KI-Projekt-SIM_002-1269263-8749364.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KI Projekt Verkehrsszenario _ Verhaltensprädiktion\n",
    "\n",
    "### Matrikelnummern: 1269263 und 8749364\n",
    "\n",
    "\n",
    "## Eingabe neuer Verkehrssituationen\n",
    "\n",
    "Für die Eingabe haben wir uns verschiedene Möglichkeiten angeschaut und uns für eine zweite csv-Datei entschieden.<br>\n",
    "Die 'input.csv' hat den Vorteil, dass die Daten leichter eingegeben werden können und für mehrere Fälle ist sie besonders geschickt. Hierbei ist die Datei gleich aufgebaut, wie auch die vorgegebenen Trainings- und Testdaten. Außerdem kann je nach Anwendungsfall die 'action' mit angegeben werden, wenn zum Beispiel reale Entscheidungen mit den Modell verglichen werden sollen. Wenn dagegen neue Daten beurteilt werden sollen, kann das Attribut auch leer gelassen werden. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importieren der benötigten Bibliotheken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importieren der Datensätze\n",
    "Hier werden die Datensätze für Training und Test, sowie Daten für die Eingabe zur Prognose einzelner Verkehrssituationen eingelesen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v                    object\n",
       "v_left               object\n",
       "v_front              object\n",
       "d_left                int64\n",
       "d_front               int64\n",
       "type_left            object\n",
       "type_front           object\n",
       "radius_curve(m)       int64\n",
       "slope_street         object\n",
       "street_type          object\n",
       "time                 object\n",
       "weather              object\n",
       "type_vehicle         object\n",
       "speed_limit(km/h)     int64\n",
       "action               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.read_csv(\"SIM_002.csv\", delimiter=\";\")\n",
    "df_input = pd.read_csv(\"Input.csv\", delimiter=\";\")\n",
    "df_all.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Konvertieren der Objekt-Zahlenwerte zu Float-Zahlenwerten\n",
    "Nachdem auffällt, dass v, v_left und v_front als object eingelesen werden, müssen diese Spalten hier noch konvertiert werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v</th>\n",
       "      <th>v_left</th>\n",
       "      <th>v_front</th>\n",
       "      <th>d_left</th>\n",
       "      <th>d_front</th>\n",
       "      <th>type_left</th>\n",
       "      <th>type_front</th>\n",
       "      <th>radius_curve(m)</th>\n",
       "      <th>slope_street</th>\n",
       "      <th>street_type</th>\n",
       "      <th>time</th>\n",
       "      <th>weather</th>\n",
       "      <th>type_vehicle</th>\n",
       "      <th>speed_limit(km/h)</th>\n",
       "      <th>action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-155</td>\n",
       "      <td>68</td>\n",
       "      <td>car</td>\n",
       "      <td>car</td>\n",
       "      <td>4761</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>night</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>250</td>\n",
       "      <td>lane_change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-147</td>\n",
       "      <td>40</td>\n",
       "      <td>car</td>\n",
       "      <td>truck</td>\n",
       "      <td>1556</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>truck</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-28</td>\n",
       "      <td>48</td>\n",
       "      <td>car</td>\n",
       "      <td>truck</td>\n",
       "      <td>3488</td>\n",
       "      <td>flat</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>dusk</td>\n",
       "      <td>rain</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>100</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-26</td>\n",
       "      <td>59</td>\n",
       "      <td>truck</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>5576</td>\n",
       "      <td>flat</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>dawn</td>\n",
       "      <td>rain</td>\n",
       "      <td>car</td>\n",
       "      <td>120</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-141</td>\n",
       "      <td>52</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>truck</td>\n",
       "      <td>2340</td>\n",
       "      <td>flat</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>night</td>\n",
       "      <td>dry</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>120</td>\n",
       "      <td>lane_change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>-179</td>\n",
       "      <td>65</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>car</td>\n",
       "      <td>1600</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>25.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>-109</td>\n",
       "      <td>42</td>\n",
       "      <td>car</td>\n",
       "      <td>truck</td>\n",
       "      <td>1973</td>\n",
       "      <td>ascending</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>night</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>120</td>\n",
       "      <td>decelerate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>36.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-38</td>\n",
       "      <td>47</td>\n",
       "      <td>car</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>3029</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>80</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>-12</td>\n",
       "      <td>54</td>\n",
       "      <td>car</td>\n",
       "      <td>car</td>\n",
       "      <td>2605</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>34.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>-123</td>\n",
       "      <td>61</td>\n",
       "      <td>car</td>\n",
       "      <td>car</td>\n",
       "      <td>4675</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>dusk</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>130</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        v  v_left  v_front  d_left  d_front   type_left  type_front  \\\n",
       "0    38.0    40.0     31.0    -155       68         car         car   \n",
       "1    23.5    23.5     22.0    -147       40         car       truck   \n",
       "2    24.5    36.5     22.0     -28       48         car       truck   \n",
       "3    33.0    36.0     31.0     -26       59       truck  motorcycle   \n",
       "4    28.0    41.0     22.0    -141       52  motorcycle       truck   \n",
       "..    ...     ...      ...     ...      ...         ...         ...   \n",
       "496  36.0    40.0     31.5    -179       65  motorcycle         car   \n",
       "497  25.5    32.5     19.5    -109       42         car       truck   \n",
       "498  36.5    36.0     31.0     -38       47         car  motorcycle   \n",
       "499  32.0    35.0     29.5     -12       54         car         car   \n",
       "500  34.5    48.5     28.5    -123       61         car         car   \n",
       "\n",
       "     radius_curve(m) slope_street               street_type   time weather  \\\n",
       "0               4761         flat                  autobahn  night     dry   \n",
       "1               1556         flat                  autobahn    day     dry   \n",
       "2               3488         flat  country_road (separated)   dusk    rain   \n",
       "3               5576         flat  country_road (separated)   dawn    rain   \n",
       "4               2340         flat  country_road (separated)  night     dry   \n",
       "..               ...          ...                       ...    ...     ...   \n",
       "496             1600         flat                  autobahn    day     dry   \n",
       "497             1973    ascending  country_road (separated)  night     dry   \n",
       "498             3029         flat                  autobahn    day     dry   \n",
       "499             2605         flat                  autobahn    day     dry   \n",
       "500             4675         flat                  autobahn   dusk     dry   \n",
       "\n",
       "    type_vehicle  speed_limit(km/h)       action  \n",
       "0            car                250  lane_change  \n",
       "1          truck                250     continue  \n",
       "2     motorcycle                100     continue  \n",
       "3            car                120     continue  \n",
       "4     motorcycle                120  lane_change  \n",
       "..           ...                ...          ...  \n",
       "496          car                250     continue  \n",
       "497          car                120   decelerate  \n",
       "498          car                 80     continue  \n",
       "499   motorcycle                250     continue  \n",
