Driver safety estimation - pandas.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Driver safety estimation\n",
    "Pointer Telocation is a developer of telocation devices for cars, focusing on fleet management and MRM (Mobile Resource Management). \n",
    "In addition to speed and location, the devices are capable of emitting different events related to driver behavior, such as harsh turns, harsh acceleration and harsh braking.\n",
    "\n",
    "The objective of this project is to estimate a specific driver's safety score, for various business scenarios. Additional use cases include driver identification, eco-scoring and theft detection by deviation from the routine driver behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data understanding and preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load packages and set display options for notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## Supress scientific notation\n",
    "pd.options.display.float_format = '{:.4f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory data analysis\n",
    "The data represents various events created by the in-car devices. Most of the events are system events and some are behavioral driver events.\n",
    "We will have two different data frames: *dfRaw* will contain all event types, and *df* will contain only events relevant for analysis, such as behavioral events (harsh turns, accelerations etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read the data from file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of dataset: 899611\n"
     ]
    }
   ],
   "source": [
    "## read data from csv\n",
    "dfRaw = pd.read_csv(\"dataset.csv\",index_col='DriverId')\n",
    "dfRaw['ts']= pd.to_datetime(dfRaw.ts)\n",
    "print('Size of dataset: {}'.format(len(dfRaw)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observe some rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EventName</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Speed km/h</th>\n",
       "      <th>ts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DriverId</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>0</th>\n",
       "      <td>Timed Event</td>\n",
       "      <td>34.1866</td>\n",
       "      <td>-118.0881</td>\n",
       "      <td>64.0000</td>\n",
       "      <td>2017-11-01 00:00:02.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1861</td>\n",
       "      <td>-118.0892</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>2017-11-01 00:00:05.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1864</td>\n",
       "      <td>-118.0896</td>\n",
       "      <td>34.0000</td>\n",
       "      <td>2017-11-01 00:00:13.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1875</td>\n",
       "      <td>-118.0889</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>2017-11-01 00:00:26.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1887</td>\n",
       "      <td>-118.0865</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>2017-11-01 00:00:35.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1882</td>\n",
       "      <td>-118.0873</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>2017-11-01 00:00:46.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1894</td>\n",
       "      <td>-118.0864</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>2017-11-01 00:00:55.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Timed Event</td>\n",
       "      <td>34.1888</td>\n",
       "      <td>-118.0861</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>2017-11-01 00:01:02.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1891</td>\n",
       "      <td>-118.0851</td>\n",
       "      <td>43.0000</td>\n",
       "      <td>2017-11-01 00:01:06.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance Event</td>\n",
       "      <td>34.1901</td>\n",
       "      <td>-118.0839</td>\n",
       "      <td>48.0000</td>\n",
       "      <td>2017-11-01 00:01:14.530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               EventName  Latitude  Longitude  Speed km/h  \\\n",
       "DriverId                                                    \n",
       "0            Timed Event   34.1866  -118.0881     64.0000   \n",
       "0         Distance Event   34.1861  -118.0892     53.0000   \n",
       "0         Distance Event   34.1864  -118.0896     34.0000   \n",
       "0         Distance Event   34.1875  -118.0889     33.0000   \n",
       "0         Distance Event   34.1887  -118.0865     32.0000   \n",
       "0         Distance Event   34.1882  -118.0873     47.0000   \n",
       "0         Distance Event   34.1894  -118.0864     24.0000   \n",
       "0            Timed Event   34.1888  -118.0861     35.0000   \n",
       "0         Distance Event   34.1891  -118.0851     43.0000   \n",
       "0         Distance Event   34.1901  -118.0839     48.0000   \n",
       "\n",
       "                              ts  \n",
       "DriverId                          \n",
       "0        2017-11-01 00:00:02.430  \n",
       "0        2017-11-01 00:00:05.600  \n",
       "0        2017-11-01 00:00:13.640  \n",
       "0        2017-11-01 00:00:26.070  \n",
       "0        2017-11-01 00:00:35.090  \n",
       "0        2017-11-01 00:00:46.330  \n",
       "0        2017-11-01 00:00:55.320  \n",
       "0        2017-11-01 00:01:02.770  \n",
       "0        2017-11-01 00:01:06.610  \n",
       "0        2017-11-01 00:01:14.530  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRaw.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains events of various types (behavioral or functional) stored in the *EventName* column, in addition to metadata such as the current location (*Latitude* and *Longitude* columns), *Speed  km/h* and time (*ts* column). *DriverId* is the unique identifier of a driver."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time in which samples were collected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Events were taken between 2017-10-31 and 2017-11-18\n"
     ]
    }
   ],
   "source": [
    "maxTime = dfRaw['ts'].max()\n",
    "minTime = dfRaw['ts'].min()\n",
    "print(\"Events were taken between \" + str(minTime.date()) + \" and \" + str(maxTime.date()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of drivers: 89\n"
     ]
    }
   ],
   "source": [
    "driverCount = len(dfRaw.index.value_counts())\n",
    "print(\"Number of drivers: {}\".format(driverCount))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of events per driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean number of events per driver 10108.0\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EventName_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DriverId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          EventName_count\n",
       "DriverId                 \n",
       "0                   17345\n",
       "1                   20694\n",
       "2                   11945\n",
       "3                     819\n",
       "4                    6811\n",
       "5                   24152\n",
       "6                    5700\n",
       "7                   12693\n",
       "8                   19606\n",
       "9                    3928"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventsPerDriver = dfRaw.groupby('DriverId').agg({\"EventName\":\"count\"}).add_suffix('_count')\n",
    "print(\"Mean number of events per driver {:0.1f}\".format(np.mean(eventsPerDriver).values[0]))\n",
    "eventsPerDriver.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Events per subscriber - drill down\n",
    "What is the distribution of events across subscribers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of drivers')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef14707b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eventsPerDriver.sort_values(by='EventName_count',ascending = False,inplace = True)\n",
    "\n",
    "eventsHist = eventsPerDriver['EventName_count'].hist()\n",
    "eventsHist.set_title(\"Distribution of events per driver\")\n",
    "eventsHist.set_xlabel(\"Number of events\")\n",
    "eventsHist.set_ylabel(\"Number of drivers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Events type\n",
    "What kind of events are there? does looking at specific events change the distribution per driver?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef145ebe80>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1411f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfReasonHist = dfRaw.groupby(['EventName'])[['EventName']].agg('count').add_suffix('_Count').reset_index().sort_values('EventName_Count',ascending=True)\n",
    "dfReasonHist.plot.barh('EventName','EventName_Count',figsize=(18,8),fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the Distance, Timed and Network events results in much less events:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of events before removing Distance Event, Timed Event, Network Event: 899611, After removing: 54242.\n",
      "Reduction of 0.94 events. Current number of events: 54242\n"
     ]
    }
   ],
   "source": [
    "events = [event for event in dfRaw.EventName if event not in ['Distance Event','Timed Event','Network Event','System Event']]\n",
    "newCount = len(dfRaw[dfRaw['EventName'].isin(events)])\n",
    "print(\"# of events before removing Distance Event, Timed Event, Network Event: {}, After removing: {}.\\nReduction of {:0.2f} events. Current number of events: {}\".format(len(dfRaw),newCount,(len(dfRaw) - newCount)/len(dfRaw),newCount))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data prep\n",
    "Remove non-behavioral events, remove drivers with too few samples and a few more cleanups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Starting data prep. Length: 899611 ***\n",
      "Removed NAs. Length: 899611\n",
      "Keeping only events that are relevant for modeling. Length: 36468\n",
      "Filtering users with too few samples. Length: 36262\n",
      "*** Done. ***\n"
     ]
    },
    {
     "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>DriverId</th>\n",
       "      <th>EventName</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Speed km/h</th>\n",
       "      <th>ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>Harsh Acceleration</td>\n",
       "      <td>34.1899</td>\n",
       "      <td>-118.0828</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>2017-11-01 14:30:12.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0</td>\n",
       "      <td>Reached max speed</td>\n",
       "      <td>34.1802</td>\n",
       "      <td>-118.1362</td>\n",
       "      <td>115.0000</td>\n",
       "      <td>2017-11-01 14:35:26.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0</td>\n",
       "      <td>Out of max speed</td>\n",
       "      <td>34.1689</td>\n",
       "      <td>-118.1440</td>\n",
       "      <td>69.0000</td>\n",
       "      <td>2017-11-01 14:36:19.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0</td>\n",
       "      <td>Harsh Braking</td>\n",
       "      <td>34.1591</td>\n",
       "      <td>-118.1414</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>2017-11-01 14:38:01.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0</td>\n",
       "      <td>Reached max speed</td>\n",
       "      <td>34.1375</td>\n",
       "      <td>-118.1473</td>\n",
       "      <td>122.0000</td>\n",
       "      <td>2017-11-01 14:39:59.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DriverId           EventName  Latitude  Longitude  Speed km/h  \\\n",
       "41          0  Harsh Acceleration   34.1899  -118.0828     49.0000   \n",
       "112         0   Reached max speed   34.1802  -118.1362    115.0000   \n",
       "130         0    Out of max speed   34.1689  -118.1440     69.0000   \n",
       "149         0       Harsh Braking   34.1591  -118.1414     98.0000   \n",
       "186         0   Reached max speed   34.1375  -118.1473    122.0000   \n",
       "\n",
       "                         ts  \n",
       "41  2017-11-01 14:30:12.120  \n",
       "112 2017-11-01 14:35:26.830  \n",
       "130 2017-11-01 14:36:19.710  \n",
       "149 2017-11-01 14:38:01.930  \n",
       "186 2017-11-01 14:39:59.440  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RELEVANT_EVENTS = ['Harsh Acceleration', 'Reached max speed', 'Out of max speed',\n",
    "       'Harsh Braking', 'Harsh Turn (motion based)',\n",
    "       'Harsh Braking (motion based)', 'Harsh Acceleration (motion based)',\n",
    "       'Harsh Turn Left (motion based)', 'Harsh Turn Right (motion based)']\n",
    "\n",
    "\n",
