mlb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as pyplot\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gd2_df = pd.read_pickle('data/gd2_df.pkl')\n",
    "covers_df = pd.read_pickle('data/covers_df.pkl')\n",
    "os_df = pd.read_pickle('data/os_df.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(gd2_df, covers_df,\n",
    "              left_on=['gid', 'team_code'],\n",
    "              right_on=['gid', 'team'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['season'] = df['date'].dt.year\n",
    "df['weekday'] = df['date'].dt.weekday\n",
    "\n",
    "df = df.sort_values(['team_code', 'date'])\n",
    "\n",
    "df[['batting_r', 'other_team_runs']] = df[['batting_r', 'other_team_runs']].astype(int)\n",
    "df[['wins', 'starter_w']] = df[['wins', 'starter_w']].astype(int)\n",
    "df['starter_game_score'] = df['starter_game_score'].astype(float)\n",
    "\n",
    "df['run_diff'] = df['batting_r'] - df['other_team_runs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['game_as_favorite'] = ((df['us_line'] < 0) ).astype(int)\n",
    "df['game_as_underdog'] = ((df['us_line'] >= 0)).astype(int)\n",
    "\n",
    "df['win_as_favorite'] = ((df['us_line'] < 0) & (df['batting_r'] > df['other_team_runs'])).astype(int)\n",
    "df['win_as_underdog'] = ((df['us_line'] >= 0) & (df['batting_r'] > df['other_team_runs'])).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "game_cols = ['gid', 'status_ind', 'date', 'weekday', 'clouds', 'hp_ump', 'temp', 'wind_dir', 'wind_speed',\n",
    "             'over_under', 'league', 'away_time_diff', 'division_game', 'game_type']\n",
    "game_df = df[game_cols].drop_duplicates('gid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in ['home', 'away']:\n",
    "    col = '{}_money_line'.format(i)\n",
    "    indexer = (os_df[col] != '') & (os_df[col].notnull())\n",
    "    os_df[col] = os_df[col].loc[indexer].astype(int)\n",
    "\n",
    "col = 'home_spread'\n",
    "indexer = (os_df[col] != '') & (os_df[col].notnull())\n",
    "os_df[col] = os_df[col].loc[indexer].astype(float)\n",
    "\n",
    "os_keep_re = '.'\n",
    "os_df = os_df.filter(regex=os_keep_re)\n",
    "game_df = pd.merge(game_df, os_df, on='gid', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "team_group = ['season', 'team_code']\n",
    "shift_cols = ['batting_avg', 'batting_obp', 'batting_ops', 'pitching_era', 'wins', 'loss']\n",
    "\n",
    "team_df = (df.groupby(team_group)[shift_cols]\n",
    "             .transform(lambda x: x.shift())).astype(float)\n",
    "\n",
    "team_df['team_runs'] = df['batting_r']\n",
    "\n",
    "team_df['team_win_pct'] = team_df['wins'] / (team_df['wins'] + team_df['loss'])\n",
    "\n",
    "team_df['team_last_5_win_pct']  = (df.groupby(team_group)['wins']\n",
    "                                     .transform(lambda x: (x.shift() - x.shift(6)) / 5))\n",
    "\n",
    "team_df['season_complete_pct'] = (team_df['wins'] + team_df['loss']) / 162\n",
    "\n",
    "no_shift_cols = ['us_line', 'decimal_line']\n",
    "team_df[no_shift_cols] = df[no_shift_cols]\n",
    "\n",
    "gb_cols = ['games_back', 'games_back_wildcard']\n",
    "team_df[gb_cols] = (df.groupby(team_group)[gb_cols]\n",
    "                      .transform(lambda x: x.shift().ffill()))\n",
    "\n",
    "team_df['season_win_pct_as_favorite'] = (df.groupby(team_group)['win_as_favorite']\n",
    "                                           .transform(lambda x: x.shift().cumsum()) /\n",
    "                                         df.groupby(team_group)['game_as_favorite']\n",
    "                                           .transform(lambda x: x.shift().cumsum()))\n",
    "\n",
    "team_df['season_win_pct_as_underdog'] = (df.groupby(team_group)['win_as_underdog']\n",
    "                                           .transform(lambda x: x.shift().cumsum()) /\n",
    "                                         df.groupby(team_group)['game_as_underdog']\n",
    "                                           .transform(lambda x: x.shift().cumsum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "oth_group = ['season', 'team_code', 'other_team_code']\n",
    "\n",
    "oth_df = (df.groupby(oth_group)['batting_r', 'other_team_runs']\n",
    "            .transform(lambda x: x.astype(int).shift()))\n",
    "\n",
    "oth_df = pd.concat([df[oth_group], oth_df], axis=1)\n",
    "\n",
    "oth_df['oth_win'] = (oth_df['batting_r'] > oth_df['other_team_runs']).astype(int)\n",
    "\n",
    "team_df['season_win_pct_against_other_team'] = (oth_df.groupby(oth_group)['oth_win']\n",
    "                                                      .transform(lambda x: x.expanding().mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "starter_group = ['season', 'team_code', 'starter_name_display_first_last']\n",
    "starter_cols = ['starter_w', 'starter_l', 'starter_s_bb', 'starter_s_ip',\n",
    "                'starter_s_h', 'starter_s_so', 'starter_era']\n",
    "\n",
    "starter_df = (df.groupby(starter_group)[starter_cols]\n",
    "                .transform(lambda x: x.shift())).astype(float)\n",
    "\n",
    "starter_df['starter_s_hr'] = (df.groupby(starter_group)['starter_hr']\n",
    "                                .transform(lambda x: x.astype(int).shift().cumsum()))\n",
    "\n",
    "starter_df['starter_s_game_score'] = (df.groupby(starter_group)['starter_game_score']\n",
    "                                        .transform(lambda x: x.shift().expanding().mean()))\n",
    "\n",
    "starter_df['starter_hr_ip'] = starter_df['starter_s_hr'] / starter_df['starter_s_ip']\n",
    "\n",
    "starter_df['starter_win_pct'] = (starter_df['starter_w'] / \n",
    "                                 (starter_df['starter_w'] + starter_df['starter_l']))\n",
