main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@ogozuacik\n",
    "\n",
    "from datetime import timedelta\n",
    "from gc import collect\n",
    "from haversine import haversine\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "import optuna\n",
    "import os\n",
    "import shap\n",
    "from sklearn.metrics import mean_absolute_error, r2_score\n",
    "from sklearn.model_selection import TimeSeriesSplit #, KFold\n",
    "from skmob import TrajDataFrame\n",
    "from skmob.preprocessing import clustering\n",
    "from tqdm import tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_DAY_IN_SECONDS = 60 * 60 * 24\n",
    "CO2_PER_MILE = 404\n",
    "NUM_BOOSTING_ROUNDS = 100\n",
    "NUM_TRIALS = 25  # higher values should do better\n",
    "KM_TO_MILES = 0.62137"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref: https://stackoverflow.com/questions/579310/formatting-long-numbers-as-strings\n",
    "def human_readable_format(value):\n",
    "    \"\"\"Converts the value to human readable format\"\"\"\n",
    "    value = float(\"{:.3g}\".format(value))\n",
    "    magnitude = 0\n",
    "    while abs(value) >= 1000:\n",
    "        magnitude += 1\n",
    "        value /= 1000.0\n",
    "    return \"{}{}\".format(\"{:f}\".format(value).rstrip(\"0\").rstrip(\".\"), [\"\", \"K\", \"M\", \"B\", \"T\"][magnitude])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_change_points_and_trip_ids(df, trip_id):\n",
    "    \"\"\"Pre-processes change points and assigns trip ids\"\"\"\n",
    "    prev_occupancy = 1\n",
    "    is_change_point = []\n",
    "    trip_ids = []\n",
    "    for occupancy in df[\"occupancy\"]:\n",
    "        if occupancy != prev_occupancy:\n",
    "            is_change_point.append(True)\n",
    "            trip_id += 1\n",
    "        else:\n",
    "            is_change_point.append(False)\n",
    "        trip_ids.append(trip_id)\n",
    "        prev_occupancy = occupancy\n",
    "    is_change_point[-1] = True if occupancy == 1 else False\n",
    "    df[\"is_change_point\"] = is_change_point\n",
    "    df[\"tid\"] = trip_ids\n",
    "    return df, trip_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading data and combining them in one dataframe\n",
    "dfs = []\n",
    "# starting from 0 to ...\n",
    "trip_id = 0\n",
    "for root, _, files in os.walk(\"cabspottingdata\"):\n",
    "    for file_ in files:\n",
    "        if not file_.endswith(\".txt\"):\n",
    "            continue\n",
    "        df = pd.read_csv(\n",
    "            os.path.join(root, file_), sep=\" \", header=None, names=[\"lat\", \"lng\", \"occupancy\", \"datetime\"])\n",
    "        df[\"datetime\"] = pd.to_datetime(df[\"datetime\"], unit=\"s\") + timedelta(minutes=-7*60)\n",
    "        taxi_id = file_.split(\".txt\")[0]\n",
    "        # user id\n",
    "        df[\"uid\"] = taxi_id\n",
    "        df, trip_id = pre_process_change_points_and_trip_ids(df, trip_id)\n",
    "        df[\"prev_lat\"] = df[\"lat\"].shift()\n",
    "        df[\"prev_lng\"] = df[\"lng\"].shift()\n",
    "        df[\"prev_datetime\"] = df[\"datetime\"].shift()\n",
    "        trip_id += 1\n",
    "        dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combining data from different taxis together\n",
    "combined_df = pd.concat(dfs).reset_index(drop=True)\n",
    "del dfs\n",
    "collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'11.2M'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of rows\n",
    "num_rows = len(combined_df)\n",
    "human_readable_format(num_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>datetime</th>\n",
       "      <th>uid</th>\n",
       "      <th>is_change_point</th>\n",
       "      <th>tid</th>\n",
       "      <th>prev_lat</th>\n",
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       "      <th>11220053</th>\n",
       "      <td>37.74654</td>\n",
       "      <td>-122.44338</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-06-09 11:18:11</td>\n",
       "      <td>new_uvreoipy</td>\n",
       "      <td>False</td>\n",
       "      <td>928700</td>\n",
       "      <td>37.74501</td>\n",
       "      <td>-122.44424</td>\n",
       "      <td>2008-06-09 11:17:13</td>\n",
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       "    <tr>\n",
       "      <th>11220054</th>\n",
       "      <td>37.75140</td>\n",
       "      <td>-122.44296</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-06-09 11:19:08</td>\n",
       "      <td>new_uvreoipy</td>\n",
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       "      <td>928700</td>\n",
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       "      <td>2008-06-09 11:18:11</td>\n",
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       "    <tr>\n",
       "      <th>11220055</th>\n",
       "      <td>37.75686</td>\n",
       "      <td>-122.44239</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-06-09 11:20:12</td>\n",
       "      <td>new_uvreoipy</td>\n",
       "      <td>False</td>\n",
       "      <td>928700</td>\n",
       "      <td>37.75140</td>\n",
       "      <td>-122.44296</td>\n",
       "      <td>2008-06-09 11:19:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11220056</th>\n",
       "      <td>37.76047</td>\n",
       "      <td>-122.44215</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-06-09 11:21:08</td>\n",
       "      <td>new_uvreoipy</td>\n",
       "      <td>False</td>\n",
       "      <td>928700</td>\n",
       "      <td>37.75686</td>\n",
       "      <td>-122.44239</td>\n",
       "      <td>2008-06-09 11:20:12</td>\n",
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       "      <th>11220057</th>\n",
       "      <td>37.76075</td>\n",
       "      <td>-122.43687</td>\n",
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       "      <td>2008-06-09 11:22:09</td>\n",
       "      <td>new_uvreoipy</td>\n",
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       "      <td>37.76047</td>\n",
       "      <td>-122.44215</td>\n",
       "      <td>2008-06-09 11:21:08</td>\n",
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       "<p>11220058 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               lat        lng  occupancy            datetime           uid  \\\n",
       "0         37.75153 -122.39447          0 2008-05-17 07:12:10    new_abboip   \n",
       "1         37.75149 -122.39447          0 2008-05-17 07:13:34    new_abboip   \n",
       "2         37.75149 -122.39447          0 2008-05-17 07:14:34    new_abboip   \n",
       "3         37.75149 -122.39446          0 2008-05-17 07:15:35    new_abboip   \n",
       "4         37.75144 -122.39449          0 2008-05-17 07:41:43    new_abboip   \n",
       "...            ...        ...        ...                 ...           ...   \n",
       "11220053  37.74654 -122.44338          1 2008-06-09 11:18:11  new_uvreoipy   \n",
       "11220054  37.75140 -122.44296          1 2008-06-09 11:19:08  new_uvreoipy   \n",
       "11220055  37.75686 -122.44239          1 2008-06-09 11:20:12  new_uvreoipy   \n",
       "11220056  37.76047 -122.44215          1 2008-06-09 11:21:08  new_uvreoipy   \n",
       "11220057  37.76075 -122.43687          1 2008-06-09 11:22:09  new_uvreoipy   \n",
       "\n",
       "          is_change_point     tid  prev_lat   prev_lng       prev_datetime  \n",
       "0                    True       1       NaN        NaN                 NaT  \n",
       "1                   False       1  37.75153 -122.39447 2008-05-17 07:12:10  \n",
       "2                   False       1  37.75149 -122.39447 2008-05-17 07:13:34  \n",
       "3                   False       1  37.75149 -122.39447 2008-05-17 07:14:34  \n",
       "4                   False       1  37.75149 -122.39446 2008-05-17 07:15:35  \n",
       "...                   ...     ...       ...        ...                 ...  \n",
       "11220053            False  928700  37.74501 -122.44424 2008-06-09 11:17:13  \n",
       "11220054            False  928700  37.74654 -122.44338 2008-06-09 11:18:11  \n",
       "11220055            False  928700  37.75140 -122.44296 2008-06-09 11:19:08  \n",
       "11220056            False  928700  37.75686 -122.44239 2008-06-09 11:20:12  \n",
       "11220057             True  928700  37.76047 -122.44215 2008-06-09 11:21:08  \n",
       "\n",
       "[11220058 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assuming dist covered is continuous data\n",
    "combined_df[\"dist_covered\"] = combined_df.apply(\n",
    "    lambda x: haversine((x[\"prev_lat\"], x[\"prev_lng\"]), (x[\"lat\"], x[\"lng\"])), axis=1)\n",
    "combined_df[\"km_per_hour\"] = combined_df[\"dist_covered\"] / \\\n",
    "    ((combined_df[\"datetime\"] - combined_df[\"prev_datetime\"]).apply(lambda x: x.total_seconds()) / 3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removal of data with speed more than 300km/h\n",
    "combined_df = combined_df[(combined_df[\"km_per_hour\"] < 300) | (combined_df[\"km_per_hour\"].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'57.3K'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of removed rows\n",
    "human_readable_format(num_rows - len(combined_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic EDA\n",
    "\n",
    "Analysis done for samples where occupancy = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>datetime</th>\n",
       "      <th>uid</th>\n",
       "      <th>is_change_point</th>\n",
       "      <th>tid</th>\n",
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       "      <th>dist_covered</th>\n",
       "      <th>km_per_hour</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>2008-05-17 07:13:34</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>37.75153</td>\n",
       "      <td>-122.39447</td>\n",
       "      <td>2008-05-17 07:12:10</td>\n",
       "      <td>0.004448</td>\n",
       "      <td>0.190620</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.75149</td>\n",
       "      <td>-122.39447</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 07:14:34</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>37.75149</td>\n",
       "      <td>-122.39447</td>\n",
       "      <td>2008-05-17 07:13:34</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.75149</td>\n",
       "      <td>-122.39446</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 07:15:35</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>37.75149</td>\n",
       "      <td>-122.39447</td>\n",
       "      <td>2008-05-17 07:14:34</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>0.051887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.75144</td>\n",
       "      <td>-122.39449</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 07:41:43</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>37.75149</td>\n",
       "      <td>-122.39446</td>\n",
       "      <td>2008-05-17 07:15:35</td>\n",
       "      <td>0.006154</td>\n",