    "def prepData(dfRaw, minRecordsPerSubscriber = 50):\n",
    "    dfRaw.reset_index(inplace=True)\n",
    "    print(\"*** Starting data prep. Length:\",len(dfRaw),\"***\")\n",
    "  \n",
    "    \n",
    "    #Remove NAs\n",
    "    dfRaw = dfRaw.dropna()\n",
    "    print(\"Removed NAs. Length:\",len(dfRaw))\n",
    "\n",
    "    ## Filter out unwanted events\n",
    "    df = dfRaw[dfRaw.EventName.isin(RELEVANT_EVENTS)]\n",
    "    print(\"Keeping only events that are relevant for modeling. Length:\",len(df))\n",
    "    \n",
    "    \n",
    "    ## Filter out users with too few samples\n",
    "    eventCountPerDriver = df.groupby('DriverId')['DriverId'].agg('count')\n",
    "    driversWithManyRecords = eventCountPerDriver[eventCountPerDriver > minRecordsPerSubscriber]\n",
    "    driversWithManyRecords.keys()\n",
    "    df = df[df.DriverId.isin(driversWithManyRecords.keys())]\n",
    "    print(\"Filtering users with too few samples. Length:\",len(df))\n",
    "\n",
    "    print(\"*** Done. ***\")\n",
    "    return(df)\n",
    "\n",
    "df = prepData(dfRaw)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of events per subscriber, while removing functional (non-behavioral) events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of drivers')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1748c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relevantEventsPerSubscriber = df.groupby('DriverId').agg({\"EventName\":\"count\"}).sort_values(by='EventName',ascending = False)\n",
    "\n",
    "eventsHist = relevantEventsPerSubscriber['EventName'].hist()\n",
    "eventsHist.set_title(\"Distribution of events per driver\")\n",
    "eventsHist.set_xlabel(\"Number of events\")\n",
    "eventsHist.set_ylabel(\"Number of drivers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Types of events:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef17519f98>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef177dd160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfReasonHist = df.groupby('EventName')[['EventName']].agg('count').add_suffix('_Count').reset_index().sort_values('EventName_Count',ascending=True)\n",
    "dfReasonHist.plot.barh('EventName','EventName_Count',figsize=(18,6),fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some insights from this chart:\n",
    "1. The number of high speed start events != number of events of end of high speed, which raises a red flag. In addition, the calculation could be inaccurate since it compares the current driver speed with a constant (e.g. 120 km/h) and not to the actual speed limit, so we will not be using it. The correct approach is to use an API that provides road segment max speed, and compare the driver's speed to the max speed. These APIs also have the option of snap-to-road which eliminated errors due to inaccurate GPS readings. For more info, see here: https://www.microsoft.com/en-us/maps/snap-to-road\n",
    "\n",
    "2. There are multiple events that represent the same thing. For example, 'Harsh Acceleration' and 'Harsh Acceleration (motion based)'. This is due to different versions of the device, or to different sensors installed on different cars.\n",
    "\n",
    "3. The motion based device has three values for harsh turning: Harsh Turn, Harsh Turn Left and Harsh Turn Right. We can further see that Harsh Turn Left + Harsh Turn Right != Harsh Turn. This is due to the time in which each event type was introduced into the system. We will use Harsh Turn and ignore the left and right values.\n",
    "\n",
    "Since some drivers have more days of data on the system than others, looking at the absolute number of events isn't accurate. Instead, we'll normalize the number of events per driver by dividing by either total drive time or total distance.\n",
    "\n",
    "In this code we calculate the total distance each driver had travelled:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>DriverId</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3761.7317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4317.0790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2509.1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>491.1896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1409.9199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5087.1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1271.6811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2598.3032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4227.1878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>906.6564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DriverId  Distance\n",
       "0         0 3761.7317\n",
       "1         1 4317.0790\n",
       "2         2 2509.1244\n",
       "3         3  491.1896\n",
       "4         4 1409.9199\n",
       "5         5 5087.1414\n",
       "6         6 1271.6811\n",
       "7         7 2598.3032\n",
       "8         8 4227.1878\n",
       "9         9  906.6564"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculate haversine distance (https://en.wikipedia.org/wiki/Haversine_formula)\n",
    "## Code taken (and modified) from here: https://stackoverflow.com/questions/29545704/fast-haversine-approximation-python-pandas\n",
    "def haversine(lon1, lat1, lon2, lat2):\n",
    "    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n",
    "\n",
    "    c = 2 * np.arcsin(np.sqrt(a))\n",
    "    km = 6367 * c\n",
    "    return km\n",
    "\n",
    "def total_distance(oneDriver):\n",
    "    dist = haversine(oneDriver.Longitude.shift(1), oneDriver.Latitude.shift(1),\n",
    "                 oneDriver.loc[1:, 'Longitude'], oneDriver.loc[1:, 'Latitude'])\n",
    "    return np.sum(dist)\n",
    "\n",
    "\n",
    "## Calculate the overall distance made by each driver\n",
    "def calculate_overall_distance_travelled(dfRaw):\n",
    "    dfDistancePerDriver = dfRaw.groupby('DriverId').apply(total_distance).reset_index(name='Distance')    \n",
    "    return dfDistancePerDriver\n",
    "\n",
    "distancePerDriver = calculate_overall_distance_travelled(dfRaw)\n",
    "distancePerDriver.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "We define a feature set as the normalized number of events per type. For each behavioral event, we count the number of events and divide by total drive distance. For over-speeding, one can calculate the total amount of time each driver was over speeding, or some metric for the ratio between the current speed and the allowed maximum speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Transform the events data frame to a features data frame (column for each type of relevant event)\n",
    "def create_feature_set(df, distancePerDriver):\n",
    "    dfEventAggByDriver = df.groupby(['DriverId','EventName'])[['EventName']].agg('count').add_suffix('_Count').reset_index()\n",
    "\n",
    "    \n",
    "    ## Pivot events into columns\n",
    "    dfEventMatrix = dfEventAggByDriver.pivot(index='DriverId',columns='EventName', values='EventName_Count').add_prefix('F_').fillna(0).reset_index()\n",
    "    dfEventMatrix = dfEventMatrix.merge(distancePerDriver, how = 'inner',on='DriverId')\n",
    "    dfEventMatrix.set_index('DriverId', inplace=True)\n",
    "    \n",
    "    featureCols = [col for col in dfEventMatrix if col.startswith('F_')]\n",
    "    dfEventMatrix[featureCols] = dfEventMatrix[featureCols].div(dfEventMatrix['Distance'], axis=0)\n",
    "\n",
    "    dfFeatureSet = dfEventMatrix[featureCols]\n",
    "    \n",
    "    return dfFeatureSet\n",
    "\n",
    "features = create_feature_set(df,distancePerDriver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>F_Harsh Acceleration</th>\n",
       "      <th>F_Harsh Acceleration (motion based)</th>\n",
       "      <th>F_Harsh Braking</th>\n",
       "      <th>F_Harsh Braking (motion based)</th>\n",
       "      <th>F_Harsh Turn (motion based)</th>\n",
       "      <th>F_Harsh Turn Left (motion based)</th>\n",
       "      <th>F_Harsh Turn Right (motion based)</th>\n",
       "      <th>F_Out of max speed</th>\n",
       "      <th>F_Reached max speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DriverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>0</th>\n",
       "      <td>0.0747</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0769</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0468</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0589</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0627</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          F_Harsh Acceleration  F_Harsh Acceleration (motion based)  \\\n",
       "DriverId                                                              \n",
       "0                       0.0747                               0.0003   \n",
       "1                       0.0243                               0.0002   \n",
       "2                       0.0769                               0.0044   \n",
       "4                       0.0468                               0.0000   \n",
       "5                       0.0627                               0.0012   \n",
       "\n",
       "          F_Harsh Braking  F_Harsh Braking (motion based)  \\\n",
       "DriverId                                                    \n",
       "0                  0.0231                          0.0122   \n",
       "1                  0.0102                          0.0021   \n",
       "2                  0.0275                          0.0120   \n",
       "4                  0.0589                          0.0206   \n",
       "5                  0.0147                          0.0049   \n",
       "\n",
       "          F_Harsh Turn (motion based)  F_Harsh Turn Left (motion based)  \\\n",
       "DriverId                                                                  \n",
       "0                              0.0069                            0.0066   \n",
       "1                              0.0000                            0.0025   \n",
       "2                              0.0052                            0.0012   \n",
       "4                              0.0369                            0.0262   \n",
       "5                              0.0177                            0.0035   \n",
       "\n",
       "          F_Harsh Turn Right (motion based)  F_Out of max speed  \\\n",
       "DriverId                                                          \n",
       "0                                    0.0040              0.0117   \n",
       "1                                    0.0019              0.0153   \n",
       "2                                    0.0000              0.0060   \n",
       "4                                    0.0078              0.0099   \n",
       "5                                    0.0092              0.0120   \n",
       "\n",
       "          F_Reached max speed  \n",
       "DriverId                       \n",
       "0                      0.0154  \n",
       "1                      0.0174  \n",
       "2                      0.0104  \n",
       "4                      0.0092  \n",
       "5                      0.0204  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are two types of systems (motion based and not), we will look at one at a time and not join the two as the values and their proportions might be incomparable. In addition, we'll ignore the inaccurate over speeding event, as noted earlier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>Harsh Acceleration</th>\n",
       "      <th>Harsh Braking</th>\n",
       "      <th>Harsh Turning</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DriverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.0069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Harsh Acceleration  Harsh Braking  Harsh Turning\n",
       "DriverId                                                  \n",
       "0                     0.0003         0.0122         0.0069\n",
       "1                     0.0002         0.0021         0.0000\n",
       "2                     0.0044         0.0120         0.0052\n",
       "4                     0.0000         0.0206         0.0369\n",
       "5                     0.0012         0.0049         0.0177"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = features.rename(columns={'F_Harsh Acceleration (motion based)': \"Harsh Acceleration\",\n",
    "                                    'F_Harsh Braking (motion based)': \"Harsh Braking\",\n",
    "                                   'F_Harsh Turn (motion based)':\"Harsh Turning\"},copy =False)\n",
    "    \n",
    "# select only motion based\n",
