project_0822_0.51538.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from xgboost import XGBRegressor\n",
    "from xgboost import plot_importance\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>N</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
       "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
       "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
       "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.980415        40.738564         -73.999481   \n",
       "2                1        -73.979027        40.763939         -74.005333   \n",
       "3                1        -74.010040        40.719971         -74.012268   \n",
       "4                1        -73.973053        40.793209         -73.972923   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "0         40.765602                  N            455  \n",
       "1         40.731152                  N            663  \n",
       "2         40.710087                  N           2124  \n",
       "3         40.706718                  N            429  \n",
       "4         40.782520                  N            435  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIhKWyFpcZlYI5Lj7hMSfqcDNwFXu/hUgB/hWm8OOAwrd/RDgh8BNUdUnIiJhirLFNRoo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21a9fb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"pickup_longitude\", y=\"pickup_latitude\", data=train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id3004672</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:58</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.988129</td>\n",
       "      <td>40.732029</td>\n",
       "      <td>-73.990173</td>\n",
       "      <td>40.756680</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3505355</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:53</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.964203</td>\n",
       "      <td>40.679993</td>\n",
       "      <td>-73.959808</td>\n",
       "      <td>40.655403</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id1217141</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:47</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.997437</td>\n",
       "      <td>40.737583</td>\n",
       "      <td>-73.986160</td>\n",
       "      <td>40.729523</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id2150126</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-30 23:59:41</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.956070</td>\n",
       "      <td>40.771900</td>\n",
       "      <td>-73.986427</td>\n",
       "      <td>40.730469</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id1598245</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:33</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.970215</td>\n",
       "      <td>40.761475</td>\n",
       "      <td>-73.961510</td>\n",
       "      <td>40.755890</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime  passenger_count  \\\n",
       "0  id3004672          1  2016-06-30 23:59:58                1   \n",
       "1  id3505355          1  2016-06-30 23:59:53                1   \n",
       "2  id1217141          1  2016-06-30 23:59:47                1   \n",
       "3  id2150126          2  2016-06-30 23:59:41                1   \n",
       "4  id1598245          1  2016-06-30 23:59:33                1   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.988129        40.732029         -73.990173         40.756680   \n",
       "1        -73.964203        40.679993         -73.959808         40.655403   \n",
       "2        -73.997437        40.737583         -73.986160         40.729523   \n",
       "3        -73.956070        40.771900         -73.986427         40.730469   \n",
       "4        -73.970215        40.761475         -73.961510         40.755890   \n",
       "\n",
       "  store_and_fwd_flag  \n",
       "0                  N  \n",
       "1                  N  \n",
       "2                  N  \n",
       "3                  N  \n",
       "4                  N  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(625134, 9)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = np.mean(train['trip_duration'])\n",
    "s = np.std(train['trip_duration'])\n",
    "train = train[(train['trip_duration'] <= m + 2*s) & (train['trip_duration'] >= m -2*s)]\n",
    "train = train[(train['pickup_longitude'] > -74.023822) & (train['pickup_latitude'] < 40.886345) & (train['pickup_longitude'] < -73.901599) & (train['pickup_latitude'] > 40.698160)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_id = test['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['source'] = 'train'\n",
    "test['source'] = 'test'\n",
    "df = pd.concat([train, test], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.jointplot(x = 'pickup_longitude', y = 'pickup_latitude', data = df, s = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1990557 entries, 0 to 1990556\n",
      "Data columns (total 12 columns):\n",
      "dropoff_datetime      object\n",
      "dropoff_latitude      float64\n",
      "dropoff_longitude     float64\n",
      "id                    object\n",
      "passenger_count       int64\n",
      "pickup_datetime       object\n",
      "pickup_latitude       float64\n",
      "pickup_longitude      float64\n",
