nyc-taxi-trip-duration/nyc-taxi-trip-eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [New York City Taxi Trip Duration - Kaggle](https://www.kaggle.com/c/nyc-taxi-trip-duration/kernels)\n",
    "\n",
    "### Data fields\n",
    "* id - a unique identifier for each trip\n",
    "* vendor_id - a code indicating the provider associated with the trip record\n",
    "* pickup_datetime - date and time when the meter was engaged\n",
    "* dropoff_datetime - date and time when the meter was disengaged\n",
    "* passenger_count - the number of passengers in the vehicle (driver entered value)\n",
    "* pickup_longitude - the longitude where the meter was engaged\n",
    "* pickup_latitude - the latitude where the meter was engaged\n",
    "* dropoff_longitude - the longitude where the meter was disengaged\n",
    "* dropoff_latitude - the latitude where the meter was disengaged\n",
    "* store_and_fwd_flag - This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip\n",
    "* trip_duration - duration of the trip in seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [16, 10]\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 노트북 안에 그래프가 표시되도록\n",
    "%matplotlib inline\n",
    "\n",
    "# 한글폰트 사용 시 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 11)\n",
      "(625134, 9)\n"
     ]
    }
   ],
   "source": [
    "t0 = dt.datetime.now()\n",
    "train = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 11 columns):\n",
      "id                    1458644 non-null object\n",
      "vendor_id             1458644 non-null int64\n",
      "pickup_datetime       1458644 non-null object\n",
      "dropoff_datetime      1458644 non-null object\n",
      "passenger_count       1458644 non-null int64\n",
      "pickup_longitude      1458644 non-null float64\n",
      "pickup_latitude       1458644 non-null float64\n",
      "dropoff_longitude     1458644 non-null float64\n",
      "dropoff_latitude      1458644 non-null float64\n",
      "store_and_fwd_flag    1458644 non-null object\n",
      "trip_duration         1458644 non-null int64\n",
      "dtypes: float64(4), int64(3), object(4)\n",
      "memory usage: 122.4+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 625134 entries, 0 to 625133\n",
      "Data columns (total 9 columns):\n",
      "id                    625134 non-null object\n",
      "vendor_id             625134 non-null int64\n",
      "pickup_datetime       625134 non-null object\n",
      "passenger_count       625134 non-null int64\n",
      "pickup_longitude      625134 non-null float64\n",
      "pickup_latitude       625134 non-null float64\n",
      "dropoff_longitude     625134 non-null float64\n",
      "dropoff_latitude      625134 non-null float64\n",
      "store_and_fwd_flag    625134 non-null object\n",
      "dtypes: float64(4), int64(2), object(3)\n",
      "memory usage: 42.9+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
       "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.980415        40.738564         -73.999481   \n",
       "2                1        -73.979027        40.763939         -74.005333   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "0         40.765602                  N            455  \n",
       "1         40.731152                  N            663  \n",
       "2         40.710087                  N           2124  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id3004672</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:58</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.988129</td>\n",
       "      <td>40.732029</td>\n",
       "      <td>-73.990173</td>\n",
       "      <td>40.756680</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3505355</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:53</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.964203</td>\n",
       "      <td>40.679993</td>\n",
       "      <td>-73.959808</td>\n",
       "      <td>40.655403</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id1217141</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:47</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.997437</td>\n",
       "      <td>40.737583</td>\n",
       "      <td>-73.986160</td>\n",
       "      <td>40.729523</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime  passenger_count  \\\n",
       "0  id3004672          1  2016-06-30 23:59:58                1   \n",
       "1  id3505355          1  2016-06-30 23:59:53                1   \n",
       "2  id1217141          1  2016-06-30 23:59:47                1   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.988129        40.732029         -73.990173         40.756680   \n",
       "1        -73.964203        40.679993         -73.959808         40.655403   \n",
       "2        -73.997437        40.737583         -73.986160         40.729523   \n",
       "\n",
       "  store_and_fwd_flag  \n",
       "0                  N  \n",
       "1                  N  \n",
       "2                  N  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### parse datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['pickup_datetime'] = pd.to_datetime(train.pickup_datetime)\n",
    "test['pickup_datetime'] = pd.to_datetime(test.pickup_datetime)\n",
    "train.loc[:, 'pickup_date'] = train['pickup_datetime'].dt.date\n",
    "test.loc[:, 'pickup_date'] = test['pickup_datetime'].dt.date\n",
    "train['dropoff_datetime'] = pd.to_datetime(train.dropoff_datetime)\n",
    "train['store_and_fwd_flag'] = 1 * (train.store_and_fwd_flag.values == 'Y')\n",
    "test['store_and_fwd_flag'] = 1 * (test.store_and_fwd_flag.values == 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trip_duration and datetimes are ok.\n"
     ]
    }
   ],
   "source": [
    "train['check_trip_duration'] = (\n",
    "    train['dropoff_datetime'] - train['pickup_datetime']\n",
    ").map(lambda x: x.total_seconds())\n",
    "duration_difference = train[np.abs(train['check_trip_duration'].values  - train['trip_duration'].values) > 1]\n",
    "print('Trip_duration and datetimes are ok.') if len(duration_difference[['pickup_datetime', 'dropoff_datetime', 'trip_duration', 'check_trip_duration']]) == 0 else print('Ooops.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "979"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['trip_duration'].max() // 3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEICAYAAABiXeIWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+P9Zm/+QYuZrCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f16908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['log_trip_duration'] = np.log(train['trip_duration'].values + 1)\n",
    "plt.hist(train['log_trip_duration'].values, bins=100)\n",
    "plt.xlabel('log(trip_duration)')\n",
    "plt.ylabel('number of train records')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEGCAYAAACtqQjWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsnXl4XFXd+D9nliSTpUnThbZpS8tW\nllIJoLKIAgUqIhLwFbfX9ae8vuLrghbBBQEXqryK4oaIr/tCRYjKIkvZCyjFQNlaKLS0TfclaZpM\nksnM+f1x7rlz587dJpksTc/nefJk7p079567fb/nfLcjpJQYDAaDwVAOYqPdAIPBYDCMH4xSMRgM\nBkPZMErFYDAYDGXDKBWDwWAwlA2jVAwGg8FQNoxSMRgMBkPZMErF4IkQ4iNCiONG8fj3CSFm+Xz3\n8SHue0i/j3iMLwghDhzkb1eXuz1DRQjxASHEVyNsd7IQ4qiRaFMpCCFeGu027C8YpTLOEEJ8Vwjx\noBDiCSFEh/X5QSHEOaXsR0r5SynlU8PVzggkgLjPd18e4r6H+vtQpJT/K6V8bZA/ryxrY8pDHP/7\n4eRM4PXD3JbBUDHaDdhfSIx2AwzlRUr5eQAhxBzgV1LKU0ezPQaDYf/CKJX9CCHEB4CDgflATkp5\noRDiZ8AhQD2wHXiPlLLTMnWsk1L+VgjxdSADnILq8aWB90spd3oc4yrgTUAt0G9tt14I8f+Aw61j\np1Cj5A9LKV+1fvdF4L1WG14Ekh77ng78EZgmhHgQuEFK+SchxLuA/wEGgC7gYinlRiHEJ4ELrZ8/\nDfwauM7x+yullA+6jtEG/AI4G5gG3A9cJqXMCiEmAT+01ieB30spb7B+9xSwFNVTvxS4GKXUHxFC\nTAO+D8wABPACsFhKuUcIUQP8xLou24C/edw63baJwDXWtlngbinlt4QQxwNLrDYlgVuA66SUUgjx\nG+Bl4CRgEnAT0Al8CJgLfF9K+TNr//dZv307MBV4DviUlLLH1Q7P6yCE+AFwHtArhDhVSvlhq3Pz\nA2ACSt58T0p5m8e5HWLdG73dQ8CVwALgC1LK91jbpYDHgGOt775jtUEAV1jX+xTUs5QCZgLnuI7l\n98zfBDwPLAQaga3Ax6WUO/zuicEDKaX5G4d/wBzgQde6DwOrgAbHugmOz9cAn7Q+X4kS+gBXAXcA\nSWv5e8BXfY7r3N9/Ad+xPn8EaANqrOVPA7+wPp+AEvr6u9MACczxOcY6x+cjgGVAhbV8DvAn63M7\nUBf0e4/vXgUutT4ngNuAC63l3wFnOb57HDjSWu4BLnDs51fAqdbn+x37EMDngZ9by5cBN1rrhXXd\nPdtnteVDgHCsawTWAPOs5UrgLyhlDkqR/sDad9K6B993LL8A1FrbPohSFrot1zuuxYdRSjjsOtjP\njbX8ADDf+pwCVgONrvNKohTYydZyzGrzl612tOn7iOokfB3VuXkKmGatn2pdhxhwKkpZzPZ5Zvye\n+V8BreSf80uAn4z2u7yv/Rmfyv7Hv6SUHY7lRUKIm4UQDwDno15ONxL4m5QyYy0vAw7y2f/xQog/\nCCHuB/7bsT8J/ENK2e2xjxOBVv2dlPIBlJCJwhnAbOAea/TxRZSgBSWUviOEODbivkAJpZ9Z7RgA\nfokaeeljfck6zn1ADUp5gxKud7p3JoSYgBJ8S619SpRQP9Pa5ETgD9ICJdSLsEY0h0opf21tpzkZ\nuFdKudrafx+q995ifS+B263dZ4BngV86llcAkx37u8nRlpsc5+4k6Dq4z/044EfWtnehRpPuAIzD\ngQ1SyuXWOeSAbwEtVjuWOs7n/ah7Mg913/9k7XspavQ20druRSnleo+2Q/Az/0vHc+53/oYAjPlr\n/2Ot/iCEOAP4FPAxVA/9YvIC2U2f43M3qgdZgBDiMFQP8z9RPeCzgQsi7CPncbwi85cPAiUIvuX+\nQkr5KyHEzcCnhRDvk1J+IeI+nceuAnqtzwPAQill1uM3m6WUvR7rcyjB7rXe+d/r2E4EkBBCCJdS\n8WqLe78Z1/pOn+3cx3eeu5Og6+Bmswz364Wdw++Anwgh/gFUSilfFUIsANqklGe5fySEAMdz7vou\n7JmPcv6GAMxIZf/DKZAOBp6QUr6MeoH+Aw9lUQJzgReklM9Yy++PuL/7gXdYvXGEEOdbbfOjQwih\nBcFDwEctvwVCiLgQokYo4lLKNKpn+z7H76UQIiiS6SJrXxUo4XOPtf4J4PPCklpWT9zep9eOpJR7\ngQ1CCO0TECizit7nfcD7rPYKwFPxWftZA/y3Pr7Fo8BCIcQR1v6rUKO1Ir9FRD5uNSUGfMLRTidB\n16EDS0hLKfcAXUKId+svXdtqVgPTLV8I1r35ij4HKeUGlKy6GPiN9ZuXgMOEEG+yfiOi3A/Cn/mP\nCCG0Yvlvn/M3BGCUyvglR3EPMOtadwtwrBDiceAPwG/Jh406t3X/zr2seQCICyGeRAmEVvLPmPs3\ndvuklM8CPwYespzFr0cpC78e7C3Ao0KI/7UU2FXAHUKIR1BC9k1AHfCkEOJR4FbgCsfv7wGeEkJc\n6rP/3UKIO4HlKD/DMmv9p4A3AE9Yx7rJ8Zt+1z6c5/tB4FwhxEPAwyjlq5XHjajAh38Cd6PMUX14\n8yHr+I8LIR4RQlxpCe73AD+wzEDLrDbf7NEOUNc9F7C8CrgdpTj2oPwM7v0EXYf7gU8KIe4VQsxA\nKfOPCiEes+7F190nZY14LgC+YJ3DQyjl9F3HZr9F+ei0oulF+Ve+Ze13OfBJn3OG/P0Jeuax9tMq\nhHgM5cxf4m6vIRhROJI2GPZvhBDrpJRzRrsdo4El0D8spVw3yk0ZFYQQv0JF7D3o8V0cuAEVWeju\nQBgcGJ+KwVBIerQbMIpk8B8d7g9kUP6iIqzR1LBXYhgPmJGKwWAwGMqG8akYDAaDoWwYpWIwGAyG\nsrFf+FQmT54s58yZM9rNMBgMhn2Kp556aoeUckopv9kvlMqcOXNYsWLFaDfDYDAY9imEECVX2jbm\nL4PBYDCUjWEbqQghEsDVwHFSykXWuhNQ2cRpYB2qQJ0s1/rhOheDwWAwRGM4RyrnoDKq4wBW2Ydv\nAh+UUn4IpdDOLNf6YTwPg8FgMERk2JSKlPKvUsp/OVbNQ1UO1QXaWlHzFpRrvcFgMBhGmZH0qTQC\nzkmddqLKVJdrfQFCiIuEECuEECu2b99elhMwGAwGQzAjGf21g8IS05OsdeVaX4CU8kZUsT6OP/54\n428xjBta29q59u7VbOpIM6MhxeJF82hpbhrtZhkMwMiOVF4B5gshKq3l81DVSMu13mAY97S2tXP5\nrc/S3pFGAu0daS6/9Vla29pHu2kGAzAyI5UMqFn0hBDfAP4ohOhCTfd5r5QyV471I3AeBsOoc+3d\nq0lnCms+pjNZrr17tRmtGMYEw65UpJRnOz4vIz83BeVebzCMdzZ1eBdR9ltvMIw0+0VGvcEwXpjR\nkKLdQ4HMaEj5/sb4YAwjicmoN4wLWtvaOXnJ/cy97A5OXnL/uPUxLF40j6pk4WubSsZZvGie5/bG\nB2MYaYxSMezz7E+Cs6W5iYtPO8RebmpIcc0FR/uOPIJ8MAbDcGCUimGfZ38TnAdPqbU/P3LpaYGm\nLOODMYw0RqkY9nn2N8H52s4e+3N3v+fstzZ+vpYgH4zBMBSMUjHs8+xvgvO1nd325719wUpl8aJ5\nJGKiYF2QD8ZgGCpGqRj2eRYvmkdFIrrzeqxRapCBc6SytzdYqbQ0N3Ho1Ly5LMwHYzAMFRNSbBg0\nYyVUtaW5ifte3MLtK7cAcMCESi4/+4h9QnDqIAPtE9JBBoBv+9fv6mFidZLdPRm6QkYq2Zxko2UG\nPHZ2A7d+8uQytr50xsozYxg+jFIxDIrBCMNS9x9F+OjtnLkbv/7oGzh82oSy7H+4KTVDvm8gy6bO\nNCcfPJlH1+wIHKm0trXzrTtfpMvapn336PqYhvuZMYwNjPnLMCiGM+Iqaoiwczsndz67uSz7HwlK\nCTJobWvnlG8/gJTw9IYOwN+nos9xW1efvW5rV9+ohVm3trXz+aXP7FdRevsrRqmMY4YzIXA4I66i\nKiyv7QB+/8T6suzfyXBdy6hBBm4loZXJwy95T+vgd21GQ4Drtmd9JmcNemb2l6TW8YQxf41ThsPU\n4GVqclOOiKuoCstvu53d/WXZP6hzvvJvz9ORztjrymm2WbxoXsF9Au8gAz8lcddzm1nyzgVF68dS\nmLVf2zV+z8xgnuGxYtbcnzEjlXFKuc1TfqYmJ+WKuGqoTnqudwsfP2HUkPL+fdjv/EYHToWiKZfZ\npqW5iWsuOJq4FfY7pa7SMzrLTxl0pr3NX37nOK2+agitHRxBiizomSn1GR5LZs39GaNUxinl7qmG\n9TbjQpQlVLW1rd3T+ZyMiyLhs3jRPFLJeNG2p86bEniMxYvmUREPz90IO+dy9frPO2aGnUty/Xua\nPa+hn5KorSw+f1DnWJkofr0vevNBQ2jp4PBre1wQ+MyU+gzvb5UVxipGqYxTyp0QGCZAc1KWxcxw\n7d2ryeSKbe81FYmi/bc0N/H1846yl2c0qF74nMk1gcdoaW7ilEPziscvdyPsnMuVXLm7J0PfQA6A\nPb3FoyLwV6BHN9V7bt/S3MQFx6rzEUCjNfo78eBJZWhxaXgVwQT4esv8wGem1Gd4LJn89meMUhmn\nKCFUvoTAMAE6dUJl4PcQzenqb+bxFrYHWYl9N/znsTx22UKqK+KhCYGQL28ypa6S5ZedXtLoAMqb\nXOk85z0+59nS3MQ3W+bby00NKabUVTCp1v+6V1ckqEzEWPOtt/GD9zYD2OHFI0lLcxP/ferB9rI2\nT775sPARpXu0FXTd97fKCmMVo1TGKS3NTVx29uH28uTailDzVJDQ9+spa97z+tmB7Ylq7y5FMLS2\ntfORX/4LgCv/9gKtbe3UVCYC62G1trVz0pJlPPHqLgA6evyd+n7nPLE6Wdas9M2dvfbnPQFC/43W\nKOOb589n+WWnM6M+FVimZdWWPcybVkc8JqitVDE5URRuuWlta+cXj6wFYNqEKlqaZ6i2hCRutjQ3\n8bFT5trL0+urAq/74kXzEIVWzbIqfxOJFg2jVMYxB07Km4G+/c4FoQolSOhrh3J1hRKycZF3LAMc\nMT042TCqvdurVlUiVuxP0e3Vjuote3pVZJCUvr1x/ZtNHXkhnslKbn1qo+f2Lc1NXHnukfZyZSLG\nrIkp2q44q6wRRZs7w0cqkK/5Nce6r7VVCU8loYTfMpav2cmabXtpbWunrkqNDvzMa8OFvuZaWW7Z\n08sf/rkBgO4QpQJw0OR8iZlff/QNgdf99COm4oxaLmdJGhMEEB2jVEaZqL2fwfSSVm3psj+H9Qqj\nCP2W5iZOOGgSR82YwCvXvI11S87hjv95EwDb9/bhR2tbu2/UmNvc1dLcxPymCcRjAq1aTps3pUgw\n+LW3I53xFVa+uRv3+DtyT7BGB//7rtdxztHT8XD3DJlNHb0k44Kainig0F9v1fya3VgNQG1loui+\n5oWfUpw9/Vkuv/VZHluj8lnKbf4Key69rnl/Nhe5Lc7nY8OuHt/tWtvaOe3aB+3lY2bV+5o1B4MJ\nAoiOUSqjyGAyx6P2klrb2vnBfS/by4+u2RHYlqhOzm1dvUyty9vxG2sqEAK2d3krFd12P7zMWnt6\nB1h4+FTWLjmHCVUJmiZWR25vJit9Fajfb7Y4zE9u9HlNqauksaaCXSE5MINhc2eaafVVNFRXsMcn\nRBjgtV09JOPCvma1lckiwewn/H760KtAeZXKV1qf5XM3Px34XAY5ybv7/CPr7N939lIRV2LKT6no\nZ8yZn7RyY+c+k+w73jBKZRQZSuZ4lHh9529u+3d74EsW1ZexvavPNnkBJOIxGqsr2OEzUgkKy/Wy\nd/f0D7B2RzdHzlDmtLqqpKeS8GtvVSLmKzj9fuM8Hzf6vCbXVtBYW0E6kyXdHy4MS2FzRy/T61PU\nVSVCRyozJ1bbOS11VcUjlSDFGY8J9vaVx/zV2tbO759Yj3vg5n4ug5zkUcxfmzrSHDatllQyzvpd\n0UOJc7K81QNMEEB0jFIZRYaaOV5KvP5ATga+ZFHKx2dzkh17+5laV5hAN6Wu0nekEtSTc9u7W9va\nect3HkRK+M3jr1mOd+9orsWL5tnC1dne+U0TfB31ixfNo8ojd+P9J/gHGWzfq3q/U2ormVRTAcDO\nbn9TX6m0trXz1Prd/GvtLl7d3s2r2/b6bnfPC1tYu6PbNjNp85d0OBKChF9dVaJsI5Vr715dpFA0\nznu+eNE82/+m0eHFYRWWQY3iZtSnmDkxxYbd3iOVkRhFlDuacjxjlMooErX3MxLx+i3NTbxt/jR7\nedqE4szuXd39ZHOyKHw4SKn4tbGpIVWkUC6/9VnbN7Oru5/Lb32WvkzWc6TS0txEfSpBVTKGIO+U\nPWJ6vW+EU0tzE+9+/SxA5W5MrlVK4rjZjZ7bA+zo6kMIZeZrrKm021YO1DmvJGs5avqzOV7d0e1j\n/lxJJqu202am13Z2k81JejM5e1uviDUt/Gory6dUgp4l5z1vaW5iUm0FlYn8fdKh0dFGKr3MaEgx\nq7Ha1/w1EqOIluYmvnxOPmhjqk/lA4NRKmWnFId61BDIxYvmkYyQAa4Z7EsWczTm/z5cHGlj+xdc\nuRGTa72VSmtbu6fgiAkiZa+nM1m27OnzdEa/8Vv3sas7Q2UixnXvPsZ2ytZUJnxt9a1t7dxiRXpN\nb6jiQyfNAYKDGHbs7WNidYUy89kjlfIoFXXOuYJ1kmKzjdd26UyWh19WfrIuh0lLR+npCDpnBFRd\nVbEPZjC0trUXPCtOBIX3dk9vhu17+7j4tENYu+Qcll92Ou88bhaViVioUtnTm2Fv3wBNDSky2Ryr\nt3T5hru73w+9vpxokyzAjR88vizVI8ZjiLIpKFlGSimA19rWzpK7VhWFQHoVwGtpbuLPK9az/JVd\ngdtpFi+ax+JbnrF7tqBKYvi9ZM5CkQIl2DrSxYJzW5dyaHuNVHbs7UNKibCEjZdfB1R48KyJqaK2\n+/V8+wZyBULfvd/O9EDBNa6rStCfzdE3kKUyEff93aaOXn50/xoguMe8vavPHtFo89duD6XiLGRY\nn0oiBHT0ZAKLGg7V/KkTQvf2DjC1Lr/+7Qum84U/P8N/n3owX3xrPldJmb+G5lMJqzj8/hNm2+fa\n2tbON+54ASnh14+tY3Zjtf1dXVXC1/zlvJYAD7+0jSfW7rLNbe73qqW5id88to6nN3YgpQq17uod\n4B2vmzGkc3XjnMa5XNdxPM4tY0YqZSSqQ10/UFv2FEYdBSmKvgH1Sr19wfTQUMmW5iZOt+pfCVTd\nrMMOqPOd5MpZKFK/uPc+v6VoW1123e1T2drZS99AjoMuv9Pucfk56BNxQdLDr+E3inJnyIddY78k\nP6/f6dIoQcmSO/b2MdkamU20lIrb/OWOzutIZ9jdkwmN1PM75+kN+eurRgXebdNKrthZ38tATjJn\nUmHU3IQy+FSCAi/eNn8a32g5mta2do656h4+e/PT7LB8Ujstc6a+DmpE6Z1j47yWAI+s2VnQQYL8\nPde9/X9v6KDCGrV++vRDgeD7Ohic0zgPx3UcLyHKRqmUkag9T78X8zt3r/L8fS4n7ZyTnE8P0U1t\nVQXTJlSxdsk5HH9gIzWV3oNSv7bc1rapaJ0zvFbT2tbOnc+pSbGcQtQvL6U3kysKm/Uzk6WScV5/\n4MSC78KusT5PtwksyAcQbP7qt893QlWCZFwUmb/CCk/6CQuvYAOAT51+COAcFRTvM5WM8943KP+Q\nW4Gus3rUzuRXUAq3a4jRX0HXcXJdZeTKzjUV3ombYdfSiftZ683kuPzWZ3l5m3pXwnKzSuW1nT12\neHNQkmoUxnOIslEqZSTMl6F7VX4Cd3NHcb5Ea1s7Jy5ZZr8gQQlgBfvqTNsFFms8kuQ0fm3xEgrb\nu/qoq0pQ5XAEX3v3as9epDviR1NbWZjg5yeEdCmU181qYG//ADnLmR12jfVIxS08g/xJbuHmtHVv\n2NVjl3IRQjCxuoJdewuVShRB4LVNS3MTcydVk4yrRE9d8v+UQ6b4zpQI+YrQVVZ1g/fd9M8Cm7w7\n815TV5UccpkWv+uYiAm27emLXNm51iMc2vl9FOJCePb2731hK1D+kjTrdnZz+HRlZwwaqUTxlYzn\nEGWjVMpIkEM9ynwk7nwJ/Zute/JO8Oc37Ynk0NOTFIES5F6mgNa2dnwsK7Zwdm77pyfX09U7UPCi\n+AmBrJRFlWlTyTinHDqFnv4sGSur2k8IVVtViWurEkgJPdY2QdFNzna7Rypeoa2pZJyqhCgYCbnN\nLxJYvmanfb6NNRVFI5UogsBvm450hguaZ7J2yTksuUBNtvXXZ4L9Fnq0+sNla+x1TjPbup09VCVj\nBUmqgB1S7AxB9hKAYTXg3GHZqWScuZOr2b63L3Jl51qfGm1RhWoqGfe9Prt7VIciqI5aVPS1mHPZ\nHbSt72Dlxk4AVqzb6bl9lIRQCH+O92WMUikjLc1NvG5mvhT5jIZ8Abwow/r/OH5mwfJgk7qklGzq\n7M2/wFXeEVFBuQbzHZEuWtDq0FXnixIUMvyxN80tWL7mgqN541wVvqt7etHNWWr7luYmrnpHPrTT\nXd+ptsryqbhGKi3NTUyrrywIbb3mgqOZVFvFXse1CcvxmVRbwS5XnopXDoMTr7lgQPlrduzt57Bp\nqvc7IaXa/qvl60JnSrz27tW2T0iTzmT57M1P84tH19KbyXHKdx4oEGa1VQkGHCHIXpUaFv/5GRbf\n8kxgDbj/ekt+ThZ9HY+cUc/2rr7IlZ1rKr3NX1GEqj5mk8+xdGDFUM1fQR3B+1Zt9wz9jpIQCsWT\ns1UmYuMmRNkolTKhezQrXuuw193+P6fYD0lQD26CJQiPmFZYlHGwdted3f30D+SYUe8wf3m8wIFt\ncsyeGORU9JorQwuPhmr1cj/55TPs4AK9X22TjmzOcrT/+DlKMX3vwtcVBS14bQ8wkM2xvaufD500\nxw5tVSHI8Ug+Gy1YGmsqixz1Lc1NfMWRw+DGay4YgJe2Ktv/YQeoookTrKKPO/b6hyzraxvFTORU\nCK1t7fzMKtVy2v8+6BtMkclJX6e45qApqr33fu7N9nWcYoWVf+GswyJVdlaJm8WKs6W5iQPqKjzP\n+8wjppKICT53xqEF0Yru7f6f1ZkZqvkrqCOY9UgmjpoQqnnb0dPtz6mKOOcdEy1abayHIhulUgb8\nejS3PLXB/hzUg6u2BOFOV6mTwdpd9QNsC+YKFWbb7+rZ+u2nIh4r8HEEKbeW5iauOf9oe53uRQJ8\n956XAGj58XL7wdeCU/tVTju8eE4NZ4+2zh555AWENgceMKF4alw/89faHd30Z3McPq2uYL27VL7f\nNREo08b9q7aybmdP0ct8zOwGz9+B91wwrW3t/PfvngJg8Z9X0trWTr2lcP2mU3bOrhnVTJTOZLny\nb89bFZ1VO3RF5yBTrBuvwo4zHTXZpk6oJJ3JcsaRBxRUs25qSPH9dx9TVNm51qXMnSQTcY6d3UBT\nQ6pgVHnOghkM5CRfbn2uKFoRx3bnHaOO0+Xy3ZUqiMMUd9TQb/B+rtbv6iGbkxx34EQ6ejKR7se+\nUC3ZKJUy4Nej+fnDa+3PQcP6rZ29xERxUp3XJEVeiYNudGl3p/kLivMx/HxA86bV0tmTfyHDlNv5\nx85kal0l73n9LJZfdjqAZwx+a1u7Y6QyQGtbO395qvBlEMA7j2uyBVBNRXHbt1qh2F5K5cHV2wD4\n0m3PFvgILvzZ4wAsuWtVoUnIlWXud20l8Psn1tvKyv0yb/IIsnBfJ41f2f5HXlaVhE89bIqnvf27\nF77Ovi5h89s46UhnPEeafsEUYeewcXeaybUVpCryx9f+wO1dfbQ0N/GWw6Zw6NRa3/D3msoE6UyW\ngWxhRyeTzbGpI83Jh0xm+WWnF4wqD7EmZHOb/UApYr1drasjMlhBHKa4o1a+AFXTzn28tTusKL1G\n9bs3ffuB0ErlXsEbuuMwVjBKpQz49VCc5eBbmpt87e4zGlKeDmB3WZGKeIyDJteE2l3dIxXtl3Db\nmNW0upPt/eue3lEz6tntmLwqygx8DdVJOixFFGQu036DPb0Zz+0k8MCq7fayFhBOwa/ze6bVFyqV\n1rZ2rvp7/uVy+gi083ZbV19hvkRFgs0dabsXG+SvCrKV6zlR3E7sUqoHfOcfKqS89elNVCTyAt9r\nXhBtk9chroMhK6VvoIYT973esLunYJQCMKVW3Qsddt7Rk/EdcYFjRNlfHPqdkzCrsbgy9cFTaovW\naZydoNqKwmfG73p/fukzoRUvvDL1obhygN7e73ru7skUKbK1O1SdtzufzeeEhVUq9wtO6Ehnxsxo\nxSiVMuDXQ5noeqmOmVVsItEv7KSayiLzF0AiFqMyEeOlb55N8+wGGgOmjwX18H3vXiXozv3hI3bh\nQfB2XFbE4xw8paagR1hfnaQjnbGjhFqam/jAiQcChcrHKeQaUhV2Fn6Qucw2f6UzkXxGtZXFI5Ut\nnb3UViaKItS8ypmE+Qh2dfexrauvoBdbCrqt7R1pknHBNecfbZttKuIx5kwq7gT4nXeHI39Hj2I+\nevIc395+S3MTZx55AFPqKnw7LKlkvOg51Eyvr/L1AWi87vXG3WlmTix85nWVBZ0g25HO2OY8L7zu\nKyiTEOTnjHFyt0dCrsbZwYhZM12GBYNkpQwcsbQ0N9E8q8FOPtUju1QyzsSaZNE9Oe3wqUjyJls3\nbt/U2h3dxAT0egRcRKlU7masJE4apVIG/EwRp80r9BcIITiwMVVkK9ZF93Y6HLTaBvx/y9ciJdyx\ncjPxmLDzNbzQvZm9tolGmVWe2aiCB7xs2Ot2dhflMzSkKugfyBUUKtTbPH75Qk8hV+8YqQSZy2zz\nV28mks/ISyFu3dPLAROKlWspOQ562+c37wkVrIBvD1S3VZevP/+4mbbZ5s2HTSmq/BxUN8uL21du\nDvx+cm0F/QOSKxwzVGrhp5+vr517VNHzmYyJ0FIjQhRXecjmJJs60kUjCV0PTo9U9qQz1KeKHe4a\nt4lK46dUwublcZajAR0IEPxFzdRzAAAgAElEQVQ8QngWe2d6gFPnTWXdknPsienedfzMosnaWtva\nOf1/HwQoGtU70c+dNv36vc6l+GtK2WYkGPHaX0KITwPNQBYYAD4PHA1cAqSBdcCVUkophDihlPUj\neyZ59Ev3+aVPk5UqlHhTRy8HTiocrr+2s4fXz5nI99/TXLSPSbWVPNeuYuDddYH6sypTeHZjynbq\ne+E3zNd+C/cLLKXktZ09nHTw5IL1ume7u6efVIUlNDvTJGLCd+6RhlSS5yxH8OJF8/jCn59hwPHG\n6BFZTUWcmFA+lcWL5nHpX1YWBBC4TS1ewmfrnt4i0xco4RF1pKEFTdBEUboOWlNDitMOn8Jfnmov\nuL7Otm7uTDPd1ab6VJIXNuUF91dan/UMOQ3Cr/qzZlJtJZ3pDM2zJwJw3btfx/nNMz231RFT2qTj\nFX3lRFrh685aXtfc9SKZrOSP/1zPPEfpnwdXKV/W1be/wC8eXcv2rt5A85eXSba1rZ1r7lQmwP+4\n4TEuXXS4vf+wnrpWDM7aYnrfixfN86xDp/ESxq1t7Xz7H6vY3NnLps40rW3thXXLrHwfIUTR+7pj\nb7/97LiZ0ZCyt+/PFvuGnNu5l8Oe7bGSODmiIxUhRC1wtpTyI1LKjwGvAQuBbwIflFJ+CKXozhRC\nxEpZP5Ln4cXZR08jB3xm4aE8dtlC6ioTBUUZ+wdybO5MM9s1KtBMqslPdHXV35/3VA7rdvYETmfr\n11PRZjW3Utne1Uc6k2XO5MJeoRYGHQ479eaOXg6YUOVZVkT/Rm/f0tzE0U319pTAzhGZEIIJqSR7\nejO0NDfxXofPyMvUUpmIk4yLougvLye914gxGRNFtbOcymCCj6kC8gpl+WWn842Wo7nmgqNtxVGf\nShS0dVNHb1HeRH0qaUdc+eUwgBpZ+JmowgTFJCsnY7VVxsddl03T0tzE8stO5/BpdcSFKDIJ+uHs\nWTsTcTvSmYJw5S+1Pmf/pr0jTX9WsjlACLprtOVH2dpk1esKhAgWqG5fRK2jzllLcxNfP+8o39/6\nBVJstmYE7eodKNh3XVWSbE7SY/mD/HyDXuHOixfNC1WQfpXKg0ZAYylxckSVipRyL9AuhDhQCDEB\nmA2sBV6UUurwmVaUoplX4voChBAXCSFWCCFWbN++3f112WnfnUbK/LC9vjpZ4DzcuFsphAM9bMWt\nbe3c+u+NdPUOcMxVd9tOZTd9A7lA85efANL2brf5S0efuM1f2mzhVIqbO71HB5qGajUrYq/1sgzk\nJCcdPKnAV6OZUJW081R0RM8/v+xtVgPLlGEJiFxOWuav4rZo53VNpVIsDakktVWJAkXsVlxnHnmA\n7zlBoTBraW7i0S+eTkzAB0+cQ0tzE61t7Zy0ZBntHWnueWFrgX2+PpWk26oeEJTDkJPS00QF3lFD\nTiZZc7y8uFkplaBZLAGmTqgqsuEHoe+5X0fn2rtX+wrJ5a94Z51DsU8lrMBilF64c3t3mf/5VlJy\nlCkkwtpS5wgeaW1r9x1BSKDOOk9nInSQgnTn82hUNWaVxyJQz7buiNRVJcZU4uRo+FR+BPwEuBG4\nD5gAOJ++ncBEoLHE9QVIKW+UUh4vpTx+ypTiXIhy85plCz7Qqgzr7KV6fa/RvSJdUqIjYI7yqkTM\nntDJC7/ZGz+zUFVtdZo7Wtva+a/fWnkStxRGwbRt2A3A+36erynlZd5xUu9IapRSsm5HN3Mne4/K\nJqQS9rXREW8Tq4Pt73v7BiwBfj8DOckf/rneU9i