    "features = features[['Harsh Acceleration','Harsh Braking','Harsh Turning']]\n",
    "\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatterplot matrix of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py:967: UserWarning: The following kwargs were not used by contour: 'label', 'color'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GAFi2bBlef/11PPLII7hy5QqSk5MhFouxcuVKzJ8/H8OHD8fx48fNA0jnzZuHbdu2YcSIETh06BBefPHFFtVIRERE9mXzdtqgoCD88MMPGDx4sHndjz/+iM6dO7e48YSEBCQkJFis27Bhg/nrnj17Yvv27Y1eFxkZic8//9xqrZs2bWpxXUREROQYNoPHa6+9hr/+9a8ICwtDTU0Npk2bhsOHD+Ptt98Woj4iIiJyITaDR9++fZGTk4Nvv/0W0dHRUCgUmD9/Pvz8/ISoj4iIiFyIzTEedXV1+OCDD9C3b19MmjQJV65cwebNm6HX64Woj4iIiFyIzeAxa9YsnDp1Ch4eHgCA++67D7/++muLZi0lIiKiO5PN4LFv3z4sX74c/v7+AIDu3bvjzTffxJ49exxeHBEREbkWm8GjXbt2OH36tMW63377DR06dHBYUUREROSabA4u/etf/4oJEyZg+PDhUKlUUKvVyMnJwezZs4Woj4iIiFyIzeDxxBNPoEePHtizZw9KS0uhVCrx4Ycf4p577hGiPiIiInIhNoMHAJw6dQpJSUlQKpXIyspCXl4egwcRERHdMptjPBYtWoRt27bBYDAAuPq4+X/+85946623HF4cERERuRabwWPXrl14//330alTJwBXb6ddv349PvvsM4cXR0RERK7FZvCQSCTQ6XQW6y5fvgy5XO6wooiIiMg12RzjkZycjKeffhp//vOfoVKpUFZWhm3btiE5OVmI+oiIiMiF2Awezz33HLp06YI9e/bgP//5D5RKJebMmWPxtFoiIiKi5mjWXS3x8fGIj483L5eUlGD16tWYOnWqwwojIiIi12NzjMc1DQ0NyMnJwaRJkzBkyBDs37/fkXURERGRC7IZPH777TcsXboUsbGxeOGFF9C7d2/k5ORgy5YtLW48MzMTI0aMwLBhw7B58+ZG2/Pz85GYmIj4+HjMnj3bfEvv4cOHMWbMGIwePRoTJkxAUVERAOC///0voqKiMHr0aIwePRqvvvpqi2skIiIi+2nyUsu//vUv/POf/8TZs2eRkJCAf/zjH0hOTjYPMm0ptVqNFStWYMeOHZDJZBg7diyioqIQGhpq3ic1NRWLFi1Cnz59MGvWLGzbtg1JSUlITU3F2rVr0bNnT2zfvh2LFi1Ceno68vLyMHHiREyePLnF9RHRVScKtPjuSCGOF+gQHuKHwf2CERbi39plEdkN+7iwmjzjMWPGDAQGBiIjIwMzZ860+0yl+/fvR3R0NHx8fODp6Yn4+HhkZ2ebtxcVFaG2thZ9+vQBACQmJiI7Oxv19fV44YUX0LNnTwBAjx49UFJSAgA4duwYfvjhByQkJGDKlCnm9UR0e04UaDH33QPI2v8bzpVcRtb+3zD33QM4UaBt7dKI7IJ9XHhNnvH48MMPsXPnTowaNQp9+/bFE088AZPJBJFIZJeGy8rKoFAozMtKpRK5ublNblcoFFCr1ZDJZBg9ejQAwGg0YvXq1RgyZAiAq0/SHT58OIYNG4atW7di+vTp+OSTT25ax6pVq7B69Wq7vCeilnDGvvjdkULU6Rss1tXpG/DdkUL+RejCnLEvOgr7uPCaDB5RUVGIiopCbW0tcnJy8Mknn6CyshKLFy9GQkICYmNjIZPJbrtho9FoEWJuDDW2ttfX12PmzJkwGAzmSysLFiwwbx83bhzeeustVFZWol27dk3WkZKSgpSUFIt1hYWFiIuLu+33RnQ7nLEvHi/QWV1/oon15BqcsS86Cvu48GwOLnV3d8ejjz6K999/H9988w3Cw8OxcuVKREdHt6jhgIAAaDQa87JGo4FSqWxye3l5uXl7dXU1nn32WRgMBqSnp0MqlcJoNCI9PR0NDZbJVSwWt6hOojtZeIif1fVhTawnamvYx4XX7NtpAUClUuG5557Drl278OGHH7ao4ZiYGBw4cAA6nQ41NTXIyclBbGyseXtQUBDkcjkOHz4MANi5c6d5e2pqKrp06YKVK1eaz7q4ubnhq6++wpdffgkAyMjIwL333gtPT88W1Ul0JxvcLxhyqWV4l0vFGNwvuJUqIrIv9nHhNWsCMWt69+7dooZVKhWmT5+O5ORk6PV6jBkzBhEREZg0aRKmTZuG3r17Y9myZZgzZw6qqqoQHh6O5ORknDhxAnv37kVoaCgef/xxAFfHh2zYsAFLlizBa6+9hjVr1sDPzw9Lly5tUY1Ed7qwEH8smDwA3x0pxIkCHcI44p9cDPu48G47eNhDQkICEhISLNZt2LDB/PW122WvFxYWhl9++cXq8bp3725zMCkR3ZqwEH/+EiaXxj4urFu61EJERETUEs0643Ho0CGUlJQ0Grj52GOPOaQoIiIick02g8e8efOwe/duhIWFQSL5fXeRSMTgQURERLfEZvDYtWsXsrKy7DJNOhEREd3ZbI7x6NSpE9zd3YWohYiIiFxck2c8srKyAACRkZF48skn8eSTTzaaAXTEiBGOrY6IiIhcSpPBY+vWreavfXx8sGvXLovtIpGIwYPoDsWneZLQ8n/T4tvD7HOuoMngsWnTJqvr6+rqIJFIOBU5kYuzFi4AIPe0Btv3njY/WOtcyWXsPXgBCyYP4H8EZFfmPnhWi44+HnCXSXBBXWnucy+M7Yu8M+UMI22MzcGl+fn5ePvtt7Fu3Tp88cUXeOWVV+Dl5YUVK1YgJiZGiBqJyMGuDxn3hvoj/G5/LN/yU6NwMaB3ICqv1PNpnmRXTYXcue8e+L0PllZCLhXjgYhAfH+0GHX6Bnx98DyOnbn6+Pq6egP2HS3CnIlR7IdOzmbwWLhwIR5++GEYjUYsWbIEb775Jnx8fLBw4ULzOBAiartOFGgx990D0DcYMaBXIMp0NVDrrD8q/EqdHtU1egT4e6Licp3FPnyaJ92Oa/3v+pC772gR7gsLsNoHDQ0mDO4bjH0/F6GsogZD7u8M7cUa1BuMkEnckHtaw+Dh5GwGjzNnzmDLli34+eefUVNTg/j4eLi5uaGkpESI+ojIwb47cjVkDIzohEP5avi2l0MmaXwp1c1NBL927pBJxLhSa0Cvbv5wl0lwIK8ERqMJ3e/yAcDxH3RrrvW/63l5SHH6wkWr+xeWVQEABvQKBEQmVNfooW8wovxiDRS+HrhQWon837T4Q1f2OWdlM3goFArs27cPmZmZGDx4MNzc3LBr1y507txZiPqIyMGOF+ggl4pRW29Anb4BFZfr0KubP86rKy32G9ArEN8c/v0/ifPqq6e+B/QKxKF8NZR+Hlb/euX4D7qZ41bOlFVcrsO99yga9UEAUPh6IO+MFkpfD/TtocBHWScb9UmVnxfWbM9l8HVSNufxmDt3LpYsWYLCwkK8/PLLOHDgAJYtW4Z58+YJUR8ROVh4iB9828uhqagBcPV0dkinDhaPCpdLxaj7XzC5Xp2+ASaTCdG9AnHohNrqX6/Xxn8QWRMe4gfgah8L8Pe82tf0DQhSeFl9XL27THI1IFfW4eRvFVb729niSygtr0bW/t8w990DOFGgFez9kG02z3gcPXoUn376Kby8vABcfQT9t99+6+i6iEggvbp1xH+Ol+DuTh3MfzGeK7mE6F6BkEpEKCi+jJBO7XHq/M1PfcdEBOJAXqnVfZo7/oOXae48g/sFo+pKPaprDdBU1KDvPQoEdvSETCJC4oPdcK60EoVlVejU0Qv+Ph7Ye+gCAODuTu3xSxN9UlNRA9/2cpRqr9gc+Mw+JzybwWPjxo14+umnzctubnygLZErOVFQjuEDQiB2E8FDLkFJeSUUvp4ov1iDguIrCFJ6IVjphZo6g9VT350U3vDxliI06OoYj9Ly6kZ/hYb976/am9ehxaKN/4GXhxQVl+t4meYOobtci/8cV6NjBzmG3N8ZBcWXcfikBoEdvRCs9EZEN38Eq9rhfMllnCm8iJExXVBZXQ+p1A1BSq+bXo65pqnge+OlwdLyauSd0WLqn+7lGBEHshk8HnzwQUyePBkPP/ww/P0tP4iWTiCWmZmJ9PR0GAwGTJgwAePHj7fYnp+fj9mzZ6O6uhqRkZFIS0uDRCJBcXExUlNTodVqERISgmXLlsHLywuXL1/Gyy+/jAsXLsDPzw8rV66EQqFoUY1Erk4iBtzlEpwo0KJQXYX7w1XI3Fdgcd38yEkNnhrRE4fyyyxChVwqRrDSCzKJGHsPn0eRphq9Qv3hLv190KlcKsbgfsE2/7LMPa3BPZ19oamosRi42hZu0+Vfzbfv0MkSPDXiD7hYWYtPvvrVot8dPSXGo4NCcDhfgwf6BuJ04SUcPFGGIKUXegV2hKe7DHKpplGfvHY55pprwffG28av1DWgTt8ANzcRBvQKRG391bMu//r+LEwm8DN0EJvBo6ioCACQk5Njsb6lM5eq1WqsWLECO3bsgEwmw9ixYxEVFYXQ0FDzPqmpqVi0aBH69OmDWbNmYdu2bUhKSkJaWhqSkpIwcuRIrFmzBmvXrkVqaipWrlyJyMhIrF+/HhkZGVi8eDFWrlx52zUS3QmUvt748It81OkbIJeKUVB82ep186OnNFdPfasrUaiugtLXA3cHdUBxeTUOnlD//h/G/+ZbGBbVBW4iWJ+T4YazGScKtBaTkl0/cNXZb9PlgNqW6Rboi61f/YLeoR2t9rui8mrcF67CpzeEkiMnNXj8wW4YMbArNBU1OF9aiWCVNyRuIvyQ+/tdl9cH3+s/p7p6g/nurWsDpK8//sETan6GDmLzusmmTZus/vvoo49a1PD+/fsRHR0NHx8feHp6Ij4+HtnZ2ebtRUVFqK2tRZ8+fQAAiYmJyM7Ohl6vx8GDBxEfH2+xHgC+/fZbJCQkAABGjRqF77//Hnq9vkV1Erm6vLNa8y/c6weZ3qhMVwPNxRq4QQQAOHZGi0JNFWrrrA86ranV4/kn7kVYiL/NQadNba+tNyAi1Ll/8XNAbcvkFWjh284dheoqq9sL1VWoqbXex86rK3GlRg+DwYh6QwOO/KJBgxHoe48CnVXtMOjeTubwcOPnVHG5DgpfD4s7um48Pj9Dx7B5xkOn02Hr1q0oLy+HyWQCABgMBhQUFGDz5s233XBZWZnFZRClUonc3NwmtysUCqjValRUVMDb2xsSicRi/Y2vkUgk8Pb2hk6ng0qlarKOVatWYfXq1bf9PojspbX64vW/8Csu1+He7h2bvG5eUHwZHbzlqDdcPUVdW9eA8ovWg0pB8WXz19ZumQR+v/be1HZNRQ3GP9Kz2e+lNdh6b22RkH3xav8zIaCjt9V+F9jRC2rdlSZfe3fw1bNupdqr+/w7txhyqfjq4FLdFfMZixs/pzp9A9xlEqj8PJsM2235M3RmNs94vPTSSzh48CC0Wi1OnToFqVSK3bt3o2/fvi1q2Gg0QiQSmZdNJpPFclPbb9wPQKPl619jazBsSkoKfvnlF4t/e/fuvZ23RNQirdUXg1Xe5q+v/jIWN3kbo087OWQSN1RcrgMAyCRuUPh6WD1ul4Dfn2Yd3sTg0mvX3pva3qOLL7rf5dv8N9MKbL23tkjIvhis8oZaV9Pk7bNBCi/IZNZ/jwcrvXHstKZRH6zTN6BUe8U8qR1g/XM6kFeCB/sHoXNAu0bbgLb9GTozm8Hj6NGjSE9Px7Rp0yCXyzF79mysW7cOBw4caFHDAQEB0Gg05mWNRgOlUtnk9vLyciiVSvj5+aGyshINDQ2NXqdUKlFeXg7g6lmZ6upq+Pj83vGIqLGIUIXFL/wfckswOvZuRPZUorOqHfr3VCLyDyoc+aUMXu4SiMVu5vEgYrEb3GUSq/9hDIjoZF4e3C/Y6j7Xxn80tX3I/c4/UaGt90Y3FxF69Sy1pqIGUeEq9L+u30WFq1BWUQOVn/VQ0knhDe2lOni5W++D138G1j4nqdgN4Xd3RMKgu/kZCsjmpRY/Pz9IpVJ07twZv/zyCwDgvvvuw7lz51rUcExMDFatWgWdTgcPDw/k5ORg4cKF5u1BQUGQy+U4fPgw+vfvj507dyI2NhZSqRSRkZHIyspCQkICMjIyEBsbCwAYPHgwMjIyMGXKFGRlZSEyMhJSqbRFdRK5upEDQwBcvaukUF2FYJU3OnjLEKTwQieF9/+eDOqGhEEhMJlMOHi8DA/1D0avRnDvAAAgAElEQVTHDh44mK9GsNILTzwUinOll3GhrApdVO0wIKITBvUJMrcRFuKPBZMH4LsjhThRoEPYDXd+2NruzNpy7c7gWv87dkYDT3cZ8L8z2P7t5eikbIeTv+lwJL8MSfE98OuFizhfWonOAe0QGuyD748UYdC9nRDduxNGPnD3TT8DW58TP0PhiEzXBm40ITU1FQaDAQsWLMDzzz+P2NhYyOVybNmyBV9++WWLGs/MzMS7774LvV6PMWPGYNKkSZg0aRKmTZuG3r174+TJk5gzZw6qqqoQHh6O119/HTKZDEVFRZg5cya0Wi0CAwOxfPlydOjQARcvXsTMmTNx4cIFtGvXDsuWLUNw8K0n1sLCQsTFxWHv3r03ff3k1/eguLy6Wcfs1NEL77465JZroTtbc/sikaOxL5K92DzjMX/+fGzYsAFGoxGLFi1CWloaqqqqLM5O3K6EhATzXSjXbNiwwfx1z549sX379kavCwoKwqZNmxqt9/Hxwbp161pcFxERETmGzeDh5eWFF198EQDQoUMH/OMf/3B4UUREROSamgweTz31VJN3i1zT0rk8iIiI6M7SZPAYN24cgKu3pM6ZMweLFy8WrCgiIiJyTU0Gj+unQ09LS2vxc1mIiIiI+KhZIiIiEgyDBxEREQmmyUstWq3W/LXJZIJOp8ONU374+3NyFSIiImq+JoPHwIEDzc9GAa7ONHo9kUiE/Px8x1ZHRERELqXJ4HHy5Ekh6yAiIqI7AMd4EBERkWAYPIiIiEgwDB5EREQkGAYPIiIiEgyDBxEREQmGwYOIiIgEw+BBREREgmlyHg9HKy4uRmpqKrRaLUJCQrBs2TJ4eXlZ7FNfX4/Zs2cjLy8P7u7uWLZsGbp164bq6mrMmjULZ8+eBQBMmTIFI0eOBADExcXB29vbfIx169YhMDBQuDdGRERETWq14JGWloakpCSMHDkSa9aswdq1a5Gammqxz6ZNm+Dh4YHdu3fj4MGDePXVV7Ft2zasX78enTp1wttvvw2tVovRo0cjKioKYrEYUqkUO3fubKV3RURERDfTKpda9Ho9Dh48iPj4eABAYmIisrOzG+337bff4tFHHwUA3HfffdDpdCguLsb999+Pp556CsDV58X4+PigvLwcx44dg8lkwtixY/H4449j9+7dwr0pIiIisqlVznhUVFTA29sbEsnV5hUKBdRqdaP9ysrKoFAozMsKhQKlpaUYOHCgeV1WVhbq6+sRGhqKwsJCDBo0CC+//DLKy8sxfvx43HPPPejWrVuTtaxatQqrV6+247uzTiRyeBPUxgnVF4lsYV8kR3J48Ni9ezdef/11i3VdunSB6Ib/iW9cBq4+Fff69SaTCW5uv5+k2b17N/7+97/jvffeg0QiwZAhQzBkyBAAQHBwMIYOHYoffvjhpsEjJSUFKSkpFusKCwsRFxfX/DfZDApfT8xdvx9q7RWb+6r8PbHguRib+5FrEaovEtnCvkiO5PDgMXz4cAwfPtxinV6vR1RUFBoaGiAWi6HRaKBUKhu9VqVSoaysDJ07dwYAlJeXm/fbtGkT3n//fbz//vvo0aMHAOCbb75Bx44d0bt3b/Mxrp1VcQZq7RUUl1e3dhlEREStplXGeEilUkRGRiIrKwsAkJGRgdjY2Eb7DR482DxQ9NChQ5DL5ejUqRP27NmDDz74AFu3bjWHDgAoKirCmjVrYDQaUV5ejq+//hoPPvigIO+JiIiIbGu10wHz5s3DzJkzkZ6ejsDAQCxfvhwAsHXrVpSVleGFF17AU089hblz52LkyJGQyWRYunQpAOCdd95BXV0dpkyZYj7eokWLMHbsWPzyyy8YNWoUjEYjXn75ZQQFBbXK+xMaL+M0xu8JEZHzabXgERQUhE2bNjVaP27cOPPXcrkcS5YsabTPv/71ryaPu3DhwhbX1tDQAAAoLS1tch+JRAKVv2ezj9nRx73ZA0xV/p7QaDTmOmwRi8XNrgPALR27rbrV70lpaSkMBoPN/QICAgS9fNecvkh3JvZFcha32hdFJpPJ5MB62qRDhw5h/PjxrV0GOaG9e/ciODhYsPbYF6kp7IvkLG61LzJ4WFFbW4u8vDwoFIqb/uUcFxeHvXv3CliZbaypeW63JqH/ymxuX7QXZ/ysAOetC2i92lyxLzrT5+wstThLHUDTtdxqX3SeWz6ciLu7OyIjI5u1r5B/cTQXa2oeZ6zpRrfSF+3FWb8vzloX4Ny12YtQfdGZvpfOUouz1AHYpxY+JI6IiIgEw+BBREREgmHwICIiIsGI58+fP7+1i2jLoqKiWruERlhT8zhjTc7AWb8vzloX4Ny1tTXO9L10llqcpQ7APrXwrhYiIiISDC+1EBERkWAYPIiIiEgwDB5EREQkGAYPIiIiEgyDBxEREQmGwYOIiIgEw+BBREREgmHwICIiIsEweBAREZFgGDyIiIhIMAweREREJBgGDyIiIhIMgwcREREJhsGDiIiIBMPgQURERIJh8CAiIiLBMHgQERGRYBg8iIiISDAMHlYYDAYUFhbCYDC0dil0h2NfJGfBvkj2wuBhRWlpKeLi4lBaWtrapdAdjn2RnAX7ItkLgwcREREJhsGDiIiIBMPgQURERIJh8CAiIiLBMHgQERGRYBg8iIiISDAMHkRERCQYBg8iIiISDIMHERERCYbBg4iIiATD4EFERESCYfAgIiIiwTB4EBERkWAYPIiIiEgwDB5EREQkGAYPIiIiEgyDBxEREQmGwYOIiIgEw+BBREREgmHwICIiIsEweBAREZFgGDyIiIhIMJLWLoCIiO5sJwq0+O5IIY4X6BAe4ofB/YIRFuLf2mWRgzB4EBFRqzlRoMXcdw+gTt8AADhXchl7D17AgskDGD5cFC+1EBFRq/nuSKE5dFxTp2/Ad0cKW6kicjSnDB6ZmZkYMWIEhg0bhs2bNze534wZM7Bjxw7zcnFxMcaPH49HHnkEzz//PKqrq4Uol4iIbtPxAp3V9SeaWE9tn9MFD7VajRUrVmDLli3IyMjAp59+itOnTzfaZ8qUKfjyyy8t1qelpSEpKQnZ2dno1asX1q5dK2TpRER0i8JD/KyuD2tiPbV9Thc89u/fj+joaPj4+MDT0xPx8fHIzs622CczMxNxcXEYPny4eZ1er8fBgwcRHx8PAEhMTGz0OiIici6D+wVDLhVbrJNLxRjcL7iVKiJHc7rBpWVlZVAoFOZlpVKJ3Nxci32effZZAMDhw4fN6yoqKuDt7Q2J5OpbUigUUKvVNttbtWoVVq9ebY/SiVqEfZGchZB9MSzEHwsmD8B3RwpxokCHMN7V4vKcLngYjUaIRCLzsslkslhuirX9mvO6lJQUpKSkWKwrLCxEXFxcMysmsg/2RXIWQvfFsBB/Bo07iNNdagkICIBGozEvazQaKJVKm6/z8/NDZWUlGhoabul1REREJBynCx4xMTE4cOAAdDodampqkJOTg9jYWJuvk0qliIyMRFZWFgAgIyOjWa8jIiIi4TjkUsurr75qdb1UKkWHDh0QHR2NgQMHWt1HpVJh+vTpSE5Ohl6vx5gxYxAREYFJkyZh2rRp6N27d5Ptzps3DzNnzkR6ejoCAwOxfPlyu7wfIiIisg+HBI/27dtj165d+OMf/4jAwECo1Wp89tlniIyMhK+vL+bNm4dx48bhmWeesfr6hIQEJCQkWKzbsGFDo/3eeOMNi+WgoCBs2rTJfm+EiIiI7MohwePHH3/ERx99hG7dupnXjR49Gi+88ALeeustjB07Fn/605+aDB5ERETkmhwyxqO4uBj+/pYjlDt06IALFy4AuHqra11dnSOaJiIiIifmkDMejz76KJ555hk888wzUCqVKCsrwwcffIBRo0bh0qVLWLhwIQYMGOCIpomIiMiJOSR4zJ49Gx9//DG2bNmC8vJyqFQqjBkzBmPGjMGpU6fQtWtX/OUvf3FE00REROTEHBI83NzckJycjOTk5EbbevbsiZ49ezqiWSIiInJyDgkeJ06cwLJly3DhwgUYjUaLbXv37nVEk0RERNQGOCR4zJo1C/fffz/+9re/mZ+dQkREROSQVHD+/Hls376doYOojZm7fj/U2itWt6n8PbHguRiBKyIiV+OQZDB06FDs2rULjz32mCMOT0QOotZeQXF5dWuXQUQuzCHBo6ioCDNnzsSSJUvg6+trse3as1SIiIjozuOQ4DFt2jRHHJaIiIjaOLsGj7y8PPTq1Qvu7u72PCwRERG5CLsGj1dffRWZmZl48cUXrW4XiUS8nZaIiOgOZtfgkZmZCQD4+uuvrW5Xq9X2bI6IiIjaGIc8JG7GjBkwmUzm5YaGBmzcuBEjRoxwRHNERETURjgkeFy+fBnPP/886uvr8eOPP+LRRx9FZmYm1q1b54jmiIiIqI1wyF0ta9euRVpaGoYNG4b6+npMmzYNf/7znyESiRzRHBEREbURdg0eubm55q+feOIJaLVanD9/Ht27d8exY8cAABEREfZskoiIiNoQuwaPpu5mSU1NBcC7WoiIiO50dg0eTd3NQkRERAQ4aIxHXV0ddu/ejfLycvPdLQaDAQUFBVi6dKkjmiQiIqI2wCHBIzU1FWfPnkWHDh1QVVWFoKAgfP/99xgzZowjmiMiIqI2wiHBY9++ffj6669RWlqKlStXYu3atcjOzsbHH3/siOaIiIiojXDIPB7t2rWDr68vQkJCcPz4cQDAI488glOnTjmiOSIiImojHBI8evXqhSVLlsBkMkGhUCArKwt79uyBh4eHI5ojIiKiNsIhl1rS0tKwdOlSVFdXY968eZgxYwaqq6vx2muvOaI5IiIiaiMcEjzWrFmDefPmwdvbGx07dkROTo4jmiEiIqI2xiGXWr766itOj05ERESNOOSMR3R0NP70pz9h0KBB8PPzswghkyZNckSTRERE1AY4JHjIZDJERETg0qVLuHTpkiOaICIiojbIIcHj9ddfd8RhiYiIqI2z+xiPjIwMvPHGG9i/fz8qKyvxzDPPoE+fPpgwYQJKSkrs3RwRERG1IXYNHosXL8b69ethNBqxePFiTJw4EUqlEp988gl69uyJtLS0Zh0nMzMTI0aMwLBhw7B58+ZG2/Pz85GYmIj4+HjMnj0bBoMBAPD555/jgQcewOjRozF69GisWLHCnm+PiIiIWsiul1q++OILZGZmwt/fHyUlJXj44Yfx0UcfwcPDA//3f/+HBx54wOYx1Go1VqxYgR07dkAmk2Hs2LGIiopCaGioeZ/U1FQsWrQIffr0waxZs7Bt2zYkJSUhLy8PM2fOxKhRo+z5toiIiMhO7HrGo6amBv7+/gCAwMBAeHp6mmcrlclkaGhosHmM/fv3Izo6Gj4+PvD09ER8fDyys7PN24uKilBbW4s+ffoAABITE83bjx07hs8//xwJCQl4+eWXObCViIjIydj1jMeNc3eIxWKLZZPJZPMYZWVlUCgU5mWlUonc3NwmtysUCqjVavPXEydORL9+/bB8+XIsWLAAb7311k3bW7VqFVavXm2zLiJHY18kZ8G+SI5k1+BRX1+P+fPnm5dramrMyyaTCfX19TaPYTQaLQKMyWSyWL7Z9jVr1pjXP/vssxg6dKjN9lJSUpCSkmKxrrCwEHFxcTZfS2RP7IvkLNgXyZHsGjymTJlisTx58uSbbrcmICAAhw4dMi9rNBoolUqL7RqNxrxcXl4OpVKJyspKfPbZZ3j66acBXA0kN55xISIiut6JAi2+O1KI4wU6hIf4YXC/YISF+Ld2WS7NrsFj6tSpLT5GTEwMVq1aBZ1OBw8PD+Tk5GDhwoXm7UFBQZDL5Th8+DD69++PnTt3IjY2Fp6ennjvvffQt29f3Hvvvfj444+bdcaDiIjavtsJECcKtJj77gHU6a+OPzxXchl7D17AgskDGD4cyCETiLWESqXC9OnTkZycDL1ejzFjxiAiIgKTJk3CtGnT0Lt3byxbtgxz5sxBVVUVwsPDkZycDLFYjJUrV2L+/Pmora1F165dsXTp0tZ+O0RE5GC3GyC+O1Jofs01dfoGfHekkMHDgZwueABAQkICEhISLNZt2LDB/HXPnj2xffv2Rq+LjIzE559/7vD6iIjIedxugDheoLO6/kQT68k+HPJ0WiIiIqHcboAID/Gzuj6sifVkHw4743Ho0CGUlJQ0mrvjsccec1STRER0BwoP8cO5ksuN1tsKEIP7BWPvwQsWZ0vkUjEG9wu2e430O4cEj3nz5mH37t0ICwuDRPJ7EyKRiMGDiIjs6nYDRFiIPxZMHoDvjhTiRIEOYbyrRRAOCR67du1CVlYWVCqVIw5PRERk1pIAERbiz6AhMIcEj06dOsHd3d0RhyYiImqEAaLtsGvwyMrKAnD17pInn3wSTz75JNq1a2exz4gRI+zZJBERuRhO6uXa7Bo8tm7dav7ax8cHu3btstguEokYPIiIqEmc1Mv12TV4bNq0yer6uro6SCQSTmFOREQ3xUm9XJ9D5vHIz883P5fliy++QP/+/RETE4P9+/c7ojkiInIRnNTL9TkkeCxcuBCRkZEwGo1YsmQJ3nzzTaxcuRKLFi1yRHNEROQiOKmX63PIXS1nzpzBli1b8PPPP6Ompgbx8fFwc3NDSUmJI5ojIiIXwUm9XJ9DgodCocC+ffuQmZmJwYMHw83NDbt27ULnzp0d0RwREbkITurl+hwSPObOnYsFCxagffv2WL58OQ4cOIBly5Zh+fLljmiOiIhciLU5OXiLretwSPA4evQoPv30U3h5eQEAlEolvv32W0c0RURELo632LoWhwwu3bhxI6RS6e+NuPEhuEREdHuu3WIrl4oR4O8JuVRsvsWW2h6HnPF48MEHMXnyZDz88MPw97dMo64ygdgPP/yAd955Bw899BCef/751i6HiMhlnS68iIERnVBbb4Cmoga9uvnDXSbByd8qbuk4vFzjHBwSPIqKigAAOTk5FutdZebSn376CS+++CIA4OzZs3jooYfQs2fPVq6KiMh1XB8S7v+DCv/ad9Z8qeW8uhJyqRhj4kJv6Xi8XOMcHBI8mprB1FXs2bMHADD68cew8/MMpKen4+23327lqoiIXMP1IUEuFeNs8SWrs5mW6WqafUzOiOo8HBI8dDodtm7divLycphMJgCAwWBAQUEBNm/e7IgmBXXy5Em4ySTIProPPncH4N///jdyc3MRERHR2qUREbVZJwq0+PfPRVDraswhwbe9HJoK6wHj1wsXm31sWzOi8jKMcBwSPF566SUAQPv27VFeXo7w8HBkZmZi3LhxjmhOcKWlpZB39Eatvh71qAfkYmzatAlvvvlma5dGRNQmXTvL4dteDpnk9+d6VVyuQ69u/jivrmz0mgA/T/xz7y8o1lTj18JLNw0M4SF+OFdyudH6sBA/XoYRmENuNzl69CjS09Mxbdo0yOVyzJ49G+vWrcOBAwcc0ZzgdBUVqHMzAhIRRG4iyJTt8P2+faisbPyDQUREN5f/mxa7/jeG42JVPYJV3uZtdfoGuMskkEstHzIql4rRs6sfPv3qV+w7Woy6egP2HryAue8ewIkCbaM2BvcLtnqMB/sH3/QyDNmfQ854+Pn5QSqVonPnzvjll18AAPfddx/OnTvniOYEVVtbC73JAJEb4BbgDS+ZOyrPamByM+H777/HyJEjW7tEIqI240SBFmv++bN5uX8PJcRuMN8yK5eKUVB8CQN6B6Le0IBCdRUUvh5wl0lwurACUeEqVNda3u3y/U+Nx200NSPqH7r6Y8323CZq44PpHMEhwaN///5ITU3FggULcPfdd2P9+vWQy+WNbq1tiyorKwGpG+ApgQkmVNXXAN5SoFKCI0eOMHgQEd2C744UQq27gl7d/KHWXUFtvQE/ndIgpncglL4eKNRUo7S8GnqDET7eMhRrRMg7o0WdvgGPRHfBN4cLG93t8lDkXVbbsjYjKnDzyzBkfw651DJ//nx06dIFRqMRixYtwoEDB7Br1y4sXLjQEc0J6tKlS4DEDXAXAx09AG8pRO1kcJNLzGd3iIioeY4X6MyXU1R+nuaBpAH+Xvji37/hv8dLcV5diX/nFmPvwUL07aFA5B9UcJdLcPlKvdVLJNVX6m+phqYuw/DBdI7hkDMeXl5e5nkuOnTogH/84x+OaKZVmM94dJBD5C4B3CUw1TZA5CnF2bNnYTAYIJE45NtKRORyrp1t+M+JUvzp4e44r65ESKcO+M3KLbQAUFldj5PntBh23104errc6jHPl1Xdch1j4kJx+sIllGir0aOzL4bc35kDSx3Erv9DPvXUUxCJRDfd56OPPrJnk4KrrKwEvKQQeUuBQC+gxgBU62GUXUGDvh6ffPIJxo4dy/BBRNQMg/sF45vDhRg96G5cqq5Dp44eEIsl+O/x3wfru7mJMKBXIGrrDTh1/iK6BfnAy1OKTv5eOF/aeFB/+C1cIrlxzhDf9nL8mFeCIffzaeqOYtf/Ha/dLmsymTBnzhwsXrzYnod3CtXV1YC7BPB1RzuPdqiX6VFbWQ/I3CCSSbBy5Ups3rwZGzduRGBgYGuXS2Q3Nv6mILotYSH+mPRYLxw4Vgz/9p7QXKzDpapKdFJ4I8DfCwfySjCgVyAO5astxnL8/Gs5/jy0O346pbE4M3ItPExd9k2z5uO4/o6WOn0DSrVXzOt5xsMx7Bo8rp8OPS0tzSWmR79RVVUVRF4SyNp7QeHVEQ3GBpxrVwWTtwwJf3oclYXl+Oabb5Ceno4FCxa0drlEdqPw9cTc9fuh/t8v5hup/D2x4LkYgauitu5EgRbv7jiG+8JUuFJbj+paA8ov1kAkArzcJRh0byCqagxWx3KUaq/gT3HdcerCRZRoqxEa7AODoQFbc07BaDQ1az4OWxOLkf3xesAtunj5EuAlRfv2PghsF4D6hnqUdSjHFc+LyDudj0/f+QceeOABnDlzprVLJbI7tfYKisurW7sMciHfHSmEyE2EAH9PZO4raHSHyqOD7kZhWZnV1548V4HKK/UIDe6AcfH34OuDF/CvfRcs9rE1Lbqr3tHizDOxMnjcoouXL0HUXo6OXv7o1C4AV/Q16OBehCueUhScOocrtTVwd3eHXq9v7VKJiJze8QId4qM644K6yupZjfPqSvTu5o8zRZcavVbp4wGZRILNX/6CfUeLm7wceLOzF4P7BWPvwQuNLte05TtanH0mVrsGD63299niTCYTdDqd+Vkt17T1uTy0FTqI2suh8OqIjl7+qDXUoYN7e5TIxYAboNGVo76+HjKZrLVLJSJyen/o4oO6+gaUNHEmraS8Gio/T/OEYtfIpWLIZRJU1dRDLhVDrbuC+8JUOGdlsOnNzl40NbGYM/wHfbuc/YF4dg0eAwcOhEgkMoeNmBjL670ikQj5+fn2bFJwxWUlkAV5wt/TD14yr6v/pJ6AmwgwAbU1NaipqYGfX9s+TUdEJIS7VO3w9aFC3KVqZ/V5LJ0D2iHnv+cR+QcV6uoNKKuogdLXA726dcRHu/MRrPCGb3s5SrVX4O0psxpQbJ29aGpisbbK2cet2DV4nDx50i7HyczMRHp6OgwGAyZMmIDx48dbbM/Pz8fs2bNRXV2NyMhIpKWlQSKRoLi4GKmpqdBqtQgJCcGyZcvg5eVll5quuVh5GRKZCh5Sd/M6Nzc3oMEEqUSKCwXnAQA9evSwa7tERK7IYGzAQ5HBuFhZZzU0BCu9cb60ErX1Bnh7yGDQVuPYGS1kUjG83CVQ+Hog78zVs+1iN7jc2Yvb4ezjVpxujIdarcaKFSuwY8cOyGQyjB07FlFRUQgNDTXvk5qaikWLFqFPnz6YNWsWtm3bhqSkJKSlpSEpKQkjR47EmjVrsHbtWqSmptq1Pr1BDzc3MYzXXUKqM9TBVK3H/RH9sGnTJgDAkCFD7NouEZErErtJsCkrH/oGIwb0CjSf1bg7qD2ClN74b54a9YYG8zTp1xSWVaH7Xb5wl0nMc3DE9g12ubMXt8PZx604ZMr0lti/fz+io6Ph4+MDT09PxMfHIzs727y9qKgItbW16NOnDwAgMTER2dnZ0Ov1OHjwIOLj4y3W25tcKoNBX4+LNRdxqfYSyqu10FbrgEt1OJd3GidOnEB8fDzPeBARNcOJgquBwmg04d+5xTh2Rot6QwOMJiDvdDnae8tQqr3SaMxCsMob93b3R0l5NUbEdHWagZPO4Nq4lRExXdE1sL3TfX+c7oxHWVkZFAqFeVmpVCI3N7fJ7QqFAmq1GhUVFfD29jbPGHptvS2rVq3C6tWrm11fYEcVSi5eROHlYphgQoOxAeXlarjp6nDhTAkeffRRzJgxo9nHI7rmVvsikaMI2RcL1ZbTm1+bn0MmEaP7XT6orW+wegnmgXuDMKhPEBIfukeQOtsaZz7z43TBw2g0Wky7bjKZLJab2n7jfgBsTt8OACkpKUhJSbFYV1hYiLi4OKv79+oZjkO/fokzAWdQUVOBGn0tas9pYVJXYdy4cfi///u/Zr1Pohvdal8kchQh+2KwytvqoNJglTeO/lqGisr63y/BXKxBF1U7DIjohEF9guxeCwnD6YJHQEAADh06ZF7WaDRQKpUW2zUajXm5vLwcSqUSfn5+qKysRENDA8RicaPX2UtYj54w7f0nar2lKOlwGaY6A3DmIrr4dcJLL71k9/aIiFxZRKgCh/PLGp3RiAj1h5e7FL9euIgO3jI82P9u/KGrc/4FT7fG6YJHTEwMVq1aBZ1OBw8PD+Tk5GDhwoXm7UFBQZDL5Th8+DD69++PnTt3IjY2FlKpFJGRkcjKykJCQgIyMjIQGxtr9/r69u0LiaYexlMXYfSRAVcMMBVW4YXFc5t1hoWIiH43cmAIACD3tAaF6ioEq7wREeqPkE4+GDmwWytXR47gdMFDpVJh+vTpSE5Ohl6vx5gxYxAREYFJkyZh2rRp6N27N5YtW28ZHa0AACAASURBVIY5c+agqqoK4eHhSE5OBgDMmzcPM2fORHp6OgIDA7F8+XK71+fj44M/j34Cm7d/AuikQG0Dxif+2SEhh4joTjByYIg5gJDrc7rgAQAJCQlISEiwWLdhwwbz1z179sT27dsbvS4oKMh8O6sjTZ06FT179sSmTZsQHR2NqVOnOrxNIiIiV+CUwcPZSaVSDB8+HMOHD2/tUoiIiNoUp5vHg4iIiFwXgwcREREJhsGDiIiIBMPgQURERIJh8CAiIiLBMHgQERGRYBg8iIiISDAMHkRERCQYBg8iIiISDIMHERERCYbBg4iIiATD4EFERESCYfAgIiIiwTB4EBERkWAYPIiIiEgwDB5EZBciUWtXQERtgaS1CyAi16Dw9cTc9fuh1l6xul3l74kFz8Xc9BgtfT0ROT8GDyKyG7X2CorLq61ua84ZkZu9nohcA4MHEQnC1hmRsLv9BK7o1vGMDFHLMXhY0dDQAAAoLS1t5UrI2QQEBEAiEe7HRsi+KJFIoPL3bHJ7Rx/3m561aM527aXam9Zws/ZV/p7QaDTm74nQxGKxzX1KS0thMBgEqMa1+yK1LbfaF0Umk8nkwHrapEOHDmH8+PGtXQY5ob179yI4OFiw9tgXqSnsi+QsbrUvMnhYUVtbi7y8PCgUipv+lRMXF4e9e/cKWJltrKl5brcmof/KbG5ftBdn/KwA560LaL3aXLEvOtPn7Cy1OEsdQNO13Gpf5KUWK9zd3REZGdmsfYX8i6O5WFPzOGNNN7qVvmgvzvp9cda6AOeuzV6E6ovO9L10llqcpQ7APrVwHg8iIiISDIMHERERCYbBg4iIiAQjnj9//vzWLqIti4qKau0SGmFNzeOMNTkDZ/2+OGtdgHPX1tY40/fSWWpxljoA+9TCu1qIiP6/vXuPi6rOHz/+moEBuSmiDBCa5lr61bwlhpFpiYqgiIv4807l1+sWuvQNtbyL7aZr4gaJadt2U9O8oHjFZb+ulvxUrLyUbdtlS+R+EbnDzJzfH67zc0IUxmFmsPfz8fDxmHM+5/I+hzf4nnM+53yEEFYjt1qEEEIIYTVSeAghhBDCaqTwEEIIIYTVSOEhhBBCCKuRwkMIIYQQViOFhxBCCCGsRgoPIYQQQliNFB5CCCGEsBopPIQQQghhNVJ4CCGEEMJqpPAQQgghhNVI4SGEEEIIq5HCQwghhBBWI4WHEEIIIaxGCg8hhBBCWI0UHkIIIYSwGik8hBBCCGE1Unjchk6nIysrC51OZ+tQxK+c5KKwF5KLwlKk8LiN3NxcgoODyc3NtXUo4ldOclHYC8lFYSlSeAghhBDCaqTwEEIIIYTVSOEhhBBCCKuRwkMIIYQQViOFhxBCCCGsxqaFR2pqKmFhYYwYMYKtW7c2uNyCBQvYs2ePcfrcuXNERUURERHBs88+y9WrVwE4c+YMgYGBREREEBERwSuvvNLsxyCEEEKIxnO01Y7z8vJISEhgz549ODk5MXHiRAIDA+natavJMsuXLycjI4OBAwca58fFxbFx40a6d+/Orl27WL16NcnJyVy6dInp06cze/ZsWxySEEIIIe7CZlc8Tp06xcCBA/H09MTV1ZWQkBCOHDliskxqairBwcGEhoYa59XW1jJ//ny6d+8OQLdu3cjJyQHg4sWLfPrpp4SHhzNnzhzjfCGEEELYB5td8cjPz8fb29s4rdVquXDhgskyM2bMAG7cWrnJycmJiIgIAAwGA0lJSQwbNgwADw8PQkNDGTFiBNu3byc2NpaPP/74jnEkJiaSlJRkkWMS4l5ILgp7IbkompPNCg+DwYBKpTJOK4piMn03tbW1LFq0CJ1OZ7y1smrVKmP7pEmTeOONNygrK8PDw6PB7cTExBATE2MyLysri+Dg4EbHIoQlSC4KeyG5KJqTzW61+Pr6UlBQYJwuKChAq9U2at2KigpmzJiBTqcjOTkZjUaDwWAgOTkZvV5vsqyDg4NF4xZCCCGE+WxWeAQFBZGRkUFxcTFVVVWkpaUxePDgRq0bFxdHp06d2LBhA05OTgCo1WqOHTvG0aNHAUhJSaFPnz64uro22zEIIYQQomlsdqvFx8eH2NhYoqOjqaurIyoqit69ezNz5kzmzZtHr169brve119/TXp6Ol27duW3v/0tcKN/yJYtW1izZg1Lly7lrbfewsvLi7Vr11rzkIQQQghxFypFURRbB2Fvbt7LTE9Pp0OHDrYOR/yKSS4KeyG5KCxF3lwqhBBCCKuRwkMIIYQQViOFhxBCCCGsRgoPIYQQQliNFB5CCCGEsBopPIQQQghhNVJ4CCGEEMJqpPAQQgghhNVI4SGEEEIIq5HCQwghhBBWI4WHEEIIIaxGCg8hhBBCWI3NRqcVQgghbrVs8ynyiiobbPdp58qqWUFWjEg0Byk8hBBC2IW8okqyCytsHYZoZja91ZKamkpYWBgjRoxg69atDS63YMEC9uzZY5zOzs5mypQpjBw5krlz51JRcSNRr1+/zqxZswgNDWXKlCkUFBQ0+zEIIYQQovFsVnjk5eWRkJDAtm3bSElJYceOHXz33Xf1lpkzZw5Hjx41mb9y5UomT57MkSNHePTRR9m4cSMAGzZsICAggMOHDzN+/Hhee+01qx2PEEIIIe7OZoXHqVOnGDhwIJ6enri6uhISEsKRI0dMlklNTSU4OJjQ0FDjvLq6Os6ePUtISAgAkZGRxvWOHz9OeHg4AKNHj+bEiRPU1dVZ6YiEEEIIcTc26+ORn5+Pt7e3cVqr1XLhwgWTZWbMmAHAuXPnjPNKSkpwd3fH0fFG6N7e3uTl5dXbpqOjI+7u7hQXF+Pj49NgHImJiSQlJVnmoIS4B5KLwl5ILormZHbhMW3aNFQqVb35Go2GNm3aMHDgQMaPH3/bZQAMBoNJm6IoDS57q9st19B6iqKgVt/5ok5MTAwxMTEm87KysggODr5rLEJYkuSisBeSi6I5mX2rpX///hQWFhIeHs6sWbOIiIigtLQUf39/nnjiCXbt2sWf/vSnBtf39fU16fxZUFCAVqu96369vLwoKytDr9fXW0+r1VJYWAiATqejoqICT09Pcw9RCCGEEBZmduFx+PBh3nvvPcaPH8+gQYMYN24cf/nLXzh9+jTjx49n8+bN7N27t8H1g4KCyMjIoLi4mKqqKtLS0hg8ePBd96vRaAgICODQoUMApKSkGNcbMmQIKSkpABw6dIiAgAA0Go25hyiEEEIICzO78Lh27Ro6nc5kXm1tLSUlJQA4OzujKEqD6/v4+BAbG0t0dDRjx45l9OjR9O7dm5kzZ3Lx4sU77nv58uXs3LmTsLAwMjMz+f3vfw/A/Pnz+fLLLxk1ahTbtm1j2bJl5h6eEEIIIZqB2X08nn32WaZNm8aECRPw8fEhLy+PTz75hOjoaPLy8oiLizM+edKQ8PBw41MoN23ZsqXecq+//rrJtL+/Px9++GG95Tw9Pdm0aZMZRyOEEEIIazC78Pjd735Ht27dSEtL4/Tp0/j4+LBixQqefPJJfvjhByIjIxkzZowlYxVCCCFEC3dPj9MGBwfftpdzly5d6NKly71sWgghhBD3IbMLj08//ZRVq1aRlZVl7Mtx81HXy5cvWyxAIYQQQtw/zC484uPjmTRpEmFhYTg4OFgyJiGEEELcp8wuPIqLi4mOjpaiQwghhBCNZvbjtFFRUbz99ttUV1dbMh4hhBBC3MfMvuKRnp7Ozz//TGJiIk5OTsD/7+Nx/vx5iwUohBBCiPuH2YXHX//6V0vGIYQQQohfgSYXHseOHWP48OF3vKrh7+9/T0EJIYQQ4v7U5MJj586dDB8+nO3bt9+2XaVSERYWds+BCSGEEOL+0+TC4+YrzW/3ynIhhBBCiDsxu49HaWkp27dv58qVKxgMBpO2P/7xj/ccmBBCCCHuP2YXHgsWLKC0tJQhQ4bIuzyEEEII0ShmFx6ZmZmcOHECNzc3S8YjhBBCiPuY2S8Qe+SRR7h69aolYxFCCCHEfc7sKx4PP/wwkydP5sknn6Rt27YmbStWrGjUNlJTU0lOTkan0/Hss88yZcoUk/bLly+zePFiKioqCAgIYOXKlZSWljJ9+nTjMmVlZZSUlPDFF19w5swZYmJi8PX1BaBHjx7S30QIIYSwI2YXHlqtlueee87sHefl5ZGQkMCePXtwcnJi4sSJBAYG0rVrV+MycXFxrF69mr59+/Lqq6+yc+dOJk+ezL59+wAwGAw8++yzxMbGAnDp0iWmT5/O7NmzzY5LCCGEEM3H7MLj4sWLrF+/3uw+HqdOnWLgwIF4enoCEBISwpEjR3jxxRcBuHr1KtXV1fTt2xeAyMhI3nzzTSZPnmzcxu7du3FxcSE8PNwYU2FhIQcOHMDf35/ly5fj5+dn7iEKIYQQwsLMLjy+/fZbqqqqzC488vPz8fb2Nk5rtVouXLjQYLu3tzd5eXnGab1ez6ZNm9i4caNxnoeHB6GhoYwYMYLt27cTGxvLxx9/fMc4EhMTSUpKMusYhLAkyUVhLyQXRXMyu/Do2bMnY8aMoX///nh5eaFSqYxtjenjYTAYTNa5OcBcY9tPnjxJ586d6datm3HeqlWrjJ8nTZrEG2+8QVlZGR4eHg3GERMTQ0xMjMm8rKwsgoOD73oMQliS5KKwF5KLojmZXXh0796d7t27m71jX19fMjMzjdMFBQVotVqT9oKCAuN0YWGhSfvf/vY3k1ezGwwG3n77bWbNmmXyXhF5x4gQQghhP8wuPG72xTBXUFAQiYmJFBcX4+LiQlpaGvHx8cZ2f39/nJ2dOXfuHP3792ffvn0MHjzY2P7ll18yc+ZM47RarebYsWN06tSJsLAwUlJS6NOnD66urvcUpxBCCCEsx+zCIzQ01OTWx60OHTp01/V9fHyIjY0lOjqauro6oqKi6N27NzNnzmTevHn06tWLdevWsWTJEsrLy+nZsyfR0dHG9a9cuWJ8bPamNWvWsHTpUt566y28vLxYu3atuYcnhBBCiGZgduGxcuVKk+nS0lI++OADhg4d2uhthIeHG59IuenmIHRw43bOrl27brvu+fPn6817+OGH79qZVAghhBC2Y3bh8fjjj9eb98QTTzBmzBief/75ewpKCCGEEPcns1+ZfjtXrlyhtrbWkpsUQgghxH3EYn089Ho92dnZ/P73v7dIYEIIIYS4/1isj4daraZDhw71OnwKIYQQQtzU5MJj1qxZbN68+bZ9PIQQQggh7qTJfTxufemXEEIIIURTWLRzqRBCCCHEnTT5VktVVZXJq8pvpzEvEBNCCCHEr0+TCw8nJ6dGDQInhBBCCPFLTS48HBwcpGOpEEIIIczS5D4eiqI0RxxCCCGE+BVocuEh/TeEEEIIYa4mFx5+fn7NEYcQQgghfgXkcVohhBBCWI1NC4/U1FTCwsIYMWIEW7durdd++fJlIiMjCQkJYfHixeh0OgD27t3LoEGDiIiIICIigoSEBACys7OZMmUKI0eOZO7cuVRUVFj1eIQQQghxZ2aP1QI33mKak5ODXq83mT927Ni7rpuXl0dCQgJ79uzBycmJiRMnEhgYSNeuXY3LxMXFsXr1avr27curr77Kzp07mTx5MpcuXWLRokWMHj3aZJsrV65k8uTJjBo1irfeeouNGzcSFxd3L4cohBBCCAsy+4rH8uXL+d3vfsfu3bs5cOCA8d/Bgwcbtf6pU6cYOHAgnp6euLq6EhISwpEjR4ztV69epbq6mr59+wIQGRlpbL948SJ79+4lPDycl19+mdLSUurq6jh79iwhISH1lhdCCCGEfTD7iseBAwc4dOgQPj4+Zq2fn5+Pt7e3cVqr1XLhwoUG2729vcnLyzN+nj59Oo899hjr169n1apVLFy4EHd3dxwdHestfyeJiYkkJSWZdQxCWJLkorAXkouiOZl9xeOBBx6gVatWZu/YYDCgUqmM04qimEzfqf2tt96if//+qFQqZsyYwcmTJ+utD9Sbvp2YmBj++c9/mvxLT083+7iEMJfkorAXkouiOTX5isfN93gEBAQwdepUpk6dioeHh8kydxvLBcDX19dkpNuCggK0Wq1Je0FBgXG6sLAQrVZLWVkZu3fv5rnnngNuFCQODg54eXlRVlaGXq/HwcGh3vaEEEIIYXtNLjy2b99u/Ozp6cmBAwdM2lUqVaMKj6CgIBITEykuLsbFxYW0tDTi4+ON7f7+/jg7O3Pu3Dn69+/Pvn37GDx4MK6urrzzzjv069ePPn368NFHHzF8+HA0Gg0BAQEcOnSI8PBwUlJSGDx4cFMPTwghhBDNqMmFx4cffnjb+TU1NTg6OuLg4NCo7fj4+BAbG0t0dDR1dXVERUXRu3dvZs6cybx58+jVqxfr1q1jyZIllJeX07NnT6Kjo3FwcGDDhg2sWLGC6upqOnfuzNq1a4EbHV4XLVpEcnIyfn5+rF+/vqmHJ4QQQohmZHbn0suXL/PnP/+ZTZs2cfDgQRYuXIibmxsJCQkEBQU1ahvh4eGEh4ebzNuyZYvxc/fu3dm1a1e99QICAti7d2+9+f7+/g0WRkIIIYSwPbMLj/j4eIYOHYrBYGDNmjX86U9/wtPTk/j4eBnPRQghhBC3ZXbh8f3337Nt2zbOnz9PVVUVISEhqNVqcnJyLBmfEEIIIe4jZhce3t7enDx5ktTUVIYMGYJarebAgQM8+OCDloxPCCGEEPcRswuPZcuWsWrVKlq3bs369evJyMhg3bp10qFTCCGEEA0yu/D48ssv2bFjB25ubsCNN48eP37cUnEJIYQQ4j5k9ptL3333XTQazf/fkNqmA90KIYQQogUw+4rH008/zezZsxk6dCjt2rUzaWvMC8SEEEII8etjduFx9epVANLS0kzmN/bNpUIIIYT49TG78JAXdQkhhBCiqcwuPIqLi9m+fTuFhYUoigKATqfjxx9/ZOvWrRYLUAghhBD3D7N7hL700kucPXuWoqIivv32WzQaDYcPH6Zfv36WjE8IIYQQ9xGzC48vv/yS5ORk5s2bh7OzM4sXL2bTpk1kZGRYMj4hhBBC3EfMLjy8vLzQaDQ8+OCD/POf/wRgwIAB/PTTTxYLTgghhBD3F7MLj/79+xMXF0dNTQ1dunRh8+bNvP/++/UerRVCCCGEuMnswmPFihV06tQJg8HA6tWrycjI4MCBA8THx1syPiGEEELcR8x+qsXNzY3f//73ALRp04a//vWvTd5GamoqycnJ6HQ6nn32WaZMmWLSfvnyZRYvXkxFRQUBAQGsXLkSR0dHzp07xx//+Efq6urw9PTkD3/4A/7+/pw5c4aYmBh8fX0B6NGjB3/84x/NPUQhhBBCWFiTC49p06ahUqnuuMwHH3xw1+3k5eWRkJDAnj17cHJyYuLEiQQGBtK1a1fjMnFxcaxevZq+ffvy6quvsnPnTiZPnkxcXBwbN26ke/fu7Nq1i9WrV5OcnMylS5eYPn06s2fPbuphCSGEEMIKmlx4TJo0CQBFUViyZAmvvfaaWTs+deoUAwcOxNPTE4CQkBCOHDnCiy++CNx4M2p1dTV9+/YFIDIykjfffJOoqCjmz59P9+7dAejWrRsfffQRABcvXqSwsJADBw7g7+/P8uXL8fPzMys+IYQQQlhekwuPW1+HvnLlSrNfj56fn4+3t7dxWqvVcuHChQbbvb29ycvLw8nJiYiICAAMBgNJSUkMGzYMAA8PD0JDQxkxYgTbt28nNjaWjz/++I5xJCYmkpSUZNYxCGFJ9p6LiqKwe/duduzYQXFxMY8++igvvPACjzzyiK1DExZm77koWjaz+3jcK4PBYHLLRlEUk+m7tdfW1rJo0SJ0Op3x1sqqVauM7ZMmTeKNN96grKwMDw+PBuOIiYkhJibGZF5WVhbBwcHmH5wQZrD3XExOTubdd9/F2dkZrVbLZ599xvnz59m8ebMUH/cZe89F0bLZbCx7X19fCgoKjNMFBQVotdoG2wsLC43tFRUVzJgxA51OR3JyMhqNBoPBQHJyMnq93mQ/Dg4OzXwkQtz/Pv/