Построение классификатора.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Загрузка и предобработка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Словарь показателей, что содержится в выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Импорт названий листов с данными\n",
    "sheets = pd.read_csv('./data/sheets.txt', header=None).values.T[0].tolist()\n",
    "\n",
    "# Импорт названий столбцов на соответствующих листах\n",
    "sheets_columns = pd.read_csv('./data/sheets columns.txt', header=None, sep='\\t').values\n",
    "\n",
    "# Словарь-  лист: названия столбцов на листе\n",
    "sname = dict((sheet,values.tolist()) for sheet,values in zip(sheets,sheets_columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Информация об отчетах\n",
    "earns = pd.read_csv('./data/earns.csv', low_memory=False)\n",
    "\n",
    "# Логарифмические доходности\n",
    "edata_before_adj_logret = pd.read_csv('./data/edata_before_adj_logret.csv', index_col=None, header=None).values\n",
    "edata_after_adj_logret = pd.read_csv('./data/edata_after_adj_logret.csv', index_col=None, header=None).values\n",
    "market_before_logret = pd.read_csv('./data/market_before_logret.csv', index_col=None, header=None).values\n",
    "market_after_logret = pd.read_csv('./data/market_after_logret.csv', index_col=None, header=None).values\n",
    "\n",
    "# Вектор бет\n",
    "beta_ = pd.read_csv('./data/beta_before_adj.csv', index_col=None, header=None, skip_blank_lines=False).values\n",
    "\n",
    "# Abnormal return \n",
    "edata_before_abn = edata_before_adj_logret - market_before_logret*beta_\n",
    "edata_after_abn = edata_after_adj_logret - market_after_logret*beta_\n",
    "\n",
    "# Рыночная капитализация компании\n",
    "mcap_before = pd.read_csv('./data/mcap_before.csv', index_col=None, header=None, skip_blank_lines =False)\n",
    "mcap_before.columns = ['mcap']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Доходности для учета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "days_before = 10\n",
    "days_after = 10+1\n",
    "\n",
    "data_before_abn = pd.DataFrame(edata_before_abn[:,-days_before:], columns=['abn_b'+str(s) for s in range(days_before,0,-1)])\n",
    "data_before_adj_logret = pd.DataFrame(edata_before_adj_logret[:,-days_before:], columns=['logret_b'+str(s) for s in range(days_before,0,-1)])\n",
    "\n",
    "data_after_abn = pd.DataFrame(edata_after_abn[:,:days_after], columns=['abn_a'+str(s) for s in range(0,days_after)])\n",
    "data_after_adj_logret = pd.DataFrame(edata_after_adj_logret[:,:days_after], columns=['logret_a'+str(s) for s in range(0, days_after)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Предсказание зная будущий показатель\n",
    "\n",
    "Расчет производится при предположении, что мы знаем каков выйдет ожидаемый показатель. Если он выходит выше ожидаемого, то прогнозируем положительную доходность в нулевой день, и отрицательную если показатель выходит ниже ожидаемого значения. Данный метод позволяет оценить насколько разница между вышедшим и ожидаемым показателем совпадает с направлением доходности в нулевой день.\n",
    "\n",
    "Примечательно, что для компаний с рыночной капитализацией меньше медианной, accuracy будет выше, чем аналогичный прогноз для компаний с капитализацией выше медианной по выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество компаний с капитализацией ниже медианного 18913\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mcap_below</th>\n",
       "      <th>mcap_above</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Revenue</th>\n",
       "      <td>57.2276</td>\n",
       "      <td>57.2777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross Profit Margin</th>\n",
       "      <td>57.088</td>\n",
       "      <td>54.4261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA</th>\n",
       "      <td>61.3731</td>\n",
       "      <td>59.2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA Per Share</th>\n",
       "      <td>58.9587</td>\n",
       "      <td>57.0914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBIT</th>\n",
       "      <td>61.172</td>\n",
       "      <td>59.3986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Operating Profit</th>\n",
       "      <td>60.8175</td>\n",
       "      <td>58.1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-Tax Profit</th>\n",
       "      <td>61.9947</td>\n",
       "      <td>59.3614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-Tax Profit Reported</th>\n",
       "      <td>60.2494</td>\n",
       "      <td>57.0674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Income</th>\n",
       "      <td>63.1818</td>\n",
       "      <td>60.0186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Income Reported</th>\n",
       "      <td>60.9574</td>\n",
       "      <td>57.7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earning Per Share</th>\n",
       "      <td>63.4519</td>\n",
       "      <td>60.4577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earning Per Share Reported</th>\n",
       "      <td>61.3405</td>\n",
       "      <td>57.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Debt</th>\n",
       "      <td>47.8764</td>\n",
       "      <td>48.1412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Asset Value</th>\n",
       "      <td>53.942</td>\n",
       "      <td>50.8967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Book Value Per Share</th>\n",
       "      <td>54.0453</td>\n",
       "      <td>51.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital Expenditures</th>\n",
       "      <td>49.0155</td>\n",
       "      <td>49.3792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cash Flow Per Share</th>\n",
       "      <td>54.7734</td>\n",
       "      <td>53.0362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Cash Flow</th>\n",
       "      <td>55.3012</td>\n",
       "      <td>53.1856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return On Assets</th>\n",
       "      <td>57.471</td>\n",
       "      <td>53.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return On Equity</th>\n",
       "      <td>56.1887</td>\n",
       "      <td>55.6589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mcap_below mcap_above\n",
       "Revenue                       57.2276    57.2777\n",
       "Gross Profit Margin            57.088    54.4261\n",
       "EBITDA                        61.3731    59.2343\n",
       "EBITDA Per Share              58.9587    57.0914\n",
       "EBIT                           61.172    59.3986\n",
       "Operating Profit              60.8175    58.1382\n",
       "Pre-Tax Profit                61.9947    59.3614\n",
       "Pre-Tax Profit Reported       60.2494    57.0674\n",
       "Net Income                    63.1818    60.0186\n",
       "Net Income Reported           60.9574    57.7408\n",
       "Earning Per Share             63.4519    60.4577\n",
       "Earning Per Share Reported    61.3405     57.808\n",
       "Net Debt                      47.8764    48.1412\n",
       "Net Asset Value                53.942    50.8967\n",
       "Book Value Per Share          54.0453     51.505\n",
       "Capital Expenditures          49.0155    49.3792\n",
       "Cash Flow Per Share           54.7734    53.0362\n",
       "Free Cash Flow                55.3012    53.1856\n",
       "Return On Assets               57.471     53.775\n",
       "Return On Equity              56.1887    55.6589"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Преаллокация с предсказанными значениями\n",