    "\n",
    "starter_df['starter_last_5_win_pct']  = (df.groupby(starter_group)['starter_w']\n",
    "                                          .transform(lambda x: (x.shift() - x.shift(6)) / 5))\n",
    "\n",
    "starter_df['starter_s_whip'] = ((starter_df['starter_s_bb'] + starter_df['starter_s_h']) /\n",
    "                                starter_df['starter_s_ip'])\n",
    "\n",
    "starter_df['starter_s_soip'] = (starter_df['starter_s_so'])/ starter_df['starter_s_ip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "add_cols = ['team_code', 'starter_name_display_first_last']\n",
    "\n",
    "hix = (df['home_away'] == 'home')\n",
    "home_df = pd.concat([df.loc[hix, add_cols], team_df[hix], starter_df[hix]], axis=1)\n",
    "home_df = home_df.rename(columns=lambda x: 'home_{}'.format(x))\n",
    "home_df = pd.concat([df.loc[hix, 'gid'], home_df], axis=1)\n",
    "\n",
    "aix = (df['home_away'] == 'away')\n",
    "away_df = pd.concat([df.loc[aix, add_cols], team_df[aix], starter_df[aix]], axis=1)\n",
    "away_df = away_df.rename(columns=lambda x: 'away_{}'.format(x))\n",
    "away_df = pd.concat([df.loc[aix, 'gid'], away_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_df = pd.merge(game_df, pd.merge(home_df, away_df, on='gid'), on='gid')\n",
    "# train_df = train_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ignore = ['gid', 'status_ind', 'date', 'home_team_code', 'home_wins', 'home_loss',\n",
    "    'home_starter_name_display_first_last', 'home_starter_w',\n",
    "    'home_starter_l', 'home_starter_s_bb', 'home_starter_s_ip',\n",
    "    'home_starter_s_h', 'home_starter_s_so', 'home_starter_s_hr',\n",
    "    'away_team_code', 'away_wins', 'away_loss', 'away_starter_name_display_first_last',\n",
    "    'away_starter_w', 'away_starter_l', 'away_starter_s_bb', 'away_starter_s_ip',\n",
    "    'away_starter_s_h', 'away_starter_s_so', 'away_starter_s_hr', 'home_team_runs', 'away_team_runs',\n",
    "    'event_id', 'event_date', 'home_abbreviation', 'away_abbreviation']\n",
    "\n",
    "# ignore = ignore + ['home_us_line', 'home_decimal_line', 'away_us_line', 'away_decimal_line']\n",
    "# ignore = ignore + ['home_season_win_pct_against_other_team', 'away_season_win_pct_against_other_team']\n",
    "\n",
    "cats = ['weekday', 'clouds', 'hp_ump', 'wind_dir', 'league', 'game_type']\n",
    "num = list(set(train_df.columns) - set(cats + ignore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = pd.concat([train_df[num].astype(float), pd.get_dummies(train_df[cats])], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = (train_df['home_team_runs'].astype(int) > train_df['away_team_runs'].astype(int)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=10, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(x, label=y, feature_names=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.588601+0.00298337\ttest-auc:0.58271+0.0171048\n",
      "[1]\ttrain-auc:0.59226+0.00276096\ttest-auc:0.587983+0.0172985\n",
      "[2]\ttrain-auc:0.594927+0.00322099\ttest-auc:0.588351+0.0174199\n",
      "[3]\ttrain-auc:0.597025+0.00295707\ttest-auc:0.590188+0.0170338\n",
      "[4]\ttrain-auc:0.598205+0.00301169\ttest-auc:0.591279+0.0168032\n",
      "[5]\ttrain-auc:0.59935+0.00257737\ttest-auc:0.592684+0.0169563\n",
      "[6]\ttrain-auc:0.600417+0.00212455\ttest-auc:0.593191+0.0175326\n",
      "[7]\ttrain-auc:0.601945+0.0021142\ttest-auc:0.592559+0.0170245\n",
      "[8]\ttrain-auc:0.603405+0.00175692\ttest-auc:0.594515+0.0165906\n",
      "[9]\ttrain-auc:0.60471+0.00182674\ttest-auc:0.595722+0.0159128\n",
      "[10]\ttrain-auc:0.605926+0.00186723\ttest-auc:0.59597+0.0158966\n",
      "[11]\ttrain-auc:0.606626+0.00184509\ttest-auc:0.596345+0.0160626\n",
      "[12]\ttrain-auc:0.607472+0.00144915\ttest-auc:0.596854+0.0163269\n",
      "[13]\ttrain-auc:0.608354+0.00151746\ttest-auc:0.596955+0.0168012\n",
      "[14]\ttrain-auc:0.609187+0.00147316\ttest-auc:0.597077+0.0171884\n",
      "[15]\ttrain-auc:0.610113+0.00141242\ttest-auc:0.597462+0.0173305\n",
      "[16]\ttrain-auc:0.610627+0.0016158\ttest-auc:0.597661+0.0167828\n",
      "[17]\ttrain-auc:0.611236+0.00174982\ttest-auc:0.598003+0.0161653\n",
      "[18]\ttrain-auc:0.611783+0.00163064\ttest-auc:0.598209+0.0163173\n",
      "[19]\ttrain-auc:0.612469+0.00155967\ttest-auc:0.598276+0.0164162\n",
      "[20]\ttrain-auc:0.613201+0.00159234\ttest-auc:0.598409+0.0159224\n",
      "[21]\ttrain-auc:0.613827+0.00160274\ttest-auc:0.598127+0.0156474\n",
      "[22]\ttrain-auc:0.614308+0.00154249\ttest-auc:0.598548+0.0155875\n",
      "[23]\ttrain-auc:0.614895+0.00157049\ttest-auc:0.599291+0.0152774\n",
      "[24]\ttrain-auc:0.615497+0.00155341\ttest-auc:0.599314+0.0152009\n",
      "[25]\ttrain-auc:0.616149+0.00161167\ttest-auc:0.599339+0.01508\n",
      "[26]\ttrain-auc:0.616734+0.00155612\ttest-auc:0.599262+0.0148774\n",
      "[27]\ttrain-auc:0.617387+0.00154031\ttest-auc:0.599243+0.0152783\n",
      "[28]\ttrain-auc:0.617881+0.00166209\ttest-auc:0.599334+0.0152859\n",
      "[29]\ttrain-auc:0.618416+0.0015678\ttest-auc:0.599629+0.015245\n",
      "[30]\ttrain-auc:0.61903+0.00167807\ttest-auc:0.599452+0.014731\n",
      "[31]\ttrain-auc:0.619704+0.00156967\ttest-auc:0.599445+0.0147541\n",
      "[32]\ttrain-auc:0.620189+0.00154136\ttest-auc:0.599699+0.0148108\n",
      "[33]\ttrain-auc:0.620851+0.00153134\ttest-auc:0.599748+0.0149002\n",
      "[34]\ttrain-auc:0.621438+0.0015079\ttest-auc:0.599727+0.0147855\n",
      "[35]\ttrain-auc:0.622047+0.00141574\ttest-auc:0.599766+0.0147358\n",
      "[36]\ttrain-auc:0.62257+0.00136956\ttest-auc:0.599996+0.0146394\n",
      "[37]\ttrain-auc:0.623153+0.00137651\ttest-auc:0.600003+0.0147438\n",
