data_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "We import the raw data and preprocess according to the previous milestones.\n",
    "\n",
    "The final version of training and testing data will be used to build several models, \n",
    "which are used to predict demand in ride sharing business.\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1: Import order data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "order_info_read = pd.read_csv('data/training_order_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "order_info = order_info_read.sort_values('time_stamp')\n",
    "order_info.columns = ['initial_row', 'order_id', 'driver_id', 'passenger_id', \\\n",
    "                      'start_district_hash', 'dest_district_hash', 'price', 'time_stamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>initial_row</th>\n",
       "      <th>order_id</th>\n",
       "      <th>driver_id</th>\n",
       "      <th>passenger_id</th>\n",
       "      <th>start_district_hash</th>\n",
       "      <th>dest_district_hash</th>\n",
       "      <th>price</th>\n",
       "      <th>time_stamp</th>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "        initial_row                          order_id  \\\n",
       "262258       262258  9a864e958859b506f5f8bee9d8dfff17   \n",
       "231406       231406  6732f74a5a59353cddb421ceb39f7c26   \n",
       "210341       210341  f4beb0739bcee0f040385d69b9809b92   \n",
       "268247       268247  1f5672197a352594fb546d7164a41f28   \n",
       "188962       188962  7bd83a5dd61f5242f82c52075ad43cbf   \n",
       "\n",
       "                               driver_id                      passenger_id  \\\n",
       "262258  a323121d71cd5247f38a4848c2039cb1  b9bd961ee676441d64c8748aa18efcda   \n",
       "231406  a57d4acc407006cbb4086f0cc60d2575  37e17d1ec5ca26d52be702ce74db64a6   \n",
       "210341  0b41511d4c63ed4bb030bd9d5ed5399e  b684445572045febd51a63a4e478b356   \n",
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       "188962  f5307b86a4d3da55e3626b9048d9b54a  d6c7bb502d01aed1c5031e17957e44ec   \n",
       "\n",
       "                     start_district_hash                dest_district_hash  \\\n",
       "262258  b05379ac3f9b7d99370d443cfd5dcc28  52d7b69796362a8ed1691a6cc02ddde4   \n",
       "231406  4725c39a5e5f4c188d382da3910b3f3f  929ec6c160e6f52c20a4217c7978f681   \n",
       "210341  b05379ac3f9b7d99370d443cfd5dcc28  4725c39a5e5f4c188d382da3910b3f3f   \n",
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       "188962  f47f35242ed40655814bc086d7514046  1cbfbdd079ef93e74405c53fcfff8567   \n",
       "\n",
       "        price           time_stamp  \n",
       "262258   45.0  2016-01-01 00:00:03  \n",
       "231406   18.0  2016-01-01 00:00:04  \n",
       "210341   31.0  2016-01-01 00:00:04  \n",
       "268247   10.0  2016-01-01 00:00:05  \n",
       "188962   24.0  2016-01-01 00:00:05  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "initial_row = order_info['initial_row']\n",
    "order_id = order_info['order_id']\n",
    "driver_id = order_info['driver_id']\n",
    "passenger_id = order_info['passenger_id']\n",
    "start_district_hash = order_info['start_district_hash']\n",
    "dest_district_hash = order_info['dest_district_hash']\n",
    "price = order_info['price']\n",
    "time_stamp = pd.to_datetime(order_info['time_stamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total order_id: 8540614\n",
      "unique order_id: 8518049\n"
     ]
    }
   ],
   "source": [
    "order_id_num = len(order_id)\n",
    "order_id_num_unique = len(order_id.unique())\n",
    "print('total order_id:', order_id_num)\n",
    "print('unique order_id:', order_id_num_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total driver_id: 8540614\n",
      "unique driver_id: 93090\n",
      "null driver_id: 1516460\n",
      "fraction of missed orders: 0.17755866264416117\n",
      "fraction of received orders: 0.8224413373558388\n"
     ]
    }
   ],
   "source": [
    "driver_id_null = pd.isnull(driver_id)\n",
    "driver_id_num = len(driver_id)\n",
    "driver_id_num_unique = len(driver_id.unique())\n",
    "driver_id_num_null = len(driver_id[driver_id_null])\n",