    "pre_vals = pd.DataFrame(index = sname.keys(), columns=['mcap_below', 'mcap_above'])\n",
    "\n",
    "# Доп фильтр по рыночной капитализации\n",
    "mcap_filter_below = (np.log(mcap_before) < np.log(mcap_before).median()).values.reshape(-1)\n",
    "mcap_filter_above = (np.log(mcap_before) >= np.log(mcap_before).median()).values.reshape(-1)\n",
    "\n",
    "print('Количество компаний с капитализацией ниже медианного', sum(mcap_filter_below))\n",
    "\n",
    "for sheet_name in list(sname.keys()):\n",
    "    \n",
    "    # Названия актуальных и фактических признаков\n",
    "    act_value = [s for s in sname[sheet_name] if 'ActValue' in s][0]\n",
    "    mean_value = [s for s in sname[sheet_name] if 'Mean' in s][0]\n",
    "    \n",
    "    # Предсказание\n",
    "    y_pred = earns[act_value] - earns[mean_value]\n",
    "    y_pred[y_pred>=0] = 1\n",
    "    y_pred[y_pred<0] = 0\n",
    "    \n",
    "    # Фактическое значение\n",
    "    y = data_after_adj_logret['logret_a0'].copy()\n",
    "    y[y>=0] = 1\n",
    "    y[y<0] = 0\n",
    "    \n",
    "    # Доп фильтр по рыночной капитализации\n",
    "    y_pred_below = y_pred[mcap_filter_below].copy()\n",
    "    y_below = y[mcap_filter_below].copy()\n",
    "    \n",
    "    y_pred_above = y_pred[mcap_filter_above].copy()\n",
    "    y_above = y[mcap_filter_above].copy()\n",
    "    \n",
    "    # Удалить строки с неизвестными данными\n",
    "    nullrows = (y_below.isnull()) | (y_pred_below.isnull())\n",
    "    y_pred_below = y_pred_below[~nullrows]\n",
    "    y_below = y_below[~nullrows]\n",
    "    \n",
    "    nullrows = (y_above.isnull()) | (y_pred_above.isnull())\n",
    "    y_pred_above = y_pred_above[~nullrows]\n",
    "    y_above = y_above[~nullrows]\n",
    "    \n",
    "    pre_vals.loc[sheet_name, 'mcap_below'] = accuracy_score(y_below.values, y_pred_below)\n",
    "    pre_vals.loc[sheet_name, 'mcap_above'] = accuracy_score(y_above.values, y_pred_above)\n",
    "\n",
    "# Вычесть accuracy из единицы, если прогноз должен быть зеркальный\n",
    "#pre_vals[pre_vals<0.5] = 1-pre_vals\n",
    "\n",
    "pre_vals = round(pre_vals*100,2)\n",
    "pre_vals.to_csv('Simple Inside forecast with mcap')\n",
    "pre_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
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3xc1vXRE3v3VFbezEydHG4PbwYPz0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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4e2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "#sns.set_palette(\"RdBu\")\n",
    "#sns.set_color_codes(\"muted\")\n",
    "sns.set_context(\"notebook\", font_scale = 1.5, rc = { \"figure.figsize\" : (24, 12), \"axes.titlesize\" : 18 })\n",
    "dp = sns.distplot((mcap_before[~mcap_before.isnull().values]), bins=2000)\n",
    "\n",
    "dp.axes.set_xlim(0, 5*1e10);\n",
    "dp.axes.set_title('Распределение рыночной капитализации компаний (NYSE)', fontsize=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выборка: X - все признаки до события, y - направление доходности в нулевой день\n",
    "\n",
    "Функция формирования выборки для прогноза направления доходности в нулевой день. \n",
    "\n",
    "Исходные данные содержат в себе оценки аналитиков о всех показателях (минимальная оценка, макс оценка, среднее, медианное значение, количество аналитиков, стандартное отклонение), логарифмические доходности до выхода события, избыточные доходности до выхода события. \n",
    "\n",
    "Параметр функции del_empty_vals позволяет удалить из выборки те признаки, которые содержать более del_empty_vals пропусков. По умолчанию = 5000. Остальные пропущенные значения устанавливаются равными нулю.\n",
    "\n",
    "Целевая переменна y устанавливается в 1, если доходность в нулевой день больше или равна нулю, и в 0 - если доходность в нулевой день меньше 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def make_xy(del_empty_vals=5000):\n",
    "    # Куммулятивные logret доходности и абнормал до события\n",
    "    X = pd.concat([earns, mcap_before, data_before_abn, data_before_adj_logret], axis=1)\n",
    "\n",
    "    # Предсказание направления гепа\n",
    "    y = data_after_adj_logret['logret_a0'].copy()\n",
    "    y[y>=0] = 1\n",
    "    y[y<0] = 0\n",
    "\n",
    "    # Удалить строки для которых неизвестна доходность\n",
    "    X = X[~y.isnull()]\n",
    "    y = y[~y.isnull()]\n",
    "\n",
    "    # Убрать лишние столбцы с актуальным показателем, временем\n",
    "    del_cols = ['Date', 'Ticker','date1', 'date2', 'event_date'] + \\\n",
    "        [s for s in X.columns if ('ActValue' in s) | ('ActSurprise' in s) | ('Date' in s)]\n",
    "    X.drop(del_cols, axis=1, inplace=True)\n",
    "\n",
    "    # Удалить признаки, где пропущенных данных больше заданного значения\n",
    "    del_cols = X.columns[(X.isnull().sum() > del_empty_vals)]\n",
    "    X.drop(del_cols, axis=1, inplace=True)\n",
    "\n",
    "    # Заполнить пропуски нулями\n",
    "    X = X.fillna(0)\n",
    "\n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выборка: X - все признаки до и после события, y - направление накопленной доходности после события за days_after\n",
    "\n",
    "Функция формирования выборки для прогноза направления накопленной доходности за days_after_cum после выхода события. \n",
    "\n",
    "Исходные данные содержат в себе оценки аналитиков о всех показателях (минимальная оценка, макс оценка, среднее, медианное значение, количество аналитиков, стандартное отклонение), логарифмические доходности до выхода события, избыточные доходности до выхода события, а также актуальные значения вышедших показателей (актуальное значение, сюрприз по показателю в %) и величину доходности в нулевой день (логарифмическая и избыточная доходность).\n",
    "\n",
    "Параметр функции del_empty_vals позволяет удалить из выборки те признаки, которые содержать более del_empty_vals пропусков. По умолчанию = 5000. Остальные пропущенные значения устанавливаются равными нулю.\n",
    "\n",
    "Целевая переменна y устанавливается в 1, если накопленная доходность за days_after_cum дней после выхода события больше или равна нулю, и в 0 - если иначе. Доходность в нулевой день не входит в y, т.к. является признаком в X.\n",
    "\n",
    "days_after_cum - за какое количество дней после выхода события просуммировать доходность. Доходность в день 0 исключается из суммирования в переменной y и находится как признак в переменной X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_xy2(days_after_cum, del_empty_vals=5000):\n",
    "    # Количество дней для расчета накопленной доходности\n",
    "    #days_logret = 10\n",
    "\n",
    "    # Куммулятивные logret доходности и абнормал до события\n",
    "    X = pd.concat([earns, mcap_before, data_before_abn, data_before_adj_logret, data_after_abn[:1], data_after_adj_logret[:1]], axis=1)\n",
    "\n",
    "    # Зависимая переменная - куммулятивная доходность\n",
    "    y = data_after_adj_logret.loc[:,'logret_a1':'logret_a'+str(days_after_cum)].cumsum(axis=1)['logret_a'+str(days_after_cum)]\n",
    "    y[y>=0] = 1\n",
    "    y[y<0] = 0\n",
    "\n",
    "    # Удалить строки для которых неизвестна доходность\n",
    "    notnans = ~y.isnull()\n",
    "    X = X[notnans]\n",
    "    y = y[notnans]\n",
    "\n",
    "    # Убрать лишние столбцы\n",
    "    del_cols = ['Date', 'Ticker','date1', 'date2', 'event_date'] + \\\n",
    "        [s for s in X.columns if ('Date' in s)]\n",