       "500          car                130     continue  \n",
       "\n",
       "[501 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = df_all.replace(',','.', regex=True)\n",
    "df_all['v'] = pd.to_numeric(df_all['v'])\n",
    "df_all['v_left'] = pd.to_numeric(df_all['v_left'])\n",
    "df_all['v_front'] = pd.to_numeric(df_all['v_front'])\n",
    "\n",
    "df_input = df_input.replace(',','.', regex=True)\n",
    "df_input['v'] = pd.to_numeric(df_input['v'])\n",
    "df_input['v_left'] = pd.to_numeric(df_input['v_left'])\n",
    "df_input['v_front'] = pd.to_numeric(df_input['v_front'])\n",
    "\n",
    "df_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Umrechnen der Geschwindigkeiten von m/s auf km/h\n",
    "Dies wird gemacht, um einheitliche Werte zu haben. Maximalgeschwindigkeit (speed limit) und die Geschwindigkeiten der Autos (v, v_left, ...) unterscheiden sich in der Einheit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v</th>\n",
       "      <th>v_left</th>\n",
       "      <th>v_front</th>\n",
       "      <th>d_left</th>\n",
       "      <th>d_front</th>\n",
       "      <th>type_left</th>\n",
       "      <th>type_front</th>\n",
       "      <th>radius_curve(m)</th>\n",
       "      <th>slope_street</th>\n",
       "      <th>street_type</th>\n",
       "      <th>time</th>\n",
       "      <th>weather</th>\n",
       "      <th>type_vehicle</th>\n",
       "      <th>speed_limit(km/h)</th>\n",
       "      <th>action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.8</td>\n",
       "      <td>144.0</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-155</td>\n",
       "      <td>68</td>\n",
       "      <td>car</td>\n",
       "      <td>car</td>\n",
       "      <td>4761</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>night</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>250</td>\n",
       "      <td>lane_change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.6</td>\n",
       "      <td>84.6</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-147</td>\n",
       "      <td>40</td>\n",
       "      <td>car</td>\n",
       "      <td>truck</td>\n",
       "      <td>1556</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>truck</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.2</td>\n",
       "      <td>131.4</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-28</td>\n",
       "      <td>48</td>\n",
       "      <td>car</td>\n",
       "      <td>truck</td>\n",
       "      <td>3488</td>\n",
       "      <td>flat</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>dusk</td>\n",
       "      <td>rain</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>100</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118.8</td>\n",
       "      <td>129.6</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-26</td>\n",
       "      <td>59</td>\n",
       "      <td>truck</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>5576</td>\n",
       "      <td>flat</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>dawn</td>\n",
       "      <td>rain</td>\n",
       "      <td>car</td>\n",
       "      <td>120</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.8</td>\n",
       "      <td>147.6</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-141</td>\n",
       "      <td>52</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>truck</td>\n",
       "      <td>2340</td>\n",
       "      <td>flat</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>night</td>\n",
       "      <td>dry</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>120</td>\n",
       "      <td>lane_change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>129.6</td>\n",
       "      <td>144.0</td>\n",
       "      <td>113.4</td>\n",
       "      <td>-179</td>\n",
       "      <td>65</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>car</td>\n",
       "      <td>1600</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>91.8</td>\n",
       "      <td>117.0</td>\n",
       "      <td>70.2</td>\n",
       "      <td>-109</td>\n",
       "      <td>42</td>\n",
       "      <td>car</td>\n",
       "      <td>truck</td>\n",
       "      <td>1973</td>\n",
       "      <td>ascending</td>\n",
       "      <td>country_road (separated)</td>\n",
       "      <td>night</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>120</td>\n",
       "      <td>decelerate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>131.4</td>\n",
       "      <td>129.6</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-38</td>\n",
       "      <td>47</td>\n",
       "      <td>car</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>3029</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>80</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>115.2</td>\n",
       "      <td>126.0</td>\n",
       "      <td>106.2</td>\n",
       "      <td>-12</td>\n",
       "      <td>54</td>\n",
       "      <td>car</td>\n",
       "      <td>car</td>\n",
       "      <td>2605</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>day</td>\n",
       "      <td>dry</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>124.2</td>\n",
       "      <td>174.6</td>\n",
       "      <td>102.6</td>\n",
       "      <td>-123</td>\n",
       "      <td>61</td>\n",
       "      <td>car</td>\n",
       "      <td>car</td>\n",
       "      <td>4675</td>\n",
       "      <td>flat</td>\n",
       "      <td>autobahn</td>\n",
       "      <td>dusk</td>\n",
       "      <td>dry</td>\n",
       "      <td>car</td>\n",
       "      <td>130</td>\n",
       "      <td>continue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         v  v_left  v_front  d_left  d_front   type_left  type_front  \\\n",
       "0    136.8   144.0    111.6    -155       68         car         car   \n",
       "1     84.6    84.6     79.2    -147       40         car       truck   \n",
       "2     88.2   131.4     79.2     -28       48         car       truck   \n",
       "3    118.8   129.6    111.6     -26       59       truck  motorcycle   \n",
       "4    100.8   147.6     79.2    -141       52  motorcycle       truck   \n",
       "..     ...     ...      ...     ...      ...         ...         ...   \n",
       "496  129.6   144.0    113.4    -179       65  motorcycle         car   \n",
       "497   91.8   117.0     70.2    -109       42         car       truck   \n",
       "498  131.4   129.6    111.6     -38       47         car  motorcycle   \n",
       "499  115.2   126.0    106.2     -12       54         car         car   \n",
       "500  124.2   174.6    102.6    -123       61         car         car   \n",
       "\n",
       "     radius_curve(m) slope_street               street_type   time weather  \\\n",
       "0               4761         flat                  autobahn  night     dry   \n",
       "1               1556         flat                  autobahn    day     dry   \n",
       "2               3488         flat  country_road (separated)   dusk    rain   \n",
       "3               5576         flat  country_road (separated)   dawn    rain   \n",
       "4               2340         flat  country_road (separated)  night     dry   \n",
       "..               ...          ...                       ...    ...     ...   \n",
       "496             1600         flat                  autobahn    day     dry   \n",
       "497             1973    ascending  country_road (separated)  night     dry   \n",
       "498             3029         flat                  autobahn    day     dry   \n",
       "499             2605         flat                  autobahn    day     dry   \n",
       "500             4675         flat                  autobahn   dusk     dry   \n",
       "\n",
       "    type_vehicle  speed_limit(km/h)       action  \n",
       "0            car                250  lane_change  \n",
       "1          truck                250     continue  \n",
       "2     motorcycle                100     continue  \n",
       "3            car                120     continue  \n",
       "4     motorcycle                120  lane_change  \n",
       "..           ...                ...          ...  \n",
       "496          car                250     continue  \n",
       "497          car                120   decelerate  \n",
       "498          car                 80     continue  \n",
       "499   motorcycle                250     continue  \n",
       "500          car                130     continue  \n",
       "\n",
       "[501 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['v'] = df_all['v'] * 3.6\n",
    "df_all['v_left'] = df_all['v_left'] * 3.6\n",
    "df_all['v_front'] = df_all['v_front'] * 3.6\n",
    "\n",
    "df_input['v'] = df_input['v'] * 3.6\n",
