纽约出租车.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from datetime import datetime, date\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LinearRegression, Ridge,BayesianRidge\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import radians, cos, sin, asin, sqrt\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [60, 30]\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score, KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv', parse_dates=['pickup_datetime'])\n",
    "test = pd.read_csv('test.csv', parse_dates=['pickup_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train shape  (1458644, 11)\n",
      " test shape  (625134, 9)\n"
     ]
    }
   ],
   "source": [
    "print( ' train shape ',train.shape)\n",
    "print( ' test shape ',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dropoff_datetime', 'trip_duration']\n"
     ]
    }
   ],
   "source": [
    "print([i for i in train.columns if i not in test.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### holiday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Date</th>\n",
       "      <th>Holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>January 01</td>\n",
       "      <td>New Years Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>January 18</td>\n",
       "      <td>Martin Luther King Jr. Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Friday</td>\n",
       "      <td>February 12</td>\n",
       "      <td>Lincoln's Birthday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monday</td>\n",
       "      <td>February 15</td>\n",
       "      <td>Presidents' Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>May 08</td>\n",
       "      <td>Mother's Day</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Day         Date                     Holiday\n",
       "0  Friday   January 01               New Years Day\n",
       "1  Monday   January 18  Martin Luther King Jr. Day\n",
       "2  Friday  February 12          Lincoln's Birthday\n",
       "3  Monday  February 15             Presidents' Day\n",
       "4  Sunday       May 08                Mother's Day"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday = pd.read_csv('NYC_2016Holidays.csv',sep=';')\n",
    "holiday.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Date</th>\n",
       "      <th>Holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>January 01 2016</td>\n",
       "      <td>New Years Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>January 18 2016</td>\n",
       "      <td>Martin Luther King Jr. Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Friday</td>\n",
       "      <td>February 12 2016</td>\n",
       "      <td>Lincoln's Birthday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monday</td>\n",
       "      <td>February 15 2016</td>\n",
       "      <td>Presidents' Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>May 08 2016</td>\n",
       "      <td>Mother's Day</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Day              Date                     Holiday\n",
       "0  Friday   January 01 2016               New Years Day\n",
       "1  Monday   January 18 2016  Martin Luther King Jr. Day\n",
       "2  Friday  February 12 2016          Lincoln's Birthday\n",
       "3  Monday  February 15 2016             Presidents' Day\n",
       "4  Sunday       May 08 2016                Mother's Day"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday['Date'] = holiday['Date'].apply(lambda x: x + ' 2016')\n",
    "holiday.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.date(2016, 1, 1),\n",
       " datetime.date(2016, 1, 18),\n",
       " datetime.date(2016, 2, 12),\n",
       " datetime.date(2016, 2, 15),\n",
       " datetime.date(2016, 5, 8),\n",
       " datetime.date(2016, 5, 30),\n",
       " datetime.date(2016, 6, 19),\n",
       " datetime.date(2016, 7, 4),\n",
       " datetime.date(2016, 9, 5),\n",
       " datetime.date(2016, 10, 10),\n",
       " datetime.date(2016, 11, 11),\n",
       " datetime.date(2016, 11, 24),\n",
       " datetime.date(2016, 12, 26),\n",
       " datetime.date(2016, 7, 4),\n",
       " datetime.date(2016, 11, 8)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holidays = [datetime.strptime(holiday.loc[i,'Date'], '%B %d %Y').date() for i in range(len(holiday))]\n",
    "holidays\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### routes from open source routing machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>number_of_steps</th>\n",
       "      <th>step_direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "      <td>5</td>\n",
       "      <td>left|straight|right|straight|arrive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>2513.2</td>\n",
       "      <td>332.0</td>\n",
       "      <td>6</td>\n",
       "      <td>none|right|left|right|left|arrive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>1779.4</td>\n",
       "      <td>235.8</td>\n",
       "      <td>4</td>\n",
       "      <td>left|left|right|arrive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>1614.9</td>\n",
       "      <td>140.1</td>\n",
       "      <td>5</td>\n",
       "      <td>right|left|right|left|arrive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0801584</td>\n",
       "      <td>1393.5</td>\n",
       "      <td>189.4</td>\n",
       "      <td>5</td>\n",
       "      <td>right|right|right|left|arrive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  total_distance  total_travel_time  number_of_steps  \\\n",
       "0  id2875421          2009.1              164.9                5   \n",
       "1  id2377394          2513.2              332.0                6   \n",
       "2  id3504673          1779.4              235.8                4   \n",
       "3  id2181028          1614.9              140.1                5   \n",
       "4  id0801584          1393.5              189.4                5   \n",
       "\n",
       "                        step_direction  \n",
       "0  left|straight|right|straight|arrive  \n",
       "1    none|right|left|right|left|arrive  \n",
       "2               left|left|right|arrive  \n",
       "3         right|left|right|left|arrive  \n",
       "4        right|right|right|left|arrive  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastrout1 = pd.read_csv('fastest_routes_train_part_1.csv',\n",
    "                        usecols=['id', 'total_distance', 'total_travel_time',  'number_of_steps','step_direction'])\n",
    "fastrout2 = pd.read_csv('fastest_routes_train_part_2.csv',\n",
    "                        usecols=['id', 'total_distance', 'total_travel_time',  'number_of_steps','step_direction'])\n",
    "fastrout = pd.concat((fastrout1,fastrout2))\n",
    "fastrout.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>number_of_steps</th>\n",
       "      <th>right_steps</th>\n",
       "      <th>left_steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>2513.2</td>\n",
       "      <td>332.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>1779.4</td>\n",
       "      <td>235.8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>1614.9</td>\n",
       "      <td>140.1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0801584</td>\n",
       "      <td>1393.5</td>\n",
       "      <td>189.4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  total_distance  total_travel_time  number_of_steps  right_steps  \\\n",
       "0  id2875421          2009.1              164.9                5            1   \n",
       "1  id2377394          2513.2              332.0                6            2   \n",
       "2  id3504673          1779.4              235.8                4            1   \n",
       "3  id2181028          1614.9              140.1                5            2   \n",
       "4  id0801584          1393.5              189.4                5            3   \n",
       "\n",
       "   left_steps  \n",
       "0           1  \n",
       "1           2  \n",
       "2           2  \n",
       "3           2  \n",
       "4           1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_turn = []\n",
    "left_turn = []\n",
    "right_turn+= list(map(lambda x:x.count('right')-x.count('slight right'),fastrout.step_direction))\n",
    "left_turn += list(map(lambda x:x.count('left')-x.count('slight left'),fastrout.step_direction))\n",
    "\n",
    "osrm_data = fastrout[['id','total_distance','total_travel_time','number_of_steps']]\n",
    "osrm_data = osrm_data.assign(right_steps=right_turn)\n",
    "osrm_data = osrm_data.assign(left_steps=left_turn)\n",
    "osrm_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>number_of_steps</th>\n",
       "      <th>right_steps</th>\n",
       "      <th>left_steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id0771704</td>\n",
       "      <td>1497.1</td>\n",
       "      <td>200.2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3274209</td>\n",
       "      <td>1427.1</td>\n",
       "      <td>141.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id2756455</td>\n",
       "      <td>2312.3</td>\n",
       "      <td>324.6</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3684027</td>\n",
       "      <td>931.8</td>\n",
       "      <td>84.2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3101285</td>\n",
       "      <td>2501.7</td>\n",
       "      <td>294.7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  total_distance  total_travel_time  number_of_steps  right_steps  \\\n",
       "0  id0771704          1497.1              200.2                7            2   \n",
       "1  id3274209          1427.1              141.5                2            0   \n",
       "2  id2756455          2312.3              324.6                9            4   \n",
       "3  id3684027           931.8               84.2                4            2   \n",
       "4  id3101285          2501.7              294.7                8            3   \n",
       "\n",
       "   left_steps  \n",
       "0           3  \n",
       "1           0  \n",
       "2           4  \n",
       "3           1  \n",
       "4           3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "osrm_test = pd.read_csv('fastest_routes_test.csv')\n",
    "right_turn= list(map(lambda x:x.count('right')-x.count('slight right'),osrm_test.step_direction))\n",
    "left_turn = list(map(lambda x:x.count('left')-x.count('slight left'),osrm_test.step_direction))\n",
    "\n",
    "osrm_test = osrm_test[['id','total_distance','total_travel_time','number_of_steps']]\n",
    "osrm_test = osrm_test.assign(right_steps=right_turn)\n",
    "osrm_test = osrm_test.assign(left_steps=left_turn)\n",
    "osrm_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weather "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Temp.</th>\n",
       "      <th>Windchill</th>\n",
       "      <th>Heat Index</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Dew Point</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Wind Dir</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Gust Speed</th>\n",
       "      <th>Precip</th>\n",
       "      <th>Events</th>\n",
       "      <th>Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-31 02:00:00</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>None</td>\n",
       "      <td>Overcast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-31 03:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Variable</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>None</td>\n",
       "      <td>Overcast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-31 04:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1016.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Calm</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Overcast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Time  Temp.  Windchill  Heat Index  Humidity  Pressure  \\\n",
       "0 2015-12-31 02:00:00    7.8        7.1         NaN      0.89    1017.0   \n",
       "1 2015-12-31 03:00:00    7.2        5.9         NaN      0.90    1016.5   \n",
       "2 2015-12-31 04:00:00    7.2        NaN         NaN      0.90    1016.7   \n",
       "\n",