    "    X.drop(del_cols, axis=1, inplace=True)\n",
    "\n",
    "    # Удалить отдельные показатели, где много пропущенных данных\n",
    "    # Количество пропусков больше которого удалить\n",
    "    del_cols = X.columns[(X.isnull().sum()>del_empty_vals)]\n",
    "    X.drop(del_cols, axis=1, inplace=True)\n",
    "\n",
    "    X = X.fillna(0)\n",
    "    \n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Случайный лес. Определить функцию классификатора\n",
    "Возвращает accuracy на тесте и важность признаков\n",
    "\n",
    "X - выборка с признаками, y - целевая переменная\n",
    "test_size - размер тестовой выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def forest_clf(X,y, test_size=0.5):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n",
    "\n",
    "    # Инициализируем и обучаем классификатор\n",
    "    rfc = RandomForestClassifier(n_estimators=1000, random_state=42, n_jobs=-1)\n",
    "    rfc.fit(X_train, y_train)\n",
    "\n",
    "    # Предсказания на тесте\n",
    "    y_pred = rfc.predict(X_test)\n",
    "    \n",
    "    # Софрмировать датафрейм с важностью признаков\n",
    "    feature_imp = pd.DataFrame(rfc.feature_importances_, index = X.columns)\n",
    "    \n",
    "    return accuracy_score(y_test, y_pred), feature_imp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Функция для отрисовки значимости признаков\n",
    "При установке положительного числа num_to_plot отрисовывются первые по важности признаки, если отрицательно - последние."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_importances(feature_imp, num_to_plot=10):\n",
    "    \n",
    "    # Отсортировать по убыванию значимости\n",
    "    feature_imp = feature_imp.sort_values(0, ascending=False)\n",
    "    \n",
    "    if num_to_plot>0:\n",
    "        imp_plot = feature_imp.iloc[:num_to_plot]\n",
    "    elif num_to_plot<0:\n",
    "        imp_plot = feature_imp.iloc[num_to_plot:]\n",
    "\n",
    "    plt.figure(figsize=(15,6))\n",
    "    ax = plt.axes()\n",
    "\n",
    "    \n",
    "    plt.bar(range(abs(num_to_plot)), imp_plot.values,\n",
    "        color=([str(i/float(abs(num_to_plot)+1)) for i in range(abs(num_to_plot))])) \n",
    "    \n",
    "    ticks = plt.xticks(range(abs(num_to_plot)), imp_plot.index)\n",
    "    \n",
    "    plt.title(u\"Значимость признаков в предсказании направления доходности\", fontsize=22)\n",
    "    plt.xlim([-1, abs(num_to_plot)])\n",
    "    plt.xticks(rotation=80);\n",
    "\n",
    "    vals = ax.get_yticks()*100\n",
    "    ax.set_yticklabels(['{:.1f}%'.format(x) for x in vals]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Предсказание до выхода события"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размерность выборки (37839, 63)\n",
      "Доля правильных ответов (для всех компаний) 0.524775369979\n",
      "Доля правильных ответов (капитализация выше медианного) 0.509249471459\n",
      "Доля правильных ответов (капитализация ниже медианного) 0.531580338266\n"
     ]
    },
    {
     "data": {
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EeXm58RcEAAAAALjshq/weXt7a9SoUVqwYIEC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\ngoOD1a1bN7e/Y+KNlJSUKCUlRVlZWcrJyVGbNm14suo6qv4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woerWrWv3cquVP//5z/rb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\nO9NwvCYVPoMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf287f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vGjZsmN2U1la///67hg8fbveerjJlypjrzOm9do6w1sGgoCC7i+zDhw/r4sWL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kJSUp31E1V0mdCUhfXx+TJ09mw4YN3H333Zw8eZJLL72UhoYG3nrrLfbu3StDw8/izCWKs2fP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\nCCGEEEKIC5QkfEIIIYQQQghxgfr/ks5Ep6xum3IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a69ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Доля тестовой выборки\n",
    "test_size = 0.4\n",
    "\n",
    "# Сформировать выборку\n",
    "X,y = make_xy(del_empty_vals=5000)\n",
    "print('Размерность выборки', X.shape)\n",
    "\n",
    "# Беги Forest, беги!\n",
    "test_acc, feature_imp = forest_clf(X,y, test_size=test_size)\n",
    "print('Доля правильных ответов (для всех компаний)', test_acc)\n",
    "\n",
    "# Вариант для разных капитализаций\n",
    "cap_above = X['mcap']>X['mcap'].median()\n",
    "test_acc, _ = forest_clf(X[cap_above],y[cap_above], test_size=test_size)\n",
    "print('Доля правильных ответов (капитализация выше медианного)', test_acc)\n",
    "\n",
    "test_acc, _ = forest_clf(X[~cap_above],y[~cap_above], test_size=test_size)\n",
    "print('Доля правильных ответов (капитализация ниже медианного)', test_acc)\n",
    "\n",
    "# Отобразить важность признаков\n",
    "plot_importances(feature_imp, 10)\n",
    "plot_importances(feature_imp, -10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Предсказание после выхода события"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размерность выборки (38087, 69)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all</th>\n",
       "      <th>mcap_above</th>\n",
       "      <th>mcap_below</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.539613</td>\n",
       "      <td>0.530717</td>\n",
       "      <td>0.551326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.54854</td>\n",
       "      <td>0.531504</td>\n",
       "      <td>0.558283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.545323</td>\n",
       "      <td>0.552245</td>\n",
       "      <td>0.549226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.546045</td>\n",
       "      <td>0.53623</td>\n",
       "      <td>0.554739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.540663</td>\n",
       "      <td>0.538593</td>\n",
       "      <td>0.544762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.547096</td>\n",
       "      <td>0.548438</td>\n",
       "      <td>0.542531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.550903</td>\n",
       "      <td>0.552376</td>\n",
       "      <td>0.548307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.551756</td>\n",
       "      <td>0.554082</td>\n",
       "      <td>0.541481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.553791</td>\n",
       "      <td>0.563796</td>\n",
       "      <td>0.551457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.559304</td>\n",
       "      <td>0.566422</td>\n",
       "      <td>0.548307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         all mcap_above mcap_below\n",
       "1   0.539613   0.530717   0.551326\n",
       "2    0.54854   0.531504   0.558283\n",
       "3   0.545323   0.552245   0.549226\n",
       "4   0.546045    0.53623   0.554739\n",
       "5   0.540663   0.538593   0.544762\n",
       "6   0.547096   0.548438   0.542531\n",
       "7   0.550903   0.552376   0.548307\n",
       "8   0.551756   0.554082   0.541481\n",
       "9   0.553791   0.563796   0.551457\n",
       "10  0.559304   0.566422   0.548307"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Количество дней для которых построить предсказание накопленной доходности\n",
    "num_days_predict = 10\n",
    "\n",
    "# Доля тестовой выборки\n",
    "test_size = 0.4\n",
    "\n",
    "# Количество пропущенных данных для удаления столбцов признака\n",
    "del_empty_vals = 5000\n",
    "\n",
    "# Для хранения результатов\n",
    "results = pd.DataFrame(index=range(1,num_days_predict+1), columns=['all', 'mcap_above', 'mcap_below'])\n",
    "\n",
    "# Фильтр по рыночной капитализации\n",