       "      <td>0.014128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        lat        lng  occupancy            datetime         uid  \\\n",
       "0  37.75153 -122.39447          0 2008-05-17 07:12:10  new_abboip   \n",
       "1  37.75149 -122.39447          0 2008-05-17 07:13:34  new_abboip   \n",
       "2  37.75149 -122.39447          0 2008-05-17 07:14:34  new_abboip   \n",
       "3  37.75149 -122.39446          0 2008-05-17 07:15:35  new_abboip   \n",
       "4  37.75144 -122.39449          0 2008-05-17 07:41:43  new_abboip   \n",
       "\n",
       "   is_change_point  tid  prev_lat   prev_lng       prev_datetime  \\\n",
       "0             True    1       NaN        NaN                 NaT   \n",
       "1            False    1  37.75153 -122.39447 2008-05-17 07:12:10   \n",
       "2            False    1  37.75149 -122.39447 2008-05-17 07:13:34   \n",
       "3            False    1  37.75149 -122.39447 2008-05-17 07:14:34   \n",
       "4            False    1  37.75149 -122.39446 2008-05-17 07:15:35   \n",
       "\n",
       "   dist_covered  km_per_hour  \n",
       "0           NaN          NaN  \n",
       "1      0.004448     0.190620  \n",
       "2      0.000000     0.000000  \n",
       "3      0.000879     0.051887  \n",
       "4      0.006154     0.014128  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taxi id based analysis of trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def day_range(series):\n",
    "    \"\"\"Returns the day range\"\"\"\n",
    "    diff = series.max() - series.min()\n",
    "    return diff.total_seconds() / A_DAY_IN_SECONDS\n",
    "\n",
    "grouped_df = combined_df[combined_df[\"occupancy\"] == 1].groupby(\"uid\").agg(\n",
    "    number_of_trips=pd.NamedAgg(column=\"tid\", aggfunc=\"nunique\"),\n",
    "    total_dist_covered=pd.NamedAgg(column=\"dist_covered\", aggfunc=\"sum\"),\n",
    "    days_active=pd.NamedAgg(column=\"datetime\", aggfunc=day_range))\n",
    "grouped_df[\"average_trip_distance\"] = grouped_df[\"total_dist_covered\"] / grouped_df[\"number_of_trips\"]\n",
    "grouped_df[\"distance_covered_per_day\"] = grouped_df[\"total_dist_covered\"] / grouped_df[\"days_active\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>total_dist_covered</th>\n",
       "      <th>days_active</th>\n",
       "      <th>average_trip_distance</th>\n",
       "      <th>distance_covered_per_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new_abboip</th>\n",
       "      <td>957</td>\n",
       "      <td>4100.611079</td>\n",
       "      <td>23.701910</td>\n",
       "      <td>4.284860</td>\n",
       "      <td>173.007624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_abcoij</th>\n",
       "      <td>28</td>\n",
       "      <td>95.550352</td>\n",
       "      <td>16.684109</td>\n",
       "      <td>3.412513</td>\n",
       "      <td>5.727028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_abdremlu</th>\n",
       "      <td>881</td>\n",
       "      <td>7656.255412</td>\n",
       "      <td>23.161088</td>\n",
       "      <td>8.690415</td>\n",
       "      <td>330.565448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_abgibo</th>\n",
       "      <td>1032</td>\n",
       "      <td>5665.364915</td>\n",
       "      <td>23.962153</td>\n",
       "      <td>5.489695</td>\n",
       "      <td>236.429713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_abjoolaw</th>\n",
       "      <td>396</td>\n",
       "      <td>2097.224778</td>\n",
       "      <td>23.200775</td>\n",
       "      <td>5.296022</td>\n",
       "      <td>90.394598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              number_of_trips  total_dist_covered  days_active  \\\n",
       "uid                                                              \n",
       "new_abboip                957         4100.611079    23.701910   \n",
       "new_abcoij                 28           95.550352    16.684109   \n",
       "new_abdremlu              881         7656.255412    23.161088   \n",
       "new_abgibo               1032         5665.364915    23.962153   \n",
       "new_abjoolaw              396         2097.224778    23.200775   \n",
       "\n",
       "              average_trip_distance  distance_covered_per_day  \n",
       "uid                                                            \n",
       "new_abboip                 4.284860                173.007624  \n",
       "new_abcoij                 3.412513                  5.727028  \n",
       "new_abdremlu               8.690415                330.565448  \n",
       "new_abgibo                 5.489695                236.429713  \n",
       "new_abjoolaw               5.296022                 90.394598  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'number_of_trips'}>,\n",
       "        <AxesSubplot:title={'center':'total_dist_covered'}>],\n",
       "       [<AxesSubplot:title={'center':'days_active'}>,\n",
       "        <AxesSubplot:title={'center':'average_trip_distance'}>],\n",
       "       [<AxesSubplot:title={'center':'distance_covered_per_day'}>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df.hist(bins=25, figsize=(16,10)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_of_trips               931\n",
       "total_dist_covered          4.86K\n",
       "days_active                  23.3\n",
       "average_trip_distance        4.96\n",
       "distance_covered_per_day      213\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# median of the metrics for all taxi ids\n",
    "grouped_df.median().apply(human_readable_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_of_trips              464K\n",
       "total_dist_covered          2.43M\n",
       "days_active                 11.7K\n",
       "average_trip_distance       2.91K\n",
       "distance_covered_per_day     111K\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum of the metrics for all taxi ids\n",
    "grouped_df.sum().apply(human_readable_format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal analysis of trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df = combined_df[combined_df[\"occupancy\"] == 1].groupby( \n",
    "    combined_df[\"datetime\"].map(lambda x : x.hour)).agg(\n",
    "        number_of_trips=pd.NamedAgg(column=\"tid\", aggfunc=\"nunique\"),\n",
    "        total_dist_covered=pd.NamedAgg(column=\"dist_covered\", aggfunc=\"sum\"))\n",
    "grouped_df.index.names = [\"hour_of_day\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hour_of_day'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df[\"number_of_trips\"].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hour_of_day'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df[\"total_dist_covered\"].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('22', '35.1K')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of trips peaks at 22\n",
    "human_readable_format(grouped_df.idxmax()[\"number_of_trips\"]), \\\n",
    "    human_readable_format(grouped_df.max()[\"number_of_trips\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('4', '6.58K')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of trips troughs at 4\n",
    "human_readable_format(grouped_df.idxmin()[\"number_of_trips\"]), \\\n",
    "    human_readable_format(grouped_df.min()[\"number_of_trips\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('21', '145K')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total dist covered peaks at 21\n",
    "human_readable_format(grouped_df.idxmax()[\"total_dist_covered\"]), \\\n",
    "    human_readable_format(grouped_df.max()[\"total_dist_covered\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('3', '31.2K')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total dist covered troughs at 3\n",
    "human_readable_format(grouped_df.idxmin()[\"total_dist_covered\"]), \\\n",
    "    human_readable_format(grouped_df.min()[\"total_dist_covered\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df = combined_df[combined_df[\"occupancy\"] == 1].groupby( \n",
    "    combined_df[\"datetime\"].map(lambda x : x.day_of_week)).agg(\n",
    "        number_of_trips=pd.NamedAgg(column=\"tid\", aggfunc=\"nunique\"),\n",
    "        total_dist_covered=pd.NamedAgg(column=\"dist_covered\", aggfunc=\"sum\"))\n",
    "grouped_df.index.names = [\"day_of_week\"]\n",
    "grouped_df.index = grouped_df.index.map({\n",
    "    0: \"Monday\", 1: \"Tuesday\", 2: \"Wednesday\", 3: \"Thursday\", 4: \"Friday\", 5: \"Saturday\", 6: \"Sunday\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day_of_week'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df[\"number_of_trips\"].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day_of_week'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more distances are covered in Sundays\n",
    "# people tend to go places that are far usually on weekends\n",
    "grouped_df[\"total_dist_covered\"].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2008-05-17 03:00:04'), Timestamp('2008-06-10 02:25:34'))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data is from 17th of May to 10th of July\n",
    "# however, it is not uniform for each taxi (later analyzed in CO2 emission)\n",
    "combined_df[\"datetime\"].min(), combined_df[\"datetime\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trip-wise analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def minutes(series):\n",
    "    \"\"\"Returns the minutes\"\"\"\n",
    "    diff = series.max() - series.min()\n",
    "    return diff.total_seconds() / 60 \n",
    "\n",
    "def return_last(series):\n",
    "    \"\"\"Returns the last value\"\"\"\n",
    "    return series.values[-1]\n",
    "\n",
    "grouped_df = combined_df[combined_df[\"occupancy\"] == 1].groupby(\"tid\").agg(\n",
    "    total_dist_covered=pd.NamedAgg(column=\"dist_covered\", aggfunc=\"sum\"),\n",
    "    trip_duration=pd.NamedAgg(column=\"datetime\", aggfunc=minutes),\n",
    "    median_speed=pd.NamedAgg(column=\"km_per_hour\", aggfunc=\"median\"),\n",
    "    end_lat=pd.NamedAgg(column=\"lat\", aggfunc=return_last),\n",
    "    end_lng=pd.NamedAgg(column=\"lng\", aggfunc=return_last))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_dist_covered    2.92\n",
       "trip_duration          8.5\n",
       "median_speed          18.1\n",
       "end_lat               37.8\n",
       "end_lng               -122\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total_dist_covered(km), trip duration (minutes), avg_speed(km/h), ignore end lat and lng\n",
    "grouped_df.median().apply(human_readable_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# removing long distance trips as they are relatively few compared to small trips\n",
    "# peak at 22km is most probably the airport\n",
    "grouped_df[grouped_df[\"total_dist_covered\"] < 50][\"total_dist_covered\"].hist(bins=100, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "end_lat     37.721040\n",
       "end_lng   -122.394495\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# San Francisco International Airport: 37.6213° N, 122.3790° W\n",