      "source                object\n",
      "store_and_fwd_flag    object\n",
      "trip_duration         float64\n",
      "vendor_id             int64\n",
      "dtypes: float64(5), int64(2), object(5)\n",
      "memory usage: 182.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "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>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>vendor_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.990557e+06</td>\n",
       "      <td>1.990557e+06</td>\n",
       "      <td>1.990557e+06</td>\n",
       "      <td>1.990557e+06</td>\n",
       "      <td>1.990557e+06</td>\n",
       "      <td>1.365423e+06</td>\n",
       "      <td>1.990557e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.075267e+01</td>\n",
       "      <td>-7.397441e+01</td>\n",
       "      <td>1.661832e+00</td>\n",
       "      <td>4.075285e+01</td>\n",
       "      <td>-7.397868e+01</td>\n",
       "      <td>7.723499e+02</td>\n",
       "      <td>1.533578e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.258285e-02</td>\n",
       "      <td>4.933976e-02</td>\n",
       "      <td>1.312092e+00</td>\n",
       "      <td>2.481170e-02</td>\n",
       "      <td>4.366096e-02</td>\n",
       "      <td>5.596175e+02</td>\n",
       "      <td>4.988713e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.639812e+01</td>\n",
       "      <td>-1.219333e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.738959e+01</td>\n",
       "      <td>-1.219331e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.073701e+01</td>\n",
       "      <td>-7.399142e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.073860e+01</td>\n",
       "      <td>-7.399226e+01</td>\n",
       "      <td>3.860000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.075495e+01</td>\n",
       "      <td>-7.397997e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.075438e+01</td>\n",
       "      <td>-7.398222e+01</td>\n",
       "      <td>6.350000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.077022e+01</td>\n",
       "      <td>-7.396364e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.076783e+01</td>\n",
       "      <td>-7.396950e+01</td>\n",
       "      <td>1.004000e+03</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.885760e+01</td>\n",
       "      <td>-6.749680e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>4.281494e+01</td>\n",
       "      <td>-6.924892e+01</td>\n",
       "      <td>1.141100e+04</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dropoff_latitude  dropoff_longitude  passenger_count  pickup_latitude  \\\n",
       "count      1.990557e+06       1.990557e+06     1.990557e+06     1.990557e+06   \n",
       "mean       4.075267e+01      -7.397441e+01     1.661832e+00     4.075285e+01   \n",
       "std        3.258285e-02       4.933976e-02     1.312092e+00     2.481170e-02   \n",
       "min        3.639812e+01      -1.219333e+02     0.000000e+00     3.738959e+01   \n",
       "25%        4.073701e+01      -7.399142e+01     1.000000e+00     4.073860e+01   \n",
       "50%        4.075495e+01      -7.397997e+01     1.000000e+00     4.075438e+01   \n",
       "75%        4.077022e+01      -7.396364e+01     2.000000e+00     4.076783e+01   \n",
       "max        4.885760e+01      -6.749680e+01     9.000000e+00     4.281494e+01   \n",
       "\n",
       "       pickup_longitude  trip_duration     vendor_id  \n",
       "count      1.990557e+06   1.365423e+06  1.990557e+06  \n",
       "mean      -7.397868e+01   7.723499e+02  1.533578e+00  \n",
       "std        4.366096e-02   5.596175e+02  4.988713e-01  \n",
       "min       -1.219331e+02   1.000000e+00  1.000000e+00  \n",
       "25%       -7.399226e+01   3.860000e+02  1.000000e+00  \n",
       "50%       -7.398222e+01   6.350000e+02  2.000000e+00  \n",
       "75%       -7.396950e+01   1.004000e+03  2.000000e+00  \n",
       "max       -6.924892e+01   1.141100e+04  2.000000e+00  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\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_datetime</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>source</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>vendor_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>id2875421</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>455.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>663.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>id3858529</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>id3504673</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>429.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>id2181028</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>435.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dropoff_datetime  dropoff_latitude  dropoff_longitude         id  \\\n",
       "0  2016-03-14 17:32:30         40.765602         -73.964630  id2875421   \n",
       "1  2016-06-12 00:54:38         40.731152         -73.999481  id2377394   \n",
       "2  2016-01-19 12:10:48         40.710087         -74.005333  id3858529   \n",
       "3  2016-04-06 19:39:40         40.706718         -74.012268  id3504673   \n",
       "4  2016-03-26 13:38:10         40.782520         -73.972923  id2181028   \n",
       "\n",
       "   passenger_count      pickup_datetime  pickup_latitude  pickup_longitude  \\\n",
