7_Model3_mixed_input_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 3: using mixed data (satellite imagery and structured data) to predict where traffic accidents will happen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook answers the question 'Does using satellite images increase the predictive capacity of a model to predict the location of traffic accidents?'\n",
    "\n",
    "Model 1 used structured data on accidents, traffic and population density to predict the severity of traffic accidents. Model 2 used satellite imagery to train a CNN to predict whether a location would be a traffic accident hot-spot or not. In this notebook, model 3 will combine structured data and satellite imagery in a mixed-imput neural network, to attempt to improve the model's predictive capability.\n",
    "\n",
    "Additional data was used for this model to get the LSOA for each latitude and longitude point, in order to match with the population data. The data is from the Greater London Authority Datastore, and contains the latitude and longitude centre point for each LSOA. Source: https://data.london.gov.uk/dataset/mylondon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining and preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns', 50)\n",
    "from scipy.spatial import cKDTree\n",
    "np.random.seed(123)\n",
    "from shapely.ops import nearest_points\n",
    "import geopandas\n",
    "from math import *\n",
    "import urllib, os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import glob\n",
    "import cv2\n",
    "\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers.core import Activation, Dropout, Dense\n",
    "from keras.layers import Flatten, Input, concatenate\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.optimizers import Adam, RMSprop\n",
    "import argparse\n",
    "import locale\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import itertools\n",
    "from keras import regularizers\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Accident_Severity</th>\n",
       "      <th>Number_of_Vehicles</th>\n",
       "      <th>Number_of_Casualties</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>1st_Road_Class</th>\n",
       "      <th>Road_Type</th>\n",
       "      <th>Speed_limit</th>\n",
       "      <th>Junction_Detail</th>\n",
       "      <th>2nd_Road_Class</th>\n",
       "      <th>Light_Conditions</th>\n",
       "      <th>Weather_Conditions</th>\n",
       "      <th>Road_Surface_Conditions</th>\n",
       "      <th>Urban_or_Rural_Area</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Two_Hour_Groupings</th>\n",
       "      <th>Time_of_Day</th>\n",
       "      <th>Was_Daylight</th>\n",
       "      <th>Was_Bad_Weather</th>\n",
       "      <th>Was_Road_Dry</th>\n",
       "      <th>log_Number_of_Casualties</th>\n",
       "      <th>log_Number_of_Vehicles</th>\n",
       "      <th>LSOA</th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motorbike_aadf</th>\n",
       "      <th>car_aadf</th>\n",
       "      <th>bus_aadf</th>\n",
       "      <th>light_goods_vehicle_aadf</th>\n",
       "      <th>heavy_goods_vehicle_aadf</th>\n",
       "      <th>Road</th>\n",
       "      <th>RCat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201301BS70003</td>\n",
       "      <td>-0.171402</td>\n",
       "      <td>51.486361</td>\n",
       "      <td>Serious</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>A</td>\n",
       "      <td>Single carriageway</td>\n",
       "      <td>30.0</td>\n",
       "      <td>T or staggered junction</td>\n",
       "      <td>Unclassified</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Fine no high winds</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Urban</td>\n",
       "      <td>9</td>\n",
       "      <td>8am-10am</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>E01002844</td>\n",
       "      <td>110.8</td>\n",
       "      <td>1634.4</td>\n",
       "      <td>860.4</td>\n",
       "      <td>14888.0</td>\n",
       "      <td>1139.8</td>\n",
       "      <td>2297.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>A3217</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201301BS70005</td>\n",
       "      <td>-0.173356</td>\n",
       "      <td>51.495115</td>\n",
       "      <td>Slight</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>Friday</td>\n",
       "      <td>A</td>\n",
       "      <td>Single carriageway</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Crossroads</td>\n",
       "      <td>A</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Other</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Urban</td>\n",
       "      <td>8</td>\n",
       "      <td>8am-10am</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>E01002821</td>\n",
       "      <td>74.6</td>\n",
       "      <td>559.6</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>28505.6</td>\n",
       "      <td>1396.2</td>\n",
       "      <td>3868.6</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>A4</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201301BS70006</td>\n",
       "      <td>-0.210767</td>\n",
       "      <td>51.518353</td>\n",
       "      <td>Slight</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>Monday</td>\n",
       "      <td>B</td>\n",
       "      <td>Single carriageway</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Crossroads</td>\n",
       "      <td>B</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Fine no high winds</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Urban</td>\n",
       "      <td>11</td>\n",
       "      <td>10am-12pm</td>\n",
       "      <td>Office hours</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>E01002878</td>\n",
       "      <td>133.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3898.2</td>\n",
       "      <td>63274.8</td>\n",
       "      <td>763.4</td>\n",
       "      <td>15253.6</td>\n",
       "      <td>3185.8</td>\n",
       "      <td>A40</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201301BS70007</td>\n",
       "      <td>-0.209675</td>\n",
       "      <td>51.516808</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-10</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>B</td>\n",
       "      <td>Single carriageway</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Crossroads</td>\n",
       "      <td>C</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Fine no high winds</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Urban</td>\n",
       "      <td>10</td>\n",
       "      <td>10am-12pm</td>\n",
       "      <td>Office hours</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>E01002831</td>\n",
       "      <td>179.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3898.2</td>\n",
       "      <td>63274.8</td>\n",
       "      <td>763.4</td>\n",
       "      <td>15253.6</td>\n",
       "      <td>3185.8</td>\n",
       "      <td>A40</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201301BS70009</td>\n",
       "      <td>-0.194332</td>\n",
       "      <td>51.492922</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>Friday</td>\n",
       "      <td>A</td>\n",
       "      <td>One way street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>T or staggered junction</td>\n",
       "      <td>Unclassified</td>\n",
       "      <td>Darkness - lights lit</td>\n",
       "      <td>Fine no high winds</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Urban</td>\n",
       "      <td>17</td>\n",
       "      <td>4pm-6pm</td>\n",
       "      <td>Rush hour</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>E01002851</td>\n",
       "      <td>272.3</td>\n",
       "      <td>869.2</td>\n",
       "      <td>1229.8</td>\n",
       "      <td>20478.6</td>\n",
       "      <td>897.2</td>\n",
       "      <td>4951.6</td>\n",
       "      <td>1251.4</td>\n",
       "      <td>A3220</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accident_Index  Longitude   Latitude Accident_Severity  Number_of_Vehicles  \\\n",
       "0  201301BS70003  -0.171402  51.486361           Serious                   2   \n",
       "1  201301BS70005  -0.173356  51.495115            Slight                   1   \n",
       "2  201301BS70006  -0.210767  51.518353            Slight                   1   \n",
       "3  201301BS70007  -0.209675  51.516808            Slight                   2   \n",
       "4  201301BS70009  -0.194332  51.492922            Slight                   2   \n",
       "\n",
       "   Number_of_Casualties        Date Day_of_Week 1st_Road_Class  \\\n",
       "0                     1  2013-01-02   Wednesday              A   \n",
       "1                     2  2013-01-04      Friday              A   \n",
       "2                     1  2013-01-07      Monday              B   \n",
       "3                     1  2013-01-10    Thursday              B   \n",
       "4                     1  2013-01-04      Friday              A   \n",
       "\n",
       "            Road_Type  Speed_limit          Junction_Detail 2nd_Road_Class  \\\n",
       "0  Single carriageway         30.0  T or staggered junction   Unclassified   \n",
       "1  Single carriageway         30.0               Crossroads              A   \n",
       "2  Single carriageway         30.0               Crossroads              B   \n",
       "3  Single carriageway         30.0               Crossroads              C   \n",
       "4      One way street         30.0  T or staggered junction   Unclassified   \n",
       "\n",
       "        Light_Conditions  Weather_Conditions Road_Surface_Conditions  \\\n",
       "0               Daylight  Fine no high winds                     Dry   \n",
       "1               Daylight               Other                     Dry   \n",
       "2               Daylight  Fine no high winds                     Dry   \n",
       "3               Daylight  Fine no high winds                     Dry   \n",
       "4  Darkness - lights lit  Fine no high winds                     Dry   \n",
       "\n",
       "  Urban_or_Rural_Area  Hour Two_Hour_Groupings   Time_of_Day Was_Daylight  \\\n",
       "0               Urban     9           8am-10am       Morning          Yes   \n",
       "1               Urban     8           8am-10am       Morning          Yes   \n",
       "2               Urban    11          10am-12pm  Office hours          Yes   \n",
       "3               Urban    10          10am-12pm  Office hours          Yes   \n",
       "4               Urban    17            4pm-6pm     Rush hour           No   \n",
       "\n",
       "  Was_Bad_Weather Was_Road_Dry  log_Number_of_Casualties  \\\n",
       "0              No          Yes                  0.000000   \n",
       "1             Yes          Yes                  0.693147   \n",
       "2              No          Yes                  0.000000   \n",
       "3              No          Yes                  0.000000   \n",
       "4              No          Yes                  0.000000   \n",
       "\n",
       "   log_Number_of_Vehicles       LSOA  population_per_hectare  bicycle_aadf  \\\n",
       "0                0.693147  E01002844                   110.8        1634.4   \n",
       "1                0.000000  E01002821                    74.6         559.6   \n",
       "2                0.000000  E01002878                   133.4           2.6   \n",
       "3                0.693147  E01002831                   179.2           2.6   \n",
       "4                0.693147  E01002851                   272.3         869.2   \n",
       "\n",
       "   motorbike_aadf  car_aadf  bus_aadf  light_goods_vehicle_aadf  \\\n",
       "0           860.4   14888.0    1139.8                    2297.0   \n",
       "1          1516.0   28505.6    1396.2                    3868.6   \n",
       "2          3898.2   63274.8     763.4                   15253.6   \n",
       "3          3898.2   63274.8     763.4                   15253.6   \n",
       "4          1229.8   20478.6     897.2                    4951.6   \n",
       "\n",
       "   heavy_goods_vehicle_aadf   Road RCat  \n",
       "0                     352.0  A3217   PA  \n",
       "1                    1003.0     A4   PA  \n",
       "2                    3185.8    A40   PA  \n",
       "3                    3185.8    A40   PA  \n",
       "4                    1251.4  A3220   PA  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accidents = pd.read_csv('data/modelling/London_accidents_merged.csv')\n",
    "accidents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.520269</td>\n",
       "      <td>-0.0950</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.519848</td>\n",
       "      <td>-0.0967</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.519030</td>\n",
       "      <td>-0.0962</td>\n",
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       "      <th>3</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.516904</td>\n",
       "      <td>-0.0981</td>\n",
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       "      <th>4</th>\n",
       "      <td>E01000003</td>\n",
       "      <td>51.522376</td>\n",
       "      <td>-0.0973</td>\n",
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      "text/plain": [
       "        lsoa   Latitude  Longitude\n",
       "0  E01000001  51.520269    -0.0950\n",
       "1  E01000001  51.519848    -0.0967\n",
       "2  E01000001  51.519030    -0.0962\n",
       "3  E01000001  51.516904    -0.0981\n",
