bxu6jkXL1rQidcQvwIrCou08ws+WB7yOFFMCDqJ1e5O4vTuW6l8Z17gO+6+7n9OgXSwYUplCn71T/vw8p6FcDXkAC3VNjYTk/cDISvIJyle6BchGc6u5faHH+Ya2s1E25EbZBgpQ1gHuQJe617v6ncixkeJj+RryXD6LxdyEUZnRtb5GPpav3nyS9SfZrSY2ZLYuMzlZCERGOcPffz+C50nAnaTt1OzSz7YEX3P16M1sZeax8COUL+oC7P2pmq6Nc8yOBzdz9hq7OGQqE7dC6YvHYfTeKxjAHWitvhJTvIAO4Y11RGJJ+hpl9kAjvCHzE3a/pofMO+//27jze1nJ8/PjnOuc0atBEUkhFCmWWQpJv8yRkLpFIJFLGIn0NocxCKkopQ75oICKVwk9SKQoVKYpKgzoN5/r9cd3r7Oes1t77DHs4Z5/P+/Xar3X2M61nrf2cZ637vu7rutuAxi2oYOXG1KCOTTPzXr//TTyDVJIkTaKIWB74DDVadSZVygOq1vwRwOmZOXOEzoTel/HlqYbrvlTQ4F7gOuAa4POZedW4vxgtNiJiyRZo2pqaeHsWQxn6d1BB009n5l/b9qtQDcM1qZIa69BKKlBzUn0aODoz/zLguQKY0RoLS7THjan/I8tQHXWvycw7xunlahHRRsX+PypA+k7gJKqD4ppu4DMidqVG1j6W6oBdmQpU3AicC1xAXV9XZOYDykPPbaO1Zf85Z9EkG/T3iognUXPjvZAqLbRvZp4zGecnjcT7mmB2YHAvhkqFH00Fqq436KRFVURsQJWv3BL4MVWCMqNKgB9Ezct8XGbuGREHAR8Gfglsnq3U5YBjdoNfm1P3xWcNcwo/BN7ZMlIdlLKQiohnAee1Xx+XmX8ch+c4DHg9Vc7+A5n5gbF+Do1uxuibSJKk8ZKZt7f09W2pL0V3UiU9Du52ug/3hbnTKL2zjSg7PyKWzcz/RsTKo2WkSPMjM+9p/1ybCq5CBVh/DLwbuKjvml2SGsG4JtWxskRbfjLVOP11d/u+DpdnUZmFp7cAVbSR48dSQar9DFAt3iLiudQIy0uoUle7U1l2H8nMSzvb9Tofvk+NoH0ksCE1t9rfqc6QXdvP/cDFEfErqmH8K2rutXv7r1XaXGydEZnTsuYWtCN3knTvIb2/V0SsQJX0eT7wVmA94FZqztpf2dGrhYn3NXW1v8F3qCy3baj72AXAsd63tCiYi+zQvwAbUXPsnUJVCNkFeFVEHA2s23b97aAAVeceNfv/Q2b+LCK2wuzQhVrUXMLDzZkIraw8cC0w0nbz89y9z9BTgB2A26iBHZoEBqkkSZp8vVrz72JArfm5aXz2fam+qy0zQKVx0Tp7jwD27Fv1O+Dq/kZeZt4QEddQHWRLAFcDb6AmPr69//idEZAbU0GtayLi95l5LZWxdT8VnLq/befIx8VbbyLsJ1IjzVcBjux25MIc8/ndExEnAk+jOiyuB17Wft+S6uBdh5qv6CnAG6n5ES6IiPOozJs/ZOYf27U6RzDEa3HytZHYq1GlW26gOqjWpObGe3Lb7BJg/8z86eCjSJPK+5rmkJk3RcRRVDBybWD7iPhVZv7eILsWdr0AVSc79GXU5/EDskMz88aIOJkKLD0W+CKVHQrwwvb5Pkd26ICM6V7Q6s72HCcNOi+zQydf1lygSwJLdduFncBmr1rHslQQaSyfu/cZd0lE7J6ZF4/l8TVvnOhSkqRJ1r58HUd1mC0F/A+weVs3zw1OG6maAHdSdeIvBw6gRkJC1X/fpLthDE1sezzwH6rjayZwVcsknB5NZ5+IiKcCh1ON05VoI+d6DUkDVOrJzB9SwUyo0eVJdV7MMfFx27bXGD2fyjy4hZqEeYvM/Gxm7kx15r4A+BSVyfBv6hrcFvgQ1dGxMlrYPZcq5XMJVe7nDVSH2A1UcGrjXoAqFnACbmmseV/TMH5CzVcLdY/btmUhZPd7lLSwiYjnRsQsKuC0PXNmh17UC1B1Po+/D5xNDd7ckArU/x14CJUZ+nEqI/QnEfG5iHhZRKwTEUvAnIH11q6Y1j1+J3vKANUki4g9qb/zuRHxwYh4BAwFNqmpDAD+0oL10wcdZ0H1AlQRYULPJHFOKkmSFgLWmteiolP65yHAEpn594h4NHAhVbLyOODQzLxmwL6fAfYBgmp8vqhb+qNtM4PKdjiIKrtwMfCWzDwPLXZ697/RgpER8VgqK3X5tugVmXnSoPtn5xpeF/gsNTDgFmqi5D/0lx2JiFWpjtwXAH+lru+BcyFo4RIRv6QySc6jMh9UE+AAACAASURBVDh/DRyfmf9p60crMSONOe9rWhBtEM+3gEdQ97T3ZOaPJ/espJG1sntntF9PpzI835uZHx+wbe9+tilwJJUdeg2Ds0O7bqGC+XNkh479q9FYiYjNqM+sJ1KDMQL4GzUA8ozM/GVEHAnsR93vnuNn1dRlkEqSpIVEK11wHFVr/mrgsMw8dsSdpIVERLwd+BiVLbU/8PVOaY9eY3MN4GvAplTW4KnA94DTqJGRD6WyCPdhaMTke4AT7UhevIw0P8BwHbsR8V7gUGo05meB943WkI2Ivahr7BHUdfbKvvUDgxgGNxYNEbEO8GCqw2NmJzg1HZjV39EvjSfvaxoLbTDPu4APADcCO2bmryb3rKTRRcSXgNcxNJ/tlpl5/kgl9yLifdQ8kitRAdkPt+VLAs+msrKe2H5W6ex6N/D8zLxgXF6MxlREbA28hKrK8eC2+D5qAO8uVMnmr2fmq6yiMXUZpJIkaSESETtQE3f2OvAPtta8FgURsSJVQmhjaqTkuzLzks763sjxTam5MF4OzKLKT19