2NDfxqdPU+fYOZAsUtBYgzpfv5EMmF+3DiVuY\nxWOChmoVqafvnQ4n3ttX2KOt15FuAUEJ+hhaITa4nNteUUNOdPb4i5v3AMVz3biZWlfpW/XYiw+f\nNIfWtnbfjs6mjnRoaX4v3L6ysKCNqCHUevs6V707PZK75MzDbMXb6CPAw9qig01a2zYG+nmaGlJ8\n5gz1LN716TfbxwlSkO99w+yi9mj/6s1PbiQmVNLv0187i7YrzqI+leT85qYxo1Bg5M1frwO+BLwD\neC9wFDAfpSg0k4Ad1l8p60eVDS4HY4MVQQXqofjsn9oA+NQf2goERClVWQ+ZWhtYpbilucnOynWa\nk951vDIxdbvi9nX7tu7pKzBlXH/fy/Y+lVlhJRt29QS+DLbJLJ1hZ3c/XX0DRSMgzYSqpK1Ed3f3\nU59KkvQJjW1ta2dzRy+3tbXzuZuftsOJO9P+wnZavRIavR4TW7mdss7qzWFh05rGGlVUMqxHW29d\nk860f1CCMzRVTzLmJsiZPKlWj1T2kIyLQD8GKKUiJZ6dj/88YbYdRKKV1YKZDYGO7BkNKd9z06X5\nvXjoJWU9WHzLSo656p6iyeqc+4f8KFS3zy/HxlmJQQcitLa18+XblHnut4+/xiVnKkH/iVMP9hTG\nYQEkeqRy06P+5XT0s6M7S+4QfS+T7NS6Cta4ppN21hIDVabpS7c9Zz/3E6uTvgp/tBhpR/00YLWU\nMgsghHgcOBWYL4SolFL2AecBDwGvlLh+VFm/s4fKRMzuBTWkKljVuafIiacT3YDQobCThlSSWY3V\nRQ+dm7rKJKlknOevWkTM8eRWJmJ2zy1MGBaHOKrlaR4mp3z7LJNZT8Y2bfmOVKqSvLJdncfO7n57\nRkU3+tpph79fnohbMHiZxjTu6/3MhnxZnVQyRnVFPHRyrcaaCnb19If2aPXorTOd8QxeEBROauXV\nvrD1Wvhv6+pjRn2VPUmaH1PrKpHAW488gL+t3IwAz3N9eWsXZ173cGiRSK0QvRzhHzrpQM/ftLap\nybs0XhWxoViptzh65O73yrl9a1s7dzy7ib19WY656h66+wdsH9Kmzl6u/vuLpJIxO9LM65z89g3K\ntAbYPlAv9Ajo/lWqYvLunn7mUGOfx9f+9hz9A5LeTNa+/nc9t5mXHe93WC2xluYmGqor7CraY4WR\nVir3AicIIW5CRX+BGrncD/xRCNEFbAfulVLmhBDfiLp+hM+jiPW7epjdWG2/1BNSSTrTA4ECXJfb\nCIvqSCXjXPmOo7j3xa2h86m8sn0vB02pKVAorW3tZLI5bnz4VZY+ucH3JQ6vMBvkU9HO/X57/15K\npbWtnYdf3k5Pf5aTl9xPdUXMV6lEGcV5tTlIqTh7oa1t7fzi0XzVg470AKlknOvefUygOWFSTQUv\nb9vre+/0MZxKpaW5ib/8eyOPWlMA+ymtsH26qa1MUJGI0T+QC/WngPKpgEo6TMQEq77+VhIeo0Rn\nhrxfmxpShbkaV9/+Aru6+6mpiNPdn7XLpbi59u7VRaNIN86SNF44o8J0RW4vBec3r0syLtiwy/t5\n1/u+ZOnT5GTxTKs6uMNvlNDUkJ/ZVNe/cwa9bNvTS2d6gK++/Ui7ThnAy9u6uO/FbfQP5KhIxCLV\nEmuoThakIowFRlSpSClzwFUeXy2z/tzbl7R+tGhta+f+VdsYyElOXnK/VUwxSWe6nwGfKBunrdjd\nK4oLyEqKepHLVm0LjP4CpVSOnZ13Mekenf6dn0KBvODyU3JfbX2e3kzO80XXAvT+1du4/RmVW/G+\nm54oCAt19y51QcCjZnjbyqOM4ryErR5RJWLCM6xZ41fp12v046SxpoJd3f287ehp/O6J9QXfOY/h\nVCqgsvcPmVrLvZe8xXffYb1kN0IIJtdUsKmzlyk+kV9OtLL417pdzG6s9lQouu3JuGB7V5/n9NS6\no6NpaW5i3rQ6zv7BIxw+fQJPvbbbHr26iXJfc1KG+gmcIxfNyUvuj2ROzmRl4NxEb18wnc8tfZpP\nLzyUS848rOA7PVI548gDuP2ZzYH3ym3+am1r5+rb1Sjtpw+uYVJNhX0OHel+sjnJvK/cFdrZ1M/9\nxOpik5kuFupUtiPpczHJj0NECcqVtvCyZ6Pb1UMmK30jprxsxaCmzz3PegCe/MoZBRFRcUGgoz7d\nn6W9I11QziKqz0a/DEEO0e17+3z9GHqkcsuKjb5hoX6hl6/u6MaLsIgfP2FbU5mgrjLBoVPz18Er\nXHmwVaAnWUrlL09tLFjvrlvmrsi82Se/xonbd+DV7qL2WH6VSCMVa5uu3gHm+JgnQSkrXXm4pbmJ\nt82fbp+jX5uarCz7FzbtQQj/zPKhzD8TRlRzcm1lgo27077RlLu6+5HS+5o+/JLKyfnzio2h5XTy\nOV8Zu1Olq1zv2Ntf4Mu8ZYV6T3RlBz9DptMP1+CKMvWqc/bZm5+m+ep7RqyMy4gnP443vEqDpDNZ\nHrbMHBe9+SCW3LWqoEfsZyt+x48eZWK1mgY4ERM0uiKiYjHhq1Ra29r51p0vIiX86rG1HDipuiSf\njftl+NJtz9px+O5z8+rJ62ieAVf7nNv7tcXrOBCctOY2SbiZOqHSjri79ZMnFYzeNKWamjTaXOe+\n7+66Ze6RypbONPMOCI9E9OqBB6FzVaIplbxS8/N5gXqetu/t4y//3sgTr+7ksANqqUrGePHqt/r6\nbSZUJe2ox4bqZIEJ1klYMuJQci6impOPnFHHv9bu5uAv3enZm9flZtwTnbW2tfM1R/FGXU7nolPm\n8iWP6Q/qqpIIoczC//fo2kBfpnvU7PWmu/1wDakKuvoGyGRzJOMx306kjiIEhn3UYkYqQ8S/jpMS\nJG+cO4kPhtTN0kytq2Lrnl62dfUxpa6y6KWMC4FX4QDdO9EvgjMMNUqPz2kDBvXQOW29brzOOchB\n7C5w6cYvAVH32rUvR0dnXf/eZt+cFs20+ipbWR0y1bsQ4WBn3wuqv+a8NpWJOFXJGJ3pDJlsjm1d\nfUyrL2/Wc2tbO/98dRcAv35sXWhvNFURt3Mn/EYq+nnS5q72jjQPv7SD6op4aCCArgnmDo12ou+r\nvu8TqvL3IMrILAiveyoc/5saUrzzuCaeXq+qV/hVLdaVDNyK2qsTCfDXZzZ5ticeE9SnkuwOCewI\n6/zptl/37mP4Rkt+qomJNYWJykH7CStJUy6MUhki/g5U9WC/7fpHWLpCzavy76+eGSgMp9VX2krF\n3UMCiAnhWaU0bBKtoPh+PyEa1dntJOHTM9Xb+7Xl7QumF63TtDQ38dhlCzl2doPdk3PPieKFbv/U\nuko7r8Br36WamiA4VNZ9bXTPfXtXH1ISmOtTKm4fVVCYtfM3evvv3/uS57Zez1NWysB52TVaqQQV\nkwRrBk8ry/3Kd8wH4MYPHBfaWQjDbU6uSMQ4ZGoNr5vVYCfiPrBqe1GJFLfA3dalQtfd76Gf0N62\nxz8SbGJ1hR1R6EVQWLZWzo9edrrntdGBAJ1WonLUuZaGE6NUhsgXzjqsyPaZjImC6BY9RNax+X4c\nUFfF7p4MG3f1eDpdlfmr+Hehk2hdcHQ++7Yyjk4qDhKiWvhFzd1Q+ytus3N79wuvldCZR03z3J8T\nZ/7Gh3/5r9AeufZjbOvqC5yjQpcv8cr890Obv9yddq9ro5WKLvkR5lMphbDQcDfuEO2d3f2eSsjv\neYpS2kWXxK8PSGZ1b6tHWl7l7geDvqfvPn4WdZUJdvdkmHdAfrQaxZemRyqTXaNSP6Eds2qAeaFL\nGAVNIeE3aj75kEmAf0dGKx0dheY1E2uU9pcTo1SGgCqwt0rV+XEIaj0XuJuwoecBlsB5dUe3XVrF\nSTyGZ0hxWLJWS3MTP/vA8QD8+P3HUVOZ4AMnHBgyalJtOefovMAPUkKtbe1ssXprImB7/cKffMgk\n+xq5fUde+37i1XwRBXcAgNf2TgVe7tnx9AsupTJJ+o1yWtvaWbejh7uf38rHfv0kUN6RSqmBBlGV\nkN/zVF0RntG+u1s9Aw+/tD10hkU9qnlirbq3TRPLK/DmTatjZ3c/O/b2c9gB+dFtlOrY27v6mFCV\noMol6P1G20Fl9CdWV7C7p5+W5ib+8wRvU7jucDlnzLzmgqOZUldFXWVxO5z7Bgrq7l182iGe245U\nfTCjVAaJ248hZf6mOWPSnYQNPZ3JhV7mr7jwdtRH8Q3MsmaXe37THvb0DtjLfky3bP9dVmmX3/2/\nN/oqIX0ttHlK4l0SxcmhU/MvuV+eiubau1eHzps+lO1LZaKjvYvmH+A5ynFXotU9yX+/trssbYDS\np5uOqoT8BOebDw0uadPa1s7tK8MnnNJMrVMBKa/t7KE+lfQ1Uw4WZ3LiTx98xW5HlPdlW1efndPj\nRAt/r4x+v2fMWWxVv+NtVxSbwluam/jkqQcDsOzzb6GluYkde/uYHBCAoaMul63aak80d9MjKvfq\nq28/omTTbjkwSmWQBPX6/F7qMDuz04/h5dOIxYTnSKWluYlvtsy3l70eoKl1VSTjgsetHr975j43\nE6uTVCRi9gjhoCn+kUKlmmGg0HkeplRK7ZEPNlQ4Ksl4zHYyL5jpPTe9XxTOjx9YU5Y2QOmBBlGV\nkBacelRVY/WeTzksOHLt2rtXh/oqnMRjwj62e9KvodLa1s7/Lc8ntjpNfe7Co02OYo+a7V19vnXU\nWpqbfJOQvZ4xPVIBNX9RfSpp+0Lc6M6cNpfu2NtnV03wwhnKr8OIdbThj+5fw+JF80oy7ZYDo1QG\nQVBl0k0daTUNqYfTujuk4mzYSCUm/CfpOtGyvX6jZb7nAxSPCZoaUjy51rJfhygVIQTT66vo6lVZ\n5kElWgYjxLd05r8767qHAq9LqT3yUteXSmtbuz0N7k2PvOrZdr9zb+/oLZsZrtRAg1KUUEtzE49f\nvpDp9VX2dQsrVjmY50Ark7DnsVS8svadCq6luYlLF6mJvW67+OSia6ZGKv7nW8oz1pBK0tOfpW8g\nqybFCwjl1opcT0+9c2+/XYnaC79QfggvRjpcGKUSgK4OOveyO2z7cNgMdLribK1HmGwmKwN77xNS\nCduR5zUvRjzmHf0FaopXCO7xzWqstnvPYeYvyCu5uZNrfHMOoHQh3trWzk2O8ijtIT6SUnvkgw0V\njoI9V7j1EjsT2JwEKbByvuilBBoMJtrtoCk1dj2qsDyYwTwHT1u11x5ds72swi+KgtN5Omu3Fybf\nSikDRypQ2jPWUJP3e6zb0cPcSf4KVPsytzhHKnX+I5WwEO+RCiN2YpSKD16ZqZff+qznvBIa50M1\nGL/KX5/eZPc4Pv6bFUUvmRqpeP+2PYJS0SavuspEqCkO8g94kOkLShfiYb1IN6UKw8GGCkchqqkv\nKJR7NF50TanRbs7qDGFKpZTnQL9fOpdob1+2rMo2ioKzlYqjooOeXiGdybJ0xYbAqMGoz9iarars\n/hu/pebd+cfzW3z36zR/ZbI5dvdkiiLQ3PiF8mtGepphk1Hvg5/wCCp54nyoSs3WdveA3dWMwT/6\nC9QkYABNDf69ID06mekofOlHa1s7y15U5Sgeeml7wXSqbvyK+/ltPxgzSalZ5qVuH5WobdfH/uzN\nT5e0n7GGU6mECbdSnoOwQqtDJUoNtRkNKSriMVupuPN+9lgzPjrPzUmUZ6y1rZ3fPfFawbreTI7F\ntzzjud9URZyG6iSbO9N2OZdJAde9ta3dMyHayUhPM2xGKj6U+tK7s9IH03sP6wH7JT+CGklNrq0g\nFRD2qZ1/L27eExju6Z7a1T2dqhel9ICH2+cxnJTS9pbmpqIphsP2M9bY7PB9LfxusO8Loj8Hwx1M\nEWUkEY8JDpxUbdeeG0zASRjX3r0ar/SeIFP4tAlVbOnstaPXpvg46u2OaIBOGY1pho1S8SEowzWK\nsijVBBPlJYsJgZR49kw27k77CjBQD+DSJzfYy0HhnsPxcjkZTp/HcDOW/DvDTWtbO79cvs5eLme+\nz0h0LMIUXGtbOxt29XDvCyocNyj4ZrAE/dbvuxkNKTZ19LLDKmnvN0L0izAMyp0aCULNX0KI90sp\nfy+EOBb4HvAnKeUNw9+00UUNn1cW1PnR5b5zuRyX/HklEFzYsBQTTBRzWdyynWZzkoRjru3WtnYe\nf2VnQel993FLKfM+Er1I3abRKs89WEpt+758roOdGiAKpZb4Lze6l68npNNVgb06/UNRdEEFLr32\nqxN9e/qz9myxfkrF733MScnaJecMssVDJ4pP5Tjg98BHgLOBHwxri8YI+qXRs/U5lcer1qyF1/7H\nAnuq3qES5SWzlYqU9o3zK73vPAcoTVEMtnpvKQyXz2MkGCv+neFmODsXo61s/aZhcCuWoSq6xYvm\nsfjPz5Bxhfwm46Jov26fjk6Ybfnxcq58x1FF12Yk3tPBEEWpTBZCfBJYIaVMCyHG1jRjw0hLs5qt\nb086w18/9SZ7vZ4Ux6/67WCPBcEvWcxyrjutX36l9929yVIewNHuRRrGBsMttEZT2fopRomqmr2n\nd4AZ9VVc+tbDh9RG/dsr//a8Xbl8YnWSr51brCT8zFkdae+y9WP1PY2iVL4DnCil/LW1PGZmXBwJ\nZk5Mce/mroJ1a7aXX6lA+EumIwedpVpKKb0R9QEc7V6kYWwwVoVWOfBTmE0NKc5vbuInD65h+WWn\nh0ZJRiGq8oxStt5d1gXG3nvqq1SEEG8i78hfLYR4s/U5uNTuOGNGfYode/vozWTtom5rtu5l2oQq\ne1rRkcJp/rLbF7E3ORhfwGg/nIbRZawKrXIQpDCf3tBBTWWiLAqlFMImGPNSOmPxPQ0aqZyCUipv\nBKYATwBvBp4DHh3+po0NdOXUzZ29zJ1cQ2tbO39fuYlM1t8pPlxo85ezVIvf/OF+I5Cx9gAaxjbj\n9ZlxKsz2jjSpZNyOlHrslR3UVIx8Cl/YjJij7SuJiu+Vk1JeAyCE+KGU8h3W5xj7iaNeo29k++40\nz2zo4PJbVxbMiDdSU3RCfqTi9Pm1NDdx7wtbuOPZLQirveOlN2kwDCdaYb7zp4+RjAv7nenuy9rF\nJke6PVDof9HsS2bHKOrYjmeTUuaEEOGz74wjdO7Hpo40P1j2ciSn+HDh5VMB7DkXnr1q0bC3wWAY\nb8ycmOIpx5QEe/sG7EKNI41WdK1t7fus2THKlUsLIW4AngGaAe/JmMcp0+qriAnYGDCP9EiV3IjZ\nI5VCpRI254LBYPCnqSHFHSs3k81J4jFBT/9AwUyjo8G+bHYMvXJSys8IIU4ADgR+KqVsG/5mjR3u\nWLkZgOuXvawmySph5sVyoycGco9UwuZcMBgM/sycWM1ATrJ1Ty8zGlLs7cvS1GDep8ESWqZFCPFr\nKeUTUsqb9zeFopORtAz3UigjaeuMxfyUSn9osT+DweCNDsbR00d09w1QOwo+lfFClNpfzwkhThdC\n7HdSyy8ZSVA8x/RIEPdIfgQ9Utnvbo/BUBb0dBHtHarSd3ff6Ju/9mWiXLnXA28FhBAiC/RLKUev\nsMwIEpR1u24UauvErC6Ac8TUP5CjI8KcCwaDwRsdjLNxlzVSGQM+lX2ZKD6VC0eiIWMRv2SkmCBw\nfpHhIubhU9nZbZXHNo56g2FQ/OO5LcQEfPfel/jjk+vpzeRGJU9lvBDFp3KoEOIhIcRjQoj7hRCH\njETDxgJ+s/flZHmnhI1K3CP6a0eXLo9tHIsGQ6m4/aabOtScQ2t37B3FVu3bRPGpXAm8V0p5EvAB\n4KphbdEYQs+JEvco1zAaU8LqdhQoFWsiHxNSbDCUjp/f9MHV+1U1qrISRal0SSk3AUgp24GukO3H\nFS3NTb5T+I70lLDCw/y13Z4dzigVg6FU/N5hd0a7ITpRlEqjzqK3IsAahrdJY4+xMv2tbf5yJPXb\nIxWjVAyGkvF7hyfVGHPyYImiVL4P3C+E+D1wD/tZ7S8YO1PCxj2iv3Z09VNbmQicm95gMHjj5zd9\n7xvKM/ne/kgUpfIMqmLxZ4FTgZXD2aCxSKnzzQ8X7uiv1rZ2/viv9eztG+DkJfePeOCAwbCvo99t\nPTLRNb/eOn/6aDZrnyZK3Ny3pJSfwZpHRQjxLeAzw9qqMchYqMWTn/lRFk09OtIVkw2G8UJLcxPz\nptVx9g8e4cSDJ3HvC1tNnsoQiHLlBlzLOc+tIiKEmAF8GZVDWAd8CTgeeI+17+VSyp9Y255Xyvrx\nTtxRpsUramUkKyYbDOOJqVb05Nod3QCjUvp+vBBFqdQKIQ6TUr4khFgANA72YNZ8LN8G/kdK2WGt\nqwc+AZyDUjS/FULcD2wuZb2UctVg27WvYJu/pBz1iskGw3hiYnUFiZhg/U5VqsUkPw6eKFfuy8B3\nhBCzgI3A4iEc7/VAO/BVIcQ04AFreZmUMgcghPgrcBqwrsT1416pOKO/ok4jbDAYwonFBFPqKtnc\n2YsQUG0CXwZNqKNeSrkD+BjwbinlR6SU24ZwvDnAESiT138CpwNvAHY6ttkJTESNiEpZX4AQ4iIh\nxAohxIrt28dHIpOO/spJOWYi0gyG8YIudVRTMfLz048nopRpuQB4CPiZtfz5IRyvB7hXStknpZRA\nK9BLoUltErDD+itlfQFSyhullMdLKY+fMmXKEJo8dhAO85eOWtGMVkSawTBe0H4V408ZGlFCis+W\nUp4CrLGWh9IVfgo4XeS7AXq/Z1j+FoB3AA8DT5a4ftxjl2mxQopbmpuICfif0w9h+WWnG4ViMAyB\nKXVVgPGnDJUoV09He+mMu9mDPZiUcpMQ4i6Uc30AeFVK+RchRBXwJyFEBnhaO92FEL8rZf14J+6a\npGsgmyMnoSIepW9gMBiCsM1fJpx4SES5es8LIf4ANAghvg88MpQDSil/Dvzcte73wO89ti1p/Xgn\nZheUVMt9A0rfVySMUjEYhooxf5WHKErll8Ay4Cjg5f1tSuGxRMzhqAc1QRdApVEqBsOQmepw1BsG\nTxRp9DMp5fNSyqVGoYwucVeZlv6sHqmYnpXBMFRe3LIHgGWrtpmyR0MgilL5pxDiw9ZkXTOs/BLD\nKBBzTdLVlzHmL4OhHLS2tfOTB16xl3XZI6NYSifKOO9oIA68xVoeAD4+bC0y+FI8UlFlWoz5y2AY\nGtfevdr2UWpM2aPBEWWO+o+NREMM4eSnE1bLxlFvMJQHU/aofBhptA+hs3t0nopRKgZDeRgrE/GN\nB4w02oew81RM9JfBUHTwmtcAACAASURBVFZM2aPy4SuNrPIsCCFOGbnmGIJw+1T6jFIxGMrCWJmI\nbzwQ5FP5oBBiB3CxEMI5cUdOSvnEMLfL4IGO/pKukUpF3IQUGwxDZSxMxDceCFIqVwMLUWVZznSs\nzwJGqYwC7umEbfNX0oxUDAbD2MBXqUgp/w38WwjxipTy1hFsk8EH2/xlR3+pAaSp/WUwGMYKUaTR\nPUKIbwgh/i6EuEoIUTPsrTJ4YpdpcY1UTPSXwWAYK0SRRt8FVgIfAl6wlg2jQFH0V9Y46g0Gw9gi\nijSSVt2vXVLKmwEzJdooka9SbMq0GAyGsUkUaVTnWq4ejoYYwom5JunKF5Q0SsVgMIwNotT+etCa\nFOsh4M3Ag8PaIoMv+Um61LKdUW8c9QaDYYwQpfbXz4UQjwLzgW9IKVcPf7MMXlg6xfap9A1kqUjE\nyM/ObDAYDKNLpNlopJQvAi8Oc1sMIQghiInC5MdKM0oxGAxjCCOR9jFiQhQkP5rER4PBMJYIlUhC\niBNGoiGGaMRiwmH+yhl/isFgGFNEkUgXD3srDJGJC1GQ/GgivwwGw1giik9loxDiV8ByoBtVUPJP\nw9oqgy/xmLCjv/oHclSa+ekNBsMYIopSeQFYZX1OArmAbQ3DTEw4kh+t6C+DwWAYK0QJKf6tEGIq\nMMmKAjOMIrGYsJVKfzZnSrQYDIYxRRRH/SeBG4GvWMvXDHejDP7EHdFffRnjUzEYDGOLKBLpKCll\nC7DeWp48jO0xhOAeqRilYjAYxhJRJJIudW/N4sHcYWqLIQIq+kt9Vo56o1QMBsPYIYqj/k4hxP1A\nlRDiz8Afh7lNhgBigsI8FRP9ZTAYxhBRHPVLhRD3AYcA66SU24a/WQY/YjFXnopJfjQYDGOIUKUi\nhDgQuAw4EHhBCLFESrlj2Ftm8CTuyqg3ZVoMBsNYIopE+gHwF+DdQCvw9WFtkSGQguivgawZqRgM\nhjFFFIn0qpTyPilll5TyUWDtcDfK4E8sJrAGKsZRbzAYxhxRJFKPEOJQACHEEUDt8DbJEERMQDYn\nkVKa5EeDwTDm8PWpCCHutL5PAe8XQuwGJmJGKqNKTCifSiYrkdJMJbxfsnIpLLsaOjdC/UxYeAUs\nuHC0W2UwAAFKRUr5tpFsiCEacSv6y8xP72J/EbQrl8LfPw2ZtFru3KCWwf9895drYxgTRIn+SgJn\nk8+kz0kpfzXYA1r7+zWwV0p5kTVfyyVAGlgHXCmllKWuH2x79jXiVkZ9vzU/valSzL4vaEtpy7Kr\n8+epyaTVeq/fDObaDAf2OW4AEQeZhfpZ5b/uY+m+7qdE6eb+EZgNdDr+hsJXgF8BMSFEDPgm8EEp\n5YdQSu7MUtcPsT37FEIIslJFfkGZRyorl8J18+HKBvV/5dLy7Xs4CRK0blYuhW/PhVs/rgQcMi9o\ng853uK6NFvpR29K5Mfr6lUvhtk9EvzbDRcE5ohQKlP+6l3otDcNCFIm0Tkr5IynlX6y/2wZ7MCHE\n+4B/AS9Zq+YBL0ope63lVmDhINbvN8QFyvxljVTKFlI8mBdytJWQVhBaWLlxC1p9juldxdsGCdrh\nFFalKkThc7/rZ3q3WQtwN37KaTjwOkdNOa97KdfSMGxEkUgbhRAfEkIcJISYLYSYGf6TYoQQxwLT\npZR3OFY3AjsdyztRwQClrvc63kVCiBVCiBXbt28fTJPHJGqSLkmfNn+VK/mx1BdyOAWtVhZX1qu/\nb88t3u/KpdD6SW8FoXEL2iDhBqPT24868ghSEsmUMvM4CTtX97UZLFE6FmEKzO/7Up/JUkZxhmEj\nSu2vKuA/gDdZy1ngE4M41oVAoxDiBqAOOA54FqUoNJOAHdZfKeuLkFLeiCrZz/HHH7/v+1wsW/HN\nnRvZGZtC76ovA1OCRyql2JejvJBOu7gXfrb9qPb0lUvhri8WK4r0LvirNau13n7Z1ZDLeLcDvAVt\nmHAZjd5+/Uzv6xlVIYoYnHt98TUPapvXtRkMUf01qYmlKX9NqUoi6rU0DCtRurmzpZTnSik/bv0N\nRqEgpbxMSnmR9fsvA48CPwbmCyEqrc3OAx4CXilx/fjGMSqIIZmS28aMhy/jHbFH/X0qpYwkophV\n3HZxP9xKqMB/gb89Pcg0BZDtL+yhhgl0L0EbJFxGq7e/8ApIVIW3xe98Zc67o+DXNhH3vjaD4a4v\nho8kVi6Fvi7/fQQpOL9z8Fu/8Ap1flH3bxgWoiiVhBBiQpmPmwWyUsoB4BvAH4UQv0aNiu4tdX2Z\n2zb28BBu8WyaSxNL/aO/opoOoppVwgSsRsTUPsOUhLs9UfbvFKxBAr2yzltoLrwCEqni9VUNo9fb\nB0DkP8YrvNuS8rTyQqXHq7lyKfR3e29/6mXFo8PB+MVWLvW/t87rFjSirJ4UrOAWXlHc2Qm67gsu\nhNpp+eX6WeVToDD6PsR9hCjmr4OAfwohNqCe/n4p5TlDOaiUcgPwX9bnZcAyj21KWr/PEsVE5SPc\nZoidbPUbqUQ1HUQ1q0Q19cisUiaJVDQlpPcbZf/OUZOf0ASYcrj3+gUXwtbnYfn3AQE1k6F7O7z7\ndzD3FO/jeY3MvExOgwll1Yp3wHGdspni/aUmQq9P0KX7XN0mKU1VvdrHpIP9ty0l3DjIn5SaqIRu\n2Kj2xE8Fh3zfd5UaiSEACRU18Pbvq++vm198rTO9sHeLZW7bDR9bBnUHBLchKrdfAiv+D3taqdEK\nzd4HiFL6/oyRaMi4oRThEuWl1qYpj5HEJjnJv0yLnx3b3cOPalbxE7BeZNLRFIqzPWH7j1eoa+kn\nNAFSjVB7AASlLSWq1PX84mtKofzwWP/jLrxCBQO4e9onfKpYoQxGOHsqdAl3XgrZ3vx3fiMC7Z9y\n4mWSAqioU0pl16vBxw/KeXES1Ano3xs8QgV1D3av8/7OLcD1/0xamVK1kgF1rW+9SK2vPUBdjyPO\nhX//BnauKY9SWbnU1R7y7Qn0IYbIgHGaUxNljvr3CCHe5/h7z0g0bJ+kVD9GWFRRgGlqIF7FdwYu\n9FYqfnZsLZid+JmRquoLlxdeoUwP5USbMsJGHvEKOO/H6oXzG1nVz4IvroVZr4eO9cXfgzrO8uuU\nwvzpSbDhn2q93/YLLoRpCyw7vYAJTWp9TWPhdn7C+daPB5tJ/ARz7+5oSllmYZejalKQSWpPO9TN\ngJ0OpeI7mt0Q3O4gH5yIKf9XEMkU1B/orVT8BDhYoxY8vrOW925V/5+3sh52rgluR1SWXe3dHii+\nhrdfopRcmAwYSvTkGDfDRfGpJK2/OuBdwOuGtUX7KlGVhA6VvfXj4VFFPgI0S4ynFlzF33Jv8nbU\n+9mxK2qLe0KHnuXdhhnHFi4vuBDe/MX8snaIuh2jmlSjt/9Co+3d4O17STXCBT+HuW+BaUeHm+H0\n+obZ0L2t+LrZpqY+a/sNcMclUFkPHRuKt9X3adNTEIvDBTfCGVeq873vysKXOajXHiRU/ARzVBIp\ndd20cLnri/7b1s+ExoMKRypBfqkwYej37NqCP4Bzr4eZx3krlSABHhXdoVrlyF4YiiAOur9Ok+y3\n58KKX+A7onHi1xG57ROlJ3jeepFSZmOE0KdaSvlbKeWvpZQ/k1KeD1SMQLv2LaKEnkbJq9DoB9Xn\nYRZIXp2uSrMVKZWVS/3NOendxds+84eivVM1ERKVxds+9gP1uW46nH8DXNmp/rtHMMkUnP1teONF\n+X3Wz1JK4u3XqVUfuVMpCl9zTY36vvGgwt54WERQwxz136koghT+QBo2PpkXON+eq7Z13qdsv1r3\n14u9o9fCosC8IqKCnpcoxJKOjoMlXIKerUPPgk1tsOGJvFD1coS72+0WclEDNvyon2Xd17nq+c66\nOj/lzCl5+W51rrdf4iGIP+6d/+Rm5VIQwudLUWiSDbr+7vPyNTtnS7duINXoboyMWAbTVTqs7K3Y\n14kSehqWV6FxRrf4CKttYjJ9GSWQCqK/9MMd1I7QdkslaN1C+e+fziulrs35B3/BharnGUtax3BE\n3CRSSmhdvgE+95xaN/VItd3WF6JFEDXOVdukO9Tywisg7lJ4zmu282X1/8dvKBQofgI82w87XsoL\nnPQu721lttiso5XFwiuUiS4I3bG4br4SaEMRzKAi3HID0bZN1qjOQ8YyMWqFmMsBIbXj3EJuKELf\neZ8mzlH77nQ9Z0Mdvbnp3KAErtf1Tu8KNjnZyt9n9HX8R9V/T0HvQkdFavyi+aD0KE0A5JipHBDF\np3KnEOIeIcR9QojngAdGoF37FlFCT6O+jIkUrH/Ct/xIDsEPeS/fu1dVujnn+kdobWtXX4Ypt6i5\nDwO9ys+gHd5h4ckLLoRDzoAD5qtjLLtamY4eWqJeyJ+cmH+hph6h/m97Pvgl0Apw4lz1f/favGMz\n2+fYzqHEVi6F5dZoSvdI/QSKjcPpOxg6N6pjz3xj8HapidHyfKJQP6t41OlHMqVGnV73774rQGZg\njkfkm3tbfa9Kzc3R5lF3eK8efV5/bDTlHxtK4dSA+xuUoR/0Ph1yJsw+IfqI06mcw3J3IHqUZtBv\nRoko0V9vG4mG7NNECT0NykR3kt5l2WU9iCXZKifxh/QJSFQvdXNnL5ff+iwALWEP1cGn5z8HRJVR\n1QC9HUpwrbkvWm2thlnwygPekVnOaChQgiasV6UVYKOlVJ7+A7T9tnDfWmE7s+wHegv3EyRQkhHD\nnoOIFD5rmU9CjxVBwelzjvo8nXu9srl7oR3b214I30/nBtXR8RxZ+rQ7NQm++Grx+pVL4fEfWQta\n+fs88yKuTKygfGBaGMeTKqItijk5DL/yPIHRiMnSTYFOBRZmtXCObLyqTHgxRioHRBprCiGOFEKc\nZP2dMNyN2ufwyooGmP/OfA86KLopKrEEHbnKotc3ncny9B03+psOqq1ZC3avK8xy90t4bDpOff7O\nXH+BBIUPccPswjBYN5m0ejmi9OxSjXlFsalN/f/XjeHJnKX01EQcXve+0rZ3m7hiSRU+Gybcj/9o\n+MgimVLbpRo9vrSUkrO3HyUaT/svwoRNz87g7zVegi3VqNrt1ZYTP+m9H0/l74MztN1p7stmPNrj\n5/sIwW2aCjMjJ1Iq4GEwI4PODdE6AzKrfLBu/54fY6hyQBTz1w3AFcBZqDLzJm9F47SRO18SHflU\nVR/sxPMUIAEMpEnIYjv6O2KPcmnmJ/5K4qRPqc/P/DHYoagF7WuPOlb69JzdD3HD7PD2p3eF9+y0\nkx+sWmCXBm8fNcvefYzzb4CX74m2fapRbX/ej5WgBqVgEhXe4bM6BLmiVvl/zvlucNu0snj791SA\nQhFSbaP9UpD3ZVVP8j9HfX+GIxxcU1Gj2n3u9cXneOLF3r8pRRjrfUYaFUh1r0o9V7ffKOhYyRTM\nfbMy39U3eW8j4gHvdgmKL5cJ6YCJfJu8KgeMUuhxlJFKSkr5HinllVLKq6SU3xj2Vu0LBNXCGkhD\n7VTVmwnLq9BCKgrJaipFsVK5NLGUauEj3M69Pj9SefKm4BdT5pSgHejz30bjfoijKJVARLHdPYog\ncQqyKMIz1Zg/RphwS6ZUxNoX16rtF1yoBPuJn4Jc1n/0KXNwZYcKQc72wb9+Dr17vLfVuUNRQ6ad\nLLgQTr28eL37OmoFVMqzFhXdrgUXwueehylH5L/70eu9BVkpyl8rxqiKKL1bnWupHTbnqDfoWOde\nD/POVvf1wJO923z+DapjVPQsDtF/5+aCG+HghTB5XnEy7mDmDCoTkWp/DXsrxipBmj5sjojubbDz\nlXAhEbUXGa+AGc3UxIt7LjOEj/lCR63ctTh8/2D5hqKUS5nlUawxRKkkU/4velWDEsLOnjhEE/rO\n0ZIWnhMChJYOVYZoowevDOcZzcE9SL3fSYeo//d8Bfp8yqy4C2WWUkRx5VK496uF69x+Jo02mXmZ\naYeCs10rlxYmHPoJsijPvLvwZVRFVD9T/cZzxBeCft5874H13OuO5MqbXRsINdLXHZBzr4eaqeqr\n6skEKpRSlaBuy8Q5ebP2dfOtHLiLSp8zqIxEUSoNQoiVQoifW383DHurxgJhGa9hAi+Tho7Xwqv/\nevUia6ZSMExONSrTy5R5VMay1FTEqbLmUWlqSNFb7Sii5z5GVGeiFkZhL6+f7ba6UYWuQt734I78\n8ey9oZRfqT1aP6G/4EI442v+v3PeNy/hpkcnbgXnpGuz//71fiGvVLIhI78obfK65qXON1KKLyMK\n7goNXmHzXu1ZcKHlz/IxBenevvP6R1FEUUc2fgK8fqa//9NZ/eHxH/vsWBaaVBdcCBdbVRve9Fn/\nkWL9LO93Q4fpu3Fe98a5Kqjmb06rSYDyGoEIsSijkP8BcuRbGiFldhwQ9sL6RU5pYhWQ6482qZLu\n2dz7NVXssHtbvnie88Vqf4q4HCCZiHHczAY60xn+evHJsPLq4qgrfYwgR7sm1agean2sWz/uv61f\n7/3ZP+eFZ6IyX1bFC3c0S98e71pZC6/wjiY78VOw6Jv+bYwSquw8Vin1l1YuhQcCju0MMnjtMf/t\nytGmUucbiSxQIphp3M9Mqcd9+R7vY/iV5ve6LoeepfbjdZ1851aZ5f1cJVNqf17Pm/Ncr5sfrJjd\n51rdCDVTYPsqddy/Xlzoh3OPLO9crJRE3Qw48ypVVLNrc16OuK/7xDnq/0DEKLQRiBCLElLsERO4\nHxBUFyksgimWVArFC7+XZuVS+OfP8sv93cWCNl5BItdPVkp6+gaoqYjnv+/tgjutUg3OCbACQ0+F\nitx5+/fyq3SWu2cxSg+zl2773z+dj87p6/IvqKjb5N6/V3G+AkHiOIfnboXpryt9wjEo7vFrhR6V\nMCeuM8jg9s+E789rFBK1TaVOSuW3fapRdWKcwvqZPxSf59HvgnfeVJ72lDo/DJR2r/wUh1OA3/PV\nfFXjs7/jf2+dJtNSJ3oDVUl62yrV0Xr2FpXpjyhWhAsuVO/YL98K8y9Q5YD2tKvpDc75rve56zyu\nKIxQhFiZ01fHEUGTHAVmz89S2c5++L00y64u7m24TQfxCuJygFxO0tOfpVorFYBJ1sP1ob8Xmm4C\nzQbSOwLKayge9EAO57Sv2hcQc/R/ujYFOx397p1zFDFYwpy4kYIMPEKEB0MpprKg7c/+tnpmtF/L\njuZymWtefSjY0VtKe0qdgKtUCszKHkEgCy6Es76uLA7p3cGdr6gRhn7nKhLQvkL5O16+Wz3LF9zo\nbWKddrT6/88blEKB/Eje69pPPNC/PU6cASrDjFEqfvi9IL4jFKFqYX3uueCchKFMnZqoJEYWZI6e\n/gGqKxyCdotKgOSA+YW/1y+XH36CPOiFHEzbnZQqUJZdXVySJEhpBQnPoRLmxNUEKZ8Lbsw/K0N5\nyUu9T6Vsr5W5syRO97ZgZV7K/ktViINBR+t5BYG4S7B06umiPIgSYegntFcuhXWuyWlzA8oM9v/b\nu/sgq+rzgOPfZ+/bLi/C7kJRXOhiJkGoocWhMyomqYBiEgKOrbbTcVCmGrU6MSIITuMq4AuGUu2M\nMdGhJYoOio0lWmoSwKiYmCqpHSgypnFMYRdNcWWBlbvu29M/zrm7dy/3fe89956zz2dmZ/eePfec\n37P37n3O7+X8fun+ju/9++A+yTK93997OX2ZU8uWGMHogZE7siuXxAvw8p1OkhhzpnNlk+mKJvmN\nl3FtEMm+dGqu44acjrsa7eFUd3SwprJvG7y63vn58S+f3gafrRks04dkIU0NhTbDZGuaSKfQpFVM\nX0m+8i17tjb9Uv5zF9p8V8j+qVPiQO71VvI9fjlfo3xkmvcutT8pXf9n4vn5lHv32vRzhyVG/aVL\ndJmkvt9z7Z8ofykupgpgSSWbWVfDyY+cYZudHzlvgs9f5kwXkq6zLSFtB7Pbf5HpzZfPh5U7qirU\n30O8u8+pqeS7QFShH+SFKPTYhf5jFpq0EucoxwdUvmUv59/bK8Pu8M+hXK9RPjLGoM6w696uoX2T\nyQopd9ZlEQqc3yuvCWFxF2/r9z5RuyypZLNvG7z6wODj44edFeWSrzzSjYIp5iosn+e4SSWs3Zzo\n7nVqKvmu3lfOK8Ni4y1Fp2sl5FP2Sl+Jl0IxydwvstUkR090Rm1d86PynSfxu2T5TEybz/6Jm28r\nxJJKNuk+sFPH4WcaylfMVViu5wwklV76gVGxUOGd3uX6UCv3scF/H9CVvBIvhWpL5qWUabh696fO\nSMpJM0t3nnRLUmdahTXtxLQZRoxWadK3jvps8qnme3SXKjCQVCLuVC2jIqHyj6KpFtk6XU15FDoQ\nwE8SsaXeCBn/BD7rgFMlmP04cZ4rHht6nsTNzOmaTNMNXki9ETTX/hVO+lZTySZb1TWZV+sYhJ2k\nEnWnvR8VDbtXQjcPHS1SBW8sExB+r21lk+meKcj/xtV8z1OOwQtVWoO3pJJNpipyKq9qBaGUpBIL\nOW+gVx+C44ec6cCr5I1ljC9kuiDsqlCfRDlH83nEkko2A8OKsyyS42WtINH8NVBTCTlLwnb+Hs6/\nFr7+996Uw5igyNQaMeYPvC9LQFifSi6zrs4842mmDrRyGaipOJ1+o6JhZ9LK7pODd+IaY/KX6WbG\ni+/wviwBYUklH8XMU1QOAx31zl39kw+9BJvmO7/7+f2eLcJjTGAkOuxTl0v407+pTHkCwJJKPqpl\nhFVSn8rimjdoemP14FKwnb/3bBEeYwJl1tWw/ACcOct5PHriwOwVpnCWVPJRLUP3woPNX3eGt1GT\nawJKY0z+6uqd758e9XT53aCxpJKPahmvn9RRP1k+Tr+PV8ObjQmSfdvg0JuDjz1cfjdobPRXvqph\n6F5S89cRnUBTusQStJsejfHC7rVD5/OD3JNnmrSspuInSXfUb+z/y+pokjMmCMo9eeYIYknFT5Jq\nKrvDX4FLk5a1DdIUGsZ4rVoG4wSAJRU/SeqoHxUNw7SLne1//k82H5Yxw1Etg3ECwJKKnyR11I+K\nhQYnvRvVkOVJxpicqmUwTgBYR72fDCQVd336xNQxqTOtGmMKVw2DcQLAaip+UhNGEaLiNn8lbny0\nmooxpkp4WlMREQEew0lmU4CtqrpFRC4AlgNx4HfAvaqqhW73MpaKEKG/JkIUd9XHU1ZTMcZUF09r\nKu4H/9+q6o3AEuAOEakB7geWquq1OInu0kK3exlHJWlNhAi9jI6GneavUDTzhJfGGOMxz5u/kmoU\ntUA7MB04qKpd7vbtwPwitg8hIt8Ukb0isvfo0aPlCaYC+kNRovRSl6ip1DWASKWLZYwxQIX6VNxm\nsAeBFqABJ7kktAP1RWwfQlWfUNU5qjpn4sSJpQ2ggrQm6tZUQhA/BqMaK10kY4wZUKmO+pXAHlX9\nBfAxTqJIaHS3Fbp9RNCaCFHppS4admoq1klvjKking8pFpFbgOOq+py76X3gPBGJqepnOH0trxWx\nfURwmr96BocUT/hCpYtkTOD19PTQ2tpKV1dX7p19qLa2lqamJiKR4U/57/XorwuB1cAOEfmBu7kF\nuA/YKiIngaPATlXtF5G8t3sZRyU5HfXufSqn2q2mYowHWltbGTt2LM3NzUjA+jBVlfb2dlpbW5k2\nbdqwj+dpUlHVN3GGEqfa7X6l7l/Q9pHgZI8QpYf7drzLdbWf8P6JCNMrXShjAq6rqyuQCQVARGhs\nbKRUA5rs5kcf2f5OG22d/UToZSxxwvSx/b04299pq3TRjAm8ICaUhFLGZknFRzb89D26+8NEpZfx\nchKA/+sdw4afvlfhkhljkm1/p425619h2uodzF3/yoi68LOk4iNHOuJ0EyZKL/V0AnCMMRzpiOd4\npjHGK9vfaeOuF/bT1hFHgbaOOHe9sH/YieX666/nww8/zGvfF198kWeeeWZY5yuWTSjpI5PH19H9\naZgIvdSLk1Q6dAyTx9fleKYxplTWvHSAd4+cyPj7dw510N3XP2RbvKePO/9lH1vfOpT2OTMnn8E9\n3/ijrOft7e2lr68vrzIuXrw4r/3KwZKKj6xcOJ3+7REa9DgPR74HwPej/8ihmXcB8ypbOGMMwGkJ\nJdf2fGzevJm33nqLe+65h6lTpxIKhThw4ADr1q3j9ddf5+DBg8TjcZqbm1mxYgVPPfUU4XCYq666\ninnz5jF16lRisRidnZ1s2bKFWCxWdFlysaTiI1fMPpv21z+j/tixgXbLSXKMSfvvgeZ6m7bbGA/k\nqlHMXf8KbWmapM8eX8dzN15Y1DmXLVvGa6+9xpo1a9i0aRPd3d1s3boVgI6ODvbv308sFmPz5s2s\nWLGC/v5++vv7CYfDHDhwgF27dhGLxVi1ahU