       "4  E01000003  51.522376    -0.0973"
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   ],
   "source": [
    "# This additional dataframe contains the latitude and longitude centre points for each LSOA\n",
    "lsoa_latlong = pd.read_csv('data/geography/UK_LSOA_bounding_boxes.csv', usecols=['lsoa', 'Latitude', 'Longitude'])\n",
    "lsoa_latlong.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LSOA</th>\n",
       "      <th>population_per_hectare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01012334</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E01012335</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E01012366</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E01033481</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E01033482</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LSOA  population_per_hectare\n",
       "0  E01012334                     0.4\n",
       "1  E01012335                    12.1\n",
       "2  E01012366                     0.3\n",
       "3  E01033481                     9.3\n",
       "4  E01033482                     6.9"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = pd.read_csv('data/population/Population_density.csv')\n",
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CP</th>\n",
       "      <th>S Ref Latitude</th>\n",
       "      <th>S Ref Longitude</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motorbike_aadf</th>\n",
       "      <th>car_aadf</th>\n",
       "      <th>bus_aadf</th>\n",
       "      <th>light_goods_vehicle_aadf</th>\n",
       "      <th>heavy_goods_vehicle_aadf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>49.915023</td>\n",
       "      <td>-6.317073</td>\n",
       "      <td>238.2</td>\n",
       "      <td>96.8</td>\n",
       "      <td>539.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>49.912343</td>\n",
       "      <td>-6.305686</td>\n",
       "      <td>87.4</td>\n",
       "      <td>79.4</td>\n",
       "      <td>629.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>221.8</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>49.917141</td>\n",
       "      <td>-6.306114</td>\n",
       "      <td>181.8</td>\n",
       "      <td>142.6</td>\n",
       "      <td>777.2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>403.8</td>\n",
       "      <td>29.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>49.917810</td>\n",
       "      <td>-6.298996</td>\n",
       "      <td>61.2</td>\n",
       "      <td>54.8</td>\n",
       "      <td>342.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>251.6</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>49.918585</td>\n",
       "      <td>-6.295094</td>\n",
       "      <td>33.6</td>\n",
       "      <td>20.8</td>\n",
       "      <td>165.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.8</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CP  S Ref Latitude  S Ref Longitude  bicycle_aadf  motorbike_aadf  \\\n",
       "0  51       49.915023        -6.317073         238.2            96.8   \n",
       "1  52       49.912343        -6.305686          87.4            79.4   \n",
       "2  53       49.917141        -6.306114         181.8           142.6   \n",
       "3  54       49.917810        -6.298996          61.2            54.8   \n",
       "4  55       49.918585        -6.295094          33.6            20.8   \n",
       "\n",
       "   car_aadf  bus_aadf  light_goods_vehicle_aadf  heavy_goods_vehicle_aadf  \n",
       "0     539.4      27.0                     379.0                      40.4  \n",
       "1     629.0       9.0                     221.8                      11.0  \n",
       "2     777.2      32.2                     403.8                      29.4  \n",
       "3     342.8       6.0                     251.6                      34.4  \n",
       "4     165.6       0.0                     150.8                      15.2  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic = pd.read_csv('data/traffic/Traffic_averages.csv')\n",
    "traffic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CP</th>\n",
       "      <th>S Ref Latitude</th>\n",
       "      <th>S Ref Longitude</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>49.915023</td>\n",
       "      <td>-6.317073</td>\n",
       "      <td>238.2</td>\n",
       "      <td>1082.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>49.912343</td>\n",
       "      <td>-6.305686</td>\n",
       "      <td>87.4</td>\n",
       "      <td>950.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>49.917141</td>\n",
       "      <td>-6.306114</td>\n",
       "      <td>181.8</td>\n",
       "      <td>1385.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>49.917810</td>\n",
       "      <td>-6.298996</td>\n",
       "      <td>61.2</td>\n",
       "      <td>689.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>49.918585</td>\n",
       "      <td>-6.295094</td>\n",
       "      <td>33.6</td>\n",
       "      <td>352.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CP  S Ref Latitude  S Ref Longitude  bicycle_aadf  motor_vehicle_aadf\n",
       "0  51       49.915023        -6.317073         238.2              1082.6\n",
       "1  52       49.912343        -6.305686          87.4               950.2\n",
       "2  53       49.917141        -6.306114         181.8              1385.2\n",
       "3  54       49.917810        -6.298996          61.2               689.6\n",
       "4  55       49.918585        -6.295094          33.6               352.4"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combining the motor vehicle traffic columns, based on the multi-collinearity found in model 1\n",
    "to_sum = ['motorbike_aadf', 'car_aadf', 'bus_aadf', 'light_goods_vehicle_aadf', 'heavy_goods_vehicle_aadf']\n",
    "traffic['motor_vehicle_aadf'] = traffic[to_sum].sum(axis=1)\n",
    "traffic.drop(to_sum, axis=1, inplace=True)\n",
    "traffic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the dataset of 'danger' locations (with traffic accidents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U-7XT79XZlNP"
   },
   "outputs": [],
   "source": [
    "def myround(x, base=.0005):\n",
    "    return base * round(x/base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding rounded lat and long columns and a grid square column to the London accident dataset\n",
    "accidents['lat_4dp'] = myround(accidents['Latitude'])\n",
    "accidents['long_4dp'] = myround(accidents['Longitude'])\n",
    "accidents['grid_square'] = round(accidents['lat_4dp'],4).map(str) + \",\" + round(accidents['long_4dp'],4).map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16218"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take random sample of 20000 to drop duplicate grid squares\n",
    "sample_20000_london = accidents.sample(n=20000, random_state=42, \n",
    "                                                     replace=False).drop_duplicates(['grid_square'])\n",
    "len(sample_20000_london)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>Accident_Index</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Accident_Severity</th>\n",
       "      <th>Number_of_Vehicles</th>\n",
       "      <th>Number_of_Casualties</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>1st_Road_Class</th>\n",
       "      <th>Road_Type</th>\n",
       "      <th>...</th>\n",
       "      <th>motorbike_aadf</th>\n",
       "      <th>car_aadf</th>\n",
       "      <th>bus_aadf</th>\n",
       "      <th>light_goods_vehicle_aadf</th>\n",
       "      <th>heavy_goods_vehicle_aadf</th>\n",
       "      <th>Road</th>\n",
       "      <th>RCat</th>\n",
       "      <th>lat_4dp</th>\n",
       "      <th>long_4dp</th>\n",
       "      <th>grid_square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56476</th>\n",
       "      <td>2014450020376</td>\n",
       "      <td>-0.415736</td>\n",
       "      <td>51.338105</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-11-29</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>A</td>\n",
       "      <td>Dual carriageway</td>\n",
       "      <td>...</td>\n",
       "      <td>1048.4</td>\n",
       "      <td>62371.6</td>\n",
       "      <td>184.6</td>\n",
       "      <td>11286.0</td>\n",
       "      <td>2170.6</td>\n",
       "      <td>A3</td>\n",
       "      <td>TA</td>\n",
       "      <td>51.338</td>\n",
       "      <td>-0.4155</td>\n",
       "      <td>51.338,-0.4155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35882</th>\n",
       "      <td>201401KD50368</td>\n",
       "      <td>0.212878</td>\n",
       "      <td>51.592871</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>A</td>\n",
       "      <td>Roundabout</td>\n",
       "      <td>...</td>\n",
       "      <td>160.6</td>\n",
       "      <td>16451.0</td>\n",
       "      <td>363.4</td>\n",
       "      <td>1797.8</td>\n",
       "      <td>483.6</td>\n",
       "      <td>A118</td>\n",
       "      <td>PA</td>\n",
       "      <td>51.593</td>\n",
       "      <td>0.2130</td>\n",
       "      <td>51.593,0.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16414</th>\n",
       "      <td>201301TX20055</td>\n",
       "      <td>-0.404606</td>\n",
       "      <td>51.477806</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-02-09</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>A</td>\n",
       "      <td>Dual carriageway</td>\n",
       "      <td>...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>29316.2</td>\n",
       "      <td>706.4</td>\n",
       "      <td>3331.6</td>\n",
       "      <td>911.0</td>\n",
       "      <td>A4</td>\n",
       "      <td>PA</td>\n",
       "      <td>51.478</td>\n",
       "      <td>-0.4045</td>\n",
       "      <td>51.478,-0.4045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124923</th>\n",
       "      <td>2017010036052</td>\n",
       "      <td>-0.161815</td>\n",
       "      <td>51.466753</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Unclassified</td>\n",
       "      <td>Single carriageway</td>\n",
       "      <td>...</td>\n",
       "      <td>954.0</td>\n",
       "      <td>12590.6</td>\n",
       "      <td>95.4</td>\n",
       "      <td>2325.0</td>\n",
       "      <td>341.4</td>\n",
       "      <td>A3220</td>\n",
       "      <td>PA</td>\n",
       "      <td>51.467</td>\n",
       "      <td>-0.1620</td>\n",
       "      <td>51.467,-0.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73930</th>\n",
       "      <td>201501SX20346</td>\n",
       "      <td>-0.231378</td>\n",
       "      <td>51.570964</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>A</td>\n",
       "      <td>Roundabout</td>\n",
       "      <td>...</td>\n",
       "      <td>220.5</td>\n",
       "      <td>9388.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>2554.5</td>\n",
       "      <td>383.0</td>\n",
       "      <td>A5</td>\n",
       "      <td>PA</td>\n",
       "      <td>51.571</td>\n",
       "      <td>-0.2315</td>\n",
       "      <td>51.571,-0.2315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Accident_Index  Longitude   Latitude Accident_Severity  \\\n",
       "56476   2014450020376  -0.415736  51.338105            Slight   \n",
       "35882   201401KD50368   0.212878  51.592871            Slight   \n",
       "16414   201301TX20055  -0.404606  51.477806            Slight   \n",
       "124923  2017010036052  -0.161815  51.466753            Slight   \n",
       "73930   201501SX20346  -0.231378  51.570964            Slight   \n",
       "\n",
       "        Number_of_Vehicles  Number_of_Casualties        Date Day_of_Week  \\\n",
       "56476                    2                     3  2014-11-29    Saturday   \n",
       "35882                    2                     1  2014-07-26    Saturday   \n",
       "16414                    2                     1  2013-02-09    Saturday   \n",
       "124923                   2                     1  2017-04-10      Monday   \n",
       "73930                    2                     1  2015-04-14     Tuesday   \n",
       "\n",
       "       1st_Road_Class           Road_Type       ...        motorbike_aadf  \\\n",
       "56476               A    Dual carriageway       ...                1048.4   \n",
       "35882               A          Roundabout       ...                 160.6   \n",
       "16414               A    Dual carriageway       ...                 294.0   \n",
       "124923   Unclassified  Single carriageway       ...                 954.0   \n",
       "73930               A          Roundabout       ...                 220.5   \n",
       "\n",
       "       car_aadf bus_aadf light_goods_vehicle_aadf heavy_goods_vehicle_aadf  \\\n",
       "56476   62371.6    184.6                  11286.0                   2170.6   \n",
       "35882   16451.0    363.4                   1797.8                    483.6   \n",
       "16414   29316.2    706.4                   3331.6                    911.0   \n",
       "124923  12590.6     95.4                   2325.0                    341.4   \n",
       "73930    9388.0    389.0                   2554.5                    383.0   \n",
       "\n",
       "         Road RCat  lat_4dp long_4dp     grid_square  \n",
       "56476      A3   TA   51.338  -0.4155  51.338,-0.4155  \n",
       "35882    A118   PA   51.593   0.2130    51.593,0.213  \n",
       "16414      A4   PA   51.478  -0.4045  51.478,-0.4045  \n",