       "   Dew Point  Visibility  Wind Dir  Wind Speed  Gust Speed  Precip Events  \\\n",
       "0        6.1         8.0       NNE         5.6         0.0     0.8   None   \n",
       "1        5.6        12.9  Variable         7.4         0.0     0.3   None   \n",
       "2        5.6        12.9      Calm         0.0         0.0     0.0   None   \n",
       "\n",
       "  Conditions  \n",
       "0   Overcast  \n",
       "1   Overcast  \n",
       "2   Overcast  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather = pd.read_csv('KNYC_Metars.csv', parse_dates=['Time'])\n",
    "weather.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1458644 1458644 2917288\n",
      "1458644 1458644 2917288\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude   latitude\n",
       "0 -73.982155  40.767937\n",
       "1 -73.980415  40.738564\n",
       "2 -73.979027  40.763939\n",
       "3 -74.010040  40.719971\n",
       "4 -73.973053  40.793209"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longitude = list(train.pickup_longitude) + list(train.dropoff_longitude)\n",
    "latitude = list(train.pickup_latitude) + list(train.dropoff_latitude)\n",
    "\n",
    "print(len(train.pickup_longitude), len(train.dropoff_longitude), len(longitude) )\n",
    "print(len(train.pickup_latitude), len(train.dropoff_latitude), len(latitude) )\n",
    "\n",
    "loc_df = pd.DataFrame()\n",
    "loc_df['longitude'] = longitude\n",
    "loc_df['latitude'] = latitude\n",
    "loc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2917288, 2)\n",
      "(2895748, 2)\n"
     ]
    }
   ],
   "source": [
    "xlim = [-74.03, -73.77]\n",
    "ylim = [40.63, 40.85]\n",
    "\n",
    "print(loc_df.shape)\n",
    "loc_df = loc_df[(loc_df.longitude> xlim[0]) & (loc_df.longitude < xlim[1])]\n",
    "loc_df = loc_df[(loc_df.longitude> xlim[0]) & (loc_df.longitude < xlim[1])]\n",
    "loc_df = loc_df[(loc_df.latitude> ylim[0]) & (loc_df.latitude < ylim[1])]\n",
    "loc_df = loc_df[(loc_df.latitude> ylim[0]) & (loc_df.latitude < ylim[1])]\n",
    "print(loc_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude   latitude  label\n",
       "0 -73.982155  40.767937     12\n",
       "1 -73.980415  40.738564      7\n",
       "2 -73.979027  40.763939     12\n",
       "3 -74.010040  40.719971      5\n",
       "4 -73.973053  40.793209      9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=15, random_state=2, n_init = 10).fit(loc_df)\n",
    "loc_df['label'] = kmeans.labels_\n",
    "loc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0Vuc/G/k5eb0ewtU+DMOkvrKC0fQUjX4vuq7RXhew95HixBKwub2eQKCCm9e0YBgm2w8P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UfL2VMfSfjyGJZU4c9LNzXyd6g4GOfhdTY0EmRpcIz8dx2PWkk3n8k8vk4leMTJXP8lqikC4w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\nmB6NcfiVUQ48f5ZwKMp7L41y4rUJBgem0Akiuz7WSf/OJm737+b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GCt7CKa6u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LbRT+Ehh7gcJ7U80Vc9z6vVvZxa7n5Vg11NAWbuNPuT+VLX/32ndj522+dPhLJx2jnXaa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\nAJZhIIqSkxV8+mmyAwMkfv1rpwy8bSsSMK+9mSZZI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r+Xdo/GNL8XgK4qbbM/JERBYEHQ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0KVm+u/0gu2JJmrwuomqOBw920qsobI8mSGgaPkHnpZ5+FlcGGe9x4pIkZGCK30MWgXKnRC7/\n4M3kcggCjPN5cYgiAcmBWxT4W2c/Hofx87vhOG1phVqXkz2JBJN9Hu7a38mcMuOcTbEUNbJEp6IS\nz2kgQEiWqXUZqUyfQ8TnEPPVmTkWhMoZnU99tqUV/JKjcI+b7E+ZU8IhDqQdvQ6RQ2mFmGp4dznM\n/5wIAjoQVXOUOYe2cFI1jXfD8aKNwgVBwC85ir5uXUvAsj7BAn4ClnPNY/2S0UWgNg+01kcS1Lqc\nhfXq+TE0XedQWiGcVYmpuUFzDBfmufZ12a9ZB3bFjW4LHZnsoD02wwSOADldp73EcShJcAw258V+\njKYZrwkCyIGhD8xSrw+9QIOBsR5nncs+b7FxBQGcPuPhLQeGnjPSEATQckZlZvtmqJv+z9Efmesx\nwQgYoKyYFs0EUa6ygf0tFpoGkVbIRI1jdQ3SMXCXD5znKjN+1rWh81k/O4dj4PPsfQ+SESirNc41\n99S+3znV6BUaqCv+2ZtjgwGuCp+LYJwjiKDrpT9fh2Nk949Dgpopg4GYdT5VgR0vQKIbIm1Q1vAv\nS3Xeeuut7bfccsu9hzvu6IeL/8dCEEXceR+vYvYRZuQUBbWrC0dVlaH/UlXkPPBS2tpQe/vQVBVB\nFIeI64uNK9XUFNKggNFHsqyM3gf/jOP003BZhNKmYN9RVYXW14d87LGktmwhl8kgSk48M6bjFkW8\nxx+P0t7OwSeeRLn7buTrvoj/2GONnox9ffTcfPOAI/6kSYYPmtdL7YUXENuyGZ56Gqqq8E+YgLJ+\nA5n1G3Cedy7pVatAyeKa3ET5ueeS3bMHTyhEy4GDkM3CD27FF40SXb4c55QpqE4n/PGPyKeegn/e\nfNRMmuiqVbBiBbljJxN+7HHk2cfBHx+AUCXZZ5+Dvj6U1kMo992P48wzkN0evKcswb1oIVpfH8lH\n/gKaTmDuHPx1daTDYYRnnqHm+9/H2d9PpqWV+Dtv45s3D62hAXQd1w030LBwIemzziL+8sske3tx\nlAUQOjqhro70O2uIbt9G7Y03kl6zhmQqTc26d6mTYE5NJW5JIq2qLO/q47SqIHfPHF9IOSq5HEsq\ny5ge8LCsO8IxHpm/tPWwI5JkYbmfu/Z1UO8UeS+d5YRggOOrK1A1jY3RJFN8Lp5o72N+uXeIXiwk\nSwXmZXbQX0j1hZwGC/b18fV4nQbAaS7z5sXsxno6MllUTaNLUamRDfapK5NlVrmPxrzOys40mWFn\nj6zsl1X3ZA2TzTIrTO3vme2LijE/R2ILUez9Yl5g1rSllX0ye4Kax5lrPlyvSRjMng23LpOVay7z\nUm/zNSvmzP9uf4zqfFq36HFKEt64B0683mgmXexvk52dKvX3a6QPvSN5OJbSLgmH0RSNNOWVUyDa\nYXhufRAPqlLrNJmZ4Ri3kSiGTL2cGcXa+xS+Fwfms6/LmhoO1MP4k+HtBwxQOtyeCiL4qwcYp1Jr\nhOIVuFbWrlSadaT3hTVFaWcTJdnwGzNTs8Ot9yiJj8DYvyCGA2EwUKlIIED/smX0v/ACbNpM8t7f\nUjN9Oq7Row1vr74+HHV1JX3BzLFMEGY9zllXhyhJVH/uqiHtlQRRRAgGDYPXhgYjFTpzJkp7O7qq\nkt66FXHMGFyAUF6O8uqrsHQpHrebbGcn1NaSTSbhwEFjwCsupzJYTud991H5qU8ZnQZeeBFOPhmm\nTSN224+RTz2VwMfPp/cH/wVAxXe+TXbtWmIvLkfLqfTGYvCznwEgpzNIkyeTUVV6fncf7NkD7e2o\nX7iW5OpVpHfuhN4+uP5LODesJ3T5ZUhuN9HJk6n5ypdxB4Okw2ECDQ3EL76I7J49dD//AqkH/wSp\nFDSMwvm1r5J1uXCVlZHcu5e+v/8dPR7HHQziHjvW6AqQTpGJxeBe4z896pYtZCdOxF1XR2bBAjK3\n3Ir7xBPxTJ+G1NBAYMlixNOWAuCYNg1l2TLEpUtxpNJsiyapyls0bOuL0qfkWFoTxC+KvNjZR3sy\nTblTYll3BL9DpMzl4uox9cQVBb8sM97n4fzaIBsiCSqdDsbkrSOOy7vVX1wfoktRCafTBN1uDibT\nNLhlBAFakml6szkqZYltsRSTvDLhbI5XesKcVRMasIlIZRiXr07cGE1SIQ0UBbTnjykTBwMre8Wh\nHdhYjwEGaaUG3ccWk9XXemMF7zMzVE1jRzxNo8dFTzZ3WLsIewynw7JXSpaqnjTTq6aVhPV88zU7\nICtls2FP6xZbmwkAGy3easVaL4mCQG3eCBcorHXQcbJ3AIiVqoQbLn02aIElANCRiritGiJ7ijKn\nwv5VUDHGcJAvdd5Iqvo0DRK9MOMCqJz0/hmUUuu0eqGVAmImyCo1dzFzWzh8itYEYsVSx9YUbdcO\nwzbDX3346y/G6pVKqVrnEUUjnWp970jTk0eyLkE00tjWFO6/iB0bSXwExo7CMKoSmw3ritGjiSUS\nqG+8Sfjmm8ld+SkqF56Eu7ERxzAgDAbSk1LeNsN8DQaKBURJKpi1mrqwnKIQXbECqboaOW9L4ZBl\n3I2NRo/IZJLO//oh7unTDHuMtjb8534Mp9OJ6nSSeOUVup9/ARIJqKmhYsoU5IkTScXjtPz612gP\nPwIeD+6ZM6k6/TRSS08luXo14ddfh85OnN++CW82S27yZMSmJtp+8Uu4+9fgdsOXv4y+Zg1RQYDu\nLrwXXkBm9Wpyxx6L8NJLqKNGEzjvPFKxGOoDD+D/zrcLtiFjvv89ZL/fYCeDhqdOeVMTWkMDrmOP\npbW7C95+B/buJXvbj6G3lwMTJyJ2dqJ1d+GaMpXExo2Ic+aQ3LSJ1KZNiNOnG7/kJ5+MOxRC9/vp\ne+EFYuvXQzJJYMF8ct09qNu20f3ggyAIKOvWg88HqRTC1KnomTTTJHC4ZZLZLAFJ5Pyacmp8hifY\nmTUVvNUXYX6+5ZCm68gOB2lV5e1omtOrXVw2upqtsRTt6Qx/aonyq4CPkMdTEOTnNI3NfWGe74lx\n2+RGOtKG2We1U0JyiMzKO82Pcst0KyqfHVOHJIpGJWXe8HVFZx9XNNbgliSm+FxsiCQQRaFghroz\nGuOFrgjn1lbglSTq82lGu/eX3XjUKqQ39VV2UNaWVqiRJbbG05xSGRjCjJn2HbLDQY0gFK2gPFxY\nwaIdfNnTn1bAaZ5rnmMyiGaYejhTU1esaMA6j+kRZm+pZM5j6v3MJu72sYqxkNYKU3Pdg47TNAOQ\nadrwlXAjAWIfVEtW7Hi7ViynwP63DZBT6jwYfC3DgYbyUcV900YaVtuPQak9Efx1I2Nm7Hosc0wz\nNdg4d6hofiQARhQHqlqL7YEgGh5jsU4IHwRPaDCYHAL23sfnWur+se7Z+22xVIqNNL3Z7IC02DqO\ngvgIjB2lYWq+JLebCVdeSd+CBXhCIdz51kaHY9dgqCWF1S/MfN0hywUgZm1C7qyrw5u3rrCmQTVV\npXzaNOT//D7dK1aQ/OEPYfKxBObMQY9EiDz4IPqiRXBgP7hdlP/n93FPmIDW0gItLWj1hkcan/wk\nNZ/8BKIkkfnzQ6RbDqIrCsJVnyX754eIX3M15dOmkXzzTXAYOgPvL39Bpc9H7rSl6JEIyf37ia1a\njee4WThiMYQTT8I9fx7hp5/BPXYM8SmTCeZBLYDs9w+pSjX3OtDQwLg776Rv/Xpco0YZFarf+S48\n95yRhly3juzChURXriSxcyeuUCXec84htX8/ZIyHY+LVV3G63XgmT6Zi5ky6//hHPGPGoKmqoa+6\n9FL8dXUo8TjpAwfoem4ZQjJJZNVq1J4eNn7icvbr8HJ3HFnu43NjXPSpGiFJZG9CodGjIIoi22Ip\nTqkM0JPNsajCYL46FZVpAQ/TAh5Gez2FSkqzb2JcUejOwc0TDJBVIYkcSKbYFE3QXO4vWEb0ZHPo\nGK2PrABgQzSBomusDcdwCAI9ikpIdlAtGwzU7KDRuun4UHmhR6QJSKxC9mLAxgyrXYUdlJnAYTjv\nLtMI1jQ3PZKUoB0Y2QGQHUCa71mBpFntae6ldX4zvWuNYvOYacS2tFIyTWmCMOv+FDvWnvKEAfuN\nQdWpuj74gWq3UTCZi5GIoEuxZyNl1azH2/tVWtfjr4WZFxpMXv9BqGgaSJ1a5ylWrVnqwW1N/x1J\nDDe2KBp+YSMJq9DfamshiIaWyw7oRgpgrDYjVvBmXff4xTBjK1QdMxiI2S0sTOBajPUqNu9IPu9S\nn/FIOgnAUJBlnq9rA3tmrtN6/FEGyD6ytjiKo5jZ6gcdD4qnSU0tmlk5aVZ5mro0QRQNxuy11yg7\n5RSyySQ9f/0rwvjxpNesQYjGcJ9wApFEAn77W1AU5K9/HZ/fR/SRv1B9y3/SsWo13P7fMH8BDbfe\nQurvf0eqqMDZ3EzbnXfC6jeouP12lK5OEi++iGfKFNL19eh/+CMsWcKEG28gpyiEn32WVEcHmXQG\n1q2D6dPwz55D2fx5RB57jMSbb+FesIDqT38Kf14jZ4Y1bVtqH8zXE11dZA4dwt3YSHTbNvz19YTX\nrCW1dSuOqkoy27ZDy0HYfwD3r++mduZMREmi56+PokQiODwe5PJyfPPm0rfiVWo+dg6eMUZKJbZ9\nB4fuuIPg+ecRWrzYYDEFgawo8kYkzklVFZS5jHZBkiiyL54suNanVZV3I3GUXI7+bI5pAS/PtfcR\nlB1MK/MRdIgkEZnmd/NEex/zyr1siSZIqSoOQWBNJMlxAQ91bpndqQxXjKqmzGUACiWXK/THNEGP\nqmkouRwHEikm5Btomx0ANkWTVLskOtJZJMHo9dieVmhPK0NaA5keZPZKRjsogsEMjv29kYCswx1X\nLJ03XJVhMQYKKGqPMVyKtNi8xV4v5TNWzDqjFLAdzlJjCJjT9dLMSan2R/+bcTjwZIILKN4SqdSY\nxVJs4RbQVagYd+TXdzg7Bmt1YzH2zX5eMWas1Nrt84ykR6T9q6m7a98IG5+AeZ+ByomlmbEjAUof\nBPQcyfWaYbdhgeKfzYecrvzI2uL/QBQzW32/YQI7a8rSHjlFIfH2O/iPm2WwSMkkme3bCylTQRRx\nH3ssOUWh48E/k9q6hYaFC6n+2tdI9fXRdsut8MYbxmBXX039nNnouo7jpm/R/+KL8PwLMHcuFVd/\nDqG9A3ncOORJk2j5/e+N935wK6E5s4m9/jrua64h0dOD/sMfwbRpVF54AclNm9AiEbynnkr4N7+B\nF5fjvOlbuEMhYo/8hdz+faSCFaAoVHzikiFADCg0PB9uz83wVlUhplIIySQ1J55o9BatribiktHd\nHjKbt8D+AwCkn3+BTCiEw+VCiUURPR6Cp55SAF+B4xfgGjUKNZ3GIcvIoxrA5UKUZdTOTjJt7URX\n/oPU+An0zz3eqFzMZHkvkWFKwIOq6wVT05ymsS2aoMwh4hRFxnjdfG2i8UdJ03Ve64myuDKA1+nk\nEw2GSezmaILZwQATAj5OrUqzOpKgJ6uiZrM82dHPyZVlOASBDdEEe2JJTqgq54RQOQBr+2MoWo7e\njEqj102nkuX1niiL8wUFXqeTMV69IKwf5XGhMxQkmOlKLO9ZfbrM7+12DiZwaE1lyOkaY7yeohov\n6/eHS1MWY5IOJ/K3nmcfw5z/cFq14UT5xSws7GncUiyYdQ/shQbDrsWVZ0AFoTQL5pCGmrf+bz/Q\nhmNc7JYLH9SGwmwzdKQxEsBhMo26ydYwoA0rdn6prgSHA1hw+HXY120yZe5yWP8QRCPgtaQoNW3o\nvpp9S4cLLe9pVqpZ+QeJ4ew/itmwFLunjzJGzIyjc1UfRSH+GUDMHMesqNTyzcjtkcsoRF57jd7H\nHycTjdL34nLEYLBgn2GaucZ27kQ5eIDyj30Mz5gxOGQZF8Cc2YWxnA4RJZEgun07iSeeILNzF0gS\nriWn4IxEERpH46iooOORR+DRx+DrNxLKKMTXr8fZ3EzyrbdI3/97kBxIJy8ifOddJLq7cc+bR/LN\nNwmMHUfgnl9THgqRfe01ys4/D2XyZEPkX1mJJxQadj+yecPWw4WaTKLremEPcvv2UXHmmZRPn4bn\nM582DvrqV/FPmkhqwwayXV24GkaR6+8jl8kQf+89Dt5/P10//wX9GzbQft99hN98CyUeh1WrSLd3\noHR14ZkxnbqvfIXAMZPYkVZJZbM4RJGl1eXUyBLbYkmmBzyM9riod8ssCpUx0StT7XayJZZCEkW6\nFJX2TJaxbonNsRQHEil6sgbT9V4syaq+GAeSad6NZZjsddPo9TCtIsAl9SHG+72M8Xk4u6aCL41v\nYEFenyaJIrPKfRwfKmdKwMPWeJpGj5tPNlQyyuPile4wqqah6TqbY6kCoGqyuPmbLI0Z5utKLsf6\nSIK0qhaqA5VcrnCsphsAz/y5zuWkK2M0MLeGCVgOJtO0JNODRPDDxUj1ZHbne3NN1q+tKaOllMn+\nHcm85v6oI7gf7WDNOkZrKjNo/uFYvra0gqblEKNtg1kE8wFtXYuZ5jJfy6nGA24E6z2isM9pfaCa\nkVMH1mq+F+8cOKbUmlSl9Jod0vtr3TPS1KsoGnOUNw6e50hTt9YwQUZONZi9SEvx43Lq4HPC+eP8\ntQOgVvbD1POg9V3Y+qyxV+b4mjb4cz+0zhD8l9pLTYPwATi4xliT/TO1Hzvctdnft993I/nPQal7\n+iiMj5ix/8eiWMPukYYoSYMsLuzjOP0+qj75iYJoX9m1E3HpqYMqMKOtrUSeeAJvczOexkZir7+O\nVFVFJpeDVaugogLh85+nonkm8bfeJr17N9kJE+Chh2HpUmrOPgtBFOm6406c06ehO2U4/TRGL1pE\nNp0ms2sXyv79ZKcYjbi9P/sptWPHkli4kOSLLxKVnIRXriJwyhKcu3fjaGqi7KqrUPfsIffII3D8\n8Yz6rx8Mapxuj5Eyjtlkkv6nn6bioosK+2e2gEru22dYVrhcyLkcZUsW4xkzxugHqihE336byKuv\nkt69ByQJYe5cfBMnktm2DdDp3bUL0mmSSgZvMkn/I3/BdfwCMn/+Mzd85zv4QyHW9McM4X0sQXdS\n4VA+/bctEieSSrIjleXL4+qZma+YzGka6/qjOESRqX5Pwf8rraqM93s4t64SvywzzuehM5Ol3q3z\nj/44m2Mp6lyq4Q2XyeIVdMrznnMNbrnQ+zKS0wtVgtsTGab4XBxKpFFyOTozWZrLjIIDM7VauO8E\nYYhWCaAjk2Wa311w3rf2ULQL1U22p7nMO8ip3xzfyrqZYVYXHklVpfVca4GBlQEzKyOt12fquA7l\n12wfZ7gQBYEaWWJDJMFsS+9L69ilUo1WcFbKEqTYfAXWzA4GirFgVr2NVX/0QXRC9nOgtAO/+b3p\nm2XXMFnNSK2O8eY6VMUwHK2fOTLm6Eji/QA4Mw6X4rQfYz3fet3WfpXWseyaL10z0rGaOtiAVdMM\nb7LayRCoGUil2vff2h5rOFPa4BgDeFpTsnYW0ASGI63a1bQBQX6xVPNwDGWxPqBHYRzdqzsKYiTs\nyYcVZqrxg6zJTNPZgYggirhGjcI7fjzKzp1k39tN1Re/iJhMoqkqOUUh1tZGx+23U37VVVSdey50\nduI76STExkZ6f/8H2LYd309/wuhFC1F37MD9/7F35nFylHX+f3d19Tk9PT09PfdMDnIfk4MkEMAQ\nSAggEBE8ABVZwQt013PVdXcF12N3dRdXXUXXc9efiyg3ikgkCSQcIRmSSQgkgZBr7szR03dXV1f/\n/qh+aqpruntmkgDBzff18pWe6qqnnnqqXzwfP9/P9/NdvIjM3Dlw550A2N9+Oen2dmzxODW33UrO\n4UTdvBnP2Wdjr60lvmUrkiwzjA31W9+GS9bStHw5qtNJZtvz2OvqUR0yHDiAw+vFfeGF2KqqGPnh\nXWhuD7ZQLc1f/xr+lvEFsxMBtA6vl5p3v4dcRYVxviTLevujX/8vqQMHkK5+B7XXXoOrspLs4CCJ\njg6Se/dSvWoVLbfdRusdt1PRthC3lsXudBJYtQrPlCnULVwITU00XX45FTNnUnPjBwi0tRH68Iep\nDgSw5XL0p3XfrkV+H6vrArR6XNhtNqZ6nBzNV9ZPhQAAIABJREFUaFRLNg6lMnSl0nSnFJo9Lpo9\nblqddl6KJXi0d4gDkRiPHw9jYzR9JXzL7u46zhyvi2UBH4OZLIlMhmwmzX8c7MbL6G+srdJjiOfd\nsoyqaSz26y78w6rK4XiSJwcjJDIZnhsa4Y+9QxyKJ8YwRMVSZ2b7DTMI6U4p5BhleFRN44VwrGgD\nbrNVhllYfySRpD0cmxDjJOZqtocQx3Im1slcRGDVbUk2W4GRrHWccveVJYmAXNyHrJwmzNqpYKJs\nn2TLG3gW29hKsQiRfCpPiMrHTKoEo1EuxDViXGsKKqeNmpaKno2lHNzNOjIzqxPr04FY/HghU2S+\n9mRiItcL8CHmZGa2yrFMI8fyBQbHRq8XYQZmAuCY2SjzemkahDsh0qe/O/M6avlqTacXKupG323O\nkv40s5Dl/vsp3pUVRI4BRGXWTZynJEbHsFH8d1eKYSy2VqdxnP4zfBPjVICfUxmnSkNW7HqRurQ7\nnVQsXYp38SI8wSByXR2pY8cYfPRRur73PbTePmKbNpHevx/H4sUk9+6l8xvf0Fmxz32OlpUrsbtc\nuN72NgYfewx+9nOQbHDdddi3biUWjZEcGmLo/gfIdR6j5o7bkfr7Se/fj2/