    "# close enough :)\n",
    "grouped_df[\n",
    "    (grouped_df[\"total_dist_covered\"] > 15) & (grouped_df[\"total_dist_covered\"] < 28)][[\"end_lat\", \"end_lng\"]].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df[grouped_df[\"trip_duration\"] < 150][\"trip_duration\"].hist(bins=100, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df[grouped_df[\"median_speed\"] < 200][\"median_speed\"].hist(bins=100, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CO2 Emission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.17M\n",
       "1    4.99M\n",
       "Name: occupancy, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# samples counts\n",
    "combined_df[\"occupancy\"].value_counts().apply(human_readable_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances_df = combined_df[combined_df[\"occupancy\"] == 0].groupby(\"uid\").agg(\n",
    "    number_of_trips=pd.NamedAgg(column=\"tid\", aggfunc=\"nunique\"),\n",
    "    total_dist_covered=pd.NamedAgg(column=\"dist_covered\", aggfunc=\"sum\"),\n",
    "    days_active=pd.NamedAgg(column=\"datetime\", aggfunc=day_range))\n",
    "distances_df[\"average_trip_distance\"] = distances_df[\"total_dist_covered\"] / distances_df[\"number_of_trips\"]\n",
    "distances_df[\"distance_covered_per_day\"] = distances_df[\"total_dist_covered\"] / distances_df[\"days_active\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>total_dist_covered</th>\n",
       "      <th>days_active</th>\n",
       "      <th>average_trip_distance</th>\n",
       "      <th>distance_covered_per_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new_egoiwroi</th>\n",
       "      <td>2</td>\n",
       "      <td>2.035594</td>\n",
       "      <td>0.123495</td>\n",
       "      <td>1.017797</td>\n",
       "      <td>16.483157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_eotcue</th>\n",
       "      <td>3</td>\n",
       "      <td>5.539578</td>\n",
       "      <td>9.586690</td>\n",
       "      <td>1.846526</td>\n",
       "      <td>0.577840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_ifeshce</th>\n",
       "      <td>8</td>\n",
       "      <td>12.721671</td>\n",
       "      <td>10.807512</td>\n",
       "      <td>1.590209</td>\n",
       "      <td>1.177114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_mfeuer</th>\n",
       "      <td>3</td>\n",
       "      <td>27.189946</td>\n",
       "      <td>0.083600</td>\n",
       "      <td>9.063315</td>\n",
       "      <td>325.240390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              number_of_trips  total_dist_covered  days_active  \\\n",
       "uid                                                              \n",
       "new_egoiwroi                2            2.035594     0.123495   \n",
       "new_eotcue                  3            5.539578     9.586690   \n",
       "new_ifeshce                 8           12.721671    10.807512   \n",
       "new_mfeuer                  3           27.189946     0.083600   \n",
       "\n",
       "              average_trip_distance  distance_covered_per_day  \n",
       "uid                                                            \n",
       "new_egoiwroi               1.017797                 16.483157  \n",
       "new_eotcue                 1.846526                  0.577840  \n",
       "new_ifeshce                1.590209                  1.177114  \n",
       "new_mfeuer                 9.063315                325.240390  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as we can see below, some taxis do not cover a lot of ground\n",
    "# this data needs to be normalized by the number of days that the taxi is active\n",
    "distances_df[distances_df[\"total_dist_covered\"] < 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.86M'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "human_readable_format(distances_df[\"total_dist_covered\"].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_of_trips               930\n",
       "total_dist_covered          3.64K\n",
       "days_active                  23.3\n",
       "average_trip_distance        3.76\n",
       "distance_covered_per_day      158\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the median in order to eliminate the effect of outliers (increasing mean)\n",
    "distances_df.median().apply(human_readable_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distances_df[\"distance_covered_per_day\"].hist(bins=25, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5778404957822241, 442.3276073169632)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# these may be faulty data due to gps, and an error calculating number of days active for taxi drivers\n",
    "distances_df[\"distance_covered_per_day\"].min(), distances_df[\"distance_covered_per_day\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily: 39.8K\n",
      "Weekly: 279K\n",
      "Monthly: 1.19M\n",
      "Yearly: 14.5M\n"
     ]
    }
   ],
   "source": [
    "# CO2 emission per taxi during idle time daily\n",
    "co2_emission_per_taxi = distances_df[\"distance_covered_per_day\"].median() * KM_TO_MILES * CO2_PER_MILE\n",
    "\n",
    "monthly_co2_emission = co2_emission_per_taxi * 30\n",
    "yearly_co2_emission = co2_emission_per_taxi * 365\n",
    "print(f\"Daily: {human_readable_format(co2_emission_per_taxi)}\")\n",
    "print(f\"Weekly: {human_readable_format(co2_emission_per_taxi * 7)}\")\n",
    "print(f\"Monthly: {human_readable_format(monthly_co2_emission)}\")\n",
    "print(f\"Yearly: {human_readable_format(yearly_co2_emission)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.8M\n"
     ]
    }
   ],
   "source": [
    "yearly_co2_emission_with_electric_car = sum([monthly_co2_emission * (0.85 ** n) for n in range(1, 13)]) \n",
    "print(human_readable_format(yearly_co2_emission_with_electric_car))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.04961608579432"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# around 60% reduction in CO2 emission assuming that the taxi cab fleet is changing at the rate of 15% per month\n",
    "100 * (yearly_co2_emission - yearly_co2_emission_with_electric_car) / yearly_co2_emission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8.72M'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From 29.9 to 11.9M --> 17.9M CO2 emission reduction per car\n",
    "human_readable_format(yearly_co2_emission - yearly_co2_emission_with_electric_car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.wikiwand.com/en/Taxis_of_the_United_States\n",
    "# 1825 taxis in San Francisco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total CO2 emission yearly: 26.5B\n",
      "Total CO2 emission yearly with taxi cab fleet changing at the rate of 15% per month: 10.6B\n",
      "Potential for yearly reduction on CO2 emissions: 15.9B\n"
     ]
    }
   ],
   "source": [
    "total_emission = 1825 * yearly_co2_emission\n",
    "total_emission_with_electric_car = 1825 * yearly_co2_emission_with_electric_car\n",
    "print(f\"Total CO2 emission yearly: {human_readable_format(total_emission)}\")\n",
    "print(\n",
    "    \"Total CO2 emission yearly with taxi cab fleet changing at the rate of \"\n",
    "    f\"15% per month: {human_readable_format(total_emission_with_electric_car)}\")\n",
    "print(\n",
    "    \"Potential for yearly reduction on CO2 emissions: \"\n",
    "    f\"{human_readable_format(total_emission - total_emission_with_electric_car)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictor for taxi drivers, \n",
    "\n",
    "Predicting the next place a passenger will hail a cab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "filtered_combined_df = combined_df[combined_df[\"is_change_point\"] == True]\n",
    "for uid in tqdm_notebook(combined_df[\"uid\"].unique()):\n",
    "    df = filtered_combined_df[combined_df[\"uid\"] == uid]\n",
    "    df[\"next_lat\"] = df[\"lat\"].shift(-1)\n",
    "    df[\"next_lng\"] = df[\"lng\"].shift(-1)\n",
    "    dfs.append(df[[\"lat\", \"lng\", \"next_lat\", \"next_lng\", \"occupancy\", \"datetime\", \"uid\", \"is_change_point\"]])\n",
    "combined_pred_df = pd.concat(dfs)\n",
    "del dfs\n",
    "collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_pred_df = combined_pred_df[combined_pred_df[\"occupancy\"] == 0].dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>next_lat</th>\n",
       "      <th>next_lng</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>datetime</th>\n",
       "      <th>uid</th>\n",
       "      <th>is_change_point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.75153</td>\n",
       "      <td>-122.39447</td>\n",
       "      <td>37.74977</td>\n",
       "      <td>-122.39724</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 07:12:10</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.75580</td>\n",
       "      <td>-122.41441</td>\n",
       "      <td>37.75053</td>\n",
       "      <td>-122.42086</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 07:56:00</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.75177</td>\n",
       "      <td>-122.43095</td>\n",
       "      <td>37.77173</td>\n",
       "      <td>-122.41838</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 08:21:50</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.77181</td>\n",
       "      <td>-122.41833</td>\n",
       "      <td>37.77181</td>\n",
       "      <td>-122.41833</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 08:46:06</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.61736</td>\n",
       "      <td>-122.38765</td>\n",
       "      <td>37.75240</td>\n",
       "      <td>-122.41842</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 09:02:58</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat        lng  next_lat   next_lng  occupancy            datetime  \\\n",
       "0  37.75153 -122.39447  37.74977 -122.39724          0 2008-05-17 07:12:10   \n",
       "1  37.75580 -122.41441  37.75053 -122.42086          0 2008-05-17 07:56:00   \n",
       "2  37.75177 -122.43095  37.77173 -122.41838          0 2008-05-17 08:21:50   \n",
       "3  37.77181 -122.41833  37.77181 -122.41833          0 2008-05-17 08:46:06   \n",
       "4  37.61736 -122.38765  37.75240 -122.41842          0 2008-05-17 09:02:58   \n",
       "\n",
       "          uid  is_change_point  \n",
       "0  new_abboip             True  \n",
       "1  new_abboip             True  \n",
       "2  new_abboip             True  \n",
       "3  new_abboip             True  \n",
       "4  new_abboip             True  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_date_features(df, date_column_name):\n",
    "    \"\"\"Creates date related features from the date column\"\"\"\n",
    "    df[f\"hour_{date_column_name}\"] = df[date_column_name].dt.hour\n",
    "    df[f\"minute_{date_column_name}\"] = df[date_column_name].dt.minute\n",
    "    df[f\"day_of_month_{date_column_name}\"] = df[date_column_name].dt.day\n",