       "124923  A3220   PA   51.467  -0.1620   51.467,-0.162  \n",
       "73930      A5   PA   51.571  -0.2315  51.571,-0.2315  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now take random sample from above to get final sample of 10000 accidents\n",
    "danger_london_accidents_sample = sample_20000_london.sample(n=10000, random_state=0, replace=False)\n",
    "print(len(danger_london_accidents_sample))\n",
    "danger_london_accidents_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create model 3 dataset just consisting of traffic and population features\n",
    "# Keep location features to get safe grid squares later\n",
    "danger_squares = danger_london_accidents_sample[['Latitude', 'Longitude', 'population_per_hectare', 'bicycle_aadf', \n",
    "                                'motorbike_aadf', 'car_aadf', 'bus_aadf', 'light_goods_vehicle_aadf', \n",
    "                                'heavy_goods_vehicle_aadf', 'lat_4dp', 'long_4dp', 'grid_square']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine traffic features for motor vehicles like we did in model 1 notebook\n",
    "to_sum = ['motorbike_aadf', 'car_aadf', 'bus_aadf', 'light_goods_vehicle_aadf', 'heavy_goods_vehicle_aadf']\n",
    "danger_squares['motor_vehicle_aadf'] = danger_squares[to_sum].sum(axis=1)\n",
    "danger_squares.drop(to_sum, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting columns in the right order\n",
    "danger_squares = danger_squares[['grid_square', 'Latitude', 'Longitude', 'population_per_hectare', \n",
    "                                 'bicycle_aadf', 'motor_vehicle_aadf']]\n",
    "\n",
    "# Rename columns for merging with safe dataset\n",
    "danger_squares = danger_squares.rename(columns={'Latitude': 'latitude', 'Longitude': 'longitude'})\n",
    "\n",
    "# Add safe column of all 0's due to being danger dataset\n",
    "danger_squares['safe'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
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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>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "      <th>safe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56476</th>\n",
       "      <td>51.338,-0.4155</td>\n",
       "      <td>51.338105</td>\n",
       "      <td>-0.415736</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>77061.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35882</th>\n",
       "      <td>51.593,0.213</td>\n",
       "      <td>51.592871</td>\n",
       "      <td>0.212878</td>\n",
       "      <td>59.6</td>\n",
       "      <td>146.6</td>\n",
       "      <td>19256.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16414</th>\n",
       "      <td>51.478,-0.4045</td>\n",
       "      <td>51.477806</td>\n",
       "      <td>-0.404606</td>\n",
       "      <td>78.8</td>\n",
       "      <td>112.8</td>\n",
       "      <td>34559.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124923</th>\n",
       "      <td>51.467,-0.162</td>\n",
       "      <td>51.466753</td>\n",
       "      <td>-0.161815</td>\n",
       "      <td>142.2</td>\n",
       "      <td>1333.6</td>\n",
       "      <td>16306.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73930</th>\n",
       "      <td>51.571,-0.2315</td>\n",
       "      <td>51.570964</td>\n",
       "      <td>-0.231378</td>\n",
       "      <td>23.6</td>\n",
       "      <td>95.0</td>\n",
       "      <td>12935.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grid_square   latitude  longitude  population_per_hectare  \\\n",
       "56476   51.338,-0.4155  51.338105  -0.415736                     3.8   \n",
       "35882     51.593,0.213  51.592871   0.212878                    59.6   \n",
       "16414   51.478,-0.4045  51.477806  -0.404606                    78.8   \n",
       "124923   51.467,-0.162  51.466753  -0.161815                   142.2   \n",
       "73930   51.571,-0.2315  51.570964  -0.231378                    23.6   \n",
       "\n",
       "        bicycle_aadf  motor_vehicle_aadf  safe  \n",
       "56476            2.6             77061.2     0  \n",
       "35882          146.6             19256.4     0  \n",
       "16414          112.8             34559.2     0  \n",
       "124923        1333.6             16306.4     0  \n",
       "73930           95.0             12935.0     0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "danger_squares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save dataset for model 3 notebook\n",
    "danger_squares.to_csv('modelling/model3_danger_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a list of danger squares to download using Google Static Maps API\n",
    "model3_danger_squares = danger_squares.grid_square"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the dataset of 'safe' locations (with no accidents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat_4dp</th>\n",
       "      <th>long_4dp</th>\n",
       "      <th>grid_square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.257</td>\n",
       "      <td>-0.5420</td>\n",
       "      <td>51.257,-0.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.257</td>\n",
       "      <td>-0.5415</td>\n",
       "      <td>51.257,-0.5415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.257</td>\n",
       "      <td>-0.5410</td>\n",
       "      <td>51.257,-0.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.257</td>\n",
       "      <td>-0.5405</td>\n",
       "      <td>51.257,-0.5405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.257</td>\n",
       "      <td>-0.5400</td>\n",
       "      <td>51.257,-0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lat_4dp  long_4dp     grid_square\n",
       "0   51.257   -0.5420   51.257,-0.542\n",
       "1   51.257   -0.5415  51.257,-0.5415\n",
       "2   51.257   -0.5410   51.257,-0.541\n",
       "3   51.257   -0.5405  51.257,-0.5405\n",
       "4   51.257   -0.5400    51.257,-0.54"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a list of all possible locations\n",
    "# Setting parameters\n",
    "lat_min, lat_max = 51.257, 51.719\n",
    "lon_min, lon_max = -0.542, 0.291\n",
    "grid_size = 0.0005\n",
    "\n",
    "# Getting a list of all grid squares\n",
    "lats = np.linspace(lat_min, lat_max, 925)\n",
    "lons = np.linspace(lon_min, lon_max, 1667)\n",
    "coords = [(round(x,4),round(y,4)) for x in lats for y in lons]\n",
    "\n",
    "# Converting to a dataframe and adding a column for the grid square name\n",
    "coords = pd.DataFrame(coords, columns=['lat_4dp', 'long_4dp'])\n",
    "coords['grid_square'] = coords['lat_4dp'].map(str) + \",\" + coords['long_4dp'].map(str)\n",
    "coords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1541975"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a list of all squares\n",
    "all_squares = coords.grid_square\n",
    "len(all_squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1472218\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grid_square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.5485,-0.4945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.3965,-0.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.668,0.0145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.368,-0.405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       grid_square\n",
       "0    51.422,0.2695\n",
       "1  51.5485,-0.4945\n",
       "2   51.3965,-0.327\n",
       "3    51.668,0.0145\n",
       "4    51.368,-0.405"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a list of safe squares (all_squares minus danger_squares)\n",
    "safe_squares = list(set(all_squares) - set(list(accidents.grid_square)))\n",
    "print(len(safe_squares))\n",
    "safe_squares = pd.DataFrame(safe_squares)\n",
    "safe_squares.columns = ['grid_square']\n",
    "safe_squares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.422</td>\n",
       "      <td>0.2695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.5485,-0.4945</td>\n",
       "      <td>51.5485</td>\n",
       "      <td>-0.4945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.3965,-0.327</td>\n",
       "      <td>51.3965</td>\n",
       "      <td>-0.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.668,0.0145</td>\n",
       "      <td>51.668</td>\n",
       "      <td>0.0145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.368,-0.405</td>\n",
       "      <td>51.368</td>\n",
       "      <td>-0.405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       grid_square latitude longitude\n",
       "0    51.422,0.2695   51.422    0.2695\n",
       "1  51.5485,-0.4945  51.5485   -0.4945\n",
       "2   51.3965,-0.327  51.3965    -0.327\n",
       "3    51.668,0.0145   51.668    0.0145\n",
       "4    51.368,-0.405   51.368    -0.405"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safe_squares_coords = safe_squares['grid_square'].str.split(',', expand=True)\n",
    "safe_squares = pd.merge(safe_squares, safe_squares_coords, left_index=True, right_index=True)\n",
    "safe_squares.columns = ['grid_square', 'latitude', 'longitude']\n",
    "safe_squares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "safe_squares[['latitude', 'longitude']] = safe_squares[['latitude', 'longitude']].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding population density data to the safe squares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to find the correct population density for each row in the 'safe squares' dataset, it is necessary to match to the nearest LSOA on the basis of latitude and longitude. This is done in a similar manner to notebook 2, by finding the nearest neighbour LSOA centre point to each safe square location. To speed up the nearest neighbour matching process, the dataframes were first converted to Geopandas GeoDataFrames, which convert the latitudes and longitudes to Geopandas x and y Points. These points can then be used in the same cKDTree function as used in notebook 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>POINT (51.422 0.2695)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.5485,-0.4945</td>\n",
       "      <td>51.5485</td>\n",
       "      <td>-0.4945</td>\n",
       "      <td>POINT (51.5485 -0.4945)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.3965,-0.327</td>\n",
       "      <td>51.3965</td>\n",
       "      <td>-0.3270</td>\n",
       "      <td>POINT (51.3965 -0.327)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.668,0.0145</td>\n",
       "      <td>51.6680</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>POINT (51.668 0.0145)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.368,-0.405</td>\n",
       "      <td>51.3680</td>\n",
       "      <td>-0.4050</td>\n",
       "      <td>POINT (51.368 -0.405)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       grid_square  latitude  longitude                 geometry\n",
       "0    51.422,0.2695   51.4220     0.2695    POINT (51.422 0.2695)\n",
       "1  51.5485,-0.4945   51.5485    -0.4945  POINT (51.5485 -0.4945)\n",
       "2   51.3965,-0.327   51.3965    -0.3270   POINT (51.3965 -0.327)\n",
       "3    51.668,0.0145   51.6680     0.0145    POINT (51.668 0.0145)\n",
       "4    51.368,-0.405   51.3680    -0.4050    POINT (51.368 -0.405)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting to a GeoDataFrame\n",
    "geo_safe_squares = geopandas.GeoDataFrame(safe_squares, geometry=geopandas.points_from_xy(safe_squares.latitude, safe_squares.longitude))\n",
    "geo_safe_squares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.520269</td>\n",
       "      <td>-0.0950</td>\n",
       "      <td>POINT (51.52026933 -0.095)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.519848</td>\n",
       "      <td>-0.0967</td>\n",
       "      <td>POINT (51.51984806 -0.09669999999999999)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.519030</td>\n",
       "      <td>-0.0962</td>\n",
       "      <td>POINT (51.51902993 -0.09619999999999999)</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>51.516904</td>\n",
       "      <td>-0.0981</td>\n",
       "      <td>POINT (51.51690356 -0.09810000000000001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E01000003</td>\n",
       "      <td>51.522376</td>\n",
       "      <td>-0.0973</td>\n",
       "      <td>POINT (51.52237596 -0.0973)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lsoa   Latitude  Longitude                                  geometry\n",
       "0  E01000001  51.520269    -0.0950                POINT (51.52026933 -0.095)\n",
       "1  E01000001  51.519848    -0.0967  POINT (51.51984806 -0.09669999999999999)\n",
       "2  E01000001  51.519030    -0.0962  POINT (51.51902993 -0.09619999999999999)\n",
       "3  E01000001  51.516904    -0.0981  POINT (51.51690356 -0.09810000000000001)\n",
       "4  E01000003  51.522376    -0.0973               POINT (51.52237596 -0.0973)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting to a GeoDataFrame\n",
    "geo_lsoa_latlong = geopandas.GeoDataFrame(lsoa_latlong, geometry=geopandas.points_from_xy(lsoa_latlong.Latitude, lsoa_latlong.Longitude))\n",
    "geo_lsoa_latlong.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ckdnearest(gdA, gdB, bcol):   \n",
    "    nA = np.array(list(zip(gdA.geometry.x, gdA.geometry.y)) )\n",
    "    nB = np.array(list(zip(gdB.geometry.x, gdB.geometry.y)) )\n",
    "    btree = cKDTree(nB)\n",
    "    dist, idx = btree.query(nA,k=1)\n",
    "    df = pd.DataFrame.from_dict({'distance': dist.astype(float),\n",