7d+5k0aJFRZUjH9an4jONnb85/UXricPutZUojjEm\nxcqF06mLhIZsq4uEWLmwdIP/586dC8CJEye44447aGlpYePGjYwZM2bIfiLC7NmzB2om5557Lh0d\nHSUrRzpWU/GbnlPptx9v9bYcxpi0rph9NuCM1jzSEWfy+DpWLpw+sL1YsViMnp4eAEaPdmYmP3z4\nMDNnzqS+vp4jR47w7rvvnva85OHCXgyLtqTiN9Ex0N15+vZxTd6XxRiT1hWzzx52Ekk1b948br/9\nds4//3xqapz2ipkzZ9LV1cXNN99MXV0dl19+OapKTU3NwD7JU68kby8XGQlrW82ZM0f37t1b6WKU\nxtN/Ab9NmZUmUmfraRtTRgcPHmTGjBmVLkZZpYtRRH6tqnMKOY71qfjNmV8EakDcl27cFEsoxpiq\nYc1ffhOOAf2gwCV/B1+5s9IlMsaYAVZT8ZtQ0tTUY8+qXDmMMSYNSyp+E4oO/nyGJRVjTHWxpOI3\noaQ7Ya2mYoypMpZU/Maav4ypfvu2wcPnwb3jne/7tlW6RJ6xpOI3ieavcC3U1Ve2LMaY0+3bBi99\nC44fBtT5/tK3hp1YCpmlGGDPnj08+OCDwzpnMWz0l9+E3eavsWdBgBcNMqZqvbwaPtqf+fetb0Pf\nZ0O39cThx7fCTMTuNgAABq5JREFUr59M/5wzvwhfXZ/1tIXMUgzQ19dX0P6lYknFbxLNX2dMrmw5\njDHppSaUXNvzkDxL8dKlS9m0aRMNDQ2cOnWKjRs30tnZyapVq2hoaOCcc85hwYIFPProo7S1tTFp\n0iRuuOGGos9dKEsqfpPoqB97ZmXLYcxIlaNGwcPnuU1fKcZNgWU7ijpl8izFy5cvZ82aNcyYMYPn\nn3+eJ554glmzZjFp0iQ2bNgwML/XrbfeyhtvvOFpQgFLKv5z6E3n+3//CA6/BfNb7G56Y6rJ/Ban\nD6Unafr7SJ2zvQQ++OADnnzSaUaLx+NMmTKFyy67jHg8zi233MKVV17JggULSnKuYlhS8ZN92+BX\n3x98nOgABEssxlSLxP/i7rXO7OHjmkpy8ZeYpbi5uZnbbruNs84aOvpzyZIlLFq0iIsuuogFCxYM\nmdXYS5ZU/GT32vQdgLvXWlIxpprMurrk/5OJWYovueQSli1bxoQJEwiFQrS0tNDa2sqzzz6LiLBw\n4UIAZsyYwerVq7n77rtZt25dScuSjc1S7Cf3jseZ9CuVwL3lXXjHmJHMZinOn92n4ieZ1kyxtVSM\nMVXCkoqfzG9xOvySlbAD0BhjhsuSip/MutpZO2XcFEBsLRVjPBTkroJSxmYd9X5Thg5AY0x2tbW1\ntLe309jY6Mk6715SVdrb26mtrS3J8SypGGNMDk1NTbS2tnL06NFKF6UsamtraWoqTd+sJRVjjMkh\nEokwbdq0ShfDF6xPxRhjTMlYUjHGGFMyllSMMcaUzIi4o15EjgL/O4xDTAA+LlFxqoHf4/F7+dMJ\nSkxBiSMhSPEUE8sfqurEQp4wIpLKcInI3kKnKqhmfo/H7+VPJygxBSWOhCDF41Us1vxljDGmZCyp\nGGOMKRlLKvl5otIFKDG/x+P38qcTlJiCEkdCkOLxJBbrUzHGGFMyVlMxxhhTMpZUjDHGlEwg5/4S\nZxrRx3CS5hRgq6puEZELgOVAHPgdcK+qqrv/cmCpqv5x0nFiwHeA8cAo9zi70pzvRuBiIAo8p6ov\nuNtHA48CPar6TT/HIyL1wEM4S082AxtV9Wc+i6HOLUMPznv/P1T18XxjqLZ4kn53BvCvwG5VfcCv\nsYjIYWCHu9tRVb270FiqLJ5zgduBXmA0cJOqdvkpDhG5GLgmabdLgbmq+lHGgqtqIL8Y7C+KAP/l\nvjC7gVp3+/3AZe7PFwFfB3alHOO7wMwc5/kC8JT7cwj4GTDOfXyd+yJs8ns8gCSVoQHY47cYUsvi\nnnuUX18T93EN8AhwFfAdn8eyq9jyV1s8OEnkycT5/BpHyj7jgGdzlTmwzV/q/hWAWqAdmA4c1MEr\nhe3AfHffX6rqjuTni0gNcDZwlYj8UEQecjN+qvnAi+5x+oCdOC8uqvpD4H+CEI+63H3GA21+iyFl\nnynAMZyrvaJUSTy3A08Bw5qTvUpimSAij4jI0yLyJZ/Hswg4CDwkIltFZIlP40h2E7ApV5kDm1Rg\noPr4INCCc3XdnvTrdqA+y9MnAnOBx1X1OuAD4E4RmSYi/+Z+LS7iuEWrhnhEJIxzdTScpomKxSAi\nF4jIz4FfAi1J/7RFqWQ8IvI14ENV/c/hxFANsQCo6p+o6reBm4H7RGS8j+NpxrlwWQ4sBW4Tkc/5\nMI5EGWLAn+HUkrIKdFIBVuI00/wCZ86bhqTfNZJ9HpxTwF4dbDt8AZijqh+o6iL368UijjscFY3H\nfXNvAB5T1WJrYBWNQVV/paqXADOAR0QkWmQc1RDPlcCXReQHOB9eS0Tkr3waywBVPQm8jfPBPByV\njOcU8LKq9qlqD/ATYJYP40i4BqcvJudFWGCTiojcAhxX1efcTe8D5yVV/ZYAr2V6vvvGbhSncxfg\nS8C+NLu+Dix2z1mDU418e/gRDFXpeNyEch9OZ/AeP8aQsk8XTpNC0f8DlY5HVa9X1ZtU9SbgH4Af\nq+qzfowlpSxh4Dzgt8XE4h6j0vG8CVyetN+FwAEfxpF4/NdAXu+toI7+uhBYDexwr+LAqTreB2wV\nkZM4bdA7U57ak/J4NbBFRD7B6bz6duq5VPWgiOwXkadxPqS2qOqxpF363S9fx+NeAV+J8wZd5MZ0\nm3sV5pcYvgpci5NQJgAPaAGjcaotnpTd+twvX8YiIo3A94ATwBhgvap2+jUeYK+IfE1E/hlnNNXL\nqvobH8YB8A3gJ6ranVe5h9mkbIwxxgwIbPOXMcYY71lSMcYYUzKWVIwxxpSMJRVjjDElY0nFGGNM\nyVhSMcYYUzKWVIwxxpTM/wOnfeEMIvM6/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106440cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train.groupby('pickup_date').count()[['id']], 'o-', label='train')\n",
    "plt.plot(test.groupby('pickup_date').count()[['id']], 'o-', label='test')\n",
    "plt.title('Train and test period complete overlap.')\n",
    "plt.legend(loc=0)\n",
    "plt.ylabel('number of records')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEhCAYAAABcN4ZbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsnXd8W9XZ+L9Hlm1Zlu3EK56J4+wd\nsoFQKARSVil7rwJpKfTlLX27aQulUN5f35YOeAthjxcKLRtaViABQhJICBlkD8eOR2Q7XrIsS/I9\nvz8eyZYdySvyzP3m44+u7j3n3nMV6Tz3mUdprTExMTExMYkWloEegImJiYnJ8MIULCYmJiYmUcUU\nLCYmJiYmUcUULCYmJiYmUcUULCYmJiYmUcUULCYmJiYmUcUULCbdQil1vVJq7gBe/32lVH6EYzcd\n5bmPqv9wQCl1klLqsW60m6aUOrE/xtQTlFIrIn0/TPofU7AMc5RSf1BKrVRKrVVK1Qa2Vyqlzu7J\nebTWT2itN/TVOLuBFYiJcOwXR3nuo+0/HIgh8ucbynzg9D4eS2/o7vhN+gHrQA/ApG/RWv8QQClV\nADyptT5lIMdjYmIy/DEFyzGMUupqYBwwHTC01pcopR4GxgMpQCVwmda6Tin1S6BIa/2MUupuwAec\nBMQBTcCVWuvqMNe4C1gMOABvoF2xUuoGYHLg2gmI9nyd1npfoN9PgMsDY9gOxIY5dzbwPJCllFoJ\nPKS1/rtS6mLg+4AfaABu0VofVEp9D7gk0P1L4Cng/pD+d2qtV3a4xveACwNjjAVu0lp/qZQ6KTC+\nBCAPOBs4EfhloKsPuF1r/ZVSanLgOjZgFPCY1voPYe7HBvw68Lm2AJu11t9XSo0P9E9GfrOrAmP1\nKqV+E9h3HJAKvAV8BPwYyAde1lr/OnD+R4GvgNMCbQ8F7qeqwzjswB8C/z8WYCVwJ/BT4FrAppRa\npLX+hlIqDfgrkBX4fP5Pa/1QmHvLAv4E5AAK2Ab8KPD5vay1PjHQTgGfAV8H0oE/h9z3H7XWrwRM\nXg8AewL3fVWHa0X6zt0RaLIAGAl4gGVa6/0dx2tylGitzb9j4A8oAFZ22HcdsAMYEbIvOWT7d8D3\nAtt3IhM/wF3IBBYbeP9H4JcRrht6vu8A/y+wfT2wEUgMvP8PZMIFWIRM/MFjXwc0UBDhGkUh21OA\nFUBc4P3ZwN8D26VAUmf9w40fUIHtpcCLge1TEKE3OvA+HfgccATeTwfWBLZtIeOxAsXBdh2udT8i\nmCwh+2KBrcCJgfcWZLL9Rcj/xSuIGcgCvAc8jQh8C/ABMDnQ9kng1ZD/t9uB/w25nycD279FJlwQ\nIfAP4Bsh35k7Q8b3LHBGyL2tAaaGubcPgEtCzvlD4JHA+3eA8YHtBcAzge0PgemB7QRgJyIQC4BG\nYF7I+VcGvx9E/s7dCayj7Xt1AfCvgf5tDsc/U2Mx+UxrXRvyfqlS6iIgE8gG/h6mjwZe11r7Au9X\nABdFOP88pdSNyBNtKiIwgud4W2vdGHKO8wPbxwOvBo9prT9USm3t5v0sAUYD78rDLxbkyRTEl/L/\nlFKPaK2/6Ob5CoHblVK5QGLIuQC2a62LQ8acBbwZuC5AUuDVCvxMKTUzsC8ZEUSuDtf6JjBRa22E\n7JsMlGitVwNorQ2l1L3Am8A9yOf4L611C4BSag2wQWvtDbz/JDCuHYHzPRHy//Yo8EmYez4TcCul\nrgi8T0Y+03AsAfKUUj8PvE9EJv5twQZKqWQgS2v9YuAetFLqT8DeQJMnEe3vbuBq4LFAn7nAAyGf\npx/RwuqAaq31+ghjivSdA3gu5Dv3CvJQZBJlTMFi0moGUEotAW4FbgT2AbcgP8xwNIdsNyJPoe1Q\nSk1Enq6vQiaaM5GnxK7OETqxBjnCFBYBhUye93Y8oLV+Uin1AvAfSqkrtNb/1emJlEoCXkYmu/XA\nJOAvIU1CTSgK+LfWelmYUz0EbAGuAWqBSEEQFo78HFsitA39jHwd9tdFaAftP0cb7QVlEAVcqrUu\ni3DtUPzAaUHB1slYw1W7DY7tVeADpdR9wDzgNsSMVa7D+AQD/sKw5qtufOdC719FGJfJUWJGhZmE\n/rDGAWu11ruRSeciwgiMHjAW2Ka13hR4f2U3z/cB8E2lVCKAUur8wNgiUauUCgrAVcC3AzZ9lFIx\nSqlEJcRorZuAJ4ArQvprpVS4iKIcxNz1KWKrv6bD+EM/u8+As5RSkwLXVQHBRGDsr2uta4CFiBYS\n7nN4C/itUir0d7kTyA74dAiM8w7kabs3XK+UCk6uNwPvhmnzAfCL4DiUUsmqTW2opf3Dxlrgh8Hj\nAU2jHVprF1CilLos0EYhZrh3A8ebEMH7Y+ANrbWhta4HGpRSlwbP0+HckQRCV9+5K5VSjsD21cDq\nCOcxOQpMwXLsYHDk029Lh33/BOYEzCnPAc/QFsIZ2rZjv47vg3wIxCilPkcmwldp+8517NM6Pq31\nFuBBYJVS6n0kxHVVhGsEx/2JUup/AhPKXcBbSqmPEVPPYsQE9XnANPQy8KuQ/u8CG5RSPw49qdZ6\nJ6JdbERMT2tom9DajV9rXYFoek8HrvspbU/KdwMvKqXeAy4DXiJ8aOyPgXhgjVLqY6XUwwFN4ALg\nvwIBBquQyT3o/A/3ORqdvF8NvKqU+hQJ0rgvzHnuCoxjXeBeXqPNuvEZcJpS6kOl1HGIhrsAWBto\n+2iY+wIRyucqpVYhwQVjgVCN8QngZ0hARZArkIeETwP/b3dHuOfQfZ195wDeBp5TSn2EBGXcHmG8\nJkdB0ClpYmIyzFFKPYk46FcO8FAGBKXUnUigxpMRjv8BcfQf6s9xDUdMH4uJybGDD/GJHKt0ev86\nkPNlcvSYGouJiYmJSVQxfSwmJiYmJlHFFCwmJiYmJlHFFCwmJiYmJlHFFCwmJiYmJlHFFCwmJiYm\nJlHFFCwmJiYmJlHFFCwmJiYmJlHFFCwmJiYmJlHFFCwmJiYmJlHFFCwmJiYmJlHFFCwmJiYmJlHF\nFCwmJiYmJlHFFCwmJiYmJlHFFCwmJiYmJlGlT9djCSyB+hTg0lovU0otQlZsawKKgDt1h7r9geVL\nz0LWQ48DfqS1diqlHg+8dwWa/o/Weo9SKgP4E7LOggZu0Vo39uV9mZiYmJhEpk/XY1FK3YUshXoJ\nsAx4Dzhba+1RSt0DrNJav9uhzyrg61prQyl1OZCmtX4gsPrdHVrrgx3aPw38Vmu9Syl1eqDvz/vs\npkxMTExMOqXPNBal1BXI+ti7ArsmAdu11p7A+1eBi5D1xkNZC8xWSn0FzAP+HNjfAPxEKZUMbAd+\nH1gPPEdrHbzGCuAnEcazDBFuJCYmzp08efJR3uHAoTV4PNDQAC0tkJICCQmgVOQ+hgGWMIbPSPuP\nFsOA5maIj++b8w9mNmzYUKW1zuiPa/X0e21oA4sanP8hGk2jtxGX14XNasPX4sNqsRIXE0dCbELY\ncRvaoLmlmfiY+EF7X8OFDRs21ANrtNbf6KptnwgWpdQcIFtr/ZxSqiCwOxWoDmlWDYwM0/1h4C9A\nBbARKAXQWn8/cG4F/By4Fng8tGNAywk7Jq31cmA5wLx58/T69et7cWcDj2GAywWHD8PatdDYKPvO\nPhtycsL3cbmgqAgKCsDh6Hp/tMbpdoPdfuwJFqXUgf661rD5XmsDt89NfXM9T37xJE3+Jk4uOBlb\nrI3CkYVkObIiCha3z4091m4Klj5GKbW7O0IF+s55fwkwSSn1EHAPsBjRPlJD2qQBVaGdlFJZwCPA\nRVrrGxEt5TehbQI+mdeAmYFdRkh/S+j74YjbDdu2werVsHUrjB4NY8fKBB4Ju12ER8c2HfcHhZYR\nhU/QYhFhFSpUonl+k+GF2+dmX80+Vuxbwas7XyU3OZfxaeOZmjEVR9yRTz2GNnB5xd3qiHOYQmWQ\n0Sf/G1rrn2qtl2mtvwv8AvgEeBCYrpSKDzQ7D1jVoWsKUBZiLlsDFIS5xCnA54HtSqXU+MD2qcCG\nqNzEIMVuh8xM2LULdu+GykqYPh2SkyP3CTfJh9vvdosG43b3zdj7+vwmQxd7rJ3MxEy2HdrGgZoD\nlNeXs6FsAzVNNRTXFeP2tf/SuH1uimqLcHlduLwuDG0+rQwm+jQqLEAL0KK19iulfgs8r5RqACoR\nZ34rWuudSql1SqlHA/1igTsAlFK/ok3IFCOCCsQs9j9KKRcSNfb9Pr6fASNoXqqthYoKmDcPTjhB\nBE00zE2RNJto0dfnNxmaBM1Z6fZ0ThxzIp8c+ISUhBRmjppJfko+3hYv9tj2Xxp7rJ2CEQUY2qCo\ntoiCEQVhNRuTgaHPBYvWugT4TmB7BeJgb4dSajnwK611hdb6gQjn+U2E/QeAi6M34sFL8Ik/Jwcu\nvlg0le4Kle74PIIaTF/R1+c3GZoEtY/RKaPJTc5lbt5ccpNyGeUYRVxMHHExcUf0sSgLjjgHhjYo\nGFFwhODpT3w+HwcPHsTj8XTZVqPRWqOUQtFJtM0AYrPZyMvLIzY2ttfn6A+NpUu01ssGegxDAZsN\n0tPB6xUTWE8iuoJCqbuO+mPZ+W7Sv4RqH/sO76PJ10SGI6NbwiIoYAaSgwcPkpSUREFBAZGCh4K0\nGC14W7zExcQRY4nppxF2H6011dXVHDx4kLFjx/b6POaUMYTweKCqCpqaoLy8Z07woBnKZuueA70/\n/CGmM98ERDjYrDZcXhfpiek0eZuodFXi8XetAQwGPB4PaWlpXQoVkHuNi4kbtMEGSinS0tK6pX11\nxuC8O5OwWK0yCX/+OTz5JKxZ0/2+QTOUx9M9gdEf/hDTmW8SpKyhjOc2P8eq/atAQXZS9oCYt4LR\nZj0NBuiOUAm2i7HEdLv9QBCNsQ0KU5hJ9ygpgZdfhuJiqK6WXJZwdGbG6q7A6A9/iOnMNwlS01TD\nB0UfUFpTyrIFy1iQt2BAnupD/T0WZTHzY3qJ+YkNIdLSJJN9zx5YuhTOPDN8u840gUihxwPBYBqL\nycDibfHi9/uxW+1My5yGzWrrsk9vtYvOzmWz2igYUQBAUW3REWHOg5kbb7yR8vLybrf/+OOP+d3v\nftcnYzF/0kOEoB9i7FjIyIDLL4cRI8L7J4JOflvXv00Tk0FBRmIGhSMLmZYxjbSEtG4JjGBSpbPR\nGbFtd4VPUFPx+D044hw44hwDHm3WU/x+Py0tLd1u39LS0qP2PcE0hQ0RXC746CPYtAlOOw0mTBCT\nWLgor6CT3+GQP69XzGj5+RB3ZOSmicmA89Whr9hSvoVr515LbnJut3JT7LF20u3p7KvZh6GNsGVf\nggKjO+cKFSSDIdqsJzzxxBN89tln/PrXv+aaa67h0UcfJTU1FbfbzR/+8AdcLhc/+clPSE1NpbCw\nkCVLlvDAAw9QWlrKqFGjuOmmm6I6HlOwDBH8fti3TzLuTz5ZIsOSk8NrJR19FyUl8MYbcO65MG5c\ndMdlhiWb9JZgYqTVYsXtc5Odks2MUTMYYRtBXExcl9pC0AdS21RLkS4iOT75CGHQUWB0dq7+FCTR\nrnF2/fXXs2rVKu666y5uv/127rrrLqZMmcI//vEPli9fzsyZMxk1ahS///3vW53zt956K5988knU\nhQqYgmXQE5y4S0rgk0+kirHbDa+9BrNmQWrqkRpLqOPdMKT68SmnQG5u9MfX0/yYrjAF1bGBoQ2c\njU4qXBUcch3i5a9e5sKpF3Jc9nE9muQtykJqQiqjR4wOKzwGq+bRXU2qN+zfv5+nnnoKgKamJvLz\n8znjjDNoamrilltu4YILLmDJkiVRvWZHTMEyyAlO3C+9BO+9B1ddJb6VM844MqIq3KTsdoumo7Vo\nPdGms8iuzoREpGPRFlQmgxO3z42z0UmCNYE3d7zJB0UfsHjs4m457YME/SZTM6f2eSHKaGsY3dWk\nekJ8fDw+n4+CggJuu+02srOz2x0/77zzOOecczjhhBNYsmRJa/u+wHwmHOTY7ZCXB/X1svZKYqL4\nUAoKRFsJNymHRoPZ7TB5MkyZ0jdhvZ1FdnUWnRbpmBmCfGxgj7VTOLKQ2JhYimqK8LX4qGmsoayh\nDL/RvScgt89NcV0xFmXp85DgoIYRrSixoCYVzXGfeuqp/OAHP2Dx4sVcf/31XHXVVVx77bXs3buX\nVatWcfPNN/P973+fpUuXAjBlyhRWrlzJL3/5y6iNIUifriA5WBlq61ZUVIiPpKQEzj8ftm8X01bH\n9VdCtQAYeJNSbzSW4YBSaoPWel5/X3eofa8BdlbtZPn65cTHxDMlcwoKxZJxS8hyZHXZtz/XYuns\nWtu3b2fKlCl9ev3+Jtw99eR7bZrChgAWi2gpt94qWkp2toQTh2sXNB/15SJe3aWzJMueJGAOZyE0\nVDmaST3Y19viZUPZBq6ceSV5KXlsrthMfko+6fYwX+4w9Kf/ZLD6agYr5s90kHP4MPz853D//fCv\nf0lZl8xMETSd1dgaTiYls/TL4ONoTEPBvs9uepafv/9zNpRtwGa14YhzkBSfZGa6DwPM/8FBzjPP\niOM+O1v8JPX1oo3s2wdOZ2ThMpyy2oeTkBwuHI3z2R5rJy85jzpPHR6fh2Z/M/ZYOxmJGTgbnWGF\nVTSz7PuC4eRSiMa9DINpZ/ji8cgyxB4PzJkjzvtt20SwpKeLYDkWnuKHk5AcLhyN89miLDgbnRTV\nFLFkzBIW5S/C7XPT6GskMzETQxv4DT8ur6v11eV1dZllfzQcjeCy2WxUV1cPC+ESLJtvO8qyHaaP\nZZBiGPDZZ7B3rzytz50LEydCTY0IlMJCmWwNo2frspiYDAY2lm1k7cG13DDnBmqba/EbfgpHFuI3\n/Gyv3E5+Sj6Hmw6Tbk+nyl3F6JTRZCZm4mx0tvp1OvPv9NQHdDR5JXl5eRw8eJDKysoe9RusBBf6\nOhr6TLAopWKBpwCX1nqZUmoRcDvQBBQBd+oOIl4pdRlwFtCMLDP8I621Uyn1A2AykIosS/xfWmsd\nWK54LhCsvPaE1npdX91Tf+J2w/79sn3VVeJrqawEn0+0FZdLsu737JFw4o5r3ns8sGOHhCp3DEvu\nL0ynu0lHDG1Q5a5if81+4i3xjLSPpLyhnOKEYqaPmo7b50YphT3WTnJ8cqvvxR5rb32tb66nuLaY\ngpEFYcu4QM8FRSTTXncEVGxs7FEtijUc6cuf+x3Ak4BFKWUB7gGu0Vpfiwi008P0uRm4Tmt9E/A2\ncElg/5+01t/RWl8M5ANTQ8b/e631dwN/w0KogAiGVatEuFRVtZXILyiQci5r1kjp/Eja965d8NRT\nco6BMpeZTneTjrh9bl7e9jLPbn6WeBVPs7+ZcSPHscW5hQpXBQCT0ye3lmexWqytJrfgX0ldCXXN\ndRyoPRAxeKCnPqBIpr1o568cK/SJxqKUugL4DNgV2DUJ2K61Di5L9ipwEfBuh65rgdlKqa+AecCf\nATpoNkm0aSiNwI1KqeuASuBurXXYb4BSahmwDGD06NG9vrf+4oUXpGzLpElw6BBcdJEkR/r9orlM\nnSol9CdNOjJs1zAkx+Xqq2H06Dand1caRLQ1DNPp3vcMte91UDC4PW7mj5nPqqJVJFgTqG6qZmP5\nRhqaG5ifO59DHGLMiDFHrHdvj5Wy+oY2Ws1hka5ztOHBhjYwtMHolPDlYkwiE3XBopSaA2RrrZ9T\nShUEdqcC1SHNqoGRYbo/DPwFqAA2AqUdzn0V8L7W+jCA1vr3IceuBH4K/CrcuLTWy4HlIIlkPb2v\n/qS+HlavFnNXdjaMGQNZWbJy5Jgx4mdJSZHaYaecIiVeQnG7Jaly4sT2QqercinRLqfSH4uFHesM\npe81wKaKTWwq3cSp409lXt48kuOS2eHcwacln2Kz2kiMT6Ssvgyf9nHJtEsYl9q+aqpFWUiOT45w\n9ugSzOwvGFFghkD3kL7QWC4BUpVSDyHaxVxgCyJcgqQBVaGdlFJZwCPA2Vprj1LqGuA3wC8Cx08G\njgP+K8J1XwOejeJ9DBivvQZvvin+lJgYKR4ZGwvTp4tpbP9+WZMlKyu8NhBJU+hKgxjKGobpzxn8\n+A0/q4pW8XnZ59yYfSMVDRWcNO0kFuUvYkLGBDaUbmB+7nxGJY0iPSGd/JT8AR1vX9TzOlaIumDR\nWv80uB3QWO4AHgTeU0rFa62bgfOAVR26pgBlIeayNcDSwHkWAZcBt3Z0+IdwCvB5dO5iYNmxAxoa\nJPKrthaSkuCrr2DhQtFYPB4oK5O8lnAaQVBTMAzReoKTbVcaxFDWMMzilYOfXdW7eGrDUzi9Tg7W\nHuS86eeRkZiBzWrjzAlnMi9nHgmxCSTHJ2NRFjx+D1aLNSraQm8qBZjZ9r2nr8ONW4AWrbVfKfVb\n4HmlVAPiD3kvtKHWeqdSap1S6tFAv1jgDqVUAqKNvAE8GFhL4Fmt9SdKqZuBhYAPcAM/7uP76TM8\nHnG422zwwQeiqUyfLmYvm00m/V27xAQWEyOLfFmtYirrGBEW5FiabIeytjWccfvcfFH2BTnJObyw\n5QV21+1mbPxYzpxyJlmOLDx+DxvKNpASn0KaPY0P93/InOw5bHVuJT85nzk5c6IyufdlmXqTI+lT\nwaK1LgG+E9heAazo2EYptRz4lda6Qmv9QIRTjYpw/r8Bf4vScAeUXbvg2WehtBTWrhXn+4kninN+\n7lzJaampEe1l82ZJmPT52krhhzMFHUuT7VDWtoYzX5Z/yZ/W/omZmTPZULKBFloozCikRbcwyjEK\ni7JQ1lCG1+/FYrFQdLiItIQ0Piv5jLEzxraaoY624KRp1upfBtwarbVeprWuGOhxDCReLygFp58u\nUVw5OXDxxSJE5s4VjcTvh507pW1mpvxVV7eF8gbXXamoEOd/MGkymLEeNIt1Vl/MxCTaZCVlUTii\nkAnpE8hLzuPsgrM5YcwJTEmfwpgRY8hMzCQrMYt397zLe7vfo8RVQmJcItfPuZ45OXNahUhnYb/d\nyZrvizL1JpExM+8HAQcOwMsvSzjxwYPw9a/LmvZ5eSIYbDbxt/j9cnzvXjmWn9/2lG63i7ApKhIh\nNW1am5/F7ZbX4uJjwyxmMnjYU72HTc5N2GPtbK7ezA1zbqDZ30x6olQw3l+zHxScOfFMKt2VzM2d\nS2xMLIWphe0W/eqocfgNP85GJzarDY/fQ5W7isKRhaaZa5Bgiu9BQEqKRHglJ4vAmD9ftJHCQtlX\nVSVrsIwaJcmRSUmSIJmYKFpI0BwWblEvlwu2bhUtpmNOSzgNxtRsTKLJ1MypzMmaw85DOxlhHUFS\nfBKZiZnYY+04G528s+cdthzaQr2nngO1B/C1+EBzhAbSUeOoclfx4f4P+WDvB2yr3Ea6Pb2d2Www\nF6w8FjAFyyDA7xfh8Ze/wEMPQXk5NDe3RXLZ7VKWZccOyV2prhaz18aN8OKLEiHmdrc59JOT24fc\nNjWJphM8H0TOijez5U2iiUVZWLt/LX/f9Xe0oUmwJlDfXI+3xYvVYiU+Jp54azzrytaRk5TD+rL1\nGNpgW+U2yhrKIgqHdHs6C3IXkJyQfITQMbPlBx7TFDbAGIaYuj79VEKKoW2lyLQ0meSdTon+2rtX\n/C9WqxyvrRVtpKZGkiRDNZIgDof4aaD9sd7mupiY9ASX18WX5V8C8FXFVyRYE1gybgnp9nS2HtpK\nWUMZUzKmsO/wPvKS86h2VxMbE8u2im2gxQRW01RDfkp+uyx8q8XKuNRxjHJIXE+oCcx01A88psYy\nwLjdsGkTfPGFvLfbZbu2VkKQn3tONJk335SIsWA0WGUlPPGEmLh8PtFWQjWSIBaLaDAdtZhIpejN\nEvUm0eT+1fdTSy0AXrys3LeSg3UHOVB7gC8rvmRs6lgsysLYkWNJiE0gKT5Jqhsn5zM5YzLV7mre\n2PkGJXUlR5i4gln4wbwXk8GD+b8xwNjtEmrscsmEfsopIlQ2bRLNJSUFZsyQiX78eNFMRo2S48XF\nEo48fbo47r/8sn1UmInJQOI3/CzMX0hhYiHnjD6H3KRcEuMTeXv32zyy/hE+3v8xhmHwcdHH5Cbn\niiMfeOqLp6hvrifLkcWYEWM4fdzpHG46TFFtEVsObcHldXV63aApLLiOi+lr6X9MU9gAU18vVYhd\nLpg1S3wtxx8vAuJHP4LZs+GSSyQTPzlZfCyzZknfiy6SQpM2mwiV//1f0Whmz4YFCyInTpqY9Aef\nlnzKnSvupLKxEkesg4TYBBqbG9nm3IarxcXi/MWMSx2Ht8XLzsqdbK3aytfHfJ0R9hG06BY8fg+O\nOActRgvPfPkME9ImMCVzSpfXDZrCDG2YSZEDhKmxDCCGAcuXS4QXiC9l3z6pA/b557J/0yapEzZy\npGg38fGykuTmzaKppAYqsM2cCRdeKI79urqeRXaZkWAmfcG9K+7lQOMB3LjZWruVBm8DHxd9zCt7\nXmFL+RaS4pPYWL6Rsvoy3tn9DvOz5zM3dy7fmvIt5mTPaQ03npg+kbMmnYXP8JGblNulkAg68x1x\nDtPXMkCYgmUACE7kLpcIEJDck5wcMXsF81hAHPr33isO+ieegI8/Fod+ampbrgqI0Bk7tq22WFVV\n9yO7zEgwk2hjaENChwGFIpFEjss+juSEZOKIw+1x8+GuD9lasZXclFySbEmMSx3HZwc/Iyk2CZfP\nhccvZQNtVhvTM6czMmEkSfFJPa71Zfpf+h/TFDYABLPkt22Dt96SfbNnS5jxvHmikaSliRaSmysZ\n9w8+KDXEzj9fBFMwIz+YYe/1ijZzww2yVothiLDpTtVfMxLs2ONoS6R0xXNbnmNt2VpSSWVm1kz2\nN+4nzZ7GosxFbD+0HbfXTam7lKK6IlYfWI3RImYrgIS4BJLiklqjwLwtXhq9jZxWeBpZjqxBcX8m\nnWN+4gOA3S7LC3/ve5JjAiJkkpNFYBiGaCrJybKmyrx5otn4fGIaGzNGNJtt20QzAXH0v/225MTE\nxbVFdnVHGxmskWCmia7v6KtcD0Mb1DfXc8MrN+DGzWEO42x2cun0S7lxwY2s2b+Gfa595DnymJo2\nldiYWHbU7UDFKOo8dVgtVlYXr+bdve9S5ZYvd0ldCa/tfI091Xu67Yg3c1kGlkE2lRwbWCwyYdbU\ntO1rbpYIsPx8ERh1daK17N1SH+FdAAAgAElEQVQrkV8ZGfKXmgoffiiC5LjjRECB9DvxRFmr5cCB\ntsl4KGsjpomu7+irXI9aTy2/+/B3ePG27ttfs584SxzbK7fz1s630GhyR+YyYdQEfnDSD1iYuZAT\n8k7g7d1vU1pbSo4jhxxHDnExcbi8LnKTc5mXPY/Pyz9nR+WObgkXM5dlYDFNYQPED3945L6qKvlb\nv16EzLhxornExMBVV0mb99+Hf/xDIsCOP14qIhcUiJYyb54In7o6CQTIzGxf9XeoLYY1lIXiYCfa\na40ETU+VjZXct+6+dsfSE9KZlT2L+1feTx11ALg9blqMFhKsCSw/fzkbyjdQ7i7nhS9e4JDvEMfn\nHs/kjMnEWGIoGFHA4jGLSben49d+3D73EWPvaPoy11IZWIbA9DL8MAxJeAwlLk7Cih96SIRHQ4MI\ngfnz4b33xIeSkQE//rFEiRmGLF/8/e+3PdFbreLAz8kRwTLUy7UMVhOdyZG4fW721exjX82+I47l\nJucyMmEkO6p2tO5bWLiQrKQsHvr8IfYe3svXx36du0+7G5vNhgcPH5Z+yPObnifLkUV9cz0WZWH6\nqOlMTJsYVgsxTV+DC/MnOwCsX9/+vcUi+SgTJ7aVdSktlVpgNTUiCGprJSLMYoH77hPhAmI2u/de\n0XRcgbyxzEwpYNlZuZah4r8YKuM81rHH2km3p3Pdc9e12+/AwdlTzuZA3QEaaGjdv825jR3OHdS4\na1h/cD3Pffkc8dZ4FuYvbG2zp2oPv17xa17b8RpV7qpOo7xM09fgwjSFDQDf/W7794YhfpPY2DZn\n/tSpIiA++kj8KErBli1SXj8pSfaNHQsJCfDAA5Ik+a1vtZXF72zJYpDJeiisLnksrYI5lAlO+k6c\n7fb78PHO9neorq3Ghw8LFsbaxlJVW8XKvSs5ZfQp7Dm8h80Vm1FK0aJbGOcYx+jk0XxV9RVv7HmD\n/1z0n6Tb08NeN9QEZpq+Bg99KliUUrHAU4BLa70ssHb97UATUATc2XENe6XUZcBZQDMQB/xIa+1U\nSo0H7g30rQN+oLVuibS/L+/raNm4sf37mBgJP374YdBakiFnzJD9770nYcjbt7dNrs3NIoBSUuSY\n2y2mr3BFKGFory45VMZ5rOM3/Gwo23Dkfvx8cuiT1vdj4sZw4vgTqXHXUH+ono3lG/FoDwtzF5Kd\nkk1ZfRnZjmwqPZXsqd3DrLRZ3DDvBqyW8FOVueTw4KSvTWF3AE8CFqWUBbgHuEZrfS0i1E4P0+dm\n4Dqt9U3A28Algf1/Bm4O9N0C3NDF/kHJJ58cua8lIAaDIlYpaGyUNVrOOkuETEWFhBKPGiXC5dxz\nxeeyf7+sv3LCCeGLUEJ438pQ8V8MlXEe61S4Krj66auP2N9C+2c8n/IxMn4k8/Lncf2s6xk3chw1\nnhp82sfGgxv5165/kRyfTKYtkxFxI7h23rWMTx0f8bqmCWxw0mc/V6XUFcBnwK7ArknAdq21J/D+\nVeC0MF3XArOVUvHAPOB1pZQdMLTW1aF9I+2PMJ5lSqn1Sqn1lZWVR3t7vcIw2qK7IhEbK/krO3bA\n88/DGWfA9ddLJn5BgWgur78uk21ysoQfX345LFkS+anefOofvgyG77Xf8LPVuZVSSjttF0889jg7\n+2v3s6ViCwtGL8Aaa6UguYDq+mpe3/k6Hq+HhLgEihuKWZS/iNPHn97qUzG0Qa2nlrKGMvyGrG5n\nZtcPTvrkf0MpNQfI1lq/FbI7FagOeV8NjAzT/WHgN8CDwD6gNNDucEibw4F9kfYfgdZ6udZ6ntZ6\nXkZGRs9u6CgIdT7v2iU5Jp2xeDH8+teSl1JbKzXBHn9cCk5mZMArr4hweeMN+fP5xHHv90d+qjef\n+ocvvf1eR2uVRUMbHKg9wF1v3tVl2+umX8ei0Yv4rPgz9lbt5Z9b/skXZV9w2oTT8OGjyFWE1Wol\nOSaZ2uZaMhIyiIuJax2j2+dmY/lGVhWtak2eNBmc9JWP5RIgVSn1EJAEzEXMVKkhbdKAdt8OpVQW\n8Ahwttbao5S6BhEyd3fomxroWx1h/6Ah1Pl8771HHh85sn2i5OLFotXExMiaK2+9JcKoogKuuELK\nttjtYiqrqYFzzoHLLmsrRglDL1/FpP+Jlm/C7XNT11zHurp17fY7cOCirbx9Jpn85hu/weP3UDCy\ngLd3vM264nWkJ6YzJXMK49LGsX/lfnxeH6/segWX4WJzxWbWl64nMzETR5wDe6yd47KPw+1zR3Tm\nmwwO+mTa0Vr/VGu9TGv9XeAXwCeIBjI9YOICOA9Y1aFrClAWYi5bAxQE3scqpUaG9o20vy/uqbfY\n7bKOfVBIhJKSIsLBGhDvU6bAt78t7y+8EL7xDXHKQ1sJ/Px8KQWTmSnv9+yBlSthxYo2H0pQmAUL\nXZqhuiYdiZZvwh5rp665Dk27GJxWoRKP/Ny/Nv5rZCZmMjplND87+WeMSR1DjVGDz+fD7XNT5a7i\nFyf/gumjplNr1GLHTmlNKW/vehtno5P65noARthGkJOUE9GZbzI46I//nRagRWvtV0r9FnheKdUA\nVALvhTbUWu9USq1TSj0a6BeLBAAA/AR4TClVD3iB/+hi/6ChslIy5Q8fbr+/TpKQ8Yu5mLFjJbIL\nRCAlJcl2drZoJK+9JqtKWq0SZjxnjhx/6SURLvHxslBY0KdiGGaorkl4opWZblEW7no7shmsmWYA\nxqSOabc/y5GFFStTM6ey/eB2Ht/xOIW2QrKTswERIDWeGl7Z8QqjHKP41tRvMS1zmhn5NUToc8Gi\ntS4BvhPYXgGs6NhGKbUc+JXWukJr/UCE82wCLuju/sGC2w2HDh2prXREKcmqD5quXC5ZITI2VopO\npqVJdeOkJPGtVFeL5mK3y3awhL7b3ZbHYhim096kbymqLWLVoc6NBHbs3L749tb3aw+uZdWBVWTZ\ns0i2J7OxUuLv93n2Ueop5WvZX+O2xbfxeennrCtdx+KCxUzJmGJGfg0hBoU+qbVeNtBj6Cvi4kTL\n2L2783bf/KYUkQzy0UdSsmXsWFi4UMxke/dCWVlb5eOyMrj1VvHTpKdL2RebTYSSzSbXNf0sJp1x\nNOXlDW1w20u3ddluXvo8cpJyACmB/8aON9hTvYdJqZOYkT2DnYd2khefh9VnpcgoIs+RxzmTzuGs\niWexq2oXE9Mnti765W3xUlJXQn5KfmtZfZPBhznl9DGHD8Pvf991u2nT2nwtFRUSCVZSIuvcz5wp\n+S8LFkilY2gzm6WkSG2wYKl8j0fMX1VVQ6sumMnA0NsaW4Y2KK4r5q2DXajiwF8v+mvr9otbX+Th\ndQ9jYDArZxbNvmYq3BWckHcCtkQRHluqtlDWUIbNamNm1sxWoQJSQv+NnW9QUlfSo/Ga9C+DQmMZ\nzlit8O67Xbc799y27bvvFr8JiJby0Ufw4otiDktOFv/KqFFw0kmSRBlK0L9is4mgMc1gJp3RWyd+\nraeWO9+984gEyHAkxiZiaAOLsnDP+/fQSKOcw13L+3vep7K5knf3vksddaSqVDLtmVQ2VlIwouCI\nc+Wn5HPupHPJT8nv0XhN+hdTsPQhVVVw7bXdaztnjvhE6uvhnXfa9jc3w7p1YuYqL5e/iRNFEE2e\n3KblBAmtBzYUHfZmqHT/0hsnvsfv4Z3d7/DU9qe6bHt+3vk0eBtweV28tO0ldrjaKhyv3beWCiqI\nJx6HxUGtUcvxecdz/PjjyUgMn5MTFxPHuNRxPRqvSf9jCpY+5J//hH/9q+t2d9whpiyXS3Jd9u5t\nO+b1tr23WiWCLDVVzGNxw9DEbBadHPxsc27jrx//teuGwJNXPYlFWTC0wbdf/3a7Y454BzRLPbGD\nxkEAKtwVnJh/InnJeVEft0n/YT4T9iFTp3av3U9+Iq92+5H+mPLytu1gWHJdXZupa7hhlp8Z/KTY\nUjhQ3UUJCaAwrpDk+GQccQ68Ld52xxJJpLy5nPSYdLKREOMcSw6jHKNIiE0w81SGOKZg6SMMA154\noet2I0e2CYja2iOPh3O+p6dLKRe3e/glP5rlZwY/SinKdFmX7Z6/5vnW7T99+qd2xxoD//LseQRy\nKGk0Gskdmcsox6iojtek/zF/vn1Efb0kRXbFyy+3bd95Z+R2oU/wLS2S32JGfZn0J37DT3FdMc9v\ner7rxsCC3AWt2y9veDlsm20N29BKkx+fz4iEEczNnRvRDBat+mYmfY+pb/YR//hH99p97Wtt2888\nE7md2w25uVLl+Ac/EB+L32+ajEyiT6Tclip3Fc9vfp47Vt3RSW/h/Lzz8Rt+rBYr68vWs92zPWw7\nL17cXje3Hn8r6Y50zp10rrn2yjDA1Fj6AK8Xbus6b4zp0yXJsb4eHn00vCksFI8H/vM/pYZY0Mdi\nmoxMok2k3JYRthGU1nZeGj/I4imLOVB7AEMbXP705Z22rTFq2FG+g29O/iZZjqyI7YbT2ivDXfsy\np6U+4MMP25YY7ozbb5c16zdsgJtu6rp9dTWsXSuCy8Skrwg3gQfL47+7reukLDt2zp54Ng3eBorr\nitnTvKfLPrsO72pd1z4Sw2ntld4mpkaLvhZsQ/9/aBARzEPpzFcS5OSTJQ9lzBjxlXQHpcQUFsn8\nFbr2y3DlWLjHgSbcBO72udlfu5+dnp1d9n/t6teYkDaBghEFfO/v3+vWNRPjE4+pUvgDrX31tWAz\nfSxRxOmUUixr13bd9r//W8q42GywdGn3zq81jBgR2fx1LOSAHAv3ONgIPt2+vvX1brU/deypWJSF\nWk8t/z707271WXto7VHX/jqaumf9TbSqS/eWvhZsg/vTH0IUFcHFF8MvftG99nPnysT4wQddryoZ\nyh//GPlpvbMckOHypG/muUSHnphC3tz1JrP+Oou/bfpbl21vmHpD6/aMP8/o/ngwuOKhK7rdvrVf\nyH0MtHlpKNHXZkVTsHSgtxPwz38uhSK7w+23t5Vi6a62EmT16sghxhaLTLjh8luCT/rBvsH79PuH\nlsAx81yiQ08m4Zv+fhNOr7Nb571m/jWt5wxdQbI7rHL3bI0+v+Fnf81+9hze06qpDBfn/lDH/Hl2\noOME3F2++KJ77ZKS4He/6/Gw2tHZ2ELHHyokOz7pB9uZVZCPTbo7CfsNP066J1QWpyxmTs4c7LF2\nXt4ePm+lM76R9I0jMvQjEQwm+KL8C5LiklrNX8Gn8OEedTXYMQVLB3pjavF42tf36oyCgrYaX1VV\nPR2d8MQTkY+Fjt/lkqgzV8iDo98PBw9KOf+8PMniN01Lxx7dNYVsdW7t9jkfu/qx1nNe+eKVPR6T\nI9MRsRx+qKAwtIGz0Uldcx1zsucwZsQYLMqCx+9hc8VmPH6PaRYbYPrUea+UigWeAlxa62VKqUXA\n7UATUATcqbXWIe0zgd+EnGIG8GdgZbj9WusXlVIrgNBltH6mta7p7ZhDqwN3l/ffb6vj1RUPhKyP\n+Z//2bPrBPH5um7j98vKlcFxBTWU2FhZgTI+Hi68UIIBjtYJblYkHjxE24H95OdPdrvtxLSJrWPw\n4OnxtfZX749YDt/tc7OvZh+ZiZnYY+04G53kJOWQmZjZqqF8VvoZj3/xOOdNOo8zJ54ZFbOY3/BT\n5a4i3Z7emrg5lIIEBoq+jgq7A3gSuEQpZQHuAc7WWnuUUvcApwOtgfFaayfw3eB7pdRLwFta68Zw\n+9u66dZjkVBKLQOWAYwOLiwfJYJrp3SHxYvl1e+H//u/3l1vWSfrbbrdsG+fTPZbtsCiRW2CIycH\nNm6U6LWxY2HPHhEsQVNZb4RCXh6UhuTMlZbKdUz6h47f62hmp1e4Knj5y+6ZtGbFzmrd/rTk015d\nz6IsESPDbFYbibGJHKw/SEp8CnnJee0y9J2NTlbsWYHb6+atXW+Rn5LP7OzZuH1ubFYbHr+nx4Lg\n71v/zuUvtSV3Vv6oknR7ulkBoBv0mbhVSl0BfAbsCuyaBGzXWgcfZV4FTuuk/8JA+8Yu9jcopf5b\nKfWMUipimqHWernWep7Wel5GRvi1HnpLcFXHrrj55rbJ++9/7921RoyAzMzIx+12KatfVCRtg7fq\ndkuC5fvvQ0OD/P3jHxIavW9f1z6W226TPJrQigIeT3uhAlJ2pr6+V7dm0gs6fq+j5cA2tMFj6x+j\nxOjeSo2v3/I69c311DfXc8NzN3TdIQzXLbou4jGP30OjrxELFj468BHlDeWsL1tPWUMZfsPP5wc/\nZ23JWmZnzSYpLok9h/fgbHRSVFtElbuqU7PYsueWoe5SLHuu7YnN5XW1EyoAGb/PwNAG9lg7o1NG\nt5rlTI6kTzQWpdQcIFtr/ZxSqiCwOxWoDmlWDYzs5DS3IWazTvdrrc8PXNMC/E0ptVdr/UHvR99z\nzjgDfvazrtvdEVJi6Zprenet0Npi4XC74dNPpTzMggUiaJxOKC4WQXDJJeLjGTlSBIrPJytS2myd\nn/cvf2l7/fOfZXtrBPP7X/4ikW+m36b/iVZ+hNvnZuHohSSRRAMNnbbNIovUhFS2HtrKW7veYlfz\nrk7bR+Ka2ZF/FPZYO+n2dN7e9TYN3gZK6koorivG0AZNviYS4xK5ddGtnDjmRIpri6lvrsfQBqNT\nRmOPteOIc0QUto/sfqT1dTnLAfi46OOwbXdU7WBy+mQsymJqLZ3QVxrLJcAkpdRDiPlrMTAPES5B\n0oCw7mul1ETArbWu6M5+AK21AbwBzIzKHfSAxx/vuk1MTNsywmVlkuzYG+6+u/PQ4E8+gYcegsZG\niVTbuVMEyKpV8NxzUmpmyhQxV82cKVpIVZUInc6YMaP9azBMec6cI9s+9hj8x3+Iqa2+fuiEMpsI\nhjbwtnhJiutaqAAsnbpUnuJHjObeT+7t9XW7mqBX7F3Bn9b+iWp3NWtK1jAxfSJur5sP93+Iz/Bx\n8tiTSU1IZWbWTCamT6TKXYXb5w4bqBAaDJCD2G6Dr4Y2iLPGMTF+4hFjOPvps3lg3QPUN9eTmpCK\nzdrFE9kxSp9oLFrrnwa3AxrLHcCDwHtKqXitdTNwHhApcP2HwJ96sD/IyYiJrV8IRlh1R7CEOu1v\nvbV31wtm3bvd4R3ufr/kx4weDRUV8NFHIsBcLnj2WfHveL3yPpjzEhQMXWkXf/sb3HNPWwKo0ymC\n4+qr4eyz4dRTYfduMa+9954Il8cek7Y1NTJ2k6GB2+dmfel67l91f7faXz//eizKQpYjC4PePUXc\nPP3mTo/Xemo5UHuABGsCZfVlHPYcJjspmzd3vYlVWfnugu+2M03ZY+2kJqSyrXIbNquNEbb2X8BQ\nP8nTVz/NL9//JXcvubv1WIothSeueILxaeOp9dTy6vZXeXjtw+xz7eO2d27jtnfEJtzws4Zho7FE\nMyihP0IaWoAWrbUf+C3wvFLqKcAGvNexsVJqFJCutd7azf1/VUo9FjhnkdZ6dV/dSChOJ/z2tzLR\ndqfg5IIFbX6MV17p+fUSE2WZ485Cg4uKJJP/+ONFwFxxhWT4b9/eJowKC0W4fPSRaBPJybLf5Qqv\nXQRzYY47Du67T85nGPIXHy++moUL5RxLl8KVVx4p9E6L6EkziTbRyN9oNTsdfLvLtvPt85kxStTY\nzYc29+p6xyUfx1/O/0unbQ7UHmBn1U6WjltKhiODy6dfzuT0ycSpOCalT8Jv+NlQtoEKVwX1zfXs\nqNpBk6+JioYKXF7XEZ9JqC9qxqgZfG/h95iQNoHNFXIPecl5LMhbQGZiJlmOLE4uOJlr5157xLiO\ntgzNYCKaIdp9XitMa10CfCewvQJY0bGNUmo58CutdYXW+hBwYZjzRNr//agPuhs89ZT4GrqTWHjl\nlWJ6stsj+yU6IyFBhNHChZEjtwxDTF/vvQezZ4uQOf10yVMZOVKKXcbHS//9+0UAjRolGldOjmgb\nzc3S1++X/qmp4o9xOkVQGYYc8/slD2bRItFEjj9ehJXNJtf59rfbfDLQ/eRRk6PnaCOWgoLJFts9\nE8/T1z+N1WLFb/i57vnreny9makz+f05v+90KWJDGxTVFFFUW0RaYhqXz7icaZnT2F65nXOmnEOW\nI4txI8exv3Y/qw+sZm7uXFxeF5mJmZwy9hRsVhtbD21l9IjRZCZmtk6cjjgHhjYoqy+jRbewqXwT\nq4pXcdGUi7DF2mj0NjJmxBgccQ6mZU4jNzmXJ1c/yf6W/a1j21m1k2mZ04ZF2HE0KxcMik9Da70s\nnN9kMJOeLpN0d0rY2+1tZUhuuaXn13rhBXnq7ywc2O0WwZCRIVqE0ylJmzt3isP/N7+RCT8uDtav\nFxNZcbGYyIqKRHhZrXKeVavg4Yflurt2yXU//1zO6XaLYCkoED/Oxx9Lm6oqaVdYKILGZGA42snB\n7XPz0YGPuPjZi7vV/ukNT7PVuZXd1bvZVd8zp70FC09d9BQnjzm503aHmw7T4Gtg6filFNcUU9NU\nw9Mbn+b/Nv0fMzJnsHj0YkmSxMJm52YONx1GoahwVZAcn4zVYsXT4uFA7QGq3FVsr9zOjqod1DfX\ns825je3V25mQOoHE+EQumHwBM7NmkhibyJqDa3A2Olt9NDlJOfzzhn+2G9uGsg3DJgkzmvXDzOrG\nvcDjgZUr5am9OzzyiPz9+c9S66snxMZ2LVRAhMKkSaIdrVghr3V1Uj4mO1tqmeXkiAmsogLOPFPM\nc+PGiTlswwbRVtLTxdyVlycaTnw8pKWJpvXJJyK08vNh6lSYOBEuuEAWLIO2bH+/H376UzGdgbTr\nKX6/CKv09La6aiZdc7RRYS6viwc/fZBt9du61f53a3/Hl8VfMiZ1DE10wyYcwn1L7mNq5tROc0z8\nhp8vyr7g0wOfcu7kc0mITaCotogH1z1Ifko+cTFxJMcnc7D+IDuqdjAhVcr1Wy3W1hwWi7IwJ1uc\nicGclsPuw9R6atlZvZNp6dPIS8ljd/VupmRMwR5rJyMxg9yk3HYC2qIsrUmgQb5W8LUeCXFDG9Q3\n1+P2uclMzOxUUxvKDM+76mPeew9ee63n/bqzqmRHfD4xMblckZMYq6pEwzAMmcRra+W1uVmEQEuL\nTNQ7d4pJ7eyzRYuJi2tb4thmk0nc45FrjRkDX30l2szixbJq5fz5ImTi4tqKXQb9KcHXzExYskTC\nmn/yE2nTWd5NJKqqYM0a0X6yIi8qaBJlHlr3EG8f6Nq3Esq/y/4NZT2/1hPrn+Dm+TcfYbrztngp\nqSshPyWfTRWbuP/T+1mYvxBDG3zl/IrTxp3GpPRJ2Kw2vC1ePH4Pu6p2UdtUy9fHfp3UhFTcPjeH\nmw6THJ+MI85BcnwyIIJze+V2Nlds5ltTvsVJY04i3Z6ORVmYljmtVUgkxyczP3f+EULDHmun7qd1\nGNro1RO+2+fmy4ovKasv49TCUztdMXMoYwqWXvDEE6IN9Ac//KFoGAcOyPotyfL7wOkUU9XFF0sY\n8f/+L9xwg/hTbDaZmGfPlhyb0lIRSK++KuYxu72tXhmIYHE6xV8SrDVms4kG5PFIYmVu7pGJoHa7\nRKAFnfkWi2gXQUFgtcpfbzL609NFqKSnmyVj+outzq08vPrhfrveLcff0i7XJMju6t08sfEJrp19\nLc9sfIYP939IXlIeGLCnag/nTzmfn5/yc3ZX7abR18gH+z/gxU0vYrfZiYuJw6IsrSZBm9WGy+tq\n1YjssXYW5i1kfOp4xowYQ1xMXDstIqjhRMKiLK1CqjfYY+3MzprNxLSJw3phM/Nn2kMMQ3wL/cWv\nfiW+kObm9vtfeEEWC3voIdFI5s0TE9aGDRKdNXKkCIWTTpLosF27RMuaOlX+Qtm6Ff76V3kN1kqz\nWsV0dtJJYurqGIkWnOxBxhcuiKG3laKhTUBZLCL0ulMdwKT3GNrg9jdup6KfXJ0TEydy6fRLKa4r\nxqIs7SbzJl8TdZ46KhsrmZY1jfMmncf4jPGUNJRw9XFXc+KYE0mKS+L40ceT7chmc/lmmo1mlhQu\nISMxo5024fK6+Lz089aESbfPzQjbCCakTWiN6HL73Gws38jK/SupcktqncvrYlvlNlzenpX+D0do\npF5QMDniHLh97mGbuW9qLD2gogLuvbdnC3MdLQ5HmyAIDeO99FIRNklJUpblssvEf/PPf8qT/tKl\nok3k5MjkvHAhXHWV7LfZ2jQMEMf9yJHyGorF0qYhdSQoNEaPjhwCHY1FudxuESyZmWYmf1/y2o7X\neP/g+/12vZ8u+SlxMXHttJXgxD81cyq3LLiFGEsMpfWlXDrjUh5Z/wg17hoyEzLZe3gvH+z7gKmZ\nU5mYNpGLpl/EGePPIC8ljz2H9zA5fXKrVuH2uSlvKGdS+iSsFmvYiDl7rJ1ZWbPIS84jNaEthztY\nH/do8zs6Ruq5fW62V25HKcXUjKnDJg8mFFNj6QHLl8uT/c6ul/0+ahYtkhDgoB8jObm9GSgzE777\nXdGeqqslhPitt0RorF8v2klJSVuIMIhgSUo6UouYMAG+8x157S5BoeFwRF54KxqLctntEmmWmWma\nwfoKv+HnxhdvRNPLchA9IIMMXrnkFU4eczLbnBIgEKxO7Gx0sq9mH4Y2KBhZwKHGQ0xMn8jO6p0Y\nhoGzycnyz5fzyPpHmDFqBqMSR/FlxZcoFLOzZ2Oz2lqFQZDMxExOLTy1tSpyuIi5YPHL5pbm1vVg\ngiHGQUFwNPkdHa9rj7UzJWMKk9MnD9tFyUyNpZtcfbWE5vYHFotksI8YEXl99/p6EXTr1sFZZ0lp\nFodDIsjWr5es+EcflXaTJokjfNo0ETQpKe1rg8XFSXRYT8fYH2vO99d1hhM9fcI+7u7jOEw3QxyP\nkpMnnMyScUvY6txKk78tisztc+NsdLZGSm2u2ExpXSkN3gae2PgE09KmcfWsq3ll2yus3L8SNJxS\neAqT0yfT4G3A7XO3CoPQydpqsbZzkEfSDjpO/qHRdeEEUk8+446RekfrpxkKmIKlm/SXUAERAEH/\nQiRT0qpVUq7/ggvED4cf7lgAACAASURBVPLii/C978HkyaJ5OJ1iGnvoIYkAy84WE97kyaLleDxD\nY8I2w457Tk+TJLfSi6zdXmDBwj1L78Eea2dqhth3QyfvwpGF2Kw2tjm3sbpkNcfnHU9NUw3jR4xn\nQvoEThp7EgvzF7KmeA3NLc18sO8D9lbv5erjrm63gmR36LjOSmd9wx0zS+d3jmlc6Cbf/Gb/XMfh\ngP/5HxEqnZmSjjtOIsIuvlgc85s3S87Ljh3S58IL4aabRDt55RUJQa6vl9IwhYVDx18RDDvu7Wqb\nxyI9TZLMJruPRyT87ISfMT51fOsTe3J8cuvTfqizvbShlFmjZpFmT+Oz0s+Ynz+fhfkLWXtwLZmJ\nmdw0/ybm5c5j9YHV/HHNH3lu03OdOsHDlbmpclexpmRNq7O+p5il8zvHFCzd5L77+qfmVW6uaCud\nRUB5vTLRLlggDvyyMokKy8iAWbPEEV9VJZrPjBkihEaNEnOXzdYWthusA3Y01Yc7niMa5wwlNOz4\naMd2rNCT/Aq/4eft77xNJr1INuohM7Jm4Gx0djoR13pqWXdwHTurd7KpfBPlDeXMzprN7OzZTEid\nQGlDKQfrDpKdlM3UjKkUpBTg8rpwNjojntPldfGV86t2EV7p9nSOzz/+iJDfjkIoUu21YCRbcV1x\nRN9LNOq2DVVMwdJNMjIk+qqvufRSMX8Fc0M6YhiilaxYIZpJQoJEZs2eLds5OSJkpk4Vk1dFBZx/\nvoQM794t/XbvFhNTMLLL5ep6AjYM0XqKimRlyq++EuFVViZhyq7Ab/ZoQozDEQw77o0ZLNpjGY6U\nNZTx+IbHcRJ5Yo4WM7Nm4mx0RpyIPX4Pm8o34fF7GJ08GgOD86ecjyPeweri1UzNmEqGPYOMxAzK\nGspwxDv4/vHf56QxJ+E3/K0hxeEmdKVUu2sFfS8dM99dXhdbD22lrKGM+uZ6XF5XRMd9V5phNIs6\nDjVMq3U3sVj6J3/lhz+Ua+3bJ5FQHaOhDh+GbdvEr5KVJXkrM2bIpP/WW7LO/emni1ZSUyMlWbKz\nRVtpahLfzPvvS3RYZmZbAmJxcZs/J5iMCLJttYowO3BAhFJJiZx34ULRjkJ/s9EIMY4Wg2ksg5Um\nXxPPfPFMn19ncuJkJqRNaC3f0hFvi5f39rzH23vf5oxxZzAjawZWixVHnINtzm2sP7ieghEFFIws\noNnfTJ2nDixQmFrIxLSJFNcVU95QzrTMaQDt/B9Bs1VoWHNQe7HH2qlyV7H10FYW5S8CwNPiYWfV\nTpLik5icPjmi8OjKpxPNoo5DjW4JFqWUA6lQrLXWf1RKjdVa7++q33Ag6Dz2eGSCLi/vu2udfLKE\nFRuGTPpOZ5vZym6Xcdx3n4Q7/+xnInw2bhTz2cSJbZqH1yvHdu2Cc84RoRIMBIiPbwtDdrvlnKNH\nS22w2lq5v9WrRXAlJLT5Nv79b/HNLF3aJniys9sy64OBAIMpimswjWWwMnbkWAqSCzhc23dRYQ4c\n3P+t+1sFBbSf3B1xDlYVreLX7/+apROXkpOUw5NfPMmZE88kITaBvJQ8Lpt5GX7tZ2P5Rj7a/xGV\njZWcWnhqazZ9MBggOImHZt0b2qCsoYyKhgpyknNaky+tMVZGp4zmrZ1v8cr2V7hd3c4pY09hTvac\nXpdsCSVaq3kORbqrsfwBWTvlxMD7XwHX98mIBhlB57HfL0/5fSVYJk2S8GGQCTEzUyZFwxABYbXC\nm29KTbDsbCnT4vGIn6WmBr78sm2NlJoaMVclJ0ttr+DiYMXFUgp/7FgxkTU2tgkyt1sqFVdVwYcf\nSo2yGTNEGCUnS9HKvDwJTQ5qN+akPfjoSRhscA2TRGsiKaRQR/TrFI1mNA9e9iCnjj213X63z82O\nqh14/V4ccQ4eW/8YG6s3MuHwBL5yfsWqfavwG37On3o+tZ5apo+SSqfbK7fT6GtkcsZkZmXPosZT\n0yoEQkuzbK7YTE5SDgYGhSMLsSor/9r9L/KS80DB/Jz5JMUnkZqQytmTzmbMiDEsyFtwTIQC9wfd\nFSxurfU/A2vZ96TfkCc1VSbrCy7ou2tMniwmqtBijcGnbcMQofLvf4tGkZYGJ5wg2zk5UmRy40YR\nfrNmyUqORUUScpyUJP6PlBQxeSUmilAIakDr10uujM0m7U86SY7NmiVaUFqaCJWgOSzo+A+al4LO\ncTj6REiT6NDdMFi/4eeL8i844fET+nQ8L930ElaLFW+Lt92iWDarjbzkPErrS3lh6wsUJBcwJ30O\nJ409ifUl6zmx4MT/z96bh7V1nmnj9yskIQ5CCCGE2FcDBoz3LV7jOGmapNmXJmmcfF3S9pp22k63\nzPXrTL+Zab9v+n39Op1ruk3bdJp0mTRttmZtnDiN48SOY8d4xwuYzViA2MVBEofz/v54eKWD0IYR\nZjF3Li5LZ3nPkSDPfZ7tftAx0IGzPWdRmlGK3ed3wzfmQ05aDspsZShIL0DPSA+SWBK8ihftg+0Y\n9lOs+mTXSTx/+nlsKtyEXSt2wWw0oyqrCimGFKSb0qGoCiSDhPbBdliSLXCanQtWDHK2EC9BZDLG\nKoHgJMd4TmKMGQA8AcDDOX+UMbYBwN8BGAHQDOB/ck2rLGPMAeCfNUssA/DvnPOnGWNvAjin2ff3\nnPM+xlg5gP81vuYAgK9wzsfi/Fwxcf48qQHPJP70p/AKwMKTqKggyfqf/ISIoKGBtMK+9jUKV+Xk\nkCjmSy/RnJU33yRyeOghYNOmoGrx8HDQ0xBqxopC6x0/TsdarUQmzc3BMJeWTLThJY+HzuWcKtCi\neTCLQpJXBvHG9Y9cOoJdv9k1o/dyS9YtWOFcETav4lW86B3pxdHOo3jiyBN4ZMUjeHDlg/jR/h8h\nzZCGb27/JkozSqGqKl5seBGvnnsV/jE/Plb5MXx82cdRaC0MVGZJBikwtAsAKu2VKLeVY0nmEhSk\nFwQ8uCWZQWkJlatXbf7jSiBeYvn/AHwfQAmAMgDxCsB/C8CvAdzLGNMB+C6AmznnXsbYdwFcD+B1\ncTDnvAvA58R7xtgzAF4O7uaBfRr8O4BdnPMexthnAHwKwM9j3Vg0GXotnngi1krTw69+NVk1GCBD\n3NVFP8XFlJhvbZ2Y53n9dfIsbrgB+PrXKQRWXQ0sXRosWxbVVEIaRfuZc3PpOj4fVbx1dxOphCa9\nI+UqJIm8LfE6GkSF1mIIbWYRb1z/D8f+gDO+mdUm+s0nfzMhr6KFSKg/9PRDuDRyCS80vACf4kPj\nUCMssOC46zj8Y370+/pxc8XNWJO3Bl7FizV5ayi0FVLNpQ1fWU1WOFIdcMtuyKMyWgda4Uh1wJHq\nmNQ3o0UiZ75f7Yjr2+Oct3HO7+Ocrxv/N+aoOMbYAwAOAhDHVgI4zTn3jr9/HkDEzhDG2Prx40Ut\n1hBj7HuMsd+MEwgYYxIAlXPeE2tNxtijjLFDjLFDXV3dcZWhqiqFh2YKGRnA/feHL6X1eCgBn5xM\nYa+bb6Z+FCGFn5ZGZcVeL+U9li2jDnu7neanlJRMXDdSs6VOR8decw15NB7PRM9ClBl3dAQ1x7Tn\nWiy0rpgsGalsebFCa2Yw4e+6O3qPiIDKVXy08qMzel8OOGA1WcPuU1Ql0M/y+Y2fx7LMZfjejd/D\nJ1Z8AkYYoYceP9z3Q+y7sA/XFl+LnWU7cWf1nXig7gFUZFbEHGMs+lr2t+0PDNRyeVxoH2zH6e7T\naOprQvtgOxRVmVCafDWXB4eDoipweVxQVCX2wSGI6rEwxl4ZPyYFQB6AfgBWAOc45x+Jct4qADmc\n898zxorHN9sA9GgO6wGQEeXyXwKFzQAAnPM7xtfWAfgpY6wRwBlggshRb6Q1Oec/x7gns2bNGh46\nRyQcZHni7PZE48YbJ2p2+f1U0puZSe/7+4PkV1REc2DOnAn2rzQ1UX9KJAXieKHTUZ9OVha9F6XO\ndjsl80+eJM9p2za6354euh8x00V4I+L4cF7JYoXWzED7d12zooYLzaxoGPQN4uWGl6MeM1386O4f\nTXgvhnflWfLQNtCGA+0HUJ1VDYvBgn/Z+S+QDBI+vebTSDGk4Bcf/AJtQ22ozanFrUtvhTHJOGGm\nSiQoqoKW/hYM+YdQbC0ONECKcw5dPIT9bfshGSTYJBtuq7oNlmQLmvqa4EglIkzRpwRmtFzt3otQ\nJ9hYsHHKOaioxMI5vwkAGGP/D8AvOOcNjLHlAO6Nse69AGyMsZ8BSAOwGsBxELkIZAIIq6fAGKsA\nFQxMGg7BOVcZYy8CqAPwXsiatkhrhkKnix6aETmBixfjWW3qSE4mRWEt2tqA3/2ODPZdd5ERLy2l\nUFVXF92nzUaexbp1FELbsiU8sQhPo7OTSEjkTsKRqKgYEx6Fw0H3cvAgScUUFlLJsixT/qa1lWT6\nhXCldjiY2bzolcwWkpOS48oZDPoG8V7zezN2Hw8tfQh3LL1jwrYz7jP45Ye/xD3V96DP24fkpGTs\nbdmLFxtexLaibah11qK5rxnVjmrsLN+Jxt5G7Fq5K1AyLAoSJIMUGMgVOtLYLbtR76pHXXYddEw3\nIfTlSHVgQ8EGOM1OjKqjeOvCWzjXcw41jhrYJTvaB9txqusUuke6kWpMhdlovuq1wCKpE8SDeHMs\nSZzzBgDgnB9ljD0S7WDO+WPi9bjH8i0APwawmzGWzDn3AbgNwNsRlvgqgB9GucQ2AM+P52oMjLEM\nznlfjDUnIFZoRpZJen6mZq8sW0Y/WmRkUBJ+3z4ilOxsCmf191NF1+uvU1nxgw8CjzxCBCDCXaEV\nWrJMeZN9+4jEzGbyhGpqiLiEorHRGCSEs2cpZyJJVG78+ONUVeZwUPittpbWvfZaqkYDJotELnol\ns4fQgVmRoHIVlwZmriHr+zd/f1K4qmOoA/Ud9ajNqoVP8eHa0mux3LkciqKg0FoIeVTG3ua9uHHJ\njXhs62PwKT4UWYsC3fSiwVGEqwQZKGMKKuwVONV1CtnmbKzNWwsd06G5vxn5lnz0jfQh25yNi4MX\noagKvIoXrQOteOXMKzh26RgeXP4gthRvgXfUiz0X9uDG8hsDXs5MJPfnkycUqgw9pXPjPK5ovKLr\nKIBVAKZytTEAY5xzhTH2HQD/zRgbAtAN6o2ZAFF1xjk/EbL9PwBI4/d8kHP+7viubwJ4nDE2CMAP\n4G/jualYoRlJIm9lprrtP/IR8iK0OHSIiMBgoEorn48S8h4P9ZgANCWyujpY0dXURB6E1Rr8Ny+P\nDP+GDUQK779PQpSXLhGxZGbS57vjDiKSnh4iocOHgdtvp7UrKoAvfIHuJy2NdNIyMiZKrKgqEW99\nPVWULc6mnx/Q6/RQ/FOPm8cLR+rkEkcGBg6Os91nMeAfwPqC9cg2Z0OFiuy0bJK/9w2hIL0A/jE/\nFFVBQ3cDdjftRnZqNrYWb4WiKpBHZeSm5ULHdJD9Mp46/hTW5a3Dy+deRq2jFnfX3I3TXafBwdE2\n0IY9jXuwo2wH3mt9DxkpGSixlsCcbMYN5TcADCjPJFFMWZGRKWWi0FoYIMWZ8FSuFlXkeInlKwAe\nA1AEymt8Jd4LcM7bQF374Jy/CeDN0GMYYz8H8I+ccxfnvBPAXWHW+WKE9Y8CSHiXiU5HBnqm8PDD\nE8NSqko5ji1byJO4eJGI5dQpoL2delU++1nKy2jn3j/5JIWsnE5qYNTriQw8HiKInTtJesVoJC9E\nNEf6fJQ76ewM9sfceiuRrcNBIbfrrqNzxf0Jj0Z4SbIMDA1RQcHliEQuYnbgNDtht9rR0d9xRa6n\nchVOsxMbCzZi2D+M3ed3Iz05HbYUG549/SwGvAOospPki8fvQYO7Ae5hN/a27MXbTW8j35qPiwMX\nYTFZcKTjCFr6WrAkewnW5q5FobUQa/LXIC05DbIiw+P3YE/zHrT0taDYWox+Xz9MehM2FGxAaUYp\nctJyoGM6bC7cDK/iDZBgdVY1qrOqZ7yf5WqReYmLWDjnzdCUAScanPNHZ2rt6eCuu4BHZ+jOQj0h\nWaafzZuJAE6cIIKxWKja68YbqXJL62WZTOSBpKaS1+DzAc8/Tz0pAq++SmSyaRN5MC0tRFb33hvM\ni4jZLGYzvTaZgpVhgsQGB4PhNlFdZjIFRwYv9qbMPoQAY6wQi16nxwNrHsCxN44l/B62OCaXUcqj\nMob8Q9havBUNXQ3wjnrxcsPLqHHW4P66+3FtybWBkcCSQUKVvQqKTcGSzCVYnbMaTf1NOOE6AZPe\nhBfOvAC3z4032t7A7/F7FJoLMTQyhOW5yzHkG0JWahY+v/bz6B7uRk5aDkZGR1CVVQWVq2HzMgAJ\nT/Z7+1FsLY5ZcXa5YSztuQvZUxGIVytMVIcJdHPOH5yZW5o7mCljaTYH+z8EJIlIoqiIjP9rr5GR\nLysDVq2in9D70espPJWURJ5FcTH9+8ILFNoaGqLw17lz1OjpclF58y23UKjLZiPCGBkJ5mvMZiKP\n0MIGs5ned3UFS4y9XkryM0bhucX8yuzCN+ZDPFVhAPDKyVdm5B7Wla6btM2kN6EkowQmPZVApuhT\ncHbgLNbkrcEDdQ/ALbsDnflaSRVbig1ltjL0jvSiub8ZKfoUZElZeKL+CXhGPBjAAI57jqPxQCO+\ntf1bWJu/FlaTFSa9CWfdZ5FuSkeRtQiKqkwYMRxu5n2xtRjGJCNcHldg+FcophPGulpCYALxeiyi\nOiwZwEcBlM/kTc0VzNRs+2eemVhmDBBpiEQ9QMa+r4/uoaCASEB0wQuCEeW+AwPkjeTm0nHl5ZRr\n6e2lhP/zzxMxPfAAhcs6Oog4VJXyKu+9RxVm+flUbaaqVAmmLWzQ6peZTEQ+JhM1YwLBbZfjvSx2\n5ScG8VaFAcCJS4mfGpmMZDy6ZrKLL7rsrSYruuVuXFN4DSpHKlHjrIFryIVCa2FA9l6IPvrH/Gjp\nb0GmlAlbig12yQ6P34M7a+/EjZU3YtA7iF8d/hX6+vtQkVOB5TnLUeuohV6nx7OnnsUTR57AUvtS\nbCzeiBR9CpxmJzbkbwiUE2shmiVdHlfU8trphLGmO954vmFKml/j1VzPM8b+bYbuZ07h8cdnZt01\na8JvF5Vqp09TJdfy5TTkqriYjLbbTdViwjNoa6PE/LXXBiVhtB32hYVAXR2NLNY2PIqiAeEl1ddT\nrsZoJJkYs5k8mdpaOkaEvkTBg8dDHlBaGnlYen14LycSQokktCt/kWguD1OpCpuJGff3V98f9klf\nGNV+bz+6Pd0wm8yoy6nDqtxVONN7hqrUPJdgNVmx3LkcZqMZbQNtePbUs6hz1mFL0RaYjeaAvpjw\nKG6pvGWSSjIA3Fl9J3LMOTAnm1F/qR7vud7DwOgAfKM+yIqMFEMKctNyJ91nrPLa6agVX23jjeMN\nhTlBXfoMQBWAFTN5U1cC8Txdd83A7KPPfIaubbNN3icM97JlwFe+Qh6EzUZGtqODyEPrGWRmUje+\nNqEeWu0W7r2278Vmo/xLXR3latato2KA//ovYPt28n5Wrgx+T6JPJS2NCCk1lQoHptJZL8sT582E\nnjsd+ZdFUoqNaNMWp4MRPoL+kf5JLcrCqJr0Jty37D4M+YaQlpwGR6oDFwcvorGvES6PC+W28kCe\nqCC9AHdW34lMKTPwlO9VvHDL7sCMFfG0H6pGnJuWi7tq7oKiKliatRRtRW0423sW1Y5qtA+2B9YT\nc+9tKTb4x/yQDFJCkvdi3UghNYGFnMiP12P5LohUGEjoMWyF1nyBqsZnuN54I/HXDic2GQqTiSqt\nBLReiDC6hYVkOFesmF7nvdlMHpTWEG/YEEzMu910zfZ2+t7y8+mY9HTK55hMQfWCeEhAValMOiWF\ncj6iaEB77nTkXxY1yWLj7ea4Wr2mBDvsWJW7CtWO6ojH6HU0/0SLkowSZEqZqHXUwqQ3ocHdEJBt\nKbOVTfDAtIY4nqd9cb18Sz6WO5fDpDchJy0nYMhFZ3lddh1GlJHAWtMNUUXrWA9de6F5KgLxEsu3\nOeft4g1jbFO0g+c6xNCrWIYrOTmxfSxGI+VNpkoEWqMtjK4YUTwyQtv0+st7Sg9HCJJEFWiqSt6I\naKD0eChMd+ECiWFWVFAeRxueAyJ7DapKZHLqVFAROdzvYDryL4uaZLHRMZj4MuM+9ME16Aok6OOF\njulgNVlhNVkx6BsE5xzyqIzekV4UppOCsTHJiN6RXtgle8Dwq1xFviU/bCVcJGIINeQi9KX1WMRQ\nsLM9Z7EqZ1VEvbNQaK+pXTdUimYhh7+0iNcMBTS7GA2P/myUY+cF4pkfcvfdib2m0UheQDzEIkp7\nQwUdhdE1m8l74DyY34hntruqUv5ElA9Hg7iWXk/37HRSg+aWLUS6QmXZODEXGnHWvJCO8Xqpki3R\nM1wWw2DxYWfZzoSuJ0FCCktBvjV/WuuYjWbUOGrgSHWg2FoMgEYMtw20YX/bfrhlUmsSisWii16I\nRop8S+ic+kjikqKz3JhkDBQNyKMyzvacxYW+C+ge7g6Ieoq1FVUJiFZqob2GWNc/5p903YUc/tIi\nlgjlSwCSAVQyxmpBoTAfgLeuwL3NKlSV8hqJREUFTWKMZPS0hlF4B0uXhicirbKw2UwJ+dCn9FBD\nK6T4m5vp/VRLhHW6oOZYTg41V9bXE0mMjlKuJ3QYmBaSRNecSuhsKlgMg8WHTCkT5VI5zsvnE7Ke\nF17cWX4nHln5SFzHx+NRCM9ElAGnGlMDSXVhnE16UyDfAgSNe2F64QTjPRVjLhkkrMpZhQJLAYZH\nhwPl22JtW4oNrQOtKEwvhNPsDNx/uGuE27aQw19axBKhvAUAGGN/xzn/wZW5pdmHMMCJLDcWlVad\nnZGPEYYxP5+8ga4umuwYbc3q6vBP6Np5LiJUJcv0vrBwemKRgtTEdQ8fBt59lxpKc3KoUiycYRfn\nCcJLNBbDYPGhqbcJvSOJqwrj4Li2LNjkGAmCUFSuonWgNWY4SGuEwyXVQ4201pBHC4FFgwjNWZIt\nAbHLfm8/Bn2DgR6ZkdERtA60wpJsCawb7hpXC4mEQ9SAAWOsZPzl7xljuZqfBa0KJQxwIp96RYmu\n3x/5GGEY+/up9+T8edLxEiGr0PBYpBkr2s8gqq7E+qWlFNKKpHQ8Fej11NW/Ywdwzz2U4P/d72IL\nd0YKlU0X0b6PRQRRaisNhJoSAQ6Oxs7GiPtFGKl3pBd7m/fCq3hhl+xTzscIRAptCUMemnDXzlyJ\nF2Itr+LFoYuH8KeTf8LhjsOQR2WszFmJ6qzqBR/Omg5i/S/4qfF/vxvy852ZvKnZhugBSU9P/Nr7\n90feJwzj8DARSmUlvRYGeCoGWZCI3U7Ha8NP4QxvpJxOLOh0dI26OipdLi0lNYBY97boWcweRkZH\noMiJFaH86ZGfYtA3GHafIILG3ka83vg6LvRdgFt2w6t4wx4fC1PNU0xngJdJb4ItxQY908On+NA+\n2B53vxAwNVK7HAKcq4j67XDOvzX+8tuc8/8hfkDjhhcsRNNeSkpi1hPlvNnZ1MwYCcK4p6aSga6u\npj4SrccRr0EWJOL1xkdG0/UizGYil4oKSuZHI6pFz2J2ca7nHI75EqMTdovzFgCAH3786cSfwh4j\niAAABnwD8I/5oapq1B6PaAj1TGIZ5MtNmKtcpYIBBny04qPYWbYT1VnVUFQFBy8eRMdQR0wSmAqp\nLaQJlpdbFTYnRSMTCUlKnGLvoUNEFElJ0QsCPB5SHFbVibPqhQGOxyArCpX0ijHC8ZLRdL0InY5C\nbLW1wXxOUxOF46bqBS1iZuFTfAlb6yXXS7DDjiQkoaU/cgxUURUYdAbUZtWie7gbfzr1p6jHTwXC\nIIuKsFBjHylEFg0qV9E13AWXxwWn2Ylsc/aE3MuprlM4fPEwXB5XVHKZauHAQqkYi5VjeYkxthvA\n3Yyx18dfvwiay7KgoaqTy2ingxMnyOCK8b+RwBiwdy/wk58Af/zj1D0It5vCbe7xOZrxegeJ8CK0\na4hJlC4XkWlHR5DsFjF7ULkKDo5kJCdsTTfcqMmswZ014adXyKMyDrQfwO+O/g7nu89jX+s+eBXv\nJM2uy4XWI0rUE788KqNruAtOsxNmo3lCWbPZaEZJRgl0Ol3Ycmct0WgT+GJfJA/rcghwrmKxKiwC\nXn8d+MtfErtmZSXNR4kEk4lkWrZuBbq7KWymKMEn/nh6NOx20heL5W2FTpyMh1Cm0iciRCsBItW2\nNhpuljtZomkRVxDyqIy3LrwFHxLntQDAbdW3ocZRE3afZJAgGSS4R9xYnr2cdMGSrTAkGRJybWGQ\nRXnyVIQeI+2XDBJKM0oDa2nXtSRbsKVoS6AxU4xPjlbtpm2MBDClJsn5KFYZ111yzn/AGLNqqsKm\n1wk1y+A89jF1dWSgEwW7Hfj856NXmvX20ox5k4mqrE6dovceD4WUmppiezDaCY/RIMvUuX/6dPxe\nUbQ8TLiciiCXqioqQZakyy8SWERiIBkkfHHDF3Fv+b0JW3Nb/jbcVHlTxJyJjulQnVWN68uuR3pK\nOga9g4H585eDqTzxx8pbxJPXCF1XSPuL0JhX8aKprwmDvkHYUmxhq920Ya4rWXwwW4hXhPK7AD4G\n4CyAjaBwWMzBX4wxA4AnAHg454+Ojzf+OwAjAJoB/E/Og2aeMeYA8M+aJZYB+HfO+dPjY42zAGQD\neJdz/n/Hz/kVACOA8edvfJ9zHrXzK56QjNNJPRmJwuAgSaGI6qxw0HobH34I7N5N81NET0qoCOV0\nwlaSFJwJo10TiOzJRMvDRGpO1OnISxENnKEClIsJ/CsLHdOhNKMUaYa0hK1pS7ZhuXN59GNSbLi1\n6lY0uBvQ2NsYmOTo8Xtg0psmhJliPZVPRRYl1IiHPv1HMvLaZkuBSPcmGSQ4Uh1o7muGTqeb0N8i\nENrTMpX+lvmYe4m3LCMNwOuc868xxiQA34vzvG+BKsjuZYzpQKXKN3POveNkdT2A18XBnPMuaAiL\nMfYMgJfH3/6DZ0Dn0QAAIABJREFUICHG2PuMsZ9yzj0gr+sxrZZZLMR6mgfI4D3zTLwrxobfTxVe\nshzZawnNUaSlkZHv7KRSXrs9WOU13e5yrcqxVvIeIC8m3PCuaN3y0UgnVOvM4Qj2CS12yF959I70\n4vHTiZsJccp1KmaFl3jKX+FcgVRDKoZHh+GW3Wjub0a+JR/tg+3gnKPGUTNlsoh1Xe16YvRxlb0K\nlmRLxCZGcQ2Vq2hwN0S9Nx3TwZHqmNDpn0jMx0bLeJ8XvQA4YyyHcy4DsTN/jLEHABwEeTkAUAng\nNOdcFK8/D+C6KOevHz9+GAA0pKIDoIC8HgAYAvBNxtgTjLHHGGNJEdZ7lDF2iDF2yO3ujnX78HhI\nJDFRMJlIkj5a1ZU21GSz0YCv8+eBV16hefNeb7DHRlUTF07SkoIkkYxMVdXUKsSmUiTgcND1FCU+\nzbLLwdUSctP+XXd3x/67VrmKo67E1t4UpxdH7GEJhRCU/Kc9/4R9Lfugqiokg4SKzAoUpBfE1TQ5\n3SQ3jyMWLq5hNppRZa/C0qylUQlDEKeQ8F8o/SiXi3h/M+8C+CmAPzDG/gxEnxLEGFsFIIdz/rJm\nsw1Aj+Z9DyZNbpiALwH4UZjtfw8Kj40BAOf8i5zzLwJ4BEASgIfDLcY5/znnfA3nfE1WrNIsAPv2\nUdI5USgqCg7LigSTiQjF7ydCeest4IknKJFvNJKnJc5vaAiGrKaCcCKUWlIQnkwiOvMjQVznzBn6\nHDMh7zJT3f1zDVP9ux70DeK91vcSdn0jjNhQtmGCYGMotAKOg75B1Lvq8WHHh3jt3GvINmcHGg57\nR3ovu2kyXgihy6lIvAjCiJfI5mNOJNGIJUL5nwjOYbkZ5H0wALG0pO8FYGOM/QwURlsN4DiIXAQy\nAbgjXLcCgMw5d4Vsvx+Al3P+dOg5nHPOGHsBwKdj3FtckOWgeu90kZ4OfPWr0Y8RY4J/8AOaSZ+V\nRbNWvF4ipYMHKecjvBi3m+5PSObHC1mOHOq6khCeEUCk6XJRqC+ezxJPdVqsvpyrVQl50DeIw22H\nE7bepuxN2FG6Y4JgoxYqV3G25yyePPIkfKM+rCtchwHfAJL1yTjmOoamviYMjw6jML0QhemTRxQn\nGlcirKQtfz7QdgB1zrp5lR9JBGL9b/wdhPdqovp4nPPHxGvGWDEo1/JjALsZY8njI45vAxBp4tBX\nAfxQu4ExdjuAKs75t6NcejuAD6LdWzyQZeCll+KrHosH6ekUVotkxMWcku9/nzTCAOAf/gG47Ta6\nh/XrKd+Sl0dP+K++SsUFfX20tnMKxTVagz6bkiraHI/LRSKWK1aQ9lgsQx+PinEs9eSrVQm5b6QP\nLzS9kJC1slgWHlz3IGqza2FMMoY1noO+QTx55En8eP+PMcSHcFv/bdi1YheK0oswqo5Cx3SBBLnK\nVRy5dAQphhTUOmrnXV4BmFgccLD9IH72wc+wa8UubC/ZPm9KhROBWH0sbQm4xhiAMc65Ml7Z9d+M\nsSEA3QB2hx7MGMsGYOecn9BsKwLwcwDPjXtBAIXDTjPG/hFA8fi2VhCBRUWsuHt9PfDcc7FWiR/Z\n2UQAkQxmfz/w5ptkVLdvpyf4V1+lkuPPfY68FJ2OxB19PuDmm6nSSlGmrg4QOppYYDaf4G02UnEe\nGIhe3CAQ6o1czr1frXplf274c8LW6ubd6Ja7YTaaJzU7KqqCjqEOnOg8gaaeJthT7BiUB3Gm8wx0\nTIfvXP8dKKqCtXlr4R/z48ilIyizlSFFn4LC9MI5+4Qfq6dEW7FW56zDrhW7YE42h/XmFjIuT6xn\nChgnp8+Ov34TwJuhxzDGfg7gHznnLs55J4C7QtZoARB2qC/n/J/DbY+GWMZHVEclCkePAr/4BfCv\n/0p5FAFFoeqo8+eBp54i0nj4YVIJzsoiArDZyHCeO0cd+Vu3khFOtPGfrSd4VaX+HTGtUlGIaEVl\nnChY0H7eUG/E4yFPrqoq/umcMzEPZj5gyDs07TV00IGP//eH+j+g0laJjyz5yAQycMtuvNDwAl5r\neA0n3SdxTeE1cPY60dDdgGdPPYvHtj6GPEteQJbe5XFhSeYS1GbXzulGwFilzqHS/dtLtgeI6GrC\nnPjtcc4fDc2nzAYUhUqMt2+f/lpOJ/CrXwHf/Cawa1fQ+GnhdgN79gB//SsRTFISEYwkUQd+dzcZ\n/BMniHw8HhLGFEY2kZVPs/UEL+T9nU4y9GfOkMd4+jR9P/Em4BMVtlyIELpX39v3Pfz8/Z9Pe71b\nim/Byw+8jI05G5En5eHJ+idxzDVR1NKWYsOSzCWU0B8DOgc60THcAWOSEYwzHO44jLeb30bHUAes\nJitqsmoCo4fnKqkAsUudwzVThhPMjDSJcqFgxj2W+YTWVuC++4Cxsemv5XDQ3PilS+l1fT0N8NLC\nZqPelp4eYNs2mmXicpHW16c+RTIoFRV07oYNwPLlE5/IE+llzNYTvJD3F4S2dGmwiVSUP6sqkb4g\nmNCyZrOZclhXW1grXsijMv508k947M3HYh8cB+5ecTdSDan49JpPo9haDL1Ojzpn3YRjvIoXrf2t\n8HM/ctNz8e6ld+GDDxZYUJpZCnlUxhn3GRiSDFhiW4LGvkY4zI7L7sa/UghN/scrtxI65Mwu2eGW\n3RM8H0VV4JbdsEv2y1Z+niuYu48Gs4B3300MqSxZAnzvexTOYowI5IYb6F8tZBk4coS8lq4uOsbp\npPDPuXP0xD48TPpia9ZQH4yYwOjxUFhtvucJwpU6i/HHYnxBczN9P4cPk8xNqAcz2zL8c71nxqQ3\n4Tdv/Gba66QgBd/e+m2k6FPwRuMbqLJXYWvxVmwt3jrpCV5RFdhSbLi9+naUWcuQkUSdBfmWfKzJ\nXwOPz4MRZQQOyYGC9AJsLNgYGD08nxBvabE4TuUqzXfR6SfJv7hlN/a37Sep/nmO+U2LCcYXvhDf\ncYxFDr2IKq1Tp4JJaTEXRQtVpY76vj6av3L0KCXk77iDDGt+PvD005Scv/Za6m155x2SfOnro0R3\nbu7ClkXxeEj+JTOTPJbkZGoOvVJEGm9RwFyvMPMqXhwYPTCtNbKQhdUlq1GaUYpMKRNF1iI408i7\n8Pg9k57YvYoXg75B9Hv7cdJ9EhtzN6LB04C7a+/GOxfeQf2lenx6zadR7ahGv7cfZ9xn0J3SjUp7\nZcJUj2caQqk4nmIDbSd/20AbRpQRpOhTAmEyySDBLtnnLcGGYpFYNBiMr3k4IqkYjUQkdXWUE2lu\nJi+jvJz2VVQEDY/HQ1VeJSVEDkePUlPmG28AN94IDA0BBw5QiXFlJSW4f/IT4JOfJE+loICS/END\nNLHR76d1ptLTMh/g8wEXLwZDZjNJpKFEEi9hzPUKs8tJHNthh1vTZuaFF4YkA1L0KTjeeRzHXccx\n4BvA5qLN0Ov0yE3LhSPVAR3TQVEVqFzFNYXX4JeHfonW3lacU88hCUk40HwAZr0ZbzW/Bb/fj1Jb\nKY65juGpE08hNy0X39j8DVTaKxP58WcM8qgcUDOOlRfSKjAvzVoaIKVOTyeGR4dRbiuH2Wie86HA\neLHAzND08LGPAS++ePnn+/1kYD75SWB0lDrnn36atn3608F8gSzTE7jPBxgMlJBfsYKqoTweCsld\nukT79+whD+UrXwG+9jXyWHp6yNsxmYD336fz/X7ybKbS0zLXYTYDq1dPHKsMJEaEMxxCiSRewliI\nFWZeTHSxk5CEuuw6LM9ZjtGLozjWeQztA+1QuYobym5A13AXzEYzJIOExt5G/LX5r0hCEva37MeI\nOgIZMhgY3mp7CxW2Cvjgw56OPXj88OP47NrPomekB8Ojw/CP+dE+2A6z0TylbncttHkPADMmOX85\n4pCikx8AXB4X6l31qMwkLy2c5xcNU83vXMlqu0Vi0WD58tjEkp5OXkl9PR1/8iSFubKygM2bydvo\n6yODWFJCRGCzkdeh0wXDOwYDGbLOTqoK45xCbElJwKZNRBI5OZTUF+XPY2OUe+npCWqGORzkDfn9\niZt4OVcQ2nMjVJ67ush7SbQxDyWShUIYvSO9kCBBRvQ8wK4lu3Cy8yTa5XbcUH4Ddl/YjSHfEJZl\nLcNn1n8G2WnZaBtow/aS7chIyUCaMQ356fnITcuFf8wPySBBHpXRMtCCjoEOjGEMFfYKDHmGcNxz\nHAVJBajJq8HDax/Ge83vQVVV3FVzFxRVwfr89Rj0DeKNpjdg0BngTHViQ+EG5KblTtkYTmf2yVQh\nqrwup5rNLtmxKmcVBnwDaOlvgVt2o8ZREyCeWIhX5XkqatCJwiKxaPCd70Tfb7NR1dirrwJbtpAx\nv/128hz+5m+CeYCDB0mGpbSUiEELMfL4/fepCk2no9JaYUC3bSPiGhmhnhWjkRL8x48TCeXmEgF9\n4QuUi0nkU/tMN0lOd31RmuxwzEzYaaEQSSjaB9pjkspq22oYTAboJB02527GPcvvwQ1VN6Cprwmf\nXPVJOM1OnOs5h3M952DSm/DRJR+dcL7Ii5j0JmSnZsOn+LC3eS+sBisGlAGYYcbWiq3YXrYdqqqi\nKKMI6aZ07G7cjX5vP/RJeqhjKtqH2nFb5W3o9fbibM9ZWE3WKRvDUE9ipiTn5VEZp7tPgzGG6qzq\nKd+nXqdHSUYJuoa70NTbBO+Yd0okFa/HZNKbYJfscQl8JgqLxDKO0OR6OOzcSSXA1dVUDmwykaGX\nZcqj6PXkyezZQ4ZP9GZoIYxXUhK93ruX+ltMJiKq6mo676mniGzsdlr/+uvJA8rJIdKprqb1FIWq\nx+LV2YqGmU5CT3d9bWnyQi1YmAk4zGF7iydgadZSHGg7gNtrbse2km0wJBlgT7WjdaAVXsULt+zG\nxaGL6BnpiSoU6VW8aHA34JmTz+D80Hk4DA7oVT1Sk1PhU3347fHfQh6RMawMI92Yjgp7Be5adhfs\nkh2Xhi5BBx22lWyDPCrDpDcFchFT8QamM/tkKpAMEpZmLQ28vhxoJfc9fg/cshuSQYJX8cYsO45X\n90z8/oRa85XAIrGM4+zZ2Mc8PS59+fDDlBOx2cjrKC0NlseuWgV86UtENJGeqs1m8kZWrQqKSup0\n5JFs3gzcfTcZ39RUuobLRQQm9MZ6e8njWb+eyGj/fgq5ORzT8whmOgk93fVDPYp4PCBxjMkUvov/\nasDjh2PPXvnvM/8NBoYxdQyrc1ej/lI96rvr0dTXBNeQCxaTBVX2KlRnVcORGpmoJIOE9fnrcWPl\njXju5HMol8oxmjSKQ12HkJqUikeueQRjfAx+xQ/JKCHfko9KeyX0Oj3aB9txvPM4VK4iNy0XHr/n\niodwpgJtviQR64i80qBvEO+3v4+NBRujJvPjzZ3MxqCwRWIZR3k5CT0OxaF48f77pO11882Tn6Al\niRojY0HkD265hdZ47jngl7+kzvM336Q1xsaILFJSiGT8fqpca22lXFBeHnku69cHFYLd7uA9TZVk\nZjoUFM/6oicEiN2bEo8HJI6x2+m7maslwTOJvzb8NeYxYxgDA4MhyQB5VEZVVhV0Oh2WZy/HipwV\nULkaV/JXiEr+w/Z/wE0VN8Etu1HfUY/GwUZ0ejuhYzrsLNsJvU4fMIoqV+HyuNA20Ia/nPsLitKL\nYEuxQTJIKEwvvCyvZT5CeCB6nR5lGWWwmqKLyMebO5mNQWGLxDKO/v74RhYDwNq1lJy/3PJeMW+e\nc/JC1qwhg1dQQAbw9ddp3003BRPyzc1UXixJlLy/eJE8F72ensbfeosKCCoq6H1rK/DBB1QEMJWk\n/mzLyU9F1j8eD0gcYzIFK72uOsT5N8rBkW5KDzw5W5Itl1VJJMI7Hyn/CDx+D64tuRYlthJ8eOlD\nvHHhDeSm5aI4ozhQquvxe7CnaQ+6h7sx4B0IhNp0TBdQPF6ZsxJWkxWKqsDlIfUnp9k57zvUw6Hf\n249zveeQakxFSUZJxO9/Lo8sXni/lcvEO+9Q7iIe3HAD9apcbk5DkogARIgGoJDXJz5BxtxqpVyO\nTkfXsNvJ4Ivqr4IC4N57g3mW7m5K7t96azAc9sEHlOvJyCDtM9GXMdeb/aYi6y+GlilK5MFkWi/p\navNUBEaG4vvDrk2pxeaizVFH9k4F2iFZj655FI29jRj2D0Ph1OciEsomvQnr89ejfbAd6/PXoza7\nNrCGPCrj0tAlVNorYTVZ4ZbdeO3caxhVR3Fb1W3ITcuN+35mo+z2cmCX7FjhXIEh/1BUVeS5PLJ4\nkVjGYTDEd9zHP04GfDqJckEYvb1kEM3miQZ9+/ZgQl5RgAsXiDyKi+mcwkKa1SJJtF9VyYPKz6eq\nKZuNPJWMDDrP7aZrzIdmv0iy/uHgdlOpdk4OeZHxEsdse2VXGocH4xvsddvq27AyZ2XC+ii0+41J\nxkBjoNDM6hruAgA4Uh0os5UhJy0nsJY4zi7ZsaN0B2wpNnj8HthSbNhStAVtg21TflKfjbLby4Go\nFpvPqsiLxDKOePIi27YBn//8RHXhwUHKCZjNQemWeAxWqAEXY4kVhc4VjY4uF+V0bDY61mIJniPL\ndP2jR4OeyuHDwYFZWoLS6RZes5/NRoSSmTk1Ipxtr+xKw66zo0PtiHrM19d9HV/e+GUYk4wBo27S\nmwIkEG7OfCxDLY/KaOprgiPVEejK13ag2yU7mvuaIRmkQPJa9IWoXEVzfzNsKTbomC7Q5S5ISEju\ni6ZCcb1o3shcDh0Bkxs75zMWiWUcDgdNcPza18LvZwx4/HEa2iWMmMtFc1YUhZ6aOzoo0X733XRc\nNHHEUAMuy1SZlpwMLFsW3Ge3Azt2BElFrOXxkHF0Oqno4IMP6Jp5edSgmZVFxwoJlCslkBjJG5gJ\nL8Hvp/Cl3z+182bbK7vS+N1Dv8M9T9wzQaJFiy1ZW/CvN/5rwCDLozIOdRzC3qa9yLZko7mvGQad\nATeU34CqrCoYk4yBLvtohlroX13ouxAgD4FAGIdhQu+GIKvC9EI4Uh042XUSlzyXsDZ3LZKTktHY\n2xhYS1s1BsRuhJzLoSMgMY2dcyXct0gs49DpaC79hQvAj0NmUObk0JyW0LG5hw8Df/4zGfSxMSr7\nTUsjEhLlyLW18T0VC2n4nJyJw8D0eio1DoUwjsJj2raNKsfq60mfDJj4VH6lntIjXWcmri9J9B2f\nOkXfmTV6EU0A88krSwS2F29H97e7UfLdEjQrzRP2bc7ajF/f/+sJRkgySDjUfgg/OvgjauAb6oJP\n9eGD9g/w8KqHUWorDTQExlONxMHR6emEZJAmJNvNRjOKrZTEb+1vxYqcFYFKMIByDatzV6N7mCZV\nehXvpPu8Eo2QVwqJ+DxzJdw3o5TGGDMwxn4/PiESjLENjLGnGWNPMMb+iTHGQo53MMZ+pvl5lzF2\nb7RzY60ZCZGkzr/xjYnvHQ7yVDZunPykvW0bkdFHPkLhmIyM4IwVq3VqSrzavEu4Zk1xvx4PiVN6\nvWQcHQ66jzVr6Jp5eXQvAOVcVDVYppyfH/l+EiX9HskbmAkvQfw+mpuDJcqLiIyf3vfTCe8/Xv5x\n7H50N0ozSids1zEddq3chU+t+hQKTAWw6q3wj/gxJA8h25yNKntV3AbPbDQjOzUbxzqPTZCD1+ZQ\nlDEFvz36W+xt3otBHynBisZMq8mKMlsZah21WOFcgYrMisC1teG5cKG6+YZEfJ65Eu6b6d/CtwD8\nGoCOMaYD8F0AuzjnD4O8peu1B3POuzjnnxM/AFwAXo50bjxrRoJ4gg6d7VFYSJItRUXAF78I/Od/\nknxLOJhMlDSvqgr2vwwMUHgmK2ui5xELZjM1TGoHVmmNvbjfgweBn/6UyEVViYycTvrXYiGC0eup\n3NjrpX/b2sib6umZeM1w68czrTEaIoX/ZmpmitlMv7Ph4fjLxa9W3FB2A/7Pzv8DI4zYnL4Z//LR\nf4ko8+FIdeBTaz6F3rFe1A/Uoxe9sFvsqLRXBvIu8SJTypwgB6+oChp7G3HMdQzyqIwKewVW5KzA\nud5zeLPxTSiqMsk46pgOep0evSO9YTv/Va6i39uPjqEOKOrC+EPwj/nR2NsI/9gUY71zADMWCmOM\nPQDgIADR014J4DTnXPxVPA/gbgCvRzh//fjxw4yxpRHObYt3TcbYowAeBYDCwsKoT9A33khGNhpU\nlcJOTz5JHoLTGewZWbuWpFqcTiKlWFVOwrDr9dSFL3pRtOEjcb9CkNJkAhobJ0vmm81kYO12uu7w\nMIXXACI9obUVKgs/X/MOFguFKOvrg7NwriaE/l1Hg47p8PVNX8dXr/lqzCSxf8yPU12nIBkkbHVu\nRXlmOXZU7IBryIUL/RewqXBTYApiNOkRkXS3S/ZA6axbduP99veRKZFrbUux4e6au9HU24TjXcfR\nI/egzFY2IefT3N+MfEv+JM0rbZXZkUtHcGnoEnaU7rji8vMzkdu40HcBfzj+B9y37L64RwnMlVDY\njBALY2wVgBzO+e8ZY8Xjm20AtM/MPQAyoizzJQB/F+PcuNfknP8cwM8BYM2aNXy6cXZZJpViIW9f\nVUWClE4n8OGHFJbS64MS7yKMJUlBAhBP78LAezzAs89SP0td3URjr73f7dtJ5fill0hzzGIhReTM\nTFpjZIR+hoeBY8co91JURPNfXK5gBVuk9ecTdDoK/w0N0fcqJPavFoT+XcdzTqwktqIq+OuFv+IH\n7/wA/f5+fGPzN3BjxY2od9XDLbtR66iFSW9C13BXQHpEkIwtxRZQOhYDrBypDjT3NUOn06E6qzpQ\nQiwZpEC4x2qyos5Zh3RTOnpGenDMdQy5lqBqcr4lH/KoPEnzSpvsX5mzEpX2ylkZlDUTBj3FkAKb\nZMOQfwiKqsTVDDpXQmEz5bHcC8DGGPsZgDQAqwEcBxGBQCYQvkyFMVYBQOacu8Y3uSOcG2n7jEOS\nqFpLUYDXXqN+CpHHeP116nVZtYpCUapKBv7iRcrB5ORMHFolDLxOFxS2DJ1BooVOR2TR20uy/bt2\nUb/KuXNEHkYjkU9BAR1nt1NYbHiYiG+hycL391MCv7+fQoEL4TPNFlSu4kLfBTx/+nk09DTAbDJD\nVmT858H/RLm9HC+degn31N2DnpEeVNmrsD5//QSSqcuuw4gyEjCwogtflC6b9CbodXo4zc5J43xF\n/8awfxjPnX4OSSwJklFCpb0SK3NWwi27A4KNAtpkvyXZElMGZaYwEwY9Ny0X1xRcg8OXDsNpdiLf\nkh/znLlS+TYjxMI5f0y8HvdYvgXgxwB2M8aSOec+ALcBeDvCEl8F8EPN+0YAtWHOjbR9xuH3k8eS\nlUVew/79FJoqLSUC+cMfqFpMhMEsluC8+sxMOlZVydBrDXx5OXkvQoJEq50lScE+GYeDCOrVV+mY\n2lra53AQkeTnB/Mv4tyFqgxst5N458gIkeoiLh+i073GUQO9qseRriN46eRL2HtxLwrMBTjVewou\n2YUvbfwS9Do9LMkWNPU1wZZiw9q8tZAMEjJZJhRVwaBvMEAuIj8i3vvH/DjWeQwrnCsCZCDCSeWZ\n5VhqX4pXz72KEmsJ/GN+mPQmlGaUTgo1iUT3bId/ZsKg63V62FPtGB0bnVJOay7gSpQbjwEY45wr\njLHvAPhvxtgQgG4Au0MPZoxlA7Bzzk+IbZHO5Zyr8aw5Ezh1Cvj1r0l+pK2NnpRraynOf+4cScQc\nOUJKyELS3uUiQ28yEfk0NATDUqLHw2ikXhgRRlPVoK6YGEdcVUXksGIF8Nhj5DkBlENpaQmSihYL\nwTuJ1Auj19P3dvjw1ZlnSSQkg4SMlAx0ejphMppwafgSlmUuw60VtyLdmI6m3iak6dNQ7QjOH3Gk\nOtA13AVHqgMdQx2wSyS3zznH0qylAePvV/xo6W/BkH8ImSmZ6BjsQEVmBSzJlkCeRORjSjNLcafh\nTrQMtkAZU6Ia7oUsVmlJtqDCXoGR0ZG4w2FzATN+l5zzNgCfHX/9JoA3Q48ZL0f+R865i3PeCeCu\nMOuEPTfS9plGaip5Ki++SKOEN20i4hgcpLHEzc3BMJjHQ6Gw4mIygpcukYqx1xvU+zp9mkhKlulY\nVaVGx4oKIhKAwm7Dw+QtidktGzbQOSdPUi/NihULb5KkQLReGNEzZDRefXmWREHlKnpHevHhpQ9x\n9NJRdA90o3GgEcddx5Fny8OGwg2wm+x48fyLWHNsDb6w4QuBJkj/mB/dw90BORY906Mwg4z96e7T\nSE5KxktnXoI52YwqexUqMiuwPn99IKkv8iTF1mKY9CaYjWbUOmrR0N0Aj98TlSzEvtPdp7E0a2lC\npOznCizJFhSlF+Fg+0GkJadNSRttNjEn6I9z/uhs38NUMTxMMX2vl4xYQwMpDKenUzgKAPbtA95+\nm3TIfv1r4KMfpQqmN98EXnmFwmjr15NBFN03djttExpgwtOQ5WBupbOT1r3lFioSEMKN4njRab/Q\n5pBEq1zz+ynn1NcXWxV5EeHh8XvwytlX8ELDCxhVR+FVvShJK4GsyHj2zLPwjnph0Bvghx9vnH8D\nN1XeFOiBOdF5Au+0vgM90yNLysKQfwgfX/ZxZKVmgTGG/PR83FN7D1SuIictB3qdHiPKSCA5L/IT\ngiTMRjMRik6H8sxymPQmuDwu2CV7oEM/NCwWZwvbvIKO6ZCVmoW89LxZT8hPBXOCWOYbRLL4oYeI\nIBoayKgBFI7asYOIh3P62bGDCKC6mgx9djb9VFRQ9ZeqklciKsUsFsrfCCMq5rwXF9MabW2kASbU\nmMU5Hg+dJ0b3trYurDkk0cJ5kgRUVk5UjF7E1OAf82PAO4Di9GIMjQzh7QtvY0vRFvAxjtG+UXzQ\n/gG2FW3DQOsANpVsmpBIX5e/DgwMB9oOwDvmxfr89ciz5MGYZESVnVzuPEseWgdaYUwyBsjEmGRE\n13BXgDC0kAxSIK/SNdyF/W37sbFgI8xG86ScitloRnVW9bwyvvHCkmzBqpxVADBvQn2LxHIZeP11\n4H//b6C0I3PgAAAgAElEQVSsjKqxVq4k411fT6GYykoiiv/6LyKdW2+lscYCFguVyIpqrdbWYFWY\nLNM6ubnB/aL3RBBPWRnw4IOUc9FCJPUFCWnnkJhMwXLnmdbwmg0IT629nT5vvArJiwiib6QPHUMd\nMOlNGBgdQKo+FaP+USiqgkxDJtYWrMWQMgRwwDXggklvChg5k96EdFM61uSvQV5aHnQ6HRRVgTHJ\nCHlUhsvjgtVkhV/xw6t4A2OHW/pbcKzzGDYVbprUe6LNq9gle6CsWcd0YRso50I11Ez0s8zHUN8i\nsVwG1q2jkNbrr1MI5uabiUi6uqiq69prqZdFlqm/Yu9e8kwUhcJYBw4QId15J3kgdnvwKfvDD4F/\n+zfgYx+jRk27PVjNBQTJoaxs8n0J0UnRoyKIwmwOilZeCQ2v2cTYWFBtej4T5ZWGylVkSpnYtWIX\nXjv3Gn7y3k8wgAGc6TkDj9+Dlbkr8fWtX0e/tx/Kuwr+eOKPyLPm4ZGVj1CYasiFzuFOtPS3IDct\nF6pKT9YujwvN/c3IkrKwr2Ufnjn1DG6quAlrc9dSuTEDVjhXRO09UbkKr+INKCQrqgKP3zOB2OYK\nQvtZEkk08ynUt0gslwGjkfTBGAPee488js5OyqXccQcZ9aYmIpVXXyUP5JZbqJHy/fdJlkWno1DW\nww8H1+zro1LkqioilPZ22if6XSKRgxbhwkWqSj/htMvma8d9OJjN1Aja2nrlvJaF4vGJDvk0YxqK\n0ouQZkqDXtHDP+aHChWcc7iH3ZAVGRwcfWN9ePro07CZbLCmWPFB+wc40nUErX2t+LDjQxSkF+Cu\n6rsCEixV9ir0e/tR31GPAksBkpOSsbFgI7LN2TE1sUKNtVt2493Wd7HCuSLqhMXZQGg/y3QbJ7XE\nJEKK8yEctkgsU4B2YuGyZcDu3ZQ7ycqiUFh7O/DCC1T5ddttVOllMNDEyaoq0uoS/SRFRcDWrbRW\nbi7te/FF8oaWLSNiys0lL0iUJJtMRDhGI90HEN+TuSxPDLdpkegy5Nk0tKLZdGTkyo0JWCgenzHJ\niEHfIPY274XZaEZeeh52lu6Ey+PCq2dfRctgC546/hSSdcmwGagn2aa3ocRWgiWZS1CXXYfO4U40\n9TVhWfYy9Mg9sEk29Pv6kWJIgV6nx00VN6F9oB33LbsP2eZsOFIdl9VNHu+ExdlAaEhuuo2ToVL6\n8yUctkgsU4AsU29KRwcl0E+fpnCV00leSUEBJfZ//3vgs58FvvMdkl0xGoG//IUI5brryPOoqZnY\nv2KzUR7G7w82WVqtwUQ8QPkW97iuQGsrFQaIdaLhSnolM21oYxGXTkd9QLIceVxxIrFQPL7ekV4c\ndR1F20AbqrOqkWxMxtbSrXjt7Gvo9nXjIzkfwbaSbWh0N2LD0g1ISUmBzWzDvpZ9ONdzDityVmBL\n0RZcV3odBn2DAeViWwqRkGSQMKaOoTa7Ftnm7CmVzYYa6/k0YXG6uR8tMXn8nnkTDlsklinAZKLc\nRno6kUNuLsm2dHeTkf/EJ2j/a69Rs57PR0/PFy5Qz0tzM20TZKD1FoxGKjuWZaowy8iY3OQojJjJ\nFDRk0Qya1ghLUvA1MHNehSRRyE2E3xK9fqhwZujnEGrHVyocthAaTwHyAj5W+TH0yr041X0KQyND\nONl9Enub9mIMY0hLSUOaMQ3vtb+HEXUE2WnZONV1Cn7VDw6Od1vfhcVIzXxn3GfAGAvMuweAfm8/\nWgZasDp3NSSDFDGcM1cGVc0WFFWZIOypJab5VPl29f3mpgGvl7yV3l5gyRLqS6moIC/FaCSP5Y47\niGDWraN+lX37gN/9johi48Zg30u0qZIOB3Xym0xU1qyVthcilkIiJprh1krha4Uuu7oo1DZdifxw\nEJ+ttZXWT9ScFwEtcYmck/ZzzJQ8/0KGMGZWkxU6nQ6lGaV4ePXD6Brswsmek6iyVuH6Jdej2lGN\nLUVboNfp8XLDy2jra0Nnfyc2FW3CqtxVsEk2SAbS9sq35E9QIZZHZXR5uqDX6dE+2D5JJ0yMJBZT\nIbX7teOKteuFHrcQ0DXchT1Ne9A13DVpnxD1nOrYgtnA4v9+U4AkkZfS0kLNkNdcQzL3nZ3A3/wN\ncN99FL667joKa+3cSUaur4+8mPXrqTpMlP6KoV3CMApj6XZTJ31fH3lCwNTnpYQm7IW3AwTLl2cq\nfKMNDyVqzouAlriA8GEos5mq8zo7pz62+GqDKPl9t/VdePwe+BQf3mh6A9uLt6PaWY0Sawm+vPnL\nuGPpHahx1OBvN/4tvrzxy1iesxxHeo/gw74Pcd59HncuvTMQ3pJHZZzvPY8TnScCs0QcqQ7sKN2B\nImtRoLteSxaCKIDJkxPDkchcUfFNNCSDhJy0nIify+P34FT3KXj8c3uy3SKxxAGtEKReT5Vgjz9O\npGCzUcnwJz9Jr8UTsyRRor2qijyZ06epQsxiIa+luZnO/4//IK9GjBgWc2CcTioKEGGzeGL5oYO7\nWluDhljcl9k8UVl5JqD1GmYiByHWFJ8nXEFCTw+NIGhpSdx1FyI8fg86PZ2oy66DSW/CG41v4MXT\nL2J/+3683PAyyjLLUJddB6vJCmOSEU6zEya9CeVZ5TDDDCOMYGC4OHQRvSO98Pg9aOlvQUtfC549\n+SwOth9Ev7cfOqaD0+yEMckIs9EMr+JFU18Tuoa7oHI1QBRCEl8bBjPpTZPmsCyEiZHhYEm2YG3e\n2qjJeVWd7MHNNSys30oCEBq6UVXSADtxgrY7HOS1DA9TFdjAABnx4eGJ6wgyuHiRwlqMkQeiqrQ9\nP5/W2rqVei9OnSJyMZmAs2eJVLSGM54Qj9Y7iGTQr3SoaCauF8+aGRnktWREm/izCKhchW/Mh6zU\nLPTIPdh7YS86Bzvx5+N/xgsNLyDHnIPlzuWB4z1+D5p6m1CXXYdl2ctgSbLAqDciLy0PXcNd8I/5\nSRY/zYliWzFOdp/EoYuHcKHvAryKFx1DHej39sOkNwXEK+VROSpReBUv3LI77OTIcJ9nrhvdaIhF\nmGajGYXWQrQOtM5pr2WRWEIQGrqRZXrqFXPoRWglM5PCVE89RXmWoiLarvVuVJUS+21tlLz/4INg\nb0p3N3D0KM1N2biRQlbt7cCePcAPfwj88Y9EaCKnMzhI6ykKbQ83hjfc4K65nmsQ35eiJDYXI6rD\n5vrnn23omA6pxlTomA4H2g6goacBO0t3It2UDkOSAeX28oCn4B/z43zPebg8Lnj8Hmwq3oT0lHTs\na92H8z3nUZpRih65B388+Ue82fQmym3l2Fq0FTlpOdjfvh+nuk7h1XOv4p2Wd9A13AVbig1WkxVt\nA23oHemNSAZTCXst1NyLgI7pYNKb0Cv3zukRzItVYSHQDt06doyeemtqaJ/ZTGGwo0cpj9LTQ3NV\nxsaCc0AGB6kkubKSwma7dpGMfno6hbxeeomS+y0t9DSdnU15GYuFvBXOibxyc6mi7MMP6Vy7nUJu\nHg+pKdfVkVejJRDRZ+P1ThxXPNOYTu+KIPJEa5p5vUGR0EVMhLbySkjOSwYJQ/4h+BU/ko3JaB5q\nxuqc1dhevD3gBTT1NuHlMy8DDOgc6kSNswbLnMsw4B9A22AbdEyHTCkTq3JXwS7Z0eftQ5IuCQXp\nBbCZbEhLToMxyQhlTEFzfzNGRkfQ4G7A4YuHsSZvDbYWb53UrQ5gSlViCzX3ooUocKjNrg2Uc881\nLD7PhUAY6vPngd/+lv7VVmBlZJDRX7mSwl8PPkhd9QKyTLL4H3wA/OIXRA5LllCTZEYGGWGvl8qO\n6+qC5bA6HZFBRwdw/fVUVZaWRppiJSVEEDodEVhZGSWm6+spdyO8K7ebJlm+/Tb9K3peZhrTSdAL\nIrfbE5uLcThI/FMoTS8iCO1TvTbMtK14G7YXb0evtxdNvU1YlbsK5ZnlkEdlnO4+Dc+oB2vz16Jt\nsA0HLx1EY08jvnjNF3FPzT147sRzeOvCW1C5CgMzIDs1Gx6fB45UB8xGMySjBEWluSrVjmrkW/LR\n2NeIwvRC3FN7D9blr4PKVSiqgq7hLjT1NUEelafsgcyX3Mt8D9nFwqLHEgEVFeRZVFRM3N7VRaGt\nzZuBu+6iBkmrZhqqw0G9LW+9BaxdS+/XrSPPRJKIcJYupdyMTkcehiwH9cI2bqR/jx4lj+j0aZoU\n2dND5DIyQl5NcXGwT0TojNntFFozGomkrtRclukk6LV9IInsB9Hr6bufiie1UORZYkE81YtxwYXp\nhdDr9BjwDmBD8QakJKUg25yNB5Y/EJjuWGmvDEyXHBgZwE3lN6EqqwrltnL4FB8GvAN4q/EtmPQm\nnO05i2pHNa4ruy4wbyVFn4Ks1Cysy1uHTCkT8qiM9oF2JLEkrM9fDwBo7m+GXbIH1I61Sf3L9UDm\nal/MdKRe5sPwrxm9I8aYAcATADyc80cZYxsA/B2AEQDNAP4n56KgNnBOOoB/BJAMIBXAvwPoAPDP\nmsOWAfh3zvnTjLE3AZzT7Pt7znnf5d6z1rjU1U3eb7NRxZZeT8RhC/FE9XrSAvu3fyNi6umhWL/V\nSh7Oa6/RurffTtc4dIiS9dddRwPCCgsp3GUwUDitqIhCYZzTa235rJgsabWSUdbryZtKBKZiZOdq\nk6DHExygFk+j5EKRZwkHlavo9/ajR+5BkbUoMO+kdaAVxdZiXOi7gBfOvICS9BKkGFLw6dWfRrWj\nOmCM9To9XB4Xnjn5DPZe2Asd06E2uxZne87CoDMgKzUL77S+g3J7OW6uuBklGSUBD0UySCiyFkGv\n06NH7kH7YDuWO5fjxiU3BsJxACYM+RLTJKdqeEOJZLpaXdNFJGKbDmFaki0osZag3lWPdFP6JFXo\nuYCZpvBvAfg1AB1jTAfguwB2cc4fBpHa9WHO+b8AfsA5/wLn/H9wzus5512c88+JHwAuAC+PH8+1\n+6ZDKsDEsE645r6CguCArb6+8DF8s5nIxGQij8TnC3olVVUU4rp0iQxyQQGQnEzHCy+ktZU8nKEh\nSvL39lJIzGQKhuXMZlpr6dKgp5DIZsRE95/MFjiP/ztZKPIs4SCPyjjQdgDPnn4WbQNtACYaN5Wr\n6PJ04a8X/ooR/whKbaWBJ2Exvx4c0EGHdfnrcLH/IsQzocfvwer81chMzkSZrQy12bVQVAVNfU1w\neVzoGu6Cy+OCPCoj15ILDg4d0yE3LZeaMsdHF5uNZuh1+kDJsXbc8FQ+pzZ0Nts5l0ihvOmE7HRM\nhyJrETYVboqqCj2bmDFiYYw9AOAggLPjmyoBnOacC1P8PIDrQs5xAuAAvsgYe5Ix9vcsRByHMbZ+\nfB1R4DvEGPseY+w3jLHPRLmfRxljhxhjh7q7uyPed6zmPouFKsL27Aker4WiUA5k1y4y/jffTKGs\n8+dJEflrX6M8Std4Y21mJrB6dVDuHqDrJSeT92K1TlQl1ladhXbfJ5IMFoKRNZvJgztyhBL5sTBf\nKum0iPvv2iBhQ8EG3Ln0ThSk0yAfrXHLScvBUvtSdAx24M+n/4yG7obAuW7Zjffb34c91Y6HVj6E\nR1Y9gryMPGSkZKDcVo4kXRKuL78e1y25DksylwS8FEeqAy39LWgdaEWqIRWtA61UhWZIBYCoOQZB\nNq0DrVOq8AolktnuVp8pYtPr9HCanXMyDAbMELEwxlYByOGcv6zZbAPQo3nfAyC0y6AIwEoA/4tz\nvgtAOoBPhBzzJQA/Em8453dwzr8J4GEAaxhjO8LdE+f855zzNZzzNVlZWRHvPVZzn9dLngpAY4JD\nh2ZduBCsJjtzhkJazc0kTOl2A5s2AfffT7kQUXl25EhwAqVY7/x5uo+6Ogq9acmjqYmIKfQpPJFk\nMNtGVlS4iTLry4EYTXD4MHl+CxFx/10zHWwpNizJXAJjknHSfqvJik+u/iQ+t/5zKM8sR4ohBYqq\nBAZ0rc1bG6j6StYn4+O1H4fFZIFP8aF7uBt5ljzcX3c/zEYzOoY6oHIVjlQHahw1qM6qphHFYDAb\nzahx1EDHdDGT8pdjlMN5ArNZgjyTxQRzuQBgpujuXgA2xtjPAKQBWA3gOIhcBDIBhNYtyQD2cs7H\nReHxLIAHAfwGABhjFQBkzrkr9IKcc5Ux9iKAOgB7EvEhwuUOJAnYsoVKf0N7V9xuKim2Wkm+paiI\nGiElifItubnkrYgYvt9PHkd6Onk6Xi9Ve5WWBkNl4a4vpkSKBspo9ztfIcuUH2FsejPsMzLo+1xs\nlIwOHdPBkerAvbX3YmPBRhSkF8AtuwPjgBVVwXOnn8OG/A1oGWhBWUYZCtMLYUwyoiSjBFaTFXqd\nHoc6DqFjsAM7SncEEvciES+65xVViYs0EjUVcrbDYVPBVIoNZjt/FA0zQiyc88fEa8ZYMSjX8mMA\nuxljyZxzH4DbALwdcuo5ACsZY0mc8zEAWwAc0+z/6v/f3vlHx1Hdh/5z16tfu5JsS+tFtmXLdvxD\nNj9tbMBJahpTUudACSWF5pCeNlAgj76QlhcKIXkP4hJI+iMNNIQmlEBKmlIOOSlNneLEdltDTMA4\neXkG2cY2wraEkSVZlqXVSpZWe98fd+/uaDS72h+zP7S6n3P2aGd2Znbu7Oh+5/sbeDTFV1+JMrHl\nDY9HOeytTvtQSDns33xTvT796UTflWPHlCC44AK1rbUycV+fmjx17asTJ2DnTlVz7MILnTUPe5fI\ncsXnU/4j/T5bRkdVUISpGZYelbMq+UCDak+q2wH7KnxsP7ydtlNttAZa+UDDB2iZ3RJvE7xh4Yb4\npH1J0yWsbFxJwBegK9TFz47+jI8u/yhej5f9p9S/8nBkOO3J0I2oLi2g9BN+qUWIWclEWFTOqqTG\nW+OogRabQlzdcWBcShkBvgI8J4T4R6Aa2GHdMOZ/+Qbwz0KIp4DFqKgyhBDnAQEp5VvWfYQQ3xRC\nfDd2zGNSyj35HpBT2Zf2dpW42NqqfCK6kvCcOYlyLXbzUiAAV1yhNBvtrNeOfP2E7uR4zreZyu2K\nxNng8aRXwXkqfL5EqLchM7weLwFfgJ+8/ROe/uXTLJ27lGpvNUvnLGVwdFCVzR9PSGyP8MRL5euJ\nWwhBJBohEo2wYeEGFs1eFI/+Gjg3wMC5gZSmHDfNWNMhKz8T7Ur7vnrDBUpYy4C8e36klB3AZ2Lv\ndwG77NsIIZ4EHpBSdkkptwHbHI5zCviEw/q7XD/pKdB+jmAwUczx8GFVVPKDH1Tmr95eZa6qqVHm\nr0WLJk+SXq9y9M+frzSZmholWOrrE2VO3n47vXBZt3IwolF13t3dSuOa7qY17SubTg75UuLk4El2\nvLOD7qFuhseGicoo8/zzGB4bZkf7DqVNVPpYNncZtZW1E564g/4gm1o24REe3njvDTYu2ojXo9od\nh8fCHOo9hJSS84Pnxx3sdm3CzadyN0xi+c6LycT856vwsaB+QUma+Eri301KeYeT36RUsfo5wmE1\n+d5yi4oEu+gipZ3oTPLGRpXv0tOTiOayorUPnYNy2WXqvY7uSrdhXDYRYU6aSTic/7L6hUQL3GJq\nX9MJu0N4aHSI2TWzuW3DbWxauonV81ZTW1lL8+xmrlp6Fd5ZXoL+YHxys07eo+OjDEeGqa+qZ+Oi\njXGfiy6P3xpoZfW81XGh4qRN9A33sf/UfvqG+1KeZzq44UgvJa2ntrKW1kDrlIKoGE7+0oxVK3Hs\nfg6PR2kVv/d78MMfqkikK69MTNKBQGbhrtFoolNkfX16E7w9IiwdDcYpIVD7hsrlKT8cVgER4fDk\nZFY7MyXzPhXhsTDtZ9oJ+oME/UHOqz2PzUs3s2zuMubXzcfr8RIaDXG07ygrG1fi9XgnPL1bn7it\nQkZn8FvX6X10roquWWZF+3ms+RpRGaV7qJvuoe64plQoSiEQQGtNo+OjHOw5yMVNF1M5qzKpFlUM\nJ/8M/ffJHbufw+NRAmThQhVqDKoKsU6UXL16arOSDrENhRKdItP1pdjPJx0NphxyVabCyceSzIeU\nbR5QKfik3ELnn3QPdRMaDeH1eGkNtHKo9xBnhs9wrP8YPUM9RMYjU5qErBqCfmoGHMOBdY6L/VhO\n+RrhsTDdQ90TNKVCkYvW44bmoIXq0b6jnOg/wcnBk5wOn56gRekwcV39uBjC0AiWHNFl7HW48TXX\nqKKTusXwiRPK/5KsHbEVHWJ76FDuSY7ZCo1yybjXeL1K4FurPCcbo7lmauIM+AIEfAH6hvt4reM1\nRsdHubz5chbNXkTAF6An3MM8/zx6w71TmoRStRzWZDrx+SqUTyfoD5ZsdJcT6ZrRUgkgLVTrKuuI\nyAgXBi+MB0ToCgrH+4/zyvFXON5/nKiMFqUwpzGF5UA0qsrf79un+q3s2QO/8zsqobG/X2kqfv/E\nzPlkxwmHE5oNqO1TmWamMtukk9OSzBRWTlqMzzf5+usx6pI7kND2sglWKLdrNhIZoeNsBz1DPRzp\nO8KJsye4dtW18e6Ps8Qs5vnnxTURPXnZsZqslsxZMmHys2o7mearuJXfUmjSFaCpTFdaqOpghsHR\nQSLRSHy70GiIwdFBVjWuYnB0kPBYuCjXavqI+xIkHFYhwgsXKjv+5Zcr7eXVV+GVV1TC4wUXTMyc\ndyIUUj3uw+GJIbapnoSnqmmWDk4TYrEz7t1mZERpkk413dzSEMvtmlV7q1k0exFXLLqCT6z5BFtW\nbIn7OHRBSf0EnKrkSngsTFeoC3+Ff0JbYacnd7cczKWcjZ6u5pBKAOljjI6PMjQ2RNAfnFBPTQue\n5tnNBP3BCde9kJTJv0LhiUbVa9myxBPxFVeoLPHjx1UYsc+XviPYKfqrujpRTt/OVDXN0qHcJkQn\nnISnvl6gNMTW1vLRNtwgPBam42wHXo+XhpoGFtQtiPs4RiIjdA910xvupdpbnfIJ3Ffho6m2iaGx\nIfqG++LCxGni1MJGm82yFQylFLWVLVMJIF0UdPHsxdRW1k4S7uGxMG/3vk3nQGda7ZzzQRlPKflB\nO9i7uhKTU329KjDZ1KSc9YsWJfwrTjW97DiZayD10/ZUNc1mOtZinXbhqQW2z+dOEmY5oZ/4x+U4\nPUM98YZb+jNdmqUr1EVvuHdK533AFyDoD9JQ0zAhGsw+cWphA+QkGNzo31KqGg8ooXKg+wB7Tuxh\nJDJCbWXthPEOnBvgQPcBFtYvpDXQmtZ1yMeYzb9ThoTDKpP+4MFEI63OTjU59ffDW2+pjo9NTUq4\ndHVNLVxCIWWOsee5pCswtHAx+RoJUmlxqQT2TCc8FqY33Mt5/vPiphZri2Cdg6Ijx1IJgKiM0hXq\noq27La6pJKsyrIWNfaLMlFwd1aWo8Vgn/pODJ9l9fDdL5yyNl9Sxjjc8FubU0Cm8Hu+ECgipyMeY\njWDJAG3+WrBAma58PvU0rCf/QEBVL25pSXQwbGpKJFImIxxW2fn2bTIxVZVTZFKu6N8pWdBEJhpe\nOYUST4XWSJbMWULLnBaWNyyfEHmlQ5F7w73UVtaybO6ylAIgPBbm8OnDcVNNOhNYsVsLl0Keih3r\ndRs8N8jx/uPUVNQ4lswP+oN8ZOlHCPqn7smtBdZUJs1sMIIlA/Tkfe5cwndinfy93kR3SUgkUuqE\nw2QEAsrxn0srYWMOS6A7R0LuJq6ZIrB1BNex/mN4hCfecMs6wesKyDopcSoB4KvwsW7+On57+W/H\nNZ9Sm7TtFFuwOWG9bnVVdSybu4y6qrpJ20WiEXrDvQR8gbT6tGiBpU1qbo65dK7eNECHDw8Opt++\n1hrCmuzJd3RUZevnUoF3Jjji0yUSUS2hI5HJnw0MqOoIr73mXGLHTrkKbLtdPd2kQ6eJN5mN3l6U\nMtemW/bvKXV/iFtYw6vrq+q5fvX1LKib2IN8JDLCtre38cjuR3in7520jptPQW+moQzQJVq0/yTd\nSVwXdmxvd37yLdfJq1iEw8q35XStd+2Cr31N5R6lQzoPBtMRpxa+2SYdpjJxhUZDtHW3xbPuc7Hn\n2/ctRX9IPgmPhekc6KS+qn6SRvJW91s8+otH2XZoG7ve2RXPuk9FPrUzI1jSRNvahchcMyi3wo6l\nTm1t8srMS5fCJZeoKtS6gnQ6lJtJzKmFb7aTzFRPvtbu4rk8Jdv3nQ6mNTdJNd4abw0rGlZw9fKr\niRLlpcMv0T3UXTRtzgiWNLCWkk/XBGZFF3ZMpuXoSSsUSjwVZ+M0TmefmeCMrqxUZstKh0rra9bA\nl76kfsfXX1fRYelQblqltfmVta5ULsdyEkq6Ai+Qc3kR+76l6A/JN/Y6YJoVjSu49zfu5aHfeoiW\n2S08t/85dhzdUTRtbub8IjkQCqle9tb+K5kwlf9D57GEQglzWSikwprT8QNopnqqnsokVy4cPgzP\nP6/+2qmsVMmrCxbAxo3pB0yUow9L15Xac2JP3ppFad9Kqgz9ZOc2E/wnmRAeC7Pv5D5ePPgiXaGJ\nXUYqZ1WyonEFQX+Qqz5wFZ+94rNcvfzqomlzZfRvkh8iEVWuRcr8TSw6uqy3V010WmORMrPjTPVU\nPVNMcs3N8OEPq7/J0BF8Hk/5a3DJCI+FGRwd5JKmSyaUpZ+KZE/NycjGZGX3nxhBo67jwrqFE0q4\nJNvug4s/yJzqObx75t0JXT4LRV6LUAohKlCthUNSyjuEEFcA/wsYBo4BX5Zy4vQphJgNPABUAX7g\nMSnlr4UQu4Ajlk3vl1KeEUIsBx6JHfMscLeUctytMfT2wq9/DatWZT4ZZ9LfQ5vLolGVsb94MZx/\n/uTvHBsbo7Ozk5EssvukVMUy+/rgzJmMdy8I1dXVNDc3U1FRkfUxGhrg6qvV+2g09bV3KsQ5U/BV\n+FjesDzjboi94V5+0fELNi7aSFNtU9LtUpXKtxeitC/bhVExeoqUGh7hYX7dfNYE18SvWarf7Xj/\ncX504EfcsOYGVjSuKOCZ5r+68f8GvgfcJITwAA8D10gpR4QQDwNXAz+z7fPXwFYp5Xu29VJK+T8c\nvkAqPWAAABBBSURBVOMx4A+llKeFELcDfww86dYAAgFYt06FGI+MZDb5ZDNpWbUOpwmxs7OTuro6\nlixZMsEpWg5IKTl9+jSdnZ0sXbo06+N4PCp0++WXYdOm1OYun09pNgMDyi/jnUH1vrOtEuzUfMsJ\ne/th63fZBYV92X5uM81Rn4z6qnpaA62cOHsiHsptRwtpf6WfRl+jY85LvsmbKUwIcTOwF9