    "df_input['v_left'] = df_input['v_left'] * 3.6\n",
    "df_input['v_front'] = df_input['v_front'] * 3.6\n",
    "\n",
    "df_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bereinigen des Datensets wo notwendig\n",
    "\n",
    "Es ist keinerlei Datensatz aufgefallen, welcher entfernt werden muss, da er ungültige Werte (beispielsweise leere Felder oder Ähnliches) enthält."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding der nicht-nummerischen Werte\n",
    "Die folgenden Features sind vom Typ object und benötigen deshalb ein Label Encoding, damit später besser mit ihnen gearbeitet werden kann.\n",
    "Dieses Vorgehen ist eine good practice, welche in vielen verschiedenen KI Modellen so oder so ähnlich bereits implementiert ist. Ein Vorteil davon ist das einfachere Vergleichen von zwei Objekten. \n",
    "Außerdem kann, wenn eine Rangordnung zwischen den Optionen besteht, das Attribut so in eine Nominalskala überführt werden. Da jeder nicht-Zahlen Wert encoded wird, ist am Ende der DataFrame einheitlich und konsistent.\n",
    "\n",
    "Das Label Encoding wird mit der Bibliothek scikit learn durchgeführt.\n",
    "Für diesen Zweck wird das Object ``type_left_encoder`` erstellt und gespeichert. Anschließend werden damit die Spalten umgewandelt. In den folgenden Encodings wird identisch vorgegangen, darum wird nicht jedes einzeln beschrieben.\n",
    "#### type-left:\n",
    "* car -> 0\n",
    "* motorcycle -> 1\n",
    "* sportscar -> 2\n",
    "* truck -> 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "car           323\n",
      "truck         107\n",
      "sportscar      36\n",
      "motorcycle     35\n",
      "Name: type_left, dtype: int64\n",
      "{'car': 0, 'motorcycle': 1, 'sportscar': 2, 'truck': 3}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(df_all['type_left'].value_counts())\n",
    "\n",
    "# Do the label encoding with sklearn\n",
    "type_left_encoder = preprocessing.LabelEncoder().fit(df_all[\"type_left\"])\n",
    "print(dict(zip(type_left_encoder.classes_, type_left_encoder.transform(type_left_encoder.classes_))))\n",
    "df_all[\"type_left\"] = type_left_encoder.transform(df_all[\"type_left\"])\n",
    "df_input[\"type_left\"] = type_left_encoder.transform(df_input[\"type_left\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### type-front:\n",
    "* car -> 0\n",
    "* motorcycle -> 1\n",
    "* sportscar -> 2\n",
    "* truck -> 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "truck         253\n",
      "car           207\n",
      "motorcycle     22\n",
      "sportscar      19\n",
      "Name: type_front, dtype: int64\n",
      "{'car': 0, 'motorcycle': 1, 'sportscar': 2, 'truck': 3}\n"
     ]
    }
   ],
   "source": [
    "print(df_all['type_front'].value_counts())\n",
    "\n",
    "# Do the label encoding with sklearn\n",
    "type_front_encoder = preprocessing.LabelEncoder().fit(df_all[\"type_front\"])\n",
    "print(dict(zip(type_front_encoder.classes_, type_front_encoder.transform(type_front_encoder.classes_))))\n",
    "df_all[\"type_front\"] = type_front_encoder.transform(df_all[\"type_front\"])\n",
    "df_input[\"type_front\"] = type_front_encoder.transform(df_input[\"type_front\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### slope_street:\n",
    "* ascending -> 0\n",
    "* decending -> 1\n",
    "* flat -> 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flat         324\n",
      "ascending     99\n",
      "decending     78\n",
      "Name: slope_street, dtype: int64\n",
      "{'ascending': 0, 'decending': 1, 'flat': 2}\n"
     ]
    }
   ],
   "source": [
    "print(df_all['slope_street'].value_counts())\n",
    "\n",
    "# Do the label encoding with sklearn\n",
    "slope_street_encoder = preprocessing.LabelEncoder().fit(df_all[\"slope_street\"])\n",
    "print(dict(zip(slope_street_encoder.classes_, slope_street_encoder.transform(slope_street_encoder.classes_))))\n",
    "df_all[\"slope_street\"] = slope_street_encoder.transform(df_all[\"slope_street\"])\n",
    "df_input[\"slope_street\"] = slope_street_encoder.transform(df_input[\"slope_street\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### street_type:\n",
    "* autobahn -> 0\n",
    "* country_road (separated) -> 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country_road (separated)    353\n",
      "autobahn                    148\n",
      "Name: street_type, dtype: int64\n",
      "{'autobahn': 0, 'country_road (separated)': 1}\n"
     ]
    }
   ],
   "source": [
    "print(df_all['street_type'].value_counts())\n",
    "\n",
    "# Do the label encoding with sklearn\n",
    "street_type_encoder = preprocessing.LabelEncoder().fit(df_all[\"street_type\"])\n",
    "print(dict(zip(street_type_encoder.classes_, street_type_encoder.transform(street_type_encoder.classes_))))\n",
    "df_all[\"street_type\"] = street_type_encoder.transform(df_all[\"street_type\"])\n",
    "df_input[\"street_type\"] = street_type_encoder.transform(df_input[\"street_type\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### time:\n",
    "* dawn -> 0\n",
    "* day -> 1\n",
    "* dusk -> 2\n",
    "* night -> 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day      274\n",
      "night    128\n",
      "dawn      51\n",
      "dusk      48\n",
      "Name: time, dtype: int64\n",
      "{'dawn': 0, 'day': 1, 'dusk': 2, 'night': 3}\n"
     ]
    }
   ],
   "source": [
    "print(df_all['time'].value_counts())\n",
    "\n",
    "# Do the label encoding with sklearn\n",
    "time_encoder = preprocessing.LabelEncoder().fit(df_all[\"time\"])\n",
    "print(dict(zip(time_encoder.classes_, time_encoder.transform(time_encoder.classes_))))\n",
    "df_all[\"time\"] = time_encoder.transform(df_all[\"time\"])\n",
    "df_input[\"time\"] = time_encoder.transform(df_input[\"time\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### weather:\n",
    "* dry -> 0\n",
    "* fog -> 1\n",
    "* rain -> 2\n",
    "* snow_ice -> 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dry         416\n",
      "rain         74\n",
      "fog          10\n",
      "snow_ice      1\n",
      "Name: weather, dtype: int64\n",
      "{'dry': 0, 'fog': 1, 'rain': 2, 'snow_ice': 3}\n"
     ]
    }
   ],
   "source": [
    "print(df_all['weather'].value_counts())\n",
    "\n",
    "# Do the label encoding with sklearn\n",
    "weather_encoder = preprocessing.LabelEncoder().fit(df_all[\"weather\"])\n",
    "print(dict(zip(weather_encoder.classes_, weather_encoder.transform(weather_encoder.classes_))))\n",
    "df_all[\"weather\"] = weather_encoder.transform(df_all[\"weather\"])\n",
    "df_input[\"weather\"] = weather_encoder.transform(df_input[\"weather\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### type_vehicle:\n",
    "* car -> 0\n",
    "* motorcycle -> 1\n",
    "* sportscar -> 2\n",
    "* truck -> 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "car           312\n",
      "truck         125\n",
      "sportscar      34\n",
      "motorcycle     30\n",
      "Name: type_vehicle, dtype: int64\n",
      "{'car': 0, 'motorcycle': 1, 'sportscar': 2, 'truck': 3}\n"
     ]
    }
   ],
   "source": [
    "print(df_all['type_vehicle'].value_counts())\n",
    "\n",
    "# Do the label encoding with sklearn\n",
    "type_vehicle_encoder = preprocessing.LabelEncoder().fit(df_all[\"type_vehicle\"])\n",
    "print(dict(zip(type_vehicle_encoder.classes_, type_vehicle_encoder.transform(type_vehicle_encoder.classes_))))\n",
    "df_all[\"type_vehicle\"] = type_vehicle_encoder.transform(df_all[\"type_vehicle\"])\n",
    "df_input[\"type_vehicle\"] = type_vehicle_encoder.transform(df_input[\"type_vehicle\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geschwindigkeit Differenz Berechnung zur action-Bewertung\n",
    "Dazu werden zum einem die Differenz der Geschwindigkeiten von `v` und `v_front` berechnet, um einschätzen zu können, ob die Geschwindigkeit beibehalten werden soll, ob verzögert oder überholt werden soll. \n",
    "\n",
    "Außerdem werden `v` und `v_left` verglichen, um bewerten zu können, ob und wie (mit oder ohne Beschleunigung) eine Überholung stattfinden soll.\n",
    "\n",
    "`v` und `speed_limit` werden verglichen, um festzustellen, wie viel schneller ein Fahrzeug noch fahren könnte, bevor es in den rechtswidrigen Bereich gelangt oder wie viel es abbremsen müsste, um den Bereich zu erreichen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v</th>\n",
       "      <th>v_left</th>\n",
       "      <th>v_front</th>\n",
       "      <th>d_left</th>\n",
       "      <th>d_front</th>\n",
       "      <th>type_left</th>\n",
       "      <th>type_front</th>\n",
       "      <th>radius_curve(m)</th>\n",
       "      <th>slope_street</th>\n",
       "      <th>street_type</th>\n",
       "      <th>time</th>\n",
       "      <th>weather</th>\n",
       "      <th>type_vehicle</th>\n",
       "      <th>speed_limit(km/h)</th>\n",
       "      <th>action</th>\n",
       "      <th>delta_vFront</th>\n",
       "      <th>delta_vLeft</th>\n",
       "      <th>delta_speed_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.8</td>\n",
       "      <td>144.0</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-155</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4761</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>lane_change</td>\n",
       "      <td>25.2</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>113.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.6</td>\n",
       "      <td>84.6</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-147</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1556</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.2</td>\n",
       "      <td>131.4</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-28</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3488</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>continue</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-43.2</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118.8</td>\n",
       "      <td>129.6</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-26</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5576</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>continue</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.8</td>\n",
       "      <td>147.6</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-141</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2340</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>lane_change</td>\n",
       "      <td>21.6</td>\n",
       "      <td>-46.8</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>129.6</td>\n",
       "      <td>144.0</td>\n",
       "      <td>113.4</td>\n",
       "      <td>-179</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "      <td>16.2</td>\n",
       "      <td>-14.4</td>\n",
       "      <td>120.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>91.8</td>\n",
       "      <td>117.0</td>\n",
       "      <td>70.2</td>\n",
       "      <td>-109</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1973</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>decelerate</td>\n",
       "      <td>21.6</td>\n",
       "      <td>-25.2</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>131.4</td>\n",
       "      <td>129.6</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-38</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3029</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>continue</td>\n",
       "      <td>19.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-51.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>115.2</td>\n",
       "      <td>126.0</td>\n",
       "      <td>106.2</td>\n",
       "      <td>-12</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2605</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>134.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>124.2</td>\n",
       "      <td>174.6</td>\n",
       "      <td>102.6</td>\n",
       "      <td>-123</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4675</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>continue</td>\n",
       "      <td>21.6</td>\n",
       "      <td>-50.4</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         v  v_left  v_front  d_left  d_front  type_left  type_front  \\\n",
       "0    136.8   144.0    111.6    -155       68          0           0   \n",
       "1     84.6    84.6     79.2    -147       40          0           3   \n",
       "2     88.2   131.4     79.2     -28       48          0           3   \n",
       "3    118.8   129.6    111.6     -26       59          3           1   \n",
       "4    100.8   147.6     79.2    -141       52          1           3   \n",
       "..     ...     ...      ...     ...      ...        ...         ...   \n",
       "496  129.6   144.0    113.4    -179       65          1           0   \n",
       "497   91.8   117.0     70.2    -109       42          0           3   \n",
       "498  131.4   129.6    111.6     -38       47          0           1   \n",
       "499  115.2   126.0    106.2     -12       54          0           0   \n",
       "500  124.2   174.6    102.6    -123       61          0           0   \n",
       "\n",
       "     radius_curve(m)  slope_street  street_type  time  weather  type_vehicle  \\\n",
       "0               4761             2            0     3        0             0   \n",
       "1               1556             2            0     1        0             3   \n",
       "2               3488             2            1     2        2             1   \n",
       "3               5576             2            1     0        2             0   \n",
       "4               2340             2            1     3        0             1   \n",
       "..               ...           ...          ...   ...      ...           ...   \n",
       "496             1600             2            0     1        0             0   \n",
       "497             1973             0            1     3        0             0   \n",
       "498             3029             2            0     1        0             0   \n",
       "499             2605             2            0     1        0             1   \n",
       "500             4675             2            0     2        0             0   \n",
       "\n",
       "     speed_limit(km/h)       action  delta_vFront  delta_vLeft  \\\n",
       "0                  250  lane_change          25.2         -7.2   \n",
       "1                  250     continue           5.4          0.0   \n",
       "2                  100     continue           9.0        -43.2   \n",
       "3                  120     continue           7.2        -10.8   \n",
       "4                  120  lane_change          21.6        -46.8   \n",
       "..                 ...          ...           ...          ...   \n",
       "496                250     continue          16.2        -14.4   \n",
       "497                120   decelerate          21.6        -25.2   \n",
       "498                 80     continue          19.8          1.8   \n",
       "499                250     continue           9.0        -10.8   \n",
       "500                130     continue          21.6        -50.4   \n",
       "\n",
       "     delta_speed_limit  \n",
       "0                113.2  \n",
       "1                165.4  \n",
       "2                 11.8  \n",
       "3                  1.2  \n",
       "4                 19.2  \n",
       "..                 ...  \n",
       "496              120.4  \n",
       "497               28.2  \n",
       "498              -51.4  \n",
       "499              134.8  \n",
       "500                5.8  \n",
       "\n",
       "[501 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['delta_vFront'] = df_all['v'] - df_all['v_front']\n",
    "df_all['delta_vLeft'] = df_all['v'] - df_all['v_left']\n",
    "df_all['delta_speed_limit'] = df_all['speed_limit(km/h)'] - df_all['v']\n",
    "df_input['delta_vFront'] = df_input['v'] - df_input['v_front']\n",
    "df_input['delta_vLeft'] = df_input['v'] - df_input['v_left']\n",
    "df_input['delta_speed_limit'] = df_input['speed_limit(km/h)'] - df_input['v']\n",
    "\n",
    "df_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### action:\n",
    "One Hot Encoding für alle ``action`` Werte um sie einzeln betrachten zu können für die Korrelationen. Dies ist eine Methode, um mit Attributen zu arbeiten, welche keine Nominalskala haben, für die Korrelationen aber trotzdem interessant sind. Bei anderen Attributen wurde darauf verzichtet, da die Korrelationen bei ihnen weniger interessant sind. Außerdem wird das Encoding nur in eine Kopie des eigentlichen Datenframes geschrieben, um es später nicht für die Vorhersage zu nutzen, sondern nur für die unten folgende Korrelationsheatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>v</th>\n",
       "      <th>v_left</th>\n",
       "      <th>v_front</th>\n",
       "      <th>d_left</th>\n",
       "      <th>d_front</th>\n",
       "      <th>type_left</th>\n",
       "      <th>type_front</th>\n",
       "      <th>radius_curve(m)</th>\n",
       "      <th>slope_street</th>\n",
       "      <th>street_type</th>\n",
       "      <th>...</th>\n",
       "      <th>type_vehicle</th>\n",
       "      <th>speed_limit(km/h)</th>\n",
       "      <th>action</th>\n",
       "      <th>delta_vFront</th>\n",
       "      <th>delta_vLeft</th>\n",
       "      <th>delta_speed_limit</th>\n",
       "      <th>accelerated_lane_change</th>\n",