       "0                1  2016-03-14 17:24:55        40.767937        -73.982155   \n",
       "1                1  2016-06-12 00:43:35        40.738564        -73.980415   \n",
       "2                1  2016-01-19 11:35:24        40.763939        -73.979027   \n",
       "3                1  2016-04-06 19:32:31        40.719971        -74.010040   \n",
       "4                1  2016-03-26 13:30:55        40.793209        -73.973053   \n",
       "\n",
       "  source store_and_fwd_flag  trip_duration  vendor_id  \n",
       "0  train                  N          455.0          2  \n",
       "1  train                  N          663.0          1  \n",
       "2  train                  N         2124.0          2  \n",
       "3  train                  N          429.0          2  \n",
       "4  train                  N          435.0          2  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['pickup_datetime'] = pd.to_datetime(df['pickup_datetime'])\n",
    "df['pickup_month'] = df['pickup_datetime'].dt.month\n",
    "df['pickup_day_of_week'] = df['pickup_datetime'].dt.dayofweek\n",
    "df['pickup_day'] = df['pickup_datetime'].dt.day\n",
    "df['pickup_hour'] = df['pickup_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['dropoff_datetime'] = pd.to_datetime(df['dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['dropoff_month'] = df['dropoff_datetime'].dt.month\n",
    "df['dropoff_day_of_week'] = df['dropoff_datetime'].dt.dayofweek\n",
    "df['dropoff_day'] = df['dropoff_datetime'].dt.day\n",
    "df['dropoff_hour'] = df['dropoff_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>dropoff_datetime</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>source</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>dropoff_month</th>\n",
       "      <th>dropoff_day_of_week</th>\n",
       "      <th>dropoff_day</th>\n",
       "      <th>dropoff_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>id2875421</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>455.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>663.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>id3858529</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>id3504673</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>429.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>id2181028</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>435.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dropoff_datetime  dropoff_latitude  dropoff_longitude         id  \\\n",
       "0 2016-03-14 17:32:30         40.765602         -73.964630  id2875421   \n",
       "1 2016-06-12 00:54:38         40.731152         -73.999481  id2377394   \n",
       "2 2016-01-19 12:10:48         40.710087         -74.005333  id3858529   \n",
       "3 2016-04-06 19:39:40         40.706718         -74.012268  id3504673   \n",
       "4 2016-03-26 13:38:10         40.782520         -73.972923  id2181028   \n",
       "\n",
       "   passenger_count     pickup_datetime  pickup_latitude  pickup_longitude  \\\n",
       "0                1 2016-03-14 17:24:55        40.767937        -73.982155   \n",
       "1                1 2016-06-12 00:43:35        40.738564        -73.980415   \n",
       "2                1 2016-01-19 11:35:24        40.763939        -73.979027   \n",
       "3                1 2016-04-06 19:32:31        40.719971        -74.010040   \n",
       "4                1 2016-03-26 13:30:55        40.793209        -73.973053   \n",
       "\n",
       "  source store_and_fwd_flag  trip_duration  vendor_id  pickup_month  \\\n",
       "0  train                  N          455.0          2             3   \n",
       "1  train                  N          663.0          1             6   \n",
       "2  train                  N         2124.0          2             1   \n",
       "3  train                  N          429.0          2             4   \n",
       "4  train                  N          435.0          2             3   \n",
       "\n",
       "   pickup_day_of_week  pickup_day  pickup_hour  dropoff_month  \\\n",
       "0                   0          14           17            3.0   \n",
       "1                   6          12            0            6.0   \n",
       "2                   1          19           11            1.0   \n",
       "3                   2           6           19            4.0   \n",
       "4                   5          26           13            3.0   \n",
       "\n",
       "   dropoff_day_of_week  dropoff_day  dropoff_hour  \n",
       "0                  0.0         14.0          17.0  \n",
       "1                  6.0         12.0           0.0  \n",
       "2                  1.0         19.0          12.0  \n",
       "3                  2.0          6.0          19.0  \n",
       "4                  5.0         26.0          13.0  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = np.vstack((df[['pickup_latitude', 'pickup_longitude']],\n",
    "                    df[['dropoff_latitude', 'dropoff_longitude']]))\n",
    "sample_ind = np.random.permutation(len(coords))[:1000000]\n",
    "kmeans = MiniBatchKMeans(n_clusters=100, batch_size=20000).fit(coords[sample_ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['pickup_cluster'] = kmeans.predict(df[['pickup_latitude', 'pickup_longitude']])\n",