CW7lXA\nQSmlftR+EbjKtk8TIIG7hBDPCiHuF4nZc1AI8ZdCiO/HBAhCCB8QlVKeTnZMy7HvEELsE0Ls6+np\nSXscXq+KJFq+PHONxSokpnKah8PK99Hbm1rDGRkZobGxseyECqjoocbGxqy0MTudnfDf/620TafI\nL2stsRdeUBWqKyrgk5/M+asLSrb3dS7Ym28lM1P5KnwT2g9bt8s0wmsmOuqd8AgP1d5qeod66R/p\nT9qz5Vj/MY6cPsLt225n/tfnM3/r/II68vPyKwkh1gHzpZQ/saxuAE5blk8Dc227tgBrgUeklH8I\nzAb+AEBK+btSyvuAPwLWCyE2x/a3NsPuczgmsf2flFKul1KunzdvXvaDywBrDkRXl6ojlswZ7/Mp\n38dU5V+AshQqGrfG1toK116rHPhdXZM/7++H//ov1a7gi19MrH/+ebjpJmfHfymSzX3ttr8iVT6J\nVSBYtzMRXtkzEhnh/dD7vN7xOgPnBiZ8FolGODl4kiO9R9h1dFd8fRdd+B/xI7YKHt/7eN7PMV+/\n4k3AKiHEt1Hmrw8D61HCRdMI2ENRwsDLUkp9tX4U2y+OlDIK/DtwEUo4WY/Z4HDMnMm1ZW0opPwm\nw8PJNRfd2jgYdC6TX0rcdtttvP/++2lt++Mf/5gf/OAHeT6jyVRXq9Bir1cJa7vW0tgI110HF100\n+bMXXlAldqaLcMkUNxMLU/Wrt5fCz0dNqplI0B/k0vmX0jfSx6nQqQkPCO1n2ln1+CpueOEGHtrz\nkOP+d710V94d+nkRLFLKL0gp74j5RL4E/Bz4FnCBEKIqttnHgd22XY8Aa4UQs2LLvwHsd/iKK4E3\nYma1CiGE1lKcjpk2ySb9XFvWgprkLr1UCZBUzbtOnCj9UOBIJML4eHrxEddddx2f+tSn8nxGzjQ1\nwWWXKa2kuzux3m5p6+9Xgsa+bvPm0v8tssFNf4XuV69rjVkjxeyl8HVNKmDGR3jlgtfjZVnDMprr\nm+k42xEPkAD46dGfpnWMZ3/9bL5ODyhMa+JxYFxKGRFCfAV4TggxCPQAO6wbxpz63wD+ObbNEMo5\njxDim4Avds57pZR7YrvdB3xXCDEAjAKfy/ZEtZ8jEFAmLB1e7EbL2tFR9QS8fLmzkNJCLc35umg8\n88wz7N27lwcffJDFixcza9Ys2traeOihh3j55Zc5ePAgw8PDLFmyhHvuuYdnn30Wr9fLjTfeyObN\nm1m8eDFVVVWEQiG+//3vU1VVNfWXZonXq651dze88w7s3at+y1dfnbjd7Nmqtpid995TpV82bcrb\nKRYFN1v7auExcG6A1ztfnxApZk3CNBFe7lJfVc/a+Wtp72un42wHoMzIz//q+bT2v33b7dx26W15\nO7+8CxYpZQfwmdj7XcAu+zZCiCeBB6SUXVLKbcA2h+PcleT4/w+4wY1z1X6OY8fUBLRmTWYCxR5e\nbBVIhw/DP/0T3HyzEi528pljkknY81Tccsst7N69m61bt/LUU08xOjrKc889B0B/fz9vvvkmVVVV\nPPPMM9xzzz1Eo1Gi0Sher5e2tjZ27txJVVUV9913Hzt27ODaa691YYTJqa9XmssTT8Abb6h179hq\n9Pl8cPas8/5zHT12Mwt7KLAVLTyqvdVc3nw51d7qCYUtrY29NCbCK3c8wkNTbRPDY8NsP7qdd8+8\ny7nIOd7qfmvCdn78DDFU8PMrieBKKeUdxT4HSJS51xN7tqYve3ix7uFy880qtNVpG58v0btlZCTR\nuMsNYZDPXI0PfehDAAwMDPD5z3+eF198kblz5/Lzn/98wnZCCNauXRvXUFpbW+nv73f3ZBzweJQj\n/847YcsWtXz8OHzOotf+/u/Do486779qVd5PMa+kEgrpko6GoRtL6e2ApPu4qTFp3BjndMMjPLTM\naeGaldfQM9RDVEbZdXgXZwcST0nn1Z5He6h90r4XcmF+zy2vR5+GeDzZt6tN5ovRjbyammDOHOdt\n7L1b3Cx66Hadq6qqKsbGxgDw+/0AdHR0sGbNGubOncvJkyc5cODApP2sEV+FjGyrrISLL4brr1cO\n+7tsuu+jj8K996q20lu3wtiYSiaV0rne2HTCDUe9XcNIFV6styu0VjLTKh1rvB4vAV+AmooaVs9b\nzRO/+8SEz9tD7dy48EZ8+Pjmx76JfFAiH5Tsf9DJde3ieeX16NMIN8xFyXwx2sSmw14bGpRmEghM\nTMiz7u+mMMjWR5SMzZs3c/fdd7Nu3To8sYu1Zs0aRkZGuPPOO6mpqWHLli1IKfF4PPFtrNn01vXF\nYOtWePDBxPKqVcq/pjXGcsnCd2OCt2sYVg1G91dxMnkV0n8yk81r1rFvatlENdWMkIhQ+fhlH+fp\n1qcLem2EzLQgVRmwfv16uc/WkCMUSs9clK0AslZI9vth/35VBLGpafJ2qY5/8OBBVq9enf4XT4GU\nibInpZIe4/YYYfJ17e0Fa9pHT48S+G6YH4UQv5RSrp96S3dZv3693PvG3rybhKxmJ7uQGTg3QHgs\nTNAfnNQzZCaaq4rBrf9yK8+8/Ux8uePuDprrUxTOS5NM7mvz68ZIV0PI1kRlNXUtWpS8sq6OTOvu\nLkxhxGg0kV9Tzth/t4YG2L4dNmyAb39b5b2kU8G41NsOFMIkZE1mtD4th8fC/Or9X7H9yHa6h7on\n7TdTzVWF5osf/SK3nn8rn77w07T9SdukbpOFwJjCYqRrLsrFRKW/IxpNPoFZM/B1yHO+KRVNJZ/Y\nfzePRxWq3Lgx8Xk6lHrRykKbhKzmL1+Fj5WNK+Pvi31uM5Vlc5fx2HWPFVUzNIIlQ9zwV4RCcOiQ\nilaqt9WQ05pNba1zrosup++WMPB4Ek/r5YzT76YDNTKh3Bp+uYlHeFhQt4A51XMchUc+osEMkymF\n61zm00lq7GYN63K+TR6pXFvJTDLhsCo/Mj7unl9MCFVKv1S0llL3+ZV6w69im5ucan6ZXiozjxL9\n9ygMdru7dTmfPc59Pmhpya5ast9fzZkzp0t+As4GXTa/utSLpZUwxTI3pRIexRZ2hsIzo01hdrOG\nfTlfJo+RkUTtqkxaHXs80NLSTGdnJ7297pVI1zkbQhRfc9GNvgzZUSwzSKokymIKOxOFVhxmtGCx\n293ty/lyzubioK+oqMipCZYTOtS6paU0HdKGzCjGhJpKeJSisDPkFyPGi4A9y77Q2P1HxiFdPugq\nw+1n2gvb2MniWykVn4qJQiseRrAUCScncLoBA7kGFtj9R6XukDakT/dQN12hLoL+YNEm1FLxqZjm\nYcXDXPESIt2AgVwDC4yGUp5obaWptomgP5j3CdWqmaRqO2yYeczIki5CiB7geA6HCJBTp8pZHhhP\nom+k+iyb7TIix3GVNIUcW4uUsjD9ry0IIXoQdCDJ5b5I/zoJPHipJIJqR6jfS6IIPDmex8Tvyf1Y\n5XpvF3JcK4BfSCm3TLXhjBQsuSKE2FeMWlD5plzHBeU9Njcp1+tkxlVYjCnMYDAYDK5iBIvBYDAY\nXMUIlux4stgnkCfKdVxQ3mNzk3K9TmZcBcT4WAwGg8HgKkZjMRgMBoOrGMFiMBgMBleZ0bXCnBBC\n3ApcpheBDVLKdZbPLwZ+CPyRlPJV274C+D/AMqAW+Fv7NsUi2biEEOuA/wmMA5XAd6WUr9j2bQCe\niC1WAf8gpfyPwpz51OQ4tmrga0Ad6kHrsJTyqwU7+QJi7u3pdW9P6/taSmleSV7AbwL3WZYbgL8D\n/gL4sMP2vwV8Jfa+BtgNzCr2OKYal2V9LbDDYf1Xgesty08DlcUeh0tjuwa437K8Hagr9jgKfZ3M\nvR1fLsl7e7rd18YUlpo/IRZ1IYSYBWwFHoCkWcBXAy8CSCmHgbeAlfk/zYyJj8vGpcD/dVg/BMwT\nihpgPuBuiWX3yHRse4BWIYRPCLESeFNKOZjPEywRzL2tmC739rS6r40pDBBCXAfcEVu8S0r5rhDi\nIuCklPJMbP0XgL+XUvaL5E1LGoDTluXTwNx8nHM6pDkuhBC3AHcDZ1FPOnb+BrgX+AfUxNMJ+PN5\n7lPh4tjOAj9F/dM2oMZZNph7e3rd22VzXxdbxSvVF/A9YGnsvR+lSn479toH/BuwzrbPV4FLLcvf\nAlYWeyzJxuXw2XLg+TSO8SLQWOyxuDE2lK367tj7SuDfgfOLPZZCXSdzb0/aruTu7el4XxuNxQEh\nRAvKzvougJRyCNhi+fzLwE4p5a9su+4GPg78MuY8WwO0F+Sk08A+LgeGUM6+VMe4EpBSytOptis0\nOYytGdgJIKUcFULsBxYAbXk50SJj7u2Uxyi5e3u63tdGsDjzOeCxFJ+Px152fgZcJYT4HjAbeFhK\nGXH/9LJm0riEEH8OXIyKOqmKbYNtm9XAn6OiS8aBz+T9TDMnq7EBfw08LIT4JCCBU8B/5vdUi4q5\ntyduU+r39rS8r03mfY7EwhW/LqW8pdjn4iblOi4o77G5SbleJzOuApyLESwGg8FgcBMTbmwwGAwG\nVzGCxWAwGAyuYgSLwWAwGFzFCJYyQwixWAjhWo8GIcRnhRCbYu+fEkLMz3D/l9w6F8PMxdzX0wsT\nblx+eHDxgUFK+bhl0QvMyvAQFW6di2FGY+7raYQRLGWKEOJDwJ8BfUA98GUp5dtCiB+hYtq9qCSq\nP5VSHo5Vtv0ScAL1D7xWSvkRIcQDqESrVahKq1uFEH+HSpbbKWMVboUQL0kpPxYLefxO7DskMC/2\n+Vzg60AIlQdxv5TyZCGuhaF8MPf1NKHY5QrMy90XsARVoXUPUBNbtxB4OfZ+N7A+9v5jwN/E3v8r\ncF7s/TLgSOz9l4lVu0WVlmi2r48t74z9vR+4OfZ+NjAae/9XwJbY+w3A48W+VuY1fV7mvp5eL6Ox\nlC8npKpCi5TyPSFEZWx9BPhl7P0h4BOx93OklKdi27cLIXrT+A4B8V4d1bF1LcCPY8c5K4Q4EFt/\nYWzT30Q9OY5kOzDDjMbc19MAI1jKl2YhRI2UclgIsZDEDS9l7PEKpdJrzgohzpdStgkhVgABh2Oe\nI2FbHkCZEV4BLgGaYusPA2uBNiFEADg/tv4I8JycXIPKYMgEc19PA4xgKT+iqKe3LwDfE0L0o2zR\nugbSmG1b3X/j88BXYk90YeCMwzb/CXxDCPE08DzwbKybXR+g/7GeBL4lhLgcVXvp9dj6h4HvCCHO\nxY7/HSnla+4M2TADMPf1NMKUdDFMIhaGeZOU8rPFPheDwS3MfV04jMZiAEAI8THgD1BPaX5UxVeD\nYVpj7uviYDQWg8FgMLiKybw3GAwGg6sYwWIwGAwGVzGCxWAwGAyuYgSLwWAwGFzFCBaDwWAwuIoR\nLAaDwWBwlf8PYcA6QUGzPuIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fb483c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100000 # number of sample rows in plots\n",
    "city_long_border = (-74.03, -73.75)\n",
    "city_lat_border = (40.63, 40.85)\n",
    "fig, ax = plt.subplots(ncols=2, sharex=True, sharey=True)\n",
    "ax[0].scatter(train['pickup_longitude'].values[:N], \n",
    "              train['pickup_latitude'].values[:N],\n",
    "              color='blue', s=1, label='train', alpha=0.1)\n",
    "ax[1].scatter(test['pickup_longitude'].values[:N], \n",
    "              test['pickup_latitude'].values[:N],\n",
    "              color='green', s=1, label='test', alpha=0.1)\n",
    "fig.suptitle('Train and test area complete overlap.')\n",
    "ax[0].legend(loc=0)\n",
    "ax[0].set_ylabel('latitude')\n",
    "ax[0].set_xlabel('longitude')\n",
    "ax[1].set_xlabel('longitude')\n",
    "ax[1].legend(loc=0)\n",
    "plt.ylim(city_lat_border)\n",
    "plt.xlim(city_long_border)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Extraction\n",
    "#### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = np.vstack((train[['pickup_latitude', 'pickup_longitude']].values,\n",
    "                    train[['dropoff_latitude', 'dropoff_longitude']].values,\n",
    "                    test[['pickup_latitude', 'pickup_longitude']].values,\n",
    "                    test[['dropoff_latitude', 'dropoff_longitude']].values))\n",
    "\n",
    "pca = PCA().fit(coords)\n",
    "train['pickup_pca0'] = pca.transform(\n",
    "    train[['pickup_latitude', 'pickup_longitude']])[:, 0]\n",
    "train['pickup_pca1'] = pca.transform(\n",
    "    train[['pickup_latitude', 'pickup_longitude']])[:, 1]\n",
    "train['dropoff_pca0'] = pca.transform(\n",
    "    train[['dropoff_latitude', 'dropoff_longitude']])[:, 0]\n",
    "train['dropoff_pca1'] = pca.transform(\n",
    "    train[['dropoff_latitude', 'dropoff_longitude']])[:, 1]\n",
    "\n",
    "test['pickup_pca0'] = pca.transform(\n",
    "    test[['pickup_latitude', 'pickup_longitude']])[:, 0]\n",
    "test['pickup_pca1'] = pca.transform(\n",
    "    test[['pickup_latitude', 'pickup_longitude']])[:, 1]\n",
    "test['dropoff_pca0'] = pca.transform(\n",
    "    test[['dropoff_latitude', 'dropoff_longitude']])[:, 0]\n",
    "test['dropoff_pca1'] = pca.transform(\n",
    "    test[['dropoff_latitude', 'dropoff_longitude']])[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 18)\n",
      "(625134, 14)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEhCAYAAABcN4ZbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11050c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2)\n",
    "ax[0].scatter(train['pickup_longitude'].values[:N], train['pickup_latitude'].values[:N],\n",
    "              color='blue', s=1, alpha=0.1)\n",
    "ax[1].scatter(train['pickup_pca0'].values[:N], train['pickup_pca1'].values[:N],\n",
    "              color='green', s=1, alpha=0.1)\n",
    "fig.suptitle('Pickup lat long coords and PCA transformed coords.')\n",
    "ax[0].set_ylabel('latitude')\n",
    "ax[0].set_xlabel('longitude')\n",
    "ax[1].set_xlabel('pca0')\n",
    "ax[1].set_ylabel('pca1')\n",
    "ax[0].set_xlim(city_long_border)\n",
    "ax[0].set_ylim(city_lat_border)\n",
    "pca_borders = pca.transform([[x, y] for x in city_lat_border for y in city_long_border])\n",
    "ax[1].set_xlim(pca_borders[:, 0].min(), pca_borders[:, 0].max())\n",
    "ax[1].set_ylim(pca_borders[:, 1].min(), pca_borders[:, 1].max())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def haversine_array(lat1, lng1, lat2, lng2):\n",
    "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
    "    AVG_EARTH_RADIUS = 6371  # in km\n",
    "    lat = lat2 - lat1\n",
    "    lng = lng2 - lng1\n",
    "    d = np.sin(lat * 0.5) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng * 0.5) ** 2\n",
    "    h = 2 * AVG_EARTH_RADIUS * np.arcsin(np.sqrt(d))\n",
    "    return h\n",
    "\n",
    "def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\n",
    "    a = haversine_array(lat1, lng1, lat1, lng2)\n",
    "    b = haversine_array(lat1, lng1, lat2, lng1)\n",
    "    return a + b\n",
    "\n",
    "def bearing_array(lat1, lng1, lat2, lng2):\n",
    "    AVG_EARTH_RADIUS = 6371  # in km\n",
    "    lng_delta_rad = np.radians(lng2 - lng1)\n",
    "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
    "    y = np.sin(lng_delta_rad) * np.cos(lat2)\n",
    "    x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\n",
    "    return np.degrees(np.arctan2(y, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'distance_haversine'] = haversine_array(\n",
    "    train['pickup_latitude'].values, \n",
    "    train['pickup_longitude'].values, \n",
    "    train['dropoff_latitude'].values, \n",
    "    train['dropoff_longitude'].values)\n",
    "train.loc[:, 'distance_dummy_manhattan'] = dummy_manhattan_distance(\n",
    "    train['pickup_latitude'].values, \n",
    "    train['pickup_longitude'].values, \n",
    "    train['dropoff_latitude'].values, \n",
    "    train['dropoff_longitude'].values)\n",
    "train.loc[:, 'direction'] = bearing_array(\n",
    "    train['pickup_latitude'].values, \n",
    "    train['pickup_longitude'].values, \n",
    "    train['dropoff_latitude'].values, \n",
    "    train['dropoff_longitude'].values)\n",
    "train.loc[:, 'pca_manhattan'] = np.abs(\n",
    "    train['dropoff_pca1'] - train['pickup_pca1']) + np.abs(\n",
    "    train['dropoff_pca0'] - train['pickup_pca0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1458644, 22)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[:, 'distance_haversine'] = haversine_array(\n",
    "    test['pickup_latitude'].values, \n",
    "    test['pickup_longitude'].values, \n",
    "    test['dropoff_latitude'].values, \n",
    "    test['dropoff_longitude'].values)\n",
    "test.loc[:, 'distance_dummy_manhattan'] = dummy_manhattan_distance(\n",
    "    test['pickup_latitude'].values, \n",
    "    test['pickup_longitude'].values, \n",
    "    test['dropoff_latitude'].values, \n",
    "    test['dropoff_longitude'].values)\n",
    "test.loc[:, 'direction'] = bearing_array(\n",
    "    test['pickup_latitude'].values, \n",
    "    test['pickup_longitude'].values, \n",
    "    test['dropoff_latitude'].values, \n",
    "    test['dropoff_longitude'].values)\n",
    "test.loc[:, 'pca_manhattan'] = np.abs(\n",
    "    test['dropoff_pca1'] - test['pickup_pca1']) + np.abs(\n",
    "    test['dropoff_pca0'] - test['pickup_pca0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(625134, 18)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'center_latitude'] = (\n",
    "    train['pickup_latitude'].values + train['dropoff_latitude'].values) / 2\n",
    "train.loc[:, 'center_longitude'] = (\n",
    "    train['pickup_longitude'].values + train['dropoff_longitude'].values) / 2\n",
    "test.loc[:, 'center_latitude'] = (\n",
    "    test['pickup_latitude'].values + test['dropoff_latitude'].values) / 2\n",
    "test.loc[:, 'center_longitude'] = (\n",
    "    test['pickup_longitude'].values + test['dropoff_longitude'].values) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 24)\n",
      "(625134, 20)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Datetime features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'pickup_weekday'] = train['pickup_datetime'].dt.weekday\n",
    "train.loc[:, 'pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\n",
    "train.loc[:, 'pickup_hour'] = train['pickup_datetime'].dt.hour\n",
    "train.loc[:, 'pickup_minute'] = train['pickup_datetime'].dt.minute\n",
    "train.loc[:, 'pickup_dt'] = (\n",
    "    train['pickup_datetime'] - train['pickup_datetime'].min()).dt.total_seconds()\n",
    "train.loc[:, 'pickup_week_hour'] = train['pickup_weekday'] * 24 + train['pickup_hour']\n",
    "\n",
    "test.loc[:, 'pickup_weekday'] = test['pickup_datetime'].dt.weekday\n",
    "test.loc[:, 'pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\n",
    "test.loc[:, 'pickup_hour'] = test['pickup_datetime'].dt.hour\n",
    "test.loc[:, 'pickup_minute'] = test['pickup_datetime'].dt.minute\n",
    "test.loc[:, 'pickup_dt'] = (\n",
    "    test['pickup_datetime'] - train['pickup_datetime'].min()).dt.total_seconds()\n",
    "test.loc[:, 'pickup_week_hour'] = test['pickup_weekday'] * 24 + test['pickup_hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 30)\n",
      "(625134, 26)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'avg_speed_h'] = 1000 * train['distance_haversine'] / train['trip_duration']\n",
    "train.loc[:, 'avg_speed_m'] = 1000 * train['distance_dummy_manhattan'] / train['trip_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 32)\n",
      "(625134, 26)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEhCAYAAABx6WukAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15c5c2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=3, sharey=True)\n",
    "ax[0].plot(train.groupby('pickup_hour').mean()['avg_speed_h'], 'bo-', lw=2, alpha=0.7)\n",
    "ax[1].plot(train.groupby('pickup_weekday').mean()['avg_speed_h'], 'go-', lw=2, alpha=0.7)\n",
    "ax[2].plot(train.groupby('pickup_week_hour').mean()['avg_speed_h'], 'ro-', lw=2, alpha=0.7)\n",
    "ax[0].set_xlabel('hour')\n",
    "ax[1].set_xlabel('weekday')\n",
    "ax[2].set_xlabel('weekhour')\n",
    "ax[0].set_ylabel('average speed')\n",
    "fig.suptitle('Rush hour average traffic speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'pickup_lat_bin'] = np.round(train['pickup_latitude'], 3)\n",
    "train.loc[:, 'pickup_long_bin'] = np.round(train['pickup_longitude'], 3)\n",
    "# Average speed for regions\n",
    "gby_cols = ['pickup_lat_bin', 'pickup_long_bin']\n",
    "coord_speed = train.groupby(gby_cols).mean()[['avg_speed_h']].reset_index()\n",
    "coord_count = train.groupby(gby_cols).count()[['id']].reset_index()\n",
    "coord_stats = pd.merge(coord_speed, coord_count, on=gby_cols)\n",
    "coord_stats = coord_stats[coord_stats['id'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAETCAYAAAB0nQK/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150e2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1)\n",
    "ax.scatter(train.pickup_longitude.values[:N], train.pickup_latitude.values[:N],\n",
    "           color='black', s=1, alpha=0.5)\n",
    "ax.scatter(coord_stats.pickup_long_bin.values, coord_stats.pickup_lat_bin.values,\n",
    "           c=coord_stats.avg_speed_h.values,\n",
    "           cmap='RdYlGn', s=20, alpha=0.5, vmin=1, vmax=8)\n",
    "ax.set_xlim(city_long_border)\n",
    "ax.set_ylim(city_lat_border)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "plt.title('Average speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[:, 'pickup_lat_bin'] = np.round(train['pickup_latitude'], 2)\n",
    "train.loc[:, 'pickup_long_bin'] = np.round(train['pickup_longitude'], 2)\n",
    "train.loc[:, 'center_lat_bin'] = np.round(train['center_latitude'], 2)\n",
    "train.loc[:, 'center_long_bin'] = np.round(train['center_longitude'], 2)\n",
    "train.loc[:, 'pickup_dt_bin'] = (train['pickup_dt'] // (3 * 3600))\n",
    "test.loc[:, 'pickup_lat_bin'] = np.round(test['pickup_latitude'], 2)\n",
    "test.loc[:, 'pickup_long_bin'] = np.round(test['pickup_longitude'], 2)\n",
    "test.loc[:, 'center_lat_bin'] = np.round(test['center_latitude'], 2)\n",
    "test.loc[:, 'center_long_bin'] = np.round(test['center_longitude'], 2)\n",
    "test.loc[:, 'pickup_dt_bin'] = (test['pickup_dt'] // (3 * 3600))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 37)\n",
      "(625134, 31)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_ind = np.random.permutation(len(coords))[:500000]\n",
    "kmeans = MiniBatchKMeans(n_clusters=100, batch_size=10000).fit(coords[sample_ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time till clustering: 62 seconds\n"
     ]
    }
   ],
   "source": [
    "train.loc[:, 'pickup_cluster'] = kmeans.predict(\n",
    "    train[['pickup_latitude', 'pickup_longitude']])\n",
    "train.loc[:, 'dropoff_cluster'] = kmeans.predict(\n",
    "    train[['dropoff_latitude', 'dropoff_longitude']])\n",
    "test.loc[:, 'pickup_cluster'] = kmeans.predict(\n",
    "    test[['pickup_latitude', 'pickup_longitude']])\n",
    "test.loc[:, 'dropoff_cluster'] = kmeans.predict(\n",
    "    test[['dropoff_latitude', 'dropoff_longitude']])\n",
    "t1 = dt.datetime.now()\n",
    "print('Time till clustering: %i seconds' % (t1 - t0).seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEICAYAAAAdjPDnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154c2cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1)\n",
    "ax.scatter(train.pickup_longitude.values[:N], \n",
    "           train.pickup_latitude.values[:N], \n",
    "           s=10, lw=0,\n",
    "           c=train.pickup_cluster[:N].values, cmap='tab20', alpha=0.2)\n",
    "ax.set_xlim(city_long_border)\n",
    "ax.set_ylim(city_lat_border)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temporal and geospatial aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gby_col in ['pickup_hour', 'pickup_date', 'pickup_dt_bin',\n",
    "               'pickup_week_hour', 'pickup_cluster', 'dropoff_cluster']:\n",
    "    gby = train.groupby(gby_col).mean()[['avg_speed_h', 'avg_speed_m', \n",
    "                                         'log_trip_duration']]\n",
    "    gby.columns = ['%s_gby_%s' % (col, gby_col) for col in gby.columns]\n",
    "    train = pd.merge(train, gby, how='left', left_on=gby_col, right_index=True)\n",
    "    test = pd.merge(test, gby, how='left', left_on=gby_col, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gby_cols in [['center_lat_bin', 'center_long_bin'],\n",
    "                 ['pickup_hour', 'center_lat_bin', 'center_long_bin'],\n",
    "                 ['pickup_hour', 'pickup_cluster'],  \n",
    "                 ['pickup_hour', 'dropoff_cluster'],\n",
    "                 ['pickup_cluster', 'dropoff_cluster']]:\n",
    "    coord_speed = train.groupby(gby_cols).mean()[['avg_speed_h']].reset_index()\n",
    "    coord_count = train.groupby(gby_cols).count()[['id']].reset_index()\n",
    "    coord_stats = pd.merge(coord_speed, coord_count, on=gby_cols)\n",
    "    coord_stats = coord_stats[coord_stats['id'] > 100]\n",
    "    coord_stats.columns = gby_cols + [\n",
    "        'avg_speed_h_%s' % '_'.join(gby_cols), \n",