8c9599106duzInj172Lt3LytWrKCiooKlS5cah00QQoi7aXLhUVRUZPynKArFxcUm84qKihq1naCgIDIyMiguLqaqqoq0tDQGDx5sbPf398fZ2Zlz584BsG/fPmN7XFwcnTp1YsOGDTg5Od04ELWaY8eOcfToUQBSUlLo06cPrq6uTT1EIcQtFEVh06ZNwI2rij4+PgCMHj2akJAQvv/+e86cOWPLEIUQLUiTb7U8+eSTqFQq4zecoKAgk3aVSsXly5fvuh0fHx9iY2OJjo6mrq6OqKgoevfuzcyZM5k3bx69evVi3bp1LFmyhPLycnr27El0dDRff/016enpdO3ald/+9rfAjf4hW7ZsYc2aNSxdupS33noLLy8v1q5d29TDE0L8wv79+/n888956qmn6NWrl0nbuHHjOHLkCOnp6QQGBtooQiFES9LkwuObb76x2M7Dw8MJDw83mbdlyxbj55uPy96qR48exle0/9LDDz98186klnT48GH8/PyMT94Icb/Izs4mPT2ds2fPkpGRgYuLCwsWLKi3XK9evXB3d+fs2bM2iFII0RLZrHNpS/fdd9+xdOlSABYtWkRUVJSNIxLi3hkMBjZt2sQHH3yATqcD4L/+67+IjY297aPpjo6O9OrVi4yMDK5du2Z8PF4IIRpis86lLd3//u//Gj+/+eabxj/SQrRUiqKwfPly3n33XbRaLUuWLOHw4cN8+OGHPPbYYw2u17t3b+DGiNVCCHE3UniY6eTJkzg4OPDMM89QWVkpo/KKFm/79u0cPnyYXr168dFHHzF27FiTd+k0pF+/fgCcPn26uUMUQtwHpPAwQ3FxMV9//TV9+vShT58+APz73/+2bVBC3IO8vDxjp+w//elPeHh4kJOfy7c/fEdWbjbXrpdSXlFObV1tvXX79OlDu3btOHz4MBUVFTaIXgjRkkgfDzPc/Gbn5tOWWm68NyQnJ8eWIQlxT9555x1qampYuHAhl3/4lj8vSeanrJ9vu2y33zxMzPNzCOwbAIBGo2H8+PFs2rSJvXv3MnXqVGuGLoRoYaTwMMP58+fB2YFPL57h04tnQCWFh2i5ioqKOHDgAB07dkRp5cD/xL+KWq3m6SeeQtuuPRWVlVRUVaLX6ymvKOeLry7wcvxi3k94my4PdgZg/PjxvPvuuxw8eFAKDyHEHUnhYYavvvoKtYczigpQAFcN2dnZtg5LCLPs3r2buro6hgx7htc3rqe1uwcJK15HcVSTXZBLnb4Ot1au/Ma/Mw/6+JP+2XFeeX0F8X9ew7vrNqJSqWjTpg19+/blzJkzdx2mQAjx6yaFRxPp9Xq+//cPKN7OqP3cUKp0qPVw5coVW4cmRJPV1NSwe/du3Nzd+Pu5z9AbDDzx9CBefns1JWWl9Zbv0fkR4ibP5eknnuJ4xknOnD9nvOVyc7iCVq1aWfUYhBAti3QubaKCggJqHQzQWoPi5ghtnDC0UvNzdpY8UitanH379lFUVIS2a0dyCvPw6uLL0XP/oNbBwH892pOAAQMICgpi6KCnGdCzH1//+1teeONVhgy6MXzBvqMHgRuDNn799dc8+OCDaDQaWx6SEMLOyRWPJsrOzgYXR2jXCpXWDUWvh7JaDPmVnD59mieffNLWIQrRKGVlZWzZsgVNaxf+XZCFxteDYipx9GtDpYOBb8qzoAzQG0CvoHFwZGTwCI6kp7Hr00P4+fiS8fkZDAYDly5doqqqiieeeMLWhyXuY7cMUC5aMCk8mqigoADaOKNq74rjA63RV9ehL6oGV0e2bdsmhYdoMTZv3kzJ9Ws4PuCBun0rdF5OqLxaobg74+jkgAoVBoMBg16Pn1s7qgqvc/TrU/T8r558dfkrgrr04bOMU/x89QqZmZkADBgwwMZHJe5n3m1dWbb5FHlFlbdt92nnyqpZQbdtE/ZDCo8mKiouRuXphEbrQQePB6hsVUVe+0rUbV04c/YsJSUltG3b1tZhCnFHV65cYcfOnWi8PdC10aDycUXt506r1u64ubjj6KBBURRq9bU4APnXSnBu7Yi63IESw40/+gbHG18/i66V3HjSC2TcItHs8ooqyS6U98W0ZNLHo4lyC/LAsxWenu3wb/0AHdo8gMbLDYObA4pa4ciRI7YOUYi72rZtGwYN6NzUqHzdcezQhvY+fnT2fojOXp3p5NmRDm388fXwoZWTK55tvajS19BO25788iIAaupqgBudSi9cuECXLl1krBYhxF1J4dFEeQX5qNq2wtdDS4c2D/BAaz88WrcBdyecWrvy4YcfoiiKrcMUokHV1dWkHjiAg6crqvYuqP3caeftQyevTnTxeuiWokOLj7s37s7uoFKjcXammjr0BgMAVZVVAFy/VkpVVZXx1elCCHEnUng0UW5BLo7urWjn6oWzoxOtnT1o7dIGlZuGttr25Ofnk5+fb+swhWhQZmYm1XU1GFzV0M4Ft/aedPDsQCfPjvi39qNDmwfwcffGs1UbPJw9aO3sgYtjK1RqByqrq3DXuACQ9XMW7b3a8d033wLSv0MI0Tg2LTxSU1MJCwtjxIgRbN26tV775cuXiYyMJCQkhMWLFxsfV83OzmbKlCmMHDmSuXPnGseHuH79OrNmzSI0NJQpU6bc6AhqYeUVFWicnXHVuKJ11+Lr4YOzoxNo1OjVN74JlpWVWXy/QljKP//5T3BSg6sjqtZOtHFri9bNG617e1q38kDjoMHVyZXWrVrTytEZB7UjqEAxGNDX6KgoLcPLvQ1l10oZNuhpUlNTcXV1ZeDAgbY+NCFEC2CzwiMvL4+EhAS2bdtGSkoKO3bs4LvvvjNZJi4ujmXLlnH06FEURWHnzp0ArFy5ksmTJ3PkyBEeffRRNm7cCMCGDRsICAjg8OHDjB8/ntdee83icdfqdajUKhxVDgCoKN5dWAAAGXFJREFUVWrUqEFR0OvkBUrC/uXl5YGDGlwccXB1xqOVO64aF1w0LjipHRjxQFcAnB2c/vNki546vQ59XR1U6zCU11BVVIaTxok2Gjfy8vIYNWoU7u7uNj4yIURLYLPC49SpUwwcOBBPT09cXV0JCQkx6Zh59epVqqurjb3kIyMjOXLkCHV1dZw9e5aQkBCT+QDHjx8nPDwcgNGjR3PixAnq6uosGreTowaDXk9FXSVZpVfJKr1Kpa4SpUZPXU0dGo0GX19fi+5TCEuqra0FFaBWo1KrUKNG/Z8XJNQa9JwuyAKgsq6KiroqymsrKK8qQ3e9CuV6DW44U1lcxuQx4/no/Q9xc3Nj5syZNjwiIURLYrPHafPz8/H29jZOa7VaLly40GC7t7c3eXl5lJSU4O7ujqOjo8n8X67j6OiIu7s7xcXF+Pj4NBhHYmIiSUlJjY67XZu2ZJWVU1hZjKuTK7X6WkrKS6C8jvKiazzer58xNiGaoqm5aC4XFxdQFKjTY6jVUVFXSWVdFSWV1/Bybcu12iqqdTWUVF6jqKKIwopCKq+XwbUaHMr1lOde46kBT3B4937Ky8tZtWoVXl5ezR63sB5r5aL4dbLZ/5AGgwHVLa+hUxTFZLqh9l8uB9SbvnUdtfrOF3ViYmKIiYkxmZeVlUVwcPBtl3+wQ0e+LPmCnLIcFBR0+jrKi6/BtRqo0jFixIg77k+IhjQ1F83l5+cHOgUq69CX11BWfZ288gIc1A7U6mpRqVSU11aQV57PldIsiksKMOSXQ2EVuoIKejzUjQufZnKt5BovvPACYWFhFo1P2J61clH8Otms8PD19TW+7RBuvBFUq9WatN/aObSwsBCtVouXlxdlZWXo9XocHBxM1tNqtRQWFuLr64tOp6OiosLi7xV4qGMnlH98SmFeLtdrylEUBUP2dRzK9KjUjvKLKexer169oEYPlXooqqK0bRFXHZ3QGXS4alxRUCivKSe/PJ/i4kJ0OaWQW4Ehv5KObX35+rPPcXR05JVXXmHcuHG2PhwhRAtjs8IjKCiIxMREiouLcXFxIS0tjfj4eGO7v78/zs7OnDt3jv79+7Nv3z4GDx6MRqMhICCAQ4cOER4eTkpKCoMH3xiwasiQIaSkpDBnzhwOHTpEQECAxQes6typM0p+FYZ/l1LTvhalWo9ypQxdQTn/Hf2cDAcu7F6fPn14sENHssryUQqr0LldJ19RKPMsw9XZDYNBT2VVBdXXym9c6civQimopK3ajZ/P/ws/Pz/WrFlDjx49bH0oQphFXrtuWzYrPHx8fIiNjSU6Opq6ujqioqLo3bs3M2fOZN68efTq1Yt169axZMkSysvL6dmzJ9HR0QAsX76cRYsWkZycjJ+fH+vXrwdg/vz5LFq0iFGjRuHh4cG6dessHnfPnj1Rl9SgZJWhlFSDzgA5lTzo48/zzz9v8f0JYWlqtZpZs2axZNlSHN2cMFwpQ1+po6JdFRWODqAoKJV1UFKDUlSFQ7keVamO4rJcRo4cycKFC6XAFi2avHbdtmzaCzI8PNz4FMpNW7ZsMX7u3r07u3btqreev78/H374Yb35np6ebNq0yfKB/mIfjz/6GP/3q89p7eNFeUk5XK9j+brl8hitaDFCQkI4duwY//jsJK5qL6qqylBdqwFnNegUlIo6qKzDzeBEec51vNq0Zf7KRYSFhTXYp0oIIRpD3lxqhhdffBEXnQPXv8vFUFTJ6vh4+vTpY+uwhGg0lUpFfHw8/Xr1ofJKIUpuBe7XVTjl1OJapOBcpEPJrqAyu4TwsNHs2rWLUaNGSdEhhLhn8tynGbp3787+/fvJzc2lTZs2PPDAA7YOSYgmc3V1ZePGjRw9epSkpCQKf8gztrVp04bI/zORqKgoHnzwQRtGKYS430jhYaa2bdvStm1bW4chxD3RaDSMHj2akSNH8sMPP3Dt2jXatm3LQw89JO+jES2OXJBrGeQvixACR0dHHnnkEVuHIcQ98W7rescnVgB6dLm/XnbXEp/QkcLjNvT6G2Ou5Obm2jgSYW98fX2teiVAclE05H7LRUdHR3zaud5xmfaere54VaO9ZyuKSqvvuq877cennSsFBQXG47VnDg4Od10mNzfXOMBqc2lqLqoURVGaMZ4WKTMzkylTptg6DGGH0tPT6dChg9X2J7koGiK5KOxFU3NRCo/bqK6u5tKlS3h7e9+xogwODiY9Pd2Kkd2dxNQ45sZk7W+Zjc1FS7HHnxXYb1xgu9jux1y0p5+zvcRiL3FAw7E0NRflVstttGrVioCAgEYta81vHI0lMTWOPcb0S03JRUux1/Nir3GBfcdmKdbKRXs6l/YSi73EAZaJRd7jIYQQQgirkcJDCCGEEFYjhYcQQgghrMZhxYoVK2wdREsWGBho6xDqkZgaxx5jsgf2el7sNS6w79haGns6l/YSi73EAZaJRZ5qEUIIIYTVyK0WIYQQQliNFB5CCCGEsBopPIQQQghhNVJ4CCGEEMJqpPAQQgghhNVI4SGEEEIIq5HC4xapqamEhYUxYsQItm7dWq/98uXLREZGEhISwuLFi41DDWdnZzNlyhRGjhzJ3LlzqaioAOD69evMmjWL0NBQpkyZQkFBgdViOnfuHFFRUURERPDss89y9epVAM6cOUNgYCARERFERETwyiuvWC2mvXv3MmjQIOO+ExISgIbPX3PHVFRUZIwlIiKCoUOH0q9fP8Ay58meWDq37SG2hvLJmrHdtGDBAvbs2WOctsZ5a2nsKQftJefsKb/MjcWsc6IIRVEUJTc3V3nmmWeUkpISpaKiQgkPD1f+9a9/mSwzatQo5YsvvlAURVFeeeUVZevWrYqiKMqsWbOUAwcOKIqiKElJScratWsVRVGUlStXKm+//baiKIqyd+9eZf78+VaL6ZlnnlEuX76sKIqifPLJJ8qcOXMURVGUv/zlL8qmTZuaFIelYlq1apWSmppab5sNnT9rxHSTXq9Xpk6dquzfv19RlHs/T/akOXLbHmJrKJ+sGVtubq4ye/ZspXfv3sru3buN85v7vLU09pSD9pJz9pRf9xKLOedErnj8x6lTpxg4cCCenp64uroSEhLCkSNHjO1Xr16lurqavn37AhAZGcmRI0eoq6vj7NmzhISEmMwHOH78OOHh4QCMHj2aEydOUFdX1+wx1dbWMn/+fLp37w5At27dyMnJAeDixYt8+umnhIeHM2fOHOP85o7p5r737t1LeHg4L7/8MqWlpXc8f9aI6abdu3fj4uJi/Hnd63myJ82R27aODW6fT9aMDW58SwwODiY0NNQ4zxrnraWxpxy0l5yzp/wyNxYw75xI4fEf+fn5eHt7G6e1Wi15eXkNtnt7e5OXl0dJSQnu7u44OjqazP/lOo6Ojri7u1NcXNzsMTk5OREREQGAwWAgKSmJYcOGAeDh4cG0adNITU1lyJAhxMbGNjqee4np5uff/e537N+/Hz8/P1atWnXH82eNmAD0ej2bNm3if/7nf4zz7vU82ZPmyG1bx3bz8y/zyZqxAcyYMYPx48ebzLPGeWtp7CkH7SXn7Cm/zI3l5v6bek4c7yna+4jBYEClUhmnFUUxmW6o/ZfLAfWmb11HrW58rWduTDfV1tayaNEidDods2fPBjBJikmTJvHGG29QVlaGh4dHs8f01ltvGefPmDGD4cOHs2DBgkafv+aICeDkyZN07tyZbt26Gefd63myJ9bIbWvHBrfPJ2vG1hBrnLeWxp5y0F5yzp7yy9xYwLxzIlc8/sPX19ek82dBQQFarbbB9sLCQrRaLV5eXpSVlaHX6+utp9VqKSwsBECn01FRUYGnp2ezxwRQUVHBjBkz0Ol0JCcno9FoMBgMJCcnG2O9ycHBodljKisr47333jPOVxQFBweHO56/5o7ppr/97W+EhYUZpy1xnuxJc+S2rWNrKJ+sGVtDrHHeWhp7ykF7yTl7yi9zYzH3nEjh8R9BQUFkZGRQXFxMVVUVaWlpDB482Nju7++Ps7Mz586dA2Dfvn0MHjwYjUZDQEAAhw4dAiAlJcW43pAhQ0hJSQHg0KFDBAQEoNFomj0mgLi4ODp16sSGDRtwcnICQK1Wc+zYMY4ePWqMtU+fPri6ujZ7TK6urrzzzjucP38egI8++ojhw4ff8fxZ4zwBfPnllwQEBBinLXGe7Elz5LatY2son6wZW0Oscd5aGnvKQXvJOXvKL3NjMfucNKkr6n1u//79yqhRo5QRI0YomzdvVhRFUWbMmKFcuHBBURRFuXz5sjJu3DglJCREeemll5SamhpFURQlKytLmTp1qhIaGqpMnz5duXbtmqIoilJSUqLMnj1bCQsLUyZMmKBcuXLFKjF99dVXyiOPPKKEhYUpY8aMUcaMGaPMmDFDURRF+fbbb5UJEyYoYWFhytSpU5Xs7GyrnaezZ88qY8eOVUaOHKnMmTNHuX79+h3PnzViUhRF6d27t1JdXW2yPUucJ3ti6dy2h9gayidrxnbTwoULTXr6W+O8tTT2lIP2knP2lF/mxmLOOVEpiqLcU6kkhBBCCNFIcqtFCCGEEFYjhYcQQgghrEYKDyGEEEJYjRQeQgghhLAaKTyE3SsuLqampsbWYQghhLAAKTyaSbdu3eqNRnvw4EGmTZtmsX1MmzaNgwcPNnr506dP061bN+O7RZpDYmIiy5Yts+g2Q0NDuX79OgCjRo3iwoULFt2+uD17yuHExER69uxJv3796Nu3LwMHDmT16tUYDIYm73Po0KF8+eWX9eZLbtkne8nD7Oxs+vXrR79+/ejduzfdu3c3Ts+YMcNiscyYMcP4DqH7lbwy/VckJSWF8PBwPvnkE8aOHWvrcBrt2rVrxs9NKbTE/WXcuHHGV9kXFBQQHR1NSkoKkZGRFtm+5Ja4kwceeIAvvvgCuPElbvny5c0y+N8777xj8W3aG7niYSPl5eXExcXxzDPP0Lt3b6Kjo42vV582bRoLFy4kMDCQTZs2cfr0aUaNGsWAAQOYOHEiX331lXE7Z8+eJTw8nH79+rFs2TIaei1LVVUV6enpxMXF8eOPP/LDDz+YtG/evJlBgwYRGBho8k1y9+7dDB06lICAAF566SWqq6sB+OKLL4iMjGTAgAHMmjXrtoMU1dbW8tprr/Hkk08ydOhQduzYYWzr0aMHixcv5vHHH+f06dP87W9/47e//S0BAQEMGjSIbdu2ARAdHQ3A8OHD+fHHH02+rR49epSRI0cyYMAA5syZY4whMTGRpUuXMnHiRB577DFmzpxJSUlJ039I4o6sncO38vb2ZtCgQXzzzTcALFq0iJdeeomgoCCWLVtGfn4+c+fO5amnnqJv3768+OKLVFVVmWxDr9czZ84cXn75ZRRFMebW6dOnmTJlCgsWLKBfv36MGTOGS5cuGdeJj48nICCA8PBw/vCHP7Bo0SJLnVJhBlvm4a1Onz7NyJEjbzudmJjICy+8wNNPP83MmTPv+Dfq5tWXrKwshg8fzpo1awgICGD48OGcPHnSuP2NGzcSGBjIiBEjSEhIsOgVoOYmhUczGjlyJAEBAcZ/ixcvNrZt2bKF2tpajh49SkZGBiqVio8//tjYnpuby/Hjx5k6dSqrVq1iwYIFnD17lqFDh/LnP//ZuFxmZibvv/8+hw8f5vDhw2RmZt42lrS0NPr164ePjw+jR4/mk08+MbYdPXqUjz/+mK1bt5Kens6FCxfYs2cPFy9e5PXXX+fNN9/k1KlT1NTU8Pbbb3P9+nXmzp3Liy++yKlTp+jbty8LFy6st8/Nmzfz3XffcfjwYd577z02b95sjE+v19O+fXtOnDhBjx49WLhwIStWrCAzM5PXXnuNNWvWUFNTwwcffADAsWPHeOihh4zb/v7771m2bBmvvfYan332GQ899BAvvfSSsf3gwYOsWLGCEydOcP36dbZv397UH5/AvnL4Vjk5OXz22Wc89dRTxnmXL1/m6NGjLFiwgLVr19KxY0eOHz/O3//+d3788cd6305XrVqFwWDg9ddfrzcgVmZmJgMHDuT06dMEBgaybt06ALZu3coXX3xBWloaGzZs4MCBA007ocIs9pqHTXHu3Dl27NhBQkIC0Li/UT///DNubm5kZGQwadIk4xW/9PR0du3axa5du/jwww9JS0uzaKzNTW61NKMjR46YDDV88OBB4y/Ec889ZxxMJycnhzZt2pjcxxw6dCguLi7AjSHaDxw4QJs2bfjv//5vZs2aZVxuypQpeHl5ATeuImRnZ982ln379jFhwgQAoqKieO6553jppZfQaDQcO3aMCRMm0KlTJwDWr1+PRqNh27ZthIaG8uijjwKwYsUKqqurOX78OD169GDo0KEAzJ49my1btpCfn2+yz/379/P666/TunVrWrduzYQJE9i/f79xXJTQ0FBatWqFRqNh3759dOjQgaKiItRqNdXV1ZSVleHs7Hzb4zl27BjDhg2jf//+AMTGxjJgwABycnIAGDRoEN27dwdujJlz5cqVhn5M4g7sKYf37NnDoUOHMBgMVFRU8Oijj9K7d29j+1NPPWUcPXjBggW4u7tTW1tLXl5evdi2bNnCxYsXSUtLMw4vfitPT0/jLZyQkBD+/ve/AzeK9OnTp+Pl5YWXlxfjx4//1Q95bw32lIfm6t+/Pz4+Psbpxv6Nev7559FoNISFhbF27VrgRh5OmDCBjh07AjB9+nT2799v0Xibk1zxsJFr164xd+5cBg8eTHx8PPn5+SaX9tq1a2f8vH79egwGAzNmzGDIkCGkpqYa21q3bm387OjoWG9EVYC8vDwyMjJYunQpgYGBTJs2jaKiItLT0wEoKSkx+YXo0KEDPj4+lJSUmIxQ6O3tTceOHcnNzeXMmTPGbx+BgYHo9Xpyc3NN9pubm8vMmTONyyUnJ5sUJzd/yR0cHDh48CBBQUFMmzbNOPjRnS515uTk4OfnZ5x2cnKiXbt2xhhuHQXY0dGxyZdNxd1ZM4cBIiMjyczM5PPPP+fzzz/nkUceITY29rb7y87OZvLkyQwbNoz169dTVlZmsq2ff/6Z2tpa4z37X2rbtq1JTDePq7Cw0OR35dYcFLZh7Tw0161xQOP+Rjk5OeHm5gbc+Dt5c5mioiKTPPT19bVorM1NrnjYyMqVKxk8eDAfffQRKpWK1atXU1tba2y/eelXr9fz008/8cYbb6DT6UhLS2PBggUMGzas0fvat28fw4cP59VXXzXO+/jjj/nkk08YOXIkWq3WpCA4c+aMcSjoW+d/++23nD17Fm9vb5555hkSExONbd9//z2dOnXiH//4h3Fe+/btSU5Oplu3bsCNX5Zb3TzGzMxMtm/fTkpKClqtltLSUvbs2XPHY/L29jZe3YAb/UkKCwuNxYxoftbM4V9yc3MjOjraeBXv1v3BjdGZ582bR3h4OABz5841WX/58uX861//Ij4+nn379jV61GgfHx+TKxxytcP2bJmHt1Kr1SbFys0n8ZqDVqtt0XkoVzxspLy8HGdnZ1QqFWfPnmXPnj3odLp6y6nVahYtWkRqaiqOjo60bdsWFxeX214ebsi+ffsYPXo0vr6+xn9RUVFkZGRw9epVQkJC2LFjB1lZWZSVlbF+/XoqKysZPnw4R48e5ZtvvqGmpoakpCQKCwsZMmQImZmZZGRkoCgKKSkpTJo0ibq6OpP9jho1iqSkJMrLyykuLmbWrFns3r27XnwVFRWo1WqcnJyorKw03se8eT40Gg0VFRUm64SGhnL06FE+//xzamtrSUhIoEuXLsbbRaL5WTOHf6m2tpaUlBT69OnTYGytWrUCblymP3HihEl+Ojo6MmHCBDQaDe+//36j9ztq1Cjee+89SkpK+Omnn26bz8K6bJmHt+rQoQM5OTl89dVXlJeXNymvmmrUqFHs3LmTq1evUlhYaOwL11LIFQ8biYuLY+nSpbz55pt07tyZiRMn3vayr0ql4o033mDVqlUsW7YMHx8fYx+Mxrhw4QLZ2dkMHjzYZH6HDh3o3bs3u3fvZt68efzwww9MnTqV6upqIiMjGTduHCqVisWLFxMTE0NpaSnBwcHMnTsXJycn3nzzTf7whz/w008/8eCDD7Jx40bjfdSbXnjhBdauXUtISAg6nY6wsDCmT59eL8ZBgwYxYMAAgoODcXFxYeTIkXTt2pUff/wRPz8/wsPDGTt2rMkv129+8xtWr17Nq6++Sl5eHo899hhJSUmNOifCMqyVwzft3r3beGncwcGBxx57jNdff/22yy5dupT4+HheeeUVHnnkEcaNG8ePP/5osoxarWbx4sXMnj3beGXkbqKiovjqq68IDg6mQ4cOBAQENPk4hGVZOw8b4ufnx/z585k1axZOTk5MnTrVpBO/JQ0aNIiIiAjGjh1L27Ztefzxxy3eJ6U5qRS5+S2EEI1y+fJlfH19jX1AEhIS0Ol0xMXF2Tgy8Wvy/fff4+7ubuznsXPnTk6dOsWGDRtsHFnjyK0WIYRopPT0dFavXk1dXR05OTkcOHCAgQMH2jos8Stz4cIFXn75ZSorK7l27RqffPJJi8pDKTyEEKKRnnvuOerq6ggKCmL8+PFMmDDB5F0iQljDmDFjeOihh3jmmWcICQmhf//+jB8/3tZhNZrcahFCCCGE1cgVDyGEEEJYjRQeQgghhLAaKTyEEEIIYTVSeAghhBDCaqTwEEIIIYTV/D/O42bRIc5xzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef145dba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\",font_scale=1.1)\n",
    "g = sns.PairGrid(features)\n",
    "g = g.map_upper(plt.scatter, edgecolor=\"w\")\n",
    "g = g.map_lower(sns.kdeplot)\n",
    "g = g.map_diag(plt.hist, edgecolor=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All features are skewed to the right with a long tail. On the diagnoal, we see a histogram of all features. On the upper triangle we see a scatterplot of each pair of features, and on the bottom triangle we see a KDE (Kernel Density Estimation) of each pair of features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example of a driver with many events of one type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Harsh Acceleration   0.0778\n",
       "Harsh Braking        0.0045\n",
       "Harsh Turning        0.1226\n",
       "Name: 51, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.loc[features['Harsh Turning'].idxmax(), ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers handling\n",