    "                             bcol : gdB.loc[idx, bcol].values })\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the nearest LSOA for each safe square point\n",
    "ckdnearest_lsoa = ckdnearest(geo_safe_squares, geo_lsoa_latlong, 'lsoa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1472218"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the name of the nearest LSOA to the safe squares list\n",
    "safe_squares_merged = geo_safe_squares.join(ckdnearest_lsoa, how='left').drop('distance', axis=1)\n",
    "safe_squares_merged = pd.merge(safe_squares_merged, lsoa_latlong, how='inner', on='lsoa')\n",
    "len(safe_squares_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry_x</th>\n",
       "      <th>lsoa</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>geometry_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>POINT (51.422 0.2695)</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>51.455248</td>\n",
       "      <td>0.193375</td>\n",
       "      <td>POINT (51.45524827 0.193374565)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>POINT (51.422 0.2695)</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>51.459721</td>\n",
       "      <td>0.189846</td>\n",
       "      <td>POINT (51.45972081 0.1898461)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>POINT (51.422 0.2695)</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>51.458117</td>\n",
       "      <td>0.189050</td>\n",
       "      <td>POINT (51.45811695 0.189049559)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>POINT (51.422 0.2695)</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>51.457611</td>\n",
       "      <td>0.192048</td>\n",
       "      <td>POINT (51.45761125 0.192048342)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.4005,0.286</td>\n",
       "      <td>51.4005</td>\n",
       "      <td>0.2860</td>\n",
       "      <td>POINT (51.4005 0.286)</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>51.455248</td>\n",
       "      <td>0.193375</td>\n",
       "      <td>POINT (51.45524827 0.193374565)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     grid_square  latitude  longitude             geometry_x       lsoa  \\\n",
       "0  51.422,0.2695   51.4220     0.2695  POINT (51.422 0.2695)  E01000374   \n",
       "1  51.422,0.2695   51.4220     0.2695  POINT (51.422 0.2695)  E01000374   \n",
       "2  51.422,0.2695   51.4220     0.2695  POINT (51.422 0.2695)  E01000374   \n",
       "3  51.422,0.2695   51.4220     0.2695  POINT (51.422 0.2695)  E01000374   \n",
       "4  51.4005,0.286   51.4005     0.2860  POINT (51.4005 0.286)  E01000374   \n",
       "\n",
       "    Latitude  Longitude                       geometry_y  \n",
       "0  51.455248   0.193375  POINT (51.45524827 0.193374565)  \n",
       "1  51.459721   0.189846    POINT (51.45972081 0.1898461)  \n",
       "2  51.458117   0.189050  POINT (51.45811695 0.189049559)  \n",
       "3  51.457611   0.192048  POINT (51.45761125 0.192048342)  \n",
       "4  51.455248   0.193375  POINT (51.45524827 0.193374565)  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safe_squares_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the distances are relatively close:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 3042.3\n",
      "Median: 1038.7\n",
      "Minimum: 0.0\n",
      "Maximum: 21588.6\n"
     ]
    }
   ],
   "source": [
    "def distance_m(lat1, lon1, lat2, lon2):\n",
    "    \"\"\"Returns the distance in metres between two points described by latitudes and longitudes using the Haversine formula\"\"\"\n",
    "    p = 0.017453292519943295\n",
    "    a = 0.5 - cos((lat2-lat1)*p)/2 + cos(lat1*p)*cos(lat2*p) * (1-cos((lon2-lon1)*p)) / 2\n",
    "    return (12742 * asin(sqrt(a)))*1000\n",
    "\n",
    "# Calculating the distances between the accident and traffic count locations in each row\n",
    "distances_m = []                    \n",
    "for index, row in safe_squares_merged.iterrows():\n",
    "    lat1 = row['latitude'] \n",
    "    lon1 = row['longitude'] \n",
    "    lat2 = row['Latitude'] \n",
    "    lon2 = row['Longitude'] \n",
    "    value = distance_m(lat1, lon1, lat2, lon2) \n",
    "    distances_m.append(value)\n",
    "\n",
    "print(\"Mean:\", round(np.mean(distances_m),1))\n",
    "print(\"Median:\", round(np.median(distances_m),1))\n",
    "print(\"Minimum:\", round(min(distances_m),1))\n",
    "print(\"Maximum:\", round(max(distances_m),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About half of LSOA centre points are 1km or less from the safe square locations, which is expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unecessary columns\n",
    "safe_squares_merged.drop(['Latitude', 'Longitude', 'geometry_x', 'geometry_y'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging the safe squares dataset with the population density based on LSOA\n",
    "safe_squares_merged = pd.merge(safe_squares_merged, population, how='left', left_on='lsoa', right_on='LSOA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lsoa</th>\n",
       "      <th>LSOA</th>\n",
       "      <th>population_per_hectare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.4005,0.286</td>\n",
       "      <td>51.4005</td>\n",
       "      <td>0.2860</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.4525,0.195</td>\n",
       "      <td>51.4525</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.454,0.1975</td>\n",
       "      <td>51.4540</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.404,0.2615</td>\n",
       "      <td>51.4040</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     grid_square  latitude  longitude       lsoa       LSOA  \\\n",
       "0  51.422,0.2695   51.4220     0.2695  E01000374  E01000374   \n",
       "1  51.4005,0.286   51.4005     0.2860  E01000374  E01000374   \n",
       "2  51.4525,0.195   51.4525     0.1950  E01000374  E01000374   \n",
       "3  51.454,0.1975   51.4540     0.1975  E01000374  E01000374   \n",
       "4  51.404,0.2615   51.4040     0.2615  E01000374  E01000374   \n",
       "\n",
       "   population_per_hectare  \n",
       "0                    20.6  \n",
       "1                    20.6  \n",
       "2                    20.6  \n",
       "3                    20.6  \n",
       "4                    20.6  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safe_squares_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding traffic data to the safe squares\n",
    "\n",
    "Traffic data is added using the same process as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CP</th>\n",
       "      <th>S Ref Latitude</th>\n",
       "      <th>S Ref Longitude</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>49.915023</td>\n",
       "      <td>-6.317073</td>\n",
       "      <td>238.2</td>\n",
       "      <td>1082.6</td>\n",
       "      <td>POINT (49.91502292 -6.317072963999999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>49.912343</td>\n",
       "      <td>-6.305686</td>\n",
       "      <td>87.4</td>\n",
       "      <td>950.2</td>\n",
       "      <td>POINT (49.91234266 -6.305685679)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>49.917141</td>\n",
       "      <td>-6.306114</td>\n",
       "      <td>181.8</td>\n",
       "      <td>1385.2</td>\n",
       "      <td>POINT (49.91714081 -6.306113756)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>49.917810</td>\n",
       "      <td>-6.298996</td>\n",
       "      <td>61.2</td>\n",
       "      <td>689.6</td>\n",
       "      <td>POINT (49.91781028 -6.298996455)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>49.918585</td>\n",
       "      <td>-6.295094</td>\n",
       "      <td>33.6</td>\n",
       "      <td>352.4</td>\n",
       "      <td>POINT (49.91858504 -6.295093656000001)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CP  S Ref Latitude  S Ref Longitude  bicycle_aadf  motor_vehicle_aadf  \\\n",
       "0  51       49.915023        -6.317073         238.2              1082.6   \n",
       "1  52       49.912343        -6.305686          87.4               950.2   \n",
       "2  53       49.917141        -6.306114         181.8              1385.2   \n",
       "3  54       49.917810        -6.298996          61.2               689.6   \n",
       "4  55       49.918585        -6.295094          33.6               352.4   \n",
       "\n",
       "                                 geometry  \n",
       "0  POINT (49.91502292 -6.317072963999999)  \n",
       "1        POINT (49.91234266 -6.305685679)  \n",
       "2        POINT (49.91714081 -6.306113756)  \n",
       "3        POINT (49.91781028 -6.298996455)  \n",
       "4  POINT (49.91858504 -6.295093656000001)  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_traffic = geopandas.GeoDataFrame(traffic, geometry=geopandas.points_from_xy(traffic['S Ref Latitude'], traffic['S Ref Longitude']))\n",
    "geo_traffic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lsoa</th>\n",
       "      <th>LSOA</th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>geometry</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>E01000374</td>\n",
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       "      <td>20.6</td>\n",
       "      <td>POINT (51.422 0.2695)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.4005,0.286</td>\n",
       "      <td>51.4005</td>\n",
       "      <td>0.2860</td>\n",
       "      <td>E01000374</td>\n",
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       "      <td>20.6</td>\n",
       "      <td>POINT (51.4005 0.286)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.4525,0.195</td>\n",
       "      <td>51.4525</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.4525 0.195)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.454,0.1975</td>\n",
       "      <td>51.4540</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.454 0.1975)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.404,0.2615</td>\n",
       "      <td>51.4040</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.404 0.2615)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     grid_square  latitude  longitude       lsoa       LSOA  \\\n",
       "0  51.422,0.2695   51.4220     0.2695  E01000374  E01000374   \n",
       "1  51.4005,0.286   51.4005     0.2860  E01000374  E01000374   \n",
       "2  51.4525,0.195   51.4525     0.1950  E01000374  E01000374   \n",
       "3  51.454,0.1975   51.4540     0.1975  E01000374  E01000374   \n",
       "4  51.404,0.2615   51.4040     0.2615  E01000374  E01000374   \n",
       "\n",
       "   population_per_hectare               geometry  \n",
       "0                    20.6  POINT (51.422 0.2695)  \n",
       "1                    20.6  POINT (51.4005 0.286)  \n",
       "2                    20.6  POINT (51.4525 0.195)  \n",
       "3                    20.6  POINT (51.454 0.1975)  \n",
       "4                    20.6  POINT (51.404 0.2615)  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_safe_squares_merged = geopandas.GeoDataFrame(safe_squares_merged, geometry=geopandas.points_from_xy(safe_squares_merged['latitude'], safe_squares_merged['longitude']))\n",
    "geo_safe_squares_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>distance</th>\n",
       "      <th>CP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.010306</td>\n",
       "      <td>16092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.027768</td>\n",
       "      <td>945657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007512</td>\n",
       "      <td>17832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.006399</td>\n",
       "      <td>73051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.013956</td>\n",
       "      <td>945571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance      CP\n",
       "0  0.010306   16092\n",
       "1  0.027768  945657\n",
       "2  0.007512   17832\n",
       "3  0.006399   73051\n",
       "4  0.013956  945571"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ckdnearest_traffic = ckdnearest(geo_safe_squares_merged, geo_traffic, 'CP')\n",
    "ckdnearest_traffic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1472218\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lsoa</th>\n",
       "      <th>LSOA</th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>geometry</th>\n",
       "      <th>distance</th>\n",
       "      <th>CP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.422 0.2695)</td>\n",
       "      <td>0.010306</td>\n",
       "      <td>16092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.4005,0.286</td>\n",
       "      <td>51.4005</td>\n",
       "      <td>0.2860</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.4005 0.286)</td>\n",
       "      <td>0.027768</td>\n",
       "      <td>945657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.4525,0.195</td>\n",
       "      <td>51.4525</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.4525 0.195)</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>17832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.454,0.1975</td>\n",
       "      <td>51.4540</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.454 0.1975)</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>73051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.404,0.2615</td>\n",
       "      <td>51.4040</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>E01000374</td>\n",
       "      <td>20.6</td>\n",
       "      <td>POINT (51.404 0.2615)</td>\n",