       "      <th>continue</th>\n",
       "      <th>decelerate</th>\n",
       "      <th>lane_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.8</td>\n",
       "      <td>144.0</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-155</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4761</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>lane_change</td>\n",
       "      <td>25.2</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>113.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.6</td>\n",
       "      <td>84.6</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-147</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1556</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.2</td>\n",
       "      <td>131.4</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-28</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3488</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>continue</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-43.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118.8</td>\n",
       "      <td>129.6</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-26</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5576</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>continue</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.8</td>\n",
       "      <td>147.6</td>\n",
       "      <td>79.2</td>\n",
       "      <td>-141</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2340</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>lane_change</td>\n",
       "      <td>21.6</td>\n",
       "      <td>-46.8</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>129.6</td>\n",
       "      <td>144.0</td>\n",
       "      <td>113.4</td>\n",
       "      <td>-179</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "      <td>16.2</td>\n",
       "      <td>-14.4</td>\n",
       "      <td>120.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>91.8</td>\n",
       "      <td>117.0</td>\n",
       "      <td>70.2</td>\n",
       "      <td>-109</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1973</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>decelerate</td>\n",
       "      <td>21.6</td>\n",
       "      <td>-25.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>131.4</td>\n",
       "      <td>129.6</td>\n",
       "      <td>111.6</td>\n",
       "      <td>-38</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3029</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>continue</td>\n",
       "      <td>19.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-51.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>115.2</td>\n",
       "      <td>126.0</td>\n",
       "      <td>106.2</td>\n",
       "      <td>-12</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2605</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>continue</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>134.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>124.2</td>\n",
       "      <td>174.6</td>\n",
       "      <td>102.6</td>\n",
       "      <td>-123</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4675</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>continue</td>\n",
       "      <td>21.6</td>\n",
       "      <td>-50.4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         v  v_left  v_front  d_left  d_front  type_left  type_front  \\\n",
       "0    136.8   144.0    111.6    -155       68          0           0   \n",
       "1     84.6    84.6     79.2    -147       40          0           3   \n",
       "2     88.2   131.4     79.2     -28       48          0           3   \n",
       "3    118.8   129.6    111.6     -26       59          3           1   \n",
       "4    100.8   147.6     79.2    -141       52          1           3   \n",
       "..     ...     ...      ...     ...      ...        ...         ...   \n",
       "496  129.6   144.0    113.4    -179       65          1           0   \n",
       "497   91.8   117.0     70.2    -109       42          0           3   \n",
       "498  131.4   129.6    111.6     -38       47          0           1   \n",
       "499  115.2   126.0    106.2     -12       54          0           0   \n",
       "500  124.2   174.6    102.6    -123       61          0           0   \n",
       "\n",
       "     radius_curve(m)  slope_street  street_type  ...  type_vehicle  \\\n",
       "0               4761             2            0  ...             0   \n",
       "1               1556             2            0  ...             3   \n",
       "2               3488             2            1  ...             1   \n",
       "3               5576             2            1  ...             0   \n",
       "4               2340             2            1  ...             1   \n",
       "..               ...           ...          ...  ...           ...   \n",
       "496             1600             2            0  ...             0   \n",
       "497             1973             0            1  ...             0   \n",
       "498             3029             2            0  ...             0   \n",
       "499             2605             2            0  ...             1   \n",
       "500             4675             2            0  ...             0   \n",
       "\n",
       "     speed_limit(km/h)       action  delta_vFront delta_vLeft  \\\n",
       "0                  250  lane_change          25.2        -7.2   \n",
       "1                  250     continue           5.4         0.0   \n",
       "2                  100     continue           9.0       -43.2   \n",
       "3                  120     continue           7.2       -10.8   \n",
       "4                  120  lane_change          21.6       -46.8   \n",
       "..                 ...          ...           ...         ...   \n",
       "496                250     continue          16.2       -14.4   \n",
       "497                120   decelerate          21.6       -25.2   \n",
       "498                 80     continue          19.8         1.8   \n",
       "499                250     continue           9.0       -10.8   \n",
       "500                130     continue          21.6       -50.4   \n",
       "\n",
       "     delta_speed_limit  accelerated_lane_change  continue  decelerate  \\\n",
       "0                113.2                        0         0           0   \n",
       "1                165.4                        0         1           0   \n",
       "2                 11.8                        0         1           0   \n",
       "3                  1.2                        0         1           0   \n",
       "4                 19.2                        0         0           0   \n",
       "..                 ...                      ...       ...         ...   \n",
       "496              120.4                        0         1           0   \n",
       "497               28.2                        0         0           1   \n",
       "498              -51.4                        0         1           0   \n",
       "499              134.8                        0         1           0   \n",
       "500                5.8                        0         1           0   \n",
       "\n",
       "     lane_change  \n",
       "0              1  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              1  \n",
       "..           ...  \n",
       "496            0  \n",
       "497            0  \n",
       "498            0  \n",
       "499            0  \n",
       "500            0  \n",
       "\n",
       "[501 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis = df_all.copy()\n",
    "actions = ['accelerated_lane_change', 'continue', 'decelerate', 'lane_change']\n",
    "for x in actions:\n",
    "    df_analysis[\"{}\".format(x)] = np.where(df_analysis.action == x, 1, 0)\n",
    "    \n",
    "df_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Korrelation Heatmap aller Attribute')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.heatmap(data=df_analysis.corr(numeric_only=True), vmin=-1, vmax=1, annot=True, cmap=\"magma\").set_title('Korrelation Heatmap aller Attribute', fontdict={'fontsize': 14}, pad=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation der Korrelationsheatmap\n",
    "Da die Geschwindigkeit ein zentraler Faktor in der Überholungsüberlegung ist, wird diese zuerst betrachtet. Sie korreliert in der Heatmap sehr stark mit ``v_left``, ``v_front`` und ``d_front``. Außerdem ist auffällig, dass eine negative Korrelation mit ``type_front`` und ``type_vehicle`` besteht. Diese kann aber wegen nicht vorhandener Rangordnung in den Attributen allerdings nicht in Betracht gezogen werden. \n",
    "\n",