      "[38]\ttrain-auc:0.623709+0.00137632\ttest-auc:0.599916+0.0146668\n",
      "[39]\ttrain-auc:0.6242+0.00147884\ttest-auc:0.600109+0.0145195\n",
      "[40]\ttrain-auc:0.624775+0.00156263\ttest-auc:0.600411+0.0143604\n",
      "[41]\ttrain-auc:0.625366+0.00170985\ttest-auc:0.600506+0.0147679\n",
      "[42]\ttrain-auc:0.625872+0.00167797\ttest-auc:0.600621+0.0147051\n",
      "[43]\ttrain-auc:0.626387+0.00166229\ttest-auc:0.60048+0.0147874\n",
      "[44]\ttrain-auc:0.627026+0.00171283\ttest-auc:0.60048+0.0145572\n",
      "[45]\ttrain-auc:0.627483+0.00159391\ttest-auc:0.600632+0.0144779\n",
      "[46]\ttrain-auc:0.62802+0.00163245\ttest-auc:0.600798+0.0146076\n",
      "[47]\ttrain-auc:0.628495+0.00166941\ttest-auc:0.600945+0.0145387\n",
      "[48]\ttrain-auc:0.628982+0.00169123\ttest-auc:0.600863+0.0146147\n",
      "[49]\ttrain-auc:0.629572+0.00174562\ttest-auc:0.600965+0.0145883\n",
      "[50]\ttrain-auc:0.629993+0.00167105\ttest-auc:0.600894+0.0147335\n",
      "[51]\ttrain-auc:0.630551+0.00158475\ttest-auc:0.600985+0.0144425\n",
      "[52]\ttrain-auc:0.631002+0.00164622\ttest-auc:0.600925+0.0145672\n",
      "[53]\ttrain-auc:0.631431+0.00169559\ttest-auc:0.600751+0.0146645\n",
      "[54]\ttrain-auc:0.631954+0.00172293\ttest-auc:0.60085+0.0148302\n",
      "[55]\ttrain-auc:0.632351+0.00175858\ttest-auc:0.601074+0.0149786\n",
      "[56]\ttrain-auc:0.632869+0.00181401\ttest-auc:0.601361+0.014955\n",
      "[57]\ttrain-auc:0.633279+0.00175614\ttest-auc:0.601335+0.0150113\n",
      "[58]\ttrain-auc:0.633664+0.00178444\ttest-auc:0.601245+0.0150492\n",
      "[59]\ttrain-auc:0.634208+0.00182127\ttest-auc:0.601035+0.0149112\n",
      "[60]\ttrain-auc:0.634747+0.00188827\ttest-auc:0.60091+0.0152755\n"
     ]
    }
   ],
   "source": [
    "param = {\n",
    "    'max_depth': 2,\n",
    "    'eta': 0.1,\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'reg_alpha': 1,\n",
    "    'reg_lambda': 1,\n",
    "    'min_child_weight': 5\n",
    "}\n",
    "history = xgb.cv(params=param, dtrain=dtrain, num_boost_round=100, folds=kf.split(x),\n",
    "                 metrics=['auc'], verbose_eval=1,\n",
    "                 early_stopping_rounds=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naive accuracy: 0.5356368367789505\n",
      "best test auc: 0.601361 on 56 boosting iterations\n"
     ]
    }
   ],
   "source": [
    "best_auc = history['test-auc-mean'].max()\n",
    "best_auc_iter = history['test-auc-mean'].idxmax()\n",
    "print(\"\"\"naive accuracy: {}\n",
    "best test auc: {} on {} boosting iterations\"\"\".format(y.mean(),\n",
    "                                                      best_auc,\n",
    "                                                      best_auc_iter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cv_preds = []\n",
    "for train_ix, test_ix in kf.split(x):\n",
    "    dtrain_k = xgb.DMatrix(x.iloc[train_ix], label=y.iloc[train_ix], feature_names=x.columns)\n",
    "    dtest_k =xgb.DMatrix(x.iloc[test_ix], feature_names=x.columns)\n",
    "    bst = xgb.train(params=param, dtrain=dtrain_k, num_boost_round=best_auc_iter)\n",
    "    cv_preds.append({'cv_index': test_ix, 'cv_pred': bst.predict(dtest_k)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv accuracy: 0.575755385282637\n",
      "cv auc: 0.6003091792250048\n",
      "line favorite accuracy: 0.57329767240133\n",
      "line favorite auc: 0.5952310246830794\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.0, 0.1]</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.1, 0.2]</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.2, 0.3]</th>\n",
       "      <td>58</td>\n",
       "      <td>0.258621</td>\n",
       "      <td>19</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.3, 0.4]</th>\n",
       "      <td>798</td>\n",
       "      <td>0.362155</td>\n",
       "      <td>573</td>\n",
       "      <td>0.349040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.4, 0.5]</th>\n",
       "      <td>3463</td>\n",
       "      <td>0.455674</td>\n",
       "      <td>3517</td>\n",
       "      <td>0.448678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.5, 0.6]</th>\n",
       "      <td>6600</td>\n",
       "      <td>0.551515</td>\n",
       "      <td>6099</td>\n",
       "      <td>0.546155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.6, 0.7]</th>\n",
       "      <td>2677</td>\n",
       "      <td>0.644378</td>\n",
       "      <td>3233</td>\n",
       "      <td>0.626353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.7, 0.8]</th>\n",
       "      <td>237</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>391</td>\n",
       "      <td>0.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.8, 0.9]</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count      mean  count      mean\n",
       "(0.0, 0.1]      0       NaN      0       NaN\n",
       "(0.1, 0.2]      0       NaN      0       NaN\n",
       "(0.2, 0.3]     58  0.258621     19  0.105263\n",
       "(0.3, 0.4]    798  0.362155    573  0.349040\n",
       "(0.4, 0.5]   3463  0.455674   3517  0.448678\n",
       "(0.5, 0.6]   6600  0.551515   6099  0.546155\n",
       "(0.6, 0.7]   2677  0.644378   3233  0.626353\n",
       "(0.7, 0.8]    237  0.683544    391  0.695652\n",
       "(0.8, 0.9]      1  1.000000      2  1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df = pd.concat([pd.DataFrame(f) for f in cv_preds], ignore_index=True)\n",
    "cv_df = cv_df.sort_values('cv_index')\n",
    "cv_df['pred_class'] = cv_df['cv_pred'].apply(\n",
    "    lambda x: 1 if x >= 0.5 else 0)\n",
    "cv_df['home_wins'] = y\n",
    "cv_df['pred_bin'] = pd.cut(cv_df['cv_pred'], bins=np.arange(0.0, 1.0, 0.1))\n",