       "      <td>0.013956</td>\n",
       "      <td>945571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     grid_square  latitude  longitude       lsoa       LSOA  \\\n",
       "0  51.422,0.2695   51.4220     0.2695  E01000374  E01000374   \n",
       "1  51.4005,0.286   51.4005     0.2860  E01000374  E01000374   \n",
       "2  51.4525,0.195   51.4525     0.1950  E01000374  E01000374   \n",
       "3  51.454,0.1975   51.4540     0.1975  E01000374  E01000374   \n",
       "4  51.404,0.2615   51.4040     0.2615  E01000374  E01000374   \n",
       "\n",
       "   population_per_hectare               geometry  distance      CP  \n",
       "0                    20.6  POINT (51.422 0.2695)  0.010306   16092  \n",
       "1                    20.6  POINT (51.4005 0.286)  0.027768  945657  \n",
       "2                    20.6  POINT (51.4525 0.195)  0.007512   17832  \n",
       "3                    20.6  POINT (51.454 0.1975)  0.006399   73051  \n",
       "4                    20.6  POINT (51.404 0.2615)  0.013956  945571  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_safe_squares_merged = geo_safe_squares_merged.join(ckdnearest_traffic, how='left', rsuffix='_traffic')\n",
    "print(len(final_safe_squares_merged))\n",
    "final_safe_squares_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_safe_squares_merged = pd.merge(final_safe_squares_merged, traffic, how='left', on='CP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "final_safe_squares_merged.drop(['lsoa', 'geometry_x', 'distance', 'CP', 'geometry_y'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 863.7\n",
      "Median: 670.9\n",
      "Minimum: 0.5\n",
      "Maximum: 5229.7\n"
     ]
    }
   ],
   "source": [
    "# Calculating the distances between the accident and traffic count locations in each row\n",
    "distances_m = []                    \n",
    "for index, row in final_safe_squares_merged.iterrows():\n",
    "    lat1 = row['latitude'] \n",
    "    lon1 = row['longitude'] \n",
    "    lat2 = row['S Ref Latitude'] \n",
    "    lon2 = row['S Ref Longitude'] \n",
    "    value = distance_m(lat1, lon1, lat2, lon2) \n",
    "    distances_m.append(value)\n",
    "\n",
    "print(\"Mean:\", round(np.mean(distances_m),1))\n",
    "print(\"Median:\", round(np.median(distances_m),1))\n",
    "print(\"Minimum:\", round(min(distances_m),1))\n",
    "print(\"Maximum:\", round(max(distances_m),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the distances are what would be expected, so this has worked as intended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_safe_squares_df = final_safe_squares_merged.drop(['LSOA', 'S Ref Latitude', 'S Ref Longitude'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a column to record that each of these is a 'safe' square, so that it can later be combined with the 'danger' squares\n",
    "final_safe_squares_df['safe'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final 'safe squares' dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "      <th>safe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.422,0.2695</td>\n",
       "      <td>51.4220</td>\n",
       "      <td>0.2695</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125775.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.4005,0.286</td>\n",
       "      <td>51.4005</td>\n",
       "      <td>0.2860</td>\n",
       "      <td>20.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>580.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.4525,0.195</td>\n",
       "      <td>51.4525</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>20.6</td>\n",
       "      <td>79.4</td>\n",
       "      <td>9458.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.454,0.1975</td>\n",
       "      <td>51.4540</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>20.6</td>\n",
       "      <td>159.0</td>\n",
       "      <td>31148.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.404,0.2615</td>\n",
       "      <td>51.4040</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>20.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1961.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     grid_square  latitude  longitude  population_per_hectare  bicycle_aadf  \\\n",
       "0  51.422,0.2695   51.4220     0.2695                    20.6           0.0   \n",
       "1  51.4005,0.286   51.4005     0.2860                    20.6           3.0   \n",
       "2  51.4525,0.195   51.4525     0.1950                    20.6          79.4   \n",
       "3  51.454,0.1975   51.4540     0.1975                    20.6         159.0   \n",
       "4  51.404,0.2615   51.4040     0.2615                    20.6           6.8   \n",
       "\n",
       "   motor_vehicle_aadf  safe  \n",
       "0            125775.2     1  \n",
       "1               580.4     1  \n",
       "2              9458.0     1  \n",
       "3             31148.8     1  \n",
       "4              1961.4     1  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_safe_squares_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_safe_squares_df.to_csv('data/modelling/final_safe_squares_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sampling 10,000 safe squares to include in model 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "safe_squares = final_safe_squares_df.sample(n=10000, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#safe_squares.to_csv('data/modelling/model3_safe_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting a list of squares to download using Google Static Maps API\n",
    "model3_safe_squares = safe_squares.grid_square"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining the dataset of safe and danger squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "danger_squares.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "      <th>safe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.572,-0.4435</td>\n",
       "      <td>51.572</td>\n",
       "      <td>-0.4435</td>\n",
       "      <td>31.1</td>\n",
       "      <td>151.8</td>\n",
       "      <td>25847.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.407,-0.1835</td>\n",
       "      <td>51.407</td>\n",
       "      <td>-0.1835</td>\n",
       "      <td>142.5</td>\n",
       "      <td>575.0</td>\n",
       "      <td>35016.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.468,0.2515</td>\n",
       "      <td>51.468</td>\n",
       "      <td>0.2515</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128692.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.319,-0.286</td>\n",
       "      <td>51.319</td>\n",
       "      <td>-0.2860</td>\n",
       "      <td>4.0</td>\n",
       "      <td>85.2</td>\n",
       "      <td>16208.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.341,0.196</td>\n",
       "      <td>51.341</td>\n",
       "      <td>0.1960</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>5928.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      grid_square  latitude  longitude  population_per_hectare  bicycle_aadf  \\\n",
       "0  51.572,-0.4435    51.572    -0.4435                    31.1         151.8   \n",
       "1  51.407,-0.1835    51.407    -0.1835                   142.5         575.0   \n",
       "2   51.468,0.2515    51.468     0.2515                     1.5           0.0   \n",
       "3   51.319,-0.286    51.319    -0.2860                     4.0          85.2   \n",
       "4    51.341,0.196    51.341     0.1960                     2.0          16.4   \n",
       "\n",
       "   motor_vehicle_aadf  safe  \n",
       "0             25847.8     1  \n",
       "1             35016.4     1  \n",
       "2            128692.0     1  \n",
       "3             16208.4     1  \n",
       "4              5928.4     1  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safe_squares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grid_square</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "      <th>safe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.338,-0.4155</td>\n",
       "      <td>51.338105</td>\n",
       "      <td>-0.415736</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>77061.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.593,0.213</td>\n",
       "      <td>51.592871</td>\n",
       "      <td>0.212878</td>\n",
       "      <td>59.6</td>\n",
       "      <td>146.6</td>\n",
       "      <td>19256.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.478,-0.4045</td>\n",
       "      <td>51.477806</td>\n",
       "      <td>-0.404606</td>\n",
       "      <td>78.8</td>\n",
       "      <td>112.8</td>\n",
       "      <td>34559.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.467,-0.162</td>\n",
       "      <td>51.466753</td>\n",
       "      <td>-0.161815</td>\n",
       "      <td>142.2</td>\n",
       "      <td>1333.6</td>\n",
       "      <td>16306.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.571,-0.2315</td>\n",
       "      <td>51.570964</td>\n",
       "      <td>-0.231378</td>\n",
       "      <td>23.6</td>\n",
       "      <td>95.0</td>\n",
       "      <td>12935.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      grid_square   latitude  longitude  population_per_hectare  bicycle_aadf  \\\n",
       "0  51.338,-0.4155  51.338105  -0.415736                     3.8           2.6   \n",
       "1    51.593,0.213  51.592871   0.212878                    59.6         146.6   \n",
       "2  51.478,-0.4045  51.477806  -0.404606                    78.8         112.8   \n",
       "3   51.467,-0.162  51.466753  -0.161815                   142.2        1333.6   \n",
       "4  51.571,-0.2315  51.570964  -0.231378                    23.6          95.0   \n",
       "\n",
       "   motor_vehicle_aadf  safe  \n",
       "0             77061.2     0  \n",
       "1             19256.4     0  \n",
       "2             34559.2     0  \n",
       "3             16306.4     0  \n",
       "4             12935.0     0  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "danger_squares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the final dataset containing safe and danger squares\n",
    "df = safe_squares.append(danger_squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['latitude', 'longitude'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('grid_square', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_per_hectare</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "      <th>safe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_square</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51.572,-0.4435</th>\n",
       "      <td>31.1</td>\n",
       "      <td>151.8</td>\n",
       "      <td>25847.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.407,-0.1835</th>\n",
       "      <td>142.5</td>\n",
       "      <td>575.0</td>\n",
       "      <td>35016.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.468,0.2515</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128692.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.319,-0.286</th>\n",
       "      <td>4.0</td>\n",
       "      <td>85.2</td>\n",
       "      <td>16208.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.341,0.196</th>\n",
       "      <td>2.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>5928.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population_per_hectare  bicycle_aadf  motor_vehicle_aadf  safe\n",
       "grid_square                                                                   \n",
       "51.572,-0.4435                    31.1         151.8             25847.8     1\n",
       "51.407,-0.1835                   142.5         575.0             35016.4     1\n",
       "51.468,0.2515                      1.5           0.0            128692.0     1\n",
       "51.319,-0.286                      4.0          85.2             16208.4     1\n",
       "51.341,0.196                       2.0          16.4              5928.4     1"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/modelling/model3_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model iteration 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lists of grid squares from the safe and danger square sets were used to download images from the Google Static Maps API using the same code described in notebook 2, into a folder called 'model3_images'.\n",
    "\n",
    "This model is based on the mixed model methodology described here: https://www.pyimagesearch.com/2019/02/04/keras-multiple-inputs-and-mixed-data/\n",
    "\n",
    "This model will be run as an initial proof of concept, for few layers and without a separated validation set. Later model iterations will improve upon this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# Getting images and reshaping\n",
    "image_folder = 'model3_images/'\n",
    "image_generator = ImageDataGenerator(rescale=1./255).flow_from_directory(\n",
    "        image_folder, shuffle=False, class_mode='binary',\n",
    "        target_size=(128, 128), batch_size=20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'danger': 0, 'safe': 1}"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the labels\n",
    "image_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "images, labels = next(image_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the ordered list of filenames for the images\n",
    "image_files = pd.Series(image_generator.filenames)\n",
    "image_files = image_files.str.split(\"\\\\\", expand=True)[1].str[:-4]\n",
    "image_files = list(image_files)\n",