    "``type_front`` korreliert zwar mit `v`, `v_left`, `v_front` und `d_front` negativ, allerdings ist auch hier keine Nominalskala vorhanden.\n",
    "\n",
    "Der wichtigste Faktor für das Training ist ``action``. Dieser Wert teilt sich hier in vier verschiedene Attribute auf. Es ist eine leichte Korrelation zwischen der ``action`` 'continue' und ``v_front`` zu erkennen. Vermutlich wird also eher 'continue' gewählt, wenn der Abstand zum vorausfahrenden Auto größer ist. \n",
    "``v_front`` korreliert außerdem auch leicht negativ mit den anderen ``action``s, dabei am stärksten mit ``lane_change``. Also je kleiner der Abstand zum vorausfahrenden Auto ist, desto eher wird abgebremst, Spur gewechselt oder beschleunigt die Spur gewechselt.\n",
    "\n",
    "Außerdem ist zwischen ``accelerated_lane_change`` und ``d_left`` eine leichte Korrelation zu erkennen. Je weiter das Auto auf der linken Spur also entfernt ist, desto größer ist die Wahrscheinlichkeit für einen beschleunigten Spurwechsel. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trainings- und Testdaten aufteilen\n",
    "\n",
    "Die Daten werden hier aufgeteilt. Um später eine Cross-Validierung möglich zu machen, werden je nach Eingabe verschiedene Intervalle gewählt. Diese sind entweder mit den Testdaten bei 0 - 100, 100 - 200 etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interval got to be between 0 and 4\n",
    "def get_test_and_train_data(interval):\n",
    "    lower_border = interval * 100\n",
    "    upper_border = lower_border + 100\n",
    "    if lower_border != 0:\n",
    "        train_data = df_all.loc[:lower_border]\n",
    "        train_data = train_data.append(df_all.loc[(upper_border+1):])\n",
    "        test_data = df_all.loc[(lower_border + 1):upper_border]\n",
    "    else:\n",
    "        train_data = df_all.loc[(upper_border):]\n",
    "        test_data = df_all.loc[(lower_border):upper_border-1]\n",
    "    return train_data, test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prüfung auf Gleichheit \n",
    "In dieser Funktion werden die Testdaten auf Gleichheit überprüft. Das bedeutet, dass die Eingabe mit allen Trainingsdaten verglichen wird und auf eine Überseinstimmung untersucht wird. Sobald Trainingsdaten mit den exakt gleichen Werten gefunden wurden, wird die gleiche ``action`` für die Testdaten übernommen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_for_same(input, train_data):\n",
    "    for index, row in train_data.iterrows():\n",
    "        if input['v'] == row['v'] and input['v_left'] == row['v_left'] and input['v_front'] == row['v_front'] and input['d_left'] == row['d_left'] and input['d_front'] == row['d_front'] and input['type_left'] == row['type_left'] and input['type_front'] == row['type_front'] and input['radius_curve(m)'] == row['radius_curve(m)'] and input['slope_street'] == row['slope_street'] and input['street_type'] == row['street_type'] and input['time'] == row['time'] and input['weather'] == row['weather'] and input['type_vehicle'] == row['type_vehicle'] and input['speed_limit(km/h)'] == row['speed_limit(km/h)'] and input['delta_vFront'] == row['delta_vFront'] and input['delta_vLeft'] == row['delta_vLeft']:\n",
    "            return True, row\n",
    "    return False, 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prüfung auf Ähnlichkeit\n",
    "\n",
    "Nachdem auf Gleichheit geprüft wurde und der Fall auftritt, dass keine genau gleichen Trainingsdaten gefunden wurden, wird auf Ähnlichkeit geprüft. \n",
    "Das heißt, dass alle Attribute der Objekte verglichen werden und nach dem ähnlichsten Fall in den Trainingsdaten gesucht wird. \n",
    "\n",
    "Um am Schluss eine Formel für das Ähnlichkeitsmaß zwischen zwei Objekten aufstellen zu können, in dem alle Attribute gleich gewichtet werden können, werden die Wertebereiche normiert.\n",
    "Dies bedeutet, dass die verschiedenen Attribute alle einen Wert bekommen, der im Bereich von 0 und 1 liegt. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similarity of speed with a value between 0 and 1 \n",
    "# default and to_compare are the input and training data\n",
    "# both, default and to_compare, contains positive values \n",
    "def similarity_v(default, to_compare):\n",
    "    return 1 - ((abs(default - to_compare))/(abs(default) + abs(to_compare)))\n",
    "\n",
    "def similarity_v_left(default, to_compare):\n",
    "    return similarity_v(default, to_compare)\n",
    "\n",
    "def similarity_v_front(default, to_compare):\n",
    "    return similarity_v(default, to_compare)\n",
    "\n",
    "# d_left contains negative values as well and therefore a different actions are necessary\n",
    "def similarity_d_left(default, to_compare):\n",
    "    if default == 0 and to_compare == 0: # both 0 \n",
    "        return 1\n",
    "    elif default >= 0 and to_compare >= 0: # both positive \n",
    "        return similarity_v(default, to_compare)\n",
    "    elif default < 0 and to_compare < 0: # both negative \n",
    "        return similarity_v(default, to_compare)\n",
    "    # if one number is negative, take its abs value and keep the distance between the numbers\n",
    "    elif default < 0 and to_compare >= 0:\n",
    "        default = abs(default)\n",
    "        to_compare += 2 * default\n",
    "        return similarity_v(default, to_compare)\n",
    "    elif default >= 0 and to_compare < 0:\n",
    "        to_compare = abs(to_compare)\n",
    "        default += 2 * to_compare\n",
    "        return similarity_v(default, to_compare)\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# similarity of positive numeric values: \n",
    "def similarity_d_front(default, to_compare):\n",
    "    return similarity_v(default, to_compare)\n",
    "\n",
    "def similarity_radius_curve(default, to_compare):\n",
    "    return similarity_v(default, to_compare)\n",
    "\n",
    "def similarity_speed_limit(default, to_compare):\n",
    "    return similarity_v(default, to_compare)\n",
    "\n",
    "def similarity_delta_vFront(default, to_compare):\n",
    "    return similarity_v(default, to_compare)\n",
    "\n",
    "# similarity of mixed numeric values -> therefore similarity_d_left is necessary\n",
    "def similarity_delta_vLeft(default, to_compare):\n",
    "    return similarity_d_left(default, to_compare)\n",
    "\n",
    "def similarity_delta_speed_limit(default, to_compare):\n",
    "    return similarity_d_left(default, to_compare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distanzberechnung der type_left, type_front und type_vehicle Fahrzeuge\n",
    "\n",
    "Die Ähnlichkeiten wurden hier und in folgenden Funktionen selbst festgelegt, dabei wurde folgende Zuordnung gemacht:\n",
    "\n",
    "0: motorcycle and truck<br>\n",
    "0.1: sportscar and truck<br>\n",
    "0.3: car and motorcycle<br>\n",
    "0.5: sportscar and motorcycle<br>\n",
    "0.6: car and truck<br>\n",
    "0.8: car and sportscar<br>\n",
    "1: same<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 = car, 1 = motorcycle, 2 = sportscar, 3 = truck\n",
    "def similarity_type_left(default, to_compare):\n",
    "    if (default == 1 and to_compare == 3) or (default == 3 and to_compare == 1): # motorcycle and truck\n",
    "        return 0.0\n",
    "    if (default == 2 and to_compare == 3) or (default == 3 and to_compare == 2): # sportscar and truck\n",
    "        return 0.1\n",
    "    if (default == 0 and to_compare == 1) or (default == 1 and to_compare == 0): # car and motorcycle\n",
    "        return 0.3\n",
    "    if (default == 2 and to_compare == 1) or (default == 1 and to_compare == 2): # sportscar and motorcycle\n",
    "        return 0.5\n",
    "    if (default == 0 and to_compare == 3) or (default == 3 and to_compare == 0): # car and truck\n",
    "        return 0.6\n",
    "    if (default == 0 and to_compare == 2) or (default == 2 and to_compare == 0): # car and sportscar\n",
    "        return 0.8    \n",
    "    if (default == to_compare): # same\n",
    "        return 1.0\n",
    "\n",
    "# same vehicles and encodings \n",
    "def similarity_type_front(default, to_compare):\n",
    "    return similarity_type_left(default, to_compare)\n",
    "\n",
    "# same vehicles and encodings \n",
    "def similarity_type_vehicle(default, to_compare):\n",
    "    return similarity_type_left(default, to_compare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distanzberechnung der Routenbeschaffenheit\n",
    "\n",
    "0.5: absteigend und flach <br>\n",
    "0.5: aufsteigend und flach <br>\n",
    "1: bei gleichen Werten<br>\n",
    "Bei den übrig bleibenden Kombinationen wird jeweils 0 zurückgegeben. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ascending = 0, descending = 1, flat = 2\n",
    "def similarity_slope_street(default, to_compare):\n",