    "cv_df['implied_probability'] = train_df['home_us_line'].apply(\n",
    "    lambda x: x / (x - 100) if x < 0 else 100/(x+100))\n",
    "cv_df['line_bin'] = pd.cut(cv_df['implied_probability'], bins=np.arange(0.0, 1.0, 0.1))\n",
    "\n",
    "print('cv accuracy: {}'.format(accuracy_score(y_pred=cv_df['pred_class'], y_true=y)))\n",
    "print('cv auc: {}'.format(roc_auc_score(y, cv_df['cv_pred'])))\n",
    "\n",
    "line_acc = accuracy_score((train_df['home_us_line'] < train_df['away_us_line']).astype(int), y)\n",
    "line_auc = roc_auc_score(y, cv_df['implied_probability'])\n",
    "print('line favorite accuracy: {}'.format(line_acc))\n",
    "print('line favorite auc: {}'.format(line_auc))\n",
    "\n",
    "pd.concat([cv_df.groupby('pred_bin')['home_wins'].agg(['count', 'mean']),\n",
    "           cv_df.groupby('line_bin')['home_wins'].agg(['count', 'mean'])], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cv_df = pd.concat([cv_df, \n",
    "                   train_df[['home_decimal_line', 'away_decimal_line',\n",
    "                             'home_us_line', 'away_us_line']]\n",
    "                  ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cv_df['home_conf'] = cv_df['cv_pred'] * cv_df['home_decimal_line']\n",
    "cv_df['away_conf'] = (1 - cv_df['cv_pred']) * cv_df['away_decimal_line']\n",
    "cv_df['bet_conf'] =  np.where(cv_df['home_conf'] > cv_df['away_conf'],\n",
    "                              cv_df['home_conf'],\n",
    "                              cv_df['away_conf'])\n",
    "cv_df['bet_wins'] =  np.where(cv_df['home_conf'] > cv_df['away_conf'],\n",
    "                              cv_df['home_wins'],\n",
    "                              1 - cv_df['home_wins'])\n",
    "cv_df['bet_decimal_line'] =  np.where(cv_df['home_conf'] > cv_df['away_conf'],\n",
    "                                      cv_df['home_decimal_line'],\n",
    "                                      cv_df['away_decimal_line'])\n",
    "cv_df['bet_return'] = cv_df['bet_wins'] * cv_df['bet_decimal_line']\n",
    "cv_df = cv_df.sort_values('bet_conf', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa64de9f588>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c0df/UqC3txRLkv741XzN/eda5ef26tDMVUq8R1X1rVd1bMoY6fKx/TR35ohm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\ngQ0AAACAkvf/AdXh0RPrrc4xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64d8e39b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.rcParams['figure.figsize'] = [14, 8]\n",
    "cv_df[['bet_return', 'bet_wins']].expanding(100).mean().plot(use_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bst = xgb.train(params=param, dtrain=dtrain, num_boost_round=best_auc_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa64da57ac8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dO4aqUr58eZYsWUJQUBDdu3fPUdehQ4fo06cPs2fPpm7dut6xlChRgueee65Q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pAAAg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\nx4DHilT7jOiuDRERERERESkgo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ctBnyOCnz2JJl/2N7+WwPpgPIAXiCZp5gCb3f2jYO77\ndpaUFZ+/AgOJJjOuB35XShyeBZ4F+HGzM3zUcv1ax8udbfI4FPHO6p1e/rpZWdSpU4f09MJtEhMT\nadeuXez2kaK2bt1KUlISX375JYsXL+aDDz4gMTExVr5371769u3L3XffTXp6OuvXr+fUU0+levXq\nfPHFF2zZsoUrr7yShg0bMmbMmNhcevToQSQSOdClV0g4HC62bjl8FO/4UrzjS/GOL8U7vhTv+FK8\nK4++9Uhl8iLnO4CV7t6p3B2455vZS0S/9D8PbAS+dvedwE4zmwe0Bda6+1dBm61mNgPoAJSWzJgC\nTDWz6dEm/qmZtano/A5GsGMl2czSgWruvt8HptaqUY01h/n5C/KDcDhcoUTEwerVqxfhcJjt27fT\ntGlTHnzwQRo0aMAtt9zCtm3b6N69O6FQiNmzZ7Np0yZuuOEGZs2aBcCVV17J119/TY0aNRg7diyJ\niYkATJ48mbFjxwJwxRVXcP311wPw3nvvMWLECGrUqEFCQgJPPfUUDRs2jNtaRURERETKomSGVKYf\nm1knd19A9FkXHwC/21cW3NbRwt1XEk101IPYczKau/u64PgXwOqgz5nAk2ZWHTgO6AiMMbM6QIK7\n7wiOLwYeKm1i7v5ZcDvKYKKJDYA1QKNS5keBOslmdoa7rwOuA+YG5Y3N7JzgNpN6FLjNJLADOKFI\n2YvAP4D/3V8w5eg3efLkEstLen5FkyZNYokMgPnz55fY9tZbb+XWW28tVn7ddddx3XXXlTmf5ORk\nVqwo90uJREREREQOGT0AVCrTGmCAmX0CnEj0dpGrgEfMbCkQAfa9qnQCMM7MIkBt4IXgoZzLgcYE\niQl3/wTIBJYBC4G/BjsaTgbeC/pdCLzh7pn7md8U4NdEbznB3b8vY34EdXYTvSVkajC/vcC4oG1P\n4Img7dvA8UXGew3I2PcA0KBsUhCbkr/FioiIiIiIHIO0M0MqhbtnAWeVcCkCdCmh/jRgWoGi88vo\neyQwskjZ50RvN6nIHP8C/KVIWWnz61vgeA7wkxLqfAScW6Q4HHwInrORVuR6Z+Cf7p5dkbmLiIiI\niIgczZTMEDlCmdkTwKVE39giIiIiIiIiASUz5JhmZmMpvsvjMXd/vjLmU5C731LZcxARERERETkS\nKZkhxzR3H1DZcxAREREREZGK0QNARURERERERKRKUTJDRERERERERKoUJTNEREREREREpEpRMkNE\n5AjUr18/kpKSSE1NjZVNnTqVlJQUEhISWLRoUZnt8/Pz+clPfkKPHj1iZUOHDuWUU04hFAoRCoWY\nNWsWAAsXLoyVtW3blhkzZsTafP/999x44420aNGCs846i2nTphUbS0REREQk3pTMkCrDzLLMbLmZ\nRcys2Dc5M0s2sxXBcXszezz+sywumHfDyp6HVC19+/YlMzOzUFlqairTp0+nS5cu+23/2GOP0apV\nq2Llt99+O5FIhEgkwmWXXRbrd9GiRUQiETIzM7npppvIy8sDYNiwYSQlJbF27VpWrVrFhRdeeAhW\nJyIiIiJycPQ2E6lqLnL37fur5O6LgLL/6VrkCNalSxeysrIKlZWUnCjJxo0beeONN7jvvvsYPXr0\nfuvXrl07drx7927MLHY+fvx4Vq9eDUBCQgINGyovJyIiIiKVT8kMqdLMrB0wPjh9q0B5OnCXu/cw\ns6HA6UAz4MfA7cC5wKXAV8Dl7p4b9DUaqAtsB/q6+2Yz+yPQH8gDVrn7NaXMpS7wBNAecOBBd59W\npM4dQL/g9K/u/qiZ1QFeBpoC1YD/dfcppc2nrHjsys0n+Z43yqoih9CdbfLoW8F4Z43ofphm84Pb\nbruNP//5z+zYsaPYtSeeeIIXX3yR9u3bM2rUKE488UQAPvzwQ/r168cXX3zBxIkTqV69OtnZ2QAM\nHjyYcDhM8+bNefLJJzn55JMP+xpERERERMqiZIZUJQ78PzPLB55x92eB54Gb3X2emY0so21z4CKg\nNbAAuNLdB5rZDKC7mb1BNBHxS3ffZmY9gWFEEw/3AKe7+x4zSyxjjMHAt+7eBsDMTix4MUhOXA90\nBAz40MzmEk2ybHL37kG9+mZWo4z5UKTfG4EbARo2bMQDbfLKmKIcSifXiiY0KiIcDpe77pYtW9i5\nc2exNtnZ2SxevJicnJxibRYsWEBubi47duwgEonw9ddfx9qnpaUxfvx4zIzx48dz7bXXMmjQoFjb\nsWPH8sUXX3DvvfdSp04ddu3axcaNG6lfvz6jR4/m5Zdf5rrrruPee++t0JoPlZycnArFTw6O4h1f\nind8Kd7xpXjHl+IdX4p35VEyQ6qSzu7+lZklAW+b2Wog0d3nBdcnEt1tUZI3g90Xy4nuftj3MILl\nQDLQEkgN+iWos28XxDJgkpm9ArxSxvx+DsR2bbj7N0XnD8xw950AZjYduCCYyygzewR43d3nm1lq\nGfMpJEjqPAvw42Zn+Kjl+rWOlzvb5FHReGf1Ti9/3aws6tSpQ3p64TaJiYm0a9eO9u3bF2sze/Zs\nFi9eTN++fdm9ezffffcdf/3rX/n73/9eqF6zZs3o0aNHsb4BXnjhBRo0aEC7du2oXbs2gwcPJiEh\ngebNm3PJJZeU2CYewuFwpY19LFK840vxji/FO74U7/hSvONL8a48+tYjVYa7fxX83BrsqOhQgeZ7\ngrZ7zSzX3T0o30v098CAle7eqYS23YEuwOXAfWbWxt0P2fYHd19rZmcDlwEPm9kcYEYZ8ylVrRrV\nWBOH2xgkKhwOVyg5EQ/Dhw9n+PDhQHR+f/nLX2KJjM2bN9O4cWMAZsyYEXtTyvr16zn11FOpXr06\nX3zxBatXryY5ORkz4/LLLyccDvPTn/6UOXPm0Lp168pZmIiIiIhIAXqbiVQJZlbHzOrtOwYuBlYA\n2WbWOajW+yCGWAM0MrNOwRg1zCzFzBKAU939XWAQUJ/oMyxK8jYwoMCcTyxyfT7wKzOrHawhA5hv\nZk2A/7r734GRwNmlzecg1idVTK9evejUqRNr1qyhadOm/O1vf2PGjBk0bdqUBQsW0L17d7p16wbA\npk2bYm8mKcvAgQNp06YNaWlpvPvuu4wZMwaA9957j7Zt2xIKhcjIyOCpp56KPejzkUceYejQoaSl\npTFx4kRGjRp1+BYtIiIiIlJO2pkhVcXJwIzglovqwD/cPdPMtgHjzcwp8ADQinL3783sKuBxM6sf\njPEosBb4e1BmwOPunl1KNw8DY4PXw+YDDwLTC4zxsZlNABYGRX919yVm1g0YaWZ7gVzg92XMZ+WB\nrlGqlsmTJ5dYnpGRUaysSZMmzJo1q1h5enp6oW2PEydOLLHP6667juuuu67Ea6eddhrz5s0r8ZqI\niIiISGVRMkOqBHf/HGhbQvniIuUDg/IwEA6OhxZpU7fA8dACxxGit5MU1bmEspLmmAP0KaE8ucDx\naKJvKCl4fTYwu4R2pc1HRERERETkmKbbTERERERERESkStHODJEKMrPrgVuLFL/v7gNKqi8iIiIi\nIiKHlpIZIhXk7s8Dz1f2PERERERERI5Vus1ERERERERERKoUJTNEREREREREpEpRMkNERERERERE\nqhQlM0REjkD9+vUjKSmJ1NTUWNnUqVNJSUkhISGBRYsWldhu9+7ddOjQgbZt25KSksKQIUNi1yKR\nCOeeey6hUIj27duzcOFCALKysqhVqxahUIhQKET//v1jbdLT02nZsmXs2tatWw/TikVEREREyk8P\nABUROQL17duXm2++md/85jexstTUVKZPn85NN91UaruaNWvyzjvvULduXXJzc+ncuTOXXnop5557\nLgMHDmTIkCFceumlzJo1i4EDBxIOhwFo3rw5kUikxD4nTZpE+/btD+n6REREREQOhnZmSInM7F/l\nqHObmdU+gL77mlmTA5vZscfMfmVmrSt7HhJfXbp0oUGDBoXKWrVqRcuWLctsZ2bUrVsXgNzcXHJz\nczGz2LXvvvsOgG+//ZYmTfRrKCIiIiJVk3ZmSInc/bxyVLsN+Dvw3/L2a2bVgL7ACmDTAU3u2PMr\n4HVg1f4q7srNJ/meNw7/jASAO9vk0bcC8c4a0f0wzuYH+fn5tGvXjnXr1jFgwAA6duwIwKOPPkq3\nbt2466672Lt3L//61w85y/Xr1xMKhahfvz4PP/wwF1xwQexanz59qFGjBldeeSX3339/LDkiIiIi\nIlJZtDNDSmRmOcHPdDMLm9k/zWy1mU2yqD8CTYB3zezdoO7FZrbAzD42s6lmVjcozzKzR8zsY6AX\n0B6YZGYRM6tlZj8zsyVmttzMxptZzQLt/hyULzSzM4Ly/zGzFWa21MzmlbGGvmb2ipm9HfR1s5nd\nEYz1gZk1COqFgvNlZjbDzE40s7PMbGGBvpLNbHlw3M7M5prZYjObbWaNg/KwmY0xs0Vm9omZnWNm\n083sUzN7uEBfvw7WEzGzZ4IED2aWY2bDgnV9YGYnm9l5wC+AkUH95ofoj1iOYtWqVSMSibBx40YW\nLlzIihUrAHj66acZM2YMGzZsYMyYMfz2t78FoHHjxnz55ZdEIhFGjx7NtddeG9vBMWnSJFauXMn8\n+fOZP38+EydOrLR1iYiIiIjso50ZUh4/AVKI7qR4Hzjf3R83szuAi9x9u5k1BO4Hfu7uO81sEHAH\n8FDQx9fufjaAmd0A3OXui8zseGAC8DN3X2tmLwK/Bx4N2n3r7m3M7DdBWQ/gAaCbu39lZon7mXtq\nMP/jgXXAIHf/iZmNAfb1+SJwi7vPNbOHgCHufpuZHWdmp7v7eqAnMMXMagBPAL90921m1hMYBvQL\nxvve3dub2a3ATKAd8B/gs2DMpKCv890918yeAnoHc6gDfODu95nZn4HfufvDZvYq8Lq7/7OkBZrZ\njcCNAA0bNuKBNnn7CYkcKifXiu7OKK99z6cory1btrBz585i7bKzs1m8eDE5OTn77SM5OZmxY8fS\ns2dPxo8fT0ZGBuFwmEaNGrFgwYIS53TSSScxefLk2C0tn376KQBnn302M2bM4Mc//nGF1nGo5OTk\nVDiGcuAU7/hSvONL8Y4vxTu+FO/4Urwrj5IZUh4L3X0jgJlFgGTgvSJ1zgVaA+8HW9CPAxYUuD6l\nlL5bAuvdfW1w/gIwgB+SGZML/BwTHL8PTDCzl4Hp+5n7u+6+A9hhZt8CrwXly4E0M6sPJLr73ALj\nTw2OXyaaeBgR/OwZzDcVeDtYZzVgc4HxXi3Q/0p33wxgZp8DpwKdiSY4Pgra1wL2vR7ie6K3kwAs\nBrruZ20AuPuzwLMALVu29Ft6/7I8zeQQCIfDXJ2eftj6z8rKok6dOqQXGSMxMZF27dqV+FDObdu2\nUaNGDRITE9m1axeDBw9m0KBBpKenc+qpp2JmpKenM2fOHM466yzS09PZtm0bDRo0oFq1anz++eds\n27aN//mf/+GEE04gOzubhg0bkpuby5NPPkm3bt2KzSdewuFwpY19LFK840vxji/FO74U7/hSvONL\n8a48SmZIeewpcJxPyX9vDHjb3XuV0sfOAxzbix67e38z6wh0BxabWTt3/7qU9gXnvrfA+V72//d/\nCjDVzKZHh/VPzawN0SRFp/2MV3CsguMZ8IK7/6mEtrnuvm+9pcVZjhG9evUiHA6zfft2mjZtyoMP\nPkiDBg245ZZb2LZtG927dycUCjF79mw2bdrEDTfcwKxZs9i8eTN9+vQhPz+fvXv3cvXVV9OjRw8A\nnnvuOW699Vby8vI4/vjjefbZZwGYN28eDzzwADVq1CAhIYFx48bRoEEDdu7cSbdu3cjNzSU/P5+f\n//zn/O53v6vMsIiIiIiIAPqyJAdnB1AP2A58AIw1szPcfZ2Z1QFOKbDjoqR2AGuA5H3tgOuAuQXq\nFtwZsQDAzJq7+4fAh2Z2KdEdD6UlM8rk7t+a2TdmdoG7zy84vrt/Zmb5wGB+2FmyBmhkZp3cfUFw\n20kLd19ZziHnADPNbIy7bw2e21HP3b8oo03BeMkxYvLkySWWZ2RkFCtr0qQJs2bNAiAtLY0lS5aU\n2LZz584sXry4WPmVV17JlVdeWay8Tp06JdYXEREREalsSmbIwXgWyDSzTe5+kZn1BSbve4An0Wdo\nlJTMmACMM7NdQCfgeqI7IKoDHwHjCtQ90cyWEd3lsG/Xx0gzO5PoLoc5wNKDXEefYD61gc+D+ewz\nBRgJnA7g7t+b2VXA48EtKtWJ3hJTrmSGu68ys/uBt8wsAcgleltNWcmMl4DnLPrQ1avc/bMKrU5E\nREREROQoo2SGlMjd6wY/w0C4QPnNBY6fIPowzH3n7wDnlNBXcpHzacC0AkVziD6ksyQj3X1QkfZX\nlHMNE4gmTorNo+A1d48QfeZHSX38BfhLkbII0KWEuukFjsMUjlvBa1Mo4Rki+2IeHP8T+Gdw/D7R\n55GIiIiIiIgIejWriIiIiIiIiFQx2pkhR6yiOzpKY2bdgEeKFK939+IPFxAREREREZEqT8kMqfLc\nfTYwu7LnISIiIiIiIvGh20xEREREREREpEpRMkNEREREREREqhQlM0REjkD9+vUjKSmJ1NTUWNnU\nqVNJSUkhISGBRYsWldhu9+7ddOjQgbZt25KSksKQIUNi14YOHcopp5xCKBQiFAoxa9YsACZNmhQr\nC4VCJCQkEIlEAJgyZQppaWmkpKQwaNCgEscUEREREYk3JTNERI5Affv2JTMzs1BZamoq06dPp0uX\nYm8GjqlZsybvvPMOS5cuJRKJkJmZyQcffBC7fvvttxOJRIhEIlx22WUA9O7dO1Y2ceJETj/9dEKh\nEF9//TV33303c+bMYeXKlWzZsoU5c+YcngWLiIiIiFSAkhly1DGzLDNbbmYRMyv2z9dmlmxmK4Lj\n9mb2ePxnWeKcri3jetjM2sdzTlK5unTpQoMGDQqVtWrVipYtW5bZzsyoW7cuALm5ueTm5mJm5R53\n8uTJXHPNNQB8/vnnnHnmmTRq1AiAn//850ybNq0iyxAREREROSyUzJCj1UXuHnL3MhMA7r7I3f8Y\nr0mVIRkoNZkhUhH5+fmEQiGSkpLo2rUrHTt2jF174oknSEtLo1+/fnzzzTfF2k6ZMoVevXoBcMYZ\nZ7BmzRqysrLIy8vjlVdeYcOGDXFbh4iIiIhIafRqVjkmmFk7YHxw+laB8nTgLnfvYWZDgdOBZsCP\ngduBc4FLga+Ay909N+hrNFAX2A70dffNZvZHoD+QB6xy92tKmcuFwGPBqQNdgBFAKzOLAC8A44Dn\ngbbAaqBWeda5Kzef5HveKE9VOQTubJNH3wrEO2tE98M4mx9Uq1aNSCRCdnY2GRkZrFixgtTUVH7/\n+98zePBgzIzBgwdz5513Mn78+Fi7Dz/8kNq1a8ee03HiiSfy9NNP07NnTxISEjjvvPP47LPP4rIG\nEREREZGyKJkhRyMH/p+Z5QPPuPuzRBMDN7v7PDMbWUbb5sBFQGtgAXCluw80sxlAdzN7A3gC+KW7\nbzOznsAwoB9wD3C6u+8xs8QyxrgLGODu75tZXWB30PYud+8BYGZ3AP9191ZmlgZ8XFpnZnYjcCNA\nw4aNeKBN3v7iI4fIybWiCY3yCofDFep/y5Yt7Ny5s1i77OxsFi9eTE5Ozn77SE5OZuzYsfTs2bNQ\neZs2bfjHP/5RqO+xY8fSsWPHQmX16tXjkUceAeC1117j+OOPr/A6DpWcnJxKG/tYpHjHl+IdX4p3\nfCne8aV4x5fiXXmUzJCjUWd3/8rMkoC3zWw1kOju84LrE4nutijJm8Hui+VANWDfExiXE70VpCWQ\nGvRLUGdzUGcZMMnMXgFeKWN+7wOjzWwSMN3dN5bwTIMuwOMA7r7MzJaV1lmQrHkW4MfNzvBRy/Vr\nHS93tsmjIvHO6p1eof6zsrKoU6cO6emF2yUmJtKuXTvaty9+F9W2bduoUaMGiYmJ7Nq1i8GDBzNo\n0CDS09PZvHkzjRs3BmDMmDF07Ngx1vfevXvp3bs38+fPp1mzZrH+tm7dSlJSEt988w233XYbL7/8\nMi1atKjQOg6VcDhcLBZy+Cje8aV4x5fiHV+Kd3wp3vGleFcefeuRo467fxX83BrsqOhQgeZ7grZ7\nzSzX3T0o30v098WAle7eqYS23YkmIS4H7jOzNu5e7J/t3X1EsMPjMuB9M+tWgfmVqVaNaqyJ060M\nEv2fV0UTFOXVq1cvwuEw27dvp2nTpjz44IM0aNCAW265