    "\n",
    "# Sorting the structured data into the same order as the images\n",
    "df_sorted = df.reindex(image_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double checking the dataframe is in the same order as the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>population_per_hectare</th>\n",
       "      <th>bicycle_aadf</th>\n",
       "      <th>motor_vehicle_aadf</th>\n",
       "      <th>safe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_square</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51.257,-0.193</th>\n",
       "      <td>2.3</td>\n",
       "      <td>47.4</td>\n",
       "      <td>20363.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               population_per_hectare  bicycle_aadf  motor_vehicle_aadf  safe\n",
       "grid_square                                                                  \n",
       "51.257,-0.193                     2.3          47.4             20363.2     0"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This image is for the correct grid square, confirming that the dataframe is in the correct order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ground Truth : 0.0')"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(images[0,:,:], cmap='gray')\n",
    "plt.title(\"Ground Truth : {}\".format(labels[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following functions will be used to pre-process the data and create the mixed-input neural network architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_structured_data(df, train, test):\n",
    "    \"\"\"\n",
    "    Pre-processes the given dataframe by minmaxscaling the continuous features (fit-transforming the training data and transforming the test data)\n",
    "    \"\"\"\n",
    "    continuous = [\"population_per_hectare\", \"bicycle_aadf\", \"motor_vehicle_aadf\"]\n",
    "    cs = MinMaxScaler()\n",
    "    trainX = cs.fit_transform(train[continuous])\n",
    "    testX = cs.transform(test[continuous])\n",
    "    return (trainX, testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_mlp(dim, regularizer=None):\n",
    "    \"\"\"Creates a simple two-layer MLP with inputs of the given dimension\"\"\"\n",
    "    model = Sequential()\n",
    "    model.add(Dense(8, input_dim=dim, activation=\"relu\", kernel_regularizer=regularizer))\n",
    "    model.add(Dense(4, activation=\"relu\", kernel_regularizer=regularizer))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_cnn(width, height, depth, filters=(16, 32, 64), regularizer=None):\n",
    "    \"\"\"\n",
    "    Creates a CNN with the given input dimension and filter numbers.\n",
    "    Adapted from the function described here: https://www.pyimagesearch.com/2019/02/04/keras-multiple-inputs-and-mixed-data/\n",
    "    \"\"\"\n",
    "    # Initialize the input shape and channel dimension, where the number of channels is the last dimension\n",
    "    inputShape = (height, width, depth)\n",
    "    chanDim = -1\n",
    " \n",
    "    # Define the model input\n",
    "    inputs = Input(shape=inputShape)\n",
    " \n",
    "    # Loop over the number of filters \n",
    "    for (i, f) in enumerate(filters):\n",
    "        # If this is the first CONV layer then set the input appropriately\n",
    "        if i == 0:\n",
    "            x = inputs\n",
    " \n",
    "        # Create loops of CONV => RELU => BN => POOL layers\n",
    "        x = Conv2D(f, (3, 3), padding=\"same\")(x)\n",
    "        x = Activation(\"relu\")(x)\n",
    "        x = BatchNormalization(axis=chanDim)(x)\n",
    "        x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "        \n",
    "    # Final layers - flatten the volume, then Fully-Connected => RELU => BN => DROPOUT\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(16, kernel_regularizer=regularizer)(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = BatchNormalization(axis=chanDim)(x)\n",
    "    x = Dropout(0.5)(x)\n",
    " \n",
    "    # Apply another fully-connected layer, this one to match the number of nodes coming out of the MLP\n",
    "    x = Dense(4, kernel_regularizer=regularizer)(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    " \n",
    "    # Construct the CNN\n",
    "    model = Model(inputs, x)\n",
    " \n",
    "    # Return the CNN\n",
    "    return model        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conducting the train test split:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using train_test_split to partition the training and testing structured data attributes and images\n",
    "(trainAttrX, testAttrX, trainImagesX, testImagesX) = train_test_split(df_sorted, images, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the labels for y as the safe column\n",
    "trainY = trainAttrX[\"safe\"]\n",
    "testY = testAttrX[\"safe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process the structured data\n",
    "(trainAttrX, testAttrX) = process_structured_data(df_sorted, trainAttrX, testAttrX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Building the model:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Laura\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\Laura\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "# Create the MLP and CNN models\n",
    "mlp = create_mlp(trainAttrX.shape[1])\n",
    "cnn = create_cnn(128, 128, 3)\n",
    " \n",
    "# Create the input to the final set of layers as the output of both the MLP and CNN\n",
    "combinedInput = concatenate([mlp.output, cnn.output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 8)                 32        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4)                 36        \n",
      "=================================================================\n",
      "Total params: 68\n",
      "Trainable params: 68\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mlp.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 128, 128, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 128, 128, 16)      448       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 128, 128, 16)      0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 128, 128, 16)      64        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 64, 64, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 64, 64, 32)        4640      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64, 64, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 32, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 32, 32, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 16384)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 16)                262160    \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 16)                64        \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 4)                 68        \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 4)                 0         \n",
      "=================================================================\n",
      "Total params: 286,324\n",
      "Trainable params: 286,068\n",
      "Non-trainable params: 256\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The final fully-connected layer head will have two dense layers (one relu and one sigmoid)\n",
    "x = Dense(4, activation=\"relu\")(combinedInput)\n",
    "x = Dense(1, activation=\"sigmoid\")(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The final model accepts numerical data on the MLP input and images on the CNN input, outputting a single value\n",
    "model1 = Model(inputs=[mlp.input, cnn.input], outputs=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15000 samples, validate on 5000 samples\n",
      "Epoch 1/5\n",
      "15000/15000 [==============================] - 281s 19ms/step - loss: 0.5126 - acc: 0.7554 - val_loss: 0.5566 - val_acc: 0.6880\n",
      "Epoch 2/5\n",
      "15000/15000 [==============================] - 297s 20ms/step - loss: 0.4613 - acc: 0.7916 - val_loss: 0.6019 - val_acc: 0.6840\n",
      "Epoch 3/5\n",
      "15000/15000 [==============================] - 298s 20ms/step - loss: 0.4468 - acc: 0.8010 - val_loss: 0.6534 - val_acc: 0.6314\n",
      "Epoch 4/5\n",
      "15000/15000 [==============================] - 277s 18ms/step - loss: 0.4374 - acc: 0.8090 - val_loss: 0.4843 - val_acc: 0.7658\n",
      "Epoch 5/5\n",
      "15000/15000 [==============================] - 281s 19ms/step - loss: 0.4256 - acc: 0.8115 - val_loss: 0.4034 - val_acc: 0.8266\n",
      "Time taken to run: 0:23:55.493697\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "\n",
    "# compile the model \n",
    "opt = Adam(lr=1e-3, decay=1e-3 / 200)\n",
    "model1.compile(loss=\"binary_crossentropy\", metrics=['acc'], optimizer=opt)\n",
    " \n",
    "# train the model\n",
    "model1_history = model1.fit([trainAttrX, trainImagesX], trainY, validation_data=([testAttrX, testImagesX], testY), epochs=5, batch_size=10)\n",
    " \n",
    "end = datetime.datetime.now()\n",
    "print(\"Time taken to run:\", end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_cf(y_true, y_pred, class_names=None, model_name=None):\n",
    "    \"\"\"Plots a confusion matrix\"\"\"\n",
    "    cf = confusion_matrix(y_true, y_pred)\n",
    "    plt.imshow(cf, cmap=plt.cm.Blues)\n",
    "    \n",
    "    if model_name:\n",
    "        plt.title(\"Confusion Matrix: {}\".format(model_name))\n",
    "    else:\n",
    "        plt.title(\"Confusion Matrix\")\n",
    "    plt.ylabel('True Label')\n",
    "    plt.xlabel('Predicted Label')\n",
    "    \n",
    "    class_names = set(y_true)\n",
    "    tick_marks = np.arange(len(class_names))\n",
    "    if class_names:\n",
    "        plt.xticks(tick_marks, class_names)\n",
    "        plt.yticks(tick_marks, class_names)\n",
    "    \n",
    "    thresh = cf.max() / 2.\n",
    "    \n",
    "    for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n",
    "        plt.text(j, i, cf[i, j], horizontalalignment='center', color='white' if cf[i, j] > thresh else 'black')\n",
    "\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cnn_evaluation(model, history, train_features, train_images, train_labels, test_features, test_images, test_labels, class_names=None, model_name=None):\n",
    "    \"\"\"\n",
    "    Evaluates the performance of a CNN with loss and accuracy plots, a confusion matrix and a classification report for the training and test sets.\n",
    "    \"\"\"\n",
    "    train_acc = history.history['acc']\n",
    "    val_acc = history.history['val_acc']\n",
    "    train_loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    epch = range(1, len(train_acc) + 1)\n",
    "    plt.plot(epch, train_acc, 'g.', label='Training Accuracy')\n",
    "    plt.plot(epch, val_acc, 'g', label='Validation acc')\n",
    "    plt.title('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.figure()\n",
    "    plt.plot(epch, train_loss, 'r.', label='Training loss')\n",
    "    plt.plot(epch, val_loss, 'r', label='Validation loss')\n",
    "    plt.title('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    results_test = model.evaluate([test_features, test_images], test_labels)\n",
    "    print('Test Loss:', results_test[0])\n",
    "    print('Test Accuracy:', results_test[1])\n",
    "    \n",
    "    y_train_pred = np.round(model.predict([train_features, train_images]))\n",
    "    y_pred = np.round(model.predict([test_features, test_images]))\n",
    "    \n",
    "    show_cf(test_labels, y_pred, class_names=class_names, model_name=model_name)\n",
    "    \n",
    "    print(classification_report(train_labels, y_train_pred))\n",
    "    print(classification_report(test_labels, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5000 [==============================] - 28s 6ms/step\n",
      "Test Loss: 0.4034484951257706\n",
      "Test Accuracy: 0.8266\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.86      0.83      2524\n",
      "           1       0.85      0.79      0.82      2476\n",
      "\n",
      "   micro avg       0.83      0.83      0.83      5000\n",
      "   macro avg       0.83      0.83      0.83      5000\n",
      "weighted avg       0.83      0.83      0.83      5000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_evaluation(model1, model1_history, testAttrX, testImagesX, testY, class_names=None, model_name=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is already performing well after only five epochs, but would benefit from additional training. It already has a higher accuracy than the best model using only images (average F1 score of 0.83 vs. 0.77). However, it uses the testing set for validation, so some data leakage may have occurred."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model iteration 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same model will be run again for additional epochs, to try and improve the accuracy. The validation set will be separated off as the first 1000 instances of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15000 samples, validate on 1000 samples\n",
      "Epoch 1/50\n",
      "15000/15000 [==============================] - 242s 16ms/step - loss: 0.3964 - acc: 0.8285 - val_loss: 0.4131 - val_acc: 0.8170\n",
      "Epoch 2/50\n",
      "15000/15000 [==============================] - 224s 15ms/step - loss: 0.3846 - acc: 0.8375 - val_loss: 0.5731 - val_acc: 0.6820\n",
      "Epoch 3/50\n",
      "15000/15000 [==============================] - 220s 15ms/step - loss: 0.3808 - acc: 0.8379 - val_loss: 0.6998 - val_acc: 0.6430\n",
      "Epoch 4/50\n",