    "df['dropoff_cluster'] = kmeans.predict(df[['dropoff_latitude', 'dropoff_longitude']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>source</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>dropoff_month</th>\n",
       "      <th>dropoff_day_of_week</th>\n",
       "      <th>dropoff_day</th>\n",
       "      <th>dropoff_hour</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>2016-03-14 17:32:30</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>id2875421</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>id3858529</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>id3504673</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>81</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>id2181028</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>train</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     dropoff_datetime  dropoff_latitude  dropoff_longitude         id  \\\n",
       "0 2016-03-14 17:32:30         40.765602         -73.964630  id2875421   \n",
       "1 2016-06-12 00:54:38         40.731152         -73.999481  id2377394   \n",
       "2 2016-01-19 12:10:48         40.710087         -74.005333  id3858529   \n",
       "3 2016-04-06 19:39:40         40.706718         -74.012268  id3504673   \n",
       "4 2016-03-26 13:38:10         40.782520         -73.972923  id2181028   \n",
       "\n",
       "   passenger_count     pickup_datetime  pickup_latitude  pickup_longitude  \\\n",
       "0                1 2016-03-14 17:24:55        40.767937        -73.982155   \n",
       "1                1 2016-06-12 00:43:35        40.738564        -73.980415   \n",
       "2                1 2016-01-19 11:35:24        40.763939        -73.979027   \n",
       "3                1 2016-04-06 19:32:31        40.719971        -74.010040   \n",
       "4                1 2016-03-26 13:30:55        40.793209        -73.973053   \n",
       "\n",
       "  source store_and_fwd_flag       ...         pickup_month  \\\n",
       "0  train                  N       ...                    3   \n",
       "1  train                  N       ...                    6   \n",
       "2  train                  N       ...                    1   \n",
       "3  train                  N       ...                    4   \n",
       "4  train                  N       ...                    3   \n",
       "\n",
       "   pickup_day_of_week  pickup_day  pickup_hour  dropoff_month  \\\n",
       "0                   0          14           17            3.0   \n",
       "1                   6          12            0            6.0   \n",
       "2                   1          19           11            1.0   \n",
       "3                   2           6           19            4.0   \n",
       "4                   5          26           13            3.0   \n",
       "\n",
       "   dropoff_day_of_week  dropoff_day  dropoff_hour  pickup_cluster  \\\n",
       "0                  0.0         14.0          17.0              14   \n",
       "1                  6.0         12.0           0.0              62   \n",
       "2                  1.0         19.0          12.0              91   \n",
       "3                  2.0          6.0          19.0              81   \n",
       "4                  5.0         26.0          13.0              84   \n",
       "\n",
       "   dropoff_cluster  \n",
       "0               43  \n",
       "1               82  \n",
       "2                4  \n",
       "3               63  \n",
       "4               78  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1990557 entries, 0 to 1990556\n",
      "Data columns (total 22 columns):\n",
      "dropoff_datetime       datetime64[ns]\n",
      "dropoff_latitude       float64\n",
      "dropoff_longitude      float64\n",
      "id                     object\n",
      "passenger_count        int64\n",
      "pickup_datetime        datetime64[ns]\n",
      "pickup_latitude        float64\n",
      "pickup_longitude       float64\n",
      "source                 object\n",
      "store_and_fwd_flag     object\n",
      "trip_duration          float64\n",
      "vendor_id              int64\n",
      "pickup_month           int64\n",
      "pickup_day_of_week     int64\n",
      "pickup_day             int64\n",
      "pickup_hour            int64\n",
      "dropoff_month          float64\n",
      "dropoff_day_of_week    float64\n",
      "dropoff_day            float64\n",
      "dropoff_hour           float64\n",
      "pickup_cluster         int32\n",
      "dropoff_cluster        int32\n",
      "dtypes: datetime64[ns](2), float64(9), int32(2), int64(6), object(3)\n",
      "memory usage: 318.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop(['id', 'store_and_fwd_flag'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1990557 entries, 0 to 1990556\n",
      "Data columns (total 20 columns):\n",
      "dropoff_datetime       datetime64[ns]\n",
      "dropoff_latitude       float64\n",
      "dropoff_longitude      float64\n",
      "passenger_count        int64\n",
      "pickup_datetime        datetime64[ns]\n",
      "pickup_latitude        float64\n",
      "pickup_longitude       float64\n",
      "source                 object\n",
      "trip_duration          float64\n",
      "vendor_id              int64\n",