Hzb9xPxWogipps39mXjZxr0ILg25nRSv5sRNVh/7hVEDz4sz8Z1s/+94YEasA5wAbAD8G3pGZvxvmOXrX40pUx++LgOnANpl51qAOk04ppbBxvOjy81STwfuaxlJErE3NtffFzLx7tO2l8WR2qIYzXAByuGumZYruDzyTmntvFkPtxROBfTLzjratwaopxiCVJEkLkYhYFjgCeD1wM/BRaqLs++xY08IuIl5IlaABeCfwhexMgNvZbmlqDoytqDmnplHzXN1GNSw/npnfbtsGONfa4qCvY3Yp4P3AvsCDOpvdB/wGeBtwYX8DNyJeSjViZwEHUh14d470fBGxPfBhqnTMBZm56bi8QEmLHe9rkqYqs0M1nP5rowXWbwOWzsy/D9q+s+006m+9DzWgY7222b3UoI1TgBN6Afqxaiu2qja3GdycPAapJElayFhrXouqVkLrFKosw2+AAzLznP5tOqPJV6BGkT+Fyqa6PzPPHbStFh8RsR3wSYbmGvgNQ4HMJwArUvfGj2Xmt/oatktQWajbAr+krsHzh3me2aM4qVKV+7dVz+1eh5K0oLyvSZpKzA7VcPo+vzaiBldsQA3OWJn6HPsS8NPMvG1ANlV3/y2BHw14mrOpuc2OycxbF/B8lwO2owZQfjszD1yQ42n+GaSSJGkhY615jbcWTMq+BsGYZOq1IOuFVOfbkcDhvUbqgG0HPqfBqcVPp1PhFcBhDAXpP0uVhrk+M29vHb2fAR4FXAJsl5l/7+sseRY1aTbA4cAnMvOmUcrJbAK8FDg6My8d1xcrabHgfU3SVGN26OJnftqIEbE8cAh1DQDcBSwDJBBUxZhTgbdm5p2DMu/aZ+gL2nZ3AN8GHg9s1tnsSuCLwJmZecU8nuMMYBNqzrNd2uI/AU8ZVAlE488glSRJCyFrzWsiRMT6wFqZedYYH/djwNuBv1GNj1PnYh/LWS7mIuLxwNepEpDHUEHOPw3ogP088Bpq7r4vZeYbBhzr01SnydXUNfj9eTiPaVQQ1+tR0gLxviZpKjI7dOrrb5u1cnizgFvaQIjhSjquSf2tdmuLjqPmK74duIcqCb8JFdj8XGa+eYRzeDFwMhWkWjsz/x0Re1CZe9sAS7Zj/hf4DvDJnIv5jCPiMcDe1GfqEm3xocAHHSg5eaZN9glIkqQHysyrM/NTBqg0HiJiiYj4MnA5NXJ7du3wMfJp4O/AWsCuEbHOKNs755QAXkh1bHyPamRe1RmtGxGxcUR8DngD1ZGbwKsjYrO2zYzOsY4EbqImXX5hC/yPqtfg9nqUNEa8r0maEmLIK4HPUQGqXwO7A68ENs/M57R/XwM8DXhvRDy8BTWmA2TmvVRpNYBnADu0AEg3A5W2bTcL61tU1ulGBqjGT/sbT+t8Vm0SER+m/uZ/pLJ5R7ITFYC8Fngzlan0rcw8MzPPBnamPs8A3tQCnsPZoD3+lZatl5nHUQGwXYAfUoHRBwMrUZ+RI7221SLiLVQJwf2pANU3gUdm5vsNUE0ug1SSJEmLn6dTDcr7qBHZAyc9nl+Z+TeGGp/bAVtExNJjdXwtfFq2wEjrYy7WHQ58HnhzZl7ZWb8ccADVoHxjW3w2de0uDbwPIDPv6x0rM6+mysJANWKf20bujsj5CyT1eF+TpCEtaLEh8A5gdeAoKiB1Qmb+sVciLTNPA84EZgJPZOh+dn/nWL+gSp8CvBh4Zuc5+p93Vnu8IDP3y8xLI2LaSPdgzZ9e9lQLKq4fEd8Cfg4cRM0JtjKwfUQ8ZJgsqgcDBwPLA98Avp6ZtzFn/OHhwBad3/fsG5DRK00P0LtmlsnMv7Z10zLznsw8g6o88xzgeZm5aw5fYn7ZiNiZyuD7JFV+9+K2326t7apJZpBKkiRp8ZPAjPYzXl/Kv0bNn7Ei8HqqQaIpJiI2jIgLgLOjJkeeIyuvjcacPtII/s4o2buBt3UbmO2YZwEfBVYFzgc2zswtqYm37wNeEBGvbrt02zfHUCN8VwD2ADZa4BcsacrzviZJwzI7dAprn11LR8RhVMWNF1LBxtOooOQrgNcyVCKv32bAasBlmfmuzLy1Hff+iHhQRBxJZWM9iyrh9xHgwMy8r+88esGpx7XHK2D2vMXd4Nh9mfn7zDyn99ncPU4LZj4N+BJVDvBZwH+AvTPzyZl5zry9QxpPBqkkSZKmmP7RaAP0RodfS3WGjbmsCZCPpMr+vT8z/zwez6NJty1VqmV5KhhJG305uxO3NUxXiojdImK7iNgqIh7WO0C3wZmZ9/RGxkbElsAP2vFvBF6bmc/OzEvarj+iAq0A74yIB7XnmtaOdRvV+IVq5P53HN8HSVOH9zVJU47ZoRpO5zNqPeB44N1t1UnA9tTfdN/MPCkzz8/Mvw9zqLWowOTVfYM79gauB/Zri04Ang+8d1AbsT/YBExrwck5yvF1A5W9z+bOMR5MXa8/AV7eFh8BrJGZXx7m/DWJDFJJkiRNMa0BuGRELN9d3mn49b4DLgvcNo7ncWpmrtXKfmgK6TQ8v0CVdFkS2Ckitm7LZzckI2J/atTkMcD3qcmTz4uIwyNimc52vU6LbI3TvagMvAuBnTLz2Lbdku05fgT8o/17far8TL//oxqmq2fm5WPy4iVNSd7XJE1FZodqNJ2//W7AVtQgiv2BN2XmzzLzujZYY7Q4wopAAEu17beIiN9Rn6vLA+dSc1LtnZm/Hi7g2Ak2rdcer8jOvGZzaSWqvP1yVObf+pl5QGbeNQ/H0AQySCVJkjTFRMSewN3AuRHxwYh4BMyeqBiGSif8JTNvmscv/PNzPqNldmkR02uoZuYdVAmNm4CHAftExNJt5P8aEXES8Amq0+LGtvtdVGmXA4ATI+LZ7ZjdzpGtqTkKEvg6cFFnXe86fjjV8Lyl/f6GiFivnduM3nlm5jda54rXoaRheV+TNEWZHapRRcQzgEMZCuoc3yvX1zNcUKmTaXcZ9Td8TkScA/yYKg95DfBmYNfM/F43UNRrh0bEEyJiqfbvaRGxAvCgttn97fnnyKQaxXVU2cntM3PnbuafFk4GqSRJkqaQqJrvb2m/PgF4DxWsen9rfAD0GhjTImKpefzCP8/664xrauh0VnyXyjqYBTwbeFXbZGtgV+AvwD7Ay6gJtF9Dld4A2Al4X0Q8AebIJlihPd4NfD8z7+01YjudvqtRDemLqIyGh1AdxwOvOa9DSaPxviZpqjA7VPNoq/b4Z+DwzLx50EadAOXsQY6dz7BbqbJ+S1OfnfcAHwO2yczPZea/+o/TgpZrAfsCL2jLZgGrUEGqgzPzwHl9MZl5b2Z+OzNPn9d9NTkMUkmSJE0hmXleZm5MjZr8KtVYWAs4mApWfYrqYAO4MjNnzkXpBmmgTgP1c9QoyRWB10XEY4A9qZGPbwK+lJkXZuZlmXkKVU7kq23fzRmaq+CetuxBVHmYO9r6OUZPRsQ6wGFtm49SHcb/pkZ+StJ8874maSowO1Rzo5MF9eD2uAJwc1s3rX/b3jXQ9/nVO8b5wFXt3zOBT2TmQZ1ls/WOExHrU/Ng7QXsGBG97Knrgcdl5mFtu3Gt/KHJZ4eEJEnSImS4L+idUW298hlnZuaewP9Qk95eTZXbeBOwOjU6PCNiud7IcYNVmle9Bmpm/ho4mWqQbgB8ENiQ6hT5Uf811kZn7kfNQzCdGtm7W+fQp1GjL1cDdouIp7X9V4yIDYADgacDp2fmj6mRuQ/PzKPH9xVLmuq8r0maKswO1Wg6f6uVO4t3aI8z+reNmuNsr4h4c0R8vs019piIWKId6ygqyLkksEtEPKzzeblE93gRsR4199UmwN+AEzLzzvZcMzPzroiY3oJj41r5Q5PPjghJkqRFQKvN3S3LsXZErBIRD4ehBsaATrP/RzVEnw98nCrhMIP6Hrgb8J2IeF0bUdnbNzoj4hbknFfr1RbX1NUJbh4FXEplCzyHyj74bWvQ9q7H3jU2vc1DcDjwL2AJYO/e9ZKZN1DXK8A2wDcj4gzgM9TcB3tRIyw/1ba/vM2RMGMsrl1Jizfva5KmCrNDNZeOb4+rUNl2T26fQUtHxPMiYo+I+C4VcPwo9Vn1BuA4au6pjwNk5veBb1DBy/WBYyLi6W3dvS3otHJEbEVdk3sB/6E+O8/vP6nMvL8vg09TVPh3liRJWri14FSvE2wjarT1BlTjcGXgl9TI7p9m5m3d7QfsvyVVH77f2cDpwDHZN0nufJzvcsB2wIeAb89PHXEtfHrXUbfUR2ddtE7bvYEPUyVDZgKvzsxvjnLc/6NGbP4Z2Dczf9h7PuA7wBbUKNyui4C3ZOYvxuK1SVo8eV+TtDiJiMOAt1OBo9OpigtfA97aKb/WbTesAJwFPLXt8+rMPLmtexhVxm1Zag6r92fmryNiRarM335UEOy0zNy5ZYz+qRPk0kImIk6nsusAbgAuA9ajsoDXA4Iq7zjoEeCDmXlIC1B+gJprrHesHwHXUm3XNYBd2n7/Ag7IzK+N64vTQs8glSRJ0gQb1Bk2F/ssDxwCvK0tugtYhqGGwc3AqVQj887+QFXveakJaU+lRj1+G3g8sFlnsyuBLwJnZuYV83iOM6hyDW+lGh4AfwKekpm3z8uxtPBo2QGzJ8wedO12OnOXAr5FdXosQU12fFj3GP3HjYhtqDJYM4G9MvOEiFiyjd5ck8pe2Bu4k7rWv5OZX+5/7vF59ZKmIu9rkhYnnYD8mlSg/KnAP4GHAntm5nEDBrn17me7Ap+nSvf9DNgmM2e2bd5PzXsL8FfgCmruq82AR1El3PbIzJ92jjsDMDtmIRQ1P9S5VDbVIFcxFLz6MTVo8vHAS9v6+4EnZeZlLVD1RipLb7jKGscC78rMG9vz+9m3GDNIJUmSNEH6v3hHxGpUbfhbcmhy41kD9lsT+BhVdgOqrMIZwO3UyLZ3UsGhBwGfy8w3j3AOL6bm2LgDWDsz/x0Re1D15reh6offA/yXasR+MjMvm4vX9hiqw21fqhMP4FBqRJ01xKeAiHgJdY3cAvwD+Fpm/qOzvteZsR1VvuMRVGP2KZl5xwgdwWtQmXyPAY7IzAOGyWpYGpjVG4EbETPSuQskLQDva5KmErNDtaAiYgtqnrKdqMGQt1OfkSdQgw+vyMzr+vb5FrAtsDTw1cx8TWfdk4HdgXWooOhlVCDzxMy8uG0znfosNEixGJsx+iaSJElaEC2DKTqlMzYBdqS+rG9BBZ0OGOEQO1Ff/K+l6n0fD9zeKctxIRWoei/wpog4MzNPG+ZYG7THv1JBrX+30ZMnUvNWvYWauH2l9nPTKK9tNWqS5bdRnXcA36TKNvxtpH21aIiaKPsI6vro2i0iPpCZvbkFepNznxYROwGvBh4JvA745IDj9jpQlqMarVDB0+4kzt3t7ukFc9smduRKmi/e1yRNJb2A+nABqj7HAdtT2aFLAY/tHmPQcan5+XYA1qQyquhkh+6L2aFTRmaeDZwdEYdTgxZXyMw/92/XAkvRPrcOBbakrqfNIuJhmXlD+7tfRAUse/stm5n/bf8OYFr/dafF07TRN5EkSdL86jXKWqNx/TbS7OfAQcCLqLrc20fEQ4bJonowVUZjeWoS2q9nTcze/R73cCrY1bNnK6XRPU5v0uReI2CZzPxrWzctM+/JzDOo4NlzgOdl5q6Z+c9hXteyEbEzVTrwk1SA6uK2