    "        'cnt_%s' %  '_'.join(gby_cols)]\n",
    "    train = pd.merge(train, coord_stats, how='left', on=gby_cols)\n",
    "    test = pd.merge(test, coord_stats, how='left', on=gby_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_freq = '60min'\n",
    "df_all = pd.concat((train, test))[[\n",
    "    'id', 'pickup_datetime', 'pickup_cluster', 'dropoff_cluster']]\n",
    "train.loc[:, 'pickup_datetime_group'] = train['pickup_datetime'].dt.round(group_freq)\n",
    "test.loc[:, 'pickup_datetime_group'] = test['pickup_datetime'].dt.round(group_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count trips over 60min\n",
    "df_counts = df_all.set_index('pickup_datetime')[['id']].sort_index()\n",
    "df_counts['count_60min'] = df_counts.isnull().rolling(group_freq).count()['id']\n",
    "train = train.merge(df_counts, on='id', how='left')\n",
    "test = test.merge(df_counts, on='id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/corazzon/codes/jupyter/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: pd.TimeGrouper is deprecated and will be removed; Please use pd.Grouper(freq=...)\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Count how many trips are going to each cluster over time\n",
    "dropoff_counts = df_all \\\n",
    "    .set_index('pickup_datetime') \\\n",
    "    .groupby([pd.TimeGrouper(group_freq), 'dropoff_cluster']) \\\n",
    "    .agg({'id': 'count'}) \\\n",
    "    .reset_index().set_index('pickup_datetime') \\\n",
    "    .groupby('dropoff_cluster').rolling('240min').mean() \\\n",
    "    .drop('dropoff_cluster', axis=1) \\\n",
    "    .reset_index().set_index('pickup_datetime').shift(freq='-120min').reset_index() \\\n",
    "    .rename(columns={\n",
    "        'pickup_datetime': 'pickup_datetime_group', \n",
    "        'id': 'dropoff_cluster_count'})\n",
    "\n",
    "train['dropoff_cluster_count'] = train[[\n",
    "    'pickup_datetime_group', \n",
    "    'dropoff_cluster']].merge(dropoff_counts, on=[\n",
    "    'pickup_datetime_group', 'dropoff_cluster'], how='left')[\n",
    "    'dropoff_cluster_count'].fillna(0)\n",
    "test['dropoff_cluster_count'] = test[[\n",
    "    'pickup_datetime_group', \n",
    "    'dropoff_cluster']].merge(dropoff_counts, on=[\n",
    "    'pickup_datetime_group', \n",
    "    'dropoff_cluster'], how='left')['dropoff_cluster_count'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 70)\n",
      "(625134, 64)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/corazzon/codes/jupyter/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: pd.TimeGrouper is deprecated and will be removed; Please use pd.Grouper(freq=...)\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Count how many trips are going from each cluster over time\n",
    "df_all = pd.concat((train, test))[[\n",
    "    'id', 'pickup_datetime', \n",
    "    'pickup_cluster', 'dropoff_cluster']]\n",
    "\n",
    "pickup_counts = df_all \\\n",
    "    .set_index('pickup_datetime') \\\n",
    "    .groupby([pd.TimeGrouper(group_freq), 'pickup_cluster']) \\\n",
    "    .agg({'id': 'count'}) \\\n",
    "    .reset_index().set_index('pickup_datetime') \\\n",
    "    .groupby('pickup_cluster').rolling('240min').mean() \\\n",
    "    .drop('pickup_cluster', axis=1) \\\n",
    "    .reset_index().set_index('pickup_datetime') \\\n",
    "    .shift(freq='-120min').reset_index() \\\n",
    "    .rename(columns={\n",
    "        'pickup_datetime': 'pickup_datetime_group', \n",
    "        'id': 'pickup_cluster_count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['pickup_cluster_count'] = train[[\n",
    "    'pickup_datetime_group', 'pickup_cluster']].merge(\n",
    "    pickup_counts, \n",
    "    on=['pickup_datetime_group', 'pickup_cluster'], \n",
    "    how='left')['pickup_cluster_count'].fillna(0)\n",
    "\n",
    "test['pickup_cluster_count'] = test[[\n",
    "    'pickup_datetime_group', 'pickup_cluster']].merge(\n",
    "    pickup_counts, on=['pickup_datetime_group', 'pickup_cluster'], \n",
    "    how='left')['pickup_cluster_count'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 71)\n",
      "(625134, 65)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Features Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['avg_speed_h' 'avg_speed_m' 'check_trip_duration' 'dropoff_datetime'\n",
      " 'log_trip_duration' 'trip_duration']\n",
      "                                                    0\n",
      "0                                           vendor_id\n",
      "1                                     passenger_count\n",
      "2                                    pickup_longitude\n",
      "3                                     pickup_latitude\n",
      "4                                   dropoff_longitude\n",
      "5                                    dropoff_latitude\n",
      "6                                  store_and_fwd_flag\n",
      "7                                         pickup_pca0\n",
      "8                                         pickup_pca1\n",
      "9                                        dropoff_pca0\n",
      "10                                       dropoff_pca1\n",
      "11                                 distance_haversine\n",
      "12                           distance_dummy_manhattan\n",
      "13                                          direction\n",
      "14                                      pca_manhattan\n",
      "15                                    center_latitude\n",
      "16                                   center_longitude\n",
      "17                                     pickup_weekday\n",
      "18                             pickup_hour_weekofyear\n",
      "19                                        pickup_hour\n",
      "20                                      pickup_minute\n",
      "21                                          pickup_dt\n",
      "22                                   pickup_week_hour\n",
      "23                                     pickup_cluster\n",
      "24                                    dropoff_cluster\n",
      "25                        avg_speed_h_gby_pickup_hour\n",
      "26                        avg_speed_m_gby_pickup_hour\n",
      "27                  log_trip_duration_gby_pickup_hour\n",
      "28                        avg_speed_h_gby_pickup_date\n",
      "29                        avg_speed_m_gby_pickup_date\n",
      "30                  log_trip_duration_gby_pickup_date\n",
      "31                      avg_speed_h_gby_pickup_dt_bin\n",
      "32                      avg_speed_m_gby_pickup_dt_bin\n",
      "33                log_trip_duration_gby_pickup_dt_bin\n",
      "34                   avg_speed_h_gby_pickup_week_hour\n",
      "35                   avg_speed_m_gby_pickup_week_hour\n",
      "36             log_trip_duration_gby_pickup_week_hour\n",
      "37                     avg_speed_h_gby_pickup_cluster\n",
      "38                     avg_speed_m_gby_pickup_cluster\n",
      "39               log_trip_duration_gby_pickup_cluster\n",
      "40                    avg_speed_h_gby_dropoff_cluster\n",
      "41                    avg_speed_m_gby_dropoff_cluster\n",
      "42              log_trip_duration_gby_dropoff_cluster\n",
      "43         avg_speed_h_center_lat_bin_center_long_bin\n",
      "44                 cnt_center_lat_bin_center_long_bin\n",
      "45  avg_speed_h_pickup_hour_center_lat_bin_center_...\n",
      "46     cnt_pickup_hour_center_lat_bin_center_long_bin\n",
      "47             avg_speed_h_pickup_hour_pickup_cluster\n",
      "48                     cnt_pickup_hour_pickup_cluster\n",
      "49            avg_speed_h_pickup_hour_dropoff_cluster\n",
      "50                    cnt_pickup_hour_dropoff_cluster\n",
      "51         avg_speed_h_pickup_cluster_dropoff_cluster\n",
      "52                 cnt_pickup_cluster_dropoff_cluster\n",
      "53                                        count_60min\n",
      "54                              dropoff_cluster_count\n",
      "55                               pickup_cluster_count\n",
      "Features 갯수 :  56 \n",
      "Feature 추출 시간: 142 seconds\n"
     ]
    }
   ],
   "source": [
    "feature_names = list(train.columns)\n",
    "print(np.setdiff1d(train.columns, test.columns))\n",
    "do_not_use_for_training = ['id', 'log_trip_duration', \n",
    "                           'pickup_datetime', 'dropoff_datetime',\n",
    "                           'trip_duration', 'check_trip_duration',\n",
    "                           'pickup_date', 'avg_speed_h', 'avg_speed_m',\n",
    "                           'pickup_lat_bin', 'pickup_long_bin',\n",
    "                           'center_lat_bin', 'center_long_bin',\n",
    "                           'pickup_dt_bin', 'pickup_datetime_group']\n",
    "feature_names = [f for f in train.columns if f not in do_not_use_for_training]\n",
    "\n",
    "print(pd.DataFrame(feature_names))\n",
    "print('Features 갯수 :  %i ' % len(feature_names))\n",
    "train[feature_names].count()\n",
    "y = np.log(train['trip_duration'].values + 1)\n",
    "\n",
    "t1 = dt.datetime.now()\n",
    "print('Feature 추출 시간: %i seconds' % (t1 - t0).seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1458644, 71)\n",
      "(625134, 65)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>train_test_mean_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>pickup_dt</td>\n",
       "      <td>0.002648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>avg_speed_h_pickup_hour_pickup_cluster</td>\n",
       "      <td>0.002680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pickup_pca0</td>\n",
       "      <td>0.002774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dropoff_pca1</td>\n",
       "      <td>0.002833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pickup_hour_weekofyear</td>\n",
       "      <td>0.002872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   feature  train_test_mean_diff\n",
       "21                               pickup_dt              0.002648\n",
       "47  avg_speed_h_pickup_hour_pickup_cluster              0.002680\n",
       "7                              pickup_pca0              0.002774\n",
       "10                            dropoff_pca1              0.002833\n",
       "18                  pickup_hour_weekofyear              0.002872"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_stats = pd.DataFrame({'feature': feature_names})\n",
    "feature_stats.loc[:, 'train_mean'] = np.nanmean(\n",
    "    train[feature_names].values, axis=0).round(4)\n",
    "feature_stats.loc[:, 'test_mean'] = np.nanmean(\n",
    "    test[feature_names].values, axis=0).round(4)\n",
    "feature_stats.loc[:, 'train_std'] = np.nanstd(\n",
    "    train[feature_names].values, axis=0).round(4)\n",
    "feature_stats.loc[:, 'test_std'] = np.nanstd(\n",
    "    test[feature_names].values, axis=0).round(4)\n",
    "feature_stats.loc[:, 'train_nan'] = np.mean(\n",
    "    np.isnan(train[feature_names].values), axis=0).round(3)\n",
    "feature_stats.loc[:, 'test_nan'] = np.mean(\n",
    "    np.isnan(test[feature_names].values), axis=0).round(3)\n",
    "feature_stats.loc[:, 'train_test_mean_diff'] = np.abs(\n",
    "    feature_stats['train_mean'] - feature_stats['test_mean']) / np.abs(feature_stats['train_std'] + feature_stats['test_std'])  * 2\n",
    "feature_stats.loc[:, 'train_test_nan_diff'] = np.abs(\n",
    "    feature_stats['train_nan'] - feature_stats['test_nan'])\n",
    "feature_stats = feature_stats.sort_values(by='train_test_mean_diff')\n",
    "feature_stats[['feature', 'train_test_mean_diff']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>train_nan</th>\n",
       "      <th>test_nan</th>\n",
       "      <th>train_test_nan_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>log_trip_duration_gby_pickup_hour</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>cnt_pickup_hour_center_lat_bin_center_long_bin</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pickup_hour_weekofyear</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>avg_speed_h_pickup_cluster_dropoff_cluster</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>cnt_pickup_cluster_dropoff_cluster</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           feature  train_nan  test_nan  \\\n",
       "27               log_trip_duration_gby_pickup_hour      0.000     0.000   \n",
       "46  cnt_pickup_hour_center_lat_bin_center_long_bin      0.081     0.081   \n",
       "18                          pickup_hour_weekofyear      0.000     0.000   \n",
       "51      avg_speed_h_pickup_cluster_dropoff_cluster      0.107     0.108   \n",
       "52              cnt_pickup_cluster_dropoff_cluster      0.107     0.108   \n",
       "\n",
       "    train_test_nan_diff  \n",
       "27                0.000  \n",
       "46                0.000  \n",
       "18                0.000  \n",
       "51                0.001  \n",
       "52                0.001  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_stats = feature_stats.sort_values(by='train_test_nan_diff')\n",
    "feature_stats[['feature', 'train_nan', 'test_nan', 'train_test_nan_diff']].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtr, Xv, ytr, yv = train_test_split(train[feature_names].values, y, test_size=0.2, random_state=1987)\n",
    "dtrain = xgb.DMatrix(Xtr, label=ytr)\n",
    "dvalid = xgb.DMatrix(Xv, label=yv)\n",
    "dtest = xgb.DMatrix(test[feature_names].values)\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'valid')]\n",
    "\n",
    "xgb_pars = {'min_child_weight': 50, \n",
    "            'eta': 0.3, \n",
    "            'colsample_bytree': 0.3, \n",
    "            'max_depth': 10,\n",
    "            'subsample': 0.8, \n",
    "            'lambda': 1., \n",
    "            'nthread': 4, \n",
    "            'booster' : 'gbtree', \n",
    "            'silent': 1,\n",
    "            'eval_metric': 'rmse', \n",
    "            'objective': 'reg:linear'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:4.22644\tvalid-rmse:4.22774\n",
      "Multiple eval metrics have been passed: 'valid-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until valid-rmse hasn't improved in 50 rounds.\n",
      "[10]\ttrain-rmse:0.41553\tvalid-rmse:0.428011\n",
      "[20]\ttrain-rmse:0.377883\tvalid-rmse:0.397793\n",
      "[30]\ttrain-rmse:0.368565\tvalid-rmse:0.393828\n",
      "[40]\ttrain-rmse:0.363539\tvalid-rmse:0.39283\n",
      "[50]\ttrain-rmse:0.358714\tvalid-rmse:0.391858\n",
      "[59]\ttrain-rmse:0.355386\tvalid-rmse:0.390888\n",
      "CPU times: user 12min 41s, sys: 4.09 s, total: 12min 45s\n",
      "Wall time: 4min 4s\n"
     ]
    }
   ],
   "source": [
    "# add more epoch\n",
    "%time model = xgb.train(xgb_pars, dtrain, 60, watchlist, early_stopping_rounds=50,\\\n",
    "                  maximize=False, verbose_eval=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modeling RMSLE 0.39089\n",
      "Training time: 398 seconds\n"
     ]
    }
   ],
   "source": [
    "print('Modeling RMSLE %.5f' % model.best_score)\n",
    "t1 = dt.datetime.now()\n",
    "print('Training time: %i seconds' % (t1 - t0).seconds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature importance analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_wo_feature = [0.39224, 0.38816, 0.38726, 0.38780, 0.38773, \n",
    "                   0.38792, 0.38753, 0.38745, 0.38710, 0.38767, \n",
    "                   0.38738, 0.38750, 0.38678, 0.39359, 0.38672, \n",
    "                   0.38794, 0.38694, 0.38750, 0.38742, 0.38673, \n",
    "                   0.38754, 0.38705, 0.38736, 0.38741, 0.38764, \n",
    "                   0.38730, 0.38676, 0.38696, 0.38750, 0.38705, \n",
    "                   0.38746, 0.38727, 0.38750, 0.38771, 0.38747, \n",
    "                   0.38907, 0.38719, 0.38756, 0.38701, 0.38734, \n",
    "                   0.38782, 0.38673, 0.38797, 0.38720, 0.38709, \n",
    "                   0.38704, 0.38809, 0.38768, 0.38798, 0.38849, \n",
    "                   0.38690, 0.38753, 0.38721, 0.38807, 0.38830, \n",
    "                   0.38750, np.nan, np.nan, np.nan]\n",
    "feature_importance_dict = model.get_fscore()\n",
    "len(rmse_wo_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n",
      "59\n"
     ]
    }
   ],
   "source": [
    "print(len(feature_names))\n",
    "print(len(rmse_wo_feature))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_name</th>\n",
       "      <th>importance</th>\n",
       "      <th>rmse_wo_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>distance_haversine</td>\n",
       "      <td>1029</td>\n",
       "      <td>0.38750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pickup_pca1</td>\n",
       "      <td>970</td>\n",
       "      <td>0.38710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pca_manhattan</td>\n",
       "      <td>939</td>\n",
       "      <td>0.38672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>direction</td>\n",
       "      <td>937</td>\n",
       "      <td>0.39359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>center_latitude</td>\n",
       "      <td>912</td>\n",
       "      <td>0.38794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dropoff_pca0</td>\n",
       "      <td>870</td>\n",
       "      <td>0.38767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>pickup_pca0</td>\n",
       "      <td>828</td>\n",
       "      <td>0.38745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>distance_dummy_manhattan</td>\n",
       "      <td>806</td>\n",
       "      <td>0.38678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dropoff_longitude</td>\n",
       "      <td>719</td>\n",
       "      <td>0.38773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>pickup_dt</td>\n",
       "      <td>657</td>\n",
       "      <td>0.38705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>avg_speed_h_pickup_cluster_dropoff_cluster</td>\n",
       "      <td>656</td>\n",
       "      <td>0.38753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>pickup_longitude</td>\n",
       "      <td>649</td>\n",
       "      <td>0.38726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>pickup_latitude</td>\n",
       "      <td>627</td>\n",
       "      <td>0.38780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>center_longitude</td>\n",
       "      <td>600</td>\n",
       "      <td>0.38694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>avg_speed_h_gby_pickup_week_hour</td>\n",
       "      <td>582</td>\n",
       "      <td>0.38747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avg_speed_h_gby_pickup_dt_bin</td>\n",
       "      <td>572</td>\n",
       "      <td>0.38727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dropoff_pca1</td>\n",
       "      <td>552</td>\n",
       "      <td>0.38738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>dropoff_latitude</td>\n",
       "      <td>544</td>\n",
       "      <td>0.38792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>avg_speed_h_pickup_hour_dropoff_cluster</td>\n",
       "      <td>501</td>\n",
       "      <td>0.38849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>cnt_pickup_hour_center_lat_bin_center_long_bin</td>\n",
       "      <td>496</td>\n",
       "      <td>0.38809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>avg_speed_m_gby_pickup_dt_bin</td>\n",
       "      <td>477</td>\n",
       "      <td>0.38750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>count_60min</td>\n",
       "      <td>459</td>\n",
       "      <td>0.38807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cnt_pickup_hour_pickup_cluster</td>\n",
       "      <td>459</td>\n",
       "      <td>0.38798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>cnt_pickup_cluster_dropoff_cluster</td>\n",
       "      <td>458</td>\n",