    "    if (default == 1 and to_compare == 2) or (default == 2 and to_compare == 1): # descending and flat\n",
    "        return 0.5\n",
    "    elif (default == 0 and to_compare == 2) or (default == 2 and to_compare == 0): # ascending and flat\n",
    "        return 0.5\n",
    "    elif default == to_compare:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distanzberechnung der Straßenart \n",
    "Bei gleichen Werten wird eine 1 zurückgegeben, bei ungleichen eine 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to different types of streets \n",
    "# same -> 1 \n",
    "# different (autobahn and country_road) -> 0\n",
    "def similarity_street_type(default, to_compare):\n",
    "    if default == to_compare:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distanzberechnung für das Wetter\n",
    "0.0: dry and snow ice<br>\n",
    "0.1: fog and snow ice<br>\n",
    "0.1: rain and snow ice<br>\n",
    "0.5: dry and fog<br>\n",
    "0.6: fog and rain<br>\n",
    "0.8: dry and rain<br>\n",
    "1: same<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dry = 0, fog = 1, rain = 2, snow_ice = 3 \n",
    "def similarity_weather(default, to_compare):\n",
    "    if (default == 0 and to_compare == 3) or (default == 3 and to_compare == 0): # dry and snow-ice \n",
    "        return 0\n",
    "    if (default == 1 and to_compare == 3) or (default == 3 and to_compare == 1): # fog and snow-ice \n",
    "        return 0.1\n",
    "    if (default == 2 and to_compare == 3) or (default == 3 and to_compare == 2): # rain and snow-ice \n",
    "        return 0.1\n",
    "    if (default == 0 and to_compare == 1) or (default == 1 and to_compare == 0): # dry and fog \n",
    "        return 0.5\n",
    "    if (default == 2 and to_compare == 1) or (default == 1 and to_compare == 2): # fog and rain\n",
    "        return 0.6\n",
    "    if (default == 2 and to_compare == 0) or (default == 0 and to_compare == 2): # dry and rain\n",
    "        return 0.8\n",
    "    if (default == to_compare): # same\n",
    "        return 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distanzberechnung für die verschiedenen Tageszeiten\n",
    "0: day and night<br>\n",
    "0.4: dusk and night<br>\n",
    "0.5: dawn and night<br>\n",
    "0.5: day and dawn<br>\n",
    "0.6: day and dusk<br>\n",
    "0.8: dawn and dusk<br>\n",
    "1: same<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dawn = 0, day = 1, dusk = 2, night = 3\n",
    "def similarity_time(default, to_compare):\n",
    "    if (default == 1 and to_compare == 3) or (default == 3 and to_compare == 1): # day and night\n",
    "        return 0.0\n",
    "    if (default == 2 and to_compare == 3) or (default == 3 and to_compare == 2): # dusk and night\n",
    "        return 0.4\n",
    "    if (default == 0 and to_compare == 3) or (default == 3 and to_compare == 0): # dawn and night\n",
    "        return 0.5\n",
    "    if (default == 0 and to_compare == 1) or (default == 1 and to_compare == 0): # day and dawn\n",
    "        return 0.5\n",
    "    if (default == 1 and to_compare == 2) or (default == 2 and to_compare == 1): # day and dusk\n",
    "        return 0.6\n",
    "    if (default == 0 and to_compare == 2) or (default == 2 and to_compare == 0): # dawn and dusk\n",
    "        return 0.8    \n",
    "    if (default == to_compare): # same\n",
    "        return 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Das ähnlichste Element finden\n",
    "Zuerst werden die Gewichtungen für jedes einzelne Attribut festgelegt. Anschließend wird die Ähnlichkeit (similarity) berechnet. Dies funktioniert über Gewichtung * Ähnlichkeit, beides sind Werte zwischen null und eins. Anschließend werden die gewichteten Ähnlichkeiten aller Attribute aufsummiert und mit dem bisher ähnlichsten Fall verglichen. Wenn der neue Fall ein höheres Ähnlichkeitsmaß erreicht, wird er selbst zum Höchsten und damit Ähnlichsten. Am Ende der Schleife wird die ähnlichste Situation zurückgegeben."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Die Gewichtung\n",
    "Bei der Gewichtung wurden verschiedene Gedankengänge und Ansätze kombiniert. Angefangen wurde mit jedem Gewicht auf eins. Es wurde damit weitergemacht, die Korrelationen aus der Korrelationsheatmap zu interpretieren und die Gewichtungen dort anzupassen, wo Auffälligkeiten gefunden wurden. Dies kann am Beispiel von ``delta_speed_limit`` gesehen werden. In der Heatmap ist zu erkennen, dass es mit keiner Option von ``Action`` stark korreliert. Die genauen Werte sind 0.0094 (accelerated_lane_change), -0.072 (continue), -0.05 (decelerate) und 0.15 (lange_change). Es wurde also die Gewichtung von ``delta_speed_limit`` verringert. Bei diesem Schritt der Korrelationen auf die Gewichtungen zu übertragen ist es wichtig, dass Scheinkorrelationen ausgelassen werden. Ein Beispiel hierfür ist `type_vehicle`. Es handelt sich hier um keine Nominalskala, Korrelationen sind also prinzipiell nicht aussagekräftig. Trotzdem ergeben sich für die vier Optionen von `Action` die folgenden Werte: -0.12 (accelerated_lane_change), 0.31 (continue), -0.18 (decelerate) und -0.18 (lange_change). \n",
    "\n",
    "Nach dem diese ersten Gewichtungen eingestellt wurden, folgte eine persönliche Einschätzung der Gewichtungen einzelner Attribute. Dies geschah vor allem für Attribute, über welche die Korrelation nichts ausgesagt hat. Beispielhaft zu nennen sind hierfür `street_type`, `slope_street`, und `time`. Z.B. bei `street_type` existieren nur zwei verschiedene Optionen, welche sich nicht groß zu unterscheiden scheinen, darum wurde es heruntergestuft.\n",
    "\n",
    "Als letzten Schritt wurde mithilfe der Accuracy versucht, die einzelnen Attribute mittels anderer Gewichtungen auszuprobieren und anzupassen. Es wurde also immer die Gewichtung eines einzelnen Attributes verändert, die Accuracy berechnet und abhängig von ihrer Veränderung die Gewichtung des Attributes weiter angepasst. Ging die Accuracy eindeutig hoch, wurde die Gewichtung erhöht. Veränderte sich die Accuracy allerdings nicht, wurde eine niedrigere Gewichtung gewählt, da das Attribut scheinbar kein großen Einfluss auf die Vorhersage hat. Trotz allem wurde minimal eine Gewichtung von 10% gewählt, um dennoch keine Attribute vollständig auszulassen. Dies wäre zwar denkbar, allerdings wird die Vorhersage in diesem Fall weniger konstant. Die Evaluation der einzelnen Testdatenintervalle zeigt, dass diese sich deutlich stärker voneinander unterscheiden. Da die durchschnittliche Accuracy sich allerdings nahezu nicht verändert, wurden alle minimalen Gewichtungen auf 0.1 gesetzt. Allgemein wurde für jedes Attribut ausprobiert, wie sehr sich mit seiner Gewichtung auch die Accuracy ändert, auch für in Schritt eins und zwei bereits angepasste Gewichtungen. Oftmals konnte bei diesen bereits angepassten Gewichtungen aber festgestellt werden, dass sie schon gut angepasst waren (Bspw. bei `delta_speed_limit` aus Schritt 1 und `street_type` aus Schritt 2). Überraschend in diesem Schritt war, dass für `v_front` ein geringeres Gewicht zu einer höheren Accuracy führt. Es wurde eigentlich davon ausgegangen, dass dieses Attribut wichtig für die vorhergesagten `Action` ist. Mit `delta_vLeft` verhielt es sich genau so, auch hier wurde fälschlicherweise erwartet, dass eine höhere Gewichtung zu einer besseren Performance führt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try to find the most similar case \n",
    "def check_for_similar(input, train_data):\n",
    "    # weights setting:\n",
    "    weight_v = 1\n",
    "    weight_v_left = 1\n",
    "    weight_v_front = 0.4\n",
    "    weight_d_left = 1\n",
    "    weight_d_front = 1\n",
    "    weight_type_left = 0.1\n",
    "    weight_type_front = 0.1\n",
    "    weight_radius_curve = 0.7\n",
    "    weight_slope_street = 0.1\n",
    "    weight_street_type = 0.1\n",
    "    weight_time = 0.1\n",
    "    weight_weather = 0.8\n",
    "    weight_type_vehicle = 0.1\n",
    "    weight_speed_limit = 1\n",
    "    weight_delta_vFront = 0.8\n",
    "    weight_delta_vLeft = 0.4\n",
    "    weight_delta_speed_limit = 0.2\n",
    "\n",
    "    max_sim = 0\n",
    "    max_row = 0\n",
    "\n",
    "    # comparing attributes from training_data and input \n",
    "    for i in range(len(train_data)):\n",
    "        row = train_data.iloc[i]\n",
    "        # similarity calculation \n",
    "        similarity = weight_v * similarity_v(input['v'], row['v'])\\\n",
    "        + weight_v_left * similarity_v_left(input['v_left'], row['v_left']) \\\n",
    "        + weight_v_front * similarity_v_front(input['v_front'], row['v_front']) \\\n",
    "        + weight_d_left * similarity_d_left(input['d_left'], row['d_left'])  \\\n",