    "We wish to remove/adjust outliers as they affect many statistical approaches. In order to remove these, we'll transform the features to normal (using a box-cox transformation) and remove based on *mean + k*std's* rule. A second approach could be to truncate the tail using some constant, but it will be more difficult to find this threshold than the standard deviation rule. A third option is to remove outliers on all three dimensions (using a multivariate normal distribution, for example).\n",
    "\n",
    "This code performs the first option, box-cox transformation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>Harsh Acceleration</th>\n",
       "      <th>Harsh Braking</th>\n",
       "      <th>Harsh Turning</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DriverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0386</td>\n",
       "      <td>0.5650</td>\n",
       "      <td>0.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0337</td>\n",
       "      <td>0.1329</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4766</td>\n",
       "      <td>0.5569</td>\n",
       "      <td>0.2748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7523</td>\n",
       "      <td>0.8953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.2851</td>\n",
       "      <td>0.6641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Harsh Acceleration  Harsh Braking  Harsh Turning\n",
       "DriverId                                                  \n",
       "0                     0.0386         0.5650         0.3484\n",
       "1                     0.0337         0.1329         0.0000\n",
       "2                     0.4766         0.5569         0.2748\n",
       "4                     0.0000         0.7523         0.8953\n",
       "5                     0.1601         0.2851         0.6641"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "\n",
    "def transform_to_normal(x,min_max_transform = False):\n",
    "    xt = np.zeros(len(x))\n",
    "    if np.count_nonzero(x) == 0:\n",
    "        print(\"only zero valued values found\")\n",
    "        return x\n",
    "    \n",
    "    valueGreaterThanZero = np.where(x<=0,0,1)\n",
    "    positives = x[valueGreaterThanZero == 1]\n",
    "    if(len(positives)> 0):\n",
    "        xt[valueGreaterThanZero == 1],_ = st.boxcox(positives+1)\n",
    "    if min_max_transform:\n",
    "        xt = (xt - np.min(xt)) / (np.max(xt)-np.min(xt))\n",
    "    return xt\n",
    "\n",
    "transFeatures = features.apply(lambda x: (transform_to_normal(x,min_max_transform = True)))\n",
    "transFeatures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot matrix of transformed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py:967: UserWarning: The following kwargs were not used by contour: 'label', 'color'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1c178438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(transFeatures)\n",
    "g = g.map_upper(plt.scatter, edgecolor=\"w\")\n",
    "g = g.map_lower(sns.kdeplot)\n",
    "g = g.map_diag(plt.hist, edgecolor=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the distribution is not symmetric yet, but not as skewed as the original data.\n",
    "Let's use the mean + k std rule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Harsh Acceleration\n",
      "found outlier with factor: 2.5 : [15 44]\n",
      "replacing outliers [15 44] with max=0.009031595984491887\n",
      "Harsh Acceleration\n",
      "found outlier with factor: 2.5 : [15 44]\n",
      "replacing outliers [15 44] with max=0.009031595984491887\n",
      "Harsh Braking\n",
      "No outliers found\n",
      "Harsh Turning\n",
      "No outliers found\n"
     ]
    },
    {
     "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>Harsh Acceleration</th>\n",
       "      <th>Harsh Braking</th>\n",
       "      <th>Harsh Turning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.0069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Harsh Acceleration  Harsh Braking  Harsh Turning\n",
       "0              0.0003         0.0122         0.0069\n",
       "1              0.0002         0.0021         0.0000\n",
       "2              0.0044         0.0120         0.0052\n",
       "3              0.0000         0.0206         0.0369\n",
       "4              0.0012         0.0049         0.0177\n",
       "5              0.0008         0.0142         0.0165"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### remove outliers\n",
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "def replace_outliers_with_limit(x, stdFactor = 2.5, normalize = False):\n",
    "    print(x.name)\n",
    "    x = x.values\n",
    "    xt = np.zeros(len(x))\n",
    "    if np.count_nonzero(x) == 0:\n",
    "        print(\"only zero valued values found\")\n",
    "        return x\n",
    "    \n",
    "    xt = transform_to_normal(x)\n",
    "\n",
    "    xMean, xStd = np.mean(xt), np.std(xt)\n",
    "    outliers = np.where(xt > xMean + stdFactor*xStd)[0]\n",
    "    inliers = np.where(xt <= xMean + stdFactor*xStd)[0]\n",
    "    if len(outliers) > 0:\n",
    "        print(\"found outlier with factor: \"+str(stdFactor)+\" : \"+str(outliers))\n",
    "        xinline = x[inliers]\n",
    "        maxInRange = np.max(xinline)\n",
    "        print(\"replacing outliers {} with max={}\".format(outliers,maxInRange))\n",
    "        vals = x.copy()\n",
    "        vals[outliers] = maxInRange\n",
    "        x= pd.Series(vals)\n",
    "    else:\n",
    "        print(\"No outliers found\")\n",
    "    if normalize:\n",
    "        #Normalize to [0,1]\n",
    "        x = (x - np.min(x)) / (np.max(x)-np.min(x))\n",
    "    return x\n",
    "\n",
    "cleanFeatures = features.apply(lambda x: (replace_outliers_with_limit(x)))\n",
    "cleanFeatures.head(6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers are found only on the harsh acceleration column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py:967: UserWarning: The following kwargs were not used by contour: 'label', 'color'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1c178320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(cleanFeatures)\n",
    "g = g.map_upper(plt.scatter, edgecolor=\"w\")\n",
    "g = g.map_lower(sns.kdeplot)\n",
    "g = g.map_diag(plt.hist, edgecolor=\"w\")\n",
    "g = g.set(ylim=((cleanFeatures.min().min()),(cleanFeatures.max().max())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations between events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef1c40d390>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1c43d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1c21aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = cleanFeatures.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "fig = plt.figure()\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(7, 7))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "#### Main hypothesis\n",
    "\n",
    "Our main assumption while analyzing this data was that the more harsh events a driver had, the more likely he/she was to be an unsafe driver. A driver with zero harsh turns, zero harsh accelerations, zero over-speeding and zero harsh braking over some distance was more likely to be classified as a safe driver. On the contrary, we can assume that a driver who made multiple harsh movements over the same distance was an unsafe driver.\n",
    "\n",
    "We used a number of different approaches for modeling:\n",
    "\n",
    "- Modeling the problem as an anomaly detection problem or using some sort of clustering to find clusters of safe/unsafe drivers.\n",
    "- Finding a multivariate metric that encompasses our assumption in multi dimensions and allowed us to estimate unsafe driving using probabilities over this multivariate distribution.\n",
    "- Reducing the dimension to 1 and trying to find a rule/rank on this one unified metric.\n",
    "    Using a univariate statistical model: modeling each event type individually by using statistical distributions.\n",
    "\n",
    "In the following section we discuss how we evaluated different approaches for modeling this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min and Max values per column before normalization\n",
      "Harsh Acceleration range:[0.0,0.009031595984491887]\n",
      "Harsh Braking range:[0.0,0.10340548522686539]\n",
      "Harsh Turning range:[0.0,0.12259498755008752]\n",
      "\n",
      "Mean and STD before standardization\n",
      "Harsh Acceleration range:[0.18775905276289825,0.25588420026047654]\n",
      "Harsh Braking range:[0.3556351512504195,0.276056089678018]\n",
      "Harsh Turning range:[0.2865774139188881,0.29888576889340224]\n"
     ]
    },
    {
     "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>Harsh Acceleration</th>\n",
       "      <th>Harsh Braking</th>\n",
       "      <th>Harsh Turning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0294</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.0564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0256</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4854</td>\n",
       "      <td>0.1156</td>\n",
       "      <td>0.0423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1989</td>\n",
       "      <td>0.3008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1306</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.1443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Harsh Acceleration  Harsh Braking  Harsh Turning\n",
       "0              0.0294         0.1183         0.0564\n",
       "1              0.0256         0.0202         0.0000\n",
       "2              0.4854         0.1156         0.0423\n",
       "3              0.0000         0.1989         0.3008\n",
       "4              0.1306         0.0475         0.1443"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Pre step: Normalize features\n",
    "minPerFeature = cleanFeatures.min()\n",
    "maxPerFeature = cleanFeatures.max()\n",
    "\n",
    "print(\"Min and Max values per column before normalization\")\n",
    "for col in range(0,len(cleanFeatures.columns)):\n",
    "    print(\"{} range:[{},{}]\".format(cleanFeatures.columns[col],minPerFeature[col],maxPerFeature[col]))\n",
    "\n",
    "normalizedFeatures = (cleanFeatures-cleanFeatures.min())/(cleanFeatures.max()-cleanFeatures.min())\n",
    "normalizedFeatures.head()\n",
    "\n",
    "\n",
    "## Standardize features after box-cox as well.\n",
    "transFeaturesScaled = (transFeatures - transFeatures.mean())/transFeatures.std()\n",
    "print(\"\")\n",
    "print(\"Mean and STD before standardization\")\n",
    "for col in range(0,len(transFeatures.columns)):\n",
    "    print(\"{} range:[{},{}]\".format(transFeatures.columns[col],transFeatures.mean()[col],transFeatures.std()[col]))\n",
    "\n",
    "\n",
    "normalizedFeatures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anomaly detection:\n",
    "\n",
    "The first approach we used was anomaly detection. We tried to see if we could highlight meaningful anomalies that might contain information about driver safety. A few approaches were evaluated, among them a method called HBOS and Local Outlier Factor (LOF). This plot shows the output of LOF on two dimensions of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "## LOF - Local Outlier Filter\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "\n",
    "X = transFeaturesScaled.values\n",
    "\n",
    "clf = LocalOutlierFactor(n_neighbors=5)\n",
    "isOutlier = clf.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1758b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Local Outlier Factor (LOF)\",fontsize=20)\n",
    "\n",
    "\n",
    "a = plt.scatter(X[isOutlier==1, 0], X[isOutlier==1, 1], c='white',\n",
    "                edgecolor='k', s=40)\n",
    "b = plt.scatter(X[isOutlier==-1, 0], X[isOutlier==-1, 1], c='red',\n",
    "                edgecolor='k', s=40)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,10)\n",
    "plt.xlabel(normalizedFeatures.columns[0],fontsize = 15)\n",
    "plt.ylabel(normalizedFeatures.columns[1],fontsize = 15)\n",
    "#plt.xlim((-0.01,1.01))\n",
    "#plt.ylim((-0.01, 1.01))\n",
    "plt.legend([a, b],\n",
    "           [\"normal observations\",\n",
    "            \"abnormal observations\"],\n",
    "           loc=\"upper right\",prop={'size': 15},frameon = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LOF model looks for anomalous points in respect to each point's neighborhood. We ran LOF on all three dimensions, however this plot shows only the first two dimensions. Even after playing with the model's parameter (number of neighbors), we did not achieve meaningful results. It appears that statistical outliers did not imply behavioral outliers in this case.\n",