    "X,_ = make_xy2(1,del_empty_vals=del_empty_vals)\n",
    "cap_above = X['mcap']>X['mcap'].median()\n",
    "\n",
    "print('Размерность выборки', X.shape)\n",
    "\n",
    "for days_after_cum in range(1,num_days_predict+1):\n",
    "    \n",
    "    # Сформировать выборку\n",
    "    X,y = make_xy2(days_after_cum,del_empty_vals=del_empty_vals)\n",
    "    \n",
    "    # Беги Forest, беги!\n",
    "    results.loc[days_after_cum,'all'], feature_imp = forest_clf(X,y, test_size=test_size)\n",
    "    results.loc[days_after_cum,'mcap_above'], feature_imp_above = forest_clf(X[cap_above],y[cap_above], test_size=test_size)\n",
    "    results.loc[days_after_cum,'mcap_below'], feature_imp_below = forest_clf(X[~cap_above],y[~cap_above], test_size=test_size)\n",
    "    \n",
    "# Сохранить и показать результаты\n",
    "results.to_csv('Предсказание накопленной доходности за разное колво дней после выхода события.csv')\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Важность признаков для последнего прогноза"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Сохранить важность признаков\n",
    "pd.concat([feature_imp.sort_values(0, ascending=False).ix[:10],\n",
    "           feature_imp_above.sort_values(0, ascending=False).ix[:10], \n",
    "           feature_imp_below.sort_values(0, ascending=False).ix[:10]\n",
    "          ], axis=1).to_csv('feature importances first 10.csv', header=['all', 'above', 'below'])\n",
    "\n",
    "pd.concat([feature_imp.sort_values(0, ascending=False).ix[-10:],\n",
    "           feature_imp_above.sort_values(0, ascending=False).ix[-10:], \n",
    "           feature_imp_below.sort_values(0, ascending=False).ix[-10:]\n",
    "          ], axis=1).to_csv('feature importances last 10.csv', header=['all', 'above', 'below'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qw4cPx6tXr/DLL79U+tl169YhLy9PZW/dZ599hrlz52LYsGFiWseOHQH8X/2oiUePHqFP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0vjc5/7HLfddtvU84mJCRITExkcHCQ1NdX7+CRJki4TLumUxKOPPsr3vvc9+vr6OHnyJFu2bKGp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\nhbLwSZIkSdIV6n8AY2qMbOrcLsMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf62fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bhgwZYjeltdUff/yhoUOH2r2nq1ixYuY6s3qvnSOsbbBPnz52J9mHDh3SxYsX\nJWV/NbV48eIaNGiQrly5YrOvWKfx/+GHH7L83Pfffy9JNu93u52Li4uKFSumpk2bqmvXrjIMI9t3\nQ+bE1dVVJUqUUIcOHdSqVSslJydn+TvdCUePHs3yhOL2NmA96f/xxx+z3LbLli1T586dFR4ebqZt\n27ZNN2/etJviPDsNGjSQJLsTJenW7zlx4kQNHz48yyvflSpVUmBgoE6dOmUz0YP1vVwDBgywOzHf\nsWOHpP/2V6mpqRo3blyWAa31XaXWPrSg7TAvfv75Z0VGRqpv377ZDsHNLL/9Z37Fxsbq2rVrql27\ndpbDVW/fzgWxbds2u22akpKiHTt2qFChQmZbs/4+/fv3txu2bi1PbmXKbT+0ftaR4bUeHh6qW7du\ntseP1NRUPfPMM+rZs6dD70nMzbRp05SamqoxY8bkmnfHjh2aNGmS3N3dtXTpUn366aeaNm1ajn3d\n7c6dO6f9+/c7vK/n9fhrNWfOHMXExJgXWpo3b67Dhw/r448/NvNUrlxZ3t7eSkxMzPLVGFeuXNGO\nHTvM4NYqMjJSlSpV0oMPPuhwvfMir8eYgvSHjshLG8Hfi4APORo4cKCKFy+uyMhIzZkzx+agGBcX\np9dff11paWmqX7++TSeXF9ar/Vu2bLG5spiYmKghQ4aYz/IVdNaovXv3Ki4uTiNHjlThwoUd/pyr\nq6vatGmjPXv2KCIiwmaY5O3uv/9+tW3bVhcvXtSoUaNshq7GxcVp3LhxkmyfYbH+f9KkSTZXmhMS\nEjRz5ky5uLjYvA+nIDIyMswDQHbPrdwJb7/9ts6fP2/+vXXrVi1btkylS5fWk08+aZc/IiJCPj4+\nds9NZObp6amNGzfqk08+sRnie/nyZW3ZskVSzs9X5MTaBq0TKVj98ccfNkPXUlJSsl3Hs88+K29v\nb0VEROjYsWOSpOeff17FihXT119/bRe0f/PNN/r2229VsmTJLLfJ7TIyMsw7IHlpv7dLSUkxA4yC\nrCcn6enpGjt2rJKTk820r7/+Wps3b9aDDz5ovnewadOm8vX1VXR0tMLCwmxOMg4fPqywsDAdPXrU\n5lmjJUuWqGTJkg7PANisWTM9+OCDio6O1oIFC2yWzZ49W3FxcXr44YftLjZZ9evXT8WLF9eCBQvM\nNm2945P5WTzpVh9mvSBhbSseHh6KiYnRpk2bNG/ePDNvRkaGeQHJ2m7vRDt0VEREhLy8vDRw4ECH\n8ue3/8zSIeWxAAAgAElEQVQv6zb+/fffbYaxpqena86cOeY2uhPb4vjx45oxY4Z5fLt586YmTJig\nc+fOqWvXruadDuvvExkZafP5ffv2mSMOHC1Tdvuh9RlFR7dx3759Jd16hth6cVS6NXTv3Xff1a+/\n/qrr169ne8xyVHp6ulavXq3u3bvnOrrg3LlzGjlypG7evKkxY8bYPOqRF8uWLVN6erqef/55h/Ln\n5/h7+PBhzZ07V8WKFTPfhzlhwgQVLlxYH3/8sc1dZ+u2DgkJsQmgr127ppEjR5qzuFrvokVFRen3\n338v8LDWnPTu3VuFChXSnDlzbIJ+wzD00Ucfaffu3fL29jYnaSlof5iTvLQR/P0Y0okcVa5cWTNm\nzNCQIUM0Y8YMrVy5Ur6+vjpz5owOHz6slJQUWSwWzZo1K9+ztT311FNasGCBNm/erI4dO6pWrVq6\nePGi9u7dq4yMDD344IM6fvy4+bBxfhmGoYCAgHxNFd2uXTt98803SkxM1Msvv5xj3nfeeUd//vmn\nNmzYoEcffVSNGjVScnKydu3apdTUVD399NPq2bOnmb9Lly766aeftGLFCnXq1EkBAQFKT0/Xnj17\ndP36dQ0YMMC8KpdXS5cuNa9eX7t2TYcPH1ZiYqJKly6tF198MV/rzI2Li4vOnz+vjh07qkmTJrp4\n8aKioqLk4eGh9957z+7ZHenWbzNmzJgchySVKlVKr7/+uqZNm6agoCA1aNBApUuX1p49e3Tx4kXV\nqlUry2dGHdGzZ0+tWLFCS5Ys0a5du1S9enUlJSVp3759cnd3V5UqVRQfH6+kpKRsn910d3fXwIED\nNW7cOH3wwQeaM2eOKlSooKlTp2rYsGEaOXKkFixYoAceeEB//vmnYmJiVKxYMb3//vtZDo+zTtoi\n3XoIPiYmRnFxcSpZsqTD9cw8WYR068Tk0KFDOnPmjLy9vfN9kSY3JUqUUHR0tNq3b68GDRro1KlT\nOnTokO677z6Fhoaaz3W4uLho+vTp6tu3r+bOnatvv/3WnFUwKipKGRkZCgwMVLt27RQbG6sZM2bo\nl19+UcWKFW1eVm5lfYnxsWPHNHnyZI0ZM0aurq6aPn26goKCNHXqVK1YsULVq1dXbGysjh07Jk9P\nT3O69qyULl1affr00Zw5czR79myNGzdOQUFBmjBhgt544w0tXrxY5cqVM9dXqVIlubi46PLly0pN\nTZWHh4dCQkL04osv6oMPPtC3336r6tWr68iRIzpx4oRKlSplDvG8E+3QUYZh6I033nB49sCC9J/5\nUb58eXN4XZcuXRQQECA3NzcdOHDArP+xY8eynPAorypUqKCPP/5YmzZtUo0aNXTw4EGdOnVKvr6+\nNpMk9enTR9u3b9f777+vH374QZUrV1Z8fLx+/fVX3XffffLy8lJSUpLOnj1r88yWI/vh+vXr9cMP\nP5h3Ch19rq9Dhw7q27evPv/8cz3zzDPy8/OTp6enDh06pNOnT6ts2bKaPn16gbeRdKsPtk5elpNx\n48bp3LlzatOmTb6DnREjRigyMlLu7u5atmyZli1blmU+67YdM2aMypYtm6fj782bNxUSEqK0tDQN\nHz7cDIqrVq2q4OBgTZ8+XaNGjdLy5cvl7u6uwMBARUdH6/vvv9fjjz+uxo0bq2jRooqKitKFCxdU\nq1YtTZgwQdKtu4bWVxDt37/f5ve3so5ImTNnTpbDnB3h5+en0aNHa9KkSQoKClL9+vXNF6//+eef\nKlu2rGbMmGHOQlvQ/jA3jrYR/P0I+JCrVq1aKSIiQvPnz9eOHTsUGRmpwoULy9fXV48//rh69OhR\noCu+FSpU0JdffqmwsDDt27dPmzZtUpkyZfTII4+of//+SkpK0qBBg7R582Z169Yt39/j6uqa72EG\nzZs3V7FixZSSkpLrCU+5cuX01VdfaeHChfr+++/1448/qmjRomrYsKFeeOEFderUye4zkydPVkBA\ngL766ivt2rVL6enpslgs6t27d4FeyRAdHW0OP3Fzc1O5cuXUpUsXDR48OM+zhjnK1dVVy5Yt09Sp\nU7V9+3a5u7urXbt2Cg4OtntvoVWHDh2ynEDgdi+//LLuv/9+LVq0SH/88YeSk5Pl7e2t3r1768UX\nX8z3TKe+vr5avHixZs2apcOHD+vPP/+Up6enunTpoldeeUXbt2/XlClTtHnz5hyHFz399NOaN2+e\nIiMjtWfPHjVs2FAdOnTQ8uXLzTuTx44dU/ny5fXcc8+pX79+2Q71yTxpi7u7u8qXL6+nnnpKr7zy\nisPTXN8+WUThwoVVsWJF9erVS6+88kq+Z87LTdmyZTVr1iyFhobqxx9/VIkSJfT000/rtddesxs+\nWKNGDUVEROiTTz7R5s2btX37dpUsWVKNGjVS79691aFDB0m3Zplbv369pFt3v3N6Vtf6rj/r/l6r\nVi2tWLFCH3/8sbZt26bIyEiVLl1azzzzjIYMGZLr82gvvfSSFi9erGXLlqlv377q0aOHihUrps8/\n/1y//fab0tLS5O3trf79++vll1/W6NGjFRkZqR9//FHt2rVTo0aNtHTpUs2dO1cHDx7U1q1bVb58\neT3zzDMKDg42TzTvVDt0hJ+fn5555hmH8xek/8yvyZMnq3r16lqzZo1+/vlnubu7q3r16ho4cKCe\ne+45tWjRQvv27dP58+cL9Cxqy5Yt1bp1a4WHh2vz5s2qUKGCBg4cqP79+9sExK1bt9a8efM0d+5c\nHTt2TDExMapYsaJ69OihV199VZ9++qkWLVpkDpW1cmQ//P3337VmzRoVL15cXbt2zXHin9uNGTNG\nAQEBWrJkiQ4dOqTDhw+rcuXKCgwM1Msvv3zHJucaPHhwlhfsMvv666/N/eudd97J93dl3l457evW\nbTt06FCVLVs2T8ff2bNn68iRI6pdu7bN7yXd2udXr16tmJgYzZ49W6+//rpcXV0VFhamVq1a6T//\n+Y85FLdatWp65ZVX1Lt3b/Ni1k8//WQ+NrF169Yc67p9+3Y98cQT+Qr4JCkwMFAPPfSQ5s+fr+jo\naP3666+qVKmSgoKC9NJLL9n9/gXtD3PiSBvB3eFi3ImHAQBAko+Pj80EBbi3xMfHq23btqpatWqW\ns4wWxM6dO9WnTx9NmTIl10DF+tzd7UPvgMxWrFih0aNHq1u3bpo0adLdLg4y8fHx0dNPP21OLJSd\nWbNm6aOPPtKmTZsKPGz1TnK0D8pLvwYUBM/wAQAAAICTIuADAAAAACfFM3wAgP/zatSoodDQUIcm\nMBowYIDNDKEA/llCQ0Mdel1I+/btVbVq1f/J+0QLwtE+KC/9GlAQPMMHAAAAAE7qH32H78aNGzp0\n6JC8vLxynM4dAAAAAJxRenq6kpKS5Ofnl+UM3P/ogO/QoUM2L9AEAAAAgHvRkiVLsnzFxz864LO+\ni2rJkiWqWLHiXS4NAAAAAPy9EhIS1KtXr2zf0/uPDviswzgrVqz4f+r9KwAAAADwd8ruETdeywAA\nAAAAToqADwAAAACcFAEfAAAAADgpAj4AAAAAcFIEfAAAAADgpAj4AAAAAMBJEfABAAAAgJMi4AMA\nAAAAJ0XABwAAAABOioAPAAAAAJwUAR8AAAAAOCkCPgAAAABwUgR8AAAAAOCk3O52Ae5VLi4ud7sI\nfzvDMPL92YoVK97BkvwzJCQk3O0iAAAA4B+OgA9wQnXr1r3bRfjb7d+//24XAQAA4P8cAj4A97yO\nHTve7SL87datW3e3iwAAAP4GPMMHAAAAAE6KgA8AAAAAnBQBHwAAAAA4KQI+AAAAAHBSBHwAAAAA\n4KQI+AAAAADASRHwAQAAAICTIuADAAAAACdFwAcAAAAAToqADwAAAACcFAEfAAAAADgpAj4AAAAA\ncFIEfAAAAADgpPIc8I0fP15