    "print('total driver_id:', driver_id_num)\n",
    "print('unique driver_id:', driver_id_num_unique)\n",
    "print('null driver_id:', driver_id_num_null)\n",
    "print('fraction of missed orders:', float(driver_id_num_null) / float(driver_id_num))\n",
    "print('fraction of received orders:', 1.0 - float(driver_id_num_null) / float(driver_id_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total passenger_id: 8540614\n",
      "unique passenger_id: 1368071\n",
      "orders per passenger: 6.242814883145685\n"
     ]
    }
   ],
   "source": [
    "passenger_id_num = len(passenger_id)\n",
    "passenger_id_num_unique = len(passenger_id.unique())\n",
    "print('total passenger_id:', passenger_id_num)\n",
    "print('unique passenger_id:', passenger_id_num_unique)\n",
    "print('orders per passenger:', float(passenger_id_num) / float(passenger_id_num_unique))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total start_district_hash: 8540614\n",
      "unique start_district_hash: 66\n"
     ]
    }
   ],
   "source": [
    "start_district_hash_num = len(start_district_hash)\n",
    "start_district_hash_num_unique = len(start_district_hash.unique())\n",
    "print('total start_district_hash:', start_district_hash_num)\n",
    "print('unique start_district_hash:', start_district_hash_num_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total dest_district_hash: 8540614\n",
      "unique dest_district_hash: 793\n"
     ]
    }
   ],
   "source": [
    "dest_district_hash_num = len(dest_district_hash)\n",
    "dest_district_hash_num_unique = len(dest_district_hash.unique())\n",
    "print('total dest_district_hash:', dest_district_hash_num)\n",
    "print('unique dest_district_hash:', dest_district_hash_num_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total price: 8540614\n",
      "unique price: 2171\n",
      "max price: 1731.0\n",
      "min price: 0.0\n"
     ]
    }
   ],
   "source": [
    "price_num = len(price)\n",
    "price_num_unique = len(price.unique())\n",
    "price_max = price.max()\n",
    "price_min = price.min()\n",
    "print('total price:', price_num)\n",
    "print('unique price:', price_num_unique)\n",
    "print('max price:', price_max)\n",
    "print('min price:', price_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total time_stamp: 8540614\n",
      "unique time_stamp: 1534340\n"
     ]
    }
   ],
   "source": [
    "time_stamp_num = len(time_stamp)\n",
    "time_stamp_num_unique = len(time_stamp.unique())\n",
    "print('total time_stamp:', time_stamp_num)\n",
    "print('unique time_stamp:', time_stamp_num_unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2: Import cluster map data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_read = pd.read_csv('data/cluster_map.csv', index_col=0)\n",
    "cluster_map_read = df_read.to_dict('dict')['0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_map_read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3: Get rid of NULL orders and out-of-town orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "driver_to_clean = driver_id_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 % finished\n",
      "5.854380024668016 % finished\n",
      "11.708760049336032 % finished\n",
      "17.563140074004046 % finished\n",
      "23.417520098672064 % finished\n",
      "29.271900123340078 % finished\n",
      "35.12628014800809 % finished\n",
      "40.98066017267611 % finished\n",
      "46.83504019734413 % finished\n",
      "52.68942022201214 % finished\n",
      "58.543800246680156 % finished\n",
      "64.39818027134817 % finished\n",
      "70.25256029601618 % finished\n",
      "76.1069403206842 % finished\n",
      "81.96132034535222 % finished\n",
      "87.81570037002024 % finished\n",
      "93.67008039468826 % finished\n",
      "99.52446041935626 % finished\n"
     ]
    }
   ],
   "source": [
    "percent = 500000\n",
    "\n",
    "for i in range(int(order_id_num)):\n",
    "    des_hash = dest_district_hash.iloc[i]\n",
    "    if des_hash not in cluster_map_read.keys():\n",
    "        driver_to_clean.iloc[i] = True\n",
    "    if i % percent == 0:\n",