    "        + weight_d_front * similarity_d_front(input['d_front'], row['d_front'])  \\\n",
    "        + weight_type_left * similarity_type_left(input['type_left'], row['type_left'])  \\\n",
    "        + weight_type_front * similarity_type_front(input['type_front'], row['type_front']) \\\n",
    "        + weight_radius_curve * similarity_radius_curve(input['radius_curve(m)'],row['radius_curve(m)']) \\\n",
    "        + weight_slope_street * similarity_slope_street(input['slope_street'], row['slope_street']) \\\n",
    "        + weight_street_type * similarity_street_type(input['street_type'], row['street_type']) \\\n",
    "        + weight_time * similarity_time(input['time'], row['time']) \\\n",
    "        + weight_weather * similarity_weather(input['weather'], row['weather']) \\\n",
    "        + weight_type_vehicle * similarity_type_vehicle(input['type_vehicle'], row['type_vehicle']) \\\n",
    "        + weight_speed_limit * similarity_speed_limit(input['speed_limit(km/h)'], row['speed_limit(km/h)']) \\\n",
    "        + weight_delta_vFront * similarity_delta_vFront(input['delta_vFront'], row['delta_vFront']) \\\n",
    "        + weight_delta_vLeft *  similarity_delta_vLeft(input['delta_vLeft'], row['delta_vLeft']) \\\n",
    "        + weight_delta_speed_limit * similarity_delta_speed_limit(input['delta_vFront'], row['delta_vFront'])\n",
    "        if similarity > max_sim: # if current similarity bigger than the previous one, use the current row \n",
    "            max_row = row\n",
    "            max_sim = similarity\n",
    "        \n",
    "     # use highest similarity and the corresponding action for return\n",
    "    return max_row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vorhersage der Action\n",
    "Es wird zuerst geschaut, ob ein gleicher Fall in den Daten gefunden wird. Falls dies der Fall ist, wird sich gleich verhalten, wie in diesem gefundenen Fall. Wenn kein identischer Fall vorhanden ist, wird mit ``check_for_similar`` nach dem ähnlichsten Fall gesucht. Für die Vorhersage wird dann die Action des gefundenen Falles genutzt. Für bessere Nachvollziehbarkeit kann die Flag ``print_output`` genutzt werden, welche die gesamten Falldaten ausgibt. Somit kann die Ähnlichkeit manuell nachvollzogen werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted action for input case nr.  1 :  continue  \t\t Real action:  continue\n",
      "Predicted action for input case nr.  2 :  continue  \t\t Real action:  continue\n",
      "Predicted action for input case nr.  3 :  decelerate  \t\t Real action:  decelerate\n",
      "Predicted action for input case nr.  4 :  continue  \t\t Real action:  continue\n"
     ]
    }
   ],
   "source": [
    "def do_prediction(input, train_data, print_output = False):\n",
    "    # first check for equal data \n",
    "    success, prediction = check_for_same(input, train_data)\n",
    "    # if no matching data was found -> check for the most similar case \n",
    "    if not success:\n",
    "        prediction = check_for_similar(input, train_data)\n",
    "    # debug mode\n",
    "    if print_output:\n",
    "        print('Input: \\n', input)\n",
    "        print(\"\\n-Prediction-\\n\")\n",
    "        print('Prediction: \\n', prediction)\n",
    "    return prediction['action']\n",
    "\n",
    "# input prediction:\n",
    "for i in range(0, len(df_input)):\n",
    "    print('Predicted action for input case nr. ', str(i+1), ': ', do_prediction(df_input.iloc[i], df_all, print_output=False), ' \\t\\t Real action: ', df_input.iloc[i]['action'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Berechnung der Accuracy\n",
    "\n",
    "Hier wird die Genauigkeit des Modells berechnet, indem mittels Cross-Validation die unterschiedlichen Durchläufe aufsummiert werden und dann durch die Länge, also die Anzahl der Durchläufe, geteilt wird. \n",
    "Im genauen werden immer 100 Einträge aus den Daten als Test-Daten verwendet, der Rest (401) als Trainingsdaten. Dies geschieht also fünf mal hintereinander, für jedes Intervall der Testdaten einmal (0-100, 101-200, ..., 401-500).\n",
    "\n",
    "Außerdem wird beim Testen das jeweilige Testobjekt nach der Vorhersage zu den Trainingsdaten hinzugefügt, wenn die flag ``add_test_line`` gesetzt ist. Dies ist nützlich, um den Datensatz ständig zu erweitern und damit eine größere Datengrundlage zu haben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy  1  is:  0.8  /  100\n",
      "accuracy  2  is:  0.79  /  100\n",
      "accuracy  3  is:  0.85  /  100\n",
      "accuracy  4  is:  0.79  /  100\n",
      "accuracy  5  is:  0.89  /  100\n",
      "accuracy total:  0.8240000000000001\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "def evaluate_accuracy(add_test_line = False):\n",
    "    accuracy_sum = []\n",
    "    for j in range(0,5):\n",
    "        train_data, test_data = get_test_and_train_data(j)\n",
    "        accuracy = 0\n",
    "        for i in range(len(test_data)):\n",
    "            row = test_data.iloc[i]\n",
    "            prediction = do_prediction(row, train_data)\n",
    "            if prediction == row['action']:\n",
    "                accuracy += 1\n",
    "            # Retain phase\n",
    "            if add_test_line:\n",
    "                train_data = train_data.append(row)\n",
    "        accuracy = accuracy / len(test_data)\n",
    "        print('accuracy ', str(j+1), ' is: ', accuracy, ' / ', len(test_data))\n",
    "        accuracy_sum.append(accuracy)\n",
    "    accuracy_average = np.sum(accuracy_sum)/len(accuracy_sum)\n",
    "    print('accuracy total: ', accuracy_average)\n",
    "evaluate_accuracy(True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ergebnisbewertung\n",
    "\n",
    "Bei der Untersuchung der Daten für unser Modell ist uns aufgefallen, dass die Daten vermehrt eine für uns nicht nachvollziehbare Schlussfolgerung der ``action`` aufweisen. \n",
    "Beispiele die dies verdeutlichen, sind unter anderem: \n",
    "* Beschleunigen (``accelerate``), obwohl das Fahrzeug schon 140 km/h fährt und zusätzlich bereits über dem speed limit ist. \n",
    "* Überholen (``lane_change``), obwohl das Fahrzeug auf der linken Seite sehr schnell angefahren kommt und es knapp ist auszuscheren.\n",
    "* Geschwindigkeit und Spur beibehalten (``continue``), obwohl das Fahrzeug sich bereits sehr nah an dem vorderen Fahrzeug befindet und schneller fährt als dieses. \n",
    "Durch diese interessanten Entscheidungen in den vorgegebenen Daten, kommen teilweise überraschende Ergebnisse für neue Testdaten heraus. Der Grund dafür liegt darin, dass das Modell auf dem Cased Based Reasoning basiert und somit nach möglichst ähnlichen Fällen schaut und anschließend deren 'action' übernimmt.\n",
    "\n",
    "Außerdem haben die Gewichtsanpassungen der einzelnen Attribute einen hohen Einfluss auf die Genauigkeit des Modells und der Vorhersage. Dabei haben uns einige Attribute und deren Einfluss auf die Ähnlichkeit überrascht, da sie teilweise mehr Einfluss haben, als wir vermutet hatten (z.B. ``radius_curve`` und ``weather``). \n",
    "\n",
    "Einen hohen Einfluss auf das Ergebnis des Modells hat auch das Festlegen der Ähnlichkeitsmaße für die Attribute. Den hierbei wird das Verhältnis einzelner Objekte zueinander bewertet und deren Ähnlichkeit beurteilt. Wird dabei etwas anders eingestuft, als es der Wahrheit entspricht, können Werte verfälscht werden. Für eine bessere Bewertung könnte in der Optimierung des Modells Expertenwissen mit einbezogen werden.\n",
    "\n",
    "Bei der Betrachtung der berechneten Accuracy fällt auf, dass der fünfte Wert am höchsten ist. Das liegt daran, dass die Hyperparameteroptimierung ausschließlich mit Trainingsdaten aus Bereich 5 stattgefunden hat, und es somit zum Overfitting in diesem Bereich kommt. Das heißt, das Modell macht damit sehr gute Vorhersagen für die Daten im Bereich 5, aber tendenziell weniger für neue Daten. \n",
    "Eine genaue Anpassung auf alles Bereiche und Daten haben wir nicht gemacht, da es sehr rechen- und zeitaufwendig ist, dieses mit der Cross-Validation zu machen. Außerdem gibt es immer eine leicht ungleiche Verteilung.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fazit\n",
    "\n",
    "Abschließend lässt sich sagen, dass die Vorhersage der Aktion des Fahrzeuges eine gute Vorhersage-Genauigkeit besitzt. Die teilweise etwas unrealistischen Vorhersagen lassen sich damit erklären, dass ähnliche Daten in dem Trainingsdatensatz enthalten sind und diese eine solche ``action`` besitzen. \n",
    "\n",
    "Die erreichte Accuracy könnte dennoch besser sein, allerdings ist es aufgrund der geringen Anzahl an Trainingsdaten nicht möglich, die Vorhersage zu verbessern, ohne in den Overfitting-Bereich zu geraten. "
   ]
  }
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