      "15000/15000 [==============================] - 220s 15ms/step - loss: 0.3527 - acc: 0.8483 - val_loss: 0.5503 - val_acc: 0.7520\n",
      "Epoch 5/50\n",
      "15000/15000 [==============================] - 217s 14ms/step - loss: 0.3295 - acc: 0.8639 - val_loss: 0.5839 - val_acc: 0.7230\n",
      "Epoch 6/50\n",
      "15000/15000 [==============================] - 214s 14ms/step - loss: 0.3047 - acc: 0.8756 - val_loss: 0.4684 - val_acc: 0.8060\n",
      "Epoch 7/50\n",
      "15000/15000 [==============================] - 215s 14ms/step - loss: 0.2801 - acc: 0.8867 - val_loss: 0.4555 - val_acc: 0.8130\n",
      "Epoch 8/50\n",
      "15000/15000 [==============================] - 215s 14ms/step - loss: 0.2675 - acc: 0.8947 - val_loss: 0.7516 - val_acc: 0.6820\n",
      "Epoch 9/50\n",
      "15000/15000 [==============================] - 215s 14ms/step - loss: 0.2414 - acc: 0.9033 - val_loss: 0.5153 - val_acc: 0.8070\n",
      "Epoch 10/50\n",
      "15000/15000 [==============================] - 217s 14ms/step - loss: 0.2316 - acc: 0.9087 - val_loss: 0.5456 - val_acc: 0.7770\n",
      "Epoch 11/50\n",
      "15000/15000 [==============================] - 218s 15ms/step - loss: 0.2154 - acc: 0.9147 - val_loss: 0.5524 - val_acc: 0.7740\n",
      "Epoch 12/50\n",
      "15000/15000 [==============================] - 216s 14ms/step - loss: 0.2091 - acc: 0.9203 - val_loss: 0.7838 - val_acc: 0.6950\n",
      "Epoch 13/50\n",
      "15000/15000 [==============================] - 213s 14ms/step - loss: 0.2027 - acc: 0.9223 - val_loss: 0.6229 - val_acc: 0.7690\n",
      "Epoch 14/50\n",
      "15000/15000 [==============================] - 2071s 138ms/step - loss: 0.1845 - acc: 0.9283 - val_loss: 0.8709 - val_acc: 0.7210\n",
      "Epoch 15/50\n",
      "15000/15000 [==============================] - 208s 14ms/step - loss: 0.1641 - acc: 0.9370 - val_loss: 0.6005 - val_acc: 0.7880\n",
      "Epoch 16/50\n",
      "15000/15000 [==============================] - 212s 14ms/step - loss: 0.1666 - acc: 0.9363 - val_loss: 0.7590 - val_acc: 0.7800\n",
      "Epoch 17/50\n",
      "15000/15000 [==============================] - 214s 14ms/step - loss: 0.1661 - acc: 0.9365 - val_loss: 0.5894 - val_acc: 0.7900\n",
      "Epoch 18/50\n",
      "15000/15000 [==============================] - 215s 14ms/step - loss: 0.1526 - acc: 0.9428 - val_loss: 0.6666 - val_acc: 0.7980\n",
      "Epoch 19/50\n",
      "15000/15000 [==============================] - 212s 14ms/step - loss: 0.1542 - acc: 0.9424 - val_loss: 0.9269 - val_acc: 0.7010\n",
      "Epoch 20/50\n",
      "15000/15000 [==============================] - 212s 14ms/step - loss: 0.1304 - acc: 0.9517 - val_loss: 0.6978 - val_acc: 0.7880\n",
      "Epoch 21/50\n",
      "15000/15000 [==============================] - 211s 14ms/step - loss: 0.1328 - acc: 0.9499 - val_loss: 0.7215 - val_acc: 0.7780\n",
      "Epoch 22/50\n",
      "15000/15000 [==============================] - 33683s 2s/step - loss: 0.1313 - acc: 0.9500 - val_loss: 0.9323 - val_acc: 0.7460\n",
      "Epoch 23/50\n",
      "15000/15000 [==============================] - 216s 14ms/step - loss: 0.1184 - acc: 0.9566 - val_loss: 0.7214 - val_acc: 0.7770\n",
      "Epoch 24/50\n",
      "15000/15000 [==============================] - 209s 14ms/step - loss: 0.1247 - acc: 0.9525 - val_loss: 0.6890 - val_acc: 0.8040\n",
      "Epoch 25/50\n",
      "15000/15000 [==============================] - 210s 14ms/step - loss: 0.1150 - acc: 0.9576 - val_loss: 0.7414 - val_acc: 0.7880\n",
      "Epoch 26/50\n",
      "15000/15000 [==============================] - 211s 14ms/step - loss: 0.1171 - acc: 0.9588 - val_loss: 0.6949 - val_acc: 0.7980\n",
      "Epoch 27/50\n",
      "15000/15000 [==============================] - 212s 14ms/step - loss: 0.1210 - acc: 0.9564 - val_loss: 0.6278 - val_acc: 0.8220\n",
      "Epoch 28/50\n",
      "15000/15000 [==============================] - 208s 14ms/step - loss: 0.1239 - acc: 0.9557 - val_loss: 0.6205 - val_acc: 0.8150\n",
      "Epoch 29/50\n",
      "15000/15000 [==============================] - 1314s 88ms/step - loss: 0.1083 - acc: 0.9621 - val_loss: 0.6340 - val_acc: 0.8080\n",
      "Epoch 30/50\n",
      "12030/15000 [=======================>......] - ETA: 42s - loss: 0.0912 - acc: 0.9677"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-373-09213df36d5f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# train the model, and validate with the first 1000 rows of the test set\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mmodel2_history\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrainAttrX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainImagesX\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtestAttrX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtestImagesX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtestY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1039\u001b[1;33m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1040\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2713\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2715\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2717\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2674\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2675\u001b[1;33m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2676\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[0;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1439\u001b[1;33m               run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1440\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "\n",
    "model2 = Model(inputs=[mlp.input, cnn.input], outputs=x)\n",
    "\n",
    "# Compile the model \n",
    "opt = Adam(lr=1e-3, decay=1e-3 / 200)\n",
    "model2.compile(loss=\"binary_crossentropy\", metrics=['acc'], optimizer=opt)\n",
    " \n",
    "# Train the model, and validate with the first 1000 rows of the test set\n",
    "model2_history = model2.fit([trainAttrX, trainImagesX], trainY, validation_data=([testAttrX[:1000], testImagesX[:1000]], testY[:1000]), epochs=50, batch_size=10)\n",
    " \n",
    "end = datetime.datetime.now()\n",
    "print(\"Time taken to run:\", end-start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model was force stopped because overfitting is clearly an issue, and validation accuracy stopped improving."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model iteration 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this iteration of the model, L1 regularization was introduced to reduce overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the MLP and CNN models\n",
    "mlp3 = create_mlp(trainAttrX.shape[1], regularizer=regularizers.l1(0.005))\n",
    "cnn3 = create_cnn(128, 128, 3, regularizer=regularizers.l1(0.005))\n",
    " \n",
    "# Create the input to the final set of layers as the output of both the MLP and CNN\n",
    "combinedInput = concatenate([mlp3.output, cnn3.output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 8)                 32        \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 4)                 36        \n",
      "=================================================================\n",
      "Total params: 68\n",
      "Trainable params: 68\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 128, 128, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 128, 128, 16)      448       \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 128, 128, 16)      0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 128, 128, 16)      64        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 64, 64, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 64, 64, 32)        4640      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 64, 64, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 32, 32, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 32, 32, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 16384)             0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 16)                262160    \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 16)                64        \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 4)                 68        \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 4)                 0         \n",
      "=================================================================\n",
      "Total params: 286,324\n",
      "Trainable params: 286,068\n",
      "Non-trainable params: 256\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(mlp3.summary())\n",
    "print(cnn3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The final FC layer head will have two dense layers\n",
    "x = Dense(4, activation=\"relu\", kernel_regularizer=regularizers.l1(0.005))(combinedInput)\n",
    "x = Dense(1, activation=\"sigmoid\", kernel_regularizer=regularizers.l1(0.005))(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The final model will accept numerical data on the MLP input and images on the CNN input, outputting a single value\n",
    "model3 = Model(inputs=[mlp3.input, cnn3.input], outputs=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15000 samples, validate on 1000 samples\n",
      "Epoch 1/15\n",
      "15000/15000 [==============================] - 223s 15ms/step - loss: 2.5254 - acc: 0.6877 - val_loss: 2.3773 - val_acc: 0.7150\n",
      "Epoch 2/15\n",
      "15000/15000 [==============================] - 217s 14ms/step - loss: 2.0202 - acc: 0.7440 - val_loss: 1.9498 - val_acc: 0.7840\n",
      "Epoch 3/15\n",
      "15000/15000 [==============================] - 219s 15ms/step - loss: 1.9584 - acc: 0.7396 - val_loss: 1.8030 - val_acc: 0.7810\n",
      "Epoch 4/15\n",
      "15000/15000 [==============================] - 216s 14ms/step - loss: 1.6637 - acc: 0.7621 - val_loss: 1.4849 - val_acc: 0.7910\n",
      "Epoch 5/15\n",
      "15000/15000 [==============================] - 220s 15ms/step - loss: 1.6794 - acc: 0.7742 - val_loss: 1.8095 - val_acc: 0.6250\n",
      "Epoch 6/15\n",
      "15000/15000 [==============================] - 220s 15ms/step - loss: 1.5279 - acc: 0.7839 - val_loss: 1.6563 - val_acc: 0.5900\n",
      "Epoch 7/15\n",
      "15000/15000 [==============================] - 223s 15ms/step - loss: 1.5475 - acc: 0.7847 - val_loss: 1.5092 - val_acc: 0.6570\n",
      "Epoch 8/15\n",
      "15000/15000 [==============================] - 345s 23ms/step - loss: 1.4272 - acc: 0.7895 - val_loss: 1.4734 - val_acc: 0.5870\n",
      "Epoch 9/15\n",
      "15000/15000 [==============================] - 367s 24ms/step - loss: 1.6741 - acc: 0.7907 - val_loss: 1.7722 - val_acc: 0.8090\n",
      "Epoch 10/15\n",
      "15000/15000 [==============================] - 377s 25ms/step - loss: 2.0173 - acc: 0.7903 - val_loss: 1.9344 - val_acc: 0.8110\n",
      "Epoch 11/15\n",
      "15000/15000 [==============================] - 360s 24ms/step - loss: 2.1438 - acc: 0.7867 - val_loss: 1.9685 - val_acc: 0.8090\n",
      "Epoch 12/15\n",
      "15000/15000 [==============================] - 375s 25ms/step - loss: 2.2136 - acc: 0.7920 - val_loss: 2.4912 - val_acc: 0.7550\n",
      "Epoch 13/15\n",
      "15000/15000 [==============================] - 363s 24ms/step - loss: 2.1941 - acc: 0.7937 - val_loss: 2.2553 - val_acc: 0.8330\n",
      "Epoch 14/15\n",
      "15000/15000 [==============================] - 343s 23ms/step - loss: 2.3190 - acc: 0.7935 - val_loss: 2.6350 - val_acc: 0.7300\n",
      "Epoch 15/15\n",
      "15000/15000 [==============================] - 342s 23ms/step - loss: 2.2074 - acc: 0.7897 - val_loss: 1.9747 - val_acc: 0.8310\n",
      "Time taken to run: 1:13:32.993110\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "\n",
    "model3 = Model(inputs=[mlp3.input, cnn3.input], outputs=x)\n",
    "\n",
    "# compile the model \n",
    "opt = Adam(lr=1e-3, decay=1e-3 / 200)\n",
    "model3.compile(loss=\"binary_crossentropy\", metrics=['acc'], optimizer=opt)\n",
    " \n",
    "# train the model, and validate with the first 1000 rows of the test set\n",
    "model3_history = model3.fit([trainAttrX, trainImagesX], trainY, validation_data=([testAttrX[:1000], testImagesX[:1000]], testY[:1000]), epochs=15, batch_size=10)\n",
    " \n",
    "end = datetime.datetime.now()\n",
    "print(\"Time taken to run:\", end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3.save('models/mixed_model_v3.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
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       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"242\" y=\"-174.3\">output:</text>\n",
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       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"340\" y=\"-197.3\">[(None, 4), (None, 4)]</text>\n",
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       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"340\" y=\"-174.3\">(None, 8)</text>\n",
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       "<g class=\"edge\" id=\"edge22\"><title>2693061170736-&gt;2693061171184</title>\n",
       "<path d=\"M344.582,-249.366C332.544,-239.991 318.367,-228.949 305.563,-218.977\" fill=\"none\" stroke=\"black\"/>\n",
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       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"206\" y=\"-102.8\">Dense</text>\n",
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       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"259.5\" y=\"-91.3\">output:</text>\n",