      "pickup_month           int64\n",
      "pickup_day_of_week     int64\n",
      "pickup_day             int64\n",
      "pickup_hour            int64\n",
      "dropoff_month          float64\n",
      "dropoff_day_of_week    float64\n",
      "dropoff_day            float64\n",
      "dropoff_hour           float64\n",
      "pickup_cluster         int32\n",
      "dropoff_cluster        int32\n",
      "dtypes: datetime64[ns](2), float64(9), int32(2), int64(6), object(1)\n",
      "memory usage: 288.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calculate the distance between two coordinates\n",
    "def haversine_np(lon1, lat1, lon2, lat2):\n",
    "    \"\"\"\n",
    "    Calculate the great circle distance between two points\n",
    "    on the earth (specified in decimal degrees)\n",
    "\n",
    "    All args must be of equal length.    \n",
    "\n",
    "    \"\"\"\n",
    "    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n",
    "\n",
    "    c = 2 * np.arcsin(np.sqrt(a))\n",
    "    km = 6367 * c\n",
    "    return km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['distance'] = haversine_np(df['pickup_longitude'], df['pickup_latitude'], df['dropoff_longitude'], df['dropoff_latitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deal with categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#use different dataframe to store numerical values\n",
    "pickup_month = pd.get_dummies(df['pickup_month'], prefix='pickup_month')\n",
    "pickup_hour = pd.get_dummies(df['pickup_hour'], prefix='pickup_hour')\n",
    "pickup_dayofweek = pd.get_dummies(df['pickup_day_of_week'], prefix='pickup_day_of_week')\n",
    "pickup_day = pd.get_dummies(df['pickup_day'], prefix='pickup_day')\n",
    "dropoff_month = pd.get_dummies(df['dropoff_month'], prefix='dropoff_month')\n",
    "dropoff_hour = pd.get_dummies(df['dropoff_hour'], prefix='dropoff_hour')\n",
    "dropoff_dayofweek = pd.get_dummies(df['dropoff_day_of_week'], prefix='dropoff_day_of_week')\n",
    "dropoff_day = pd.get_dummies(df['dropoff_day'], prefix='dropoff_day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickup_cluster = pd.get_dummies(df['pickup_cluster'], prefix='pickup_cluster')\n",
    "dropoff_cluster = pd.get_dummies(df['dropoff_cluster'], prefix='dropoff_cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "passenger_count = pd.get_dummies(df['passenger_count'], prefix='passenger_count')\n",
    "vendor_id = pd.get_dummies(df['vendor_id'], prefix='vendor_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop old categorical columns and concat new numerical columns\n",
    "df_numerical = df.drop(['pickup_cluster', 'pickup_day', 'pickup_day_of_week', 'pickup_hour', 'pickup_month', 'dropoff_cluster', 'dropoff_day', 'dropoff_day_of_week', 'dropoff_hour', 'dropoff_month', 'passenger_count', 'vendor_id'], axis = 1)\n",
    "df_numerical = pd.concat([df_numerical, pickup_month, pickup_day, pickup_dayofweek, pickup_hour, pickup_cluster, dropoff_month, dropoff_day, dropoff_dayofweek, dropoff_hour, dropoff_cluster, passenger_count, vendor_id], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>source</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>pickup_month_1</th>\n",
       "      <th>pickup_month_2</th>\n",
       "      <th>pickup_month_3</th>\n",
       "      <th>...</th>\n",
       "      <th>passenger_count_1</th>\n",
       "      <th>passenger_count_2</th>\n",
       "      <th>passenger_count_3</th>\n",
       "      <th>passenger_count_4</th>\n",
       "      <th>passenger_count_5</th>\n",
       "      <th>passenger_count_6</th>\n",
       "      <th>passenger_count_8</th>\n",
       "      <th>passenger_count_9</th>\n",
       "      <th>vendor_id_1</th>\n",
       "      <th>vendor_id_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.765602</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>train</td>\n",
       "      <td>455.0</td>\n",
       "      <td>1.497580</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.731152</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>train</td>\n",
       "      <td>663.0</td>\n",
       "      <td>1.804374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.710087</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>train</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>6.381090</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.706718</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>train</td>\n",
       "      <td>429.0</td>\n",
       "      <td>1.484566</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.782520</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>train</td>\n",
       "      <td>435.0</td>\n",
       "      <td>1.187842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 355 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dropoff_latitude  dropoff_longitude  pickup_latitude  pickup_longitude  \\\n",
       "0         40.765602         -73.964630        40.767937        -73.982155   \n",
       "1         40.731152         -73.999481        40.738564        -73.980415   \n",
       "2         40.710087         -74.005333        40.763939        -73.979027   \n",
       "3         40.706718         -74.012268        40.719971        -74.010040   \n",
       "4         40.782520         -73.972923        40.793209        -73.973053   \n",