1haRqa+Eb34DeXlxr\n1qCmUgz+4AfkWlvIVPpIbdyI88orcM6Zw+B//AeSLBP45CfI9fXhP2cFFXV1k16PUmlbmyShVXjp\nuvU2Yr29ZBUFTVVx+f2EbrsVbXgY76zZJDdvxh4K4WxsxDFrlu7Hpmk4vF4q6uqoueYaXFOmkNM0\nXM3N2GtqdO+2FStQDx4k09+PNjyM0t0N8TjxnbvIdvewxF/BypoqpnrdvJZQDMC1rrGWf104gzva\nZnJNcx1TvR4a8izYbK+TXx7rZ4bHRZvfy0BG5W2BCgbTGR4/HialqvSmM9Q47LwaSxuVk3MrXPym\nZ4D7uwaptMNAJks2p/HMQJjnBkd4tHeQrlSaRCbDb7oGOBRP6OAInbm6tqGal2MpGlxO1tUFClKR\nSjbLa7HEGFDU4HLQEUmMARaC5Znq9RjpTslmoy6ferQCsWKgRFy/zMI0WVstmUGTOSVqtpto9bqZ\n4nUbDJXwChPniPub2SnrOEV/d6a5q5rGgXjaWKOJpDona91RMK7wv1KVwhNKbWzW45GuiV073n87\nzQyMmUURTNfR7XDs+VEQZQViAoDAKHtjnYe/WTcILZauOtmU62QAqA0d4AwfKZxvOW1VDh2AiBTn\neGH92ZgZxOopMOUc/ZgZ1Ii045Sl0Lldtw0Rv4/ho6PPmdPAWzP2nsUArrimVFpRP1D6Ok3TgdjT\nP4DDW0BNlW+Wbv3NFUuzmr8vNsabHGfSlGXiVAroT1W8HnPRVJXYCy/gbGjA1dJigFBbvtm3ZLcj\nL1yI7eFH8L3vBmouvhiA8D33EK+pgSc2QrCalkvWoqZSRA8cILplK9ruDl0U+vnPE6qpIePzMfKD\nH5JobICjx6j44I14nU5s06fjmDGDofsfIPHQg/qkbryRit5eEntexHPddQx+7evwyitIf/3XhBa1\nwSuvUPPpT+Py+zl2551kB4do+NhHCywrJvrsyT178LS1Fb3WHQggn3sustvNyBNPIIdCVCxejJRI\nINvAd8H5OH26rUN0xw6ie/eSi0SJd3RQuWwZ6a4uRp58kqyiMHDffch+P5muLgjWoIXDeNraDM8z\nTVVxNunWH5IsM90kcl+X99AS4XU4dAd2h4POZJrelELIKXMkniSlakz1uvE6HOyNJtmHjVq3gzkV\nbiMlJksSfzWlloq8BYbX4eDLs6agZLP8oWcQWZKodcjsVTVm+9w02WxG4+/rm0PIkoRDslPjsPNS\nLEXOpvNYws+qL5Wh3ung8f4wftnGa3GFVbkcLru9wD/LnJo0VxEWq6C06tBKCdXN6UnzmNbUJ4y1\nzigGcMwCfetxoGgFZCmBvfWYOM9pt3Np/h2PVwlpvb81SmnGVE0zFTZI4KvLO7hbUkXFQJFVWF6q\n16B1U5xoRZ3orwiFlg6ty/R2NuV8usSrsT6D+f4wNl1VLvU1kRDrM1HdmBDyx/r0e8rOUUavpC1G\nGZuRYu8lUMbgVJIAaex7kWT9fQan58/LjxmaqXcj0FSdVRs5Bq9uBacdVn9Bb8nk8OqpUGs1q7h/\nKX8zSYKKmrHfGbo2TU93tl0NP7tWr/Jc9TcguctX95orbUVqu5idx6kwmT3FcQaMjROnExB7PcNm\n0wX+mqqS6Oggq6poqop7yhTUdJr4H/9I8MO34Js9m8jmzXhaWpAuuIDMI4+AZMP1T/+EJxhk8He/\nQ1NVlFmz4Le/hXNW0HrFFTi8XnIjI2Q/9lFi3/0upNIo+w+QmTUL78qVpLfvIB6LQXcPvPtdNF58\nEY6ZMwk/8ntsBzRoWwg1QbRt20ipKlXrryK5aRPKWWfhrKoi+PGP4zsB4GzWgRWLrKKQ6+0hk0gg\nt7XhCQZ11quhAcecORz/4V3YAN+K5VS0teGsrqbymmsMc1lbVRXus87Cd7Ze3GB3OlFTKeIdu8mR\nw+X3Y5MkUseOkXzpJdxz5ujVlnlQdiSeZE8kwYJKD9N9uk5JbNQivdfgchieUQG7jWWLZ+Jz6i1y\nrmqsQcvleM1hpz+dYUAZQbbZWFxVwZFUhkqnEyWb5YmBCOtqq5BsNqPN0cvxNGtqqxjI6IJ5sbm7\n86B1itdNtyQxJ89YiVZKXck0IadepddW5UXVcpxT7ee4ohrjmJkncayYvYPVFBUoOG6+VsTRRIr+\ndIazAz6DuTK3JjI/S7GKSrPbvwDAVv2aGeAVm6s1rFWR1mcACnzZToT1Mt9HzFWA0jHzs9mgelr+\nocukHCtqoWe3rhWK9Y1uXrJTP2bdgM3jTRSoQCGgsmqVhIcYlAcgqlLYU7LY/a3MimCrJhvFgGY5\nDZhxLJ8iNK9lOa+wnIbBIAlwE+0pBMNmbZYVhFrBsUhpmtm4VBie+3+w7nP6OWoKHrwDUkchpcKa\n23Sg5q+HZ7+lX+N0wJ5N8I6vQ9/uQvNdK+ApBnyUBGz5Tzj/o5AM679Fg8lsHK1uDc6EjzwMnS/o\nf5vBVjEGVqyNeH7rueLZT0Md2Rkwdibyjv+LUPv7AfC0taH09JDp7cM9ZQpyba3uO5ZMMvzQQySP\nHEWaO5eh2z4BTgfS3/8D3p4eAKqvuYau55+H2z4B1dX4P/lJUtu3o9jtuJYvR4lGQcng/NCH8M+e\nxcgfHsXR0EC8vg4eewymTKHh5ptx5nLYEgm8c+eQcDjgR88iz55F9Tvfia+hASmRIDZvHslHHsE5\nYwbeUOiEgbMwtrWawYKuG8stXkz/L36J8sILVLzzahyahlxdTeMNNwA6o6UcOJA347WjHDhARpLI\nptNEt2zFVhPEd/bZZF55BbmtDYfXi1wTRDqwn6yiIMmykb40N2tH05iSr54UG6x5o1Y1jaOJFHsj\nCWb73LQPjbB5KMo3505h50jcYJ2OxJPsi6UIOmUgh4YOUCrtetWj025nbchv9JJcGQows7LCAEAt\neRPXYoyRABdKNqt7jjllY1/dORKjrdLLpsEoU71ubOOIzK0gpBTrBYXmpcK1HnQAIlKKIj1ZzDLC\nfE8zoAIdeIUcdn7bPcgF1T5mVFYUzLuY35f1vfSmM2VtLqzPZrb4sM7TuvbligLMgFPL5YzfQdHn\nLwUAxHdiEy0m2hb6IasgO6fpLYrMm58x8TIVb9YqQxh7v2ICfnG+qsC+x2Du5WMBmTXM4vHKRh3U\nNC3RQc1ELTLMoBEKQYIVeFjBkXUtBeCynlfMdFWACBhrDivmZZ2bdQ6gO+0HpuSvkaG6Qe9PKbvh\nye/Ayz8BQsAAjFwJZ12k3+vqH8BDn9Q92Xr/CZ67G3b/CCQ7nP2hwvuLf7NK8c4E7grY/xhITqhq\nAeRRVozcKKsVmAaVTfrnnE030TW/JzNTVtmYb0ZO8R6b5ZjINznOgLEzAeiMjZRnY2yShK25mezg\noN6D8ZVXCF5xBbLbjXPOHLTHHiN24AAk4vCBj1Jd4SUzNEQ6EiG5dy+p730fXC645WZs27djP/8C\nXK0tHPnnf4GNG5G/+AVqFy9GPXyY4E0fpOvhR+Dzf6tP5JvfIPfyyyjBIL6lSxk5epTIv36L4Ne/\nht3pJPo//4N2zjnICxcy/Pm/xXnFFYSuuspo5XQiIWw/RC9OM6hLhcNojz+O6/rrqfvQX+ENhcgq\nCqm9e3F4vQZ4cy1fTmJgAGd9Hc7Zsw3Gy71gAZHNmwGQpk41UsBKby9aRiWxezfORl10nNq3z2DG\nHA0NZHp79UnU1rIvljKAk9jwd43EWeT30pRvFN3qcXF5fZCgx4PPqRrpuka3k+bGmoKN/2gixZFk\nhgvy5wxksjTlxxbmsjDq7dWfVgv0V2amR7jOL7E0yx5QdGZptI+k3g6olNmqACRWYCKiWIVlKcbL\nqiErFVZg1ZD3cXPLLt7bVMNAJjumItN8X7OFhgBqvekMdU65AKiZgas1FStsPdoqC5urm+dnZQvr\nnPKYllDW5zUXG5jXrHAByqQSreyBlWGyflfVPCrGF2OXAiTWMDNA5nuYr61stAjPzUyUDI0LRlNs\n44VYBsHwjXTp4LL57EIwJyKTKmxMLcLKABVjXMoxdFbAZQa3VgG+sR7qKBDW1NGiBQEwxXpaQYf4\nnEnpADSrQqwfZAfG2nfvgppp+gK990548hdQmS+IyirQshJuehhazoF3/Uxvrt64Dha+ZyzrKK55\n4dew9Aa9QECE3akDZ5Gatkl5D7M8mBI6QCWqj5uTQctC/x6dKRUhwJWvXv9bPHMpq5LJsLVvcJxx\n4D8TJUNNpXR/sePHcTU36677v/oVyWyWzJYtcPEapl52KVTp5qAj995LLBBA/d738f7LP1M/fTrJ\nTAbl6WeofPvldH33e9jaFhLweLD5/ThnzWJk40aiP/0Z9PbCzR+ifsFCpNYWclkNqSZI949+jGfh\nApw+H8knNlJ5/XVULVjAyIsvMnjXXbT8679SmQeRJxOiA4EtEDA0YKCzXv2PP07lrFl4Z8wwgJqm\nqgWFHUosxtG//QKV71iPuvclgu95N97p00kePszQQw8jNTaS6thF9VVX4W1r4/g992APBAhdeSXa\n0JBhRGtm5sT46c5OpOZmXHl9lwAar8USyJKEDcjmchxPZ6h1OWjxuNgxHKU3raBpGrJk5/L6aqNN\nkbBqULJZnHa7kcYSm72SzRrnAsQUBbdcuPkXS7vBKCDpTKYNQCLAS1cyTat3dEMzg4uQw25YaHRE\nEiwrkmI0P3upVKBIL+6OJJjvc+ObAEgX6yDA647hKMurK435HUmkmF5R3GjWOj+RqrVqyMpdA5DI\nZHgxmqTWKTPVci/rOEo2S0ckUbQ11ESsNMbEeNVxJzJWuTSeNazNtIudZ9UJifF99aPABCb+HNaU\nqqbpwMHcWNp8fOfdOqAQgEzMUYA065wns6ZWoGddO/Pfgh1sWqKfu+8xHYRWNo7OXbBLalJnlWS3\nDm5EQ/XwMchmdFAzeBB23w99B+Bd34VoP/S9CE2LABsMvKLrwlqWw4HHwV0NVY0QG9DTlr56fezQ\nnNH5h4/pYFcArf6XweUr1O6Z38HwYejbq//bvEy/10gXhA/Br26A2zaB3QuvPAqPfRk+thkal4ze\nz8xKTqa/6Kn83ZeJMw78fwFRrn3R6x2aqhJtb0c5dAjv8uU4NQ1bLIa8ahWZz/8tHD+O96abUF0u\nIj/7Gf716wmHQvBPXwO3m+qzziIjSYTv+hGO+fNQUykkh0yFJEF9PbEXdiJ17CYdCEAmg/e/fky1\ny400dQpDDz5E/NlnYWAAentJJpNkZJmqj3wYr9PJwMaNpDdtpuGrXz0lQAzybGAgwMB//w+hmz5o\nADKbJOGfO5dMfz+56dML+nz23/8Adpfe07Nq7Vps06YRXL0aVq/G4fUaBSC+tWtwNDTgqq/DNWcO\nqaNHSR85SkW+z6hmsxmGusJHTunuBsAeCpHavx9//jnNzIgsSQUMSYPLQU9a72mos1SVxjX9ikqL\n3Y6qaaRVFXDSl87QryQNxkvL5TgcT/JSNMklea2YX4I7X+vhuqYgc/yVBayL1dvLqoESQFEcyxU5\nVxQU9Csqi/1eZEkqYJtKabpECPBjZqR2RxJ40Li3Z4jrm0OGxq1YqJpmABtxn2qH3ZhjUJbYMhih\n2e0cM06xVOML4ZihVbMCMvHZKvhXNY0hVWOR30u/MrYyzQqunHa7sVbmMLN8k/JWK3XuiWxWZqas\nFBtULEQzbWs6TVxXjKUzM1Llxi4GlGAsWIz1gZYrPE+cYwViQk93YAPMuayQTZtM4UJW1TV5pbzT\nxvwtFZ4793IdoB14HOrn5wfNwaGt8PvPwfxrYd6l4GvSwZEwuBXjVtTAKxth8TU66ArNhOC00eIC\nAeBsEsy9Qv/XJkHwrFHgI4BYpEuvtjS3fVIVHdDNvlQHabG+UdAtgGYqDPf/PahH4H136/eunqrP\n88a7ITRXH3/ZzXpqtX5RoXbQJhWmh0u9e2urqdOsR+UZMPYmhej9WCoEUwOMSZ293iFAoF124Lv0\nUr33pN2O6nAw9P3/1Fmsr/wj6patDO3cReV176Vn69PwLV3cKX39a7j8fobvvpvAh29h+KmniD74\nELU330wuHGZkwway6TTuK64g9v3/hFgUTzCImslAezsVc+cQr6yEr30NWlupvumD2NNpEg8/TKQ6\niHT0CJXvu4HAtGmn9LlltxtPXjsnwJRNknC1tJA5frzg3EwiQfyZpwl99rMk/vAHkkND5Hp7SXd2\noo2MkJYk7KEQkaefJv7qq9hlGRoaiD61BUIhMrEYnrlzsUmSkaZUUymSe/bgW7bMAGYAvlWrjDSs\nAC+96QwZTePPx0dYUOnBZrPRk0rTn86gajkOxNNcZqrOA93lfttQhFfjKZo9LuySZHiIgQ4mWjwu\nWjwunHY7dfkqv8+e1Ug4O+rDZQZEpRpVi8/Cd8vqwC8AlwCKZgDR6nUb4MrqpG9mlrRcjhfCMUJ5\nYCrStUuqKuhKpnlvU0VZIAaFPmedyTRJVeXpIV3jFnLYcdrtZQGdFSjV5Ruci/U0e5GZgZKZoTTr\ny1osacpiIdbM2oxcrOt4raAmBLJOxWY1HiMmvov26DogczoNdCd3THoyK6CaiAi7HMtkvbbKBFLE\nv0bFobuQTRPHrUBMfD/RVkClvNOs15r/NgMKce85l+spWknS9VeBqbD2y7qVxS/eCR/fOMo6msfT\nVEglYOE7R8eSnIUFB2aNnTGPIiDbWwO7fgPT3qYDOik/Xv18fW6B1tE0pPlaTwDUEVj1RXjuv/VU\nqDeo32P6RaPvLX4cZl2uM5WiqESSx75PMzsr5m3VG56G6cozaco3IcazUxDxZjBjmqoazclTR4+S\nHRoiV1FB4sUXGdmyFW3rVvjA+5n2rnehOhxIsszgr/+X+KaNcOAVuPlm6hbMR/L7kaZOJblnD8M/\n+SnVn/0MLrsdSZbJyg7CW54ivfcleP55+Nzn8JJDbW+n6tZbyRw4QOShh9FmzcKzYD65Z5+j8pK1\nJN1uYv/yr9Tf+e9Un3VWwbqcTGcCc6ipFKm9e3EvWGCI6TVVZeippwheeKHxvtRUiv4HH6T+2mtR\nYjHCDz0MHg/Vl19G4plncMyYQcWMGWQSCcKPb6DibRdgi0aRamqI7thBeMsW6q+7DimZRK6rI5fN\nktj7EjktS/Dtb0eSZdKdnSh9+v9b9y07W2fRTMyYWWclgI25v6P4F/QN3Gm3G43AvQ4HKVVl10ic\nBreTBpeDDf1hqh12VtboaWdzEYBI4QngIDZ8cR/REkmcazVEtaYxrToo8R2MghjBmBUT8VvTgtbr\nXwjHDA3bRNN2wmcs5NBTt4/3h5lf6WGaKW1YKg1ofi8CXJpBllgf8Tww2mTcPPdiz1gsyn1f9trJ\ngKxTkcYZr7Kv1H3ERlrVPBaoTLZxufVeMHYu462Jpo02/B6vddJkgayYX6km4aWuKbU+oI8lqkSf\n/QGc9wkduIgUoq9eB0X7H4V9f4Krvq2nEovNXzy71QKl2DyVBER7dS1a0xK9ClLLgE0Gfx1Ej0Nl\nvb6GAvwpCdjwdbjkyzrQ8pZoZadpGN0DGhZC7Hghy2edOxTO21qJ+wbFmTTlaRzCTmE8T6w3gw3L\n9PaiqSo2ScIzbRq5KVOwSRL2mhoiiQQ88wyBpUvRPB7iDzxA9Q03IJ23En76U/jcZ6l2u5Hb2kg8\n9xzpTZtRXS6ks5fi0DQyAwN4zz+f3GuvkcGGVFeH9v73wWN/JOFy4/vIhwn/7OeoFRW4Vr2NygUL\niD74IBVXXgEOB7Gv/hNERgzWyjx3ASBPds1ktxv3ggWk9u7Fu3gxkizrgOpPf8K7aBHeUIicppE6\nclR33VcUZLcbW0UFVZesRUok8F14IckXXwTyhRFVVWQPH8Zmk3A0NkI8jrelFdnlxj17tsHAOZua\nSHd15asy9b+F6a7xjjSNBpeDI4kU5HLY82BDaLdEmqvJ7eRoIkV3Ko2a1XDZ7SwN+OhN6yL6Jrud\nnrSC324jYLfhlmXW1QXoSqWNjdwMxIACcGFuaRRy2Nk5EqMvlWGOz0WFw1lUoC9CMDjm42ZgUngf\nqSQQA8YYuorxl1RVGM9aqp3QmHcvSQXnXloXKABzZjbLmgYsFPXbjDUSgLk9HCPosBdoz8xVj+VE\n+5ONstdMhhE4Ff/9KXY/syC/qBg/z4IVAz2CSZtME+pSG3W5ORYbw8qcmedkBZaTAWIjx0arOq0V\nkqUArH5A11YVWyexrlkVWlaMpg0DrTrg2f84VDVA+2/AU4StKpkmLTL3KpO3mezWbSoCU/Q5TDlH\nB4UivRmYCiOdupHvlJX6OU4vrPuHQoF/qYj1jTJi4mculZm7GUC+CUBsMnH6cHT/x2Ky5qSvZ4iU\nqKaqOBoakGQZNZXSDV9lmfDhwwz+9KdUVVQQ/PnP8NbVEf/zn6lYv57hp58m2/4C0o0f4KzrrkNu\nbib+8MNIfj+2i1ajHjiAd/pZuOfPR1UUIvfdR1yW0bZswXv2UkJz5hD4/OchUIXyx8fIpJLkOo8h\nJZJ4gkGqP/IRpOFh4gcPQk837s9/Hl9e7C7iVJvz2p1O5Lo63WMsD1JzapZo3rxVTaVIvtCO59xz\niW3ZSmT7dtKdx0jv24c9FNK9xIaGyCoK4WefJb7tOVxz5+JdvIj0sWNUrV2Le+ZMkGyGrYZ4juzg\nIDlNMwBmuquLyNaniR88SOroURIdHSRSKfaMxI0Nu8GUGmtwOQxmp8XjYom/ghavm3k+t3FMVEAe\ni6d4cnCEO1/tIqYoyJJEX95jrDulFLBYQAEIEd81uBwMZLK0VXq5pLaKV+OjzBKMAguz872qaRxL\npHh+ODLGlV+AGvN9RJid8s0hxhbNwjuT6YJnnQygMZ9rrWwsNgfzM5nXy6xrk/Pp4H2x1Bjz2GOJ\nVNGWTeM590+kIXnJ78XG+UZFsZSb0PiYW/2UawRtvtZvYYPE+eM9UzmQNFFwWgyQmJ3exefJ/Lco\nhw4uyqXQzGOLuQSmFpq8FgubNAp6xHUON8y+RBfvB6dCpFMHaNZnBf2dCABc7B7iJyaAmWhEbr5/\nrG8UDMrOPPNlqQSeCBAz1kWkY0t0JLCCMxHm+b+Rv/8JxhkwdiYAyKbTJDo69D/8fkYeeYR0JMLw\na6/R96W/IzdjBpUXXYQ7Hif5wgs45s8n/MwzDP/P/5A4chhPbZ0udj/nHALXX8/QU0+R+MevUP2p\nvyF05RWkt2/H4fFgO+88Ip/+DOzdC42NyFOmwLZtNH/uc0gXXwQbN8GSJTgXzNfTo394FCWXQwaq\n/v3fmXrllUVB16lkEQUT2P/rXzPc0YHT56Pl779M8O1vJ6dpaENDBK6+Gm8ohP/ii6g691y8ixbh\nWbhQZ8Jk2dCDEY3hnDkT2e0mefgw/T/+sQ6sXn1VLwoQm3o+PWxuzeRsasLZ2IgNyPT16UzZwoVs\niaS4qKbSqEzcNRLncDzJkXiS5wZH+G3XAIlMhs5kmj2RBClV5f4evU+liN50hpU1VXxoaiNfmN2K\n1+FA1TQy2WwBy2P9LEKkAoXR7JNDMSSbjXmVHsMOAkbBFejslwB62VwOW/4/5NZigFIgxKyJMoOg\nYh5jYr6lUorjhZLNjrlGsuldAJrczoL2SSlVZedIvACgie/EvbwOh6HhMz9Pq9c9xjLErK+b7LzN\n55bsi2nd2N+MsJlAlWj1M1EjzmIgRYC6iQCyUxkCIJg/T5QRE71BBaAq1cmg3NgCiBV7n+J4se4F\nI0dhw7/A2ddB7VSKWoKoil7lmtPG7xAg5mfWZ4l2WeZ0Z3wAnvuJXgF6okyV+VnFZ2tLJitALwWa\nT6M4A8bOBOlIhOzQEPj9upXFwYN4Lr6Y4w8/zNAvfon33e/G43SSev555Bkz8J9/Plo2S+y/fgKD\nQwTXr6f2mneS2L2b4QceIB2JwEsvwdlL8be0kG5vx7lsGd7zziN8110wNAS33Yp7YIDk7t143v52\nki++iPrHx5A//nFCa9YQ/vc76fv5L/BeeQWJVw+S2LWL4NKlbxijmFUUIjva6fvOfxDr7UU9eJB4\nRweRJ54gm8kgu90GMMsqCiNPPcXIE0+gplI6o5bLIckyvgtX4coL9H2zZ9N8xx34Zs3CllFwz5un\ns4/BIGp/P3JdHXaH02DFQDedrVp/FQ5PvjrTZmNN0MdQXpMlBOvTKjy0et2cE/RzfUstXoeDKV43\ny6orme338e6mGsKqZmzOaVVFliTcsozX4eBoIsX24QgHYimUPCATUQocBR12jiZSvBRLsTroY0jV\nCwCElsx6vQA0LR4XZ/m8rAj6xzQHHw9wmNN/Wi7HsWSKBpejYM7iu2JjlQMp4piSzbLh+AipPDC2\nXmO+T8hhZyCTHVPdKFKt5rD6gon1KOUnVDUAAAAgAElEQVRhZj7PPAdzWrpUFBtn9MsSzMupjvF6\nFprnY06JTSYEyyaMPt+sKMWIFVtXTdOLE47tKN6s3XpdMabQzCKWA2pWaxHQDV+3/AhW3qw73J97\ni15Jab1/tGe0cXip92IeW/SxtMt6Y/aRzlG/sKwKg6/Ccz+DprbR+032d2d+JvFZgD/xezMDdPMa\nidT2ZEDzGxin12zOxBsaOU1DicUY+PGPsbW2oh4+rDeznjePWHs70Uf/iBIepvaStQTWrcO1fDnR\nBx4gV1mJEolALIb3H/4ef2sr6X37kKqrsTc1E33ySUilqTjvfFx+P65lyxj+5S8Z2LQJ9u2Hm2+m\n0iZhb27GFgwS/u1vGdmyFcfUqTTfcD2Ori5qvvkNvGdNJ7JtG5lNmwjecovRw/GNCIfXi7xoEezb\nR98fHsW9YAH+FSuoWrcOZ2OjkUp0NDTooCyewDljBkp3N+ENG4jmU4uRzZuJbNtGbOdOcpqGOxDA\n4fVSuWYN2cFBRrZtY+ixx8jE42QVBU/bwoK0q5pKEf3jH3HOmU1O0xh54gkyR44y0L6To9E4xxIp\ngynqTKbZMRwt0HmBDnLcskxAlgwfrmeGYwb7Ixifc4NVXFQbKGC2ioUAQblcDockscjv1asvnTK7\nRuIcSaTYPhwlkckUXGcGMWYQYrV8GC/MQv2O4RgxReE3eTZQMFadyXRBKs/K1BVLPwrmTrLZWBvy\nG+tgZvfMKVthvGu2FxFrKcBSOeBX6jszeDWfV66TQLl1Kv5lmRTYRGK8c4WZqUhzWe9t1W2daAjQ\nAGP7OL5RUU5TVWxdRYqxdXlp8X05FqeYbm4iYCnSpTNTv75FB0ZzL9PF+8Hpo8yVADQiDRicNjHQ\noqag+wVdYK9pei/LSDe4dHsdoj26Hcaqv4YZawptTCabPrSmIa0VqeZUtpmVM6daTzMgBmcE/P9n\nI6dpJI8cwdnYSNXVVyMrCvYVK8j09ZF1uQjvaIe6WqrWrCWnaUQ3Pwk2G+7LLmP4wYeQXU6qf/gD\nfNksUjBI9sW9aNksajqFOjgIM2fiQGeY3IEAwZtvpufXv0Y6byUNV16BvaaGkd//nvj+/eRaW3E4\nnQTXrsETDKK1tZH+85/RsJHq6KDixg9QfdZZb9i6iJRnZV0tylfvQH3m2QIrksimzdh8Pmx2icrl\ny9GGh/Gfey6eadNweL2GLUVO08im06gDA3jz/S9zmkZWUcgOD6MoCjYgffQoajpN9tAhvOeei8tf\nRcXSJcY8skpGt9uYNg3H9Ol4Wltoa2xAdjiwORwGuGlwOdgVjqJqGv2Kamz0NiClqjx2fIQWj4tw\nNsd7mmoMACEAh9OuC8xVTSORyeB1OIpu6Kqm0ZNUwAZne910pxTDm6wh33Q7q2lGv0u3LI9JRYqY\nqFi9ZBUjMKhqXFDtwy3LNMmFac9i1ZvltFhHEkkGlSxnB3w0ue1jwI855ZrNaahF+hqaxzf7rlnX\nsFiD8WLrYq5eLbcWJxyTZQqsQvhi14kNUripF6vEm6gp7HhzL9an8GRtOSa7aYsN33x+qfSeMW4J\nRswKtCbi6D+R+fmb9T6S6ShcdTvsvAewwfwr88zWkbE+YKXC/KxZNV81mRt97pFjenXm5jth7ZdG\n52uTCk1hS7WNMtZoglHKGsT872nIhpnjjLXF/9FQYjGGHngA98yZeBYsINPXR/y550ik06S274De\nXgKf/hTS8DBqVxfu88/H1dzM8H33Ezv4Kk2f/jT248fRPB6cPp/uqfXyyygvvog375OlHj8OkQia\n10v48GES3/0etd/7Lh6HA21omJyvgmg0SuSLX4TmFurvuB13IEDkvvtwXHwx8Y4OfLW1+FeuNGwm\nXs8wV2WqqRSd//kDQh94P33f/R6OC1fhzWbxX3SRDqYGBlAOHqRq7VrSXV1Ed7Tj8HrxLFyAZLeT\nzWSIPv20DtokiepLL0U9fpxsOo3S10+qu4tsJEr9Ddcb1ZRZRUE9fhx3a6vhK6apKgMbN1I5Zw5y\nbS1DDzyAo7UVLRIBIHbBKlqqKpElCSWbZcdwlHOC/gKbCQFABMAye4RZN/oj8STbh0Z4IRzlQ9Ma\nmVXpG5P2E7YTgOFjpmqaydNMb9Zd55QZUrUx3lrWFJvZu6zYcbNfl/k8s5WHiBOye8iH0HkJpqtU\nNagwx82RI5fT16BU2tAMqsz3+VN/mMvy1ZqlwKi5M4B4tpOttDxlYdbslGKFBEAZPjrawsYKmopt\nxONtmOWuE/YFJwLszOObAcNENWDDR0b7cmZVvWKw9ZzJz+dk5l4u+l+Cuz8KF30CokPg8sKCq0et\nJDRNB1LjabmKvausqrNiDjekY7D536F+jm72arWqKAd0x/tdvQXjjLXFmSgZmUSC9Msv4122DHI5\n4k8/jXvuXNyrVzP0pS/BCzuRb/8KteedB+iasvBvfsNAMonW3YOztpbskaPkWltIb9mKdM4KMuEw\nkfvvx3fRRVTMnKn7iR06BK2tdP/bv8GeF/F//WsEZ84k0dGBY9ZM4rt2kX5qC/JV66lcu4ah730f\nubUV19o19H/zm3D4MP5f/vINAWJQWJWZSSRI7dyJ/eYP4bvheoY/cCOJlStJdnWRGxzEPW8e/osv\n1i/M5ZDranEvWIAr76qfVRQc115rVKYqPT0oPT2oA4NoWhZyOXLRiN4X0+0m09uLPRgkfuAA7tbR\nKqF0JEJ04yb855yD3ekkeM01erVmKqUXCvT30+N00uhx0a+onB3QvYI6IgkW5x3dBRDw5tspiU08\n5LAb6S9xfGqFhyq7jUPxNM3uUeuFo4kUvSmFBrfTAB6dyTQNLh2MmNN0ZqatwW4vAHtiPKsmrRho\nEWDR2napGIgU55u9z8S9zM9cLoQeTU9Fqga7Vgz82G02cthocDuKVl2an9PMpoHOli30e8YUSJjD\n7OYvfMvEc5Ri297QKMXaiM1UsGE5DSRbYa9EcX05tscM5sqxXubrijXWLhXlAI8Qppufc7zIaTqr\nFGgFJP1vs4bJDNTGi1LnnAxIG+mEh78IzUuh9yBMXa7bUCRHwB0wvY8Jzq9Y4+9wp57WHD6iz3XO\n5bqha7Hry40NJ89qvgVB3Bkw9hcaIk1mFbyrqRTRJ5/E3tqKNjxMLhzGt3o1OU0jsWMH+Cqxf+Ur\nNJ97LtH2dhyhEI6KClJz56J9858J3HE7lXPnkti9m+h3voP93JVk9u7F1t1Dza23IudZI1tdHa55\n8wg/8ywcOULFp/6GUFMT8f5+PHkfL1tVFe6ZM9HiMeRjxwh99jOkXnqJ4e9/H2JxeMc7xthYvN4h\nUoMOrxdmzjDWEVWFrVtRLruU1ve/H5skEX3ySXK5HFRUEN+xg5GnniJ4ySX4li4l+tRT2KuqkKqr\nCW/ZgruuDvfcuXgXLUJ2u/Vm40eOGoUAcl0dOU3Dkxf1g86KKfv3I4VqsEkSiY4O5Lo6bLW1JJ59\nVnfmz2/OdkkymaTaDY8wq0+XCCWb5U99wwBcVl+tNwrPA5kEEp+Y0YRblg3gI3RQgMHCCc3U2aZq\nQBgFDELEbma0xPFibY7MuqymfLpTy+XoT2cK0o5mx3+zwaz4W5jVCsCo5XJM8bon5TUm7gWMYeRE\nFWSxVkoiirGA5nGb3S66U8qErDcaXI6CilHzGr2p7BgUB0qVjbqYPqfpPlhVJv0OlGY9rGyXucVN\nuXST+FyqsbY1JsLCmY9PxCzULhf21gx3Qrx/1HjVDNROJE4m9Rrp1tOFKrDui3nwJev/M4vzJwNi\nrOdlFTj0FFTWwbHt4A/pYO9EoxTTOl6cihT1mxRvrdmeiQlFTtNId3YSbW8nq4yWyWuqSnZgAHna\nNLSREVyNjfjX6pqw3l/9Ck1V8V+0mvoZZ+k9EV99lchzz3G8sxPl61/HefOHCCxaRHLTJpz19fje\n+16UbduI3nsftgXzcWqabkQaCpHcswelpwd7TRDH3Lk443HiqRSdn/gEI0ePYmttJfXMs/iWLqHu\nhhuoXL2a7J49DPf0wLPPQTIJT2wkvGdPwTO8UeuXHBqCzU9y7Nv/pjdF/7u/A8CWVgw2K7BuHdWX\nXkpw1SrqbrmF6jVr8C1diux2U7VmDRVLl5Lp60ft7sZ73nlow8PEtm7VOzDs3UvihXaUWIxERwep\nY8dI7tmDPRTSWyrlNWrexYupPu88HF4v3sWLcTU3I7vdVK5erZvD2kZ7IAoDVrM4XnxnDafdzmX1\n1by9IahrrUwmrg0uB16Hw0jZiQIByWajN50xGlU3uZ1jgJi4l+ibKUCEYLTKCfWLWWnIklQU7FmN\naM3nLzU57xdrMj5eWHVlRuVmIlXAtDnt9gIgNt74kuVdmSUi1upPyWYzWDGhgTNbdpRKa76pYRZO\nB1rzRp+MWitYmayJjFMOtIkwi74FGCpXkFBMZ1Yqsupo1eN4YRaQB1p0YCjJhUDtRGOimifzMysJ\nSIZ1wf7Lf4Zr/hkqm3Rw1r1LPyerjBZZiArEEwmHG5a+T//cuweW3FDY/Pxkwvo+y4n83wLasFLx\n1pvxmRg3bJKEXFeHOjCA0tOj21WkUqj9/dgCAXLhEbKDQ0bPy97f/Y7Egw+R7usndNVVVC5bhsvv\nJ/Se9+BeuJDU/96N7+/+jtZrr0U7cgT36tWkd+/GFo/jOm8luN0kHn2UpKqS3LdPT/fNmkW6uxut\nq4umT32KyiVLcY6MUH377Yz87OeMPPwIlde8E9+sWeTCYd0iwuWCjt3g9eL7q5uo+86d0NtLvKND\nZ5LC4dd97YRuzBMM4rz2GmouvZSaG27AVuUHQLn3d/Tfcw/R9nbdjyyv7coeOoSjro7s4CBKd7fh\nou9bdjb+1atx+f14Fi7EM3cukixTsXgxleedB5EInrY2PFOn6s7/L71E4sUXSXd1oakq2tAQlStW\nGLoys5+a2t9fYHabzWnsHIkXbMrFTFfF8d50pgC0iU3enOITxq7Czb7B5TAaVQ9kCjd/s7eW1WfL\nDCCshqnWsIKMUg2xodCIttj55m4BE7HOKBVivYpVZ3anFKOS03y8FOslvmvNs3WCRRPVnOL5zDYe\nxQCiOYQdx2kByMS/klRcczWZVN1EwEfY4i8mNuRy15QyMbWGTQJ/fXGvrnLjx/p0F3oza3eyMZG1\nEKBFScAzP4SjW6FvG1z6Oaidr4Ov7t16OyE1BTt+BUee0Rk8wUSeSCVqVsVotxScDk7fiT1jsTAD\nLPMzlgPbIt7IqtqTjDNg7C80BHPjbm1FU1XdbsLvJ/PKK3gXL6Ji8SLsTqfuCTYyQuWtH6fu6ndg\ni8WMqj/Z7cYVCOBaMJ/qmTPJhcPYp09H3bMHadYs0kePInV1E7j2WoIf+xix3/4W1W43+jFmBwbw\nzJmLOxBA9rhxzJqFfPQo3otWU/WO9aS3bCEVDiMFgzibmnQ2qquTwE9/QuP69biiUTzz5+OeN4/Y\n/v103XEHiYEBYLR906kOoRuTZBlvUxPexYsIP/Qwud/9Tj9Bkqhev96ojgSQZBn3ggXkIhEc9fU4\nm5rQVJWRJ54gsWcPNkliZNs2ops36z0oOzrI9PYi19UZYEo8j3fRIvxr1uBqbjbGt0kSSnc3Sne3\nbqOhqqT27sUeChlpaMlmo9XjHmOKKqosraBMy+XoLQJOzJt8RtM3eFH5BxhpP6fdXnAvAVbqnLIB\nLmyMTfEJ0BBTlAm5yBcL8UzWKGUhYbSAsvTCnMi9zesozFmFhk7YYIQcdjYNRgk5Cn3EyqUQzeBK\ngDORwjXPdaJpSKfdzrraqjFeZm96nIzuZ6LflVoiK0gzz2kyLaECE9R6GfNTx3YJeD1DABPzc8lu\naD0b7v40hM6Buev1c0e6dOAVPgbde+CsVaMO/cWYyInYTqiKXqwwcABe3Qhnf+DUsWIirGlqGN+O\nZTyG9DSLM5qxv+AQrI0ky7hmzULO942UZBm5tRU1lSL8m3vQvF6C8+ZDJIKc980SYME7fTqum27C\n7nQaIECaN4/Er36Fd+VKvG1tZA4eJLt/P773vpfoxk0M/v4PKIcO0fCJ2wxvMEdDg974OpHElh7G\nFovhvOACBn/yEwgGcZx7LuknNhL45CdpWKLbOnja2gCItbejptN4Lr6Y7KFDZP1+1P5+g9k71SEq\nG5VXDxKvDlJ7w/WweBGRD94Erx0iFQ6j7NyJe+5cXM3NRkpRTSYNsGR3Oo1UYvLIEdL7D+CaMwfP\n9Ol6GvTwYQZ/+UuqLrsMbWgYTcuSfu01nNOmYZcduFqacTU342xqAjD+FexYsd6msiQxtcKjv3NT\n6kyAMLPeqMHloD6f3rMK6gXIcNll1oZcup7MbtctM/L6LZESNKfLhLZJ1TTsNhuNbmdefG7jWDLF\nVK8HWZK4uKaSF6NJQk7ZqMIsF+aKyVIGqOK5rOk7s4WEeT3MBQDlKjCLFQsIIAswxevW+3rWVpWt\njCw1vhmQlUvhTiROOyB2olFO91NMwF9l8RcTn8st42T+uzGZc1UF9v8J5lwG0htQaKFppRuYDx8C\njsNl/zla0RicDv5GGNgPrcsY47xfTANYTusnzvHVQXJIf+aT0YqVC6Hdm6jI35qOPs2F/afvzM7E\nhMIQmJcINZUyPgtXd8AADMEPvJ/KlhbsbtdoJWFvL2oyRXLPHjRV1Rth51Ny8owZpLdupeK88/Cv\nWAFdXXjb2nBMn05yyxayA8chmSD4zqtx+f3GfYVI3VXpI3jNNTgaGtBefpmqG28kPTDI8Fdup+K6\n91I9Y4Yxv+zAgK6v6uxE6e8nd/Qo8owZBgBLDg29HksKgNPnI/ihvyK6cSM5TaN+0SLsX/oSzNe1\ncZWrV+NsbCR17JjRdSDW3k7kuW2MPPEESiyGsn8/OU3D7nTiX3MxFTNmIMnyaA9QbwWu5mZczU34\nV6yg5l3vwjNlCs7GBr0NUv450126W7kZeJbqRGAz2T5AYXrQuuHbTCkycb6qabQPR430pNfhMFio\nfkVlkd9bFiyItGGj24nTbjdE9S9GkijZrGFAe3bAR4vHxa58GyHz/c2MlappRqshMVcBeKytj6xA\nRrBw5r6P5lTsRIBPsXOFjs1cFCAqKoV+zRyTMX190wX5r0dMlpmwpqXMY5QT8JuZkFIpUnOcqD6q\nXMhOHYjJb1DFq3hOq+FtKgxP/xpCq6H1/MJ3cHw/bPhnUPLt0YYOFV8L81qXZRMlHext/3+w6JpT\nz4pBoYmweX7lwpyOFr8N9Y3VH08mzoCxt3DkNI10Vxfpzs4xgCynaXq68KGHyCQSaMPDkGeUMokE\n4Q0biLW34/T5qL70UjxTpxo6J3soRC4ygmvePLIDA6PNsjUNWyxG9TvfSfXb3oYtFiPX2Ig2NIQt\nnqB6/XqqV11I7Q03UDlvHmp/PwD2UIhERwfZwUGq1q7F6fPpVZyrVuFraMC5dAkEAlTU1+PIV09m\nenuRgkGUnh4cHi/Byy+n5sYbUV97DaW7mzTQ84//SHJo6HVLWXpDITyLFyHJMvFdu3DFohCLYRP2\nFQMDuFtbca9ejcPrxV5djau5Cd+qVRCJ4MqvQaa3j2w+vZpJJDj+i18SbW8HXwUArpYWoyjA3dpa\nwPjlNA21v7+gZ2WpEIxmNg96igEyKxtTLNS85qsjkjAAFOjMV7+iGuDNDCgEaAIdwAjTWSF0Fyk0\nMQ9ZknSRvymFqmSzvBCOcdQklDcL8gFDyC4E+uXAS6nU3XjPD6PVkNZrxHf9ytj3oeVydFlSr+LZ\nVE0rCgrHA1+lgNxbJk4mVWQWlY/XQNyapit1nghVGbu5n4r/jmjaiQGxk7m3AEtinEgXRLtheCe8\n8x90/ZZYv5wG7mpY92Vw+fWUau+eUTuKYmMX+2x+F4FWfawVHwBv6MSfo1xYXfYnElYw6avXCxde\nDxB+CuIMGHurRy5HLpcr2KxzmobS3U12YICq9etxeL04Zs0iuW0btkCATF8fcjCIe8ECQE9nCt2S\n6LfoaWvTrzMJxIWeyuH1Ync6ybrdHP/2t1GdTpxzZmNPpahcsRynz4cky8a1kiwj19biqK83WDah\nyxrcvoP4j/+LqptuomLGDINFy2YyJA8fJjs0hPucFRCJQDQKPp8OKvfsof722wGIv/rq6wLINFVF\n7dMBZeXy5VRddRVEo6S7uow0bmJggN6vfpXk0BD+c8/F2dyM2tdHzufTnzUQwNO2kFze/BXA+7YL\nsFdXo3R2Ef7zn1FTKRIdHUYVZXLPHgNg2yTJAHXjPaNYV3tezwWjHmHmxtbm9J6wcrDql0SloFUM\nL7RNks1GJJ02mCAraLKmBgWIM4e4V3dKIeTQvcoEY2Zm94ROq1QDbXEPswBeRLHUXTkwY77erK8z\nX2sFt+bvzHo9EQ0uB0cSSbYPR8cAsnJzKsaelSrIOG1jMvosEQJQQPmqSuv5E0lDCZYk1gdNS0Y3\n91OhLzrRMU62ktEcIjUXzveE9AQLdVbRHr3Ss36h/lmSYe4Voy2RJjPf+ABkUnp6cvAgdDwAmcTJ\nP0OpOBENnvmZbNLkAd0bGGfA2Fs0xMYs19UB6MyTyQJCbP52p1MHD6+8gmvZMj1NNjSkV+7tfYn4\nzl1oqlrYnLqhwdCbWTVZ5r89wSB1X/wi6fZ2wg8/jC0QMK6znmuz28cAivgrr5B77SAN37mTwLSp\nJPe8iNLTq+u1enqIPvMMaU0jct994Pdjq6oi9txzDD76R2yVes+zztvvoOuuu1BisVOyrtYQaypY\nQxIJFJvN8AVLPPss7nXrSG7bxsjWpxnZ9jzHH3qI4//938T7++n/wQ/pve9+uu/4KrHeXgbvvZfw\n4xuIP7cNR2Mj9uqgYWHhamnB7nQanwXzmdq7d0LMGOhrLjZswWCVSs0VY4iEh5YAWMWqArtTCkPJ\nJF/Ye5j9kVgBaDKnFc1jmq0ZRKiaRkpV6Umm2TUSJ6WqRnViqWpMcf9S4KXL1JNyMulBwOhgYGbm\nzONZwax1LFmSCkT+5vOPp1XqXHJRACbOUy2beDFrjomsw2kXk9XpWBmN8cYoBvis6U3x2QzyzAzW\niYDGicxjotedTCWjNdQU7LoXAkt0MCbuIeZnFevLzslr6GQX/OpGuPtW+ME7IBOFGW97/fRiJxsi\nzXkax+kJEc9E2cgkErqeKpvVKxbzQvdMby80NJA4dIiRBx+k5iMfIasouJqbkaqrGbz/fnznnINn\nwQLdb6w2hKulBUmWjabXkxXEV9TV4V63zqi+LBY2ScLV3Gx8Bh3kpHbtwn/llTh9PhIdHXgXL8Im\nSaS7unDPn0+6u5vsvv34rr5at+iIRMhWVaE8v53AJWuhq4v6L36ByIYNJ7GapSOnaeTicV2/pig6\nkHS7CX/nP3DcfDMVixeR2H+AZGQE9/r1SOk0vqVL8E5phaoqvKEQ8iduwyZJhOvr8ASDuN79blLH\njum2FPX1pLu7yfT1jameBN26wtnYaOjHJvpuzKJ2K5iaiCZJnCPa8RS7xud0cn1LLbMqKwy7CpF6\nbKv00JlMM8XrLmjJZA5V03h2cISD8RTvbqpBliT2RJMs9nuNc4oZwxY7Lr6b4i38/ZnF9GIOpdKD\ngtWqdzsMxtA8nvjeqgmzjmVu+yS+lyWJ5dWVBWlVAcBEBaXwZDOzglbgZi0OKPUcRd/xRIxLT6c4\nEUAjQoAuq2nseGDpVIi7T3SM8dg/a5RL2abCcGALrProWIuJUmnHyYSmwcAr0Lsd0Kt/+clamP8+\nmHre2PZHp0OcSJrzDY4zzNhpGOXSUZlEguO/+x2ZeFxnvmpqsDudBtjJ9PYiu93UfOQjpPfto+eu\nH5EcGtLd3IeGSR04oKcOg0Gw2ZBkmUwiUSDun+hcRNidznFbFlnBhOx2U33NNbgDgYLqwKyiMPLk\nU6Rf3kf15ZfjXbGc5LZthB97jExVFY5YjPrPfBp3IIB7wQIqm5oIXHAB4Q0bTrk5rE2SkAMBlO5u\nBh5+hJ4f/BAGByEUomLxItSDB3EtXADHOontaCe1bx+xF17AXltL4oknyCQSEInoPTrjcZTubmS3\nG9+sWVSddx6ulhZShw5hr6kp0IiJVK05zWteO5FOLvZuxDFbkU3Zmu4CxrAxIor5VpnTm067nQtr\nq3HmqyyFv5mWy9GTUtgzEudYMlWU9RHA7byaKq5vqcXndOKWZZZWVRRYZpSKYuySGWyJc8zVj1aW\nzXq9OF9UfIpj4lxRVWmu6iyVNjUzgeJ8qzFuMdbS/H2xtLIVCBZ7v0XZskxqVB/1FinzB058rmYG\nqJTY/3SMyQCx8DG9irLYGsluXR81/YKx1ZKnKrwhqL/AcqwOXv6D7nF2OsZpDMTgDBg77SKrKMZm\nDBSkp7KKQqanh2w4TPLYMeTaWiSHA01VDb2Qs6kJyW7HHQjgnD2bZE8PvT/5CcN/+hPVl+qO8TlN\nI7p5M/Hdu0lHIow88gi2PCgyhxkYvB4hWgEJD63I9u3Ed+5EDlThXay3DZKSSfwXX0zOX8Xgd79L\nLliD0+cjvGEDySNHjMrE1Mv7TjkYk91uat6xHldLC/4Vy2n5zKfhwlWwfTvpvFlr7SWX0Prtb9F0\n0wep/ehHyQ7rLYZ8l12mV6LW1eGor8fucBrgSgCsnKaRzXuHiRCGvcKsV6y/YOcyiQSpo0d1m5D8\nteYCi0xvL5qqjl5nEsebtUbimDWlKMIsfi9mJwGFaU7RBxNgaoWHtzcEDWBj9iMzpzFlScJtqgoV\nac5+RTU6CUAh4BJzNVdXmkGLFZCIz6VYJPN15QCg+XnF53ICfAHkzHMvZQorxrBWhprTyqX0aNZ7\njplTVoWe3dC4SNfMvB6+Sycqzi93fCL6q3Iaq4mkN0/XGG89hWi+VLVoToNEBGIDo+2YTmVIEuQy\n0Ld59NhV34Y1n4LDz8BrT+nO/28l4H8axFvwl/qXG0K8bQ+FdO1P/u+soqCmdKsJV2srwRtuINXe\nrgu+Mxniu3aRPHSI5J49wKgnlR5EVFMAACAASURBVKwo1H78Y5BRkeyyIZ7XhoaovOgiKhYuxBaL\nGSJ/a5gbZ79eYZMknE1N+jMDNlnGd+GFOLxeQ0sFIGlZHCvOwZ5VdfDpryL18svEdu4kp2k4Z88u\n0Kudqrm5mpsJb9igm7n6fDBrFgwM0PNfPyG6fQex9nbSO3YwsmEDkUcfZeSFnRy/5x76772Xzm9+\nk6EtW1B6enDOnDFmHSVZpvrd7y669mb9Xk7TiLW3M/TYYwzeex/JY8dwNDQYekEBvMyFEQarlmdU\nRLNp82ZfTKRvFb8LoANjRfmCvelMpg3QJcY3pzetTvLinlZgIQBHyGFndyRBSlUNwKVksxxJJGkP\nx0ipagGItIIQ873KsWxW249ybZqKuelPpBLSDGDN9xKsmWATreyhGehary8XY74X6RmHu7y26kTj\nRETrpa6xCvHLpe2KWR2cSJxugGGi62nW01kjMQCZw1Az9fVjgyqboP5cWPYx+PQeWP5Rvefl+bfq\n/m4bvwWHnoR07PRb49M0Tm/e7i88xAYqwiZJyLW1Y5zdM729pHt6kPPpLG8ohOOv/xqH12uwYjlN\nKzACzWkaWbcbdfNmGm67FZffb3iI2UMhvRrS4TA271LxegIxEZqqEn/6aTznnova30/sT39Cvvpq\no0PAyO9/j83rpcLtxnnWWaT37cNZXwez9K4A0aeeQhsJo6ZSpxyQpSMRhjdvxr1iBUP3/Bae3wZA\n9XvfQ+XcuYbha1ZRdF3Z/Q9QnZ+70t1N8uhR0t3dqP3HyUWj+JYuNc6PPvkkjmnTxtxTpJ/NLZB8\ny5ZRoWlkFYX0y/t0cf/LL+tVr5aKV3PkNA07pfVF5rQbjOqsRBTTSVmF5OKY2Ri1GHAx656UbNYA\niOa0oLg+mAdki/xecvnrbNiMVKYAdC0eyRhbADehlwPGFC1Yo1zar9R5k4liBRPis0gFr6utos4p\n0xFJsNjvLagALaYVm3SYN+Ri2qqTEa6fiGi91DXm4+NVRp4KDdCpeP5THaeikKCiDlxzXj+bCdB1\nYTf9Rre0MPfkrJuv69Q0FR76W5h9KSy6GmpmjFZ2nomicWZl3qQwp6BEGkqwROaqQ7vTiVxXh81m\nw2a3Gyk9waZkBwZ0A9Xh4QLdUd/WrQzdey/25mbc+SpHTVVR02lSe/eiplKTEoW/niHJMq6ZM3UN\nVksLVevXkx0YoO+xxxj81a+Qm5sJnHMOgXXrkJJJNJeL+GuH6P/u91D6+nAvX05Ololu3nzKUpWa\nqhLr7aX/nt+ibd9BvKODhltupvKWW6CuDjmdJrZzJ+EHHyJx6BDhhx8huXcvNiC+9WkSTz+tN0zf\nsgU1kcDZ1IjXBJbtTicVF1xA+pVXxlRK5jSN7OAQ6UjE+E7oxmS3m4qlS7A7nQb4Nr93MXeRrhTp\nTJtFL9ZpqTo0s1/AmN6PQjtWKt0mmDFxbbF0mgBbWi7HrpE4IYed3nRmDCMk2Wyc5fOypKqCfkWl\n1ikzxetmitdtABUzgEupatnKwhMGMScR41lxQGEqWPT7FP5sIibKiJ1QnOzGX05EPpF7FxtPALFS\n7JD5mNmWotg5E2GXTicgJuJk34emggt48f7X1+TUGxwLhu0yVDbASC988H91IPbY1+Huj+h+Zqer\nnuw0iDPM2JsUIqUEusO62t+Pd/HighST2ferYsmSAgZLbMDiXHFtTtOIdHYy/OW/h1mzqL72WoOJ\niWzahD0YxD1vHsr+/bgXLDgtwJhNkvBMn46aSpF++WWkYJBcRQXxhx8h8OFbqM63R7JJErk5c+j9\n+c9xVFVR96m/QZJlIn/4A5UrVhQA0hMNoc0KP/MMIw89TKayEiIRJL8fh9dL7cqVRN92AZXz5unM\n5NKluh3F9OnYJIlUczOulhZA15zZPvMZXfuWB01ifJEyzqTSenukoSEcXu9oKtnr4fhdd+FbtYqq\nlSt1G42ODsCGd/Ei1P5+I00Jo+Berqsj09tr/LZKpZrN1ZZA0SpB8X0ik2HTYLTAuBUKWTRV0wwW\nbSJMVF3+fiKNaGbIxHmyJJHRsjwxEOeyuoDxvbk9kpLN8sRAhLUh/xjPtFMR1upEUWE6XpSypbCG\neSx9baVJp0NPKk5m4z+VrJJ1vIm0Qip2TPxdUQvx43pFpV0uzbSVA4VvpTCvgzsAbdfAQKcOzHgd\nuwFY10rT9AKCGRdCLgeeGrjwNr1dkvz/2XvzMDmu8t7/U9V7T8+i2VetlmTtkiUvQggj7wvGCC4E\nLrEdIIHkBrKQQEhC7pOQ+8sPfr+QkGDi3FwS4rCEYBaDMLYBWxZClmVrG40l2ZIsaaTZZzRrTy/V\n1VX3j+pz5nR19cxosyXT7/PomZ7uqlOnTpd0vvq+3/f7BuHAt2HFOxzW7mpb48scpdV4A0MAjFBL\niwRT4n3bskicOiU3WGHMWgxsWKajpUp2djpu7/PmUvvRjzo6JxwmpvLWW50G1/E44RUrLnlK72Ij\n09+Pb8ECxwetqoq63/sE1dddJ9OrRk+PA3zmL6B661bssTHGn3iCrGkSnjsXy8zOfJEiIdZvbPdu\nBrdtg/gktX/0ScrffjNksyS7upjM6dPqbroJQ2mobpmmbDtlplLOn4EBx8z1hRc499hjjO95kZGn\nnmJ01/MMfu1rxI8fZ2LfPpJHjzDa0cHZP/kMfd/4phwnXFlJ/e/+LpU33eT0yhwaIrpmjWTF/PX1\neQyqCrpsUSk5MFCQhnY7ywMFAngRQuR/OJ5iS015XrpNTSmKFkX1Qf+MIngxh+ZwkPZx53/J7ubm\nInTNaX4ugJiYz17FPFWwS6IQ4FIDMZVxM7JZnh4YzaswnS7c+rqrwhNstnGpWSX3eMWAk1d1pDA0\nFb/HGpyihbI6x9rC7eA/XVyIBu5KCHVtfEFYsgUO/3Sq5dFsYraMojjGvVbid4CqudD/qmMwu/h2\naF4HtUsdIPbTv4azL1x9a3yZQ7Ovon8gNmzYYO/du/eNnsbrEmYqxcgPfkDV/ffLlKTYeNPd3dLK\nInX2LFplJRNPPklw1SrSx4+j+3zobW1EFywg9dJLxDZvxhcMOgxMR8cVCcTMVIqhb38bfH4q7r6L\naG2tTN0aPT15bOHIL39JxQ03EN+5k8iNN8p763/4YRo+/nEJQGcTAkylDh8msHgxA9/6T5LdXVTd\ncw+cPEnV/fczevo0VfPnk+ntJXXsGPqyZfR+5jPU/9Efk3hhN3Z5Oal9+yi/8y5Gf/Qjgs1NzLn1\nVqo2bnQsRXp7CTY1YfT2ysbi/nDYAc+nTxOZPx8jHpfMmJiXCryFNk28r4Iw9ViRmgzPnev9eQ6M\nCX8r0dzbtm1aIqE83RcgPcQADoxNFmiaxJji2KI+V7kwslnaxxOsq3RaQamaNbX6UFz3wNik1IeJ\neewdmWDDnPI8Ju1yhXo/lm3TmUgyLxo5b9A307qIY9wWGqWYJtxpTRWomYbDxKgb/mzW9GJSr1dS\ndO91vL8e/AEsvGXm48Uaur3ZpjtWgGA3M+b+Dtznnt0Dz34R3v0lqGw9/3u7ykLTtH22bW+Y6bir\n/Il784Y/HC4AYkIHJBgRyzRJdHQwuet5gps2kT5xAn91NZW33UbFsmWkXnqJ8PXXYw4MOLoh3fHz\nutKAGDj3W/v+91Nx913SoyvT1yfNVgWgyBoGiYMHsS2L2ObNaPE4Vi7FV/vRj87od6aGZZoYPT1O\nWm/xYqcIYukSom1taPE45XffjS8YpDwWY2LfPoItLfhzPTxJG+g11YSvvRbGx6n+9V8ndt06YuvW\n0vTxj1O1caPTBiocJjJvngSMQveVSSQw4nF0v5/UmTOEKirQ/f48nZg6T9GfUrWugELtodHTg+nq\nJ6rak6gaL6EHa42EaMsZtKpsl/xudMfMVdU0eem8ZgIcwrZiTUVUjqmOYVoWZxMp6YCvtlgSaVBd\n0y4pEJuJrXKL7udFIxd0HRXQTXfNYizhJY+rnZWYrvLSyrU7Evco2JqZ7llldq7msCyoWQKtt8KT\nf+fYTMwUYg1nYz6rrrdXEYYIrx6duu6wZMvvgF985eI1ZFf7c6xECYxdoWFbFvboqExbAbIKUqQ0\nfcEgVXfcQfj6DUz88Ick+/rx1dTgD4cdc9EcWMkkk3kGoldqaLqOffYsFffeiz8cJmsYTB48iJXJ\nSIsPALK53pvnzjm9MhsbMVMpJnbsYHzPnlk31Ra6KyuTYXDbNoa+/nW0lhYq3/EOMl1dpI4ccby8\nYjGGnnqK0dOnGf7+9xnfvh1sm4mnnkYPBql697tJv3aS3oe/QmrE+YdPtJgSIMhMpRj5xS9InDrF\n+JEjdP3d33Hmf/5PJo4dY+L550mcPMnIrl30fP7zjOzaJXtTCiDmq62VekJVVyh8yQTzFWptpSyn\nsVPtMVQ2zcsnzKviTw3LtqVezLLtPJNXL38vr/MFE+eluRKp0qzrfGGBIVzqhZbsUoCVC00fXsg5\nqi+au12TALddyfTlFeyL1NLVmopzs1zF0ppusCAYnJnu+UoV9M8U6j1ZOUPYYAzu/DQM7oT+Q7Mb\nZ7oUcbFjZ5qPVwTCTpXlKz9xbDguNK7W57hIXLk7cynQq6uJ79xJbPNmMv39ZIeGCK9YkQeobMsi\nuedF/IuuwZ+YJHHgAOG5c6Urfraigvi2bYTb2q5oICYYnMDixdIMVtN10DR8dXWE2tokKCl/y0bC\nc+dOsUX9/Y59xOgoZiJB+fXXF4yfNYyCvplC5G6ZJukTJ4ht3Ur/n/4ZZbfcQmTxNURWrsRMpZh8\n9VWyu3YxeO4clffey5xNm8iOTxBcMB+A+M9/jjEwQDY+QSAaZfDRRwnW1FCxeTO+QAA0jUwigdnd\nTTadJnv2LE0f/zjgMILje/YQbG4mMn8+ZWvWSI81QN5zdmgIPTdfMX+xbgJUZvr6HNPf3LnuQhAh\n7HfrDtUUoQBmAviIz1V7BWHyKhgcwbbNJNyvD/oLGC0BSER6tJgNhGCMmsPO+TPZPcwmLXghwOdC\nzlHTraJwQgWnIk1sM2U3ohYpXJKwLMex3cYxDL3aQIdXKnIm9sb9ejb3fLWsyXQpWvFo1iyBuuvh\nG38Af/CUI6J/PeY1m8KOQBT8UTi5A1a91wFo5xtXK3guEiXN2BUYgg0JNDZKEGH09KBVVZF+5RXs\nbFayY+nubiZPnEBLpoi+ZSPpri7Mzk6M4RHKb7+NoGWhVVUV6KjcmqQ3OizTZPzFF8kMDhJsbCRQ\nX0+opYXk6dMkXnqJOVu3OoUJINmh5OnTZIeH8dfWEmxuJj0+Tu8/fpmWT/4h4aqqvLZBEzt2UH7z\nzZ4p2kwiwdBjj1G9dSvje/YQamlh/JVXMMfGSf3yl5TfsoXx48dp/vCH0Xp7KVu3juTp08Tb26m+\n+245f9XrS7CQQh9mWxaTJ05Qds01kqHK9PWRzWQY+8UvCFbXEF21Ej0QANsm1Nqap/cS47sBlRgr\naxiYg4MFfSxFs3GhMbQty5mXAsC6kmlpDNscDmJaFj8bHOPW2gopjPfqlaiGFzhRz3Hrv8TnAlSp\nnmeez4dLlzbTdS/Um8t9n5cKFKnjuLVhKkMmTG9Fn87ZVG7OOq52PdTVWOV4OcKrgtSt2wLnvc5f\nwtfeCfd+AdZ/5PVZv9l8T5YFr/0cnvn/4W2fgKX3TNlkXG19VGeIS64Z0zTNp2naAU3Tfpz7fYGm\naXs0TTuuadp/aZrmKUTSNG21pmm7NU07rGlah6Zp4dz7z2ma9qqmaQdzf14H2H5lhwAOgukQtha2\nZTk2BuPjhK69Fl9NDcmODiZeecXZpFMOEDOOHyd96BDh668nq2v0f/4LJEdGChzeL3ebowsJTdcJ\nNTdTtno10VWryPT3O5YdCxcSe8tbnLRsrjOBCF8wSOjaawnPnSvtO8xzQ0weOOCYo3Z1kWhvR9P1\nokAMcPRm730v9tgYsfXrMSYnmfjqV0m3H6T6Ix+m6vbbCQaDRKqrJRAMNjeT7uwk3dVF8vBhErt3\nO30+jx/HHBx0zGv37WPk8cdlelWbnJT3mmhvx7Ztgk1NlL/lLVTefhuR+fMJNDTIeam+YeluR8sS\nUNgxqRHLpWizhiF96FTNmNAYigIOM5PJS7V5pQFvra1gKJMtAF0i3SY8tNQUpzv1pl5D6L/cjJfQ\nqBXTSYkUnrieOu5MfmYXAsTUMdXWTdOdM5tw6+PUfpRiDYVX21Amy6rySIHn2EWHl8bnaorznfsV\n9O/bJY3ZVJ6K9F3zBlj7IDzx5zDw8us3v9kc03aTw96NnJlq23SpOitchTFrZkzTtE8CG4AK27bf\noWnad4Dv27b9bU3T/hlot237Edc5fmA/8IBt2+2aptUAo7ZtZzVNew74Y9u2Z011XanM2PmwTMWO\nVdNIIoyeHvTqaqzhYcx0mmBTE+mjR520VSjEua/8Ew1/9EnH0HV4GK2qyhG4t7eTPHWKijvuIAR5\n+qILmfPrFUIjBXDuiSeoufdeyS6Nv/QS9vg4kRtvxOrsJLxiBbZlMfbMM8Q2b8YcHGTsqafR5s2l\n+m1vI1RRIQHIdOlZFfCM7t5N6lAHkdWrscuilF1zDf5wmJFf7mLof/9vWv7+7wiB0zsyHqfzf/0v\n2j7zGcJVVYADDjOJBNmhIdmSSjBj4jU4YCzd1YW/vp7J9nbHY271aoJNTQ5wyoE02V8ymyX1yitU\n3nqrA/hydidiHE3XpZ5QhPrdqilaUZVp2TaabaMpVYpq2kz1IVPZM69KSJiq0rRsm7nRsBTkC+ZH\ndeD3qhb0Siuqlhtq0213WvVSxmyYMfW+ZmLgijFr07F7l/P+rtpQ03KzrYq80tz1X89Q10lUVlZt\nho99AyJVb+zcRJgG7Pk/8NK34Te/N5VGLTFj0w7WCtwLfDX3uwbcAnw3d8ijwLs8Tr0DOGTbdjuA\nbdvnbNu+cDOoKzDUKsdin7uP9WKkNF2XzbrzbAqOHsWOxZh84QUy/f2Eli3DV1MD3d3U/+EfEKqo\nwBoeJjM5yfj27Yw/9xyje/ZgGwb+TAYqKpx2St3decUAVxoQAyRzkzUMSCRktaNtWRKIJXbvJrB4\nMdmhIdlRYOzZZwm1tFD70INULlvG5K5dZA1jxoIFwTilu7owenvxB4OU33oL5ddvoPq66wjGYk6H\ng/o6aGpk4rkdaFVVTB44SHzfPuxEkonnnsPo7ZWgMX30KFY2K6/tru5MdnQ4vTSbmx1mr6mJ6KZN\nhFpaZDeFQEODZN9Cra1E5s2j8tZbpdec8BgDR7AfbG6WZrFeoDurVFdKLzvbdtKk2SzdLuG4rbBb\n6k/BbkGhiN3L5FS466v9GafzFMv7blxsnVfbossBVNxjegExwZZ53XOxY93vdyXTnJ5MSgDrNYcS\nEMuF8Ak7H7+wYnoilXF5szFn7gIHEY1r4Za/gNFD8MoPp4o4Xq/5FLue7ofmVU7BwdCJqWOESW+x\n8d6kMdsd+UvApwGxGoLhEk92F9Dicd4SwNY07WlN0/ZrmvZp1+dfy6Uo/yIH8ApC07SPapq2V9O0\nvYODg7Oc7usXYnMUNgxqiFSZlzGnO8xUirFt2yRg0nSd8Ny5TsubRII5W7cSamlhbPduxp55Bq2y\nUjIy/vp6iMcp27SJyltuIRQto/Z978NXU8PEk09ix2JOj8dnnrlk7YIuR4jenLrfT/V73iMNTH3B\nIBVbthCMxZxei8Egvtpa0keP4p8zh+zEhHS1D8+bR3jp0mmvI+wyMn19YNsEGhsJt7VRtm4dTEww\nsXcvZirlmMzW1hKMxSi77jrGn34KM5XCX19HoKyM2v/2HsrXryfU0iLBV2TVKvSczscNxEWvUU3X\nJZjSq6sZ/tZ/OvPPOeqnDh/OY9E0XZfWGEBBNeVMwFplW/PWur4eW9MKwJGWY7X2j8Yxstk8cb2u\naVJwrobQfLkbb7t7Q6oNxd3nqyGA21Ame/mqC4vEdKlB1W4DpgCTCjqLHSvGVjsOzMSq/cqHZTm+\nV8JJf7ZMlxeDpqbA3mSVeNPej88P1z0IcxY4/SIHXr789y7mkzUdsDV2tvB6up5j6RJQ0ZhvTTJy\nprBK9M30fXnEjE+1pmnvAAZs296nvu1xqNe/YH7grcAHcz+3app2a+6zD9q2vQrYnPvzgNf1bdv+\nF9u2N9i2vaGurm6m6b4hIQTWyY4OuYlapsnkgYMY/f15m3KxjdMXDFJ1//15TIrYhAONjQSiUTKJ\nBPFfOqxPqLVVaoZ0v5/g0qWM/+Qn+MNhGn7zI4QqKjBfe42y228n0dHB2M9+RtYfKBB+X0mh6bqs\nILVMk4kdO7AyGccdP6ed81VXA856hVesIDJ/Pv716+n7/vcZf/FFJ/3q85FoP+TZ8zE9Pu6Ma5oE\nm5sJNjfLwgB/OOys4969DO/cSeL0acyBAUKtrVSsWwexmAO45s2j4oYbqHn724kuWJDHvom2R27/\nLwHW1SIE8f0GW1skQBNecMLsVpyfGBoiefp0HrCfKdQ5qDoy8Zk5MIAPWF8VkyALpvpR1iuAQQUf\nwpfMXW0pvMBUsb36UxzrBn/q+Wqo156tdupCNVbFtG5e4ZVi9Wqo7j5WsIPN4aC0CSlWHDAbvdqv\nRAiGS21GPVMU27h9fmhe6/x8k1XizXg/sXp4798DIXjqSxAqv/T3rjJgYj6aDpUt+V0SRKTjsP1h\nII3TtikXRhxOPwdZhTh4s31fHjGbO9sEvFPTtNPAt3HSk18CqnKaMIBWoMfj3C5gh23bQ7ZtJ4Cf\nANcB2Lbdnfs5AXwLuOEi7uMNDcFiqS2NAIJNjZStWTPjxik2TS+BuWp2Orl/P+Hly6jOmZGKDd4y\nTcfKotJJcwprhPCKFWjxOLZl42towDj2KmYqlQcOriRQZpkmxquvErnxRoeN2rSJ8Ny5JF47iVZV\nRfL0acyhIYzeXrKGQerwEc7t3cvQxz9B4pFHSAwPA+Cvq8NXW5PXrihrGCRPn2bou98jcuONeRWP\nenW1k87LVSRmRkaI73qezNAQ/vp6soZBbOlSYps2EYhG5Xei+/1kEvmmhaLZu1pwIFiobG489XnQ\n/X5iuf6W4jO3dcn40aOc/dM/ZeSZZ8+L2fRiYr1YWr+u54npBSgT2i+vXoleQvyZmB71WHdqb7r0\n5Wz9wIqBOvU6052nplLPl4mbDlh5XcPrnoxsVs5dmOwKIPqmaqV0vnG+G3CxjdtymcG+2Tb2me6n\n+Tr4wCNw+gew+58vrUjeylmnjOYYMDH2SCecew2GT8Jo59T74jvI5v79fPgu+MtK58/n22Db78P/\n0wDb/9/Z399VHjPenW3bf2rbdqtt2/OB9wPP2rb9QWA78N9yhz0E/NDj9KeB1ZqmRXPA7WbgiKZp\nfk3TagE0TQsA7wBep1KPyxPqJipYB7EpqzqxYnoxr1SScFrPJBIkTp1i7Gc/p/zmmwnGYs4GW1FB\nZnBQGp1W3XlHHqATrFowGqX+/vupfeABGB8HnPTY+IsvkuzsvGIAmabr6HPmYBw7RiaRYGLHDiYH\nBuj5ysOc27GDkaeeIrhoEaGWFufeFl9DIJmk7p++QvNjj9Fwxx2ku7uZ2LGD1JkzUhMW37efyQMH\noKKC9LFjZA2D+P79sqfk+PbtjO3eTeLQIcJtbTQ9+CCtf/xH1L7jHWQNg55/+AeSw8Ok29sx4nEm\nDxwk3dVFenyc/ocfxojn939TWywJdk41bHVrB8NtbQVMmgrqKpYtY+4XvkDtr73vvDoMiDUVYwF5\n13WDNAFAbBezBRQwWe73ZtI5uR37xfldyTRnEqlpKyBnWx2patLU680E5tTxvZi76UKtLJ1pbtPd\ng5HN8lT/CC8Mj8mK1QHDlPoyL21ZKVxRTDOlvnex7MoV8m/lBceCW2DxVvjFlx326VKFWNuqNqcy\nsnu/w2yZKTiyDTpfgEzKAWdC+wfgEx0tznmPu+Pz8PXfhbEuGD1z6eZ7BcbFQM0/AT6padoJHA3Z\nvwJomvZOTdM+B2Db9gjwd8BLwEFgv23bTwAh4GlN0w7l3u8G/s9FzOWKCgGuRMpKMBACHKjNpVUg\n5GVJYEWjxHfuJNzWRu0Dv47fMMgaBplEgtEf/Yh0Nkvq5EnGn3lGnqeGLxiUzaWzp07hq60FYPTQ\nIRIHDpA6e1YyR280KBMMo15VhZlKkR4aIv3qq4RzZqh6tAx/JIKZShHv62P8xRdJZzJoJ04QzTnU\nA5Rt2oQ9OekI5ZuaKFu3Fn99PaGKCirvvgvGxkjnii6yQ0MEFiwg1NpKdPVqZ70WLZLticzBQQg5\n4vis5VQfBhrq0aurCUSjlG3alAeQnPT0AQCZboQphlO2RMrpw0Txh9Hbm9fSyJ3aDsZiJHbvvmDN\nnwBlXppFNzh0yzdVMKOyT257jGIh9GcCIKlpzGJGr+6YLVMlNGnudOlsGLuZjr1YGw03m6gawAZ9\nPu6or6I1MsVGCl1ZayREayRUEvRPF7PVFF0sELvadUvBKNz/eVj0dvj6h+D0Ly7NuELbB04auGk1\njPXAZD8ceRaGjjlVnQNHHLDmDzlA7dU9MwxcCa99A/5+BfzjzbD3X+D5v3MqMd9kcV71o7ZtPwc8\nl3t9Eo/Uom3bPwJ+pPz+DeAbrmMmgfXnPdurKNQNT2zC6a4uzMFBfDU1JA4dInXiNWI33Thl8Jn7\nx17aTlRUMLljB8FFi5zKu4oKfLW1JDs6oKKCpO7DfuIJ6n//92VqMt3djT5nTp7JqwApkVWr0P1+\nJnp6OPfwV2j43F8R0jRSZ86QPn6cyPLlBJua3tDelbZlET90iOzoKHpDAxXXX0+oqYlsZyd6wI9W\nWUnPPz1Ccu9LEIlCIkHkD/+A5MsvE16+nPSxY8Q2byaybBnm4CCapskUodbWJpt324cOSVBk9Pah\n5aoRjZ4eySIBBBoaCNfXEYhGiW3YgC8YxIzFGH38h5Tfczch5VgRmWQSQJq0iupH9ado5u2rrcUc\nGCCbThOZP99ZA9uWoEw8PLh8xQAAIABJREFUG7rfT/jaay+6i4JXylwFaRoUbPxuwCFeq2zSdBYP\nuqZRn0v9uY8VYONigYZqA+GVLj1fl333mGLe9UF/gRGr1/GznavoAdoaCRH2+5nr8+UxdK938cJV\nG6+HpujNoluK1cO7vgiPfwr+/SPw4P+eXTPx6UKsDTjAbLTLacP03Q857w3smjp2yfvh2Ldh4b1A\nZ25Ot8PvfwPOvgRaFkJzoKwGzvwSvvex3Ljl8ONPOa9/+lfw2cGpZvBX+3dCyYH/koXwb4JCD6+s\nYaD7/bI6zxoedn7mWvkkjxzBX11DsKmRUGur3MBHf7kLc2SY8nXrpBWCpuukRkcZ2bmT0YcfpuaL\nX6T6mmuI79xJdONGJtvbmdjzIk2//bECs1cxl9Gnf4rW2sKcNWskK6JXVzu+VUePElq2zDMd9np4\nk1mmyfiePRiTk2ReeYWqe+4hMn++Y3tx+jTRhQtJvHYSKiswenuZPHKE2Pz5aGgE6usJtjRjj45K\nrZe4j9TZs/jr6rCGhx392NGjVN1+u+PRlnO3F2sRbG4mOTyMnkySmZyUbOLwt76Ff9E1MHyOrO5D\nzxgQjVJ1442E29oc8JVIMPi1f6f2oQdl9afaF1J8B6IjgDU8DBUVxHf+kootb5c2FP76elKHD0t2\nTVhnXAktrbxARzEg4uWZdan9s2bruH8+1/Ua08hmaR9PsKo8IjsTqMefTaRoUfzQZjOusMgotnam\nZV1aF/5SlELE0DH4j/8O48fhw0/C3LfM/lwvACSYw1gDHPspfOeDsxvrfV+D5e92XmdNR/T/2k/h\n8HNwMGddqt0A9ouF5y55CN72EDSu8m5MfgXEJXfgL0XxCkTLNEm0t8vm0GrKJzk8zPj27WQNg0Bj\nI/5wmEBjo+wdGWppIbpiBbH11xFqbZVjpkZHGX/m54SXLiXQ2CgrDFOjo4xu24Yvm2XOZz9LJJnC\n6O0lunGj49C/dClWIuE5V/merlGxbFle6kq4xgcWL3a8sjwqES+3a7/wBgu3tVH9trdRdc89BJub\nZQEDExPOvOIThKuq8BkGc66/nuiqVURWrWRkx3OS7bJMk6HHHpNWIXY2S3znTny1tUTmz5e+XSLU\nFlSp0VF6/uzPGT97loFv/xf9X/sa5771Lcrf9S6M48eovPde6re+iznvehf25CT+ujoJUgPRKLUP\nPYg9OprXIkldP2t4mPKbb8YfDuOrrcUeHSW4aKFTWZmr8LSGhwmvWEG6u5tEe7uTmn6duyZ4pSBn\nK6YHB7yoonURxbRoFxpq8YEabnH8+TT49hLyB30+VpVH6JhIYmTz7RIt26Y3Zcyo7fLSpxUL0ZZK\nNGQvRSkuWWRNmByG2/4YCMG/fczRZbnD69+b6dK1FS2QGp0eiC0Un81xfvS+OjXWL78In5sP3/y1\nKSAGDhBb+zuFYx17FL56C/yf34CzLxS/5lUQJTA2y3ALrNUQ/lhq5Zym6ySGhhh45BG0nLO68BwT\n4CLZ2SmtE8DRD2X6+rCiUYwDB6j7xCeoWL7cSZFt3uw4zj/xBNH166m+6y4aNm0ismol2aFz0oFf\nTySoeNvmglSjZMD8fqpuvx1/OCwLBMRGb2WzDjBYulQyMKnRUXmPxTzSLsXaZg2D4Z/8RFpO+IJB\nQq2txHfuJH78OCOPP05w6VLHSyzXLF3DAXAjP/whydOnSR8/If3BMokEma5ukqdPAxCZP5+KLVum\n3PCHhx3LjJzGa/LgQXw53Vm4qoqKD/0G1dddR8Xmt1J7331U3Xkn4aoqjBxzNXnwoMNYlZcXsFWB\naFSurQDoIj0phPzCNyw7NOQAsrGxvCpNsdbZoXMFzeEvV6jPtZc2TLVmcAOqrmQ6TzRvWhbt44m8\nhuPuOB9gVyzUa6pASJ3/bObiNTcvIX/Y72dNRVSK60X4dZ0Nc8plBep04aVP81qDoM/HrbUVwJTJ\nrhD3F5/4VaxnKsXrFz4/tG6Ale+D9/4TcAae+P8cTZeIYqDLK10rjjXi8BVPlyogBG/5zNQ1Qouc\nnzs/Dyd/Do/+Dmz/G2BMmefmqdcHH4GtXwHfksKh+5+Af70Tzjw/ywW48qIExmYZasWjm6HQdD2v\nsbM5MIARjzP+5JPoNTX4IxH06mqy585J4DF58CCJjpfRc75ZqcOHndRlNEr86afRW1qwz56V4M8a\nHnbm0NpGIBqVoMgfDkuBeurwYQKNjVTedFOe67q4ptjgRUWfOTDgNB8/ehR/fT3BpiZHQ/Xqq45v\n2vAwPZ/7XB4gu5hQiwQESBEgMZNIMPHSS6THx2XHAF8wSGzzZqILFhDOtSbKJBKM7Hoe27Ic5uj4\ncUILFhBduJA599ztFEn09mK+9hq1H/soejJZUD3odrGPrFpFUKl0NOJxJrf9GCMex2cYhOfOxR8K\nOXOdiDO+Zw8Tu19g/KWXMM6c8RTV635/3jVEQYeITF8fgGxjpBZ5iDAHBvDV1sj0tGAJL0e4mU+3\nXQXgyXKJcAMrYXjqTrFN59o/XXgBkOm8uNTKSsBzLtPFdEAp6PN5Vm0Ki5DzAZfTWXcIQHhwbJLa\ngA/Lttk/GudMIuV9jTeDwLwUr18Iv7Vl74Z7vwjHfgD/tBEOfxcmh/J1YO5w/zuk644xb18HJD0Y\nqsgN8KkjUBaB+AGgEu75GNzw+87n33gATn1r6vi3fgp+6xn4rc/Dx3ZMvf+Dz8JvfxM2/TG89ffg\nPV/Nv86/3X3VivvfeAHKVRQyFeXBEAm2S7jDmwMD1LznPQ4QOncO49VXZT/F7NAQkZUrsbNZsufO\n4W9pIbxiBZOvvUZy7z4q7r0HLR7HikTyU4mpFCQmHc+tjg78dXX46+ul55ht21Ou7Dl2zjJNkqdP\nk3jpJaruv99pqJ3zuyLXSimyahWA1ChJnVIiQc2HP+y0XLpIvVLWMBjfvp3QkiUEm5pIHT6Mv66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3ndCczBQbSyMk8bBa8UmS8YJLJqpZOuDQQof8smKpYvd3pyxmLSGiQ7NIQ/HKb6Pe8mWlsr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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13a60a04ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "for label in loc_df.label.unique():\n",
    "    plt.plot(loc_df.longitude[loc_df.label == label],loc_df.latitude[loc_df.label == label],'.', alpha = 0.3, markersize = 0.3)\n",
    "\n",
    "plt.title('Clusters of New York')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### date feature, including holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train shape  (1458644, 11)\n",