324GqKaGiFiLmu/s+VSWwc+AS6mO2ycBX4mIZ7RrKDvX3WepCbKXBF4fEY9p63vXYbfDdgaVvQcVPJ1Db7ve/4/MdKSlpPnmfU3SVJND5UtfAhwTEUdExIERsXpnm2xBp5lU0OmGtupVEbFcyxqNQccFfkeVBF8K2Lgtu7dtc11mnpiZzwVeCOzUC1D17p/e3xYdvWsgM6/NzJt6AareZ11n/f2ZeV/LAL4E+D0VoLwL+Hfbpjs4o9d2vav3e2sjG6ASYJBKkiRpXLUG4dJRExVfTjXeZlJzVRwFvAJ4LUMl8vptRo1YvCwz35WZt7bj3h8RD4qII4E/As+iRmt/BDiwfzR2pwHwuPZ4BcweIdkNjt2Xmb/PzHOiTO8eJyKmRcTTqA6+77Tn/Q+wd2Y+OTPPmbd3SAuDXsOx1/DsdFLsATyDyt7bCngx1TlxMFXyZRUqi299gN511xqrJ1IjatcHjmwB2/4Rur39V6MarRchSWPA+5qkxUVEPCEizqIGtO1OzQ/7EeC0iNixs+ns7FDgR1QwvZcdOui4vfvmqNmh7Z/3tOyqae3+aHboIma4gGLvs65/fQtULU+Vj4T6jHxAvKEzKGOOQRpSj0EqSZKkcdDpFFuP6gh7d1t1ElVi441UTfeTMvP8zPz7MIdai6oHfnVnBBpR9eSvB/Zri06gRoW/d4SSDF3T2gi2OTrXug2P/tFtLavrcOAnwMvb4iOANbolPbToaJ0I0wY0HLP9vV9EBUE/mpmXAje3bT4NfKgdZidgh4hYoR2zl3XwJWrk7X3UnC/HRMRWbZtVIuLR1BxmO1KdJIdnq00vSfPL+5qkxYnZoZpMEbEsFRTtDYT8XmbePYmnpEWUc1JJkiSNg07DbDdqpPaN1CTFX+1lQ8HQBMcjHGpFqnTCUlklA7cAjgSe0NafC3wCOCsz7xrhfHrBpvXa4xXteA+oPz+ClajRmcsB36Mytq6cy301Qea27n/brjdP2hOB11PtgyWAY4B/UfOWfSUzL4c5OiHuiIivA1tTWQi7Ab8CftpGVE7LzBsj4mhgXeAh1LWze0T8lsouWBV4TDudU6k5EiTpAbyvSVrc9doMvfth5764B0PZoR+nBrHdDLwLeA01B+47gfcAl3ezQ6PmpD2IoezQ7c0O1XD6P4sjYilq8OWrqJLyp1ADGqV5ZpBKkiRpnETEM6iJZKHKbxzfDVDB8KUOOo2Ay6hJa58TEecAz26bXEMFp07OzH/17Tu9lQN8AnBlZs5sWVjLUZ1z0Oammsc64NcBbwDuyszT52E/TYAYmjQ7Ox0ZwwZB23bLAe8D3tG3emsqsLoSNYfaAybUbuVcPgLsTJXK2ikirsjMf1CBVagOkx3az+3UNXQzNe/ZP4FfAkdm5ilj8R5Imlq8r0la3PUqKcxldujlnSDWp6l5p75CZYdeGBHXZeZtUfMI3UdlYG0NPJ2h7NBvZOYPW3BqRSoAYXaoZl97EbEiVe3jZcBbqPblhcBRrQ06L4MgJcAglSRJ0njaqj3+mWrU3Txoo05jcvYX+s4otVupEZHrUgGqe4BPAcdk5h8HHac1DtaiSg59H/hB69hbhWpEHJyZh83ri8nMe4Fvz+t+Gl/dv3v7/U3AyhHxv92O3E6HRNc7GerI/SFV8mV9at6Bh7Xla7XHB3QKZ+ZvI+IIahTursB5EfHtdg3OaNkHnweeRmUY/BvYqz2uCfyxk/Vgg1YS4H1N0tRmdqgWJRGxPnWtrU21Rx8NbAs8qm3yDeDdmXkNzPMgSAlwTipJkqQxFzF78uDeBLIrUKOsZ4+G7G7baUzeP+AY5wNXtX/PBD6RmQd1ls3WGd22PjXSey9gx4joZU9dDzyuF6CKB85TpUVIlGmdv/t2EfFH4DNUJ8MTu9v3OnIjYvOIeHpEPAJ4O5Wt90LgFZn5fGpE7kltt7uBZ0fEkiN0pnyGmp/g4VSH7rpteS/g+hMquLk0sDmwUWbenpmzS0627WzQSos572uSprLOvSF7bYL+tkFXLzs0Ij4KXAzsQwWqXkOVVvsGfdmhffvfA3wEuJeh7NDVe6fTHo+n2hv3AbdQ81ndTAXil6WyQ1+ambtm5rUL9AZokRMRSwJfp66/X1IlJfehAlSXAttn5ssz85r2GR7DHkwagUEqSZKkMdbp9Fq5s3iH9jijf9uI2DAi9oqIN0fE5yPi1cBjImKJdqyjqNGMSwK7RMTDOqMql+geLyLWA/YHNgH+BpyQmXe255qZmXdFxPQWHLPzbBHVRudn6wx9XEScTmXNrQf8Cfhf4A+d7SMilo+InwBnA28CXg4sRWXmnUZ1TECV63gLcCXVAbsR8KzhziUzrwc+1H7dEXh+RCzdru3e9X408F3g+Zn5f337ex1K8r4macrqddz3ZYe+J/rKl3buL1392aE/pcr4PZS6l63EKNmhwBHt112BzXrtgJYdej/weeAfwDIMZYeuSlWF2DRb+VIHuC1+WqDzSGANKth5HnAy8MrM3ChbCfjOZ/ioGYLSIOG1I0mSND4i4vnAWVSD8TfAGzPzoohYmgoiPZKa92Ib4E6GMq8A/g58JzP3a8f6CvBSqvH4Q+CQzPxVWzedqhn/NGoE+ZZUSYYP0GqDj/NL1QTpdma0DLnDgP3a6juojtmv9MptDNj/58BmVJbBcsDMzHzcoOeIiK2AM6iRtZ8G/jczbxlUoiYilgXObMf+KXBgZv6mrRu0/VyVuZE09XlfkzRVteBUt2TfdlTAaD3gL8CLcsAcTxGxOTUn7T+o+aauAg4Gfp6ZN0fEJlQ2yyuo7NBjgf1bQGHQeawB/AJ4BBVgODgzr+retyLiSIbuvbt0g+9h6dLFXkTsTWVS3Qn8LTPvbssHld2V5plBKkmSpHHURoJv3X69gepEW4+aW2o9qtRGDvMI8MHMPCQi1qGCTi/vHOtHwLVUxtYawC5tv38BB2Tm18b1xWnC9Hd+tobi4cDybdEJVHmq33RH5Ha2782j8hTgZ1T5lgS+mZkva1l79w7Y77tUFsGlwKGZOeycZBHxAiqACvAJ4EOZeUvfNtMGnZ+kxY/3NUlTWTewExGPo+4hvTbBn4APAyd1OvuDCrR/F3geVYbvCiqrcy/ga8B9LaMzqMFtF1BzRv0CeG9m/myE83k9Q9UZ3g4cl5l3d+6lGwIfBD6TmT8du3dCU1ErU2nmlMaMQSpJkqRxFDU/1LnAKsNschVDwasfAxsAj6eypqDmv3hSZl7WAlVvpEoaLTXM8Y4F3pWZN7bnd2T3FNI6TD9BXSNQJTc+DpyVmXfN5TE+Dezbfj0nM583YJvprQzMo6mOFKjOkff3as4Puq4i4hRq7pcvUyN6/zsPL0/SYsj7mqSpxOxQTVVeExpPBqkkSZLGWURsQdWA34ka5X07NU/GCVRH2RWZeV3fPt8CtqXmzvhqZr6ms+7J1ATy61D16C8DbgJO7JUMaSUAZ9mQmBraiNn3AIe2RTdRmXXfyMyb5/IYvQ6N1alyHWsB11OlZi4cMC9Cr0P3I8CB1BxnH87Mo0Z4jnWBdTPzzPl4mZIWI97XJE0lZodK0vybNtknIEmSNNVl5tmZ+SZgU+DJwA6ZuWlmfiEzz+oFqCJiegxNmHwoVRIwqQmOH9a2icy8KDP3y8ztM/NpwJsy88DMvDjK9My83wDV1NH+lpdS8xPQ/n1+m5cght+ztGtiVnv8BzUBMtRcZi9qz9HfIdG7fg4BbqY6f7eNiCe1Yz7geTPzT72O3HBybUkj8L4maSrpfe+OiBdExCXAF6gA1XnUHLR7Z+avhwsAZZvXp2U3HUuV8J4GrN6WzxGg6tyP3tYenwDsEBGPausH3c/OAr7Vfl0BmDlgGwNUkiacQSpJkqRx1mskZua1mXlTZv65LZ/et/7+NoJyRmZeAvyeaqDeBfy7bdMdodn7LndX7/csTmw8Nf2I6rSACnZuFxFLduYmeICIWDMiPgwc1LfqU8CvgAcBL4iIrdv2s4/TOn9nZOZM4B1t8XOoDt2lRnretr/XoaTReF+TNCW0gWLvpTKVHk9lh+4L7JyZ35ub8qWd7/YfojI9AdaLiGf2radlhU7PzL9QGVtQc1lt3dYPN1jt3cC2mbm35UslLSwMUkmSJI2z4RqJvc6u/vUtULU8NSEywJ0M+N7WG+nY29+Rj1Nb69w4Griauja2oeYVeICIWDEiXg18n+rIPSwi1u50aCQ1RwLAY4GXR8SK/R20nVG9xwK/pkbdvhh4fltutp6k+eZ9TdJUYXaoJM0/g1SSJEkLmaiJjd8K9CZJ/l5m3j2Jp6SFx2VAb+6UZwI7RsQqnRIzS7Q50E4AjgM2ojp/X5WZV8McwdEfAN8ElqQ6hXdpy/snzu6VoHx7e1wFuGE8XpykxZL3NUlThdmhkjQfDFJJkiRNov6GY0QsBWwPvAq4HziJoRIeWsy1zoRvAL8ApgNb0kb/R8QG1Kjbs4DtqNG1783MdTLz693jdEbOfhC4DXgU8OKIWLut75aTua/NhXYe8KLMXCszfzt+r1LS4sT7mqSpwuxQSZo/BqkkSZImUWek+IoR8XjgYOArwLpUQ/OoXmN1Ek9TC5frqMm4ATYAXtJG4J4F7EPNY3Y8sEZmfgjmyBoAZs9jEJnZzWB4KvDStn64Sb2/M+h4krSAvK9JmirMDpWkeRQG1CVJkiZeRKwP/AtYG3g28GhgW2rkN9So8ndnB21zmgAADUlJREFU5jWTcX5auEXEisAXgZcAdwHLtFUXAm/PzAvadtOBWYNG0UbEtFYmZiUqIPpoKpNh/8z8dW/9BLwcSfK+JmnKiIi1qGoIzwIuBw7NzFNadug+wBup4HsC7+sF3/uOMb0F3x8PnEdlSJ0B7JuZV/ffz1qQPiPihb3guyQtKgxSSZIkTbCIWBK4AHgosEbf6kuBd2Xm6W3bAEt16IEi4nnAqVSnxV3ARzPz0LYugGmjzTXQ6QB5HfAlYCZwMvBa5ymQNNG8r0maCtr96uVUBijAd4CrgFcDD2vLjgcOzMx/tn1m9Er3dY/TAk8fAQ4EbgI+mZkfHvSc3fbCoONJ0sLKcn+SJEkTLDPvoebYWAO4lxodeTLwyszcqBOgmp7N5J2tFmK/oEpDQs1fdmeb0wyYu8mwO+VkjqbK0ywFXGJHrqRJ4n1N0iKvfXf/AXBKW7QNNe/Uw6js0E0zc/fM/GdETG8BpkEBpd7cUx8F/gKsBmwXEU+DB8y1N0d7wQCVpEWJmVSSJEmTJCL2Bn4J3An8LTPvbssd+ai5EhEbUnMVrE+VtjokM8+ci/1WpcpL/iIz/9SWPRG4zRKTkiaT9zVJU4XZoZI0d8ykkiRJmiSZ+cXMvDgzr8rMuyNi2ggjKaVB/sDQ5NxPA3aIiIfAUKnIrohYJiJ2pMrOHAd8urcuMy/JzGt6I3rH/cwlaTDva5KmCrNDJWkuGKSSJEmaRN1Os8wcOBG8NJzWQfFN4Oy2aEvgBW1dd16Caa00zBeB7wKbAbczVIZmjmN6HUqaLN7XJE0VmTkTOIYKvi8HvAR4Xls34j0pIlaNiFdHxLqdxa8EHp2ZR4zTKUvSpDBIJUmSNInsNNOCyswbqKyDWcB6VNbBY6CCoBHxKOADwE+ozg2ATwJrZOZxE32+kjQa72uSphCzQyVpFAapJEmSpEXfD4ET2r83B7aJiFWAPYAzgPdQI3jPBDbMzLdl5p0RMcNODkkLKe9rkhZ5ZodK0ujCe5okSZK06IuITYFvAw8BLgLuBZ7RVv8J2D8zT2vbToMqMTkJpypJc8X7mqSpIiJeBHyDShg4BTg4M69sQfVHAq8F9qOC71DZoe/LzDsn43wlaSKZSSVJkiRNDb8Cvtz+/SSqI/ce4IDMfEynI3d6m//MjlxJCzvva5KmCrNDJWkYZlJJkiRJU0RErAf8FFgDOBp4Z2be3NbNyMz7JvP8JGleeV+TNFWYHSpJgxmkkiRJkqaI1qGxI/D3zPx1WzYdmOXcBZIWRd7XJE0VEbEEcDCVNZVAUNmh787MIzrbTW9zWUnSYsEglSRJkjQFOQJX0lTjfU3Sos7sUEl6IINUkiRJkiRJkjTOzA6VpAcySCVJkiRJkiRJE8jsUEkqBqkkSZIkSZIkSZI04aZN9glIkiRJkiRJkiRp8WOQSpIkSZIkSZIkSRPOIJUkSZIkSZIkSZImnEEqSZIkSZIkSZIkTTiDVJIkSZIkSZIkSZpwBqkkSZIkSZIkSZI04QxSSZIkSZIkSZIkacIZpJIkSZIkSZIkSdKEM0glSZIkSZIkSZKkCWeQSpIkSZIkSZIkSRPOIJUkSZIkSZIkSZImnEEqSZIkSZIkSZIkTTiDVJIkSZIkSZIkSZpwBqkkSZIkSZIkSZI04QxSSZIkSZIkSZIkacIZpJIkSZKkBRQRj4iIL0bEnyPi7ojI9rPzZJ+bJEmSJC2sZkz2CUiSJEnSoiwiHgH8Blh1ss9FkiRJkhYlZlJJkiRJ0oJ5LxWgug84CNgEeEL7+ckkntccIuK4lt11zWSfiyRJkiSBmVSSJEmStKC2bI/fzczDJ/VMJEmSJGkRYiaVJEmSJC2Yh7fHKyf1LCRJkiRpEWOQSpIkSZIWzJLt8d5JPQtJkiRJWsQYpJIkSZKkeRQRe7T5nbKz+JDesvZzXN8+0yNi94j4QURcHxEzI+LfEXFeRLwtIpYZ4fmmRcQWEfHxiDg/Iv4VEfdGxK0RcXFb/ohh9n1/O8/d26JH9p1n/+ugs/z9o7wPP2vb/WzAus07x9m8vYY9I+KnEfHPiJjV/x4t6PvU9n9KRHwlIq6MiDsj4u6I+FtE/CYiPhcRO0ZEjHQMSZIkSRPDOakkSZIkaZy1ANL3gI36Vq0MbNp+3hgR22XmoLKBBwOHDFi+YjvmRm3/V2bmqWN35mNmaeCHDM3fNdCCvk8RsT/wcR44IHPN9vNkYB9geeCOeX8ZkiRJksaSQSpJkiRJmnffBf5f+/el7fELwOc729wCEBGrAOcBawEzgS8D5wDXAMsB/wPsB6wLnBERT87M//Q93wzgBuBU4ALgL8Dd7ZjPogIvywEntv2v6Oz7eeBbwGHATsD1wFbz/9Lny0eBJ1IBqOOAa4GHAiv0NljQ9ykinshQgOpq4LPAxcDNVFDqscDzqPdAkiRJ0kLAIJUkSZIkzaPMvBW4FaBTOe7GzLxswOafpgIv1wLPy8yr+9b/LCK+CZwLPBo4EHhP3zZHAx/IzP55ry4C/i8iPgNcCDwceDfwqs653gjcGBG3tkX3DnOe4+mJwGGZ+b4RtlnQ9+lFVIDqTmCTzPxn3/7nAkdHxIrAf+f7lUiSJEkaM85JJUmSJEnjJCIeBezWft13QOAFgMz8LfC59useA9ZfMyBA1V1/HfCx9uvCOOfSlcD7h1s5Ru/T6r3nGhCg6h7jP5k5a9QzliRJkjTuDFJJkiRJ0vjZDphOZe6cMcq2P2+Pa7S5mYYVEStExNoRsWFEPD4iHs9QdtAKwNoLctLj4OTMvH+E9WPxPt3QHjeIiKfP32lKkiRJmkiW+5MkSZKk8fPU9rgscN88JDitDvy1uyAiHgkcAOwAPHKU/Vel5q1aWFwyyvqxeJ9OAt4FLAWcHxFnAqdR81z9PjNzns5YkiRJ0rgzk0qSJEmSxs9D5nO/Zbu/RMQ2wOXAvoweoAJYZj6fd7zcMsr6BX6fMvMPwMvac80Atge+AFxKzcl1fEQ8ez6fR5IkSdI4MJNKkiRJksbP9Pb4L+B587Df7DmZImJV4EQqIHMH8HHgh8Cfgf9k5j1tuy2An/R2W7DTHnMjlfqDMXifADLz2xHxY2p+q62AZwOrUZllrwReGRFfBfZ0XipJkiRp8hmkkiRJkqTx8+/2uDxwxSjzMg3nRcCD2793ycwfD7PdyvNx7OEkFegarfrGg8bo+cbifQIgM/8DfKn9EBGPA3YC3gysAewO/Bb41IKcsCRJkqQFZ7k/SZIkSRo/v22PSzE079K82rA93jxCgIq5OP68zMl0e3tcabgNoiaOWncejjmSsXifBsrMKzLzI8AzgTvb4peM5XNIkiRJmj8GqSRJkiRp/HyfoeDQW+fzGL0KGEtHxMA2XEQsC7xqlOPc3R6Xmovn7JXRGylgtA1DGV4LaizepxFl5t+AK9uvq47Hc0iSJEmaNwapJEmSJGmcZOYfgW+2X18aEW8bafuIWDsiXta3+Kr2uCwDMoAiYjpwNFXKbiQ3tMeHRMTyo2x7Tnt8RkRsOuA5Vwc+M8ox5tpYvE8RsXNEDBs0i4i1gPXbr1cPt50kSZKkieOcVJIkSZI0vt5IZSQ9GvhEROwEfA34PTATWAXYCNga2AI4FTips/8pwIeoDKhjI2Jj4CzgP1QpwDcDTwHOBx4QUOr4RXucBhwVEZ8B/tVbmZl/6mz7JWAfqs34/Yg4FDgPWLI9x9uAJagA2npz/1aMaEHfp7cCX4+I04CzgSuo92ildtw3A8u0bY8ao3OWJEmStAAMUkmSJEnSOMrMm1s20inAs4HntJ/h