    "        print(i / float(order_id_num) * 100, '% finished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/afs/ir.stanford.edu/group/bil/env/jupyter/lib/python3.5/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "order_info_cleaned = order_info[~driver_to_clean]\n",
    "time_stamp_cleaned = pd.to_datetime(order_info_cleaned['time_stamp'])\n",
    "order_info_cleaned['time_stamp'] = time_stamp_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5914153 orders left\n"
     ]
    }
   ],
   "source": [
    "order_left_num = len(order_info_cleaned)\n",
    "print(order_left_num, 'orders left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>initial_row</th>\n",
       "      <th>order_id</th>\n",
       "      <th>driver_id</th>\n",
       "      <th>passenger_id</th>\n",
       "      <th>start_district_hash</th>\n",
       "      <th>dest_district_hash</th>\n",
       "      <th>price</th>\n",
       "      <th>time_stamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262258</th>\n",
       "      <td>262258</td>\n",
       "      <td>9a864e958859b506f5f8bee9d8dfff17</td>\n",
       "      <td>a323121d71cd5247f38a4848c2039cb1</td>\n",
       "      <td>b9bd961ee676441d64c8748aa18efcda</td>\n",
       "      <td>b05379ac3f9b7d99370d443cfd5dcc28</td>\n",
       "      <td>52d7b69796362a8ed1691a6cc02ddde4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2016-01-01 00:00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231406</th>\n",
       "      <td>231406</td>\n",
       "      <td>6732f74a5a59353cddb421ceb39f7c26</td>\n",
       "      <td>a57d4acc407006cbb4086f0cc60d2575</td>\n",
       "      <td>37e17d1ec5ca26d52be702ce74db64a6</td>\n",
       "      <td>4725c39a5e5f4c188d382da3910b3f3f</td>\n",
       "      <td>929ec6c160e6f52c20a4217c7978f681</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2016-01-01 00:00:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210341</th>\n",
       "      <td>210341</td>\n",
       "      <td>f4beb0739bcee0f040385d69b9809b92</td>\n",
       "      <td>0b41511d4c63ed4bb030bd9d5ed5399e</td>\n",
       "      <td>b684445572045febd51a63a4e478b356</td>\n",
       "      <td>b05379ac3f9b7d99370d443cfd5dcc28</td>\n",
       "      <td>4725c39a5e5f4c188d382da3910b3f3f</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2016-01-01 00:00:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268247</th>\n",
       "      <td>268247</td>\n",
       "      <td>1f5672197a352594fb546d7164a41f28</td>\n",
       "      <td>90f993e801c394a351321377de6820b3</td>\n",
       "      <td>7d61be2772673f4fb728c867fba64833</td>\n",
       "      <td>82cc4851f9e4faa4e54309f8bb73fd7c</td>\n",
       "      <td>82cc4851f9e4faa4e54309f8bb73fd7c</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2016-01-01 00:00:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188962</th>\n",
       "      <td>188962</td>\n",
       "      <td>7bd83a5dd61f5242f82c52075ad43cbf</td>\n",
       "      <td>f5307b86a4d3da55e3626b9048d9b54a</td>\n",
       "      <td>d6c7bb502d01aed1c5031e17957e44ec</td>\n",
       "      <td>f47f35242ed40655814bc086d7514046</td>\n",
       "      <td>1cbfbdd079ef93e74405c53fcfff8567</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2016-01-01 00:00:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        initial_row                          order_id  \\\n",
       "262258       262258  9a864e958859b506f5f8bee9d8dfff17   \n",
       "231406       231406  6732f74a5a59353cddb421ceb39f7c26   \n",
       "210341       210341  f4beb0739bcee0f040385d69b9809b92   \n",
       "268247       268247  1f5672197a352594fb546d7164a41f28   \n",
       "188962       188962  7bd83a5dd61f5242f82c52075ad43cbf   \n",
       "\n",
       "                               driver_id                      passenger_id  \\\n",
       "262258  a323121d71cd5247f38a4848c2039cb1  b9bd961ee676441d64c8748aa18efcda   \n",
       "231406  a57d4acc407006cbb4086f0cc60d2575  37e17d1ec5ca26d52be702ce74db64a6   \n",
       "210341  0b41511d4c63ed4bb030bd9d5ed5399e  b684445572045febd51a63a4e478b356   \n",
       "268247  90f993e801c394a351321377de6820b3  7d61be2772673f4fb728c867fba64833   \n",