      " test shape  (625134, 9)\n"
     ]
    }
   ],
   "source": [
    "print( ' train shape ',train.shape)\n",
    "print( ' test shape ',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for df in (train,test):\n",
    "    df['year']  = df['pickup_datetime'].dt.year\n",
    "    df['month'] = df['pickup_datetime'].dt.month\n",
    "    df['day']   = df['pickup_datetime'].dt.day\n",
    "    df['hr']    = df['pickup_datetime'].dt.hour\n",
    "    df['minute']= df['pickup_datetime'].dt.minute\n",
    "    df['store_and_fwd_flag'] = 1 * (df.store_and_fwd_flag.values == 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.assign(log_trip_duration = np.log(train.trip_duration+1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_data = pd.DataFrame(index = range(len(train)))\n",
    "time_test = pd.DataFrame(index = range(len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def restday(yr,month,day,holidays):\n",
    "    '''\n",
    "    Output:\n",
    "        is_rest: a list of Boolean variable indicating if the sample occurred in the rest day.\n",
    "        is_weekend: a list of Boolean variable indicating if the sample occurred in the weekend.\n",
    "    '''\n",
    "    is_rest    = [None]*len(yr)\n",
    "    is_weekend = [None]*len(yr)\n",
    "    i=0\n",
    "    for yy,mm,dd in zip(yr,month,day):        \n",
    "        is_weekend[i] = date(yy,mm,dd).isoweekday() in (6,7)\n",
    "        is_rest[i]    = is_weekend[i] or date(yy,mm,dd) in holidays \n",
    "        i+=1\n",
    "    return is_rest,is_weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rest_day,weekend = restday(train.year,train.month,train.day,holidays)\n",
    "train['rest_day'] = rest_day\n",
    "train['weekend'] = weekend\n",
    "\n",
    "rest_day,weekend = restday(test.year,test.month,test.day,holidays)\n",
    "test['rest_day'] = rest_day\n",
    "test['weekend'] = weekend\n",
    "\n",
    "train['pickup_time'] = train.hr+train.minute/60\n",
    "test['pickup_time'] = test.hr+test.minute/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for df in (train,test):\n",
    "\n",
    "    df['hr_categori'] = np.nan\n",
    "    df.loc[(df.rest_day == False)&(df.hr <= 9)&(df.hr >= 7), 'hr_categori'] = \"rush\"\n",
    "    df.loc[(df.rest_day == False)&(df.hr <= 18)&(df.hr >= 16), 'hr_categori'] = \"rush\"\n",
    "    df.loc[(df.rest_day == False)&(df.hr < 16)&(df.hr > 9), 'hr_categori'] = \"day\"\n",
    "    df.loc[(df.rest_day == False)&(df.hr < 7)|(df.hr > 18), 'hr_categori'] = \"night\"\n",
    "    df.loc[(df.rest_day == True)&(df.hr < 18)&(df.hr > 7), 'hr_categori'] = \"day\"\n",
    "    df.loc[(df.rest_day == True)&(df.hr <= 7)|(df.hr >= 18), 'hr_categori'] = \"night\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train shape  (1458644, 21)\n",
      " test shape  (625134, 18)\n"
     ]
    }
   ],
   "source": [
    "print( ' train shape ',train.shape)\n",
    "print( ' test shape ',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dropoff_datetime', 'trip_duration', 'log_trip_duration']\n"
     ]
    }
   ],
   "source": [
    "print([i for i in train.columns if i not in test.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### distance feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.join(osrm_data.set_index('id'), on='id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = test.join(osrm_test.set_index('id'), on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train shape  (1458644, 26)\n",
      " test shape  (625134, 23)\n"
     ]
    }
   ],
   "source": [
    "print( ' train shape ',train.shape)\n",
    "print( ' test shape ',test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weather feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8739, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hr</th>\n",
       "      <th>Temp.</th>\n",
       "      <th>Precip</th>\n",
       "      <th>snow</th>\n",
       "      <th>Visibility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  day  hr  Temp.  Precip  snow  Visibility\n",
       "22      1    1   0    5.6     0.0     0        16.1\n",
       "23      1    1   1    5.6     0.0     0        16.1\n",
       "24      1    1   2    5.6     0.0     0        16.1\n",
       "25      1    1   3    5.0     0.0     0        16.1\n",
       "26      1    1   4    5.0     0.0     0        16.1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather['snow']= 1*(weather.Events=='Snow') + 1*(weather.Events=='Fog\\n\\t,\\nSnow')\n",
    "weather['year'] = weather['Time'].dt.year\n",
    "weather['month'] = weather['Time'].dt.month\n",
    "weather['day'] = weather['Time'].dt.day\n",
    "weather['hr'] = weather['Time'].dt.hour\n",
    "weather = weather[weather['year'] == 2016][['month','day','hr','Temp.','Precip','snow','Visibility']]\n",
    "print(weather.shape)\n",
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "train = pd.merge(train, weather, on = ['month', 'day', 'hr'], how = 'left')\n",
    "test = pd.merge(test, weather, on = ['month', 'day', 'hr'], how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train shape  (1458644, 30)\n",
      " test shape  (625134, 27)\n"
     ]
    }
   ],
   "source": [
    "print( ' train shape ',train.shape)\n",
    "print( ' test shape ',test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## clustering and find speed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 40.76793671, -73.98215485],\n",
       "       [ 40.73856354, -73.98041534],\n",
       "       [ 40.7639389 , -73.97902679],\n",
       "       ...,\n",
       "       [ 40.74866486, -73.87660217],\n",
       "       [ 40.89178848, -73.85426331],\n",
       "       [ 40.76937866, -73.96932983]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_ind = np.random.permutation(len(coords))[:1000000]\n",
    "kmeans = MiniBatchKMeans(n_clusters=8, batch_size=10000).fit(coords[sample_ind])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>pickup_cluster</th>\n",
       "      <th>dropoff_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.763939</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pickup_latitude  pickup_longitude  dropoff_latitude  dropoff_longitude  \\\n",
       "0        40.767937        -73.982155         40.765602         -73.964630   \n",
       "1        40.738564        -73.980415         40.731152         -73.999481   \n",
       "2        40.763939        -73.979027         40.710087         -74.005333   \n",
       "3        40.719971        -74.010040         40.706718         -74.012268   \n",
       "4        40.793209        -73.973053         40.782520         -73.972923   \n",
       "\n",
       "   pickup_cluster  dropoff_cluster  \n",
       "0               5                1  \n",
       "1               5                0  \n",
       "2               5                0  \n",
       "3               0                0  \n",
       "4               1                1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[:, 'pickup_cluster'] = kmeans.predict(train[['pickup_latitude', 'pickup_longitude']])\n",
    "train.loc[:, 'dropoff_cluster'] = kmeans.predict(train[['dropoff_latitude', 'dropoff_longitude']])\n",
    "test.loc[:, 'pickup_cluster'] = kmeans.predict(test[['pickup_latitude', 'pickup_longitude']])\n",
    "test.loc[:, 'dropoff_cluster'] = kmeans.predict(test[['dropoff_latitude', 'dropoff_longitude']])\n",
    "\n",
    "train[['pickup_latitude', 'pickup_longitude','dropoff_latitude', 'dropoff_longitude','pickup_cluster','dropoff_cluster']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train shape  (1458644, 32)\n",
      " test shape  (625134, 29)\n"
     ]
    }
   ],
   "source": [
    "print( ' train shape ',train.shape)\n",
    "print( ' test shape ',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## count features\n",
    "a = pd.concat([train,test]).groupby(['pickup_cluster']).size().reset_index()\n",
    "b = pd.concat([train,test]).groupby([ 'dropoff_cluster']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_cluster</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>622419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>447386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>47396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>62049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>867622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>36884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pickup_cluster       0\n",
       "0               0  622419\n",
       "1               1  447386\n",
       "2               2       3\n",
       "3               3   47396\n",
       "4               4   62049\n",
       "5               5  867622\n",
       "6               6      19\n",
       "7               7   36884"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dropoff_cluster</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>562177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>26725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>63874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>811732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>107538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dropoff_cluster       0\n",
       "0                0  562177\n",
       "1                1  511704\n",
       "2                2       3\n",
       "3                3   26725\n",
       "4                4   63874\n",
       "5                5  811732\n",
       "6                6      25\n",
       "7                7  107538"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.merge(train, a, on = ['pickup_cluster'], how = 'left')\n",
    "test = pd.merge(test, a, on = ['pickup_cluster'], how = 'left')\n",
    "\n",
    "train = pd.merge(train, b, on = ['dropoff_cluster'], how = 'left')\n",
    "test = pd.merge(test, b, on = ['dropoff_cluster'], how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['speed'] = train['total_distance'] / train['trip_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.415604</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.790649</td>\n",
       "      <td>2513.2</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.207533</td>\n",
       "      <td>11060.8</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.147786</td>\n",
       "      <td>1779.4</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.712414</td>\n",
       "      <td>1614.9</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.145598</td>\n",
       "      <td>1393.5</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.000293</td>\n",
       "      <td>1705.1</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.861380</td>\n",
       "      <td>10642.0</td>\n",
       "      <td>1551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.140000</td>\n",
       "      <td>1310.7</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.540245</td>\n",
       "      <td>6786.8</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.088697</td>\n",
       "      <td>3935.0</td>\n",
       "      <td>1274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.961525</td>\n",
       "      <td>5596.6</td>\n",
       "      <td>1128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.043627</td>\n",
       "      <td>2276.6</td>\n",
       "      <td>1114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.145769</td>\n",
       "      <td>1337.9</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.670156</td>\n",
       "      <td>8017.6</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.250237</td>\n",
       "      <td>896.8</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.918264</td>\n",
       "      <td>4442.7</td>\n",
       "      <td>2316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.159644</td>\n",
       "      <td>3040.7</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.548899</td>\n",
       "      <td>4673.9</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.307968</td>\n",
       "      <td>1332.3</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15.134568</td>\n",