    "    df[f\"week_of_month_{date_column_name}\"] = \\\n",
    "        (df[date_column_name].apply(lambda d: (d.day-1) // 7 + 1))\n",
    "    df[f\"day_of_week_{date_column_name}\"] = (df[date_column_name].dt.dayofweek + 1)\n",
    "    df[f\"is_wknd_{date_column_name}\"] = (df[date_column_name].dt.weekday // 4)\n",
    "    return df  # .drop(date_column_name, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_pred_df[\"next_lat\"][(combined_pred_df[\"next_lat\"] > 37.5) & (combined_pred_df[\"next_lat\"] < 37.85)].hist(bins=100, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD4CAYAAADy46FuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcPklEQVR4nO3df5BU5b3n8fcnQICr/DCQzBLHm2FLckuBiMUEMSmrhpirk5tkNRWpS2IJVryZrOKPVJFUJNktzWbZwq149RKv5pIlC3pzQ4gmC5UEXa/a62aDEEiIiGjEOJuMUBjQGCYR1iHf/aMfSDP0nO7p6enuaT6vqq45/T3nOef5eob5+pzn9GlFBGZmZgN5S707YGZmjc2FwszMMrlQmJlZJhcKMzPL5EJhZmaZRte7A5WaOnVqtLW11fy4f/jDHzjjjDNqftxac57NxXk2l6HkuWPHjoMR8fbBtBmxhaKtrY3t27fX/Li5XI6Ojo6aH7fWnGdzcZ7NZSh5Svq/g23jS09mZpbJhcLMzDK5UJiZWaYRO0dhZqeHN998k56eHo4cOVJy20mTJrFnz54a9Kq+yslz3LhxtLa2MmbMmCEfz4XCzBpaT08PEyZMoK2tDUmZ2x4+fJgJEybUqGf1UyrPiODQoUP09PQwffr0IR/Pl57MrKEdOXKEKVOmlCwS9meSmDJlSlmjsHK4UJhZw3ORGLxq/jdzoTAzs0yeozCzEaXt1h9WdX/dKz9c1f01I48ozEagtlt/eOJlI8fdd9/NH//4x8xt2traOHjwYI16VB4XCjOzGimnUDQiFwozsxK6u7s577zz+PSnP83MmTO57LLLeOONN3jxxRfp7Oxk7ty5XHLJJTz33HP09fXx3ve+l1wuB8Dy5cv50pe+xKpVq9i3bx8LFixgwYIFFR8TYMeOHbznPe/h4osv5vOf/zyzZs0azvRdKMzMyvHCCy+wdOlSdu/ezeTJk3nooYfo6uria1/7Gjt27OCrX/0qN9xwA6NHj2bt2rVcf/31PProozz88MPcdttt3Hzzzbzzne/kiSee4Iknnqj4mAA33HADX//619myZQujRo0azrQBT2abmZVl+vTpzJkzB4C5c+fS3d3NT37yExYuXHhim6NHjwIwc+ZMrrnmGj760Y+yZcsW3vrWt1btmL/73e/o7e3lfe97HwCf/OQn+cEPflB5YmVwoTAzK8PYsWNPLI8aNYoDBw4wefJkdu7cWXT7Xbt2MXnyZA4cOFC1Y77xxhtERMX7q5QLhZmNKFm3s9byER4TJ05k+vTpfPe732XhwoVEBE8//TQXXHAB3/ve9zh06BBPPvkkH/nIR9i2bRuTJ09mwoQJHD58mKlTp1Z83LPOOoszzzyTp556ivnz57N+/foqZlWc5yjMzCr0rW99izVr1nDBBRcwc+ZMNm7cyMGDB7n11ltZs2YN7373u7nxxhu55ZZbAOjq6uJDH/pQWZPZWe655x66urq4+OKLiQgmTZpUjXQG5BGFmVkJbW1tPPPMMyfef+5znzux/PDDD5+y/S9/+csTyzfffPOJ5Ztuuombbrop81jd3d0ATJ06dcBjnnfeeTz99NMArFy5kvb29jIzqYwLhZnZCPPII49w991309fXx7ve9S7Wrl07rMdzoTAzq4OLLrroxF1Sxz3wwAPMnj27ZNuPf/zjXHvttcPUs1O5UJhZw4uIpnuC7NatW4d1/9W8O8qT2WbW0MaNG8ehQ4fqclvoSHX8i4vGjRtXlf2VHFFIGgc8CYxN2z8YEbdJuh34NPDbtOkXI+JHqc1y4DrgGHBzRDyS4nOBtcB44EfALRERksYC9wNzgUPA30ZEd1UyNLMRrbW1lZ6eHn7729+W3PbIkSNV++PYyMrJ8/hXoVZDOZeejgIfiIheSWOAH0vanNbdFRFfLdxY0vnAImAm8E7gXyW9OyKOAfcBXcBT5AtFJ7CZfFF5LSLOlbQIuAP426GnZ2Yj3ZgxY8r+Os9cLseFF144zD2qv1rnWfLSU+T1prdj0itrDHgFsD4ijkbES8BeYJ6kacDEiNgS+THk/cCVBW3WpeUHgUvVbBckzcxGqLImsyWNAnYA5wL/GBFbJX0IuFHSYmA7sCwiXgPOJj9iOK4nxd5My/3jpJ+/AYiIPkmvA1OAkx7KLqmL/IiElpaWE09nrKXe3t66HLfWnGdjWza778RyOf0fqXkOlvMcHmUVinTZaI6kycD3Jc0ifxnpK+RHF18B7gQ+BRQbCURGnBLrCvuxGlgN0N7eHh0dHeV0v6pyuRz1OG6tOc/Gdm3BFxZ1X91RcvuRmudgOc/hMai7niLid0AO6IyIAxFxLCL+BHwDmJc26wHOKWjWCuxL8dYi8ZPaSBoNTAJeHUzfzMxseJQsFJLenkYSSBoPfBB4Ls05HPcx4PhnzTcBiySNlTQdmAFsi4j9wGFJ89P8w2JgY0GbJWn5KuDx8L1wZmYNoZxLT9OAdWme4i3Ahoj4gaQHJM0hf4moG/gMQETslrQBeBboA5amS1cA1/Pn22M3pxfAGuABSXvJjyQWDT01MzOrhpKFIiKeBk65DysirsloswJYUSS+HTjlO/si4giwsH/czMzqz5/MNjOzTC4UZmaWyYXCzMwyuVCYmVkmFwozM8vkQmFmZplcKMzMLJMLhZmZZXKhMDOzTC4UZmaWyYXCzMwyuVCYmVkmFwozM8vkQmFmZpnK+ipUMxtebYVfbbryw3XsidmpPKIwM7NMLhRmZpbJhcLMzDKVLBSSxknaJukXknZL+nKKv03So5JeSD/PKmizXNJeSc9LurwgPlfSrrRulSSl+FhJ30nxrZLahiFXMzOrQDkjiqPAByLiAmAO0ClpPnAr8FhEzAAeS++RdD6wCJgJdAL3ShqV9nUf0AXMSK/OFL8OeC0izgXuAu4YempmZlYNJe96iogAetPbMekVwBVAR4qvA3LAF1J8fUQcBV6StBeYJ6kbmBgRWwAk3Q9cCWxObW5P+3oQuEeS0rHNmlLhnU5mjaysOQpJoyTtBF4BHo2IrUBLROwHSD/fkTY/G/hNQfOeFDs7LfePn9QmIvqA14EpFeRjZmZVVtbnKCLiGDBH0mTg+5JmZWyuYrvIiGe1OXnHUhf5S1e0tLSQy+UyujE8ent763LcWnOew2/Z7L6i8XL6U9i2nO19PptLrfMc1AfuIuJ3knLk5xYOSJoWEfslTSM/2oD8SOGcgmatwL4Uby0SL2zTI2k0MAl4tcjxVwOrAdrb26Ojo2Mw3a+KXC5HPY5ba85z+F07wKWn7qs7BtW2nO19PptLrfMs566nt6eRBJLGAx8EngM2AUvSZkuAjWl5E7Ao3ck0nfyk9bZ0eeqwpPnpbqfF/doc39dVwOOenzAzawzljCimAevSnUtvATZExA8kbQE2SLoO+DWwECAidkvaADwL9AFL06UrgOuBtcB48pPYm1N8DfBAmvh+lfxdU2Y2SH4UiA2Hcu56ehq4sEj8EHDpAG1WACuKxLcDp8xvRMQRUqExM7PG4k9mm5lZJhcKMzPL5EJhZmaZ/H0UZiOcP+Ftw80jCjMzy+RCYWZmmVwozMwskwuFmZllcqEwM7NMLhRmZpbJhcLMzDK5UJiZWSYXCjMzy+RCYWZmmfwID7MRwo/qsHrxiMLMzDK5UJiZWSYXCjMzy+Q5CrMmVTinsbbzjDr2xEa6kiMKSedIekLSHkm7Jd2S4rdLelnSzvT6m4I2yyXtlfS8pMsL4nMl7UrrVklSio+V9J0U3yqpbRhyNTOzCpRz6akPWBYR5wHzgaWSzk/r7oqIOen1I4C0bhEwE+gE7pU0Km1/H9AFzEivzhS/DngtIs4F7gLuGHpqZmZWDSULRUTsj4ifpeXDwB7g7IwmVwDrI+JoRLwE7AXmSZoGTIyILRERwP3AlQVt1qXlB4FLj482zMysvgY1R5EuCV0IbAXeD9woaTGwnfyo4zXyReSpgmY9KfZmWu4fJ/38DUBE9El6HZgCHOx3/C7yIxJaWlrI5XKD6X5V9Pb21uW4teY8h9+y2X1F4wP1Z6Dty+Hz2VxqnWfZhULSmcBDwGcj4veS7gO+AkT6eSfwKaDYSCAy4pRY9+dAxGpgNUB7e3t0dHSU2/2qyeVy1OO4teY8h9+1A3yArvvqjkFtX461nWf4fDaRWudZ1u2xksaQLxLfiojvAUTEgYg4FhF/Ar4BzEub9wDnFDRvBfaleGuR+EltJI0GJgGvVpKQmZlVVzl3PQlYA+yJiL8viE8r2OxjwDNpeROwKN3JNJ38pPW2iNgPHJY0P+1zMbCxoM2StHwV8HiaxzAzszor59LT+4FrgF2SdqbYF4FPSJpD/hJRN/AZgIjYLWkD8Cz5O6aWRsSx1O56YC0wHticXpAvRA9I2kt+JLFoKEmZmVn1lCwUEfFjis8h/CijzQpgRZH4dmBWkfgRYGGpvpiZWe35ER5mZpbJhcLMzDK5UJiZWSYXCjMzy+RCYWZmmVwozMwskwuFmZllcqEwM7NMLhRmZpbJhcLMzDK5UJiZWSYXCjMzy+RCYWZmmVwozMwskwuFmZllKvs7s83sZG0F32HdvfLDdeyJ2fDyiMLMzDK5UJiZWaaShULSOZKekLRH0m5Jt6T42yQ9KumF9POsgjbLJe2V9LykywvicyXtSutWSVKKj5X0nRTfKqltGHI1M7MKlDOi6AOWRcR5wHxgqaTzgVuBxyJiBvBYek9atwiYCXQC90oalfZ1H9AFzEivzhS/DngtIs4F7gLuqEJuZmZWBSUnsyNiP7A/LR+WtAc4G7gC6EibrQNywBdSfH1EHAVekrQXmCepG5gYEVsAJN0PXAlsTm1uT/t6ELhHkiIihpyhmZ3Ek/A2WBrM3+J0SehJYBbw64iYXLDutYg4S9I9wFMR8c8pvoZ8MegGVkbEB1P8EuALEfERSc8AnRHRk9a9CFwUEQf7Hb+L/IiElpaWuevXr68o6aHo7e3lzDPPrPlxa815lrbr5ddPLM8+e9KQ2hcq3NdA2wzW9EmjTuQ51H43Mv/elrZgwYIdEdE+mDZl3x4r6UzgIeCzEfH7NL1QdNMisciIZ7U5ORCxGlgN0N7eHh0dHSV6XX25XI56HLfWnGdp1xb+n/nVg99HYftChfsaaJvBWtt5xok8h9rvRubf2+FR1l1PksaQLxLfiojvpfABSdPS+mnAKyneA5xT0LwV2JfirUXiJ7WRNBqYBLw62GTMzKz6yrnrScAaYE9E/H3Bqk3AkrS8BNhYEF+U7mSaTn7Selua6zgsaX7a5+J+bY7v6yrgcc9PmJk1hnIuPb0fuAbYJWlnin0RWAlskHQd8GtgIUBE7Ja0AXiW/B1TSyPiWGp3PbAWGE9+3mJziq8BHkgT36+Sv2vKzMwaQDl3Pf2Y4nMIAJcO0GYFsKJIfDv5ifD+8SOkQmNmZo3Fn8w2M7NMLhRmZpbJhcLMzDL5MeNmDaatSp+dMKsWFwqz08Cul1+v2of37PTjS09mZpbJhcLMzDK5UJiZWSYXCjMzy+RCYWZmmVwozMwskwuFmZll8ucozKrAXy9qzcwjCjMzy+RCYWZmmVwozMwskwuFmZllcqEwM7NMJQuFpG9KekXSMwWx2yW9LGlnev1NwbrlkvZKel7S5QXxuZJ2pXWrJCnFx0r6TopvldRW5RzNzGwIyhlRrAU6i8Tviog56fUjAEnnA4uAmanNvZJGpe3vA7qAGel1fJ/XAa9FxLnAXcAdFeZiZmbDoGShiIgngVfL3N8VwPqIOBoRLwF7gXmSpgETI2JLRARwP3BlQZt1aflB4NLjow0zM6u/oXzg7kZJi4HtwLKIeA04G3iqYJueFHszLfePk37+BiAi+iS9DkwBDvY/oKQu8qMSWlpayOVyQ+h+ZXp7e+ty3FpznqUtm91XNF7u/gZqPxxaxhc/XrOdY//eDo9KC8V9wFeASD/vBD4FFBsJREacEutODkasBlYDtLe3R0dHx6A6XQ25XI56HLfWnGdpA31jXPfV5e2vlt84t2x2H3fuOvWfe7l9HSn8ezs8KrrrKSIORMSxiPgT8A1gXlrVA5xTsGkrsC/FW4vET2ojaTQwifIvdZmZ2TCrqFCkOYfjPgYcvyNqE7Ao3ck0nfyk9baI2A8cljQ/zT8sBjYWtFmSlq8CHk/zGGZm1gBKXnqS9G2gA5gqqQe4DeiQNIf8JaJu4DMAEbFb0gbgWaAPWBoRx9Kurid/B9V4YHN6AawBHpC0l/xIYlEV8jIzsyopWSgi4hNFwmsytl8BrCgS3w7MKhI/Aiws1Q8zM6sPP2bcrMra+k1S+7HjNtL5ER5mZpbJhcLMzDK5UJiZWSYXCjMzy+TJbLMa6j/RbTYSeERhZmaZXCjMzCyTC4WZmWVyoTAzs0wuFGZmlsmFwszMMrlQmJlZJhcKMzPL5EJhZmaZXCjMzCyTH+FhdhorfKSIvzfDBuJCYTYIzfysJhcNG0jJS0+SvinpFUnPFMTeJulRSS+kn2cVrFsuaa+k5yVdXhCfK2lXWrdKklJ8rKTvpPhWSW1VztHMzIagnDmKtUBnv9itwGMRMQN4LL1H0vnAImBmanOvpFGpzX1AFzAjvY7v8zrgtYg4F7gLuKPSZMzMrPpKFoqIeBJ4tV/4CmBdWl4HXFkQXx8RRyPiJWAvME/SNGBiRGyJiADu79fm+L4eBC49PtowM7P6q3SOoiUi9gNExH5J70jxs4GnCrbrSbE303L/+PE2v0n76pP0OjAFONj/oJK6yI9KaGlpIZfLVdj9yvX29tbluLXmPItbNrtv0Mco3H8l7auhZfzgjj1Sz71/b4dHtSezi40EIiOe1ebUYMRqYDVAe3t7dHR0VNDFocnlctTjuLXmPIu7toLJ7O6r/7z/StpXw7LZfdy5q/x/7oV9Hkn8ezs8Ki0UByRNS6OJacArKd4DnFOwXSuwL8Vbi8QL2/RIGg1M4tRLXWZWQ74DygpV+oG7TcCStLwE2FgQX5TuZJpOftJ6W7pMdVjS/DT/sLhfm+P7ugp4PM1jmJlZAyg5opD0baADmCqpB7gNWAlskHQd8GtgIUBE7Ja0AXgW6AOWRsSxtKvryd9BNR7YnF4Aa4AHJO0lP5JYVJXMzMysKkoWioj4xACrLh1g+xXAiiLx7cCsIvEjpEJjZmaNx5/MNithqJ/GbuZPc9vpwQ8FNDOzTC4UZmaWyYXCzMwyuVCYmVkmFwozM8vkQmFmZplcKMzMLJMLhZmZZXKhMDOzTC4UZmaWyYXCzMwyuVCYmVkmFwozM8vkQmFmZplcKMzMLJMLhZmZZfIXF5klhV8w1L3yw3XsiVljGdKIQlK3pF2SdkranmJvk/SopBfSz7MKtl8uaa+k5yVdXhCfm/azV9IqSRpKv8zMrHqqcelpQUTMiYj29P5W4LGImAE8lt4j6XxgETAT6ATulTQqtbkP6AJmpFdnFfplZmZVMBxzFFcA69LyOuDKgvj6iDgaES8Be4F5kqYBEyNiS0QEcH9BGzMzq7OhzlEE8D8lBfBPEbEaaImI/QARsV/SO9K2ZwNPFbTtSbE303L/uFndFM5XmJ3uhloo3h8R+1IxeFTScxnbFpt3iIz4qTuQushfoqKlpYVcLjfI7g5db29vXY5ba6djnstm99W3M8OoZXzl+Y2k34PT8fe2FoZUKCJiX/r5iqTvA/OAA5KmpdHENOCVtHkPcE5B81ZgX4q3FokXO95qYDVAe3t7dHR0DKX7FcnlctTjuLV2OuZ5bROPIpbN7uPOXZX9c+++uqO6nRlGp+PvbS1UPEch6QxJE44vA5cBzwCbgCVpsyXAxrS8CVgkaayk6eQnrbely1SHJc1PdzstLmhjZmZ1NpQRRQvw/XQn62jgXyLiYUk/BTZIug74NbAQICJ2S9oAPAv0AUsj4lja1/XAWmA8sDm9zMysAVRcKCLiV8AFReKHgEsHaLMCWFEkvh2YVWlfzAajcKJ62ey+pr7kZFYNfoSHmZll8iM8zCyTH21iHlGYmVkmFwozM8vkQmFmZplcKMzMLJMns+204Gc3mVXOIwozM8vkQmFmZpl86clGDN/Pb1YfHlGYmVkmjyhsRBpodOFRh1n1eURhZmaZPKKwEW+gW199S6xZdbhQWN0M9Ifcl4zMGosvPZmZWSaPKKymBns5yJePzOrPhcKGxVD+wLs4mDUWX3oyM7NMDTOikNQJ/AMwCvhvEbGyzl2yEvx//manh4YoFJJGAf8I/DXQA/xU0qaIeLa+PWseg/2jvmx2H9e6EFg//kDj6akhCgUwD9gbEb8CkLQeuAJwoRgk/1++1Uo5v2suJs1BEVHvPiDpKqAzIv4uvb8GuCgibuy3XRfQld7+FfB8TTuaNxU4WIfj1przbC7Os7kMJc93RcTbB9OgUUYUKhI7pYJFxGpg9fB3Z2CStkdEez37UAvOs7k4z+ZS6zwb5a6nHuCcgvetwL469cXMzAo0SqH4KTBD0nRJbwUWAZvq3CczM6NBLj1FRJ+kG4FHyN8e+82I2F3nbg2krpe+ash5Nhfn2VxqmmdDTGabmVnjapRLT2Zm1qBcKMzMLNNpWygkLZS0W9KfJLUXxP9a0g5Ju9LPD6T4X0j6oaTnUruijxiR1CbpDUk70+vrBetykp4vWPeOJs1zbtrvXkmrJBW7/XlE5Fmwn7+U1CvpcwWxpjmfBfsplmfTnE9J8wrO1y8kfaxgXdOczxJ5Dv58RsRp+QLOI/+hvRzQXhC/EHhnWp4FvJyW/wJYkJbfCvxv4ENF9tsGPDPAMU86VhPnuQ24mPznYzYXaz9S8izYz0PAd4HPNeP5LJFn05zPtN3otDwNeKXgfdOczxJ5Dvp8NsRdT/UQEXsA+hfTiPh5wdvdwDhJYyPij8ATaZv/J+ln5D/v0dBqnaekacDEiNiS3t8PXEn+F3LYDGeekq4EfgX8ofo9H5xa59ls5zNtd9w4inywt5ZqnWel5/O0vfRUpo8DP4+Io4VBSZOBjwKPDdBuuqSfS/pfki7pt+6/p+Hgf6zFEL5M1czzbPIfoDyuJ8UawaDzlHQG8AXgywPssynOZ0aeTXU+0/qLJO0GdgH/PiL6ClY3xflM64vlWdH5bOoRhaR/Bf5NkVVfioiNJdrOBO4ALusXHw18G1gV6SGG/ewH/jIiDkmaC/wPSTMj4vfA1RHxsqQJ5If41wD3DzqxU/vaMHlS5uNYKlGnPL8M3BURvUX+bjTT+Rwoz2Y7n0TEVmCmpPOAdZI2R8QRmut8Fs2TCs9nUxeKiPhgJe0ktQLfBxZHxIv9Vq8GXoiIuwc45lHgaFreIelF4N3A9oh4OcUPS/oX8k/NHfIvYoPl2cPJQ+GqPY6lHnkCFwFXSfqvwGTgT5KORMQ9zXQ+GSBP8n8wm+l8Fh5/j6Q/kJ8DaKp/n/2OX5hnRf8+fempnzSc+yGwPCL+T791/xmYBHw2o/3blf9+DST9W2AG8CtJoyVNTfExwEeAZ4Yjh3IMV54RsR84LGl+GrovBjL/r2k4DTXPiLgkItoiog24G/gvEXFPs53PgfJstvOp/GOCRqfld5GfSO5utvM5UJ4Vn89Ss93N+gI+Rr66HgUOAI+k+H8gP5m3s+D1DvKVN4A9BfG/S23+HfCf0vLHyU8+/QL4GfDRFD8D2AE8ndb/AzCq2fJM69rJ/yN7EbiH9ASAkZhnv2PcTrobqNnO50B5Ntv5JH85aXda/zPgymY8nwPlWen59CM8zMwsky89mZlZJhcKMzPL5EJhZmaZXCjMzCyTC4WZmWVyoTAzs0wuFGZmlun/AwPEtHIPsaWjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_pred_df[\"next_lng\"][(combined_pred_df[\"next_lng\"] > -122.7) & (combined_pred_df[\"next_lng\"] < -122.3)].hist(bins=100, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>next_lat</th>\n",
       "      <th>next_lng</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>datetime</th>\n",
       "      <th>uid</th>\n",
       "      <th>is_change_point</th>\n",
       "      <th>hour_datetime</th>\n",
       "      <th>minute_datetime</th>\n",
       "      <th>day_of_month_datetime</th>\n",
       "      <th>week_of_month_datetime</th>\n",
       "      <th>day_of_week_datetime</th>\n",
       "      <th>is_wknd_datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.75153</td>\n",