3Na3/3UR8UYqW2pp4KD203Uy8GVgpDmrzgYupOZmenn76YrOc/4+Ig4EjqCCPEf2bXszsDPwQcYoSLWg71OzLPDi9jPILOCQzPzugpyrJEmSpLFhkEqSJEmSxllm/gN4TkRsB7wM2ARYncpGupXKSLoA+F5m/nzA/sdGxB+Bd1CZTA+msqB+BxybmadExOajnMOsiPgf4EBgB2Ad4EF0glN92x8ZEZcD+wNPpwJA1wOnA4dn5l8jBu463xbwfXoZsD2wObBB229Vai6ua4GfA0dl5iVjetKSJEmS5ltk5uhbSZIkSZIkSZIkSWNo2mSfgCRJkiRJkiRJkhY/BqkkSZIkSZIkSZI04QxSSZIkSZIkSZIkacIZpJIkSZIkSZIkSdKEM0glSZIkSZIkSZKkCWeQSpIkSZIkSZIkSRPOIJUkSZIkSZIkSZImnEEqSZIkSZIkSZIkTTiDVJIkSZIkSZIkSZpwBqkkSZIkSZIkSZI04QxSSZIkSZIkSZIkacIZpJIkSZIkSZIkSdKEM0glSZIkSZIkSZKkCWeQSpIkSZIkSZIkSRPOIJUkSZIkSZIkSZImnEEqSZIkSZIkSZIkTTiDVJIkSZIkSZIkSZpwBqkkSZIkSZIkSZI04QxSSZIkSZIkSZIkacIZpJIkSZIkSZIkSdKEM0glSZIkSZIkSZKkCWeQSpIkSZIkSZIkSRPOIJUkSZIkSZIkSZImnEEqSZIkSZIkSZIkTbj/D5dYBNRsXopkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5), dpi=200)\n",
    "\n",
    "sns.barplot(\n",
    "    x = importances['features'],\n",
    "    y = importances['importance'],\n",
    "    palette='winter_r'\n",
    ")\n",
    "\n",
    "plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 50 rounds.\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[16]\tTrain's multi_logloss: 0.367465\tTrain's accuracy: 0.868056\tValidation's multi_logloss: 0.542608\tValidation's accuracy: 0.765432\n"
     ]
    }
   ],
   "source": [
    "best_iteration = history.best_iteration\n",
    "\n",
    "best_model = lightgbm.train(\n",
    "    params, \n",
    "    lgbm_train_data,\n",
    "    valid_sets=[lgbm_train_data, lgbm_validation_data],\n",
    "    valid_names=['Train', 'Validation'],\n",
    "    evals_result=evaluation_results,\n",
    "    feval=accuracy,\n",
    "    num_boost_round=best_iteration,\n",
    "    verbose_eval=20,\n",
    "    early_stopping_rounds=50\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results = pd.DataFrame(best_model.predict(test_data))\n",
    "test_predicted = pd.DataFrame(np.argmax(test_results.values, axis=1), columns=['Predicted'])\n",
    "test_df_concat = pd.concat([test_predicted, test_labels.reset_index(drop=True)], axis=1)\n",
    "test_df_cm = confusion_matrix(test_df_concat['Survived'], test_df_concat['Predicted'])\n",
    "\n",
    "model_results['lightgbm'] = accuracy_score(test_labels.values, test_predicted.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Predicted</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Predicted  Survived\n",
       "0          1         1\n",
       "1          1         1\n",
       "2          1         0\n",
       "3          0         0\n",
       "4          0         0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_concat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77600c2748>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3, 2), dpi=200)\n",
    "\n",
    "sns.set(font_scale=0.5)\n",
    "\n",