       "      <td>7355.4</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.113957</td>\n",
       "      <td>3334.3</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.196927</td>\n",
       "      <td>3044.3</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.779192</td>\n",
       "      <td>5558.2</td>\n",
       "      <td>1163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10.513843</td>\n",
       "      <td>26126.9</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.127903</td>\n",
       "      <td>5296.1</td>\n",
       "      <td>1283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8.507788</td>\n",
       "      <td>9613.8</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6.145821</td>\n",
       "      <td>4265.2</td>\n",
       "      <td>694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.832287</td>\n",
       "      <td>2526.4</td>\n",
       "      <td>892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.898284</td>\n",
       "      <td>13748.9</td>\n",
       "      <td>2331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458614</th>\n",
       "      <td>4.964497</td>\n",
       "      <td>1678.0</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458615</th>\n",
       "      <td>5.338156</td>\n",
       "      <td>4574.8</td>\n",
       "      <td>857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458616</th>\n",
       "      <td>2.545492</td>\n",
       "      <td>1242.2</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458617</th>\n",
       "      <td>4.344231</td>\n",
       "      <td>903.6</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458618</th>\n",
       "      <td>4.576022</td>\n",
       "      <td>1679.4</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458619</th>\n",
       "      <td>4.494209</td>\n",
       "      <td>3181.9</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458620</th>\n",
       "      <td>4.750775</td>\n",
       "      <td>5520.4</td>\n",
       "      <td>1162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458621</th>\n",
       "      <td>2.111932</td>\n",
       "      <td>4973.6</td>\n",
       "      <td>2355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458622</th>\n",
       "      <td>2.123732</td>\n",
       "      <td>1172.3</td>\n",
       "      <td>552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458623</th>\n",
       "      <td>4.459928</td>\n",
       "      <td>2470.8</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458624</th>\n",
       "      <td>5.298684</td>\n",
       "      <td>805.4</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458625</th>\n",
       "      <td>2.299101</td>\n",
       "      <td>4602.8</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458626</th>\n",
       "      <td>6.424180</td>\n",
       "      <td>1567.5</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458627</th>\n",
       "      <td>3.402614</td>\n",
       "      <td>2994.3</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458628</th>\n",
       "      <td>4.992023</td>\n",
       "      <td>1752.2</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458629</th>\n",
       "      <td>4.576705</td>\n",
       "      <td>2750.6</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458630</th>\n",
       "      <td>3.572495</td>\n",
       "      <td>1961.3</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458631</th>\n",
       "      <td>6.617836</td>\n",
       "      <td>2263.3</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458632</th>\n",
       "      <td>5.983912</td>\n",
       "      <td>4649.5</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458633</th>\n",
       "      <td>4.291420</td>\n",
       "      <td>4201.3</td>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458634</th>\n",
       "      <td>5.854012</td>\n",
       "      <td>5690.1</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458635</th>\n",
       "      <td>3.405485</td>\n",
       "      <td>807.1</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458636</th>\n",
       "      <td>2.826375</td>\n",
       "      <td>2261.1</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458637</th>\n",
       "      <td>5.620000</td>\n",
       "      <td>4271.2</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458638</th>\n",
       "      <td>7.707005</td>\n",
       "      <td>3190.7</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458639</th>\n",
       "      <td>2.872237</td>\n",
       "      <td>2234.6</td>\n",
       "      <td>778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458640</th>\n",
       "      <td>11.661069</td>\n",
       "      <td>7638.0</td>\n",
       "      <td>655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458641</th>\n",
       "      <td>12.920812</td>\n",
       "      <td>9871.5</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458642</th>\n",
       "      <td>4.365416</td>\n",
       "      <td>1628.3</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458643</th>\n",
       "      <td>5.728283</td>\n",
       "      <td>1134.2</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1458644 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             speed  total_distance  trip_duration\n",
       "0         4.415604          2009.1            455\n",
       "1         3.790649          2513.2            663\n",
       "2         5.207533         11060.8           2124\n",
       "3         4.147786          1779.4            429\n",
       "4         3.712414          1614.9            435\n",
       "5         3.145598          1393.5            443\n",
       "6         5.000293          1705.1            341\n",
       "7         6.861380         10642.0           1551\n",
       "8         5.140000          1310.7            255\n",
       "9         5.540245          6786.8           1225\n",
       "10        3.088697          3935.0           1274\n",
       "11        4.961525          5596.6           1128\n",
       "12        2.043627          2276.6           1114\n",
       "13        5.145769          1337.9            260\n",
       "14        5.670156          8017.6           1414\n",
       "15        4.250237           896.8            211\n",
       "16        1.918264          4442.7           2316\n",
       "17        4.159644          3040.7            731\n",
       "18        3.548899          4673.9           1317\n",
       "19        5.307968          1332.3            251\n",
       "20       15.134568          7355.4            486\n",
       "21        5.113957          3334.3            652\n",
       "22        7.196927          3044.3            423\n",
       "23        4.779192          5558.2           1163\n",
       "24       10.513843         26126.9           2485\n",
       "25        4.127903          5296.1           1283\n",
       "26        8.507788          9613.8           1130\n",
       "27        6.145821          4265.2            694\n",
       "28        2.832287          2526.4            892\n",
       "29        5.898284         13748.9           2331\n",
       "...            ...             ...            ...\n",
       "1458614   4.964497          1678.0            338\n",
       "1458615   5.338156          4574.8            857\n",
       "1458616   2.545492          1242.2            488\n",
       "1458617   4.344231           903.6            208\n",
       "1458618   4.576022          1679.4            367\n",
       "1458619   4.494209          3181.9            708\n",
       "1458620   4.750775          5520.4           1162\n",
       "1458621   2.111932          4973.6           2355\n",
       "1458622   2.123732          1172.3            552\n",
       "1458623   4.459928          2470.8            554\n",
       "1458624   5.298684           805.4            152\n",
       "1458625   2.299101          4602.8           2002\n",
       "1458626   6.424180          1567.5            244\n",
       "1458627   3.402614          2994.3            880\n",
       "1458628   4.992023          1752.2            351\n",
       "1458629   4.576705          2750.6            601\n",
       "1458630   3.572495          1961.3            549\n",
       "1458631   6.617836          2263.3            342\n",
       "1458632   5.983912          4649.5            777\n",
       "1458633   4.291420          4201.3            979\n",
       "1458634   5.854012          5690.1            972\n",
       "1458635   3.405485           807.1            237\n",
       "1458636   2.826375          2261.1            800\n",
       "1458637   5.620000          4271.2            760\n",
       "1458638   7.707005          3190.7            414\n",
       "1458639   2.872237          2234.6            778\n",
       "1458640  11.661069          7638.0            655\n",
       "1458641  12.920812          9871.5            764\n",
       "1458642   4.365416          1628.3            373\n",
       "1458643   5.728283          1134.2            198\n",
       "\n",
       "[1458644 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['speed','total_distance','trip_duration']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pick_up_speed = train[['speed', 'pickup_cluster']].groupby(['pickup_cluster']).mean().reset_index()\n",
    "pick_up_speed = pick_up_speed.rename(columns = {'speed': 'ave_pickup_speed'})\n",
    "drop_off_speed = train[['speed', 'dropoff_cluster']].groupby(['dropoff_cluster']).mean().reset_index()\n",
    "drop_off_speed = drop_off_speed.rename(columns = {'speed': 'ave_dropoff_speed'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pickup_cluster  ave_pickup_speed\n",
      "0               0          5.067469\n",
      "1               1          5.494199\n",
      "2               2          0.000000\n",
      "3               3         11.242965\n",
      "4               4          8.405111\n",
      "5               5          5.047294\n",
      "6               6         12.841142\n",
      "7               7          6.225725\n",
      "   dropoff_cluster  ave_dropoff_speed\n",
      "0                0           4.986800\n",
      "1                1           5.976817\n",
      "2                2           0.000000\n",
      "3                3          11.935437\n",
      "4                4           8.552266\n",
      "5                5           4.691077\n",
      "6                6          35.699330\n",
      "7                7           6.885238\n"
     ]
    }
   ],
   "source": [
    "print(pick_up_speed)\n",
    "print(drop_off_speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.merge(train, pick_up_speed, on = ['pickup_cluster'], how = 'left')\n",
    "test = pd.merge(test, pick_up_speed, on = ['pickup_cluster'], how = 'left')\n",
    "\n",
    "train = pd.merge(train, drop_off_speed, on = ['dropoff_cluster'], how = 'left')\n",
    "test = pd.merge(test, drop_off_speed, on = ['dropoff_cluster'], how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.drop(['speed'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train shape  (1458644, 36)\n",
      " test shape  (625134, 33)\n"
     ]
    }
   ],
   "source": [
    "print( ' train shape ',train.shape)\n",
    "print( ' test shape ',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'vendor_id', 'pickup_datetime', 'dropoff_datetime',\n",
       "       'passenger_count', 'pickup_longitude', 'pickup_latitude',\n",
       "       'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',\n",
       "       'trip_duration', 'year', 'month', 'day', 'hr', 'minute',\n",
       "       'log_trip_duration', 'rest_day', 'weekend', 'pickup_time',\n",
       "       'hr_categori', 'total_distance', 'total_travel_time', 'number_of_steps',\n",
       "       'right_steps', 'left_steps', 'Temp.', 'Precip', 'snow', 'Visibility',\n",
       "       'pickup_cluster', 'dropoff_cluster', '0_x', '0_y', 'ave_pickup_speed',\n",
       "       'ave_dropoff_speed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id  store_and_fwd_flag\n",
       "0          2                   0\n",
       "1          1                   0\n",