       "      <td>-122.39447</td>\n",
       "      <td>37.74977</td>\n",
       "      <td>-122.39724</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 07:12:10</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.75580</td>\n",
       "      <td>-122.41441</td>\n",
       "      <td>37.75053</td>\n",
       "      <td>-122.42086</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 07:56:00</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.75177</td>\n",
       "      <td>-122.43095</td>\n",
       "      <td>37.77173</td>\n",
       "      <td>-122.41838</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 08:21:50</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.77181</td>\n",
       "      <td>-122.41833</td>\n",
       "      <td>37.77181</td>\n",
       "      <td>-122.41833</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 08:46:06</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>46</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.61736</td>\n",
       "      <td>-122.38765</td>\n",
       "      <td>37.75240</td>\n",
       "      <td>-122.41842</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-17 09:02:58</td>\n",
       "      <td>new_abboip</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat        lng  next_lat   next_lng  occupancy            datetime  \\\n",
       "0  37.75153 -122.39447  37.74977 -122.39724          0 2008-05-17 07:12:10   \n",
       "1  37.75580 -122.41441  37.75053 -122.42086          0 2008-05-17 07:56:00   \n",
       "2  37.75177 -122.43095  37.77173 -122.41838          0 2008-05-17 08:21:50   \n",
       "3  37.77181 -122.41833  37.77181 -122.41833          0 2008-05-17 08:46:06   \n",
       "4  37.61736 -122.38765  37.75240 -122.41842          0 2008-05-17 09:02:58   \n",
       "\n",
       "          uid  is_change_point  hour_datetime  minute_datetime  \\\n",
       "0  new_abboip             True              7               12   \n",
       "1  new_abboip             True              7               56   \n",
       "2  new_abboip             True              8               21   \n",
       "3  new_abboip             True              8               46   \n",
       "4  new_abboip             True              9                2   \n",
       "\n",
       "   day_of_month_datetime  week_of_month_datetime  day_of_week_datetime  \\\n",
       "0                     17                       3                     6   \n",
       "1                     17                       3                     6   \n",
       "2                     17                       3                     6   \n",
       "3                     17                       3                     6   \n",
       "4                     17                       3                     6   \n",
       "\n",
       "   is_wknd_datetime  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_pred_df = create_date_features(combined_pred_df, \"datetime\")\n",
    "combined_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validate_lgbm(data, label, params):\n",
    "    \"\"\"Cross-validates lgbm using time  cross validation\"\"\"\n",
    "    return lgb.cv(\n",
    "        params,\n",
    "        lgb.Dataset(data, label=label),\n",
    "        num_boost_round=NUM_BOOSTING_ROUNDS, # more rounds can be better\n",
    "        folds=TimeSeriesSplit(), #KFold(), TimeSeriesSplit() \n",
    "        metrics=\"mae\")\n",
    "\n",
    "def objective(trial, data, label):\n",
    "    \"\"\"Optuna objective for maximizing auc in lgbm cross validation\"\"\"\n",
    "    params = {\n",
    "        \"objective\": \"regression_l1\", # regression_l1, regression, huber, fair, poisson, quantile, mape, gamma, tweedie\n",
    "        \"metric\": \"mae\",\n",
    "        \"lambda_l1\": trial.suggest_float(\"lambda_l1\", 1e-8, 10.0, log=True),\n",
    "        \"lambda_l2\": trial.suggest_float(\"lambda_l2\", 1e-8, 10.0, log=True),\n",
    "        \"num_leaves\": trial.suggest_int(\"num_leaves\", 2, 256),\n",
    "        \"feature_fraction\": trial.suggest_float(\"feature_fraction\", 0.4, 1.0),\n",
    "        \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.4, 1.0),\n",
    "        \"bagging_freq\": trial.suggest_int(\"bagging_freq\", 1, 7),\n",
    "        \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 5, 100)}\n",
    "    cv_results = cross_validate_lgbm(data, label, params)\n",
    "    return cv_results[\"l1-mean\"][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lat', 'lng', 'next_lat', 'next_lng', 'occupancy', 'datetime', 'uid',\n",
       "       'is_change_point', 'hour_datetime', 'minute_datetime',\n",
       "       'day_of_month_datetime', 'week_of_month_datetime',\n",
       "       'day_of_week_datetime', 'is_wknd_datetime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_pred_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_pred_df = combined_pred_df.sort_values(\"datetime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross validation mae: 0.00915\n"
     ]
    }
   ],
   "source": [
    "lng_data = combined_pred_df[[\n",
    "    \"lat\", \"lng\", \"hour_datetime\", \"minute_datetime\", \"day_of_month_datetime\", \"week_of_month_datetime\", \n",
    "    \"day_of_week_datetime\", \"is_wknd_datetime\"]]\n",
    "# data[\"uid\"] = data[\"uid\"].astype(\"category\")\n",
    "lng_labels = combined_pred_df[\"next_lng\"]\n",
    "\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING) # INFO, WARNING\n",
    "study = optuna.create_study(direction=\"minimize\")\n",
    "study.optimize(\n",
    "    lambda trial: objective(trial, lng_data, lng_labels), \n",
    "    n_trials=NUM_TRIALS, n_jobs=-1) # more trials is better\n",
    "lng_mae = round(study.best_value, 5)\n",
    "print(f\"Best cross validation mae: {lng_mae}\")\n",
    "\n",
    "lng_params = study.best_params\n",
    "# parameters that are not tuned are left out of the best params dict\n",
    "lng_params[\"objective\"] = \"regression\"\n",
    "lng_params[\"metric\"] = \"mae\"\n",
    "lng_params[\"verbosity\"] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross validation mae: 0.01351\n"
     ]
    }
   ],
   "source": [
    "lat_data = combined_pred_df[[\n",
    "    \"lat\", \"lng\", \"hour_datetime\", \"minute_datetime\", \"day_of_month_datetime\", \"week_of_month_datetime\", \n",
    "    \"day_of_week_datetime\", \"is_wknd_datetime\"]]\n",
    "lat_labels = combined_pred_df[\"next_lat\"]\n",
    "# data[\"uid\"] = data[\"uid\"].astype(\"category\")\n",
    "\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING) # INFO, WARNING\n",
    "study = optuna.create_study(direction=\"minimize\")\n",
    "study.optimize(\n",
    "    lambda trial: objective(trial, lat_data, lat_labels), \n",
    "    n_trials=NUM_TRIALS, n_jobs=-1) # more trials is better\n",
    "lat_mae = round(study.best_value, 5)\n",
    "print(f\"Best cross validation mae: {lat_mae}\")\n",
    "\n",
    "lat_params = study.best_params\n",
    "# parameters that are not tuned are left out of the best params dict\n",
    "lat_params[\"objective\"] = \"regression\"\n",
    "lat_params[\"metric\"] = \"mae\"\n",
    "lat_params[\"verbosity\"] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation mae in kms: 1.7039273042745329\n"
     ]
    }
   ],
   "source": [
    "# san francisco lat long 37.7749° N, 122.4194° W \n",
    "cross_validation_mae = haversine((37.7749, -122.4194), (37.7749 + lat_mae, -122.4194 + lng_mae))\n",
    "print(f\"Cross validation mae in kms: {cross_validation_mae}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# baseline model: next passenger is in the same location as the drop off point\n",
    "pred_df = pd.DataFrame([\n",
    "    combined_pred_df[\"next_lat\"] - combined_pred_df[\"lat\"], combined_pred_df[\"next_lng\"] - combined_pred_df[\"lng\"]]).T\n",
    "pred_df = pred_df.abs()\n",
    "pred_df.columns = [\"pred_lat\", \"pred_lng\"]\n",
    "pred_df[\"mae_in_kms\"] = pred_df.apply(\n",
    "    lambda x: abs(haversine((37.7749, -122.4194), (37.7749 + x[\"pred_lat\"], -122.4194 + x[\"pred_lng\"]))), axis=1)\n",
    "pred_means = pred_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pred_lat      0.016725\n",
       "pred_lng      0.012630\n",
       "mae_in_kms    2.437720\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pred_lat      0.038954\n",
       "pred_lng      0.025736\n",
       "mae_in_kms    4.755497\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred_lat</th>\n",
       "      <th>pred_lng</th>\n",
       "      <th>mae_in_kms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291968</th>\n",
       "      <td>0.13473</td>\n",
       "      <td>0.00525</td>\n",
       "      <td>14.988405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440263</th>\n",
       "      <td>0.06634</td>\n",
       "      <td>0.08442</td>\n",
       "      <td>10.460357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313868</th>\n",
       "      <td>0.03822</td>\n",
       "      <td>0.29048</td>\n",
       "      <td>25.875417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260305</th>\n",
       "      <td>0.01836</td>\n",
       "      <td>0.24338</td>\n",
       "      <td>21.485510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157994</th>\n",
       "      <td>0.30296</td>\n",
       "      <td>0.27799</td>\n",
       "      <td>41.585714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444941</th>\n",
       "      <td>0.17107</td>\n",
       "      <td>0.02744</td>\n",
       "      <td>19.174068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449602</th>\n",
       "      <td>0.26177</td>\n",
       "      <td>0.20388</td>\n",
       "      <td>34.164443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250705</th>\n",
       "      <td>0.09655</td>\n",
       "      <td>0.13621</td>\n",
       "      <td>16.074593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195466</th>\n",
       "      <td>0.05515</td>\n",
       "      <td>0.11051</td>\n",
       "      <td>11.483710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355439</th>\n",
       "      <td>0.16841</td>\n",
       "      <td>0.00298</td>\n",
       "      <td>18.728191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33037 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pred_lat  pred_lng  mae_in_kms\n",
       "291968   0.13473   0.00525   14.988405\n",
       "440263   0.06634   0.08442   10.460357\n",
       "313868   0.03822   0.29048   25.875417\n",
       "260305   0.01836   0.24338   21.485510\n",
       "157994   0.30296   0.27799   41.585714\n",
       "...          ...       ...         ...\n",
       "444941   0.17107   0.02744   19.174068\n",
       "449602   0.26177   0.20388   34.164443\n",
       "250705   0.09655   0.13621   16.074593\n",
       "195466   0.05515   0.11051   11.483710\n",
       "355439   0.16841   0.00298   18.728191\n",
       "\n",