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       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"322.5\" y=\"-114.3\">(None, 8)</text>\n",
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       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"322.5\" y=\"-91.3\">(None, 4)</text>\n",
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       "<!-- 2693061171184&#45;&gt;2693061171240 -->\n",
       "<g class=\"edge\" id=\"edge24\"><title>2693061171184-&gt;2693061171240</title>\n",
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       "<polygon fill=\"black\" points=\"272.5,-139.607 269,-129.607 265.5,-139.607 272.5,-139.607\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 2693061171632 -->\n",
       "<g class=\"node\" id=\"node26\"><title>2693061171632</title>\n",
       "<polygon fill=\"none\" points=\"180.5,-0.5 180.5,-46.5 357.5,-46.5 357.5,-0.5 180.5,-0.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"206\" y=\"-19.8\">Dense</text>\n",
       "<polyline fill=\"none\" points=\"231.5,-0.5 231.5,-46.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"259.5\" y=\"-31.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"231.5,-23.5 287.5,-23.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"259.5\" y=\"-8.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"287.5,-0.5 287.5,-46.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"322.5\" y=\"-31.3\">(None, 4)</text>\n",
       "<polyline fill=\"none\" points=\"287.5,-23.5 357.5,-23.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"322.5\" y=\"-8.3\">(None, 1)</text>\n",
       "</g>\n",
       "<!-- 2693061171240&#45;&gt;2693061171632 -->\n",
       "<g class=\"edge\" id=\"edge25\"><title>2693061171240-&gt;2693061171632</title>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualising the model\n",
    "SVG(model_to_dot(model3, show_layer_names=False, show_shapes=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model3, to_file='mixed_model3_v3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000/4000 [==============================] - 39s 10ms/step\n",
      "Test Loss: 2.018479386329651\n",
      "Test Accuracy: 0.802\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.90      0.83      7476\n",
      "           1       0.88      0.72      0.80      7524\n",
      "\n",
      "   micro avg       0.81      0.81      0.81     15000\n",
      "   macro avg       0.82      0.81      0.81     15000\n",
      "weighted avg       0.82      0.81      0.81     15000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.89      0.82      2013\n",
      "           1       0.86      0.72      0.78      1987\n",
      "\n",
      "   micro avg       0.80      0.80      0.80      4000\n",
      "   macro avg       0.81      0.80      0.80      4000\n",
      "weighted avg       0.81      0.80      0.80      4000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_evaluation(model3, model3_history, trainAttrX, trainImagesX, trainY, testAttrX[1000:], testImagesX[1000:], testY[1000:], class_names=None, model_name=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model still performs well even when the data leakage issue between the validation and test sets has been removed, with an average F1 score of 0.8 for the test set. It also has fewer False Negatives, which is preferred. It also does not suffer from overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model iteration 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to try and improve accuracy, this model adds an extra layer to the final fully-connected layer head, tries using an RMSprop optimizer (as used in CNN model 2), and trains for longer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15000 samples, validate on 1000 samples\n",
      "Epoch 1/30\n",
      "15000/15000 [==============================] - 350s 23ms/step - loss: 1.7350 - acc: 0.6873 - val_loss: 1.0153 - val_acc: 0.7550\n",
      "Epoch 2/30\n",
      "15000/15000 [==============================] - 330s 22ms/step - loss: 1.0521 - acc: 0.7351 - val_loss: 1.0805 - val_acc: 0.7200\n",
      "Epoch 3/30\n",
      "15000/15000 [==============================] - 330s 22ms/step - loss: 1.0367 - acc: 0.7494 - val_loss: 1.0293 - val_acc: 0.7580\n",
      "Epoch 4/30\n",
      "15000/15000 [==============================] - 331s 22ms/step - loss: 1.0288 - acc: 0.7602 - val_loss: 1.0132 - val_acc: 0.7950\n",
      "Epoch 5/30\n",
      "15000/15000 [==============================] - 331s 22ms/step - loss: 1.0352 - acc: 0.7686 - val_loss: 0.9556 - val_acc: 0.8030\n",
      "Epoch 6/30\n",
      "15000/15000 [==============================] - 357s 24ms/step - loss: 1.0406 - acc: 0.7710 - val_loss: 0.9497 - val_acc: 0.8080\n",
      "Epoch 7/30\n",
      "15000/15000 [==============================] - 256s 17ms/step - loss: 1.0108 - acc: 0.7824 - val_loss: 0.9807 - val_acc: 0.7760\n",
      "Epoch 8/30\n",
      "15000/15000 [==============================] - 216s 14ms/step - loss: 0.9890 - acc: 0.7856 - val_loss: 0.9158 - val_acc: 0.8160\n",
      "Epoch 9/30\n",
      "15000/15000 [==============================] - 214s 14ms/step - loss: 0.9647 - acc: 0.7944 - val_loss: 1.1436 - val_acc: 0.6480\n",
      "Epoch 10/30\n",
      "15000/15000 [==============================] - 214s 14ms/step - loss: 0.9528 - acc: 0.7987 - val_loss: 0.9002 - val_acc: 0.8000\n",
      "Epoch 11/30\n",
      "15000/15000 [==============================] - 213s 14ms/step - loss: 0.9498 - acc: 0.7983 - val_loss: 0.8835 - val_acc: 0.8030\n",
      "Epoch 12/30\n",
      "15000/15000 [==============================] - 213s 14ms/step - loss: 0.9458 - acc: 0.7996 - val_loss: 0.9031 - val_acc: 0.8010\n",
      "Epoch 13/30\n",
      "15000/15000 [==============================] - 212s 14ms/step - loss: 0.9344 - acc: 0.7999 - val_loss: 0.8684 - val_acc: 0.8170\n",
      "Epoch 14/30\n",
      "15000/15000 [==============================] - 209s 14ms/step - loss: 0.9145 - acc: 0.8064 - val_loss: 0.8795 - val_acc: 0.8260\n",
      "Epoch 15/30\n",
      "15000/15000 [==============================] - 5511s 367ms/step - loss: 0.9202 - acc: 0.8103 - val_loss: 0.9088 - val_acc: 0.8150\n",
      "Epoch 16/30\n",
      "15000/15000 [==============================] - 207s 14ms/step - loss: 0.9225 - acc: 0.8116 - val_loss: 0.8845 - val_acc: 0.8310\n",
      "Epoch 17/30\n",
      "15000/15000 [==============================] - 207s 14ms/step - loss: 0.9166 - acc: 0.8152 - val_loss: 0.9099 - val_acc: 0.8120\n",
      "Epoch 18/30\n",
      "15000/15000 [==============================] - 209s 14ms/step - loss: 0.9200 - acc: 0.8196 - val_loss: 0.8926 - val_acc: 0.8130\n",
      "Epoch 19/30\n",
      "15000/15000 [==============================] - 210s 14ms/step - loss: 0.9186 - acc: 0.8239 - val_loss: 0.9605 - val_acc: 0.7840\n",
      "Epoch 20/30\n",
      "15000/15000 [==============================] - 210s 14ms/step - loss: 0.9068 - acc: 0.8259 - val_loss: 0.8727 - val_acc: 0.8130\n",
      "Epoch 21/30\n",
      "15000/15000 [==============================] - 207s 14ms/step - loss: 0.8931 - acc: 0.8295 - val_loss: 0.9051 - val_acc: 0.8170\n",
      "Epoch 22/30\n",
      "15000/15000 [==============================] - 5429s 362ms/step - loss: 0.9013 - acc: 0.8322 - val_loss: 0.9323 - val_acc: 0.8100\n",
      "Epoch 23/30\n",
      "15000/15000 [==============================] - 209s 14ms/step - loss: 0.9064 - acc: 0.8338 - val_loss: 0.9500 - val_acc: 0.8120\n",
      "Epoch 24/30\n",
      "15000/15000 [==============================] - 209s 14ms/step - loss: 0.8960 - acc: 0.8401 - val_loss: 0.9125 - val_acc: 0.8120\n",
      "Epoch 25/30\n",
      "15000/15000 [==============================] - 209s 14ms/step - loss: 0.8737 - acc: 0.8448 - val_loss: 1.0372 - val_acc: 0.7690\n",
      "Epoch 26/30\n",
      "15000/15000 [==============================] - 211s 14ms/step - loss: 0.8776 - acc: 0.8484 - val_loss: 0.9553 - val_acc: 0.7910\n",
      "Epoch 27/30\n",
      "15000/15000 [==============================] - 216s 14ms/step - loss: 0.8928 - acc: 0.8455 - val_loss: 0.9720 - val_acc: 0.8030\n",
      "Epoch 28/30\n",
      "15000/15000 [==============================] - 229s 15ms/step - loss: 0.8762 - acc: 0.8525 - val_loss: 0.9451 - val_acc: 0.8150\n",
      "Epoch 29/30\n",
      "15000/15000 [==============================] - 219s 15ms/step - loss: 0.8767 - acc: 0.8521 - val_loss: 0.9681 - val_acc: 0.7870\n",
      "Epoch 30/30\n",
      "15000/15000 [==============================] - 223s 15ms/step - loss: 0.8768 - acc: 0.8523 - val_loss: 1.0109 - val_acc: 0.7740\n",
      "Time taken to run: 4:54:56.527568\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "\n",
    "# create the MLP and CNN models\n",
    "mlp4 = create_mlp(trainAttrX.shape[1], regularizer=regularizers.l1(0.005))\n",
    "cnn4 = create_cnn(128, 128, 3, regularizer=regularizers.l1(0.005))\n",
    " \n",
    "# create the input to our final set of layers as the *output* of both the MLP and CNN\n",
    "combinedInput = concatenate([mlp4.output, cnn4.output])\n",
    "\n",
    "\n",
    "# our final FC layer head will have two dense layers, the final one being our sigmoid head\n",
    "x = Dense(4, activation=\"relu\", kernel_regularizer=regularizers.l1(0.005))(combinedInput)\n",
    "x = Dense(4, activation=\"relu\", kernel_regularizer=regularizers.l1(0.005))(x)\n",
    "x = Dense(1, activation=\"sigmoid\", kernel_regularizer=regularizers.l1(0.005))(x)\n",
    "\n",
    "\n",
    "# our final model will accept numerical data on the MLP input and images on the CNN input, outputting a single value\n",
    "model4 = Model(inputs=[mlp4.input, cnn4.input], outputs=x)\n",
    "\n",
    "\n",
    "# compile the model \n",
    "opt = RMSprop(lr=1e-4)\n",
    "model4.compile(loss=\"binary_crossentropy\", metrics=['acc'], optimizer=opt)\n",
    " \n",
    "# train the model, and validate with the first 1000 rows of the test set\n",
    "model4_history = model4.fit([trainAttrX, trainImagesX], trainY, validation_data=([testAttrX[:1000], testImagesX[:1000]], testY[:1000]), epochs=30, batch_size=10)\n",
    " \n",
    "end = datetime.datetime.now()\n",
    "print(\"Time taken to run:\", end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "model4.save('models/mixed_model_v4.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000/4000 [==============================] - 26s 7ms/step\n",
      "Test Loss: 1.01718044757843\n",
      "Test Accuracy: 0.7855\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.95      0.91      7476\n",
      "           1       0.95      0.87      0.90      7524\n",
      "\n",
      "   micro avg       0.91      0.91      0.91     15000\n",
      "   macro avg       0.91      0.91      0.91     15000\n",
      "weighted avg       0.91      0.91      0.91     15000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.84      0.80      2013\n",
      "           1       0.82      0.73      0.77      1987\n",
      "\n",
      "   micro avg       0.79      0.79      0.79      4000\n",
      "   macro avg       0.79      0.79      0.78      4000\n",
      "weighted avg       0.79      0.79      0.78      4000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_evaluation(model4, model4_history, trainAttrX, trainImagesX, trainY, testAttrX[1000:], testImagesX[1000:], testY[1000:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately this model performs slightly worse than model iteration 3. Although it achieves a considerably higher score for the training set, there is significant overfitting. Therefore, model iteration 3 is the preferred model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and potential directions for future work\n",
    "\n",
    "The best model produced was version 3, which had an average F1 score of 0.8 and had no overfitting. Versions 2 and 4 both achieved accuracy scores for the training set above 0.9, but they exhibited more overfitting, which is why version 3 is preferred.\n",
    "\n",
    "This model performs slightly better than the best version of model 2 (0.77), which only used satellite images as inputs and did not include the extra structured data. Therefore, depending on the context, based on the current best versions it might be preferable to put the best version of the CNN-only model into production, rather than the mixed-input model (due to the extra data processing involved in the mixed-input model). However, there is plenty of room for improvement in model 3, including tuning of both the CNN and MLP components, as well as the final fully-connected layers. In particular, model 3 did not benefit from the pre-trained VGG19 model which was used in model 2. Nevertheless, model 3 is a successful proof of concept of the mixed-input approach.\n",
    "\n",
    "To try and improve the accuracy of the model, some of the following could be tried in further model iterations:\n",
    "- Adding additional layers to the MLP component (which is currently only two layers)\n",
    "- Using a pre-trained model as the base for the CNN (which increased accuracy in model 2)\n",
    "- Parameter tuning of both the CNN and MLP components (e.g. experimenting with regularization and optimization)\n",
    "- Parameter tuning of the final fully-connected layer head\n",
    "- Adding additional data to the structured dataset about the local area\n",
    "- Limiting the 'safe square' part of the dataset to only squares that contain roads of some type, i.e. excluding fields, forests and bodies of water"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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