       "      <td>0.38721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cnt_center_lat_bin_center_long_bin</td>\n",
       "      <td>455</td>\n",
       "      <td>0.38709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>avg_speed_h_pickup_hour_pickup_cluster</td>\n",
       "      <td>454</td>\n",
       "      <td>0.38768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>avg_speed_m_gby_dropoff_cluster</td>\n",
       "      <td>444</td>\n",
       "      <td>0.38673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>avg_speed_h_center_lat_bin_center_long_bin</td>\n",
       "      <td>390</td>\n",
       "      <td>0.38720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dropoff_cluster_count</td>\n",
       "      <td>381</td>\n",
       "      <td>0.38830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>log_trip_duration_gby_pickup_date</td>\n",
       "      <td>340</td>\n",
       "      <td>0.38746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>avg_speed_h_pickup_hour_center_lat_bin_center_...</td>\n",
       "      <td>339</td>\n",
       "      <td>0.38704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>log_trip_duration_gby_pickup_dt_bin</td>\n",
       "      <td>327</td>\n",
       "      <td>0.38771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>avg_speed_h_gby_pickup_date</td>\n",
       "      <td>322</td>\n",
       "      <td>0.38750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>avg_speed_m_gby_pickup_date</td>\n",
       "      <td>315</td>\n",
       "      <td>0.38705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pickup_hour</td>\n",
       "      <td>290</td>\n",
       "      <td>0.38673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pickup_hour_weekofyear</td>\n",
       "      <td>280</td>\n",
       "      <td>0.38742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>avg_speed_m_gby_pickup_cluster</td>\n",
       "      <td>274</td>\n",
       "      <td>0.38701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pickup_minute</td>\n",
       "      <td>262</td>\n",
       "      <td>0.38754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>avg_speed_h_gby_pickup_cluster</td>\n",
       "      <td>257</td>\n",
       "      <td>0.38756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log_trip_duration_gby_pickup_cluster</td>\n",
       "      <td>254</td>\n",
       "      <td>0.38734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>log_trip_duration_gby_pickup_week_hour</td>\n",
       "      <td>254</td>\n",
       "      <td>0.38719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>pickup_cluster</td>\n",
       "      <td>250</td>\n",
       "      <td>0.38741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>pickup_week_hour</td>\n",
       "      <td>235</td>\n",
       "      <td>0.38736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>log_trip_duration_gby_pickup_hour</td>\n",
       "      <td>225</td>\n",
       "      <td>0.38696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>dropoff_cluster</td>\n",
       "      <td>222</td>\n",
       "      <td>0.38764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>avg_speed_h_gby_dropoff_cluster</td>\n",
       "      <td>219</td>\n",
       "      <td>0.38782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>avg_speed_h_gby_pickup_hour</td>\n",
       "      <td>217</td>\n",
       "      <td>0.38730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>cnt_pickup_hour_dropoff_cluster</td>\n",
       "      <td>201</td>\n",
       "      <td>0.38690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>log_trip_duration_gby_dropoff_cluster</td>\n",
       "      <td>199</td>\n",
       "      <td>0.38797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>avg_speed_m_gby_pickup_hour</td>\n",
       "      <td>170</td>\n",
       "      <td>0.38676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>avg_speed_m_gby_pickup_week_hour</td>\n",
       "      <td>148</td>\n",
       "      <td>0.38907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>pickup_cluster_count</td>\n",
       "      <td>144</td>\n",
       "      <td>0.38750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>pickup_weekday</td>\n",
       "      <td>109</td>\n",
       "      <td>0.38750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>passenger_count</td>\n",
       "      <td>85</td>\n",
       "      <td>0.38816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vendor_id</td>\n",
       "      <td>41</td>\n",
       "      <td>0.39224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>store_and_fwd_flag</td>\n",
       "      <td>1</td>\n",
       "      <td>0.38753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         feature_name  importance  \\\n",
       "0                                  distance_haversine        1029   \n",
       "4                                         pickup_pca1         970   \n",
       "9                                       pca_manhattan         939   \n",
       "30                                          direction         937   \n",
       "52                                    center_latitude         912   \n",
       "12                                       dropoff_pca0         870   \n",
       "40                                        pickup_pca0         828   \n",
       "7                            distance_dummy_manhattan         806   \n",
       "3                                   dropoff_longitude         719   \n",
       "27                                          pickup_dt         657   \n",
       "29         avg_speed_h_pickup_cluster_dropoff_cluster         656   \n",
       "45                                   pickup_longitude         649   \n",
       "33                                    pickup_latitude         627   \n",
       "11                                   center_longitude         600   \n",
       "19                   avg_speed_h_gby_pickup_week_hour         582   \n",
       "2                       avg_speed_h_gby_pickup_dt_bin         572   \n",
       "10                                       dropoff_pca1         552   \n",
       "49                                   dropoff_latitude         544   \n",
       "48            avg_speed_h_pickup_hour_dropoff_cluster         501   \n",
       "28     cnt_pickup_hour_center_lat_bin_center_long_bin         496   \n",
       "20                      avg_speed_m_gby_pickup_dt_bin         477   \n",
       "23                                        count_60min         459   \n",
       "5                      cnt_pickup_hour_pickup_cluster         459   \n",
       "39                 cnt_pickup_cluster_dropoff_cluster         458   \n",
       "6                  cnt_center_lat_bin_center_long_bin         455   \n",
       "21             avg_speed_h_pickup_hour_pickup_cluster         454   \n",
       "37                    avg_speed_m_gby_dropoff_cluster         444   \n",
       "17         avg_speed_h_center_lat_bin_center_long_bin         390   \n",
       "14                              dropoff_cluster_count         381   \n",
       "34                  log_trip_duration_gby_pickup_date         340   \n",
       "32  avg_speed_h_pickup_hour_center_lat_bin_center_...         339   \n",
       "44                log_trip_duration_gby_pickup_dt_bin         327   \n",
       "46                        avg_speed_h_gby_pickup_date         322   \n",
       "22                        avg_speed_m_gby_pickup_date         315   \n",
       "8                                         pickup_hour         290   \n",
       "18                             pickup_hour_weekofyear         280   \n",
       "38                     avg_speed_m_gby_pickup_cluster         274   \n",
       "24                                      pickup_minute         262   \n",
       "13                     avg_speed_h_gby_pickup_cluster         257   \n",
       "1                log_trip_duration_gby_pickup_cluster         254   \n",
       "42             log_trip_duration_gby_pickup_week_hour         254   \n",
       "47                                     pickup_cluster         250   \n",
       "51                                   pickup_week_hour         235   \n",
       "31                  log_trip_duration_gby_pickup_hour         225   \n",
       "43                                    dropoff_cluster         222   \n",
       "50                    avg_speed_h_gby_dropoff_cluster         219   \n",
       "26                        avg_speed_h_gby_pickup_hour         217   \n",
       "53                    cnt_pickup_hour_dropoff_cluster         201   \n",
       "41              log_trip_duration_gby_dropoff_cluster         199   \n",
       "35                        avg_speed_m_gby_pickup_hour         170   \n",
       "36                   avg_speed_m_gby_pickup_week_hour         148   \n",
       "25                               pickup_cluster_count         144   \n",
       "54                                     pickup_weekday         109   \n",
       "16                                    passenger_count          85   \n",
       "15                                          vendor_id          41   \n",
       "55                                 store_and_fwd_flag           1   \n",
       "\n",
       "    rmse_wo_feature  \n",
       "0           0.38750  \n",
       "4           0.38710  \n",
       "9           0.38672  \n",
       "30          0.39359  \n",
       "52          0.38794  \n",
       "12          0.38767  \n",
       "40          0.38745  \n",
       "7           0.38678  \n",
       "3           0.38773  \n",
       "27          0.38705  \n",
       "29          0.38753  \n",
       "45          0.38726  \n",
       "33          0.38780  \n",
       "11          0.38694  \n",
       "19          0.38747  \n",
       "2           0.38727  \n",
       "10          0.38738  \n",
       "49          0.38792  \n",
       "48          0.38849  \n",
       "28          0.38809  \n",
       "20          0.38750  \n",
       "23          0.38807  \n",
       "5           0.38798  \n",
       "39          0.38721  \n",
       "6           0.38709  \n",
       "21          0.38768  \n",
       "37          0.38673  \n",
       "17          0.38720  \n",
       "14          0.38830  \n",
       "34          0.38746  \n",
       "32          0.38704  \n",
       "44          0.38771  \n",
       "46          0.38750  \n",
       "22          0.38705  \n",
       "8           0.38673  \n",
       "18          0.38742  \n",
       "38          0.38701  \n",
       "24          0.38754  \n",
       "13          0.38756  \n",
       "1           0.38734  \n",
       "42          0.38719  \n",
       "47          0.38741  \n",
       "51          0.38736  \n",
       "31          0.38696  \n",
       "43          0.38764  \n",
       "50          0.38782  \n",
       "26          0.38730  \n",
       "53          0.38690  \n",
       "41          0.38797  \n",
       "35          0.38676  \n",
       "36          0.38907  \n",
       "25          0.38750  \n",
       "54          0.38750  \n",
       "16          0.38816  \n",
       "15          0.39224  \n",
       "55          0.38753  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = ['f%i' % i for i in range(len(feature_names))]\n",
    "f1 = pd.DataFrame({'f': list(feature_importance_dict.keys()),\n",
    "                   'importance': list(feature_importance_dict.values())})\n",
    "f2 = pd.DataFrame({'f': fs, \n",
    "                   'feature_name': feature_names, \n",
    "                   'rmse_wo_feature': rmse_wo_feature[:56]})\n",
    "feature_importance = pd.merge(f1, f2, how='right', on='f')\n",
    "feature_importance = feature_importance.fillna(0)\n",
    "\n",
    "feature_importance[['feature_name', 'importance', 'rmse_wo_feature']].sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAJHCAYAAAA63JoZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4v8AEM5+L+BMui4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157b3d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = feature_importance.sort_values(\n",
    "    by='rmse_wo_feature', \n",
    "    ascending=False)\n",
    "feature_importance = feature_importance[feature_importance['rmse_wo_feature'] > 0]\n",
    "\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    ax.scatter(feature_importance['importance'].values, \n",
    "               feature_importance['rmse_wo_feature'].values,\n",
    "               c=feature_importance['rmse_wo_feature'].values, s=500, \n",
    "               cmap='RdYlGn_r', alpha=0.7)\n",
    "    for _, row in feature_importance.head(5).iterrows():\n",
    "        ax.text(row['importance'], row['rmse_wo_feature'], row['feature_name'],\n",
    "                verticalalignment='center', horizontalalignment='center')\n",
    "    ax.set_xlabel('xgb feature importance')\n",
    "    ax.set_ylabel('rmse without feature')\n",
    "    ax.set_ylim(np.min(feature_importance['rmse_wo_feature']) - 0.001,\n",
    "                np.max(feature_importance['rmse_wo_feature']) + 0.001)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEICAYAAAC55kg0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11578ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ypred = model.predict(dvalid)\n",
    "fig,ax = plt.subplots(ncols=2)\n",
    "ax[0].scatter(ypred, yv, s=0.1, alpha=0.1)\n",
    "ax[0].set_xlabel('log(prediction)')\n",
    "ax[0].set_ylabel('log(ground truth)')\n",
    "ax[1].scatter(np.exp(ypred), np.exp(yv), s=0.1, alpha=0.1)\n",
    "ax[1].set_xlabel('prediction')\n",
    "ax[1].set_ylabel('ground truth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test shape OK.\n",
      "Valid prediction mean: 6.466\n",
      "Test prediction mean: 6.466\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD7CAYAAAB+B7/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYZRiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167aad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 85241 seconds\n"
     ]
    }
   ],
   "source": [
    "ytest = model.predict(dtest)\n",
    "print('Test shape OK.') if test.shape[0] == ytest.shape[0] else print('Oops')\n",
    "test['trip_duration'] = np.exp(ytest) - 1\n",
    "test[['id', 'trip_duration']].to_csv('beluga_xgb_submission.csv.gz', index=False, compression='gzip')\n",
    "\n",
    "print('Valid prediction mean: %.3f' % ypred.mean())\n",
    "print('Test prediction mean: %.3f' % ytest.mean())\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True)\n",
    "sns.distplot(ypred, ax=ax[0], color='blue', label='validation prediction')\n",
    "sns.distplot(ytest, ax=ax[1], color='green', label='test prediction')\n",
    "ax[0].legend(loc=0)\n",
    "ax[1].legend(loc=0)\n",
    "plt.show()\n",
    "\n",
    "t1 = dt.datetime.now()\n",
    "print('Total time: %i seconds' % (t1 - t0).seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "FOREVER_COMPUTING_FLAG = False\n",
    "xgb_pars = []\n",
    "for MCW in [10, 20, 50, 75, 100]:\n",
    "    for ETA in [0.05, 0.1, 0.15]:\n",
    "        for CS in [0.3, 0.4, 0.5]:\n",
    "            for MD in [6, 8, 10, 12, 15]:\n",
    "                for SS in [0.5, 0.6, 0.7, 0.8, 0.9]:\n",
    "                    for LAMBDA in [0.5, 1., 1.5,  2., 3.]:\n",
    "                        xgb_pars.append({'min_child_weight': MCW, 'eta': ETA, \n",
    "                                         'colsample_bytree': CS, 'max_depth': MD,\n",
    "                                         'subsample': SS, 'lambda': LAMBDA, \n",
    "                                         'nthread': -1, 'booster' : 'gbtree', 'eval_metric': 'rmse',\n",
    "                                         'silent': 1, 'objective': 'reg:linear'})\n",
    "\n",
    "while FOREVER_COMPUTING_FLAG:\n",
    "    xgb_par = np.random.choice(xgb_pars, 1)[0]\n",
    "    print(xgb_par)\n",
    "    model = xgb.train(xgb_par, dtrain, 2000, watchlist, early_stopping_rounds=50,\n",
    "                      maximize=False, verbose_eval=100)\n",
    "    print('Modeling RMSLE %.5f' % model.best_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 데이터는 https://www.kaggle.com/gaborfodor/from-eda-to-the-top-lb-0-367 커널에 있는 데이터로\n",
    "커널 제한 때문에 이미 생성 된 파라메터와 결과를 데이터프레임으로 가져와서 시각화 했다고 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "paropt = pd.DataFrame({'lambda':[1.5,1.0,1.0,1.5,1.5,1.0,1.5,1.0,1.5,2.0,0.5,1.0,0.5,1.5,1.5,0.5,1.0,1.5,0.5,2.0,1.0,2.0,2.0,1.5,1.5,2.0,1.5,2.0,1.5,0.5,1.0,1.0,2.0,1.5,1.0,1.0,0.5,2.0,1.0,0.5,0.5,2.0,1.0,1.0,0.5,0.5,1.5,0.5,1.5,2.0,2.0,2.0,2.0,0.5,1.5,1.0,1.5,2.0,2.0,0.5,1.5,1.0,0.5,1.0,1.5,2.0,1.0,1.0,2.0,2.0,1.0,0.5,0.5,1.0,1.5,2.0,0.5,1.0,1.5,1.0,1.0,1.5,1.5,1.5,0.5,1.5,1.0,1.5,2.0,2.0,2.0,1.0,2.0,0.5,2.0,0.5,1.5,0.5,2.0,0.5,1.0,1.5,1.5,1.5,2.0,0.5,0.5,1.0,2.0],\n",
    "                       'eta':[.1,.1,.05,.05,.05,.15,.15,.1,.1,.05,.15,.15,.15,.1,.1,.1,.1,.05,.15,.05,.05,.05,.15,.15,.05,.05,.05,.05,.15,.15,.15,.15,.1,.05,.05,.1,.1,.1,.1,.1,.05,.15,.15,.15,.1,.1,.05,.05,.15,.15,.15,.1,.1,.05,.05,.05,.05,.05,.15,.1,.1,.15,.1,.1,.05,.15,.15,.15,.1,.05,.05,.05,.05,.15,.1,.1,.1,.1,.05,.05,.05,.15,.15,.1,.1,.1,.1,.05,.15,.15,.1,.1,.1,.05,.05,.1,.1,.1,.1,.1,.05,.15,.15,.15,.15,.05,.05,.15,.15],\n",
    "                       'min_child_weight': [50,50,20,100,10,50,100,100,75,10,10,50,50,100,75,100,50,10,20,10,75,20,50,75,100,100,10,20,75,75,75,20,10,75,10,100,100,10,20,20,50,50,100,20,50,100,100,75,20,75,20,50,20,10,20,20,20,75,20,75,100,10,10,20,10,20,100,75,75,10,100,50,100,100,50,10,75,75,50,10,75,75,50,75,20,100,100,50,20,20,50,50,75,20,50,100,75,75,100,75,10,10,20,20,10,10,75,50,20],\n",
    "                       'subsample':[.8,.9,.8,.6,.6,.6,.9,.6,.5,.9,.8,.9,.7,.5,.5,.9,.7,.7,.5,.8,.5,.9,.6,.6,.8,.8,.8,.7,.5,.5,.9,.9,.5,.6,.7,.8,.8,.6,.9,.7,.8,.6,.6,.9,.7,.7,.8,.6,.6,.5,.9,.8,.7,.6,.6,.6,.5,.9,.8,.5,.7,.6,.8,.6,.8,.8,.6,.7,.9,.5,.7,.5,.9,.7,.8,.9,.9,.7,.8,.5,.7,.8,.6,.8,.8,.5,.9,.5,.5,.7,.8,.6,.6,.8,.7,.6,.6,.6,.7,.7,.8,.6,.5,.9,.7,.6,.9,.5,.5],\n",
    "                       'rmse': [.380,.380,.377,.378,.378,.386,.382,.382,.383,.374,.386,.381,.385,.383,.383,.379,.381,.376,.389,.375,.381,.374,.385,.385,.378,.377,.375,.376,.385,.386,.382,.384,.384,.379,.376,.380,.380,.382,.380,.382,.378,.385,.384,.383,.383,.383,.379,.381,.386,.387,.381,.380,.380,.377,.377,.377,.379,.376,.382,.385,.382,.386,.380,.382,.375,.383,.385,.384,.379,.378,.380,.381,.378,.384,.380,.377,.379,.383,.380,.380,.380,.383,.385,.381,.379,.386,.380,.383,.387,.383,.382,.384,.385,.377,.380,.383,.383,.383,.382,.382,.377,.386,.388,.382,.384,.379,.378,.387,.388]\n",
    "                       })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEICAYAAABxiqLiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117652780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEICAYAAABxiqLiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nADEzs0wcIGZmlsn/B7JQyZ17cUxNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118aef2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEICAYAAABxiqLiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nk/8PL7zlfX+BJSAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196fa438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEICAYAAABxiqLiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJFVigEiSKjFAJEmVGCCSpEr+P/LOT6PqT/qKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1d9dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, par in enumerate(['lambda', 'min_child_weight', 'subsample', 'eta']):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax = sns.boxplot(x=par, y=\"rmse\", data=paropt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 참고\n",
    " * [From EDA to the Top (LB 0.367) - Kaggle](https://www.kaggle.com/gaborfodor/from-eda-to-the-top-lb-0-367)"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}