       "[33037 rows x 3 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df[pred_df[\"mae_in_kms\"] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZWa5+C4WklZL2S3q1KHaWpM2SdqXnCUX7lkpql7RT0jVF8UsltaV996VbopJum7ouxbdIaixqszC9x650j24zMxtkJzKjWAXM7RVbAjwVEdOAp9JrJE0nu5XpBanNg5LqUpuHgMVk99GeVtTnIuCDiDgPuBe4O/V1FtltVy8HZgB3FhckMzMbHP0Wioh4luxe1sXmA6vT9mrguqL4oxFxOCLeAtqBGZImAWdGxPMREcAjvdr09LUBmJ1mG9cAmyPiQER8AGzm4wXLzMyqrN97ZvehPiL2AUTEPknnpPhk4IWi4zpS7Eja7h3vabMn9dUt6SAwsTheos1xJC0mm61QX19PoVAoMy2oHwu3N3cDVNTPcNLZ2VkzuULt5QvOuVZUK+dyC0VfVCIWOfFy2xwfjFgBrABoaWmJ1tbWfgfal/vXbuSetuxj2X1j+f0MJ4VCgUo+s+Gm1vIF51wrqpVzuVc9vZuWk0jP+1O8A5hSdFwDsDfFG0rEj2sjaRQwnmypq6++zMxsEJVbKDYBPVchLQQ2FsUXpCuZppKdtN6alqkOSZqZzj/c1KtNT1/XA0+n8xhPAHMkTUgnseekmJmZDaJ+l54kfQ9oBc6W1EF2JdJdwHpJi4B3gBsAIuI1SeuB14Fu4NaIOJq6uoXsCqqxwOPpAfAwsEZSO9lMYkHq64CkvwK2peOWRUTvk+pmZlZl/RaKiPhyH7tm93H8cmB5ifh2oKlEvItUaErsWwms7G+MZmZWPf5mtpmZ5XKhMDOzXC4UZmaWy4XCzMxyuVCYmVkuFwozM8vlQmFmZrlcKMzMLJcLhZmZ5XKhMDOzXC4UZmaWa6DvRzGiNC557Nj27rvmDeFIzMyGjmcUZmaWy4XCzMxyuVCYmVkuFwozM8vlQmFmZrkqKhSS/ruk1yS9Kul7ksZIOkvSZkm70vOEouOXSmqXtFPSNUXxSyW1pX33pftqk+69vS7Ft0hqrGS8ZmZ28souFJImA38KtEREE1BHdr/rJcBTETENeCq9RtL0tP8CYC7woKS61N1DwGJgWnrMTfFFwAcRcR5wL3B3ueM1M7PyVLr0NAoYK2kUcBqwF5gPrE77VwPXpe35wKMRcTgi3gLagRmSJgFnRsTzERHAI73a9PS1AZjdM9swM7PBUfYX7iLi55K+BbwD/D/gyYh4UlJ9ROxLx+yTdE5qMhl4oaiLjhQ7krZ7x3va7El9dUs6CEwE3isei6TFZDMS6uvrKRQK5aZF/Vi4vbn7Y/FK+jzVdXZ2juj8equ1fME514pq5Vx2oUjnHuYDU4FfAt+X9JW8JiVikRPPa3N8IGIFsAKgpaUlWltbc4aR7/61G7mn7eMfy+4by+/zVFcoFKjkMxtuai1fcM61olo5V7L09HngrYj4RUQcAX4AXAm8m5aTSM/70/EdwJSi9g1kS1Udabt3/Lg2aXlrPHCggjGbmdlJqqRQvAPMlHRaOm8wG9gBbAIWpmMWAhvT9iZgQbqSaSrZSeutaZnqkKSZqZ+berXp6et64Ol0HsPMzAZJJecotkjaAPwE6AZ+Srb8cwawXtIismJyQzr+NUnrgdfT8bdGxNHU3S3AKmAs8Hh6ADwMrJHUTjaTWFDueM3MrDwV/XpsRNwJ3NkrfJhsdlHq+OXA8hLx7UBTiXgXqdCYmdnQ8DezzcwslwuFmZnlcqEwM7NcLhRmZpbLhcLMzHK5UJiZWS4XCjMzy+VCYWZmuVwozMwslwuFmZnlcqEwM7NcLhRmZpbLhcLMzHK5UJiZWS4XCjMzy+VCYWZmuSoqFJJ+Q9IGSW9I2iHpCklnSdosaVd6nlB0/FJJ7ZJ2SrqmKH6ppLa07750S1TSbVPXpfgWSY2VjNfMzE5epTOKbwP/JyLOBy4iu2f2EuCpiJgGPJVeI2k62a1MLwDmAg9Kqkv9PAQsJruP9rS0H2AR8EFEnAfcC9xd4XjNzOwklV0oJJ0JXEV2X2si4lcR8UtgPrA6HbYauC5tzwcejYjDEfEW0A7MkDQJODMino+IAB7p1aanrw3A7J7ZhpmZDY5K7pn9GeAXwP+WdBHwInAbUB8R+wAiYp+kc9Lxk4EXitp3pNiRtN073tNmT+qrW9JBYCLwXvFAJC0mm5FQX19PoVAoO6n6sXB7c/fH4pX0earr7Owc0fn1Vmv5gnOuFdXKuZJCMQq4BPiTiNgi6dukZaY+lJoJRE48r83xgYgVwAqAlpaWaG1tzRlGvvvXbuSeto9/LLtvLL/PU12hUKCSz2y4qbV8wTnXimrlXMk5ig6gIyK2pNcbyArHu2k5ifS8v+j4KUXtG4C9Kd5QIn5cG0mjgPHAgQrGbGZmJ6nsQhER/wrskfSfUmg28DqwCViYYguBjWl7E7AgXck0leyk9da0THVI0sx0/uGmXm16+roeeDqdxzAzs0FSydITwJ8AayV9EvgX4A/Jis96SYuAd4AbACLiNUnryYpJN3BrRBxN/dwCrALGAo+nB2QnytdIaiebSSyocLxmZnaSKioUEfES0FJi1+w+jl8OLC8R3w40lYh3kQrNUGtc8tix7d13zRvCkZiZDS5/M9vMzHK5UJiZWS4XCjMzy+VCYWZmuVwozMwslwuFmZnlcqEwM7NcLhRmZpbLhcLMzHK5UJiZWS4XCjMzy+VCYWZmuVwozMwslwuFmZnlcqEwM7NcLhRmZpar4kIhqU7STyX9OL0+S9JmSbvS84SiY5dKape0U9I1RfFLJbWlffelW6KSbpu6LsW3SGqsdLxmZnZyBmJGcRuwo+j1EuCpiJgGPJVeI2k62a1MLwDmAg9KqkttHgIWk91He1raD7AI+CAizgPuBe4egPGamdlJqKhQSGoA5gH/UBSeD6xO26uB64rij0bE4Yh4C2gHZkiaBJwZEc9HRACP9GrT09cGYHbPbMPMzAZHRffMBv4WuAMYVxSrj4h9ABGxT9I5KT4ZeKHouI4UO5K2e8d72uxJfXVLOghMBN4rHoSkxWQzEurr6ykUCmUnVD8Wbm/uzj2mkv5PRZ2dnSMupzy1li8451pRrZzLLhSSrgX2R8SLklpPpEmJWOTE89ocH4hYAawAaGlpidbWExlOafev3cg9bfkfy+4by+//VFQoFKjkMxtuai1fcM61olo5VzKjmAV8QdIfAGOAMyV9B3hX0qQ0m5gE7E/HdwBTito3AHtTvKFEvLhNh6RRwHjgQAVjNjOzk1T2OYqIWBoRDRHRSHaS+umI+AqwCViYDlsIbEzbm4AF6UqmqWQnrbemZapDkmam8w839WrT09f16T0+NqMwM7PqqfQcRSl3AeslLQLeAW4AiIjXJK0HXge6gVsj4mhqcwuwChgLPJ4eAA8DayS1k80kFlRhvGZmlmNACkVEFIBC2n4fmN3HccuB5SXi24GmEvEuUqE5lTQueezY9u675g3hSMzMqs/fzDYzs1wuFGZmlsuFwszMcrlQmJlZLhcKMzPL5UJhZma5XCjMzCyXC4WZmeVyoTAzs1wuFGZmlsuFwszMcrlQmJlZLhcKMzPL5UJhZma5XCjMzCxXNW5cVFN8bwozG+nKnlFImiLpGUk7JL0m6bYUP0vSZkm70vOEojZLJbVL2inpmqL4pZLa0r770i1RSbdNXZfiWyQ1VpCrmZmVoZKlp27g9oj4bWAmcKuk6cAS4KmImAY8lV6T9i0ALgDmAg9Kqkt9PQQsJruP9rS0H2AR8EFEnAfcC9xdwXjNzKwMZReKiNgXET9J24eAHcBkYD6wOh22Grgubc8HHo2IwxHxFtAOzJA0CTgzIp6PiAAe6dWmp68NwOye2YaZmQ2OATmZnZaELga2APURsQ+yYgKckw6bDOwpataRYpPTdu/4cW0iohs4CEwciDGbmdmJqfhktqQzgH8E/iwiPsz5B3+pHZETz2vTewyLyZauqK+vp1Ao9DPqvtWPhdubu8tqW8n7DqXOzs5hO/Zy1Fq+4JxrRbVyrqhQSBpNViTWRsQPUvhdSZMiYl9aVtqf4h3AlKLmDcDeFG8oES9u0yFpFDAeONB7HBGxAlgB0NLSEq2trWXndP/ajdzTVt7HsvvG8t93KBUKBSr5zIabWssXnHOtqFbOlVz1JOBhYEdE/E3Rrk3AwrS9ENhYFF+QrmSaSnbSemtanjokaWbq86ZebXr6uh54Op3HMDOzQVLJjGIW8FWgTdJLKfYN4C5gvaRFwDvADQAR8Zqk9cDrZFdM3RoRR1O7W4BVwFjg8fSArBCtkdRONpNYUMF4zcysDGUXioh4jtLnEABm99FmObC8RHw70FQi3kUqNGZmNjT8zewB5G9pm9lI5N96MjOzXC4UZmaWy4XCzMxyuVCYmVkuFwozM8vlq56qxFdAmdlI4RmFmZnlcqEwM7NcXnoaBF6GMrPhzDMKMzPL5UJhZma5vPQ0yLwMZWbDjWcUZmaWyzOKIeTZhZkNB55RmJlZLs8oThGeXQyM4s+xmD9Ts/INi0IhaS7wbaAO+IeIuGuIh1RV/mPXv74+oxM5ftXc0wd6OGYj2ilfKCTVAQ8Avwd0ANskbYqI14d2ZINvJBSQk/0Db2ZD75QvFMAMoD0i/gVA0qPAfKDmCkVfKv3je3tzN1+roT/gbT8/eEL5DqcCbFZNw6FQTAb2FL3uAC4vPkDSYmBxetkpaWcF73c28F4F7YedP62xnE80X909CIMZPDX13zhxzifn033tGA6FQiVicdyLiBXAigF5M2l7RLQMRF/DRa3lXGv5gnOuFdXKeThcHtsBTCl63QDsHaKxmJnVnOFQKLYB0yRNlfRJYAGwaYjHZGZWM075paeI6Jb034AnyC6PXRkRr1XxLQdkCWuYqbWcay1fcM61oio5KyL6P8rMzGrWcFh6MjOzIeRCYWZmuVwoEklzJe2U1C5pyVCPpxokrZS0X9KrRbGzJG2WtCs9TxjKMQ40SVMkPSNph6TXJN2W4iM2b0ljJG2V9HLK+X+k+IjNGbJfcZD0U0k/Tq9HdL4AknZLapP0kqTtKTbgebtQcNzPhPw+MB34sqTpQzuqqlgFzO0VWwI8FRHTgKfS65GkG7g9In4bmAncmv7bjuS8DwNXR8RFwO8AcyXNZGTnDHAbsKPo9UjPt8fvRsTvFH1/YsDzdqHIHPuZkIj4FdDzMyEjSkQ8CxzoFZ4PrE7bq4HrBnNM1RYR+yLiJ2n7ENkfksmM4Lwj05lejk6PYATnLKkBmAf8Q1F4xObbjwHP24UiU+pnQiYP0VgGW31E7IPsjypwzhCPp2okNQIXA1sY4XmnZZiXgP3A5ogY6Tn/LXAH8Oui2EjOt0cAT0p6Mf2UEVQh71P+exSDpN+fCbHhTdIZwD8CfxYRH0ql/pOPHBFxFPgdSb8B/FBS0xAPqWokXQvsj4gXJbUO8XAG26yI2CvpHGCzpDeq8SaeUWRq+WdC3pU0CSA97x/i8Qw4SaPJisTaiPhBCo/4vAEi4pdAgezc1EjNeRbwBUm7yZaNr5b0HUZuvsdExN70vB/4Idky+oDn7UKRqeWfCdkELEzbC4GNQziWAads6vAwsCMi/qZo14jNW9JvppkEksYCnwfeYITmHBFLI6IhIhrJ/r/7dER8hRGabw9Jp0sa17MNzAFepQp5+5vZiaQ/IFvn7PmZkOVDO6KBJ+l7QCvZTxG/C9wJ/AhYD/xH4B3ghojofcJ72JL0n4H/C7Tx7+vX3yA7TzEi85Z0IdlJzDqyfwyuj4hlkiYyQnPukZae/jwirh3p+Ur6DNksArLTCN+NiOXVyNuFwszMcnnpyczMcrlQmJlZLhcKMzPL5UJhZma5XCjMzCyXC4WZmeVyoTAzs1z/H51kmKScHCXEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seems like some of the taxis going to airport come back without a passenger\n",
    "pred_df[pred_df[\"mae_in_kms\"] < 50][\"mae_in_kms\"].hist(bins=100, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline mae in kms: 2.165774861432214\n"
     ]
    }
   ],
   "source": [
    "baseline_mae = haversine((37.7749, -122.4194), (37.7749 + pred_means[\"pred_lat\"], -122.4194 + pred_means[\"pred_lng\"]))\n",
    "print(f\"Baseline mae in kms: {baseline_mae}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.213'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lgb model is 22.4% better in terms of mae\n",
    "human_readable_format((baseline_mae - cross_validation_mae) / baseline_mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Digging deeper into the LGB Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = int(len(lat_data) * 0.7)\n",
    "lat_train_df, lat_test_df = lat_data.iloc[:threshold], lat_data.iloc[threshold:]\n",
    "lat_train_labels, lat_test_labels = lat_labels.iloc[:threshold], lat_labels.iloc[threshold:]\n",
    "\n",
    "lng_train_df, lng_test_df = lng_data.iloc[:threshold], lng_data.iloc[threshold:]\n",
    "lng_train_labels, lng_test_labels = lng_labels.iloc[:threshold], lng_labels.iloc[threshold:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(X_train, y_train, X_test, y_test, params):\n",
    "    \"\"\"Trains a model\"\"\"    \n",
    "    training_data = lgb.Dataset(X_train, label=y_train)\n",
    "    test_data = lgb.Dataset(X_test, y_test)\n",
    "    print(\"Preparing final model...\")\n",
    "    bst = lgb.train(\n",
    "        params, training_data,\n",
    "        num_boost_round=NUM_BOOSTING_ROUNDS, \n",
    "        keep_training_booster=True, valid_sets=[training_data, test_data])\n",
    "    return bst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing final model...\n",
      "[1]\ttraining's l1: 0.022549\tvalid_1's l1: 0.0234727\n",
      "[2]\ttraining's l1: 0.0215614\tvalid_1's l1: 0.0225326\n",
      "[3]\ttraining's l1: 0.0207096\tvalid_1's l1: 0.0216991\n",
      "[4]\ttraining's l1: 0.0199731\tvalid_1's l1: 0.0210158\n",
      "[5]\ttraining's l1: 0.019334\tvalid_1's l1: 0.0203818\n",
      "[6]\ttraining's l1: 0.0187794\tvalid_1's l1: 0.0199134\n",
      "[7]\ttraining's l1: 0.0182926\tvalid_1's l1: 0.0194494\n",
      "[8]\ttraining's l1: 0.0178706\tvalid_1's l1: 0.0190637\n",
      "[9]\ttraining's l1: 0.017504\tvalid_1's l1: 0.018713\n",
      "[10]\ttraining's l1: 0.0171824\tvalid_1's l1: 0.0184065\n",
      "[11]\ttraining's l1: 0.016901\tvalid_1's l1: 0.0181607\n",
      "[12]\ttraining's l1: 0.0166526\tvalid_1's l1: 0.0179149\n",
      "[13]\ttraining's l1: 0.0164342\tvalid_1's l1: 0.0177667\n",
      "[14]\ttraining's l1: 0.0162423\tvalid_1's l1: 0.0175998\n",
      "[15]\ttraining's l1: 0.0160661\tvalid_1's l1: 0.0174561\n",
      "[16]\ttraining's l1: 0.0159085\tvalid_1's l1: 0.0173221\n",
      "[17]\ttraining's l1: 0.0157706\tvalid_1's l1: 0.017199\n",
      "[18]\ttraining's l1: 0.0156493\tvalid_1's l1: 0.0171167\n",
      "[19]\ttraining's l1: 0.0155415\tvalid_1's l1: 0.0170736\n",
      "[20]\ttraining's l1: 0.0154465\tvalid_1's l1: 0.0169754\n",
      "[21]\ttraining's l1: 0.015361\tvalid_1's l1: 0.0169245\n",
      "[22]\ttraining's l1: 0.0152872\tvalid_1's l1: 0.0168497\n",
      "[23]\ttraining's l1: 0.015221\tvalid_1's l1: 0.016816\n",
      "[24]\ttraining's l1: 0.0151643\tvalid_1's l1: 0.0167547\n",
      "[25]\ttraining's l1: 0.0151122\tvalid_1's l1: 0.0167278\n",
      "[26]\ttraining's l1: 0.0150639\tvalid_1's l1: 0.016701\n",
      "[27]\ttraining's l1: 0.0150196\tvalid_1's l1: 0.0166809\n",
      "[28]\ttraining's l1: 0.0149809\tvalid_1's l1: 0.0166466\n",
      "[29]\ttraining's l1: 0.0149433\tvalid_1's l1: 0.0166248\n",
      "[30]\ttraining's l1: 0.0149123\tvalid_1's l1: 0.0166167\n",
      "[31]\ttraining's l1: 0.0148823\tvalid_1's l1: 0.0165928\n",
      "[32]\ttraining's l1: 0.0148579\tvalid_1's l1: 0.0165683\n",
      "[33]\ttraining's l1: 0.0148355\tvalid_1's l1: 0.0165669\n",
      "[34]\ttraining's l1: 0.0148179\tvalid_1's l1: 0.0165523\n",
      "[35]\ttraining's l1: 0.0147993\tvalid_1's l1: 0.0165363\n",
      "[36]\ttraining's l1: 0.0147777\tvalid_1's l1: 0.0165219\n",
      "[37]\ttraining's l1: 0.0147572\tvalid_1's l1: 0.0164899\n",
      "[38]\ttraining's l1: 0.0147391\tvalid_1's l1: 0.0165002\n",
      "[39]\ttraining's l1: 0.014724\tvalid_1's l1: 0.0164981\n",
      "[40]\ttraining's l1: 0.014709\tvalid_1's l1: 0.0164847\n",
      "[41]\ttraining's l1: 0.0146946\tvalid_1's l1: 0.0164777\n",
      "[42]\ttraining's l1: 0.0146818\tvalid_1's l1: 0.0164846\n",
      "[43]\ttraining's l1: 0.0146697\tvalid_1's l1: 0.0165036\n",
      "[44]\ttraining's l1: 0.0146594\tvalid_1's l1: 0.0164738\n",
      "[45]\ttraining's l1: 0.0146485\tvalid_1's l1: 0.0164693\n",
      "[46]\ttraining's l1: 0.0146376\tvalid_1's l1: 0.0164651\n",
      "[47]\ttraining's l1: 0.0146299\tvalid_1's l1: 0.0165184\n",
      "[48]\ttraining's l1: 0.0146242\tvalid_1's l1: 0.0164899\n",