jx47NMc/Bgwf1wgsvqG7duurQoYMiIiJslkdERKhFixZq1qyZFi5c\naLNsw4YNCgoKymuxAAAAAAC3cTjgMwxDH374ob766qsc850/f179+/dX7dq1tWLFCgUGBmrs2LHa\nvn27JOnChQuaMGGC3n33XYWHh2vmzJk6duyYJCk9PV1hYWEaNmxYAaoEAAAAAJAkN0cyxcXFacyY\nMTp69KgqV66cY96vv/5aJUqU0NixY+Xq6qoaNWro8OHDWrBggVq0aKG4uDiVKFFCbdq0kSTVrFlT\nR48eVc2aNbVixQpVr15d/v7+Ba8ZAAAAANzjHLrDt3fvXlWqVEmrVq1SlSpVcswbFRWlxo0by9X1\nv6sOCAjQ3r17ZRiGKlWqpEuXLik2NlZJSUk6ceKEvL29lZKSojlz5mjo0KEFqxEAAAAAQJKDd/i6\ndu2qrl27OrTChIQE1apVyyatfPnySk5O1oULF+Tl5aXg4GB16dJFktSjRw/5+/tr/vz5CggIUM2a\nNfNYBQAAAABAVhwK+PLixo0b8vDwsEmz/p2amipJGjBggAIDA5WRkaGSJUvq8uXLWrhwoZYtW6at\nW7dqypQpcnNz09ixY9WsWbM7XUQAAAAAuCfc8YCvSJEiZmBnZf27aNGiZlrx4sXN/3/yySfq1KmT\nvLy81K1bN82bN0+GYei1115TZGSkChcufKeLCQAAAABO744HfBUrVlRSUpJN2pkzZ1SsWDGVLFnS\nLn9iYqKWL1+uVatWKTY2Vq6uruakLYZh6Pjx4/L19b3TxQQAAAAAp3fHX7zesGFDRUVFyTAMM23n\nzp1q0KCBzUQuVuHh4Xr++efl6ekpFxcXZWRkmMvS0tLudPEAAAAA4J5R4IAvNTVVSUlJ5rDNbt26\n6fz583rrrbcUGxurRYsWafXq1erfv7/dZ48fP66NGzeay6pVq6b09HStWbNG3333nVxcXFStWrWC\nFhEAAAAA7kkFDviio6PVokULRUdHS5I8PT01f/58HT58WE899ZQWL16sqVOnZjn5SlhYmIKCgsyh\nnkWLFtXEiRP13nvvKSwsTKGhoSpSpEhBiwgAAAAA96Q8P8O3aNEim7+bNGmiI0eO2KTVq1dPy5cv\nz3VdM2fOtEvr2LGjOnbsmNdiAQD+Jn369LnbRfjbffHFF3e7CAAA5Msdf4YPAAAAAPB/AwEfAAAA\nADgpAj4AAAAAcFIEfAAAAADgpAj4AAAAAMBJEfABAAAAgJMi4AMAAAAAJ0XABwAAAABOioAPAAAA\nAJwUAR8AAAAAOCkCPgAAAABwUgR8AAAAAOCkCPgAAAAAwEkR8AEAAACAkyLgAwAAAAAnRcAHAAAA\nAE7K7W4XAAAAZzZy5Mi7XYS/3fvvv3+3iwAA+P+4wwcAAAAAToqADwAAAACcFAEfAAAAADgpAj4A\nAAAAcFIEfAAAAADgpAj4AAAAAMBJEfABAAAAgJMi4AMAAAAAJ0XABwAAAABOioAPAAAAAJwUAR8A\nAAAAOCm3u10AAACAzKZNm3a3i/C3GjFixN0uAgAnRsAHAADwD7Zo0aK7XYS/VWBg4N0uAvCPwpBO\nAAAAAHBSBHwAAAAA4KQI+AAAAADASRHwAQAAAICTIuADAAAAACdFwAcAAAAAToqADwAAAACcFAEf\nAAAAADgpAj4AAAAAcFIEfAAAAADgpAj4AAAAAMBJEfABAAAAgJNyu9sFAAAAAP4u69evv9tF+Nt1\n6NDhbhcBdxF3+AAAAADASRHwAQAAAICTIuADAAAAACfl0DN86enpmjFjhlauXKlr166pZcuWGj9+\nvDw9PbPM//PPP+uDDz7QsWPH5Onpqe7du6t///5ycXGRJEVERGjatGlKT0/Xq6++qqCgIPOzGzZs\n0JIlS7Rw4cICVw4AAABA/h04cOBuF+Fv5+/vf7eLcEc5dIdv1qxZWrlypaZOnarFixcrISFBgwcP\nzjLviRMnNGDAAD3yyCNatWqVRowYofDwcH355ZeSpAsXLmjChAl69913FR4erpkzZ+rYsWOSbgWW\nYWFhGjZs2B2qHgAAAADcu3IN+FJTU/XFF19o2LBhat68uWrXrq3p06dr79692rt3r13+bdu2qUiR\nIho0aJDuv/9+derUSa1bt9a2bdskSXFxcSpRooTatGmjBg0aqGbNmjp69KgkacWKFapevbrTRdUA\nAAAAcDfkGvDFxMTo2rVrCggIMNOqVKkib29vRUVF2eUvW7asLl68qNWrVysjI0O///67oqKi5Ofn\nJ0mqVKmSLl26pNjYWCUlJenEiRPy9vZWSkqK5syZo6FDh97B6gEAAADAvSvXZ/gSEhIkSRUqVLBJ\nL1++vLkssw4dOqhbt24aMWKE3nzzTaWnp+uxxx7Ta6+9Jkny8vJScHCwunTpIknq0aOH/P39NX/+\nfAUEBKhmzZoFrhQAAAAAwIE7fMnJyXJ1dZW7u7tNuoeHh1JSUuzyX758WadOnVL//v21fPlyTZ06\nVT/99JM++ugjM8+AAQO0e/du7dy5U//+9791+fJlLVy4UIMGDdLWrVvVqVMnde7cWT///PMdqCIA\nAAAA3JtyvcNXpEgRZWRkKC0tTW5u/82empqqokWL2uWfNm2aChUqpBEjRkiSatWqpbS0NE2YMEGB\ngYEqU6aMJKl48eLmZz755BN16tRJXl5e6tatm+bNmyfDMPTaa68pMjJShQsXLnBFAQAAAOBek+sd\nvkqVKkmSkpKSbNLPnDljN8xTkvbv328+r2dVt25d3bx5U6dPn7bLn5iYqOXLl2vAgAGKjY2Vq6ur\n/P39VbduXRmGoePHj+epQgAAAACAW3IN+Hx9fVW8eHHt2rXLTIuPj9epU6fUuHFju/wVK1bUkSNH\nbNKOHj0qV1dXVa1a1S5/eHi4nn/+eXl6esrFxUUZGRnmsrS0tDxVBgAAAADwX7kO6fTw8FDPnj0V\nGhqqMmXKqFy5cnr77bcVEBCgevXqKTU1VZcuXdJ9990nDw8P9enTR6+++qpmz56tLl266NixY5oy\nZYp69uypEiVK2Kz7+PHj2rhxo9atWydJqlatmtLT07VmzRqlp6fLxcVF1apV+59UHAAAAACcXa4B\nnyQNHTpUaWlpGjlypNLS0tSyZUuNHz9ekhQdHa0+ffroiy++UJMmTdS6dWt99NFHmj17tj755BN5\nenqqe/fuevXVV+3WGxYWpqCgIJUsWVKSVLRoUU2cOFETJ06Um5ubQkNDVaRIkTtYXQAAAAC4dzgU\n8Lm5uSkkJEQhISF2y5o0aWI3hLNdu3Zq165druudOXOmXVrHjh3VsWNHR4oFAAAAAMhBrs/wAQAA\nAAD+mQj4AAAAAMBJEfABAAAAgJMi4AMAAAAAJ0XABwAAAABOioAPAAAAAJwUAR8AAAAAOCkCPgAA\nAABwUgR8AAAAAOCkCPgAAAAAwEkR8AEAAACAkyLgAwAAAAAnRcAHAAAAAE6KgA8AAAAAnBQBHwAA\nAAA4KQI+AAAAAHBSBHwAAAAA4KQI+AAAAADASRHwAQAAAICTIuADAAAAACdFwAcAAAAAToqADwAA\nAACcFAEfAAAAADgpAj4AAAAAcFIEfAAAAADgpAj4AAAAAMBJEfABAAAAgJMi4AMAAAAAJ0XABwAA\nAABOioAPAAAAAJwUAR8AAAAAOCkCPgAAAABwUgR8AAAAAOCkCPgAAAAAwEkR8AEAAACAkyLgAwAA\nAAAnRcAHAAAAAE6KgA8AAAAAnBQBHwAAAAA4KQI+AAAAAHBSBHwAAAAA4KQI+AAAAADASRHwAQAA\nAICTIuADAAAAACdFwAcAAAAATsqhgC89PV0ffPCBWrRoofr162vIkCE6e/ZstvkTEhI0ZMgQ1a9f\nX82aNdOECROUnJxsLo+IiFCLFi3UrFkzLVy40OazGzZsUFBQUL4qAwAAAAD4L4cCvlmzZmnlypWa\nOnWqFi9erISEBA0ePDjLvKmpqXrxxRd18eJFLV26VGFhYdqyZYvef/99SdKFCxc0YcIEvfvuuwoP\nD9fMmTN17NgxSbcCy7CwMA0bNuwOVQ8AAAAA7l1uuWVITU3VF198oXHjxql58+aSpOnTp6tt27ba\nu3evGjRoYJN/1apVSkpK0rJly3TfffdJkgYPHqylS5dKkuLi4lSiRAm1adNGklSzZk0dPXpUNWvW\n1IoVK1S9enX5+/vf0UoCAAAAwL0o1zt8MTExunbtmgICAsy0KlWqyNvbW1FRUXb5t2/frocfftgM\n9iTp2Wef1fLlyyVJlSpV0qVLlxQbG6ukpCSdOHFC3t7eSklJ0Zw5czR06NA7US8AAAAAuOfleocv\nISFBklShQgWb9PLly5vLMvvzzz/VtGlTzZgxQ999951cXFzUoUMHDR06VIULF5aXl5eCg4PVpUsX\nSVKPHj3k7++v+fPnKyAgQDVr1rwT9QIAAACAe16uAV9ycrJcXV3l7u5uk+7h4aGUlBS7/FevXtXy\n5cvVqlUrffjhh0pMTNS7776rc+fOKTQ0VJI0YMAABQYGKiMjQyVLltTly5e1cOFCLVu2TFu3btWU\nKVPk5uamsWPHqlmzZneoqgAAAABwb8k14CtSpIgyMjKUlpYmN7f/Zk9NTVXRokXtV+jmpvvuu0+h\noaEqVKiQ6tSpo7S0NL3++usaPXq0ypQpI0kqXry4+ZlPPvlEnTp1kpeXl7p166Z58+bJMAy99tpr\nioyMVOHChe9EXQEAAADgnpJrwFepUiVJUlJSkvl/STpz5ozdME/p1tDPwoULq1ChQmaadZjmqVOn\nzIDPKjExUcuXL9eqVasUGxsrV1dXc9IWwzB0/Phx+fr65qNqAAAAAHBvy3XSFl9fXxUvXly7du0y\n0+Lj43Xq1Ck1btzYLn+jRo3022+/6ebNm2ba77//rkKFCsnb29suf3h4uJ5//nl5enrKxcVFGRkZ\n5rK0tLQ8VwgAAAAAcEuud/g8PDzUs2dPhYaGqkyZMipXrpzefvttBQQEqF69ekpNTdWlS5d03333\nycPDQy+88IIWLVqkUaNGKTg4WImJiXr//ffVtWtXu7t7x48f18aNG7Vu3TpJUrVq1ZSenq41a9Yo\nPT1dLi4uqlat2v+k4gAAAADg7HIN+CRp6NChSktL08iRI5WWlqaWLVtq/PjxkqTo6Gj16dNHX3zx\nhZo0aSJPT08tWbJEU6ZM0TPPPKNixYrpySef1PDhw+3WGxYWpqCgIJUsWVKSVLRoUU2cOFETJ06U\nm5ubQkNDVaRIkTtYXQAAAAC4dzgU8Lm5uSkkJEQhISF2y5o0aaIjR47YpNWsWVOffvppruudOXOm\nXVrHjh3VsWNHR4oFAAAAAMhBrs/wAQAAAAD+mQj4AAAAAMBJEfABAAAAgJMi4AMAAAAAJ0XABwAA\nAABOioAPAAAAAJwUAR8AAAAAOCkCPgAAAABwUgR8AAAAAOCkCPgAAAAAwEkR8AEAAACAkyLgAwAA\nAAAnRcAHAAAAAE6KgA8AAAAAnBQBHwAAAAA4KQI+AAAAAHBSBHwAAAAA4KQI+AAAAADASRHwAQAA\nAICTIuADAAAAACdFwAcAAAAAToqADwAAAACcFAEfAAAAADgpAj4AAAAAcFIEfAAAAADgpAj4AAAA\nAMBJEfABAAAAgJMi4AMAAAAAJ0XABwAAAABOioAPAAAAAJwUAR8AAAAAOCkCPgAAAABwUgR8AAAA\nAOCkCPgAAAAAwEkR8AEAAACAkyLgAwAAAAAnRcAHAAAAAE6KgA8AAAAAnBQBHwAAAAA4KQI+AAAA\nAHBSBHwAAAAA4KQI+AAAAADASRHwAQAAAICTIuADAAAAACdFwAcAAAAAToqADwAAAACclEMBX3p6\nuj744AO1aNFC9evX15AhQ3T27FmHvuDVV19VYGCgTVpERIRatGihZs2aaeHChTbLNmzYoKCgIIfW\nDQAAAADInkMB36xZs7Ry5UpNnTpVixcvVkJCggYPHpzr55YtW6YtW7bYpF24cEETJkzQu+++q/Dw\ncM2cOVPHjh2TdCuwDAsL07Bhw/JeEwAAAACAjVwDvtTUVH3xxRcaNmyYmjdvrtq1a2v69Onau3ev\n9u7dm+3nTpw4obCwMNWvX98mPS4uTiVKlFCbNm3UoEED1axZU0ePHpUkrVixQtWrV5e/v38BqwUA\nAAAAyDXgi4mJ0bVr1xQQEGCmValSRd7e3oqKisryM+np6Ro1apT69++vGjVq2CyrVKmSLl26pNjY\nWCUlJenEiRPy9vZWSkqK5syZo6FDhxawSgAAAAAAyYGALyEhQZJUoUIFm/Ty5cuby243d+5cSVK/\nfv3slnl5eSk4OFhdunRR69at1blzZ/n7+2vRokUKCAhQzZo181wJAAAAAIA9t9wyJCcny9XVVe7u\n7jbpHh4eSklJsct/6NAhffbZZ1q+fLlcXbOOJwcMGKDAwEBlZGSoZMmSunz5shYuXKhly5Zp69at\nmjJlitzc3DR27Fg1a9Ysn1UDAAAAgHtbrgFfkSJFlJGRobS0NLm5/Td7amqqihYtapM3JSVFb775\npoYOHaoHHnggx/UWL17c/P8nn3yiTp06ycvLS926ddO8efNkGIZee+01RUZGqnDhwnmtFwAAAADc\n83IN+CpVqiRJSkpKMv8vSWfOnLEb5rl//37FxsZq2rRpmjZtmqRbgWFGRobq16+vNWvWqHLlyjaf\nSUxM1PLly7Vq1SrFxsbK1dXVnLTFMAwdP35cvr6+BaslAAAAANyDcg34fH19Vbx4ce3atUtdu3aV\nJMXHx+vUqVNq3LixTV5/f3+tX7/eJm369On666+/NG3aNJUvX95u/eHh4Xr++efl6emppKQkZWRk\nmMvS0tLyVSkAAAAAgAMBn4eHh3r27KnQ0FCVKVNG5cqV09tvv62AgADVq1dPqampunTpku677z4V\nKVLEbihniRIlskyXpOPHj2vjxo1at26dJKlatWpKT0/XmjVrlJ6eLhcXF1WrVu3O1BQAAAAA7jG5\nBnySNHToUKWlpWnkyJFKS0tTy5YtNX78eElSdHS0+vTpoy+++EJNmjTJ05eHhYUpKChIJUuWlCQV\nLRumAgcAACAASURBVFpUEydO1MSJE+Xm5qbQ0FAVKVIkj1UCAAAAAEgOBnxubm4KCQlRSEiI3bIm\nTZroyJEj2X520qRJ2S6bOXOmXVrHjh3VsWNHR4oFAAAAAMhBru/hAwAAAAD8MxHwAQAAAICTIuAD\nAAAAACdFwAcAAAAAToqADwAA/D/27js+qjLt//hn0nvvhABJgEDoQlgkLCIIPqhrWQVFRBQRUMGC\nIspSXFAEXHikiA8ClkVBqYoKCCIIKgiGZkIgCemQQhLS6yS/P3AGIijurr+dmPm+/zHMnMRrzus+\nZ851l+sWEZFmSgmfiIiIiIhIM6WET0REREREpJlSwiciIiIiItJMKeETERERERFpppTwiYiIiIiI\nNFNK+ERERERERJopJXwiIiIiIiLNlBI+ERERERGRZkoJn4iIiIiISDOlhE9ERERERKSZUsInIiIi\nIiLSTCnhExERERERaaaU8ImIiIiIiDRTSvhERERERESaKSV8IiIiIiIizZQSPhERERERkWZKCZ+I\niIiIiEgzpYRPRERERESkmVLCJyIiIiIi0kwp4RMREREREWmmlPCJiIiIiIg0U0r4REREREREmikl\nfCIiIiIiIs2UEj4REREREZFmSgmfiIiIiIhIM6WET0REREREpJlSwiciIiIiItJMKeETERERERFp\nppTwiYiIiIiINFNK+ERERERERJopJXwiIiIiIiLNlBI+ERERERGRZkoJn4iIiIiISDOlhE9ERERE\nRKSZUsInIiIiIiLSTCnhExERERERaaaU8ImIiIiIiDRTSvhERERERESaKSV8IiIiIiIizZQSPhER\nERERkWZKCZ+IiIiIiEgz9ZsSPqPRyD/+8Q9iY2Pp3r07kyZN4vz58794/Oeff87tt99Ot27duOmm\nm1ixYgVGo9H8/pYtW4iNjaVPnz688847jX53586djB49+t/6MCIiIiIiInLJb0r4lixZwubNm5k3\nbx5r1qwhJyeHiRMnXvXYvXv38uyzz3LPPffwySefMHnyZN566y3efPNNAIqKipg1axazZ89m2bJl\nLF68mOTkZOBiYrlo0SKeeeaZ3+njiYiIiIiIWC+7ax1QU1PDe++9x9/+9jf69u0LwMKFCxk4cCBx\ncXH06NGj0fHr1q1j8ODBjBw5EoCwsDBSUlLYtGkTjz/+OJmZmbi5uTFgwAAAIiMjSUpKIjIykk2b\nNhEeHk6XLl1+788pIiIiIiJida6Z8CUmJlJeXk5MTIz5tdDQUFq0aMHhw4evSPgmTJiAi4tLo9ds\nbGwoKSkBIDg4mOLiYlJSUvDw8CA9PZ0WLVpQXV3N8uXLWbFixe/xuURERERERKzeNRO+nJwcAAID\nAxu9HhAQYH7vcj8fnSsrK2Pt2rX069cPAH9/fx5//HFuu+02AO677z66dOnCypUriYmJITIy8t/7\nJCIiIiIiItLINRO+yspKbGxssLe3b/S6g4MD1dXV1/zdxx57jOrqaiZPnmx+ffz48TzwwAPU19fj\n7u5OSUkJ77zzDuvWrWPv3r3MnTsXOzs7pk2bRp8+ff7NjyYiIiIiImLdrpnwOTk5UV9fT11dHXZ2\nlw6vqanB2dn5F3+vsLCQxx57jOTkZFavXk2LFi0ave/q6mr++a233uLmm2/G39+fu+++mxUrVtDQ\n0MBjjz3G7t27cXR0/Hc+m4iIiIiIiFW7ZsIXHBwMQH5+vvlngLy8vCumeZpkZWUxZswYysvLWbNm\nDVFRUb/493Nzc9mwYQNbt24lJSUFGxsb87TQhoYGUlNTf/X3RURERERE5OquuS1DVFQUrq6ufP/9\n9+bXsrKyyM7OplevXlccX1BQwKhRo6ivr2ft2rXXTNaWLVvGsGHD8PPzw2AwUF9fb36vrq7uX/ks\nIiIiIiIicplrjvA5ODgwYsQI5s+fj7e3N76+vrz00kvExMTQrVs3ampqKC4uxtPTEwcHB1566SWK\niop49913cXJyIj8/HwCDwYCfn1+jv52amsquXbvYsWMHAK1bt8ZoNPLZZ59hNBoxGAy0bt369//U\nIiIiIiIiVuCaCR/AU089RV1dHc899xx1dXX069ePGTNmAHDkyBFGjRrFe++9R9euXdm5cyf19fXc\nc889jf6Gra0tCQkJjV5btGgRo0ePxt3dHQBnZ2fmzJnDnDlzsLOzY/78+Tg5Of0en1NERERERMTq\n/KaEz87OjqlTpzJ16tQr3uvduzenTp0y//vkyZO/+X++ePHiK14bMmQIQ4YM+c1/Q0RERERERK7u\nmmv4RERERERE5I9JCZ+IiIiIiEgzpYRPRERERESkmVLCJyIiIiIi0kwp4RMREREREWmmlPCJiIiI\niIg0U0r4REREREREmiklfCIiIiIiIs2UEj4REREREZFmSgmfiIiIiIhIM6WET0REREREpJlSwici\nIiIiItJMKeETERERERFpppTwiYiIiIiINFNK+ERERERERJopJXwiIiIiIiLNlBI+ERERERGRZkoJ\nn4iIiIiISDOlhE9ERERERKSZUsInIiIiIiLSTCnhExERERERaaaU8ImIiIiIiDRTSvhERERERESa\nKSV8IiIiIiIizZQSPhERERERkWZKCZ+IiIiIiEgzpYRPRERERESkmVLCJyIiIiIi0kwp4RMRERER\nEWmmlPCJiIiIiIg0U0r4REREREREmiklfCIiIiIiIs2UEj4REREREZFmSgmfiIiIiIhIM6WET0RE\nREREpJlSwiciIiIiItJMKeETERERERFpppTwiYiIiIiINFNK+ERERERERJopJXwiIiIiIiLNlBI+\nERERERGRZkoJn4iIiIiISDOlhE9ERERERKSZUsInIiIiIiLSTCnhExERERERaaaU8ImIiIiIiDRT\nvynhMxqN/OMf/yA2Npbu3bszadIkzp8//4vHnzhxgnvvvZeuXbsyePBgtmzZ0uj9LVu2EBsbS58+\nfXjnnXcavbdz505Gjx79L38QERERERERaew3JXxLlixh8+bNzJs3jzVr1pCTk8PEiROvemxhYSGP\nPPII0dHRbNq0iQceeIBp06axf/9+AIqKipg1axazZ89m2bJlLF68mOTkZOBiYrlo0SKeeeaZ3+nj\niYiIiIiIWC+7ax1QU1PDe++9x9/+9jf69u0LwMKFCxk4cCBxcXH06NGj0fHr16/Hzc2NadOmYWNj\nQ0REBAkJCaxevZrY2FgyMzNxc3NjwIABAERGRpKUlERkZCSbNm0iPDycLl26/H/4qCIiIiIiItbl\nmiN8iYmJlJeXExMTY34tNDSUFi1acPjw4SuOP3z4ML169cLG5tKfjomJIS4ujoaGBoKDgykuLiYl\nJYX8/HzS09Np0aIF1dXVLF++nKeeeup3+mgiIiIiIiLW7ZojfDk5OQAEBgY2ej0gIMD83s+P79ix\n4xXHVlZWUlRUhL+/P48//ji33XYbAPfddx9dunRh5cqVxMTEEBkZ+ZuDNxqNjWL8I7Gzu+apb3ay\nsrL+7d81GAy/YyR/DP/J+aqvr/8dI/lj+E/OV21t7e8YyR/Df3K+qqurf8dI/hj+k/NVUVHxO0by\nx/CfnC+A0tLS3ymSP4b/9HxduHDhd4rkj+E/PV8FBQW/UyR/HP/JOcvLy/sdI/lj+E/b2H+bKRcy\n5UY/d82so7KyEhsbG+zt7Ru97uDgcNUv/aqqKhwcHK44Fi5ODwUYP348DzzwAPX19bi7u1NSUsI7\n77zDunXr2Lt3L3PnzsXOzo5p06bRp0+fX4wtPz8fgPvvv/9aH6PJCQ8Pt3QI/3UDBw78t3/Xw8Pj\nd4zkj+E/OV/WSOfrX6Pz9a/R+frX7Nq1y9Ih/KGsW7fO0iH8oSxdutTSIYg0Sfn5+bRq1eqK16+Z\n8Dk5OVFfX09dXV2jUamamhqcnZ2verwpsbv8WKDR8a6uruaf33rrLW6++Wb8/f25++67WbFiBQ0N\nDTz22GPs3r0bR0fHq8bWqVMn3n//ffz9/bG1tb3WRxEREREREWlWjEYj+fn5dOrU6arvXzPhCw4O\nBi5mjKaf4eLw7s+neQIEBQWZR94uP9bFxQV3d/crjs/NzWXDhg1s3bqVlJQUbGxszEVbGhoaSE1N\nJSoq6qqxOTk50bNnz2t9BBERERERkWbraiN7Jtcs2hIVFYWrqyvff/+9+bWsrCyys7Pp1avXFcdf\nd911HD58mIaGBvNrBw8epEePHo0KuZgsW7aMYcOG4efnh8FgaLT2qK6u7lrhiYiIiIiIyC+45gif\ng4MDI0aMYP78+Xh7e+Pr68tLL71ETEwM3bp1o6amhuLiYjw9PXFwcODuu+9m5cqVzJw5kwcffJBv\nv/2WTz/9lLfeeuuKv52amsquXbvYsWMHAK1bt8ZoNPLZZ59hNBoxGAy0bt36d//QIiIiIiIi1sDQ\ncPlQ3C+oq6vjtddeY/PmzdTV1dGvXz9mzJiBj48PBw8eZNSoUbz33nv07t0bgKNHjzJnzhxOnTpF\nSEgIkyZN4pZbbrni706aNIlOnTrx6KOPml/bsWMHc+bMwc7OjlmzZtG/f//f8eOKiIiIiIhYj9+U\n8ImIiIiIiMgfzzXX8ImIiIiIiMgfkxI+Efmv08QCERHrdHlxPhH571DCJ7+ooaFBD+a/kc7Tv8Zg\nMDT6t9qa/Depvf2yn58Xnadr0zn611ytYrv8a9Tm/j319fVWe+60hk/kd1RXV4ed3TWL31q18vJy\n0tLScHR0xN/fH09PT0uH1CQVFhYSHx9PbW0tgYGB+Pn54e7ujpOTkx6Y5P+rEydOEB4ejqurKwA1\nNTU4ODhYOKqmqaGhoVEHVkNDA7W1tTpfvyAnJ4f09HR69OiBvb09NTU15Ofn06JFC0uH9ofx8zYn\nV1dVVUVeXh5hYWGWDqVJUMInVyguLub7778nLy8Pf39/goKCCAwMxMfHB3t7e0uH12SYbrqFhYVs\n376dM2fO4OXlRXh4uPkB3dXVFS8vL6tPAuvr67GxsWH37t2sXLmSoqIivL298fb2xs/Pj5CQELy8\nvLjxxhvx9/e3dLgWV1payksvvcSnn36Ko6MjNTU1uLu7ExoaSkREBFFRUYwZM8bSYTZ5dXV1bNiw\ngbq6Ou69914qKio4ePAgFRUVDBo0yJzQyEUNDQ18+OGHrFy5knnz5tGlSxcmT55MWloaAwYMYMKE\nCTg5OVk6TIsz3fuLi4t57bXXyM3NZfHixWRmZrJ582YaGhp44IEHCAkJsXSoTYLp/n/mzBnmz59P\nYWEhq1at4ujRo0yfPh2DwcAjjzzC/fffb+lQm6zk5GTmz5/PhAkT6N69O9u3bycrK4sbbriByMhI\nS4fXZJjaWmZmJosXL8bT05O//e1vxMXFsWTJEmxtbZk0aRJdunSxdKj/ddb9FCpmpi+wjIwMXnjh\nBeLi4ggLC8PR0REnJyc8PT3x9PSkXbt2jbbRsGamc7ZkyRK2b9+Oh4cHpaWlFBYWYmNjQ2BgII6O\njrz22mt06tTJ0uFalKk3cuHChYSGhjJgwACKi4vJzMwkKSmJEydOkJeXR8+ePa064TN9WR08eJB9\n+/axZMkSevfuzdmzZzl16hSJiYkcOXKEc+fOKeH7FUajEVtbW9atW8fbb7/N//zP/1BVVcWcOXPY\ntWsXDg4OnDt3jnHjxqmnnEv3spSUFJYvX86IESNo06YNK1euZN++fQwbNoxNmzbRtWtXbrzxRkuH\na3H19fXY2tqyYsUK4uPjGT9+PKWlpcyZM4fExETq6upITU1l3rx5msHApfva2rVrKS0tZfbs2eTl\n5bF48WKio6Np0aIFmzZtomfPnrRv397S4TY558+f57nnnsPHx4egoCC++uornnrqKQICAvi///s/\n1q1bR0REhKXDbBJMY1grVqwgLS2NcePGkZqayuzZs82dp2vXriU8PBw3NzcLR/vfpYRPgEtfYDt2\n7KCkpIR169bh4eHBqVOnSE1NJTMzk9OnT+Pu7m7pUJsM07S6TZs2MWfOHG677Tbg4vSnU6dO8eOP\nP/Ltt9/i4+MDWPc0DIPBQHl5Oenp6axevZqAgADze7W1tZw7d47U1FRatWplwSgtz/RldeLECXr3\n7s1NN90EgIeHB1FRUdx+++2NjpNft2bNGkaMGMGYMWPYsmUL33//PXPnzuXChQts2LCBgQMH0rZt\nW0uHaXGm+/+hQ4cIDAxk3LhxnDhxgt27dzNq1CiefvppjEYj69evV8J3mZ07dzJ27FgGDx7MW2+9\nRV5eHmvXrsXT05MxY8Zw+vRpevXqZdX3frjU4Xfo0CFGjRpF+/btWbhwITY2NjzxxBN06NCB22+/\nnZMnTyrhu4wpUT506BBVVVWsWLGCs2fPsmbNGm666Sbmz5/P1KlT+ec//8msWbMsHW6TYGpre/bs\n4eWXX+bPf/4zixYtwmAwMHPmTOrr63n66adJTU2lc+fOVnVtKuGTRs6cOUNsbCxdu3YFoE2bNo3e\nr6ystERYTdb58+fx9fWlX79+5tccHBzo3LkznTt35r777jO/bi03lV9SWFhI7969uXDhQqOEz97e\nnrCwMM2zB2xtbQHo168fRUVFpKamXnENgtrStZjOY2FhIYMHDwZg7dq19OnThwEDBmBra8vChQu1\nzuonpvaUk5Nj7qD65JNPqKurY+jQoeZjNJ3zIlP7MhqN5ul0GzduZMCAAQQFBeHs7ExWVhbe3t6W\nDLPJMJ0vR0dHCgoKKCoqYsOGDQwfPtw8MlVQUGD1HX4/Z+rYO3XqFK1bt8bW1pb9+/eTk5PD1KlT\ncXZ2pmXLliQnJ1s40qbDxsbGXAXW19eX+vp6Nm7cyL333ku7du0wGAzk5+cTGBgIWNd3qVb+C3Bp\ntKpdu3aUl5dTU1Nz1eOcnZ3/m2E1eY6OjgwZMoQ1a9ZQWVmJ0Wi0dEhNjulLq6amhoaGBl555RW+\n//57MjMzf7GdWSvTF9VHH33ERx99xIIFC/jyyy85c+YMRUVF5nMo11ZaWkpUVBQffPAB69ev59ix\nY/z1r3/FwcGBpKQkampq9ID5E9P9/8Ybb6SiooJHH32ULVu2cOedd9K+fXu+//57Dhw4wPXXX2/h\nSJuO6upq/vSnP7Fw4UJeeOEF0tLSGD58OAaDgT179uDg4GBOBq3pofLXPPjgg3z66aeMHz+e+vp6\nRo0aRWlpKYsWLcLJyYmOHTtaOsQmxZQot2zZkpqaGtavX8/KlSvp0aMH/fr1o6ysjPj4eKtcj/Zr\namtrGTJkCDNnzmTKlClUV1czYsQIbG1t2bhxI25ubo06na2FRvgEuPSFlJuby+bNm8nNzeXPf/4z\nrVq1Ijg4GH9/fzw8PCwcZdNhWiO0efNm3n77bQBSU1Pp0qULrVu3JiAgAA8PD3x8fKw+STZNmdiz\nZw+nTp2iqqqK6dOnExwcjK+vL0FBQXh6enLjjTda/eJz04O3q6srN9xwAz/++CN79uzBxsYGf39/\nwsPDCQ0NZdq0aRqdugZ3d3cefvhh5s+fz86dO7nnnnvo2bMn77//PuvXr2fgwIGWDrHJ6dq1KwMH\nDmT79u2MGzeOkSNHsmHDBhYuXEj//v0ZMGCApUNsMhwdHXnssceYPXs2WVlZvPzyy7Ru3ZpFixbx\n8ccf89e//hWw7qn8P3fDDTeQnZ1Nfn4+s2fPxsnJiZkzZ3L06FEmTZqEo6OjpUNskv7yl7/w9ddf\nM336dHr37s0TTzxBUlISCxYsoKSkRNflzzg6OjJ8+HDS09PJzc1lzpw5ODk5MWHCBOLj4xk3bhxw\nacqstVCVTjErKyvj2WefNZeybWhowMHBATc3N7y8vAgKCmL69OmWDrNJSUlJYe/evaSkpBAXF0d2\ndra5oiLAnDlzGDJkiIWjbBpqamrIzMwkNzeXpKQkzpw5w9mzZyktLSUtLY3XXnuN2NhYq7sJX0te\nXh7JyckkJCRw4sQJcnNzWbdunaXD+kMwGo2cPHkSBwcH2rVrR0ZGBkuXLsXFxYXx48cTFBRk6RCb\nLNN1GB8fT0JCAjfffLPWcF9FbW0tdnZ2GAwG8vLy+OijjwgICGDYsGGWDq3JKi4uxsPDg4aGBk6f\nPk1FRQU9evSwdFhNXllZGY6Ojtjb2/Phhx/y6aef8vzzz1t9Ubifu7yTpaqqCicnJ/Ly8ti7dy8u\nLi7ccsstFo7QMpTwyVUVFBSQnp7OmTNnSE1NJTk5GRcXFxYtWmTp0Jq8nJwc4uPjOXjwIHfeeScd\nOnRQEvMrSktLOXfuHC1btrT60VCTc+fOkZaWRmhoKC1btrR0OH9YlZWVxMfHU1NTQ9euXXF1daW6\nuhqDwaAR0qtITU1l06ZNxMfHExERweOPP05+fr4K2/yCM2fO8OWXX3L06FHGjRtHq1atVJXzV6xZ\ns4bvv/+ew4cP89xzz9GhQwfs7e1VYfIaTJ2lBw4cwM3NjcGDB2M0Gq2uyuRvYUr2du/ezbfffkt5\neTkPPfQQXl5eVjmN83Ka0ilmRqOR/Px8qqurcXJyonv37o163dQ3cKVz586xdetWXFxc6NGjB5GR\nkQQFBREUFNRoypi1J3u1tbV88sknrF69Gjc3N7p160ZwcDCRkZH4+/tTUlJCu3btLB2mRZk6BQ4c\nOMCKFStITk6msrIST09P/Pz86Nq1K7a2tvTr148+ffpYOtwm79SpU7z88suUlZWRkJDA6tWrSUlJ\nwcnJibvvvtvS4TUZpnZ3/PhxlixZQnFxMRcuXMDNzY2zZ8/y4osvMnToUG3H85PLp6hPnTqVbt26\nsWfPHoYNG0ZcXBxJSUlMnjzZXPzG2pna14oVK9i8eTMDBgxg3759ODs7c+LECd544w1mzZpF//79\nLR1qk2JqZxcuXGDVqlW8//771NXVMWDAAKKjo/nggw+44447tH7vZwwGA+vWrWP16tW0adOGvXv3\nMnz4cDZu3Mjp06eZN2+e1SZ+SvjEfEP+6quvWLBgAenp6fj5+REQEEBgYCA9evSgrKyMgQMH6uZy\nmfz8fGbMmEF8fDxGo5Hi4mIA/Pz8iIyMxMHBgRUrVlg4Sssyta1jx46xePFievToQU1NDe+++y6+\nvr4UFBTg5uZGly5drL58ualD5a233qK6upqZM2eyePFigoODcXV15d1338VgMGj6zm9gNBp55ZVX\n8Pf355lnnuGRRx4hICCAzMxM5s6dS0BAgB4wf3L5HmkuLi689dZbzJ8/n6KiIjp27EivXr3Ytm0b\nQ4YMUZEbLj5QmopPPfnkk9xxxx3069ePoKAgbG1tef3112nTpg1jxoyx2nvZ5Uydne+99x6zZs1i\n0KBBfPzxx/j5+TFgwAB27tzJJ598Qu/evVUF9jKmrVL++c9/cujQIT755BPeffddCgsL8fb25siR\nI6SkpPDmm29qZsxlysrKWL16NWPHjuWee+6hV69e+Pr6ctddd/HII4+wbds2HnzwQUuHaRHWPewg\njbz++uu0b9+ezz//nIaGBsLCwqitreUf//gHq1ev1gjfT0yVOL/55huSkpJYtmwZzz//POHh4Sxe\nvJiAgAAOHDhAhw4dGh1vjUxtZu/evYSFhbFo0SKGDh3K4MGD+eabb3jooYfw8fHhoYceanS8NTI9\nGB07doynn36agQMHEhgYyNChQ5k9eza33347Dz74oBbo/wpT+0lKSuLUqVPMmzePkJAQDAYDQUFB\n3Hrrrdxyyy1s3LjRwpE2HaZ2d+TIEfN642+//dbcsTBhwgTKy8tJTU21WIxNxeXtq7i4mOHDh1NS\nUoLBYCA4OJjY2Fief/55Pv/8cyV7lzGtbR8wYAD5+flUVlbSunVrHB0dGTt2LN99952SvV+we/du\nbrvtNkJDQ0lOTiYiIgJfX1/mzJlDbm4uhw8ftnSITYLp2kxISKCyspJ77rmH48eP4+DggL+/P+3b\nt2fUqFF8/vnnFo7UcpTwCTY2NhiNRlJTU5kyZQrh4eG0atWKhx9+mMWLFxMbG8tzzz1HdHS0pUNt\nEkw3lh9++IGYmBi6d++OwWAgOjqa/v37M2PGDAYNGmSe0mkqrWzNkpKSiImJAS6OjPr4+HDhwgXG\njh1L27ZtzdsRWPNDksFgMC/KDw0NBcDT05Pq6mpcXFy477772Ldvn9VPD/41pmszJSWFkJAQ7Ozs\nOHLkCIGBgbi5ueHq6krnzp1JS0uzbKBNiKk9BQcHm/fzys3NJSoqCrhYMbaoqEj7ZF4mPz+f4OBg\namtrOXHiBH5+fuYq1g4ODub9aq25s+9yVVVVhISEcODAAfP+hKYpr/n5+Ur2rsL03FBXV4erqytw\n8Xu0Q4cONDQ0EB0dTVlZmdaM/kxRURH+/v4AZGVlERISYm5fTk5O5mvSGq9NPTkIAGlpafj4+ODp\n6cmFCxcIDAzk7NmzuLi4MGrUKDZv3oydnWYAw6UHpNLSUvPmnTU1NTg4OFBQUEC3bt0oKSnh5MmT\nwKW91ayR6UsrICCAjIwMANzc3CgtLaW+vh4fHx/OnDlDXV0dYN0jfHBxrWPHjh1ZsWKFeepObm4u\ncLHdFRUVqXT5rzBdmxERERiNRrZt28bRo0cJDw8HLk73OXDggPb7uor777+frVu3snbtWsrLy2nT\npg1lZWXMnDmToKAg8zm0ZqYOqbZt2+Lt7c2bb77JoUOHaN++PXCxE3Dbtm307t0b0P0MLp6DiIgI\nBgwYwBtvvMGKFSsICgqivr6e9evXs3z5cm6++WZLh9kk1dfX8z//8z9s2rSJiooKqqqq6NOnDwaD\ngS1btmA0Gs1tz9qZrs2+ffvi6urKjBkz+OyzzwgJCQHg6NGjfPzxx8TGxgLWeW3qCV6Ai/uWtGrV\nik2bNnHPPfdgY2NDfn4+cLEsvOmB3No1NDSYHyqHDh3KzJkz6dWrF6GhoXz66afmnqT09HRz5TFr\nHrUyGTlyJPfddx/r16+nQ4cOvPHGG5SUlJCWlkZeXp55+pi1j155e3tz22238fHHH1NcXExIg34+\n7QAAIABJREFUSAirVq0iODiYjz/+WInKb9DQ0EBUVBR/+ctfWL16NcnJyfTs2ZPNmzfzz3/+E4AX\nXnjBwlE2PYMGDSIuLo4lS5bg6OjI0KFDqa2tJSQkhOeff97S4TUJDQ0N1NbWEhAQwPjx43nhhRc4\ne/YsPj4+PPbYYyQmJhIREcH9998P6H4Gl77/Hn74YXJycjh48CAFBQV06tQJb29v7rjjDkaPHm3Z\nIJsg016/o0aNYvv27cTExGA0Gpk+fTr19fUcOnSIESNGqAPwZ9zc3HjyySd54403+PHHHwkKCuKe\ne+7hxIkT3HnnnQwfPhywzplX2pbByl1eJGPp0qXs2bOHxYsXs3btWr7++mtGjRrFxo0badeuHbNm\nzbJssE1MWVkZy5YtIyoqihtuuIEhQ4YQExPD+fPnycvL45NPPsHFxcXSYTYZx48fB6Bz5848+OCD\nnDhxAqPRyNChQ3n11VctHF3TUVtbS2FhIYGBgRQUFDBt2jR++OEHevfuzZgxY+jevbulQ2zyKioq\nMBqNbNq0iX379nHy5Elqa2sZMGAAo0aN0vR0oLq6mvr6epydnfn000/x9PSkX79+JCYmcuLECWpq\navDw8CAmJobAwECrLqj0c/v37yc2NpaysjK+/PJLTpw4wblz5+jatSv333+/eQqeNTPNRnBxceGp\np55i9uzZuLu7k5mZSVZWFgaDAXd3d12LvyIlJQVvb288PDz48MMPOX78OLm5udjY2HDLLbfw17/+\n1dIhNjnr1q3jnnvuISkpie+++4709HQcHR3p27cvffv2tcpEz0QJnzTaI668vBw7OzvS0tJ4+eWX\nSUhI4Prrr2fixIlWvxdTbm4uhYWF5mIsP3fw4EFef/11vLy8eOCBB1Q6/xfU1tZSUVHBF198ga+v\nL3369FGVsZ9JTEwEoH379tTU1JCRkYGPjw++vr4WjqxpqqurMz8c2dracuedd/L11183OqahoYGK\nigrs7e21Bx8X95D76quvaNeuHQsWLKBv375MnDgRBweHRtP39+3bR1BQkFXf/2tra9m5cyfV1dW4\nu7vzxBNP8NVXXxEcHHzFsRkZGVrvCHzxxRccOHCAoKAgFi5cyKJFi2jXrh2BgYG4uLiYnzmWLl3K\n2LFjNVL1k8zMTHJzc+nUqROTJ0+mQ4cOPPHEE+b7V319Pe7u7lRVVWFjY2P197La2loOHjxIfX09\nTk5OPPHEExw8ePCKzqna2lq+/vrrRttlWRslfFasT58+fPTRR7Rs2ZLNmzcTERFBVFSU+QZSV1dn\nXpwu8O6775KcnMzs2bPZu3cvmzdvplu3brRq1YrQ0FBatWqFvb29+UajzdYv2b9/P2+//Tbnzp1j\n4sSJ9OjRA4PBYLX74fyS9PR0Xn75ZS5cuMDx48f55JNPOHHiBB07dvzFjga5OFq1evVqPvvsM+zs\n7EhKSuLRRx8lJCSEyMhIWrZsiZ+fHykpKSxcuJBly5ZZOmSLy83N5cEHHyQtLQ0bGxs8PT2JiIgg\nLCyMiIgI2rRpg6OjIxMnTmTRokVWv43FZ599xuuvv46trS3Z2dnccMMN+Pr6EhwcTGhoKEFBQZw5\nc4ZXXnmFuLg4S4drcUeOHGHq1KlUVVWRm5tLmzZt8PLyIjAwkBYtWhAcHExWVhbvvPOOuYNLLn4H\nPPjgg9TW1lJSUkJMTAw33ngjISEhBAcH4+vri62tLWPHjuWxxx6z6gQGLtZPWLVqFa+//jp2dnbU\n19cTFRVFREQEHTt2pFOnTub9+NasWcPmzZutdraC1vBZqaqqKh566CFCQkIoKytj7ty5VFVVmQtp\ntGnThuuuu47o6Giqqqpo06aNpUO2uC5dutC1a1cAcnJySE9PJzs729zT5uLiQkhICG5ubtx+++2N\nNq23RqaEd/369axatYrBgwdz8OBBzp8/z7fffsvrr7/OnDlzzIuorV1RURGzZ8/Gzs6O4cOHk5qa\niru7OwkJCSxevJh33nlH1+EvcHR0ZMCAARiNRvbu3YuzszPff/89JSUlNDQ04OzsjIuLC6mpqerA\n+klgYCDbt2+nrKyMP/3pT4wcOZIzZ85w/Phxtm3bRlVVFY6OjkRFRWkaMRAbG0tQUBDr1q3DaDRi\nMBg4efKkObkzGAyUlJSYt02pq6uz6kJn3bt3Z8eOHWzZsoWNGzdy1113ceLECdLS0khKSqKkpAQv\nLy8mTZpk6VCblLCwMBYvXsyJEyeYPXs26enpzJ8/n+rqahwcHAgODsbb25usrCzatWtn6XAtzsHB\ngbFjx/LAAw8wffp04uPjiYmJ4fjx4xw4cICioiLq6upwdHTkrrvuAi6uj7TGa1MjfAJcnCteVFRE\nZmYmqamppKSkkJGRQUZGBt7e3uzZs8fSITYpRqOR/Px88vLyyMrKIisri3PnzlFQUMDp06eZMWMG\n119/vVWP8pl60YYOHcrIkSMZMWIEQ4YMYcqUKcTGxjJhwgScnZ159dVXcXd3t3S4FmNqI7t27WLB\nggXs2LGDhIQEJkyYwN69e8nMzOTFF1+kR48ePP3005YOt8nbsmULdXV1XHfddWRkZJCVlUVOTg5n\nz54lICCAW2+9VeuGfmJqe2fOnLmiCmdubi6pqamEhIRoiuJlTpw4gZ2dHR06dKCsrIzs7GzS09NJ\nS0sjMDCQfv364ePjY7WjCCamR8va2loaGhqumLJ57tw5ysvLCQsLs/ppib9kyZIlTJw4EbhYPC8+\nPp5jx46RkZHBwIEDueWWWywcYdOSm5tLfX29uVOvoqKCvLw8srOzMRgMtG/fHl9fX6u9NpXwWanD\nhw+TkJBAq1atCAgIIDAwEHd3d+zt7YGLN+nS0lLy8vKora2lc+fOFo7Y8iZNmkS7du1o3749LVu2\nJCAgwLyXkEllZSU5OTkEBwdrbyEurgm9/vrr+fLLL/H09CQmJoZNmzbRpk0b4uPjmTBhAtu3b7fq\n4jamamzLli3j2LFjrFixgjfffJMDBw7wzjvvADBv3jzS0tJYvny5ZYNtwhoaGhpV0f05ax9x+TUp\nKSns2rWL4OBgOnToQFhYmNZU/UxDQwP19fXY2tqSnp4OXNxuRuuPf115eTkbNmzgzJkz9OjRgw4d\nOhAREWEunmG6/8kll5+TlJQUWrZsqaT4N4qLiyM3N5egoCACAwPx8/NrdO6sNdkDTem0Wtu2beOj\njz4iICAALy8vAgIC8PX1JSwsjKCgIEJCQswXjJeXl6XDtbji4mLOnz9PQkICOTk5wMWpFx4eHrRu\n3ZqoqCjatm1LixYtCAwMVLL3k6KiItq2bcvZs2fNi6pN0xKLioqoqqrCxcXFqm/CpgQlPDycHTt2\nkJ2dTVJSknnj66qqKk6fPm3eukKuzmAwYDAY2LVrF8uWLaO2tpYePXoQGBhI27ZtCQ0NJSsri/79\n+yuZ4dJD5WeffcYbb7yB0WgkLy8PGxsb3N3dadGiBdXV1dx9993mUubWzGAwYGtry4oVK1i+fDmV\nlZX4+Pjg7e1NZGQk1113HUVFRYwZMwY3NzdLh9tkzJw5kwMHDhAYGMiHH34IXDyXpn0e16xZoxHk\ny5g6FRITE3nnnXc4dOgQDQ0NBAUFERQURLdu3XBzc6Njx47m7wi5aNasWXz22WcEBARQV1dHYGAg\nHh4eBAUFkZGRwaJFi6y6gq4SPis1ffp0HnroITIyMti1axfr16/HwcEBT09PXFxcKC4uJjAwEF9f\nXyZPnmz1c8U9PT354IMPgItTUd544w22bdtGdHQ02dnZ7Nu3z1yGulOnTub9vqxVfX09BoOB0NBQ\nYmJiePbZZ+nevTve3t5kZmaSkZHB6tWr6devn/l4a+3lNSW6N998M1999RUvvvgiR48eZeTIkRw+\nfJilS5dSUlLC4MGDLRxp05eXl8crr7xC27ZtCQsL45///CfBwcHk5OTg7OxMUFCQzuPPvP/++7Rv\n357HH3+c6dOnExYWRmhoKEuXLiUsLIxWrVpZOsQmIzc3l//7v//j4YcfpmfPnowZM4Y77riDffv2\nsWPHDgIDA3nyySctHWaTUVxczJdffsmrr75K586duffee5kxYwZHjx5l5cqVDBs2zLwxtlxkmnS3\ncuVKEhISGDVqFBs2bDBv1/PKK68A8OKLLyrh49KIXXJyMtu3b+e5557DwcGBmTNn8qc//Yl9+/ax\nZ88ebrnlFqtO9kAJn1ULDQ0lNDSUHTt2MHz4cJ566imcnJxIT0/n6NGjLFiwgNraWry9vS0dapNQ\nW1uLvb09W7dupaCggNWrV9OlSxfz+4sXL2b79u3mTXetearK5dPqnnjiCWxsbNiyZQu1tbU888wz\nJCcn06dPHyZPnnzF8dbKYDDw+OOPs2rVKkpLS3n//fdZtWoVffv2Zfr06dp0/VeY1qL98MMPACxb\ntoyUlBSOHTvGhx9+yIYNG3j99deZMGGChSNtOkz3ptOnTzNlyhQiIiLw9fWlb9++3HbbbRgMBrKy\nsrTekUvt69ixY/j4+DBx4kQSExPp1q0bjzzyCIMGDWLRokXm+5m1Mz2Enzx5End3d4YMGUJ2dra5\n+mtsbCwlJSW0adNG06x/xtQB+O233/L3v/+dQYMG8c0333DjjTdyww03MGPGDFq3bm0uQGLtTJ3F\ncXFxBAQEMGzYMOLj4+nZsycvvfQSmZmZTJ48mT/96U+ApnSKFcvMzGTDhg388MMP5mmIERERRERE\nUFpaSlpaGv7+/haOsmkw3SQOHz5Mx44d6dKlC0aj0VwBauzYsSQnJ5sfpKw1iTl48CBOTk74+Pjg\n5eWFu7s7Tz31FLfeeisJCQnk5+czffr0RluAWOMNODc3l927dxMdHU1ISAi+vr60atWKv//971RU\nVJirvwKaVn0Npl7xxMREoqOjsbOzIzMzk9DQUDIzM7njjjs4duwYRUVFFo60aSkrK8PDwwNPT08A\n3NzczOdoyJAhjBs3TtNfudS+0tLSzNMPi4qKCA4OJi8vz7w2bePGjXTr1s2qi3XBpYfq/Px8QkND\ngYtr+dzd3cnNzSUiIoLQ0FC+/fZbHnroIQtH27TY2NhQV1eH0Wg0j+B5eHhgb29PUFAQjz76KC+/\n/DIqv9FYdnY27du3By5WUff29iYxMZGoqCjat29vXnerhE+sjqnRX7hwgZCQEA4dOkS/fv0afVGF\nhoaycuVKZs2aZdlgmwhTT2RoaCiHDx+mtLQUd3d3c4JXX19PQkKCuXKWtd5YJk+eTGBgIK1bt8bd\n3Z3AwEDzPoX9+/c3V+S09kXo27Zt49VXXwUuti0PDw9atmxJdHQ0Xbt2pV27drRu3VpFIX4D0z3L\nw8ODsrIyysvLcXV1pbq6murqauzt7cnPzycwMNDCkTYtBoOBbt26MWPGDF599VUCAgJITk4G4Pz5\n81RUVFj9dQqXRkPbt2/Ptm3bOHLkCJ6enhQXF5uPycrKMm9Ob+0P46brsUePHixbtozZs2czevRo\nqqqqyMvLA+DkyZPmZFAaKy0tpUOHDnz66aeMHDkSLy8vzp8/D4C3tzdnz5616srWl7v82vz888/Z\ns2cPPj4+FBYWUlVVBUBqaqq5doA1X5tK+KyUKRExLTZfsGABBoOBTp064eDgwJ49e/jggw/MewrJ\nJWPHjmXYsGGMHj2aIUOGEB0djbu7O0uWLMHV1ZVevXoB1jnCV1RUhL29PfHx8WRlZeHr60tVVZX5\nwdHHx4egoCDzdOLRo0dbOmSLadu2Le3bt6euro4+ffrQtm1b0tLSiIuLY+vWrZSWluLs7IytrS1P\nP/20eaqwXMl0P7vrrrtYs2YNy5cv57777iMtLY2srCzs7e1JTExkxIgRFo60aXF1dWXkyJHm6dbh\n4eHMnTsXNzc39u3bZ54GJRf16dOHtWvXsnHjRl544QVyc3NZtWoVERER/Pjjj+Zr1Bo7+q6mRYsW\nPPPMM+YtUTw8PFi5ciXr168nKyuLOXPmWDrEJsnb25ubbrqJjRs3ctNNN9GiRQvWrFlDly5d2Lhx\no9bVXsXQoUP55ptvOH36NMOHD+f8+fPs2LGDPXv2kJKSwtSpUwHrfC4z0bYMQmJiIq+++ioHDhzA\n2dkZR0dHbG1t6d+/PxMmTKBly5aWDrHJiY+PZ9WqVZw+fZrz589TVlZGx44dmTt3LhEREZYOz2KM\nRiNJSUl88cUXJCcn061bN4YMGYK7uztHjhwhLi6O5ORkkpOT8fHxYe3atVY7EgoXE+R169axdu1a\nAgMDGTFiBP3798fOzo6srCzS09OJi4tjwIABXH/99ZYO9w8hNTWVvLw8evXqxbRp09i+fTtVVVXc\ncMMNLFiwQBUUr6KmpgYHBwcaGhqYO3cuX3zxBYMGDeKBBx7Qw+VVFBUV4e3tzc6dO1m+fDmFhYXc\ncsstPPHEExqR/xUVFRW8+uqr5OXlMWzYMGJjYzWC/AtqamrMWzIUFhby/PPPc+bMGVq1asUjjzyi\n4lNXUVZWRk1NDT4+Pnz88cfMmzePyspKpkyZwvDhw6062QMlfFbv8r2pTA/iVVVVuLm50a9fP63f\n+BVlZWUkJCSYq1H6+fmZ9zGUi4vOV69ezfHjx7ntttsYP358o/Wg1dXVODo6Wm3Cd3lRn+LiYtav\nX8/x48cJDg7mjjvuoH379tjY2FBbW4udnZ1VnqPfw44dOzAYDPTu3du8Vk0uOXnyJHl5ebi5uRES\nEkJwcLD2LPwFlZWVZGdnm0er4GJl2IaGBk0Xvorz58+za9cuGhoaCAkJITw8nNDQUN3LfoXp+7Cq\nqorq6mqcnJxwdHTk/PnzxMfHExUVpbZ2FTU1NZSWllJRUYGzszN+fn7ApWJ7ooRPgC+++IKTJ09S\nX1/PmDFjMBgMmh/+K/bv38/bb79Nfn4+zz77LNHR0dTU1BAcHGzVlTlNfl6wICEhgXXr1pGUlESf\nPn34y1/+QlhYmNX3tsGl7SsMBgPl5eV89913fPTRRxQWFnLjjTdy//33K0m5BtMDUllZGZ9++imb\nNm3C09OTDh060L17d7p06YKvry9wZdu0djNnzmTbtm14e3vj5uaGq6srvr6+hIaG4uHhwciRI61+\nxMrUZjIyMli5ciX79+/HYDDg6upKWFgYXbp0ITQ0lI4dO9K6dWtLh2txpvP1448/8uKLL5KXl0do\naCg1NTXmPR49PT3p3r07Y8aMsXS4TdLOnTvZuHEjdXV1eHt7ExgYSHh4OG3atMHR0ZF27dqpQ4ZL\nbS0xMZEFCxbwzTff4OPjQ0hICK1bt6Zdu3aEhoYSFRVFeHi4pcO1OCV8VqysrIy3336bDz/8EBsb\nGxoaGli7di1z5syhW7dujB8/3tIhNhmmG8v69etZtWoVgwcPZtWqVUyZMgUPDw8WL17M7NmziY2N\ntdoRK5OrJb0XLlwgLi7OvFZo+PDh9O/f36rP06/Zvn07W7ZsITU1lbvuuotx48ZZOqQmyzQatXTp\nUj744AM6deqEk5MTKSkpnD17ltraWurq6nj88ceZOHGipcNtMs6ePcutt97KpEmTaNWqFampqaSn\np3Pu3DkKCgqoq6vj448/tnSYFmdqX/PmzWPHjh3ceuut+Pv7k5GRwdmzZzl//jwpKSkMHz6c5557\nzuo7/Uzn67XXXuOHH35g8uTJGAwGMjMzyc7OJj8/n5SUFGJiYnQ9XkVZWRlDhw4lJCSEsLAwCgoK\nKCoqorKyEltbWxwdHVm3bp1GrbjU1qZOncrp06cZNWoUNjY2HD58mPj4eLKzs7lw4QLjxo3j6aef\ntvoOP3URWCFTo9+/fz9ffPEFK1eupKqqiqlTpxIaGkrXrl354IMPuOGGG7Sx509Micnbb7/NqFGj\nGDFiBDt27CA0NJTY2Fi2bt3K2rVr6dq1q9WPjpoedvLy8nBxcaGiooKTJ09y/vx5vLy8+Pzzz/nq\nq6/Yv3+/edqFNaqsrKSsrAxHR0fOnj3LDz/8QHp6Og0NDZSUlJCcnExWVhbx8fGWDrVJM12bu3fv\nZvTo0YwcOZLa2lpKSkq4cOECBQUFnDp1ipiYGAtH2jRcvlFx69atzYWTLi/QVVxcbK4KaO1M7evI\nkSOMGTPGXJilurqakpISioqKyM7ONlcBtOYHSrj0+QsKCvjzn/9Mz549AbjuuuvMx1y4cMEisTVl\npuvyxx9/xGAwsHbtWnPbKykpITMzk+TkZAoKCpTs/cTU1pKSkhg5ciR33HEHAH/5y1/Mx5SUlGA0\nGgEVU1LCZ4UuT/g6d+5MVFQUc+fONZeUfuCBB/juu+/44osvlPD9xDTlLjs7m8GDB1NbW0teXh7h\n4eE4OjoyefJkJkyYYNU9u0VFRWzevJn8/HwqKyspKiri2LFjnD9/noiICDw8PDAYDNx888106dLF\nqpO99evXc+jQIbKzs4mPj8fJyYl27drh5OSE0WgkIiKCqVOnEhISYl4rJFdnuubCw8MJDg7GxcUF\nAE9PT3PBqT//+c9W/2VvYjoP4eHhtG7dml27djFo0KBGx3h6emoq8U9M7evOO+8kNzeXkpISPDw8\ncHR0xN/fH39/f9q1a2c+3trbmekh/L777mPx4sUkJCTQsWPHRsdoX9ErmdqNk5MTXbt2JSMjw1ww\nycPDg+joaKKjoy0ZYpNjams333wzKSkpVz3m8u9Pa782lfBZIdNFUltba/4yS05OpkePHgDmKnbW\nPlL1c0VFRbRt25azZ89SX1+Pk5OTuVe3qKiIqqoqXFxcrHZK5969e5k/fz52dnYEBAQQExPDjBkz\n6NChAwaDAR8fH1Vk+8msWbMwGo1ERkby5JNP0q5dO9zd3YmMjDQnLPKvefjhh5kyZQr29vZ07NgR\nd3d3c9Vhax91uZzp/uTk5ERlZSV/+9vf+Prrr+natSudOnUiMjLSqjuufklYWBgvv/wyVVVVxMbG\nEhISgqenJy4uLtjZ2emc/UxeXh5xcXFMnDiRbt260blzZzp06ECbNm3w8/PTNfkLOnbsaJ4SO2nS\nJMLDw9W2rqF3796MHj2ajIwMrrvuOtq0aUOrVq0ICQnRM8dllPBZIdON9t577+X555/n66+/Jjk5\nmVGjRgGwZcsW0tLSzFMxrJ2psEZoaCgxMTE8++yzdO/eHW9vbzIzM8nIyGD16tX069fPfLw13qDz\n8vKwt7cnPDycoKAgKisr2bFjB/Hx8bRs2RJ/f398fX3N/7VmY8aM4eTJk5w6dYr58+fj4eFBQEAA\nLVq0IDw83Pxg5O3tTUhIiFV2IPwWpuTl7NmzLF++nOTkZCZPnkxQUBCtWrUiMjKSVq1a0bZtW03p\n/InpnD399NNkZ2fTpUsXEhMT2bVrF8XFxdjZ2WE0GtmwYYPVz/AwnaszZ87wv//7v/j6+vLJJ5/w\n9ttvY29vT2BgIKGhofTs2ZPHH3/c0uFanOl8paSkMG3aNGJiYmjVqhWnT5/mvffeIz8/n9raWkJD\nQ9m1a5elw21STDOvVq5cyZEjRygqKuKrr77CxsYGPz8/cwGScePGNap2ba1M5+vw4cM89dRTODg4\nkJ+fz9atW82VrW1tbenWrRvTpk2zdLhNghI+K9XQ0EB0dDQDBw5kxowZFBUVsWzZMv73f/+XtLQ0\nHn300SumYViry3sin3jiCWxsbMzFR5555hmSk5Pp06cPkydPvuJ4azJq1Cj69+9PcnIyeXl5nD59\nmpycHE6fPk1NTQ12dnZ4eHhQV1fH5MmTrbpD4ZlnngGgqqqK/Px8Tp8+TXx8PAkJCezevZv333+f\n6upqPD09OXjwoIWjbbpMnSuff/458fHxLF26FC8vL+Li4jh+/Dhff/01aWlp9O3bVwnfT2xsbDAa\njRw/fpz33nuPsLAwampqKC4upqCggJycHBITEwkNDbV0qBZnal+7du3iwoULrFy5krCwMPLy8khN\nTSUxMZFDhw5x7tw54OoFq6yJKeGLi4ujRYsWLF68GLhYXKOyspILFy6QmZlJRUWFhSNtekzPDW++\n+Sbjx48nJiaGqqoqzp07R1paGunp6ezcuZOHHnrIwpE2DaZ6k19++SXt27fntddeM2+bkp2dTU5O\nDklJSQQEBAC6NkFVOq1KRUUF1dXVeHt7N3r90KFD7N+/n8LCQpydnenRowc33XST1V8cAAcPHsTJ\nyQkfHx+8vLxwd3enrq6O5ORkEhISyM/Pp0+fPkRFRWnqwGUSExPNowO5ublkZmaSmppKRkYGp0+f\nZsqUKURERFjt9NdrqaioID09nZycnEbFNKQxUy/vzJkzcXV1ZcqUKVc9zrTuSi7KycnhmWeeYcGC\nBbRo0eKK900bsVs7U/taunQp5eXlPP/885YOqUkz3c8//vhjvvvuO/7+97+rHf0LcnNzGTFiBNu2\nbWt03qqrqykvL6e8vNy8Ltnamdra0qVLMRqNPPnkk1c9TveyS2xnzZo1y9JByH/HRx99xJQpU4iL\ni+PAgQMkJydTXFxMeHg4N954I4MGDaJfv35ERkZa7SjVz91777388MMPJCYm8sMPP5g3KXZ3d6dz\n58707t0bPz8/q7+hGI1Gvv76a1JTU8nJyeG+++7jzjvvxMPDw7yhc3R0NNdffz033XSTuddNyd7F\nL/kPP/yQ1atX4+XlhZeXF3Z2dgQHB5vXiMovMxgMZGdnk5SURGxs7FWvRUdHRwtE1vSYpqeXlJRw\n8uRJtm7dSkREBHDxPNrY2GBjY6POvssYDAbOnTvHl19+iYeHByEhIaqS+AtMD+FpaWls2LCBhIQE\nXF1dqampoaGhQWsdr8FoNFJYWEh8fLx57butrS12dnY4OzurkNLPGAwGioqK2LhxI35+flfda0/t\n7RJN6bQiphEDV1dXIiIiyMzM5LPPPqO+vh4PDw/zegQvLy8GDRpk9T1JRUVF2NvbEx8fT1ZWFr6+\nvlRVVVFRUYGDgwM+Pj4EBQURGhr6/9h77+ioqvWP+zOTnkkPkE5CeiCAIEZBIqJSldhMm1D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sJoNIr3HS+mo6NDSku8AhaLhT179jBhwgTa29tpampCp9NRU1ODSqUiMTGRm266SZpjF1FVVYXF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5cf7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importances(feature_imp, 10)\n",
    "#plot_importances(feature_imp_above, 10)\n",
    "#plot_importances(feature_imp_below, 10)\n",
    "\n",
    "plot_importances(feature_imp, -10)\n",
    "#plot_importances(feature_imp_above, -10)\n",
    "#plot_importances(feature_imp_below, -10)"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}