    "### Multivariate analysis\n",
    "\n",
    "The second approach was to find a multivariate representation of the distribution and then use it to calculate the probability of one driver being unsafe when compared to a general population of drivers. Essentially we look for drivers that lay on the tail of a multivariate right skewed distribution. We didn't pursue with this approach since most methods assume normal distribution, have other unmet assumptions, or do not have a robust implementation we could employ.\n",
    "### Dimensionality reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef17b6b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pricipal components explained variance ratio: [0.7275472  0.16648689 0.10596592].\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ef11167c88>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef11308208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef11167390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PCA\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=3)\n",
    "\n",
    "principalComponents = pca.fit_transform(normalizedFeatures)\n",
    "column_names = ['principal component {}'.format(i) for i in range(normalizedFeatures.shape[1])]\n",
    "\n",
    "\n",
    "plt.bar(x = column_names,height = pca.explained_variance_ratio_)\n",
    "plt.title(\"Percentage of explained variance\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Pricipal components explained variance ratio: {}.\".format(pca.explained_variance_ratio_))\n",
    "\n",
    "\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = column_names)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "df = normalizedFeatures\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,10)\n",
    "\n",
    "\n",
    "## Show correlation matrix of the original features and the first principal component\n",
    "pcAndOriginal = pd.concat([principalDf.iloc[:,0].reset_index(drop=True), normalizedFeatures], axis=1)\n",
    "sns.set(style=\"ticks\")\n",
    "histplot = pcAndOriginal['principal component 0'].hist(figsize = (2.5,2.5))\n",
    "histplot.set_title(\"principal component 0 histogram\")\n",
    "sns.pairplot(pcAndOriginal,y_vars=['principal component 0'],\n",
    "                  x_vars=['Harsh Acceleration', 'Harsh Braking', 'Harsh Turning'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first principal component was the only one that preserved the order of the data (has positive correlation with all original features). However, since this component only explained ~70% of the variance, it was not accurate enough to be used as a metric for ranking.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The selected model: comparing each driver to a homogeneous population\n",
    "\n",
    "In the end, we decided to use the technique of modeling the distribution of each event, and then evaluating each individual car by comparing it to general data from the overall homogeneous population. Since all events had a skewed distribution, we decided to use an exponential or gamma distribution. In this approach, instead of using a multivariate model that might be difficult to explain, we used a weighted sum of probabilities. For each event distribution, we estimated the Cumulative Distribution Function (CDF) value and performed a weighted sum across all event types. This is a naïve approach as it ignores the dependency between events, but helps in making the model easily explainable.\n",
    "Additionally, it is possible to evaluate the goodness-of-fit or the SSE measure between the data and the proposed distribution to detect drifts or changes in the data that might affect the model once it is finally deployed to production. This figure shows histograms for each event (normalized between 0 and 1), together with a fitted Exponential density function:\n",
    "### Model parameters selection\n",
    "\n",
    "In order to decide what is the right weight for each factor, one can refer to accident statistics relevant to this population. For example, the [National Motor Vehicle Crash Causation Survey](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811059) has details on causes with respect to over speeding, aggressive driving, accelerating and more.\n",
    "### Segmentation of drivers\n",
    "As noted earlier, we wish to compare each driver to his or her homogeneous population. Pointer's system contain metadata on each driver, such as the type of vehicle, country, and device type. We divided all drivers into distinct groups where each group has a specific value of the triplet {vehicle, country, device}. Only groups with 1,000 drivers or more were evaluated – and for distinct groups having less than 1,000 drivers, no prediction was made for any driver. In order to generalize this approach from 242 drivers to all drivers in all segments, the pipeline proposed here is duplicated per segment for which the models' assumptions hold.\n",
    "### Model assumptions\n",
    "\n",
    "The primary assumption of this approach is that the population segment is homogeneous, i.e. no confounding effect could cause drivers to be different from others in the population, other than the mode of driving. In addition, this approach might not be appropriate for population segments in which a large majority of the drivers are dangerous drivers. In order to overcome the latter, it is suggested to reject population segments in which the drivers distributions don't follow a predefined distribution (e.g. Exponential) using [goodness of fit statistical tests](https://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm), such as the [Anderson-Darling](https://www.itl.nist.gov/div898/handbook/eda/section3/eda35e.htm) test or the [Kolmogorov-Smirnov](https://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm) test.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract statistics from the fitted distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting distribution parameters for feature: Harsh Acceleration (74 values)\n",
      "Extracting distribution parameters for feature: Harsh Braking (74 values)\n",
      "Extracting distribution parameters for feature: Harsh Turning (74 values)\n",
      "Fitted parameters:\n",
      "{\n",
      "  \"Harsh Acceleration\": {\n",
      "    \"arg\": [],\n",
      "    \"loc\": 0.02564752667369235,\n",
      "    \"scale\": 0.3035523938874951\n",
      "  },\n",
      "  \"Harsh Braking\": {\n",
      "    \"arg\": [],\n",
      "    \"loc\": 0.008563681894357935,\n",
      "    \"scale\": 0.10105576180418932\n",
      "  },\n",
      "  \"Harsh Turning\": {\n",
      "    \"arg\": [],\n",
      "    \"loc\": 0.0037259420919605983,\n",
      "    \"scale\": 0.09982577201370954\n",
      "  }\n",
      "}\n",
      "Calculating scores for feature: Harsh Acceleration\n",
      "Calculating scores for feature: Harsh Acceleration\n",
      "Calculating scores for feature: Harsh Braking\n",
      "Calculating scores for feature: Harsh Turning\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Harsh Acceleration_CDF</th>\n",
       "      <th>Harsh Braking_CDF</th>\n",
       "      <th>Harsh Turning_CDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.6623</td>\n",
       "      <td>0.4099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1084</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7801</td>\n",
       "      <td>0.6533</td>\n",
       "      <td>0.3203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.8480</td>\n",
       "      <td>0.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2923</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>0.7554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Harsh Acceleration_CDF  Harsh Braking_CDF  Harsh Turning_CDF\n",
       "0                  0.0124             0.6623             0.4099\n",
       "1                  0.0000             0.1084             0.0000\n",
       "2                  0.7801             0.6533             0.3203\n",
       "3                  0.0000             0.8480             0.9490\n",
       "4                  0.2923             0.3199             0.7554"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "\n",
    "\n",
    "def fit_distribution_params(series):\n",
    "    print(\"Extracting distribution parameters for feature: \" + series.name + \" (\" + str(len(series)) + ' values)')\n",
    "    xPositive = series[series>0]\n",
    "  \n",
    "    probs = np.zeros(len(series))\n",
    "    if(len(xPositive)>0):\n",
    "        params = st.expon.fit(xPositive)\n",
    "        arg = params[:-2]\n",
    "        loc = params[-2]\n",
    "        scale = params[-1]\n",
    "        #print('params = {}, {}, {}.'.format(arg,loc,scale))\n",
    "        return arg, loc, scale\n",
    "\n",
    "def calculate_score_for_series(x, fittedParams,verbose = False):\n",
    "    \n",
    "    print(\"Calculating scores for feature: \"+ x.name)\n",
    "    xPositive = x[x>0]\n",
    "  \n",
    "    probs = np.zeros(len(x))\n",
    "    if(len(xPositive)>0):\n",
    "        arg = fittedParams[x.name]['arg']\n",
    "        loc = fittedParams[x.name]['loc']\n",
    "        scale = fittedParams[x.name]['scale']\n",
    "        probs[x>0] = st.expon.cdf(xPositive, loc=loc, scale=scale, *arg)\n",
    "        if verbose:\n",
    "            probs_df = pd.DataFrame({'Event value':x.values.tolist(),'Event probability':probs},index = None)\n",
    "            probs_df = probs_df.sort_values(by = 'Event value')\n",
    "            print(probs_df)\n",
    "    return probs\n",
    "\n",
    "## Store each fitted distribution parameters for later use\n",
    "fittedParams = {}\n",
    "for col in normalizedFeatures.columns:\n",
    "    arg,loc,scale = fit_distribution_params(normalizedFeatures[col])\n",
    "    fittedParams[col] = {}\n",
    "    fittedParams[col]['arg'] = arg\n",
    "    fittedParams[col]['loc'] = loc\n",
    "    fittedParams[col]['scale'] = scale\n",
    "import json\n",
    "print('Fitted parameters:')\n",
    "print(json.dumps(fittedParams, indent=2))\n",
    "\n",
    "##Commulative distribution/density function\n",
    "perFeatureScores = normalizedFeatures.apply(calculate_score_for_series,args=(fittedParams,False),axis=0).add_suffix(\"_CDF\")\n",
    "\n",
    "perFeatureScores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Harsh Acceleration\n",
      "sse: 88.81292779966475\n",
      "Harsh Braking\n",
      "sse: 24.760400326313338\n",
      "Harsh Turning\n",
      "sse: 98.53717270817495\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of drivers')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef17aa46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot each fitted distribution\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "\n",
    "DIST = st.expon\n",
    "\n",
    "def create_pdf(dist, params, size=10000):\n",
    "\n",
    "    # Separate parts of parameters\n",
    "    arg = params[:-2]\n",
    "    loc = params[-2]\n",
    "    scale = params[-1]\n",
    "\n",
    "    start = dist.ppf(0.01, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n",
    "    end = dist.ppf(0.99999, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99999, loc=loc, scale=scale)\n",
    "    x = np.linspace(start, end, size)\n",
    "    y = dist.pdf(x, loc=loc, scale=scale, *arg)\n",
    "    pdf = pd.Series(y, x)\n",
    "\n",
    "    return pdf\n",
    "\n",
    "## fit exponential distribution\n",
    "fig, axs = plt.subplots(1,3, figsize=(15, 6), facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = .5, wspace=.001)\n",
    "\n",
    "axs = axs.ravel()\n",
    "i=0\n",
    "for col in normalizedFeatures:\n",
    "    print(col)\n",
    "    feature = normalizedFeatures[col]\n",
    "    x = feature.values[feature.values > 0] # only fit positive values to keep the distribution tighter\n",
    "   \n",
    "    params = DIST.fit(x)\n",
    "\n",
    "    # Separate parts of parameters\n",
    "    arg = params[:-2]\n",
    "    loc = params[-2]\n",
    "    scale = params[-1]\n",
    "   \n",
    "    #Plot\n",
    "    pdfForPlot = create_pdf(DIST,params)\n",
    "    pdfForPlot.plot(lw=2, label='PDF',ax=axs[i])\n",
    "    \n",
    "    #Plot\n",
    "    feature[feature.values >0].plot(kind='hist', bins=30, density=True, alpha=0.5, label=feature.name, legend=True,ax=axs[i])\n",
    "    axs[i].set_ylabel('')\n",
    "    axs[i].set_xlabel('')\n",
    "    ## Calculate SSE\n",
    "    yhist, xhist = np.histogram(x, bins=60, density=True)\n",