      "[49]\ttraining's l1: 0.0146149\tvalid_1's l1: 0.0164869\n",
      "[50]\ttraining's l1: 0.0146062\tvalid_1's l1: 0.0165113\n",
      "[51]\ttraining's l1: 0.0145975\tvalid_1's l1: 0.0165318\n",
      "[52]\ttraining's l1: 0.0145925\tvalid_1's l1: 0.0165577\n",
      "[53]\ttraining's l1: 0.0145871\tvalid_1's l1: 0.0165396\n",
      "[54]\ttraining's l1: 0.0145814\tvalid_1's l1: 0.016536\n",
      "[55]\ttraining's l1: 0.0145775\tvalid_1's l1: 0.0165332\n",
      "[56]\ttraining's l1: 0.0145716\tvalid_1's l1: 0.0165445\n",
      "[57]\ttraining's l1: 0.0145682\tvalid_1's l1: 0.0165426\n",
      "[58]\ttraining's l1: 0.0145639\tvalid_1's l1: 0.0165434\n",
      "[59]\ttraining's l1: 0.0145591\tvalid_1's l1: 0.0165319\n",
      "[60]\ttraining's l1: 0.0145544\tvalid_1's l1: 0.0165367\n",
      "[61]\ttraining's l1: 0.0145501\tvalid_1's l1: 0.0165379\n",
      "[62]\ttraining's l1: 0.0145468\tvalid_1's l1: 0.0165307\n",
      "[63]\ttraining's l1: 0.0145442\tvalid_1's l1: 0.0165324\n",
      "[64]\ttraining's l1: 0.0145407\tvalid_1's l1: 0.0165364\n",
      "[65]\ttraining's l1: 0.0145385\tvalid_1's l1: 0.016526\n",
      "[66]\ttraining's l1: 0.0145365\tvalid_1's l1: 0.0165282\n",
      "[67]\ttraining's l1: 0.0145344\tvalid_1's l1: 0.0165278\n",
      "[68]\ttraining's l1: 0.0145306\tvalid_1's l1: 0.0165272\n",
      "[69]\ttraining's l1: 0.0145271\tvalid_1's l1: 0.0165231\n",
      "[70]\ttraining's l1: 0.0145258\tvalid_1's l1: 0.0165239\n",
      "[71]\ttraining's l1: 0.0145194\tvalid_1's l1: 0.0165092\n",
      "[72]\ttraining's l1: 0.0145138\tvalid_1's l1: 0.0165086\n",
      "[73]\ttraining's l1: 0.0145075\tvalid_1's l1: 0.0164983\n",
      "[74]\ttraining's l1: 0.014502\tvalid_1's l1: 0.0164959\n",
      "[75]\ttraining's l1: 0.0144987\tvalid_1's l1: 0.0164965\n",
      "[76]\ttraining's l1: 0.0144962\tvalid_1's l1: 0.016503\n",
      "[77]\ttraining's l1: 0.0144915\tvalid_1's l1: 0.0164989\n",
      "[78]\ttraining's l1: 0.014485\tvalid_1's l1: 0.0164966\n",
      "[79]\ttraining's l1: 0.0144804\tvalid_1's l1: 0.016497\n",
      "[80]\ttraining's l1: 0.0144774\tvalid_1's l1: 0.0164952\n",
      "[81]\ttraining's l1: 0.0144742\tvalid_1's l1: 0.016497\n",
      "[82]\ttraining's l1: 0.0144719\tvalid_1's l1: 0.0164936\n",
      "[83]\ttraining's l1: 0.0144682\tvalid_1's l1: 0.0164917\n",
      "[84]\ttraining's l1: 0.014466\tvalid_1's l1: 0.0164936\n",
      "[85]\ttraining's l1: 0.0144601\tvalid_1's l1: 0.0164928\n",
      "[86]\ttraining's l1: 0.0144543\tvalid_1's l1: 0.0164932\n",
      "[87]\ttraining's l1: 0.0144493\tvalid_1's l1: 0.0164934\n",
      "[88]\ttraining's l1: 0.0144441\tvalid_1's l1: 0.0164958\n",
      "[89]\ttraining's l1: 0.0144396\tvalid_1's l1: 0.0164972\n",
      "[90]\ttraining's l1: 0.0144351\tvalid_1's l1: 0.016498\n",
      "[91]\ttraining's l1: 0.0144321\tvalid_1's l1: 0.0164989\n",
      "[92]\ttraining's l1: 0.0144284\tvalid_1's l1: 0.016502\n",
      "[93]\ttraining's l1: 0.0144238\tvalid_1's l1: 0.0165016\n",
      "[94]\ttraining's l1: 0.0144196\tvalid_1's l1: 0.0165044\n",
      "[95]\ttraining's l1: 0.0144158\tvalid_1's l1: 0.0165049\n",
      "[96]\ttraining's l1: 0.0144136\tvalid_1's l1: 0.0165132\n",
      "[97]\ttraining's l1: 0.0144106\tvalid_1's l1: 0.0165142\n",
      "[98]\ttraining's l1: 0.014406\tvalid_1's l1: 0.016512\n",
      "[99]\ttraining's l1: 0.0144016\tvalid_1's l1: 0.0165113\n",
      "[100]\ttraining's l1: 0.0143983\tvalid_1's l1: 0.0165146\n",
      "Preparing final model...\n",
      "[1]\ttraining's l1: 0.0144819\tvalid_1's l1: 0.0146098\n",
      "[2]\ttraining's l1: 0.0137959\tvalid_1's l1: 0.0139229\n",
      "[3]\ttraining's l1: 0.0132002\tvalid_1's l1: 0.0133234\n",
      "[4]\ttraining's l1: 0.0126808\tvalid_1's l1: 0.012803\n",
      "[5]\ttraining's l1: 0.0122254\tvalid_1's l1: 0.0123494\n",
      "[6]\ttraining's l1: 0.011836\tvalid_1's l1: 0.011954\n",
      "[7]\ttraining's l1: 0.0114967\tvalid_1's l1: 0.0116105\n",
      "[8]\ttraining's l1: 0.011203\tvalid_1's l1: 0.0113149\n",
      "[9]\ttraining's l1: 0.0109497\tvalid_1's l1: 0.0110644\n",
      "[10]\ttraining's l1: 0.0107257\tvalid_1's l1: 0.0108368\n",
      "[11]\ttraining's l1: 0.0105352\tvalid_1's l1: 0.0106407\n",
      "[12]\ttraining's l1: 0.0103706\tvalid_1's l1: 0.0104693\n",
      "[13]\ttraining's l1: 0.0102249\tvalid_1's l1: 0.0103198\n",
      "[14]\ttraining's l1: 0.0100981\tvalid_1's l1: 0.0101938\n",
      "[15]\ttraining's l1: 0.00999373\tvalid_1's l1: 0.0100889\n",
      "[16]\ttraining's l1: 0.00989819\tvalid_1's l1: 0.00999865\n",
      "[17]\ttraining's l1: 0.00981614\tvalid_1's l1: 0.00991345\n",
      "[18]\ttraining's l1: 0.00974305\tvalid_1's l1: 0.00984066\n",
      "[19]\ttraining's l1: 0.00968051\tvalid_1's l1: 0.00978154\n",
      "[20]\ttraining's l1: 0.00962492\tvalid_1's l1: 0.00972326\n",
      "[21]\ttraining's l1: 0.00957707\tvalid_1's l1: 0.00968112\n",
      "[22]\ttraining's l1: 0.00953561\tvalid_1's l1: 0.00964691\n",
      "[23]\ttraining's l1: 0.00949929\tvalid_1's l1: 0.00961086\n",
      "[24]\ttraining's l1: 0.00946748\tvalid_1's l1: 0.00957936\n",
      "[25]\ttraining's l1: 0.00943754\tvalid_1's l1: 0.00955274\n",
      "[26]\ttraining's l1: 0.00941296\tvalid_1's l1: 0.00952751\n",
      "[27]\ttraining's l1: 0.00938946\tvalid_1's l1: 0.0095047\n",
      "[28]\ttraining's l1: 0.00937023\tvalid_1's l1: 0.00948648\n",
      "[29]\ttraining's l1: 0.00935028\tvalid_1's l1: 0.00947217\n",
      "[30]\ttraining's l1: 0.00933223\tvalid_1's l1: 0.00945836\n",
      "[31]\ttraining's l1: 0.00931421\tvalid_1's l1: 0.00944642\n",
      "[32]\ttraining's l1: 0.0093006\tvalid_1's l1: 0.00943124\n",
      "[33]\ttraining's l1: 0.00928762\tvalid_1's l1: 0.00941867\n",
      "[34]\ttraining's l1: 0.00927536\tvalid_1's l1: 0.00940843\n",
      "[35]\ttraining's l1: 0.00926466\tvalid_1's l1: 0.00940261\n",
      "[36]\ttraining's l1: 0.00925425\tvalid_1's l1: 0.00939571\n",
      "[37]\ttraining's l1: 0.00924402\tvalid_1's l1: 0.0093858\n",
      "[38]\ttraining's l1: 0.00923776\tvalid_1's l1: 0.00938344\n",
      "[39]\ttraining's l1: 0.00922966\tvalid_1's l1: 0.00937805\n",
      "[40]\ttraining's l1: 0.00922048\tvalid_1's l1: 0.00937375\n",
      "[41]\ttraining's l1: 0.00921446\tvalid_1's l1: 0.00936682\n",
      "[42]\ttraining's l1: 0.00920911\tvalid_1's l1: 0.00936267\n",
      "[43]\ttraining's l1: 0.00920305\tvalid_1's l1: 0.00936315\n",
      "[44]\ttraining's l1: 0.00919679\tvalid_1's l1: 0.00936259\n",
      "[45]\ttraining's l1: 0.00919149\tvalid_1's l1: 0.00936011\n",
      "[46]\ttraining's l1: 0.00918541\tvalid_1's l1: 0.00935569\n",
      "[47]\ttraining's l1: 0.00918068\tvalid_1's l1: 0.00935188\n",
      "[48]\ttraining's l1: 0.00917794\tvalid_1's l1: 0.00934971\n",
      "[49]\ttraining's l1: 0.00917506\tvalid_1's l1: 0.00935064\n",
      "[50]\ttraining's l1: 0.00917331\tvalid_1's l1: 0.00934986\n",
      "[51]\ttraining's l1: 0.00916897\tvalid_1's l1: 0.00934778\n",
      "[52]\ttraining's l1: 0.00916723\tvalid_1's l1: 0.00934917\n",
      "[53]\ttraining's l1: 0.00916206\tvalid_1's l1: 0.00934525\n",
      "[54]\ttraining's l1: 0.00915948\tvalid_1's l1: 0.00934479\n",
      "[55]\ttraining's l1: 0.00915753\tvalid_1's l1: 0.00934593\n",
      "[56]\ttraining's l1: 0.00915479\tvalid_1's l1: 0.00934607\n",
      "[57]\ttraining's l1: 0.00915325\tvalid_1's l1: 0.00934725\n",
      "[58]\ttraining's l1: 0.0091515\tvalid_1's l1: 0.00934667\n",
      "[59]\ttraining's l1: 0.00914889\tvalid_1's l1: 0.00934619\n",
      "[60]\ttraining's l1: 0.00914801\tvalid_1's l1: 0.00934632\n",
      "[61]\ttraining's l1: 0.00914574\tvalid_1's l1: 0.00934561\n",
      "[62]\ttraining's l1: 0.00914469\tvalid_1's l1: 0.00934544\n",
      "[63]\ttraining's l1: 0.00914214\tvalid_1's l1: 0.00934465\n",
      "[64]\ttraining's l1: 0.00914083\tvalid_1's l1: 0.00934344\n",
      "[65]\ttraining's l1: 0.00913927\tvalid_1's l1: 0.00934369\n",
      "[66]\ttraining's l1: 0.00913769\tvalid_1's l1: 0.0093446\n",
      "[67]\ttraining's l1: 0.00913531\tvalid_1's l1: 0.00934446\n",
      "[68]\ttraining's l1: 0.00913437\tvalid_1's l1: 0.00934338\n",
      "[69]\ttraining's l1: 0.00913179\tvalid_1's l1: 0.00934081\n",
      "[70]\ttraining's l1: 0.00912938\tvalid_1's l1: 0.00934006\n",
      "[71]\ttraining's l1: 0.00912787\tvalid_1's l1: 0.00934137\n",
      "[72]\ttraining's l1: 0.00912551\tvalid_1's l1: 0.00934014\n",
      "[73]\ttraining's l1: 0.00912379\tvalid_1's l1: 0.00934163\n",
      "[74]\ttraining's l1: 0.00912312\tvalid_1's l1: 0.00934134\n",
      "[75]\ttraining's l1: 0.00912237\tvalid_1's l1: 0.00934156\n",
      "[76]\ttraining's l1: 0.00912081\tvalid_1's l1: 0.0093412\n",
      "[77]\ttraining's l1: 0.00911942\tvalid_1's l1: 0.00934119\n",
      "[78]\ttraining's l1: 0.0091157\tvalid_1's l1: 0.00934266\n",
      "[79]\ttraining's l1: 0.00911429\tvalid_1's l1: 0.00934354\n",
      "[80]\ttraining's l1: 0.00911283\tvalid_1's l1: 0.00934499\n",
      "[81]\ttraining's l1: 0.00911151\tvalid_1's l1: 0.00934349\n",
      "[82]\ttraining's l1: 0.00911042\tvalid_1's l1: 0.00934326\n",
      "[83]\ttraining's l1: 0.00910941\tvalid_1's l1: 0.00934314\n",
      "[84]\ttraining's l1: 0.00910745\tvalid_1's l1: 0.00934242\n",
      "[85]\ttraining's l1: 0.00910605\tvalid_1's l1: 0.00934214\n",
      "[86]\ttraining's l1: 0.00910564\tvalid_1's l1: 0.0093421\n",
      "[87]\ttraining's l1: 0.00910511\tvalid_1's l1: 0.00934265\n",
      "[88]\ttraining's l1: 0.00910437\tvalid_1's l1: 0.00934299\n",
      "[89]\ttraining's l1: 0.00910335\tvalid_1's l1: 0.00934278\n",
      "[90]\ttraining's l1: 0.00910221\tvalid_1's l1: 0.00934284\n",
      "[91]\ttraining's l1: 0.00910126\tvalid_1's l1: 0.00934293\n",
      "[92]\ttraining's l1: 0.00909974\tvalid_1's l1: 0.00934273\n",
      "[93]\ttraining's l1: 0.00909808\tvalid_1's l1: 0.0093417\n",
      "[94]\ttraining's l1: 0.00909644\tvalid_1's l1: 0.00934195\n",
      "[95]\ttraining's l1: 0.00909559\tvalid_1's l1: 0.00934196\n",
      "[96]\ttraining's l1: 0.00909506\tvalid_1's l1: 0.00934274\n",
      "[97]\ttraining's l1: 0.00909384\tvalid_1's l1: 0.00934301\n",
      "[98]\ttraining's l1: 0.00909221\tvalid_1's l1: 0.00934265\n",
      "[99]\ttraining's l1: 0.00909076\tvalid_1's l1: 0.00934211\n",
      "[100]\ttraining's l1: 0.00909019\tvalid_1's l1: 0.00934039\n"
     ]
    }
   ],
   "source": [
    "lat_bst = train_model(lat_train_df, lat_train_labels, lat_test_df, lat_test_labels, lat_params)\n",
    "lng_bst = train_model(lng_train_df, lng_train_labels, lng_test_df, lng_test_labels, lng_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lat Model\n",
      "Train R2 Score: 0.4332069112858069\n",
      "Test R2 Score: 0.36606660762470344\n",
      "Train MAE: 0.014398326830194572\n",
      "Test MAE: 0.016514592895563236\n",
      "Lng Model\n",
      "Train R2 Score: 0.5037399820448495\n",
      "Test R2 Score: 0.46574477541796433\n",
      "Train MAE: 0.00909019266339119\n",
      "Test MAE: 0.009340391953006196\n"
     ]
    }
   ],
   "source": [