       "188962  f5307b86a4d3da55e3626b9048d9b54a  d6c7bb502d01aed1c5031e17957e44ec   \n",
       "\n",
       "                     start_district_hash                dest_district_hash  \\\n",
       "262258  b05379ac3f9b7d99370d443cfd5dcc28  52d7b69796362a8ed1691a6cc02ddde4   \n",
       "231406  4725c39a5e5f4c188d382da3910b3f3f  929ec6c160e6f52c20a4217c7978f681   \n",
       "210341  b05379ac3f9b7d99370d443cfd5dcc28  4725c39a5e5f4c188d382da3910b3f3f   \n",
       "268247  82cc4851f9e4faa4e54309f8bb73fd7c  82cc4851f9e4faa4e54309f8bb73fd7c   \n",
       "188962  f47f35242ed40655814bc086d7514046  1cbfbdd079ef93e74405c53fcfff8567   \n",
       "\n",
       "        price          time_stamp  \n",
       "262258   45.0 2016-01-01 00:00:03  \n",
       "231406   18.0 2016-01-01 00:00:04  \n",
       "210341   31.0 2016-01-01 00:00:04  \n",
       "268247   10.0 2016-01-01 00:00:05  \n",
       "188962   24.0 2016-01-01 00:00:05  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_info_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first order:\n",
      "initial_row                                      262258\n",
      "order_id               9a864e958859b506f5f8bee9d8dfff17\n",
      "driver_id              a323121d71cd5247f38a4848c2039cb1\n",
      "passenger_id           b9bd961ee676441d64c8748aa18efcda\n",
      "start_district_hash    b05379ac3f9b7d99370d443cfd5dcc28\n",
      "dest_district_hash     52d7b69796362a8ed1691a6cc02ddde4\n",
      "price                                                45\n",
      "time_stamp                          2016-01-01 00:00:03\n",
      "Name: 262258, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('first order:')\n",
    "print(order_info_cleaned.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last order:\n",
      "initial_row                                      259969\n",
      "order_id               3831a13720589ff58a3c047fa649c80f\n",
      "driver_id              3ca1d4ef5e4802ef869c85dc973dee67\n",
      "passenger_id           234f2901f37d77513e985890603df6c9\n",
      "start_district_hash    ca064c2682ca48c6a21de012e87c0df5\n",
      "dest_district_hash     52a4e8aaa12f70020e889aed8fd5ddbc\n",
      "price                                                 9\n",
      "time_stamp                          2016-01-21 23:59:58\n",
      "Name: 8326243, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('last order:')\n",
    "print(order_info_cleaned.iloc[len(order_info_cleaned)-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4: Cut orders into pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6048 time slots\n"
     ]
    }
   ],
   "source": [
    "interval_in_minute = 5 # one slot is 5 minutes long\n",
    "interval_num = int(24 * 60 * 21 / interval_in_minute) # 24 hours * 60 minutes * 21 days\n",
    "print(interval_num, 'time slots')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def delta_second(time_stamp_1, time_stamp_2):\n",
    "    '''\n",
    "    Returns the time in second between 2 time stamps\n",
    "    '''\n",
    "    delta_time = time_stamp_2 - time_stamp_1\n",
    "    return int(delta_time.total_seconds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 % finished\n",
      "8.454295991327921 % finished\n",
      "16.908591982655842 % finished\n",
      "25.362887973983767 % finished\n",
      "33.817183965311685 % finished\n",
      "42.27147995663961 % finished\n",
      "50.725775947967534 % finished\n",
      "59.18007193929545 % finished\n",
      "67.63436793062337 % finished\n",
      "76.0886639219513 % finished\n",
      "84.54295991327922 % finished\n",
      "92.99725590460713 % finished\n"
     ]
    }
   ],
   "source": [
    "start_time = order_info_cleaned['time_stamp'].iloc[0]\n",
    "delta_time = pd.Timedelta(minutes=interval_in_minute)\n",
    "end_time = start_time + delta_time\n",
    "\n",
    "start_row = 0\n",
    "end_row = None\n",
    "\n",
    "orders_in_group = [] # a list of DataFrame, whose length should be 6048\n",
    "\n",
    "delta_time = pd.Timedelta(minutes=interval_in_minute)\n",
    "\n",
    "for i in range(order_left_num):\n",
    "    if i % percent == 0:\n",
    "        print(i / float(order_left_num) * 100, '% finished')\n",