hW3bttG9e3dCoRCzZ89m06ZN3HDDDcya\nNYvNmzfTp08f8vPz2bt3L1dffTU9evQAYODAgUQiEcyM5ORknnnmmdh48+bN49RTTy2UyAC49dZb\nWbp0KQAPPPBApSUyREREREQKUjJDjipmVgdIcPcdwfHFwENAtpl1dvf3gN4HMcQaoJGZdXL3BWZW\nA2gBfAKc6u7vmtl7wDVEn6mRXcIcm7v7cmC5mZ0DnAVsAOoVqDa6nI9nAAAgAElEQVSP6ANB3zGz\nVCDtIOYsVdDkyZNLLM/IyChW1qRJE2bNmgVAWloaS5YsKbHtxIkTSx0vPT290Ctc9zcPEREREZHK\npGSGHG1OBmYEt21UB/7h7plmtg0Yb2ZOgQeAVpS7f29mVwGPm1n9YIxHgbXA34MyAx5392KJjMBt\nZnYR0d0eK4E3g+N8M1sKTACeBp43s0+IJkoWH+icRUREREREjjZKZshRxd0/J/oGkKLli4uUDwzK\nw0A4OB5apE3dAsdDCxxHiN5OUlTncs7xllIu/bTIeYlvQxERERERETnWJVT2BEREREREREREKkI7\nM0QOEzO7Hri1SPH77j6gMuYjIiIiIiJytFAyQ+Qwcffngecrex4iIiIiIiJHG91mIiIiIiIiIiJV\nipIZIiIiIiIiIlKlKJkhIiIiIiIiIlWKkhkiIkeYfv36kZSURGpqaqxs6tSppKSkkJCQwKJFi0ps\nt3v3bjp06EDbtm1JSUlhyJAh+20/adIkQqFQ7JOQkEAkEgHg+++/58Ybb6RFixacddZZTJs27TCt\nWERERESkYpTMEDlEzCzRzP5Q2fOQqq9v375kZmYWKktNTWX69Ol06dKl1HY1a9bknXfeYenSpUQi\nETIzM/nggw/KbN+7d28ikQiRSISJEydy+umnEwqFABg2bBhJSUmsXbuWVatWceGFFx7ilYqIiIiI\nHBi9zUTk0EkE/gA8VdkTkaqtS5cuZGVlFSpr1arVftuZGXXr1gUgNzeX3NxczKzc7SdPnsw111wT\nOx8/fjyrV68GICEhgYYNG5Z3CSIiIiIih5WSGSKHzgiguZlFgLeBrcDVQE1ghrsPMbNkIBP4ADgP\n+Ijo61sfBJKA3u6+0MyGAs2BM4CGwJ/d/bn9TWBXbj7J97xxiJclpbmzTR59KxDvrBHdD+NsovLz\n82nXrh3r1q1jwIABdOzYsdxtp0yZwsyZMwHIzs4GYPDgwYTDYZo3b86TTz7JySeffFjmLSIiIiJS\nEbrNROTQuQf4zN1DRJMZZwIdgBDQzsz27e8/AxgFnBV8rgU6A3cB9xboLw34KdAJeMDMmsRjEVK1\nVatWjUgkwsaNG1m4cCErVqwoV7sPP/yQ2rVrx57TkZeXx8aNGznvvPP4+OOP6dSpE3fdddfhnLqI\niIiISLlpZ4bI4XFx8FkSnNclmtz4Eljv7ssBzGwlMMfd3cyWA8kF+pjp7ruAXWb2LtHEyCtFBzKz\nG4EbARo2bMQDbfIOz4qkmJNrRXdnlFc4HC533S1btrBz585ibbKzs1m8eDE5OTn77SM5OZmxY8fS\ns2fP/bYfO3YsHTt2jI3n7hx//PE0aNCAcDhM06ZNefzxxyu0hkMtJyenUsc/1ije8aV4x5fiHV+K\nd3wp3vGleFceJTNEDg8Dhrv7M4UKo7eZ7ClQtLfA+V4K/056kT6LnkcL3Z8FngVo2bKl39L7lwc8\naamYcDjM1enph6XvrKws6tSpQ3qR/hMTE2nXrh3t27cv1mbbtm3UqFGDxMREdu3axeDBgxk0aFCh\nPkpqv3fvXnr37s38+fNp1qxZrPyXv4z+XUpPT2fChAmcc845xeYTT+FwuFLHP9Yo3vGleMeX4h1f\nind8Kd7xpXhXHt1mInLo7ADqBcezgX5mVhfAzE4xs6QK9vdLMzvezE4C0ok+X0OOAb169aJTp06s\nWbOGpk2b8re//Y0ZM2bQtGlTFixYQPfu3enWrRsAmzZt4rLLLgNg8+bNXHTRRaSlpXHOOefQtWtX\nevToAVBqe4B58+Zx6qmnFkpkADzyyCMMHTqUtLQ0Jk6cyKhRo+IUARERERGRsmlnhsgh4u5fm9n7\nZrYCeBP4B7AgeJtEDvBrIL8CXS4D3iX6AND/dfdNh3jKcoSaPHlyieUZGRnFypo0acKsWbMASEtL\nY8mSJcXq7GtbUnuI7rzY9wrXgk477TTmzZtX3mmLiIiIiMSNkhkih5C7X1uk6LESqqUWqN+3wHFW\nwWvAMnf/zaGcn4iIiIiIyNFAt5mIiIiIiIiISJWinRkiRyB3H1rZcxARERERETlSaWeGiIiIiIiI\niFQpSmaIiIiIiIiISJWiZIaIiIiIiIiIVClKZoiIiIiIiIhIlaJkhohInPTr14+kpCRSU394A+9/\n/vMfunbtyplnnknXrl355ptvirXbsGEDF110Ea1btyYlJYXHHntsv+2zsrKoVasWoVCIUChE//79\nY20uueQS2rZtS0pKCv379yc/P7/QeNOmTcPMWLRoEQBffPEFZ599NqFQiJSUFMaNGxer+9vf/pa2\nbduSlpbGVVddRU5ODgAzZ84kLS2NUChE+/btee+99wDYvXs3HTp0iI0/ZMiQWF+DBw+Otbn44ovZ\ntGkTAAsXLiQUCnHDDTfQtm1bZsyYsd+1jBs3jjZt2hAKhejcuTOrVq064LWIiIiIyBHI3fXRR5+j\n5NOiRQuX+Hn33XcrVH/u3Lm+ePFiT0lJiZXdfffdPnz4cHd3Hz58uA8cOLBYu02bNvnixYvd3f27\n777zM88801euXFlm+/Xr1xcap6Bvv/3W3d337t3rV1xxhU+ePDl27bvvvvMLLrjAO3bs6B999JG7\nu+/Zs8d3797t7u47duzw0047zb/66qtCfbm733777bG57Nixw/fu3evu7kuXLvWWLVvGxtyxY4e7\nu3///ffeoUMHX7BgQbG+HnvsMb/pppvc3X3nzp2em5vr7777rm/atMkbNWrkubm5Za6lYF8zZ870\nbt26HfBajlUV/fstB0fxji/FO74U7/hSvOOrKsYbWORHwHeXg/1oZ4aUycySzWxFJc/h3socvyxm\n9q84jJFoZn843OPI4delSxcaNGhQqGzmzJn06dMHgD59+vDKK68Ua9e4cWPOPvtsAOrVq0erVq34\n6quvyt2+qBNOOAGAvLw8vv/+e8wsdm3w4MEMGjSI448/PlZ23HHHUbNmTQD27NnD3r17i/Xl7uza\ntSvWV926dWPHO3fujB2bGXXr1gUgNzeX3Nzc2LV9fRVtU7t2bapXj75JfPfu3YXmW9paSuvrQNYi\nIiIiIkee6pU9AZFyuBf4v8qeREnc/bw4DJMI/AF4an8Vd+Xmk3zPG4d/RgLAnW3y6HvPG2SN6H7A\nffz73/+mcePGAPzoRz/i3//+d5n1s7KyWLJkCR07dtxv+/Xr1xMKhahfvz4PP/wwF1xwQexat27d\nWLhwIZdeeilXXXUVAB9//DEbNmyge/fujBw5stC4+8rXrVvHyJEjadKkSeza9ddfz6xZs2jdujWj\nRo2Klc+YMYM//elPbN26lTfe+OHvZX5+Pu3atWPdunUMGDAgthaA++67jxdffJH69evz7rvvxso/\n/PBD+vbty/bt25k4cWIsuVHaWgDGjh3L6NGj+f7773nnnXcOai0iIiIicmTRzgwpj2pm9pyZrTSz\nt8yslpmFzOwDM1tmZjPM7EQAMwub2RgzW2Rmn5jZOWY23cw+NbOH93VoZr82s4VmFjGzZ8ysWkkD\nm9kIoFZQb1JZbc3s6WDclWb2YIE+ssxseFB/kZmdbWazzewzM+tf0rhBu7Fm9ovgeIaZjQ+O+5nZ\nsOA4J/iZHqz9n2a22swmWRn/rBvM6c9mtjxYyxlB+cnBWEuDz3nACKB5MP+RpfUpVZ+ZlbkbICcn\nhyuvvJJHH3200M6Dkto3btyYL7/8kkgkwujRo7n22mv57rvvYnVnz57N5s2b2bNnD++88w579+7l\njjvuKPUL/KmnnsqyZctYt24dL7zwQqGkyfPPP8+mTZto1aoVU6ZMiZVnZGSwevVqXnnlFQYPHhwr\nr1atGpFIhI0bN7Jw4UJWrPhh89ewYcPYsGEDvXv35sknn4yVd+zYkQkTJvDRRx8xfPhwdu/eXepa\n9hkwYACfffYZjzzyCA8/HPvPzwGtRURERESOLNqZIeVxJtDL3X9nZi8DVwIDgVvcfa6ZPQQMAW4L\n6n/v7u3N7FZgJtAO+A/wmZmNAZKAnsD57p5rZk8BvYEXiw7s7veY2c3uHgIws1ZltL3P3f8TJDfm\nmFmauy8LuvrS3UPB+BOA84HjgRXAOEo2H7gAeBU4BWgclF8AvFRC/Z8AKcAm4P1gjPdKCyrwrbu3\nMbPfAI8CPYDHgbnunhGsoy5wD5C6LwZFmdmNwI0ADRs24oE2eWUMKYfSybWiuzPC4XC522zZsoWd\nO3fG2pxwwglMmzaNk046ia+//pp69eqV2F9eXh5/+tOf6NixIw0aNKhw+5NOOonJkyfTsmXLQuUt\nWrTgqaeeYs+ePSxZsoRzzz0XiD5Y9JJLLmHYsGHF2px00kmMGzeOCy+8sFB5y5YtefbZZzn99NOL\njb9q1SpmzpxJ/fr1C5UnJyczduxYevbsWai8WbNm/H/27ju6qip74Ph3kwQhRAglKCAa6SEJhCJF\nYHgYKSKDIF1UkFGHGXFQUMj8UMexBgEFBUUZFFRARFCKCNgeAiO9FwGBUJShhCKhJ+zfH/fm+dJD\nSQDZn7Wyct+5p7+wXHd7zrlxcXE0a9bMl5Z6IGdycjLjx4/PcixBQUFp0m+88UamTp3KQw89lOm8\nnO9YrhVJSUnn9bdtLo7Nd/6y+c5fNt/5y+Y7f9l8Xz4WzDC5sUNVV7vXK4CKQKiqznfTxgNT/PLP\ncH+vAzao6l4AEdkOlAca4wQ4lrn/F7kwsD+XfYnNpmxn98E+ECfwUB1IDWb49ylEVY8Bx0TktIiE\nquqRTNpaADwhItWBjUBxESkDNAT+kUn+paq6xx3raiCc7IMZk/x+v+Fe3wE8CKCqKcDR1FUvWVHV\n94D3AG6uUEmHrbN/1vmlf3Qyw9YFktDdk+syCQkJFClSBI/HKdOlSxe2bt1Khw4diI+Pp2vXrr57\nqVSVHj160KhRI4YPH57mXlblDxw4QIkSJQgICGD79u0cOHCATp06UbBgQY4dO0aZMmVITk7mnXfe\nITY2ljZt2nD06FFfvR6Ph6FDh1K3bl327NlDyZIlKVy4MIcPH2bbtm289tprREVFsW3bNipVqoSq\nMmvWLBo1aoTH4+Hnn3+mYsWKiAgrV65ERGjbti0HDx4kKCiI0NBQTp486Tujw+PxsHXrVipXrgzA\nW2+9RZ06dfB4POzYsYPy5cuzcOFCbr31Vv73v//RoUMHChUqlOlY0tc1c+ZMqlWrhsfjuaCxXKu8\nXu81Pf78ZvOdv2y+85fNd/6y+c5fNt+Xjz31mNw47XedgnOGQ27yn0tX9hzO35wA41X1nxfQl0zL\nisitwFPAbap6WETG4ay8yG2fMlDVX0QkFGgF/ACUADoDSW4wJL3085TTvy/N4vqCFQ4KYPNFnN9g\nzo/X6z2vQEa3bt3wer0cPHiQm266iX//+9/ExcXRuXNnxo4dyy233MKnn34KwK+//srDDz/M7Nmz\nWbRoER999JHvVaMAr7zyCq1bt86y/A8//MBzzz1HUFAQBQoUYPTo0ZQoUYJ9+/bRtm1b3+GXzZo1\nS/Pa1sxs2rSJ/v37IyKoKk899RTR0dGcO3eOHj168Ntvv6Gq1KxZk3feeQdwXu/64YcfEhQUROHC\nhZk8eTIiwt69e+nRowcpKSmcO3eOzp0706ZNGwDi4uLYvHkzBQoU4JZbbvG9NnXhwoXEx8dz+vRp\nihYtyttvv02pUqWyHcvIkSP55ptvCAoKonjx4owfP/6Cx2KMMcYYY648FswwF+IocFhEmqjqAuAB\nYH4OZfx9C0wXkTdUdb+IlACuV9WdWeQ/KyJBqno2q7JAUeA4zkqGG4C7AO+FDS+NxTjbZ+4ASgKf\nuT+XQhec8zC6AD+6ad8CfwOG+20zOYYzRnOVmzRpUqbp3377bYa0smXLMnv2bAAaN26M8xatjEqW\nLJlp+Q4dOtChQ4cM6TfccAPLli3Lsa/+yyWbN2/O2rVrM+QpUKAAixYtyrT8wIEDGThwYIb0GjVq\nsGrVqkzLTJ06NdP0Bx54gAceeCDD//nIbiwjRozINP1CxmKMMcYYY648FswwF6oHMFpEgoHtQMbN\n6FlQ1Y0i8gwwT0QKAGeBx4CsghnvAWtFZKWqds+srKouFpFVwE/AbpwzKy6FBUALVf1ZRHbirM5Y\ncInqLi4ia3FWdHRz0/oC74nIX3BWd/xNVX8UkUXivCL3K1V9+hK1b4wxxhhjjDFXJQtmmGypagIQ\n5fd5qN/tBpnk9/hde/FbHZHu3mQgV68KUNWBwEC/z5mWVdWeWZQP97seh3MAaIZ7WZQdC4x1r88C\nRdLdD3F/e0k71j7Z1esa4o7Nv759wD2Z9OO+XNRnjDHGGGOMMdcEezWrMcYYY4wxxhhjriq2MsNc\nMURkCXBduuQHVHVdHrcbDXyULvm0qta/BHV/DqR/t+PAnFaEGGOMMcYYY4zJmgUzzBXjUgQPLrDd\ndUBMHtXdPi/qNcYYY4wxxphrmW0zMcYYY4wxxhhjzFXFghnGGGOMMcYYY4y5qlgwwxhjjDHGGGOM\nMVcVC2YYY0w+6NWrF6VLlyYqyvemYw4dOkTz5s2pXLkyzZs35/Dhw7ku62/YsGGICAcPHvSlrV27\nloYNGxIZGUl0dDSnTp1KU6Zt27ZZ1meMMcYYY8yVzoIZxhiTD3r27MmcOXPSpMXHxxMbG8vWrVuJ\njY0lPj4+12VT7d69m3nz5nHzzTf70pKTk7n//vsZPXo0GzZswOv1EhQU5Ls/bdo0QkJCLsGojDHG\nGGOMuTwsmGEuiIiEi8j688jfU0TK5mWf/qhEpJ2IVL/c/TAX509/+hMlSpRIkzZ9+nR69OgBQI8e\nPfjiiy9yXTbVk08+yWuvvYaI+NLmzZtHjRo1qFmzJgAlS5YkICAAgKSkJF5//XWeeeaZix6TMcYY\nY4wxl4u9mtXkl57AeuDXy9yPq1E7YBawMaeMJ8+mEB73Zd73yADQPzoZz0WU37dvH2XKlAHgxhtv\nZN++fedVfvr06ZQrV84XtEi1ZcsWRISWLVty4MABunbtyoABAwB49tln6d+/P8HBwRfRc2OMMcYY\nYy4vC2aYixEoIhOA2sAG4EEgAngdCAEO4gQxGgF1gQkichJoCGwCxgN/BoKATqr6k4iUAN4HKgAn\ngEdVda2IrAOaAEfdep9U1Q9F5EPgI1X9On3nRKQnTiCgCFAZGAoUBB4ATgOtVfWQiMQAo4FgYBvQ\nC7gB+FBV67l1hQMzVTVaROqkH6Oq7hURL7DK7WcRdz7+CUQDk1X1Gbeu+4F/uH1ZAvxdVVNEJAkY\nAbQBTgL3ABWBtkBTEXkG6KCq29KN81HgUYBSpcJ4Ljo5+2/NXDI3FAav15vr/P/73/84fvy4r0xy\ncnKa8ikpKVnWl77sqVOniIuLY8iQIXi9Xk6dOsWiRYsoVqwYmzdv5ptvvmH06NFcd9119O/fn4CA\nAIoVK8bSpUu55557WLx4cZr6rgZJSUlXVX+vdjbf+cvmO3/ZfOcvm+/8ZfOdv2y+Lx8LZpiLURX4\ni6ouEpH3gceA9sA9qnpARLoAL6tqLxHpAzylqsuB1CXxB1W1toj8HXgKeBj4N7BKVduJyB3Ah0AM\nsAgnKLIT2I4TMPgQJzDyt2z6GAXUAgoBPwMDVbWWiLyBE2wY7tbzuKrOF5EXgH+p6hMiUlBEblXV\nHUAXYLKIBAFvpR8jTgAE4Iyq1hWRvsB0oA5wCNjmtlnarauRqp4VkbeB7m4figCLVXWQiLwGPKKq\nL4nIDGCWqn6W2QBV9T3gPYCbK1TSYevsn3V+6R+dTGePJ9f5ExISKFKkCB63TLly5ahatSplypRh\n7969lC1b1ncvp7Lr1q0jMTGRPn36AHDw4EEef/xxli5dStOmTTlx4gT33HMPAMuWLePcuXOcPXuW\nHTt20LNnT5KTk9m/fz/PP//8VfMfYK/Xm+X8mEvP5jt/2XznL5vv/GXznb9svvOXzfflY0895mLs\nVtVF7vXHwP/hBA++doMVAcDebMpPc3+vAO51rxsDHQBU9TsRKSkiRYEFwJ9wghnvAI+KSDngsKoe\nz6aN71X1GHBMRI4CM930dUANESkGhKrqfDd9PDDFvf4UJ/AQ7/7ughPAyW6MM/zq36CqewFEZDtQ\n3h1fHWCZW74wsN8tcwZnO0nqnDTPZlyZKhwUwOb4u8+3mLlAFxsEaNu2LePHjycuLo7x48f7gg+5\nER0dzf79+32fw8PDWb58OaVKlaJly5a89tprnDhxgoIFCzJ//nyefPJJ7r77bv72Nyf2l5CQQJs2\nba6aQIYxxhhjjDH+7ABQczE03edjOA/wMe5PtKq2yKb8afd3CjkH1n7AWY3RBPACB4COOEGO7Jz2\nuz7n9/lcLtqcDHQWkSqAqupWQMh+jP71p2870C0/3q98VVV93s1zVlVT5zQ3c2KuIt26daNhw4Zs\n3ryZm266ibFjxxIXF8fXX39N5cqV+eabb4iLiwPg119/pXXr1tmWzU7x4sXp168ft912GzExMdSu\nXZu777YglzHGGGOM+eOwhyVzMW4WkYaq+iNwH7AYeCQ1zd2SUUVVN+AEOq7PRZ0LcLZdvCgiHpyt\nKL8Bv4lIKaCgqm4XkYU4W1P6XMwAVPWoiBwWkSaqugDnPI357r1tIpICPIsT2ADYDIRlMcbc+BaY\nLiJvqOp+94yQ61V1ZzZlcjt35go2adKkTNO//fbbDGlly5Zl9uzZOZb1l5CQkObz/fffz/33359l\n/vDwcNavz/ULiYwxxhhjjLmi2MoMczE2A4+JyCagOM5ZEh2BwSKyBlgN3O7mHQeMFpHVIlI4mzqf\nB+qIyFqc7R09/O4tAba41wuAcsDCSzCOHsAQt80Y4AW/e5OB+3G2nKCqZ8h6jDlS1Y3AM8A8t72v\ngTI5FPsEeFpEVolIxdy2ZYwxxhhjjDF/VLYyw1wQVU0AqmVyazXO2Rbp808FpvolhfvdWw7OGy5V\n9RDOG0gya/MBv+v/kkMwTlXH4QRRUj+HZ3ZPVVcDDbKoYyjOW1D807Iao8fv2ouzHSaze5P5faWH\nf/kQv+vPgM/c60VA9cz6Z4wxxhhjjDHXIluZYYwxxhhjjDHGmKuKrcwwVz0RaQkMTpe8Q1XbX47+\nGGOMMcYYY4zJWxbMMFc9VZ0LzL3c/TDGGGOMMcYYkz9sm4kxxhhjjDHGGGOuKhbMMMYYY4wxxhhj\nzFXFghnGGJPHRowYQVRUFJGRkQwfPjzD/cOHD9O+fXtq1KhBvXr1WL9+ve/enDlzqFq1KpUqVSI+\nPt6X/vTTT1OtWjVq1KhB+/btOXLkCAATJkwgJibG91OgQAFWr16dpr22bdsSFRWVR6M1xhhjjDEm\n71kwwxhj8tD69esZM2YMS5cuZc2aNcyaNYuff/45TZ5XXnmFmJgY1q5dy4cffkjfvn0BSElJ4bHH\nHuOrr75i48aNTJo0iY0bNwLQvHlz1q9fz9q1a6lSpQqvvvoqAN27d2f16tWsXr2ajz76iFtvvZWY\nmBhfW9OmTSMkJARjjDHGGGOuZhbMMJeciPw3F3meEJHgC6i7p4iUvbCe5R0R8YjIrAssO1tEQt2f\nv1/qvpnLa9OmTdSvX5/g4GACAwNp2rQp06ZNS5Nn48aN3HHHHQBUq1aNhIQE9u3bx9KlS6lUqRIV\nKlSgYMGCdO3alenTpwPQokULAgOdM5wbNGjAnj17MrQ9adIkunbt6vuclJTE66+/zjPPPJNXwzXG\nGGOMMSZf2NtMzCWnqrfnItsTwMfAidzWKyIBQE9gPfDrBXXuCqSqrQFEJBz4O/D2hdZ18mwK4XFf\nXpqOmWwlxN+dq3xRUVEMGjSIxMREChcuzOzZs6lbt26aPDVr1mTatGk0adKEpUuXsnPnTvbs2cMv\nv/xC+fLlffluuukmlixZkqGN999/ny5dumRInzx5si/4AfDss8/Sv39/goPPO45ojDHGGGPMFcVW\nZphLTkSS3N8eEfGKyGci8pOITBDHP4CywPci8r2bt4WI/CgiK0VkioiEuOkJIjJYRFYC3YC6wAQR\nWS0ihUUkVkRWicg6EXlfRK7zK/eam75URCq56Z1EZL2IrBGRH7IZw2IRifT77BWRuiJSxG1nqdvu\nPZmULSEiX4jIWreeGm56iIh84PZprYh08OtrKSAeqOiObYiIfCgi7fzqnZBZe+bKFhERwcCBA2nR\nogWtWrUiJiaGgICANHni4uI4cuQIMTExvPXWW9SqVStDnqy8/PLLBAYG0r179zTpS5YsITg42Hc2\nxurVq9m2bRvt27e/NAMzxhhjjDHmMhJVvdx9MH8wIpKkqiEi4gGmA5E4KykWAU+r6kIRSQDqqupB\n90F+GnCXqh4XkYHAdar6gpvvbVV9za3bCzylqstFpBCwFYhV1S0i8iGwUlWHu+XGqOrLIvIg0FlV\n24jIOqCVqv4iIqGqeiSLMTwJhKrqv0SkDOBV1aoi8gqwUVU/FpFQYClQC7jN7VcbEXkLOKiq/xaR\nO4DXVTVGRAa743rCbaO4qh5OnQsgBJilqlHu/abAk6raTkSKAauByqqanK6vjwKPApQqFVbnueFj\nLuyLM+clulwxkpKSzvv8iTFjxhAWFka7du0yva+qdOvWjbFjx5KQkMC4ceMYMmQI4BzuCfgCF3Pm\nzGHmzJkMGzaMQoUKpaln1KhRFCtWjPvvvx+A6dOn89FHHxEYGEhKSgpHjhzJ8kDSK9WFzLe5cDbf\n+cvmO3/ZfOcvm+/8ZfOdv67G+W7WrNkKVa2bc84rm20zMXltqaruARCR1UA4sDBdngZAdWCRiAAU\nBH70uz85i7qrAjtUdYv7eTzwGJD6dDbJ7/cb7vUiYJyIfIoTQMnKp8A84F9AZ+AzN70F0FZEnnI/\nFwJuTle2MdABQFW/E5GSIlIUuBPwHWCgqoezaR9VnS8ib4tImFvf1PSBDDffe8B7ADdXqKTD1tk/\n6/yQ0N2D1+vF4/HkmHf//v2ULl2aXbt2sWLFChYvXkxoaFqhkiwAACAASURBVKjv/pEjRwgODqZg\nwYKMGTOGFi1acPfdd5OcnMywYcO45ZZbKFeuHH379mXixIlERkYyZ84cZsyYwfz58wkLC0vT3rlz\n5+jevTsLFiygQoUKAHg8Ht54w/lnkJCQQJs2bTK85eRKl9v5NpeGzXf+svnOXzbf+cvmO3/ZfOcv\nm+/Lx556TF477XedQuZ/cwJ8rardsqjj+AW2remvVbW3iNQH7gZWiEgdVU3MUNBZuZHobhHpAvT2\n62sHVd2cZgAiN1xgH3PyIXA/ThDkoZwyFw4KYHMuz3Iw+adDhw4kJiYSFBTEqFGjCA0NZfTo0QD0\n7t2bTZs20aNHD0SEyMhIxo4dC0BgYCAjR46kZcuWpKSk0KtXLyIjnd1Pffr04fTp0zRv3hxwDgFN\nrfOHH36gfPnyvkCGMcYYY4wxfzQWzDCXyzHgeuAgsBgYJSKVVPVnESkClPNbcZFZOYDNQHhqOeAB\nYL5f3i4451B0wV3pISIVVXUJsERE7gLKAxmCGa7JwACgmKquddPmAo+LyOOqqiJSS1VXpSu3AOgO\nvOhutTmoqr+JyNc4K0fSbDPJYmypxuFsZfmfqm7Mop/mCrdgwYIMab179/ZdN2zYkC1bMvtzh9at\nW9O6desM6elf7+rP4/GwePHiLO+Hh4ezfv367LpsjDHGGGPMFc0OADWXy3vAHBH5XlUP4LylZJKI\nrMUJPFTLotw4YLS7ZUVwVitMcc/COAeM9stb3K2vL/CkmzbEPYBzPfBfYE02ffwMZ0XEp35pLwJB\nwFoR2eB+Tu95oI7bdjzQw01/ye3TehFZAzTzL+SuEFnk3h/ipu0DNgEfZNNPY4wxxhhjjLmm2MoM\nc8mpaoj72wt4/dL7+F2/Bbzl9/k7nEM009cVnu7zVGCqX9K3OAdwZmaIqg5MV/7e3I3CF0gITJd2\nEvhrJnm9uGNV1UNAhtMdVTWJ3wMb/unhftf3+d8TkWCgMr+f/2GMMcYYY4wx1zxbmWHMFUpE7sRZ\nlfGWqh693P0xxhhjjDHGmCuFrcwwf0jpV3RkRURaAoPTJe9Q1faXvFPnSVW/AW653P0wxhhjjDHG\nmCuNBTPMNU1V5+Ic6mmMMcYYY4wx5iph20yMMcYYY4wxxhhzVbFghjHGGGOMMcYYY64qFswwxpg8\nNGLECKKiooiMjGT48OEZ7h89epQ///nP1KxZk8jISD74wHkL7+bNm4mJifH9FC1a1Ff+0KFDNG/e\nnMqVK9O8eXMOHz4MQGJiIs2aNSMkJIQ+fXwvD+LEiRPcfffdVKtWjcjISOLi4nz3Xn/9dapXr06N\nGjWIjY1l586dvnsBAQG+9tu2bZsn82OMMcYYY8yFsGCGMcbkkfXr1zNmzBiWLl3KmjVrmDVrFj//\n/HOaPKNGjaJ69eqsWbMGr9dL//79OXPmDFWrVmX16tWsXr2aFStWEBwcTPv2zrm08fHxxMbGsnXr\nVmJjY4mPjwegUKFCvPjiiwwdOjRDX5566il++uknVq1axaJFi/jqq68AqFWrFsuXL2ft2rV07NiR\nAQMG+MoULlzY14cZM2bk1TQZY4wxxhhz3iyYYXIkIr1F5MF8aKediFTP63YulogkXWT5niIy8lL1\nx1y5Nm3aRP369QkODiYwMJCmTZsybdq0NHlEhGPHjqGqJCUlUaJECQID057N/O2331KxYkVuucV5\nuc306dPp0aMHAD169OCLL74AoEiRIjRu3JhChQqlKR8cHEyzZs0AKFiwILVr12bPnj0ANGvWjODg\nYAAaNGjgSzfGGGOMMeZKZsEMkyNVHa2qH+ZDU+2AKz6YcT7EYf/OrlFRUVEsWLCAxMRETpw4wezZ\ns9m9e3eaPH369GHTpk2ULVuW6OhoRowYQYECaf9kPvnkE7p16+b7vG/fPsqUKQPAjTfeyL59+3Ld\npyNHjjBz5kxiY2Mz3Bs7dix33XWX7/OpU6eoXbs2DRo08AVMjDHGGGOMuRJcU69mFZEvgPJAIWAE\ncBhoqKr9RKQv0FdVK4hIBeAjVW0kIs8BfwYKA/8F/gpUAKaoam233srA5NTPmbQbD7QFkoF5qvqU\niIQBo4Gb3WxPqOoiEann9q0QcBJ4SFU3i0gk8AFQECcI1UFVt4pIP6CXW8d/VHW4iIQDXwELgduB\nX4B7VPVkJn0rDXylqnVEpCawGrhFVXeJyDYgGhgAJKnqUBHxAkuAZkAo8BdVXZDFuHsC7YFiQDng\nY1X9t3vvQeApQIG1wDvuHDUVkWfc8W3LpM5HgEfdefgZeEBVT4hIJ+BfQApwVFX/lE2f6qpqH/fz\nLGCoqnrdFRcjgDbu3N+jqvtE5FZgIhACTE9X39NAZ+A64HNV/Zc7/3PdeaoDtBaRO4B/AkeANcBp\nt3w48D5QCjiA833vEpGKwASgiNvmE6oaktmY/J08m0J43Jc5ZTOXQEL83TnmiYiIYODAgbRo0YIi\nRYoQExNDQEBAmjxz584lJiaG7777jm3bttG8eXOaNGlC0aJFAThz5gwzZszg1VdfzbQNEUFEctXn\n5ORkunXrxj/+8Q8qVKiQ5t7HH3/M8uXLmT9/vi9t586dlCtXju3bt3PHHXcQHR1NxYoVc9WWMcYY\nY4wxeemaCmYAvVT1kIgUBpYBLXEe1AGaAIkiUs69/sFNH6mqLwCIyEdAG1WdKSJHRSRGVVcDD+EE\nGjIQkZI4D/TVVFVFJNS9NQJ4Q1UXisjNOA+/EcBPQBNVTRaRO4FXgA5Ab2CEqk4QkYJAgIjUcduu\nDwiwRETm4wRpKgPdVPUREfnUrePj9P1T1f0iUkhEirrjXg40EZGFwH43UJC+WKCq1hOR1jgBhDuz\nmfN6QBRwAlgmIl/iBAqeAW5X1YMiUsL9XmYAs1T1s2zqm6aqY9y5fQn4C/AW8BzQUlV/8Zvj81UE\nWKyqg0TkNeAR4CWc7+odVf1QRB5LzSwiLXDmuR7O/M8QkT8Bu9z0Hqq6WETKAP/GCWwcBb4HVrnV\nvAWMV9XxItILeBNnhcoInO97koj0zq7TIvIoToCHUqXCeC46+QKHb86H1+slKSkJr9ebbb6KFSsy\nbNgwAMaMGUNYWFiaMkOHDuW+++7zBRGKFy/OhAkTiIiIAGDhwoXceuutbNq0iU2bNgFQtGhRpk6d\nSsmSJUlMTOT6669PU+dPP/3EL7/8kqFvgwcPpnDhwsTExKS5t2LFCt58802GDx/Ojz/+mKbM1q1b\nAahWrRoff/wxTZs2ze0UXXK5mW9z6dh85y+b7/xl852/bL7zl813/rL5vnyutWDGP0SkvXtd3v0J\nEZHr3euJwJ9wHupTN7Y3E5EBQDBQAtgAzAT+AzzkrozogvNAm5mjwClgrLsKYJabfidQ3S9QUFRE\nQnBWMYx3V3soEOTe/xEYJCI34TzQbxWRxjirAY4DiMg0t+8zgB1uoAVgBRCezbz8F2jkjv0VoBXO\nw3mmKy785ianegG+VtVEv/41xlk9MUVVDwKo6qEc6vAX5QYxQnFWSsx10xcB49zAzbSsCufgDL9/\nPyuA5u51I5xgEMBHwGD3uoX7kxqYCMEJYuwCdqrqYje9PuBV1QMAIjIZqOLeawjc61f3a37p7dzr\niUDGEx1dqvoe8B7AzRUq6bB119o/68sjobsHr9eLx+PJNt/+/fspXbo0u3btYsWKFSxevJjQ0N/j\nbbVq1eLQoUN4PB727dvHvn376NSpE6VKlQJg9OjR/P3vf0/TTpcuXdi6dSsdOnQgPj6erl27prmf\nkJBAUlJSmrRnnnmG4OBgpkyZkmYby6pVq3j77bf55ptvqFy5si/98OHDBAcHc91113Hw4EG2bdvm\ne/PJ5ZKb+TaXjs13/rL5zl823/nL5jt/2XznL5vvy+eaeeoREQ9OAKGhu9rAi7OV4784qxs24zy8\n98J5kOwvIoWAt3G2JewWkefdMgBTcVYlfAesSH1gT89dYVEPiAU6An2AO3C2ijRQ1VPp+jkS+F5V\n27tbELxuPRNFZAlwNzBbRP6aw5BP+12n4GyTycoPOEGQW3C2NAzECaRktV8hte4Ucv4b0hw+n69x\nQDtVXeNuGfEAqGpvEamPMz8rRKROFt9JMmnPivE/KfGsqqb2L/3YMuu3AK+q6rtpEp3v7Xgux3NJ\nFQ4KYHMutj+Y/NOhQwcSExMJCgpi1KhRhIaGMnr0aAB69+7Ns88+S8+ePYmOjkZVGTx4sC+Qcfz4\ncb7++mvefTfNnxhxcXF07tyZsWPHcsstt/Dpp5/67oWHh/Pbb79x5swZvvjiC+bNm0fRokV5+eWX\nqVatGrVrO7vh+vTpw8MPP8zTTz9NUlISnTp1AuDmm29mxowZbNq0ib/+9a8UKFCAc+fOERcXd1kD\nGcYYY4wxxvi7ZoIZOCseDruBjGpAAzd9AfCC+7MK5yyIk6p61G+7wkF31URH4DMAVT0lInNxznr4\nS1aNuuWCVXW2iCwCtru35gGPA0PcfKlbVorhnHEB0NOvngrAdlV9092WUgMnCDHOPZNDcLazPHAB\nc7MAeBn4QVXPicghoDXOGQ8Xq7mIlMDZWtIOJ1h0EvhcRF5X1cTUbSbAMeD6HOq7HtgrIkFAd9y5\nEpGKqroEZ6vNXTgrbTILZiQAf3cP5SxH1itq/C0CuuJs0+nulz4XeFFEJqhqkrtF6Wwm5ZcAI9wt\nR78BnXDOzQAnmNYVZ1VGd35fDbMYZzXIZPe+uUotWJBxgVPv3r/vHCpbtizz5s3LtGyRIkVITMz4\nZ1yyZEm+/fbbTMskJCRkmv57nC6tb775JtP022+/nXXr1mV6zxhjjDHGmMvtWnrLwhwgUEQ2AfE4\nD4vgPDyWx3mQTwF24xyciaoeAcYA63EeXJelq3MCcA4nMJGV64FZIrLWrbefm/4PoK6IrBWRjThn\nYoCzzeBVEVlF2mBTZ2C9iKzGOYPiQ1VdibNSYSnOA/N/VHUV50lVE3CCIannhCwEjqjq4fOtK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m0rL5zl823/nL5jt/2Xznr6txvoHlegU8u1zsT04rMyYCfwZmuL9Tf+qo6v15El0xVy0R6S0i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KKIbBCRrSLSSkQ+EZFtIvL/TnUoIg+LyDoRSRSRt0XEr7CBRcRPRGaJyBYRSRKRv4hIV6Al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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64df91cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pyplot.rcParams['figure.figsize'] = [14, 16]\n",
    "xgb.plot_importance(bst, importance_type='gain')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Anaconda (mlb)",
   "language": "python",
   "name": "mlb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}