       "\n",
       "  source  trip_duration  distance  pickup_month_1  pickup_month_2  \\\n",
       "0  train          455.0  1.497580               0               0   \n",
       "1  train          663.0  1.804374               0               0   \n",
       "2  train         2124.0  6.381090               1               0   \n",
       "3  train          429.0  1.484566               0               0   \n",
       "4  train          435.0  1.187842               0               0   \n",
       "\n",
       "   pickup_month_3     ...       passenger_count_1  passenger_count_2  \\\n",
       "0               1     ...                       1                  0   \n",
       "1               0     ...                       1                  0   \n",
       "2               0     ...                       1                  0   \n",
       "3               0     ...                       1                  0   \n",
       "4               1     ...                       1                  0   \n",
       "\n",
       "   passenger_count_3  passenger_count_4  passenger_count_5  passenger_count_6  \\\n",
       "0                  0                  0                  0                  0   \n",
       "1                  0                  0                  0                  0   \n",
       "2                  0                  0                  0                  0   \n",
       "3                  0                  0                  0                  0   \n",
       "4                  0                  0                  0                  0   \n",
       "\n",
       "   passenger_count_8  passenger_count_9  vendor_id_1  vendor_id_2  \n",
       "0                  0                  0            0            1  \n",
       "1                  0                  0            1            0  \n",
       "2                  0                  0            0            1  \n",
       "3                  0                  0            0            1  \n",
       "4                  0                  0            0            1  \n",
       "\n",
       "[5 rows x 355 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numerical = df_numerical.drop(['pickup_datetime', 'dropoff_datetime'], axis = 1)\n",
    "df_numerical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.heatmap(df['pickup_cluster'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seperate the dataframe back to training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_numerical = df_numerical[df_numerical['source'] == 'train']\n",
    "test_numerical = df_numerical[df_numerical['source'] == 'test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# train_numerical.to_csv('train_numerical000.csv', index=False)\n",
    "# test_numerical.to_csv('test_numerical000.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dropoff_latitude', 'dropoff_longitude', 'pickup_latitude',\n",
       "       'pickup_longitude', 'source', 'trip_duration', 'distance',\n",
       "       'pickup_month_1', 'pickup_month_2', 'pickup_month_3',\n",
       "       ...\n",
       "       'passenger_count_1', 'passenger_count_2', 'passenger_count_3',\n",
       "       'passenger_count_4', 'passenger_count_5', 'passenger_count_6',\n",
       "       'passenger_count_8', 'passenger_count_9', 'vendor_id_1', 'vendor_id_2'],\n",
       "      dtype='object', length=342)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_numerical.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = train_numerical.drop(['trip_duration', 'source'], axis = 1)\n",
    "y_train = train_numerical['trip_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1365409,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "       objective='reg:linear', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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03GJXd1+doF1ERERERKTKU8G5B8Izj/tz/mBgcMVEIyIiIiIiUjWo4BSRtCsqKmL48CGs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\n/H/NtrbC8nQrXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e328b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_importance(model)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(14, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choose features according to feature_importance_, then fit and predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = ['distance', 'dropoff_latitude', 'pickup_day_of_week_6', 'dropoff_longitude']\n",
    "target = ['trip_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "       objective='reg:linear', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train[features], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "train    1365409\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_numerical['source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_numerical[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = np.absolute(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'id': test_id, 'trip_duration': predictions}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = pd.DataFrame(data = d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit.to_csv(\"submit0822.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "#submit[submit['trip_duration'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test[test['id'] == 'id0466318']"
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}