    "    cur_time = order_info_cleaned['time_stamp'].iloc[i]\n",
    "    if delta_second(cur_time, end_time) < 0: # reach an end\n",
    "        end_row = i\n",
    "        orders_in_group.append(order_info_cleaned.iloc[start_row:end_row])\n",
    "        start_time = end_time\n",
    "        end_time = start_time + delta_time\n",
    "        start_row = end_row\n",
    "        #print('start_time:', start_time)\n",
    "        continue\n",
    "           \n",
    "orders_in_group.append(order_info_cleaned.iloc[start_row:]) # add the last group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6048 data points for training and testing\n"
     ]
    }
   ],
   "source": [
    "group_num = len(orders_in_group)\n",
    "print(group_num, 'data points for training and testing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.arange(group_num)\n",
    "order_distribution = np.zeros(t.shape)\n",
    "for i in range(len(orders_in_group)):\n",
    "    order_distribution[i] = len(orders_in_group[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h2KHw4wcW5o45a6sTx0IzHqiPVUZDDiNQrQAbiBgvjkcWEMbQNAMu4XhMCHEb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\nQhWHoiiKEoUqDkVRFCWK/wexZ8vJssxSKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d4ce8f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, order_distribution)\n",
    "plt.xlabel('time slots')\n",
    "plt.ylabel('#orders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5: Build training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def day(i):\n",
    "    '''\n",
    "    Returns an integer ranging from 0 to 6, \n",
    "    which indicates the day of week corresponding to the time slot i.\n",
    "    '''\n",
    "    date = int(i) / 288\n",
    "    #print('date:', date)\n",
    "    day = int(date) % 7\n",
    "    return int(day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hour(i):\n",
    "    '''\n",
    "    Returns an integer ranging from 0 to 23, \n",
    "    which indicates the hour of day corresponding to the time slot i.\n",
    "    '''\n",
    "    group = int(i) / 12\n",
    "    hour = int(group) % 24\n",
    "    return int(hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y.shape: (6048, 66)\n",
      "X.shape: (6048, 4453)\n",
      "0.0 % finished\n",
      "4.9603174603174605 % finished\n",
      "9.920634920634921 % finished\n",
      "14.880952380952381 % finished\n",
      "19.841269841269842 % finished\n",
      "24.801587301587304 % finished\n",
      "29.761904761904763 % finished\n",
      "34.72222222222222 % finished\n",
      "39.682539682539684 % finished\n",
      "44.642857142857146 % finished\n",
      "49.60317460317461 % finished\n",
      "54.563492063492056 % finished\n",
      "59.523809523809526 % finished\n",
      "64.48412698412699 % finished\n",
      "69.44444444444444 % finished\n",
      "74.40476190476191 % finished\n",
      "79.36507936507937 % finished\n",
      "84.32539682539682 % finished\n",
      "89.28571428571429 % finished\n",
      "94.24603174603175 % finished\n",
      "99.20634920634922 % finished\n"
     ]
    }
   ],
   "source": [
    "Y = np.zeros((interval_num, start_district_hash_num_unique))\n",
    "print('Y.shape:', Y.shape)\n",
    "\n",
    "feature_dim = 24 + 7 + 66 + 66 * 66\n",
    "X = np.zeros((interval_num, feature_dim))\n",
    "print('X.shape:', X.shape)\n",
    "\n",
    "percent = 300\n",
    "\n",
    "for i in range(interval_num):\n",
    "    orders = orders_in_group[i]\n",
    "    hour_time = hour(i)\n",
    "    day_time = day(i)\n",
    "    X[i, hour_time] = 1\n",
    "    X[i, 24 + day_time] = 1\n",
    "    for j in range(len(orders)):\n",
    "        sta = cluster_map_read[orders['start_district_hash'].iloc[j]] - 1\n",
    "        des = cluster_map_read[orders['dest_district_hash'].iloc[j]] - 1\n",
    "        X[i, 24 + 7 + sta] += 1\n",
    "        X[i, 24 + 7 + 66 + 66*sta + des] += 1\n",
    "        Y[i, sta] += 1\n",
    "    if i % percent == 0:\n",
    "        print(i / float(interval_num) * 100, '% finished')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
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