    "sns.heatmap(test_df_cm, annot=True, annot_kws={'size':  8}, cmap='PuBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare the Other Algorithm    ※ Hyper Parameters not tuned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_svm_model = LinearSVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsm_history = linear_svm_model.fit(train_data, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsm_test_results = pd.DataFrame(linear_svm_model.predict(test_data))\n",
    "lsm_test_predicted = pd.DataFrame(np.argmax(lsm_test_results.values, axis=1), columns=['Predicted'])\n",
    "lsm_test_df_concat = pd.concat([lsm_test_predicted, test_labels.reset_index(drop=True)], axis=1)\n",
    "lsm_test_df_cm = confusion_matrix(lsm_test_df_concat['Survived'], lsm_test_df_concat['Predicted'])\n",
    "\n",
    "model_results['linear_svm'] = accuracy_score(test_labels, lsm_test_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(1, 2), dpi=200)\n",
    "\n",
    "sns.set(font_scale=0.5)\n",
    "sns.barplot(\n",
    "    x = list(model_results.keys()),\n",
    "    y = list(model_results.values()),\n",
    "    palette='winter_r'\n",
    ")\n",
    "\n",
    "plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 1-10 Features \n",
    "上位 1 ~ 10の `importances`の特徴のみで学習してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame()\n",
    "for n in range(len(importances)):\n",
    "    input_data = train_data[importances['features'][0:n+1]]\n",
    "    linear_svm_model = LinearSVC()\n",
    "    lin_svc_pipe = Pipeline([('lin_svc_model', linear_svm_model)])\n",
    "    scores = cross_val_score(lin_svc_pipe, input_data, train_labels,\n",
    "                            cv=5, scoring='accuracy')\n",
    "    results = results.append(pd.DataFrame([scores]))\n",
    "results = results.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Feature\n",
    "全特徴量を用いた場合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data = train_data\n",
    "linear_svm_model = LinearSVC()\n",
    "lin_svm_model = LinearSVC()\n",
    "lin_svc_pipe = Pipeline([('lin_svc_model', linear_svm_model)])\n",
    "scores = cross_val_score(lin_svc_pipe, input_data, train_labels,\n",
    "                        cv=5, scoring='accuracy')\n",
    "results = results.append(pd.DataFrame([scores]))\n",
    "results = results.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data treatment for plotting\n",
    "グラフ表示用にデータを整形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_comp = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'All']\n",
    "treatment_results = results.T\n",
    "treatment_results.columns = label_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f773bfd08d0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,2), dpi=200)\n",
    "sns.barplot(data=treatment_results, palette='RdPu')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}