       "2          2                   0\n",
       "3          2                   0\n",
       "4          2                   0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['vendor_id','store_and_fwd_flag']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vendor_train = pd.get_dummies(train['vendor_id'], prefix='vi', prefix_sep='_')\n",
    "vendor_test = pd.get_dummies(test['vendor_id'], prefix='vi', prefix_sep='_')\n",
    "store_and_fwd_flag_train = pd.get_dummies(train['store_and_fwd_flag'], prefix='sf', prefix_sep='_')\n",
    "store_and_fwd_flag_test = pd.get_dummies(test['store_and_fwd_flag'], prefix='sf', prefix_sep='_')\n",
    "cluster_pickup_train = pd.get_dummies(train['pickup_cluster'], prefix='p', prefix_sep='_')\n",
    "cluster_pickup_test = pd.get_dummies(test['pickup_cluster'], prefix='p', prefix_sep='_')\n",
    "cluster_dropoff_train = pd.get_dummies(train['dropoff_cluster'], prefix='d', prefix_sep='_')\n",
    "cluster_dropoff_test = pd.get_dummies(test['dropoff_cluster'], prefix='d', prefix_sep='_')\n",
    "\n",
    "train['dayofweek'] = train['pickup_datetime'].dt.dayofweek\n",
    "test['dayofweek'] = test['pickup_datetime'].dt.dayofweek\n",
    "\n",
    "month_train = pd.get_dummies(train['month'], prefix='m', prefix_sep='_')\n",
    "month_test = pd.get_dummies(test['month'], prefix='m', prefix_sep='_')\n",
    "dom_train = pd.get_dummies(train['day'], prefix='dom', prefix_sep='_')\n",
    "dom_test = pd.get_dummies(test['day'], prefix='dom', prefix_sep='_')\n",
    "hour_train = pd.get_dummies(train['hr'], prefix='h', prefix_sep='_')\n",
    "hour_test = pd.get_dummies(test['hr'], prefix='h', prefix_sep='_')\n",
    "hour_cate_train = pd.get_dummies(train['hr_categori'], prefix='hc', prefix_sep='_')\n",
    "hour_cate_test = pd.get_dummies(test['hr_categori'], prefix='hc', prefix_sep='_')\n",
    "dow_train = pd.get_dummies(train['dayofweek'], prefix='dow', prefix_sep='_')\n",
    "dow_test = pd.get_dummies(test['dayofweek'], prefix='dow', prefix_sep='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vi_1</th>\n",
       "      <th>vi_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vi_1  vi_2\n",
       "0     0     1\n",
       "1     1     0\n",
       "2     0     1\n",
       "3     0     1\n",
       "4     0     1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendor_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m_1</th>\n",
       "      <th>m_2</th>\n",
       "      <th>m_3</th>\n",
       "      <th>m_4</th>\n",
       "      <th>m_5</th>\n",
       "      <th>m_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   m_1  m_2  m_3  m_4  m_5  m_6\n",
       "0    0    0    1    0    0    0\n",
       "1    0    0    0    0    0    1\n",
       "2    1    0    0    0    0    0\n",
       "3    0    0    0    1    0    0\n",
       "4    0    0    1    0    0    0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.drop(['id','vendor_id','store_and_fwd_flag','dayofweek','hr_categori','minute',\n",
    "                    'month','day','hr','pickup_cluster','dropoff_cluster','dropoff_datetime', 'trip_duration'],axis = 1)\n",
    "Test_id = test['id']\n",
    "test = test.drop(['id','vendor_id','store_and_fwd_flag','dayofweek','hr_categori','minute',\n",
    "                  'month','day','hr','pickup_cluster','dropoff_cluster'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Train_Master = pd.concat([train,\n",
    "                          vendor_train,\n",
    "                          store_and_fwd_flag_train,\n",
    "                          cluster_pickup_train,\n",
    "                          cluster_dropoff_train,\n",
    "                         month_train,\n",
    "#                          dom_train,\n",
    "#                           hour_train,\n",
    "                          hour_cate_train,\n",
    "                          dow_train\n",
    "                         ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Test_master = pd.concat([test, \n",
    "                         vendor_test,\n",
    "                         store_and_fwd_flag_test,\n",
    "                         cluster_pickup_test,\n",
    "                         cluster_dropoff_test,\n",
    "                         month_test,\n",
    "#                          dom_test,\n",
    "#                           hour_test,\n",
    "                         hour_cate_test,\n",
    "                          dow_test\n",
    "                        ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Train_Master = Train_Master.drop(['pickup_datetime'],axis = 1)\n",
    "Test_master = Test_master.drop(['pickup_datetime'],axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Train, Test = train_test_split(Train_Master, test_size = 0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = Train.drop(['log_trip_duration'], axis=1)\n",
    "Y_train = Train[\"log_trip_duration\"]\n",
    "X_test = Test.drop(['log_trip_duration'], axis=1)\n",
    "Y_test = Test[\"log_trip_duration\"]\n",
    "\n",
    "Y_test = Y_test.reset_index().drop('index',axis = 1)\n",
    "Y_train = Y_train.reset_index().drop('index',axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_train, label=Y_train)\n",
    "dvalid = xgb.DMatrix(X_test, label=Y_test)\n",
    "dtest = xgb.DMatrix(Test_master)\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'valid')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:5.72047\tvalid-rmse:5.73117\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 5 rounds.\n",
      "[1]\ttrain-rmse:5.43631\tvalid-rmse:5.44664\n",
      "[2]\ttrain-rmse:5.16655\tvalid-rmse:5.1765\n",
      "[3]\ttrain-rmse:4.91029\tvalid-rmse:4.91987\n",
      "[4]\ttrain-rmse:4.6669\tvalid-rmse:4.67614\n",
      "[5]\ttrain-rmse:4.43578\tvalid-rmse:4.44463\n",
      "[6]\ttrain-rmse:4.21626\tvalid-rmse:4.22479\n",
      "[7]\ttrain-rmse:4.00786\tvalid-rmse:4.01608\n",
      "[8]\ttrain-rmse:3.80999\tvalid-rmse:3.81789\n",
      "[9]\ttrain-rmse:3.6222\tvalid-rmse:3.62972\n",
      "[10]\ttrain-rmse:3.4439\tvalid-rmse:3.45113\n",
      "[11]\ttrain-rmse:3.27462\tvalid-rmse:3.28151\n",
      "[12]\ttrain-rmse:3.11407\tvalid-rmse:3.12063\n",
      "[13]\ttrain-rmse:2.96161\tvalid-rmse:2.9678\n",
      "[14]\ttrain-rmse:2.81684\tvalid-rmse:2.82271\n",
      "[15]\ttrain-rmse:2.67949\tvalid-rmse:2.68512\n",
      "[16]\ttrain-rmse:2.54923\tvalid-rmse:2.55454\n",
      "[17]\ttrain-rmse:2.42567\tvalid-rmse:2.43074\n",
      "[18]\ttrain-rmse:2.3085\tvalid-rmse:2.31326\n",
      "[19]\ttrain-rmse:2.19764\tvalid-rmse:2.20206\n",
      "[20]\ttrain-rmse:2.09214\tvalid-rmse:2.09629\n",
      "[21]\ttrain-rmse:1.99207\tvalid-rmse:1.99596\n",
      "[22]\ttrain-rmse:1.89739\tvalid-rmse:1.90097\n",
      "[23]\ttrain-rmse:1.80749\tvalid-rmse:1.81086\n",
      "[24]\ttrain-rmse:1.72235\tvalid-rmse:1.72543\n",
      "[25]\ttrain-rmse:1.64173\tvalid-rmse:1.64452\n",
      "[26]\ttrain-rmse:1.56545\tvalid-rmse:1.56797\n",
      "[27]\ttrain-rmse:1.49313\tvalid-rmse:1.49537\n",
      "[28]\ttrain-rmse:1.42472\tvalid-rmse:1.4267\n",
      "[29]\ttrain-rmse:1.36046\tvalid-rmse:1.36218\n",
      "[30]\ttrain-rmse:1.29932\tvalid-rmse:1.30079\n",
      "[31]\ttrain-rmse:1.24155\tvalid-rmse:1.24275\n",
      "[32]\ttrain-rmse:1.18694\tvalid-rmse:1.18782\n",
      "[33]\ttrain-rmse:1.1355\tvalid-rmse:1.13614\n",
      "[34]\ttrain-rmse:1.08679\tvalid-rmse:1.08709\n",
      "[35]\ttrain-rmse:1.04099\tvalid-rmse:1.041\n",
      "[36]\ttrain-rmse:0.997755\tvalid-rmse:0.99745\n",
      "[37]\ttrain-rmse:0.957017\tvalid-rmse:0.956351\n",
      "[38]\ttrain-rmse:0.918678\tvalid-rmse:0.917683\n",
      "[39]\ttrain-rmse:0.882708\tvalid-rmse:0.881349\n",
      "[40]\ttrain-rmse:0.848843\tvalid-rmse:0.847146\n",
      "[41]\ttrain-rmse:0.817151\tvalid-rmse:0.81515\n",
      "[42]\ttrain-rmse:0.787331\tvalid-rmse:0.785004\n",
      "[43]\ttrain-rmse:0.759441\tvalid-rmse:0.756777\n",
      "[44]\ttrain-rmse:0.733277\tvalid-rmse:0.730294\n",
      "[45]\ttrain-rmse:0.708853\tvalid-rmse:0.705547\n",
      "[46]\ttrain-rmse:0.686011\tvalid-rmse:0.682357\n",
      "[47]\ttrain-rmse:0.664691\tvalid-rmse:0.660727\n",
      "[48]\ttrain-rmse:0.644808\tvalid-rmse:0.640503\n",
      "[49]\ttrain-rmse:0.626361\tvalid-rmse:0.621765\n",
      "[50]\ttrain-rmse:0.609172\tvalid-rmse:0.604278\n",
      "[51]\ttrain-rmse:0.593236\tvalid-rmse:0.588038\n",
      "[52]\ttrain-rmse:0.578424\tvalid-rmse:0.572888\n",
      "[53]\ttrain-rmse:0.564738\tvalid-rmse:0.558947\n",
      "[54]\ttrain-rmse:0.552144\tvalid-rmse:0.546068\n",
      "[55]\ttrain-rmse:0.540596\tvalid-rmse:0.534221\n",
      "[56]\ttrain-rmse:0.529746\tvalid-rmse:0.52312\n",
      "[57]\ttrain-rmse:0.519824\tvalid-rmse:0.512993\n",
      "[58]\ttrain-rmse:0.510617\tvalid-rmse:0.503529\n",
      "[59]\ttrain-rmse:0.502198\tvalid-rmse:0.49498\n",
      "[60]\ttrain-rmse:0.494439\tvalid-rmse:0.486986\n",
      "[61]\ttrain-rmse:0.487338\tvalid-rmse:0.479732\n",
      "[62]\ttrain-rmse:0.48075\tvalid-rmse:0.472947\n",
      "[63]\ttrain-rmse:0.47473\tvalid-rmse:0.466718\n",
      "[64]\ttrain-rmse:0.469159\tvalid-rmse:0.460998\n",
      "[65]\ttrain-rmse:0.464129\tvalid-rmse:0.455769\n",
      "[66]\ttrain-rmse:0.459502\tvalid-rmse:0.450988\n",
      "[67]\ttrain-rmse:0.455193\tvalid-rmse:0.446603\n",
      "[68]\ttrain-rmse:0.451285\tvalid-rmse:0.442518\n",
      "[69]\ttrain-rmse:0.447686\tvalid-rmse:0.438758\n",
      "[70]\ttrain-rmse:0.444441\tvalid-rmse:0.435469\n",
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    {
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      "[329]\ttrain-rmse:0.386392\tvalid-rmse:0.382578\n",
      "[330]\ttrain-rmse:0.386328\tvalid-rmse:0.382543\n",
      "[331]\ttrain-rmse:0.386306\tvalid-rmse:0.382534\n",
      "[332]\ttrain-rmse:0.386267\tvalid-rmse:0.382554\n",
      "[333]\ttrain-rmse:0.386209\tvalid-rmse:0.382533\n",
      "[334]\ttrain-rmse:0.386143\tvalid-rmse:0.382515\n",
      "[335]\ttrain-rmse:0.386116\tvalid-rmse:0.38251\n",
      "[336]\ttrain-rmse:0.386086\tvalid-rmse:0.382525\n",
      "[337]\ttrain-rmse:0.386031\tvalid-rmse:0.382518\n",
      "[338]\ttrain-rmse:0.385985\tvalid-rmse:0.38249\n",
      "[339]\ttrain-rmse:0.38594\tvalid-rmse:0.38246\n",
      "[340]\ttrain-rmse:0.385884\tvalid-rmse:0.382513\n",
      "[341]\ttrain-rmse:0.385831\tvalid-rmse:0.382502\n",
      "[342]\ttrain-rmse:0.385795\tvalid-rmse:0.382484\n",
      "[343]\ttrain-rmse:0.385767\tvalid-rmse:0.382491\n",
      "[344]\ttrain-rmse:0.385714\tvalid-rmse:0.382478\n",
      "Stopping. Best iteration:\n",
      "[339]\ttrain-rmse:0.38594\tvalid-rmse:0.38246\n",
      "\n",
      "Modeling RMSLE 0.38246\n"
     ]
    }
   ],
   "source": [
    "xgb_pars = {\n",
    "    'objective':          'reg:linear',\n",
    "    'learning_rate':      0.05,\n",
    "    'max_depth':          7,\n",
    "    'subsample':          0.8,\n",
    "    'colsample_bytree':   0.7,\n",
    "    'colsample_bylevel':  0.7,\n",
    "    'silent':             1,\n",
    "    'reg_alpha':          1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_pars, dtrain, 500, watchlist, early_stopping_rounds=5,\n",
    "      maximize=False, verbose_eval=1)\n",
    "print('Modeling RMSLE %.5f' % model.best_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fscores = model.get_fscore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#top_50_list = sorted(fscores, key=fscores.get, reverse=True)[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13a0cff5630>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13a231ebb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(model, max_num_features=70, height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = model.predict(dtest)\n",
    "# pred = xgb_param_30.predict(dtest)\n",
    "pred = np.exp(pred) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.concat([Test_id, pd.DataFrame(pred)], axis=1)\n",
    "submission.columns = ['id','trip_duration']\n",
    "submission['trip_duration'] = submission.apply(lambda x : 1 if (x['trip_duration'] <= 0) else x['trip_duration'], axis = 1)\n",
    "submission.to_csv(\"submission.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}