    "def print_statistics(bst, train_df, train_labels, test_df, test_labels):\n",
    "    \"\"\"Prints regression statistics\"\"\"\n",
    "    train_predictions = bst.predict(train_df)\n",
    "    test_predictions = bst.predict(test_df)\n",
    "    print(f\"Train R2 Score: {r2_score(train_labels, train_predictions)}\")\n",
    "    print(f\"Test R2 Score: {r2_score(test_labels, test_predictions)}\")\n",
    "    print(f\"Train MAE: {mean_absolute_error(train_labels, train_predictions)}\")\n",
    "    print(f\"Test MAE: {mean_absolute_error(test_labels, test_predictions)}\")\n",
    "\n",
    "print(\"Lat Model\")\n",
    "print_statistics(lat_bst, lat_train_df, lat_train_labels, lat_test_df, lat_test_labels)\n",
    "print(\"Lng Model\")\n",
    "print_statistics(lng_bst, lng_train_df, lng_train_labels, lng_test_df, lng_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x338.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampled_df = lat_train_df.sample(10000)\n",
    "explainer = shap.TreeExplainer(lat_bst)\n",
    "shap_values = explainer.shap_values(sampled_df)\n",
    "shap.summary_plot(shap_values, sampled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x338.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampled_df = lng_train_df.sample(10000)\n",
    "explainer = shap.TreeExplainer(lng_bst)\n",
    "shap_values = explainer.shap_values(sampled_df)\n",
    "shap.summary_plot(shap_values, sampled_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "sk_combined_pred_df = TrajDataFrame(combined_pred_df, timestamp=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_df = clustering.cluster(\n",
    "    sk_combined_pred_df[[\"lat\", \"lng\", \"datetime\", \"uid\"]], cluster_radius_km=0.5, min_samples=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# skmob dataframes are buggy with group bys\n",
    "cluster_df = pd.DataFrame(cluster_df).groupby(\"cluster\").agg(\n",
    "    number_of_trips=pd.NamedAgg(column=\"lat\", aggfunc=\"count\"),\n",
    "    median_lat=pd.NamedAgg(column=\"lat\", aggfunc=\"median\"),\n",
    "    median_lng=pd.NamedAgg(column=\"lng\", aggfunc=\"median\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>median_lat</th>\n",
       "      <th>median_lng</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>385535</td>\n",
       "      <td>37.787150</td>\n",
       "      <td>-122.415010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18252</td>\n",
       "      <td>37.616340</td>\n",
       "      <td>-122.386320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5995</td>\n",
       "      <td>37.764560</td>\n",
       "      <td>-122.452890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3782</td>\n",
       "      <td>37.762630</td>\n",
       "      <td>-122.449450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2876</td>\n",
       "      <td>37.760655</td>\n",
       "      <td>-122.441290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2316</td>\n",
       "      <td>37.753660</td>\n",
       "      <td>-122.434015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1960</td>\n",
       "      <td>37.753610</td>\n",
       "      <td>-122.435815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1723</td>\n",
       "      <td>37.749620</td>\n",
       "      <td>-122.442660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1539</td>\n",
       "      <td>37.747740</td>\n",
       "      <td>-122.437630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1406</td>\n",
       "      <td>37.746740</td>\n",
       "      <td>-122.440350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         number_of_trips  median_lat  median_lng\n",
       "cluster                                         \n",
       "0                 385535   37.787150 -122.415010\n",
       "1                  18252   37.616340 -122.386320\n",
       "2                   5995   37.764560 -122.452890\n",
       "3                   3782   37.762630 -122.449450\n",
       "4                   2876   37.760655 -122.441290\n",
       "5                   2316   37.753660 -122.434015\n",
       "6                   1960   37.753610 -122.435815\n",
       "7                   1723   37.749620 -122.442660\n",
       "8                   1539   37.747740 -122.437630\n",
       "9                   1406   37.746740 -122.440350"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 10 drop off locations\n",
    "cluster_df.nlargest(10, \"number_of_trips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top 10 pick up locations\n",
    "sk_combined_pred_df = TrajDataFrame(\n",
    "    combined_pred_df[[\"next_lat\", \"next_lng\", \"datetime\", \"uid\"]], latitude=\"next_lat\", longitude=\"next_lng\", \n",
    "    timestamp=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_df = clustering.cluster(\n",
    "    sk_combined_pred_df[[\"lat\", \"lng\", \"datetime\", \"uid\"]], cluster_radius_km=0.5, min_samples=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>median_lat</th>\n",
       "      <th>median_lng</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>397686</td>\n",
       "      <td>37.78736</td>\n",
       "      <td>-122.414170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21988</td>\n",
       "      <td>37.61614</td>\n",
       "      <td>-122.390070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6819</td>\n",
       "      <td>37.76444</td>\n",
       "      <td>-122.436810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4059</td>\n",
       "      <td>37.76270</td>\n",
       "      <td>-122.433780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2907</td>\n",
       "      <td>37.76086</td>\n",
       "      <td>-122.435920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2309</td>\n",
       "      <td>37.75799</td>\n",
       "      <td>-122.434070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1872</td>\n",
       "      <td>37.75518</td>\n",
       "      <td>-122.434955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1596</td>\n",
       "      <td>37.75125</td>\n",
       "      <td>-122.434000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1412</td>\n",
       "      <td>37.75313</td>\n",
       "      <td>-122.436585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1251</td>\n",
       "      <td>37.75117</td>\n",
       "      <td>-122.435120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         number_of_trips  median_lat  median_lng\n",
       "cluster                                         \n",
       "0                 397686    37.78736 -122.414170\n",
       "1                  21988    37.61614 -122.390070\n",
       "2                   6819    37.76444 -122.436810\n",
       "3                   4059    37.76270 -122.433780\n",
       "4                   2907    37.76086 -122.435920\n",
       "5                   2309    37.75799 -122.434070\n",
       "6                   1872    37.75518 -122.434955\n",
       "7                   1596    37.75125 -122.434000\n",
       "8                   1412    37.75313 -122.436585\n",
       "9                   1251    37.75117 -122.435120"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# skmob dataframes are buggy with group bys\n",
    "cluster_df = pd.DataFrame(cluster_df).groupby(\"cluster\").agg(\n",
    "    number_of_trips=pd.NamedAgg(column=\"lat\", aggfunc=\"count\"),\n",
    "    median_lat=pd.NamedAgg(column=\"lat\", aggfunc=\"median\"),\n",
    "    median_lng=pd.NamedAgg(column=\"lng\", aggfunc=\"median\"))\n",
    "# top 10 pick-up locations\n",
    "cluster_df.nlargest(10, \"number_of_trips\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot the points using: https://mobisoftinfotech.com/tools/plot-multiple-points-on-map/\n",
    "\n",
    "Red circle for drop-offs.\n",
    "\n",
    "Yellow square for pick-ups.\n",
    "```\n",
    "37.78715,-122.41501,red,circle,386K\n",
    "37.61634,-122.38632,red,circle,18.3K\n",
    "37.76456,-122.45289,red,circle,6K\n",
    "37.76263,-122.44945,red,circle,3.78K\n",
    "37.760655,-122.44129,red,circle,2.88K\n",
    "37.75366,-122.434015,red,circle,2.32K\n",
    "37.75361,-122.43581499999999,red,circle,1.96K\n",
    "37.74962,-122.44266,red,circle,1.72K\n",
    "37.74774,-122.43763,red,circle,1.54K\n",
    "37.74674,-122.44035,red,circle,1.41K\n",
    "37.78736,-122.41417,yellow,square,398K\n",
    "37.61614,-122.39007,yellow,square,22K\n",
    "37.76444,-122.43681,yellow,square,6.82K\n",
    "37.7627,-122.43378,yellow,square,4.06K\n",
    "37.76086,-122.43592,yellow,square,2.91K\n",
    "37.75799,-122.43407,yellow,square,2.31K\n",
    "37.755179999999996,-122.434955,yellow,square,1.87K\n",
    "37.75125,-122.434,yellow,square,1.6K\n",
    "37.75313,-122.43658500000001,yellow,square,1.41K\n",
    "37.75117,-122.43512,yellow,square,1.25K\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unused codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using skmob, for sanity checking \n",
    "# sk_combined_df = skmob.TrajDataFrame(combined_df, timestamp=True)\n",
    "# due to large number of samples this code block takes a lot of time\n",
    "# the idea was to remove trajectories with invalid gps data where\n",
    "# the speed is over 300km/h \n",
    "\n",
    "# removal of trajectories with incorrect data\n",
    "# idle_df = skmob.preprocessing.filtering.filter(idle_df, max_speed_kmh=300)\n",
    "# detection.stay_locations(df, stop_radius_factor=2.0, minutes_for_a_stop=20.0, spatial_radius_km=1.0, leaving_time=True)\n",
    "# temp_df = clustering.cluster(df, cluster_radius_km=0.1, min_samples=1)\n",
    "# temp_df.cluster.value_counts()\n",
    "# kms covered by taxi cabs roaming without passengers by taxi id \n",
    "# for this analysis, I used skmob functions instead of my implementation that calculates distance\n",
    "# distances_df = distance_straight_line(sk_combined_df[sk_combined_df[\"occupancy\"] == 0])\n",
    "# getting number of days active for each taxi\n",
    "# this could have been done better by checking if the taxi is working in day X\n",
    "# right now, it only looks into max and mins\n",
    "# distances_df[\"num_days_active\"] = combined_df.groupby(\"uid\").agg(\n",
    "#     num_days_active=pd.NamedAgg(column=\"datetime\", aggfunc=day_range))[\"num_days_active\"].values\n",
    "# distances_df[\"distance_normalized_per_day\"] = distances_df[\"distance_straight_line\"] / distances_df[\"num_days_active\"]\n",
    "# distances_df = distances_df.set_index(\"uid\")\n",
    "# for i in cluster_df.nlargest(10, \"number_of_trips\").apply(lambda x: \",\".join([str(x[\"median_lat\"]), \n",
    "# str(x[\"median_lng\"]), \"yellow\", \"square\", str(x[\"number_of_trips\"])]), axis=1).values:\n",
    "#     print(i)\n",
    "# import dataframe_image as dfi\n",
    "# # pd.DataFrame(distances_df.median().apply(human_readable_format), columns=[\"median\"]\n",
    "# dfi.export(combined_pred_df.head(), \"new_data_model.png\")"
   ]
  }
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