    "    xhist = (xhist + np.roll(xhist, -1))[:-1] / 2.0\n",
    "    histPdf = DIST.pdf(xhist, loc=loc, scale=scale, *arg)\n",
    "    sse = np.sum(np.power(yhist - histPdf, 2.0))\n",
    "    print(\"sse:\",sse)\n",
    "    i+=1\n",
    "axs[1].set_xlabel('Events per km')\n",
    "axs[0].set_ylabel('Number of drivers')\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a single metric\n",
    "The commulative metric is the sum (or weighted sum) of CDFs per event type. here we assume that features have equal weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_joint_score(perFeatureScores):\n",
    "    driverScores = perFeatureScores\n",
    "    featureCols = [col for col in driverScores if col.startswith('Harsh')]\n",
    "    driverScores['metric'] = (driverScores[featureCols].sum(axis = 1) / 3.0)\n",
    "    driverScores = driverScores.sort_values('metric')\n",
    "    driverScores['rank'] = (driverScores.metric.rank(method=\"min\")-1)/((len(perFeatureScores)-1)*1.0)\n",
    "    return driverScores\n",
    "\n",
    "driverScores = calculate_joint_score(perFeatureScores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the entire population won't be available on test time, we fit a simple linear model to find the relation between the metric (weighted sum of CDFs) and the rank (the position in the population). We will use the metric value at test time, and this model to estimate the driver's hypothetical rank in this population. Since we are doing in-population analysis, and this is a very simple model, there's no need for train/test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6946498430580719"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "def get_linear_model(perFeatureScores):\n",
    "    driverScores = calculate_joint_score(perFeatureScores)\n",
    "    reg = LinearRegression()\n",
    "    x = driverScores['metric'].values.reshape(-1, 1)\n",
    "    y = driverScores['rank'].values.reshape(-1, 1)\n",
    "    reg.fit(x,y)\n",
    "    return reg\n",
    "    \n",
    "\n",
    "def get_rank(regModel,metric):\n",
    "    rank = regModel.predict(metric)\n",
    "    rank = np.asscalar(np.where(rank > 1,1,rank))\n",
    "    rank = np.asscalar(np.where(rank < 0,0,rank))\n",
    "    return rank\n",
    "    \n",
    "regModel = get_linear_model(perFeatureScores)\n",
    "sampleRank = get_rank(regModel,0.5)\n",
    "sampleRank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Harsh Acceleration_CDF</th>\n",
       "      <th>Harsh Braking_CDF</th>\n",
       "      <th>Harsh Turning_CDF</th>\n",
       "      <th>metric</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Harsh Acceleration_CDF  Harsh Braking_CDF  Harsh Turning_CDF  metric  \\\n",
       "32                  0.0000             0.0000             0.0000  0.0000   \n",
       "29                  0.0000             0.0000             0.0000  0.0000   \n",
       "70                  0.0000             0.0000             0.0000  0.0000   \n",
       "68                  0.0000             0.0000             0.0000  0.0000   \n",
       "67                  0.0000             0.0000             0.0000  0.0000   \n",
       "6                   0.0000             0.0000             0.0000  0.0000   \n",
       "27                  0.0000             0.0000             0.0000  0.0000   \n",
       "25                  0.0000             0.0000             0.0000  0.0000   \n",
       "51                  0.0000             0.0000             0.0000  0.0000   \n",
       "20                  0.0000             0.0000             0.0000  0.0000   \n",
       "\n",
       "     rank  \n",
       "32 0.0000  \n",
       "29 0.0000  \n",
       "70 0.0000  \n",
       "68 0.0000  \n",
       "67 0.0000  \n",
       "6  0.0000  \n",
       "27 0.0000  \n",
       "25 0.0000  \n",
       "51 0.0000  \n",
       "20 0.0000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Top 10 safest drivers?\n",
    "driverScores.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Harsh Acceleration_CDF</th>\n",
       "      <th>Harsh Braking_CDF</th>\n",
       "      <th>Harsh Turning_CDF</th>\n",
       "      <th>metric</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.9813</td>\n",
       "      <td>0.8590</td>\n",
       "      <td>0.6334</td>\n",
       "      <td>0.8767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.7543</td>\n",
       "      <td>0.4610</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>0.7026</td>\n",
       "      <td>0.8904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.9596</td>\n",
       "      <td>0.2898</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7498</td>\n",
       "      <td>0.9041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.8187</td>\n",
       "      <td>0.8948</td>\n",
       "      <td>0.6243</td>\n",
       "      <td>0.7793</td>\n",
       "      <td>0.9178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.8374</td>\n",
       "      <td>0.6871</td>\n",
       "      <td>0.8154</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>0.9315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.9596</td>\n",
       "      <td>0.7658</td>\n",
       "      <td>0.6326</td>\n",
       "      <td>0.7860</td>\n",
       "      <td>0.9452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.9421</td>\n",
       "      <td>0.7005</td>\n",
       "      <td>0.7953</td>\n",
       "      <td>0.8127</td>\n",
       "      <td>0.9589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.4887</td>\n",
       "      <td>0.9844</td>\n",
       "      <td>0.9724</td>\n",
       "      <td>0.8152</td>\n",
       "      <td>0.9726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.9075</td>\n",
       "      <td>0.7541</td>\n",
       "      <td>0.8275</td>\n",
       "      <td>0.8297</td>\n",
       "      <td>0.9863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.9596</td>\n",
       "      <td>0.5761</td>\n",
       "      <td>0.9755</td>\n",
       "      <td>0.8371</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Harsh Acceleration_CDF  Harsh Braking_CDF  Harsh Turning_CDF  metric  \\\n",
       "39                  0.0598             0.9813             0.8590  0.6334   \n",
       "59                  0.7543             0.4610             0.8924  0.7026   \n",
       "44                  0.9596             0.2898             1.0000  0.7498   \n",
       "42                  0.8187             0.8948             0.6243  0.7793   \n",
       "72                  0.8374             0.6871             0.8154  0.7800   \n",
       "48                  0.9596             0.7658             0.6326  0.7860   \n",
       "7                   0.9421             0.7005             0.7953  0.8127   \n",
       "37                  0.4887             0.9844             0.9724  0.8152   \n",
       "16                  0.9075             0.7541             0.8275  0.8297   \n",
       "15                  0.9596             0.5761             0.9755  0.8371   \n",
       "\n",
       "     rank  \n",
       "39 0.8767  \n",
       "59 0.8904  \n",
       "44 0.9041  \n",
       "42 0.9178  \n",
       "72 0.9315  \n",
       "48 0.9452  \n",
       "7  0.9589  \n",
       "37 0.9726  \n",
       "16 0.9863  \n",
       "15 1.0000  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## top 10 most risky drivers?\n",
    "driverScores.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributions of CDFs, rank and metric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef2175c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "g = sns.PairGrid(driverScores)\n",
    "g = g.map_upper(plt.scatter, edgecolor=\"w\")\n",
    "g = g.map_diag(plt.hist, edgecolor=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating safety for an arbitrary subscriber\n",
    "The following process estimates the score of a new driver:\n",
    "1. Calculate features - number of events per km\n",
    "2. Calculate CDFs by estimating the CDF per feature value using the fitted functions during 'training'. We use the function's parameters to estimate the CDF per new value.\n",
    "3. Calculate the weighted sum metric. The value of this metric corresponds to the point in the population this new driver resides in.\n",
    "4. Calculate a rank within a population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****** Estimated safety scores ******\n",
      "Calculating scores for feature: Harsh Acceleration\n",
      "   Event probability  Event value\n",
      "0             0.8244       0.5536\n",
      "Calculating scores for feature: Harsh Braking\n",
      "   Event probability  Event value\n",
      "0             0.3255       0.0484\n",
      "Calculating scores for feature: Harsh Turning\n",
      "   Event probability  Event value\n",
      "0             0.3101       0.0408\n",
      "Calculating scores for feature: Harsh Acceleration\n",
      "   Event probability  Event value\n",
      "0             1.0000      22.1445\n",
      "Calculating scores for feature: Harsh Braking\n",
      "   Event probability  Event value\n",
      "0             1.0000       1.5473\n",
      "Calculating scores for feature: Harsh Turning\n",
      "   Event probability  Event value\n",
      "0             1.0000       2.2839\n",
      "------\n",
      "Rank for safe driver = 0.6785927595653235\n",
      "Metric for safe driver = 0.486647447960139\n",
      "------\n",
      "Rank for risky driver = 1.0\n",
      "Metric for risky driver = 0.9999999186733707\n"
     ]
    }
   ],
   "source": [
    "safeDriver = [{'Harsh Acceleration':0.005,\n",
    "                       'Harsh Braking':0.005,\n",
    "                       'Harsh Turning':0.005}]\n",
    "\n",
    "riskyDriver = [{'Harsh Acceleration':0.2,\n",
    "                       'Harsh Braking':0.16,\n",
    "                       'Harsh Turning':0.28}]\n",
    "\n",
    "safeDriverDf = pd.DataFrame(safeDriver,index = None)\n",
    "riskyDriverDf = pd.DataFrame(riskyDriver,index = None)\n",
    "\n",
    "print('****** Estimated safety scores ******')\n",
    "def get_score_one_driver(driverDf,fittedParams,minPerFeature,maxPerFeature):\n",
    "\n",
    "    normalized = (driverDf - minPerFeature) / (maxPerFeature - minPerFeature)\n",
    "    scoreDF = pd.DataFrame()\n",
    "    for col in normalized:\n",
    "        scoreDF[col] = calculate_score_for_series(normalized[col],fittedParams,True)\n",
    "    scoreDF = calculate_joint_score(scoreDF)\n",
    "    return scoreDF\n",
    "\n",
    "\n",
    "safeDriverWithMetric = get_score_one_driver(safeDriverDf, fittedParams,minPerFeature,maxPerFeature)\n",
    "riskyDriverWithMetric = get_score_one_driver(riskyDriverDf, fittedParams,minPerFeature,maxPerFeature)\n",
    "\n",
    "safeDriverMetric = safeDriverWithMetric['metric'].values[0]\n",
    "riskyDriverMetric = riskyDriverWithMetric['metric'].values[0]\n",
    "\n",
    "\n",
    "print('------')\n",
    "print(\"Rank for safe driver = {}\".format(get_rank(regModel,safeDriverMetric)))\n",
    "print(\"Metric for safe driver = {}\".format(safeDriverMetric))\n",
    "print('------')\n",
    "print(\"Rank for risky driver = {}\".format(get_rank(regModel,riskyDriverMetric)))\n",
    "print(\"Metric for risky driver = {}\".format(riskyDriverMetric))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two alternatives here for results: the metric (weighted sum), which gives a score with regards to the amount of events of each type, and the rank, which shows on which percentile of the population the driver is. Depending on the use case, one can decide on the right metric.\n",
    "Since the relation between these two metrics is not 100% linear, we get different results than the training set. In this case we get a rank of 0.28 and metric of 0.15 for a driver with almost 0 events."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deployment on a Spark cluster\n",
    "The PySpark code is very similar to the pandas code. It performs the feature calculation and normalization on a spark cluster, where each driver's data is computed separately. Then it aggregates the data into one node and performs the population statistics on this node. This process is utilized for each segment in the population, where segments are groups of drivers with similar attributes (e.g. same type of vehicle, same country, same device installed, same device firmware/software).\n",
    "\n",
    "For the PySpark version of this analysis, open [this notebook](Driver safety estimation - pyspark.ipynb)"
   ]
  }
 ],
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